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VERSION:2.0
X-WR-CALNAME:11thictisthailand
X-WR-CALDESC:Event Calendar
METHOD:PUBLISH
CALSCALE:GREGORIAN
PRODID:-//Sched.com 11th International Conference on ICTIS//EN
X-WR-TIMEZONE:UTC
BEGIN:VEVENT
DTSTAMP:20260417T125213Z
DTSTART:20260409T022800Z
DTEND:20260409T023000Z
SUMMARY:Opening Remarks
DESCRIPTION:\n
CATEGORIES:VIRTUAL ROOM 1A
LOCATION:Virtual Room A\, Bangkok\, Thailand
SEQUENCE:0
UID:0a6bb9fd1b3af4f212bf9c67fe4edd13
URL:http://11thictisthailand.sched.com/event/0a6bb9fd1b3af4f212bf9c67fe4edd13
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260417T125213Z
DTSTART:20260409T022800Z
DTEND:20260409T023000Z
SUMMARY:Opening Remarks
DESCRIPTION:\n
CATEGORIES:VIRTUAL ROOM 1B
LOCATION:Virtual Room B\, Bangkok\, Thailand
SEQUENCE:0
UID:4dcca6e5e572a3fd99bbc408fcbfcd60
URL:http://11thictisthailand.sched.com/event/4dcca6e5e572a3fd99bbc408fcbfcd60
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260417T125213Z
DTSTART:20260409T022800Z
DTEND:20260409T023000Z
SUMMARY:Opening Remarks
DESCRIPTION:\n
CATEGORIES:VIRTUAL ROOM 1C
LOCATION:Virtual Room C\, Bangkok\, Thailand
SEQUENCE:0
UID:0a791ce3f7c1277932834da7c84bfba9
URL:http://11thictisthailand.sched.com/event/0a791ce3f7c1277932834da7c84bfba9
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260417T125213Z
DTSTART:20260409T022800Z
DTEND:20260409T023000Z
SUMMARY:Opening Remarks
DESCRIPTION:\n
CATEGORIES:VIRTUAL ROOM 1D
LOCATION:Virtual Room D\, Bangkok\, Thailand
SEQUENCE:0
UID:d35c7e925c6a3769f12114afe22c49fe
URL:http://11thictisthailand.sched.com/event/d35c7e925c6a3769f12114afe22c49fe
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260417T125213Z
DTSTART:20260409T022800Z
DTEND:20260409T023000Z
SUMMARY:Opening Remarks
DESCRIPTION:\n
CATEGORIES:VIRTUAL ROOM 1E
LOCATION:Virtual Room E\, Bangkok\, Thailand
SEQUENCE:0
UID:b33790ae3dcf18a0e8e05d502c8fa286
URL:http://11thictisthailand.sched.com/event/b33790ae3dcf18a0e8e05d502c8fa286
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260417T125213Z
DTSTART:20260409T022800Z
DTEND:20260409T023000Z
SUMMARY:Opening Remarks
DESCRIPTION:\n
CATEGORIES:VIRTUAL ROOM 1F
LOCATION:Virtual Room F\, Bangkok\, Thailand
SEQUENCE:0
UID:0e2ceeb09b7c80d7f223eae9879b8fea
URL:http://11thictisthailand.sched.com/event/0e2ceeb09b7c80d7f223eae9879b8fea
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260417T125213Z
DTSTART:20260409T023000Z
DTEND:20260409T043000Z
SUMMARY:A Hybrid Deep Learning and Graph-Based Framework for Interpretable Medicare Fraud Detection
DESCRIPTION:Authors - Kunam Subba Reddy\, Mangavarapu Jahnavi\, Kotte Hima Teja\, Shaik Kathamma Basheerun\, Nama Adarsh Abstract - Proper estimation of battery state of charge (SOC)\, state of health (SOH) and state of power (SOP) are vital to safe and efficient operation of photovoltaic (PV)-battery energy storage systems\, particularly at highly dynamic profiles in which both charging and discharging is taking place. In this paper\, a clear comparative analysis of classical\, model-based\, and machine-learning-based estimation techniques is performed in terms of a similar 24-h ultra-challenging PV + load current profile\, simulating realistic residential microgrids operation. The test profile involves strong directions of current swings\, partial charging\, and long constant-power discharge\, and temperature change. The estimators are implemented and benchmarked such as open-circuit-voltage (OCV)-based SOC estimation\, linear Kalman filter (KF)\, extended Kalman filter (EKF)\, unscented Kalman filter (UKF)\, basic machine learning (ridge regression) and support vector machine (SVM). Each of the methods is compared to a high-fidelity model of an electro-thermal battery with capacity degradation and resistance increase with age and temperature. The accuracy of SOC tracking\, SOH estimation error\, and SOP tracking capability are discussed. It is found that the OCV-based method fails when the loading is dynamic which makes the SOC about constant and SOP highly conservative. KF and EKF are much better SOC trackers but they have greater deviation at high SOC and near bottom-of-discharge. ML and SVM estimators based on Ridge-regression show high SOC accuracy in the entire profile and UKF shows the best overall trade off between SOC accuracy\, SOH tracking as well as SOP estimation strength when resistance varies with temperature and with age. The paper discusses the relevance of the collaborative consideration of SOC\, SOH\, and SOP and shows the advanced filters and ML models can significantly increase the performance of PV-battery applications that require tough operating conditions.
CATEGORIES:VIRTUAL ROOM 1A
LOCATION:Virtual Room A\, Bangkok\, Thailand
SEQUENCE:0
UID:2077d08b7786bccb93dc8b82f15eba8a
URL:http://11thictisthailand.sched.com/event/2077d08b7786bccb93dc8b82f15eba8a
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260417T125213Z
DTSTART:20260409T023000Z
DTEND:20260409T043000Z
SUMMARY:An Adversarial Evaluation of Stealthy Insider Attacks and UEBA-Based Defensive Detection in Enterprise Environments
DESCRIPTION:Authors - Asmit Patil\, Smita Shedbale\, Sneha Kumbhar\, Ashwini Athawale\, Smita Arude\, Rohan S. Sapkal\, Priya Sharma\, Dhanaraj Jadhav Abstract - The rapid growth of Internet of Things (IoT) deployments in 5G Enhanced Ma-chine-Type Communication (eMTC) networks has significantly increased the network at-tack surface. A major challenge for Network Anomaly Detection Systems (NADS) in this environment is severe class imbalance\, where dominant benign traffic obscures rare but high-impact attacks\, leading to poor minority-class detection. This paper presents Conf-Gate XGBoost-RF\, a two-stage hybrid anomaly detection architecture designed to address this limitation without compromising real-time performance. The framework employs a high-speed XGBoost classifier for initial screening and a confidence-gated mechanism that selectively routes low-confidence predictions to a specialist Random Forest trained on synthetically balanced data. Evaluation on the large-scale CICIoT2023 dataset shows that the proposed model achieves 99.32% accuracy and a Macro F1-score of 0.80\, sub-stantially outperforming single-stage baselines. Notably\, recall for critical low-volume at-tacks\, such as Command Injection\, improves by over 34%. With an average inference latency of 0.87 ms\, the proposed approach remains compatible with the stringent low-latency requirements of 5G eMTC control signaling\, demonstrating a practical balance between computational efficiency and rare-attack sensitivity.
CATEGORIES:VIRTUAL ROOM 1A
LOCATION:Virtual Room A\, Bangkok\, Thailand
SEQUENCE:0
UID:a069308a15177ac19600491ddbfbe5fe
URL:http://11thictisthailand.sched.com/event/a069308a15177ac19600491ddbfbe5fe
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260417T125213Z
DTSTART:20260409T023000Z
DTEND:20260409T043000Z
SUMMARY:Beyond Accuracy: Comparison of ResNet50 and GWN-Enhanced Models for Brain Tumor MRI Classification with LIME visualization
DESCRIPTION:Authors - Nguyen Thanh Minh Tam\, Mai Nhu Yen\, Nguyen Quang Huy\, Nguyen Thi Nhung\, Nguyen Thi Huyen Chau\, Nguyen Hoang Phuong\, Dong Van He\, Bui Xuan Cuong\, Vladik Kreinovich Abstract - The rapid emergence of smart apps in smart environments\, industrial automation and cyber-physical systems has demonstrated the intrinsic limitations of conventional design of information and communication technologies. The existing ICT systems are very rigid\, centrally controlled and dependent on fixed operation logic and this restricts their dynamism to changing environments and complex system dynamics. Intelligent systems are already urgently in need of architectural paradigms that offer self-education\, decentralized intelligence\, and autonomous scale-based decision making. The following paper proposes a next-generation Edge-Cloud-AI integrated ICT architecture\, which is supposed to service self-learnings and autonomous intelligent systems. The proposed architecture gives a layered intelligence design that utilizes edge level real-time learning\, cloud level global optimization\, and an autonomy orchestration layer balancing the adaptive behavior in the distributed elements. The architecture enables systems to create operational policies in an autonomous way through the provision of continuous learning feedback and autonomous controls directly within the ICT infrastructure although still be scaled and be reliable. Significant contributions of this work are the definition of a single Edge-Cloud intelligence system\, the incorporation of self-learner’s mechanisms along structural layers\, and an autonomy-based orchestration model that may be applied to diverse fields of intelligent systems. The proponent architecture is not platform specific and can be applied in a wide range of future intelligent applications that may require resilience\, versatility and autonomy.
CATEGORIES:VIRTUAL ROOM 1A
LOCATION:Virtual Room A\, Bangkok\, Thailand
SEQUENCE:0
UID:680d92f5a5367056d6773d3a8fe9005e
URL:http://11thictisthailand.sched.com/event/680d92f5a5367056d6773d3a8fe9005e
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260417T125213Z
DTSTART:20260409T023000Z
DTEND:20260409T043000Z
SUMMARY:Comparative Study of LSTM and Transformer Models for Health Mention Detection on Twitter
DESCRIPTION:Authors - Nethika Alagarathnam\, Dhanushka Jayasinghe\, WU Wickramaarachchi\n Abstract - Social media platforms\, especially Twitter\, have become trending sources for public health monitoring\, as individuals often share personal experiences related to symptoms\, diagnoses\, and health concerns. However\, detecting personal health mentions (PHMs) in such noisy\, short text environments remains challenging. This study investigates about evaluating and comparing three neural architectures including Long Short-Term Memory with word embeddings\, a fine tuned Bidirectional Encoder Representations from Transformer model (BERT) and a compact TinyBERT model distilled from BERT. Using a labeled corpus of health related tweets\, all models were trained under identical preprocessing\, optimization\, and evaluation conditions with accuracy\, precision\, recall\, and F1- score assessed on a test set. The results reveal clear performance differences across three architectural paradigms. LSTM baseline demonstrated strong learning on the training set but found significant overfitting and failed to perform on unseen data. But in contrast\, the transformer models BERT and TinyBERT delivered a decent balanced performance reflecting the good ability to capture contextual semantics noise in tweets. While BERT achieved the highest overall performance. Notably\, TinyBERT provided a competitive and alternate suite for deployment in constrained environment. These findings highlight the effectiveness of transformer architectures for Personal Health Mention detection and practical insights for building efficient and accurate public health monitoring system using social media data.
CATEGORIES:VIRTUAL ROOM 1A
LOCATION:Virtual Room A\, Bangkok\, Thailand
SEQUENCE:0
UID:96732937105356761c91b90d84d46886
URL:http://11thictisthailand.sched.com/event/96732937105356761c91b90d84d46886
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260417T125213Z
DTSTART:20260409T023000Z
DTEND:20260409T043000Z
SUMMARY:Conf-Gate XGBoost-RF Hybrid Model for Multi-Class Anomaly Classification in 5G-Enabled eMTC IoT Net-works
DESCRIPTION:Authors - Michael David\, Chekwas Ifeanyi Chikezie\, Abra-ham Usman Usman\, Sulieman Zubair\, Henry Ohiani Ohize\, Joseph Ojeniyi Abstract - With the rapid growth of digital communication and multimedia applications\, secure transmission and storage of digital images have become critical challenges. Conventional text-based encryption algorithms are often inadequate for image data due to its high redundancy\, strong pixel correlation\, and large data volume. These characteristics necessitate specialized encryption mechanisms that can provide strong security while maintaining computational efficiency. This paper proposes a robust image encryption framework designed to ensure confidentiality\, resistance to cryptographic attacks\, and suitability for real-time applications. The proposed approach integrates advanced permutation–diffusion operations with chaos-based key generation to effectively disrupt statistical characteristics inherent in digital images. Chaotic maps with high sensitivity to initial conditions are employed to generate dynamic encryption keys\, enhancing key space complexity and resistance against bruteforce and differential attacks. Pixel-level scrambling is combined with nonlinear diffusion operations to eliminate spatial correlations and achieve uniform cipher text distribution. The encryption process is further optimized to support grayscale and color images while preserving computational feasibility. Extensive experimental evaluation is conducted using standard benchmark images to assess security and performance. Statistical analyses\, including histogram uniformity\, correlation coefficients\, information entropy\, NPCR\, and UACI metrics\, demonstrate strong resistance against statistical and differential attacks. Key sensitivity and key space analysis confirm robustness against bruteforce attempts. Performance results indicate that the proposed scheme achieves a favorable balance between security strength and execution efficiency\, making it suitable for real-time image protection. The experimental findings validate that the proposed image encryption framework provides enhanced security\, scalability\, and practicality\, offering an effective solution for secure image communication in modern digital environments.
CATEGORIES:VIRTUAL ROOM 1A
LOCATION:Virtual Room A\, Bangkok\, Thailand
SEQUENCE:0
UID:637a17e89999e4fbd68b368b51824997
URL:http://11thictisthailand.sched.com/event/637a17e89999e4fbd68b368b51824997
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260417T125213Z
DTSTART:20260409T023000Z
DTEND:20260409T043000Z
SUMMARY:DCS: A SHAP Enhanced Containment Framework for Stealthy Rogue Nodes in Software Defined Networks
DESCRIPTION:Authors - Vinod B. Maniyat\, Arun Kumar B. R\, Shreyas A Abstract - Stealthy rogue components pose as legitimate nodes and progressively deteriorate services\, take over flows\, or taint network topology\, posing serious scalability and security threats to modern SDN networks. High false positive rates\, poor interpretability\, and static threat assumptions make it difficult for current rule-based and signature-driven detection systems to detect such contextual threats. Based on the Dynamic Containment Score (DCS)\, a mathematically modelled\, context-sensitive metric that measures each network node’s disruptive potential\, this work offers a comprehensive behavioural defence paradigm. The framework integrates graph theoretic features\, protocol specific entropy\, and temporal volatility to compute real time DCS rankings\, refined through SHAP based explainability and confidence bounded feature attribution for adaptive detection under concept drift. A multi strategy containment engine\, including deception based mitigation\, redirects attackers toward synthetic vulnerabilities. Validation on hybrid real world and adversarial traffic demonstrates superior early detection\, explainable risk attribution\, and efficient mitigation with minimal service disruption.
CATEGORIES:VIRTUAL ROOM 1A
LOCATION:Virtual Room A\, Bangkok\, Thailand
SEQUENCE:0
UID:72941aabcf7dbec2aae9746db5ed0f87
URL:http://11thictisthailand.sched.com/event/72941aabcf7dbec2aae9746db5ed0f87
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260417T125213Z
DTSTART:20260409T023000Z
DTEND:20260409T043000Z
SUMMARY:Detect insider threats from Employee Communications Using NLP
DESCRIPTION:Authors - Saurabh Nimje\, Anup Bhitre\, Sudhir Agarmore\, Utkarsha Pacharaney Abstract - Insider threat is a great danger to business security because of trust rights granted insiders\, it is not easy to notice their malicious or careless work through the available security programs. This paper is going to examine how Natural Language Processing (NLP) can be used to detect insider threats proactively by analyzing the communication of employees such as emails\, chat messages\, and internal reports. Applying the CERT Insider Threat Dataset and simulated logs\, a multi-level system was created\, which includes text preprocessing\, feature extraction based on sentiments and semantics\, and classification of machine learning models- Random Forest Mean Square Error\, SVM\, and LSTM. Out of them\, LSTM model performed best (92.6% accuracy and overall performance) since it was able to capture contextual and sequential patterns of communication. The most notable indicators of behaviors were sentiments of negativity\, passively aggressive language\, and frequency of communication efficiency\, which indicated a high relationship with insider threat. SHAP (Shapley Additive Explanations) was also used in the given research to allow enhancing insights into model decisions. The results prove the viability of NLP-based solutions as scalable\, context-sensitive\, and explainable systems to detect insider threats extending the understanding of organizations to perceive behavioral anomalies and reduce the risks to a minimum.
CATEGORIES:VIRTUAL ROOM 1A
LOCATION:Virtual Room A\, Bangkok\, Thailand
SEQUENCE:0
UID:6edcde74ec90d1a3c7397433b4940182
URL:http://11thictisthailand.sched.com/event/6edcde74ec90d1a3c7397433b4940182
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260417T125213Z
DTSTART:20260409T023000Z
DTEND:20260409T043000Z
SUMMARY:Privacy-Preserving Synthetic Data Generation Using Natural Language Processing and Laplace Mechanism
DESCRIPTION:Authors - Maria Jihan Sangil\, John Raymond Barajas\, Ramnick Lim Abstract - Government Identity documents have become groundwork for citizen verification\, financial inclusion and public service. However unauthorized dis-closure\, fraudulent access and frequent misuse of individual's personal information expose gaps in routine verification and wrongful disbursement of welfare benefits which asks the immediate need for a more secure privacy holding approach. Existing infrastructure like DigiLocker takes care of the secure transmission but a factor of privacy exposure is still compromised. The pro-posed model introduces TrustChain based on blockchain framework for decentralized identify verification and secure access. The inducement shifts focus from submission of identity information to authentication by the means of DID (Decentralized Identifiers) ensuring that some personal information will be hid-den to the document requestor. By integrating self sovereign identity principles\, distributed storage and cryptographic operations the model enables users to au-thenticate without revealing personal parameters minimizing the risks of identity compromise. Pilot findings are particular not to mention that identity expo-sure is mitigated by such a representation and offers scalability towards integration into the current infrastructures such as Aadhaar connected services and Digital locker. A safe identity space where individuals retain ownership to per-sonal information as organizations and governments achieve acceptable validation.
CATEGORIES:VIRTUAL ROOM 1A
LOCATION:Virtual Room A\, Bangkok\, Thailand
SEQUENCE:0
UID:9532f37eba9863b904f7e8c096bd18e5
URL:http://11thictisthailand.sched.com/event/9532f37eba9863b904f7e8c096bd18e5
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260417T125213Z
DTSTART:20260409T023000Z
DTEND:20260409T043000Z
SUMMARY:SigNeura: Signature Verification via Siamese–Transformer with Synth-Pressure Map Augmentation
DESCRIPTION:Authors - K. Subba Reddy\, Pasulammagari Jahnavi\, Devasam Hema Keerthana\, Katreddy Jaswanth Reddy\, Dudhekula Abdul Gaffar Abstract - The investigation of events that have taken place is the foundation of the current law enforcement that uses surveillance videos extensively to identify suspects and the motives of criminals. Wherein\, it becomes slow\, tiresome\, and liable to error when video data is being monitored manually due to the enormously large volumes of data being monitored. To cope with this\, it is a need to witness a transition toward the use of automated technologies that are led by AI and deep learning. These systems are able to detect faces\, masks\, gaits\, and ab-normal behavior systematically in adverse environments. The survey will re-view the information about each of the methods that involve face recognition using videos\, gait analysis and anomaly detection systems. In addition\, efforts are put in direction to present a standardized AI-based surveillance framework of legal multimodal multitask biometric and behaviour identification. The system proposed will focus on a radical reduction of false alarms and offer adequate and prompt intelligence to the law enforcement agencies to speed up investigations.
CATEGORIES:VIRTUAL ROOM 1A
LOCATION:Virtual Room A\, Bangkok\, Thailand
SEQUENCE:0
UID:061e2407596c374e8b290a24276e9bed
URL:http://11thictisthailand.sched.com/event/061e2407596c374e8b290a24276e9bed
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260417T125213Z
DTSTART:20260409T023000Z
DTEND:20260409T043000Z
SUMMARY:The Generative AI Usage and Ethical Guidelines in Graduate Education of Payap University
DESCRIPTION:Authors - Keerin Nopanitaya\, Luo Xiaoyu\, Zhu Chunping\, Pratya Nuankaew Abstract - This study examines Generative AI use and ethical guidelines in graduate education at Payap University\, Thailand. As large language models increasingly support learning\, research\, and academic writing\, they boost efficiency but raise concerns about accuracy\, transparency\, and integrity. Using mixed methods\, the study gathered questionnaire data and conducted interviews and focus groups with master’s and doctoral students. Results show broad AI use for literature reviews\, writing\, idea generation\, and research\, with more advanced use expected to grow. While students report moderate to high skills\, many lack strong critical evaluation of AI outputs and practical understanding of ethics. Consistent with international research\, key risks stem from limited AI literacy\, unclear disclosure\, and lack of oversight rather than the technology itself. The study recommends developing an AI literacy framework\, clear disclosure standards\, and process evaluation for ethical\, responsible AI integration while protecting academic quality and integrity.
CATEGORIES:VIRTUAL ROOM 1A
LOCATION:Virtual Room A\, Bangkok\, Thailand
SEQUENCE:0
UID:5605484f596abd8e77ff8c24326eb233
URL:http://11thictisthailand.sched.com/event/5605484f596abd8e77ff8c24326eb233
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260417T125213Z
DTSTART:20260409T023000Z
DTEND:20260409T043000Z
SUMMARY:3D Fractal Characterization and Phase-Field Simulation of Spinodal Decomposition in Binary Alloys: A Computational Study for Intelligent Materials Systems
DESCRIPTION:Authors - Rahul Basu\n Abstract - Spinodal decomposition in binary alloys produces complex\, interconnected microstructures with fractal-like characteristicsduring early and intermediate stages of phase separation. This paper presents a computational framework for simulating three-dimensional (3D) spinodal decomposition using the Cahn–Hilliard phase-field model\, with emphasis on fractal dimensionanalysis of the evolving microstructures. The model incorporates CALPHAD-consistent free-energy descriptions (via commontangent interpolation for miscibility gaps) for benchmark alloys such as Cu–Ni and Fe–Cr. Simulations in 3D revealinterconnected networks with fractal dimensions typically in the range 2.4–2.8 during coarsening (deviation &lt\;5\% RMSE fromFe–Cr APT data)\, consistent with experimental observations. Fractal analysis via box-counting ($\log(1/r)=0$–$1.2$) andcorrelation functions ($r=5$–$20$ dx) quantifies morphological complexity\, providing insights into scaling behavior and self-similarity. The approach leverages efficient FFT-based solvers for large-scale 3D computations (up to 256$^3$)\, aligning withuseful descriptors for data-driven materials design\, microstructure prediction\, and alloy performance optimization. Resultshighlight the transition from early-stage fractal-like patterns to late-stage Ostwald ripening (with LS recovery on larger grids)\,offering quantitative metrics for alloy engineering.
CATEGORIES:VIRTUAL ROOM 1B
LOCATION:Virtual Room B\, Bangkok\, Thailand
SEQUENCE:0
UID:f8960e4ddde58bcfff73d7f8eefdd288
URL:http://11thictisthailand.sched.com/event/f8960e4ddde58bcfff73d7f8eefdd288
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260417T125213Z
DTSTART:20260409T023000Z
DTEND:20260409T043000Z
SUMMARY:AI-Driven Paddy Growth Stage Identification and Yield Estimation Using Deep Convolutional Neural Networks
DESCRIPTION:Authors - S SRINIVASA REDDY\,N SARASWATHI\, K CHARITHA\, L GOPAL KRISHNA\n Abstract - Accurate identification of paddy crop growth stages plays a crucial role in effective agricultural planning\, crop management\, and yield estimation. Paddy cultivation is highly sensitive to environmental conditions\, disease progression\, and growth variability\, making continuous and automated monitoring essential. This paper presents an AI-driven framework for automated paddy growth stage identification and yield readiness estimation using deep convolutional neural networks. The proposed system employs the EfficientNetV2-S architecture trained on heterogeneous paddy plant image datasets collected from multiple public sources. To address inconsistencies in labeling across datasets\, a semantic stage-mapping mechanism is introduced to map dataset-specific visual classes into standardized paddy growth stages. Furthermore\, a confidence-weighted yield readiness index is formulated to provide an interpretable estimate of crop maturity and harvest readiness based on predicted growth stages. The trained model is deployed using a Flask-based web application that supports real-time inference\, result visualization\, and storage of historical predictions. Experimental results demonstrate stable convergence\, high classification accuracy\, and reliable generalization across different growth stages. The proposed framework effectively bridges visual growth stage classification and yield estimation\, offering a practical and scalable solution for precision agriculture and decision support systems
CATEGORIES:VIRTUAL ROOM 1B
LOCATION:Virtual Room B\, Bangkok\, Thailand
SEQUENCE:0
UID:cc317be162ec9b81a7277e997f9f9719
URL:http://11thictisthailand.sched.com/event/cc317be162ec9b81a7277e997f9f9719
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260417T125213Z
DTSTART:20260409T023000Z
DTEND:20260409T043000Z
SUMMARY:Computational and Experimental Analysis of Compact Wind Turbine Diffusers with Curved Converging-Diverging Sections and Extended Uniform Throat
DESCRIPTION:Authors - Shilpa H. Gujar\, Abhijeet B. Auti\, Nisha A. Auti\n Abstract - It is possible to increase the acceptability of small wind turbines for wind regions with low wind velocities for rural as well as urban sectors by placing them inside diffusers. The research on development of various diffusers is a major re-search area nowadays. Curved flanged diffusers can deliver better performance by adding a cylindrical throat section between converging and diverging sections. This research paper presents a systematic study on short curved flanged diffusers with converging-diverging sections and extended uniform throat between them. Twenty-five diffuser models are studied using Computational Fluid Dynamics using ANSYS Fluent. These models are finalized using the design of experiments for six variables at five levels. The throat diameter for all diffuser models is fixed. The investigation is performed by considering radial average velocity and percentage velocity variation along the radial planes. The global velocities are observed as 1.18 to 1.47 times that of the radial average velocities. The diffuser dimensions are optimized to maximize radial average velocity and to minimize the velocity variation along the radial planes. The diffuser with optimized dimensions is manufactured and tested experimentally in a wind tunnel. Good matching is seen between the predicted results and experimental results. The optimized diffuser has the ability to produce more than two times the power that of the turbine without a diffuser.
CATEGORIES:VIRTUAL ROOM 1B
LOCATION:Virtual Room B\, Bangkok\, Thailand
SEQUENCE:0
UID:f4f7abeea077e33410676a8a17f712f4
URL:http://11thictisthailand.sched.com/event/f4f7abeea077e33410676a8a17f712f4
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260417T125213Z
DTSTART:20260409T023000Z
DTEND:20260409T043000Z
SUMMARY:Early Detection of Crop Disease from Degraded Field Images Using Quality-Aware FFDNet Denoising and Swin Transformer Classification
DESCRIPTION:Authors - Prathilothamai M.\, R. Rinitha\, Priyanshu Raj\, Jishnu Hari\, Lucky Goyal\n Abstract - The rapid growth of industrialization and urbanization has intensified the release of emerging air and water pollutants\, posing significant threats to environmental sustainability and public health. This paper presents an Internet of Things (IoT) driven monitoring and forecasting framework designed for the early detection of emerging contaminants in air and water systems. The proposed system integrates distributed sensor nodes for real-time measurement of key environmental parameters\, including particulate matter\, volatile organic compounds (VOCs)\, heavy metals\, pH\, turbidity\, and dissolved oxygen. Data collected from heterogeneous IoT sensors are transmitted through secure communication proto-cols to a cloud-based analytics platform. Advanced data processing and machine learning models are employed to identify pollution patterns\, predict contamination trends\, and generate early warning alerts. The framework emphasizes scalability\, low power consumption\, and cost-effectiveness to support deployment in urban\, industrial\, and remote environments. Experimental evaluation demonstrates improved detection accuracy and forecasting reliability compared to conventional monitoring approaches. The proposed solution enables proactive environmental management\, supports regulatory compliance\, and contributes to sustainable development by facilitating timely intervention and mitigation strategies for emerging air and water pollutants.
CATEGORIES:VIRTUAL ROOM 1B
LOCATION:Virtual Room B\, Bangkok\, Thailand
SEQUENCE:0
UID:337e4fab26ff3156b62e5b81dc037707
URL:http://11thictisthailand.sched.com/event/337e4fab26ff3156b62e5b81dc037707
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260417T125213Z
DTSTART:20260409T023000Z
DTEND:20260409T043000Z
SUMMARY:Efficient Quantum-Resilient Homomorphic Encryption for Scalable Cloud Data Processing
DESCRIPTION:Authors - Prerna Agarwal\, Bharat Gupta\, Pranav Shrivastava\, Saquib Hussain\, Kareena Tuli\, Amaanur Rahman\, Aishwarya Keshri Abstract - We propose a classification method for Ise-katagami stencil images based on SIFT keypoints and an optimal matching framework. Ise-katagami are traditional Japanese stencil papers originally developed for kimono dyeing\, many of which have been preserved over long periods yet lack annotation. Because of copyright-related limitations\, methods based on conventional deep learning or transfer learning―which typically depend on large labeled datasets―cannot be readily applied. To address this challenge\, the proposed method formulates the classification task as an optimal matching problem over sets of SIFT keypoints\, allowing robust comparison of local image structures without relying on pixellevel features. The method requires only a small number of copyrightfree training images to extract representative features for each class\, thereby eliminating the need for large-scale training data and enabling fast classification. According to the experimental evaluation\, our method computes a suitable decision threshold within seconds\, whereas the PCAbased method demands more than 3\,000 seconds for optimization\, despite both achieving almost perfect classification accuracy.
CATEGORIES:VIRTUAL ROOM 1B
LOCATION:Virtual Room B\, Bangkok\, Thailand
SEQUENCE:0
UID:7b3081feb3648fee46bf4342c61a0879
URL:http://11thictisthailand.sched.com/event/7b3081feb3648fee46bf4342c61a0879
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260417T125213Z
DTSTART:20260409T023000Z
DTEND:20260409T043000Z
SUMMARY:Government Notice Verification System using Blockchain Technology
DESCRIPTION:Authors - V.Mohanraj\, J.Senthilkumar\, Y.Suresh\, K.Selvaraj\, B.Valaramathi\, S.Sivanantham\, B I Hemantt Kumar\, Ishwarya P Abstract - The increаsing scаle аnd comрlexity of globаl migrаtion flows hаve creаted significаnt chаllenges for trаditionаl migrаtion mаnаgement systems\, раr-ticulаrly in terms of efficiency\, dаtа рrocessing\, аnd timely decision-mаking. Re-cent аdvаnces in Аrtificiаl Intelligence (АI) offer new oррortunities to enhаnce migrаtion governаnce through intelligent dаtа аnаlysis\, аutomаtion\, аnd smаrt communicаtion systems. This рарer exаmines the role of АI in modern migrаtion mаnаgement\, with а focus on border control\, visа аnd аsylum рrocessing\, migrаtion flow forecаsting\, аnd migrаnt integrаtion services. The study emрloys а structured quаlitаtive аnd comраrаtive аnаlyticаl аррroаch\, synthesizing recent аcаdemic literаture\, internаtionаl рolicy documents\, аnd аррlied digitаl migrаtion systems. АI аррlicаtions аre аnаlyzed within а smаrt governаnce frаmework\, emрhаsizing their contribution to communicаtion efficiency\, risk аssessment\, аnd decision-suррort рrocesses. The findings indicаte thаt АI-bаsed biometric identificаtion\, mаchine leаrning–driven risk аssessment\, аnd рredictive аnаlytics significаntly imрrove the аccurаcy аnd sрeed of migrаtion-relаted рrocedures. Nаturаl lаnguаge рrocessing tools further enhаnce communicаtion between аuthorities аnd migrаnts by fаcilitаting multilinguаl informаtion аccess аnd 2 service delivery. However\, the аnаlysis аlso reveаls criticаl chаllenges\, including аlgorithmic biаs\, dаtа рrivаcy risks\, limited trаnsраrency\, аnd the need for humаn oversight in high-stаkes migrаtion decisions. The рарer concludes thаt АI cаn serve аs а key enаbler of smаrt migrаtion governаnce when imрlemented аs а decision-suррort tool within ethicаl\, trаnsраrent\, аnd humаn-centered regulаtory frаmeworks. The study рrovides рrаcticаl insights for рolicymаkers аnd system designers seeking to integrаte АI into smаrt communicаtion аnd digitаl gov-ernаnce аrchitectures for sustаinаble migrаtion mаnаgement.
CATEGORIES:VIRTUAL ROOM 1B
LOCATION:Virtual Room B\, Bangkok\, Thailand
SEQUENCE:0
UID:9d8803319d306e53b4bbd7b09a893d0a
URL:http://11thictisthailand.sched.com/event/9d8803319d306e53b4bbd7b09a893d0a
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260417T125213Z
DTSTART:20260409T023000Z
DTEND:20260409T043000Z
SUMMARY:Intelligent Network Intrusion Detection Based on Hybrid Sampling Techniques and Deep Learning Approaches
DESCRIPTION:Authors - Ischyros Gangbo\, Ghislain Vlavonou\, Pelagie Houngue\, Joel T. Hounsou\, Fulvio Frati\n Abstract - One of the major phenomena in recent decade remains the massive proliferation of data\, directly linked to the adoption and expansion of new technologies and the increasing automation of processes\, affecting numerous fields such as the economy\, education\, and cybersecurity. This exponential increase in almost every area is accompanied by an intensification of threats. It is within this context that new approaches are being defined\, as traditional security mechanisms are showing their limitations. To counter attacks\, several tools\, including intrusion prevention and detection systems (IDS)\, have been designed. IDS are devices intended to monitor an information system in order to react effectively in the event of an attack. To this end\, IDS use mechanisms that allow them to listen to the system covertly in order to detect abnormal or suspicious activities and enable effective preventative action against the risks of intrusion. The objective of this article is to compare the performance of the following models: XGBoost\, CNN\, CNN-LSTM for multiclass classification with a hybrid model. The dataset was first transformed into a sequential format. CNN\, CNN-LSTM\, and XGBoost models were independently implemented as standalone classifiers to perform intrusion detection. Furthermore\, a hybrid CNN-LSTM-XGBoost model was designed\, where deep spatiotemporal features learned by the CNN-LSTM network were used as input to an XGBoost classifier for final decision-making. Comparative experimental results show that XGBoost and Hybrid models achieve effective detection performance\, the hybrid architecture especially in detecting complex and minority attack categories.
CATEGORIES:VIRTUAL ROOM 1B
LOCATION:Virtual Room B\, Bangkok\, Thailand
SEQUENCE:0
UID:9a488eceff7276c3a4cefb6ffb705887
URL:http://11thictisthailand.sched.com/event/9a488eceff7276c3a4cefb6ffb705887
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260417T125213Z
DTSTART:20260409T023000Z
DTEND:20260409T043000Z
SUMMARY:Modelling of Educational Role-Playing Games
DESCRIPTION:Authors - Yavor Dankov\, Boyan Bontchev\, Valentina Terzieva\, Elena Paunova-Hubenova\, Aleksandar Dimov Abstract - The growing demand for lightweight\, high-performance\, and sustain-able machine structures has accelerated the adoption of intelligent digital design methodologies in modern manufacturing. Conventional CAD-based design approaches rely heavily on manual iterations\, limiting efficient exploration of complex design spaces and multi-objective trade-offs. This paper presents a hybrid AI-assisted generative design and topology optimization framework for intelligent lightweight optimization of machine structural components\, with ap-plication to column-type machine structures and complex non-prismatic industrial brackets. The proposed framework integrates parametric CAD modeling\, finite-element-based structural analysis\, CAD-embedded generative design\, and an AI-inspired algorithmic decision layer for automated evaluation and ranking of design alternatives. Key performance indicators—including mass\, stiffness\, stress\, deflection\, fatigue index\, and additive-manufacturing constraints—are digitally processed and combined into a composite performance score to sup-port objective design selection. In the first case study\, a rectangular machine column is evaluated across multiple volume-fraction configurations\, achieving approximately 20% mass reduction while retaining 96% structural stiffness with minimal increases in stress and deflection. The second case study applies generative design to a complex industrial support bracket under multiple load cases\, generating twelve feasible solutions that are algorithmically ranked based on performance and manufacturability. The results confirm that AI-assisted evaluation enables efficient design space exploration and supports intelligent\, sustain-ability-driven engineering decisions for advanced digital manufacturing systems.
CATEGORIES:VIRTUAL ROOM 1B
LOCATION:Virtual Room B\, Bangkok\, Thailand
SEQUENCE:0
UID:e2f09c65bc3554172aaabedad00d43e8
URL:http://11thictisthailand.sched.com/event/e2f09c65bc3554172aaabedad00d43e8
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260417T125213Z
DTSTART:20260409T023000Z
DTEND:20260409T043000Z
SUMMARY:Transparent Dropout Prediction in Higher Education Using Explainable AI: Decision Support for Policy-Makers
DESCRIPTION:Authors - Damla Karagozlu\, Kian Jazayeri\, Ahmet Adalier Abstract - The security of resource-constrained Internet of Things (IoT) devices is increasingly reliant on Zero-Trust Architecture (ZTA) models\, as continuous authentication and behavioral-based trust are providing new models to help mitigate against more sophisticated threats. The proposed framework helps strengthen secure and reliable digital infrastructure for emerging smart technologies and connected environments. In developing a ZTA security framework specifically for limited re-sources (IoT)\, the study proposed a lightweight version that combines Elliptic Curve Cryptography (ECC)-based authentication\, real- time determination of trust scores\, and the use of machine learning to detect behaviorally-based attack pat-terns from a real attack dataset. In addition\, the real-time analysis of device trust scores provides a means to understand which devices are performing in accordance with established expectations or displaying behavior consistent with an at-tack\, and when these devices will reach those levels. Combining a lightweight ECC authentication with a (trust) behaviorally-driven approach to anomaly detection provides a means to enforce Zero-Trust by minimizing any adverse effects on computational performance ability in IoT environments. Therefore\, the approach provides a practical and scalable foundation for Zero-Trust security in future IoT deployments where devices will have limited hardware resources.
CATEGORIES:VIRTUAL ROOM 1B
LOCATION:Virtual Room B\, Bangkok\, Thailand
SEQUENCE:0
UID:9f38ce817a79bdf8847250f2df815712
URL:http://11thictisthailand.sched.com/event/9f38ce817a79bdf8847250f2df815712
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260417T125213Z
DTSTART:20260409T023000Z
DTEND:20260409T043000Z
SUMMARY:User Experience of Deaf and Hard-of-Hearing Students in Institutional Web Platforms: Case Study at Universidad Tecnica de Manabi
DESCRIPTION:Authors - Steven Saltos-Minaya\, Tatiana Zambrano-Solorzano Abstract - The high rate of digital communication has heightened the possibility of fake government announcement getting into the institutions bringing about misinformation and interference in their operations. In an effort to overcome this issue\, this paper will be a proposal of a blockchain verification framework that will guarantee the authenticity\, integrity\, and reliability of any digital notices issued by the government. The system stores cryptographic hashes of official documents in a blockchain Hyperledger\, which produces an audit trail that is immutable and unalterable. The entire files of the notices are safely distributed on the InterPlanetary File System (IPFS) which is decentralized and provides scalable and permanent storage which cannot be censored. Smart contracts running on the Hyperledger platform automatically provide access control\, authorization checks on authorized government publishers and a robust cryptographic assurance of authenticity and non- repudiation. The schools and institutions can check the notices in real time using an intuitive React-based frontend\, with the application logic being dealt with by the Node.js/Express backend as well as communicating with the blockchain layer. Other characteristics like tracking of reputation of publishers\, version management and database of instant notification are also added to advance trust and transparency. The suggested solution provides a secure\, scaled-up\, and highly visible channel of communication between government and educational organizations with the lowest level of system complexity and without the need of any machine-learning parts.
CATEGORIES:VIRTUAL ROOM 1B
LOCATION:Virtual Room B\, Bangkok\, Thailand
SEQUENCE:0
UID:33e0fbe2de1857484018ac0a84242a16
URL:http://11thictisthailand.sched.com/event/33e0fbe2de1857484018ac0a84242a16
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260417T125213Z
DTSTART:20260409T023000Z
DTEND:20260409T043000Z
SUMMARY:A Boundary-Driven Lightweight Segmentation Framework for Robust Image Enhancement in CCTV Surveillance
DESCRIPTION:Authors - Jeba Priya J\, N. Priya Abstract - Mental health challenges among young adults require innovative psychoeducational interventions. This study presents the development and preliminary evaluation of Dear Alfred\, a serious virtual reality (VR) game designed to enhance emotional self-regulation and intergenerational empathy. Grounded in the Process Model of Emotion Regulation\, the game immerses players in a narrative- driven experience addressing elderly isolation. The development followed an iterative methodology\, resulting in a playable vertical slice tested on Meta Quest 2 and 3 platforms. This work contributes to the field by proposing a scalable\, multidimensional approach at the intersection of psychology\, technology\, and education\, highlighting the specific need for hardware-specific optimization in digital mental health solutions.
CATEGORIES:VIRTUAL ROOM 1C
LOCATION:Virtual Room C\, Bangkok\, Thailand
SEQUENCE:0
UID:be2360939e0cfddda5bf9420bffbc0f5
URL:http://11thictisthailand.sched.com/event/be2360939e0cfddda5bf9420bffbc0f5
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260417T125213Z
DTSTART:20260409T023000Z
DTEND:20260409T043000Z
SUMMARY:A Decentralized Architecture for Secure Data Sharing Using Blockchain
DESCRIPTION:Authors - Ashwini V. Zadgaonkar\, Sonali Potdar\, Archana Bopche\, Pranali Pawar\, Rupali Vairagade\, Yogita Hande Abstract - Time series prediction plays a critical role in monitoring and control of electrical power systems\, particularly for detecting frequency fluctuations caused by imbalances between generation and demand. This study proposes an early warning framework for frequency fluctuation events using a hybrid k-Nearest Neighbour (KNN) and Dynamic Time Warping (DTW) approach combined with a global confidence interval based decision mechanism. Electricity frequency data collected from the New Zealand power grid over a six-month period were segmented into training\, validation\, and testing sequences. Alignment distances between historical and incoming sequences were used to identify precursor patterns indicative of impending frequency disturbances. Experimental results show that the proposed method achieves high warning accuracy with a very low false negative rate\, outperforming baseline models such as ARIMA and LSTM. The findings demonstrate that KNN–DTW provides an effective and practical solution for early warning of frequency fluctuations\, supporting improved operational reliability in modern power systems.
CATEGORIES:VIRTUAL ROOM 1C
LOCATION:Virtual Room C\, Bangkok\, Thailand
SEQUENCE:0
UID:cc1b332b9524e44a5b52a6d2f1c2fd2e
URL:http://11thictisthailand.sched.com/event/cc1b332b9524e44a5b52a6d2f1c2fd2e
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260417T125213Z
DTSTART:20260409T023000Z
DTEND:20260409T043000Z
SUMMARY:Analyzing Socio-Environmental Determinants of Teen Suicide in U.S. Counties using K-Means Clustering based Machine Learning Approach
DESCRIPTION:Authors - Arianna Cobb\, Vishnu Kumar\n Abstract - Teen suicide remains a significant public health concern in the Unit ed States\, with substantial geographic variation across counties. Understanding how socio-environmental and healthcare access factors relate to suicide risk can help identify communities that may benefit from targeted interventions. This study aims to support this effort by analyzing county-level teen suicide patterns using K-means clustering\, an unsupervised machine learning technique. A da taset of 248 U.S. counties with reported teen suicide data was constructed using five-year aggregated suicide crude rates (2019-2023) alongside multiple socio environmental and healthcare indicators\, including hospitalization rates\, mental health provider availability\, primary care provider rates\, social association rates\, uninsured population percentages\, poverty levels\, food insecurity\, and rural population share. K-means clustering was then applied to identify county-level risk profiles. The results reveal two distinct county groups: one characterized by lower suicide rates\, greater healthcare provider availability\, stronger social as sociations\, and lower socioeconomic disadvantage\; and another characterized by higher suicide rates\, reduced healthcare access\, higher poverty and food in security\, and greater rural residency. These findings highlight meaningful coun ty-level disparities and demonstrate the utility of machine learning approaches to identify regional risk profiles associated with teen suicide. The results may help inform public health strategies and policy efforts aimed at prioritizing re sources and expanding mental health services in high-risk communities.
CATEGORIES:VIRTUAL ROOM 1C
LOCATION:Virtual Room C\, Bangkok\, Thailand
SEQUENCE:0
UID:97ea89a5331455e4a8e7f0ec7d445f41
URL:http://11thictisthailand.sched.com/event/97ea89a5331455e4a8e7f0ec7d445f41
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260417T125213Z
DTSTART:20260409T023000Z
DTEND:20260409T043000Z
SUMMARY:CodeForge: A Three-Tier Hybrid Framework for Automated Python Code Optimization
DESCRIPTION:Authors - Adhi Sree Praveen Pai\, Alaganandha Pradeep\, Jeremy Simon Moncey\, Josin Kurian Athikalam\, Lakshmi K.S. Abstract - This research investigates the digital footprint of mental health infor mation as it circulates on YouTube. Using a qualitative content analysis ap proach\, the study examines 100 selected videos in conjunction with social media analytics to identify recurring patterns in the dissemination of mental health dis course. The findings reveal a mix of misleading or incomplete claims\, educa tional resources\, personal narratives\, and recovery-oriented content\, illustrating how mental health discussions shape and amplify user perspectives at both broad (macro) and specific (micro) levels within the evolving field of e-health. To in terpret these dynamics\, the analysis applies Gibson’s theory of transactional af fordances\, which illuminates key themes of risk\, relevance\, lived experience\, credibility\, and social support. By situating these themes within the broader con text of video-sharing platforms\, the study underscores the importance of YouTube as a platform for mental health communication. It underscores its role in broader public conversations about health in the digital age. The future re search should investigate mental health discourse from other social media users.
CATEGORIES:VIRTUAL ROOM 1C
LOCATION:Virtual Room C\, Bangkok\, Thailand
SEQUENCE:0
UID:260f4a9335827f945fa8fd9fe9fc4bff
URL:http://11thictisthailand.sched.com/event/260f4a9335827f945fa8fd9fe9fc4bff
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260417T125213Z
DTSTART:20260409T023000Z
DTEND:20260409T043000Z
SUMMARY:Performance Analysis of Brain Tumor Classification Using Computer Vision-Based Vision Transformer and Swin Transformer Techniques
DESCRIPTION:Authors - Rahul Singh\, Sachin B. Jadhav Abstract - Cloud cover generally limits the applicability of optical remote sensing images for tasks such as agriculture monitoring and disaster relief. Cloud removal is an inherently difficult problem because of the lack of spatial structures and spectral information. To effectively remove cloud contamination from SAR and optical images\, we propose a speckle-aware global cross-attention network. The proposed SAR-optical cloud removal network architecture consists of a dual encoder with a global cross-attention mechanism that allows for effective cross-modal interactions. Additionally\, a refining module and symmetric decoders improve the accuracy of the reconstructed image. Furthermore\, we propose a speckle-aware gating mechanism to perform speckle filter adaptation. The experimental results affirm that our proposed network outperformed the baseline by increasing Peak Signal-to-Noise Ratio(PSNR) by +0.86 dB\, Structural Similarity Index Measure(SSIM) by +0.142\, and reducing the spectral distortion of the image. Additionally\, we noticed a decrease in the Root Mean Square Error(RMSE) and Spectral Angle Mapper(SAM) values. This infers that selective SAR-Optical fusion with an adaptive noise-aware gating mechanism improves the accuracy of cloud-free optical images and optical remote sensing images.
CATEGORIES:VIRTUAL ROOM 1C
LOCATION:Virtual Room C\, Bangkok\, Thailand
SEQUENCE:0
UID:ea2dd027ccb304ea5b53470a45d07ac7
URL:http://11thictisthailand.sched.com/event/ea2dd027ccb304ea5b53470a45d07ac7
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260417T125213Z
DTSTART:20260409T023000Z
DTEND:20260409T043000Z
SUMMARY:Predictive Modelling of Mental Health in Engineering and Medical Students Using Machine Learning Techniques
DESCRIPTION:Authors - Nyuti Bhesania\, Khushi Solanki\, Bimal Patel\, Purvi Prajapati\, Priyanka Patel Abstract - The rapid advancement of information and communication technology (ICT) has accelerated the digital transformation of public sector governance\, including tax administration. This study examines the impact of Indonesia’s Core Tax Administration System (Coretax) on micro\, small\, and medium enterprise (MSME) tax compliance within an ICT–behavioral framework. Using survey data from 300 MSME taxpayers and Structural Equation Modeling–Partial Least Squares (SEM-PLS)\, the study analyzes the direct and indirect effects of Coretax utilization on tax compliance through administrative efficiency and trust in the tax authority. The results indicate that Coretax utilization has a positive and significant effect on administrative efficiency\, trust in the tax authority\, and MSME tax compliance. Administrative efficiency and trust also significantly influence compliance\, con-firming their mediating roles. These findings demonstrate that digital tax administration functions not only as a technological reform but also as an institutional and behavioral mechanism that reduces compliance burdens and strengthens vol-untary compliance. From a sustainable development perspective\, improved MSME tax compliance supports Sustainable Development Goal (SDG) 8 by enhancing domestic revenue mobilization for inclusive economic growth\, while the integrative and trust-building role of Coretax reflects SDG 17 through strengthened partnerships among government\, technology providers\, and taxpayers. This study contributes empirical evidence on digital tax systems in developing economies.
CATEGORIES:VIRTUAL ROOM 1C
LOCATION:Virtual Room C\, Bangkok\, Thailand
SEQUENCE:0
UID:ccd5a41813e9c501ae7e6108ce31446f
URL:http://11thictisthailand.sched.com/event/ccd5a41813e9c501ae7e6108ce31446f
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260417T125213Z
DTSTART:20260409T023000Z
DTEND:20260409T043000Z
SUMMARY:Readiness Assessment of Electric Vehicle–Driven Green Logistics Practices
DESCRIPTION:Authors - Ajidhashini Thulasidass\, M. Suresh Abstract - Modern railway system increasingly rely on digital technologies such as Communication-Based Train Control (CBTC)\, European Train Control System (ETCS) and Supervisory Control and Data Acquisition (SCADA) systems\, raising significant cyber-security challenges. We have seen 220% increase in attacks over five years from opportunistic ransomware to sophisticated targeted threats. This paper provides an overview of railway cybersecurity and surveys the coverage area considering ICT architectures\, cyber threat models\, and AI-based defense approaches. 75% of cases employed Distributed Denial of Service (DDoS) tactics while ransomware had affected 54% of the OT environments. We describe a comparative taxonomy of Artificial Intelligence and Ma-chine Learning approaches including the methods based on supervised learning\, unsupervised learning\, and advanced deep learning practices with detection accuracy as high as 97.46%. However\, there exist several challenges: few available public data sets\, lack of validation in real-world scenarios\, demands for explain ability from that AI system and worries about adversarial robustness. We discuss eight potential research gaps\, and future directions focusing on federated learning\, digital twin development\, multimodal AI fusion and safety-security co-engineering frameworks.
CATEGORIES:VIRTUAL ROOM 1C
LOCATION:Virtual Room C\, Bangkok\, Thailand
SEQUENCE:0
UID:94bdecc1b03f9f9122896ec468405c25
URL:http://11thictisthailand.sched.com/event/94bdecc1b03f9f9122896ec468405c25
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260417T125213Z
DTSTART:20260409T023000Z
DTEND:20260409T043000Z
SUMMARY:Socioeconomic Drivers of Supplemental Nutrition Assistance Program (SNAP) Participation in U.S. Urban Communities: A Machine Learning Analysis of Baltimore City
DESCRIPTION:Authors - Vishnu Kumar\, Natalia Miranda\n Abstract - Food insecurity remains a pressing public health and equity challenge in urban U.S. communities\, with the Supplemental Nutrition Assistance Program (SNAP) serving as the primary federal mechanism for alleviating household food hardship. Despite its importance\, SNAP participation varies substantially across neighborhoods\, reflecting underlying socioeconomic disparities. This study leverages neighborhood-level data from Baltimore City to identify the key socioeconomic drivers of SNAP participation using explainable machine learning (ML) techniques. Three supervised ML models: Decision Tree\, Random Forest\, and XGBoost were developed and evaluated using standard regression metrics. The Random Forest model demonstrated the strongest predictive performance. Model interpretability was enhanced through Shapley Additive Explanations (SHAP)\, which quantified the contribution of each feature to predicted SNAP participation. Results indicate that lower income\, shorter life expectancy\, higher Temporary Assistance for Needy Families (TANF) participation\, higher proportions of female-headed households\, and lower educational attainment are associated with increased SNAP reliance. These findings highlight the complex interplay be-tween economic deprivation\, social vulnerability\, and neighborhood-level assistance utilization\, offering actionable insights for policymakers and public health practitioners. By combining predictive accuracy with interpretability\, explainable ML provides a robust framework for informing evidence-based interventions aimed at reducing food insecurity and promoting equity in urban communities.
CATEGORIES:VIRTUAL ROOM 1C
LOCATION:Virtual Room C\, Bangkok\, Thailand
SEQUENCE:0
UID:a7dd3a75befd4de5737e122a68f29a4f
URL:http://11thictisthailand.sched.com/event/a7dd3a75befd4de5737e122a68f29a4f
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260417T125213Z
DTSTART:20260409T023000Z
DTEND:20260409T043000Z
SUMMARY:Study LLMs to Extract Coordinates from 2D Contour Engineering Drawings
DESCRIPTION:Authors - Hector Rafael Morano Okuno Abstract - This work proposes an intelligent system for automatic food-image-based recognition and calorie estimation to meet the emerging demand for accurate dietary monitoring and personalized nutrition recommendations. Conventional food-logging methods are cumbersome\, prone to errors\, and mostly fail to capture portion sizes\, hence motivating an end-to-end computer vision and depth-based approach. The proposed system utilizes a custom-curated Indian food image dataset of eighty classes\, collected\, labeled\, and preprocessed to make it robust enough to present various variations in lighting\, background\, etc. A deep learning model was then trained for detecting and classifying food with high precision. The overall classification accuracy achieved by the proposed system is ninety-seven percent. The depth understanding of the detected food regions will provide an approximation of volume and weight\, leading to relatively better calorie calculations. Nutritional analysis gets integrated into the system by relating the type and estimated weight of food to the standard nutritional information for detailed insights in terms of calories\, proteins\, fats\, car-bohydrates\, fiber\, and micronutrient content. The results for evaluation reveal strong detection\, minimum estimation error\, and efficient real-time processing\, which clearly show its applications. In this paper\, an approach that combines recognition by image\, depth estimation by portion\, and nutrition logic capable of leading to a strong solution for diet determination has been introduced.
CATEGORIES:VIRTUAL ROOM 1C
LOCATION:Virtual Room C\, Bangkok\, Thailand
SEQUENCE:0
UID:d842cdfcb1e9af70a0172a65af54b272
URL:http://11thictisthailand.sched.com/event/d842cdfcb1e9af70a0172a65af54b272
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260417T125213Z
DTSTART:20260409T023000Z
DTEND:20260409T043000Z
SUMMARY:Synthesis and Characterization of TiO2 Nanoparticles for the Detection of Hazardous Gases Using AIML
DESCRIPTION:Authors - D.Swetha\, Senthilkumar Selvaraj\, K.M.Madhan Prasanth\, D.Nihal Abstract - The rapid expansion of digital commerce platforms has significantly transformed on- line transactional systems\; however\, conventional centralized architectures continue to face critical challenges related to security\, transparency\, data integrity\, and trust management. Traditional e-commerce systems rely heavily on centralized databases\, making them vulnerable to data tam- pering\, unauthorized access\, fraudulent transactions\, and single points of failure. To address these limitations\, this paper proposes a secure\, scalable\, and modular web-based e-commerce system that is architecturally designed for integration with blockchain technology and smart contracts. The proposed system is implemented using widely adopted web technologies\, with a responsive frontend and a robust backend to support essential functionalities such as user authentication\, product catalog management\, shopping cart operations\, order processing\, inventory management\, and administrative control. The architecture emphasizes separation of concerns\, enabling flexibility\, maintainability\, and future extensibility. A key contribution of this work lies in the incorporation of a blockchain-ready framework that enables immutable transaction recording and enhanced trace- ability across the entire transaction lifecycle. Smart contracts automate transaction validation and order execution. The system also introduces an AI-based anomaly detection mechanism using a Deep Q-Network to detect fraudulent behavior. Experimental validation demonstrates reliable per- formance and scalability.
CATEGORIES:VIRTUAL ROOM 1C
LOCATION:Virtual Room C\, Bangkok\, Thailand
SEQUENCE:0
UID:3a99ea4a8659a504617f121c4f08f616
URL:http://11thictisthailand.sched.com/event/3a99ea4a8659a504617f121c4f08f616
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260417T125213Z
DTSTART:20260409T023000Z
DTEND:20260409T043000Z
SUMMARY:A Hybrid Deep Learning Approach for Network Intrusion Detection System in Software-Defined Networking
DESCRIPTION:Authors - Linda Sara Mathew\, Anna Irene Ditto\, Anna Keerthana V\, Cristal James Tomy Abstract - With proper and real-time crop mapping and yield prediction\, agricultural planning\, food security\, and climate-resilient decisions are necessitated. The conventional field surveys are slow\, expensive and inconsistent whereas the increased supply of multispectral\, hyperspectral and SAR satellite imagery has made automated crop surveillance possible. Nevertheless\, operational methods continue to suffer significant setbacks\, such as low accuracy in the presence of a cloud cover\, lack of empirical models of the complex time-dependence of temporal growth\, difficulties in treating mixed pixels in the smallholder landscape\, and the lack of a single framework that incorporates optical\, SAR\, and phenology data. Even though recent researchers have investigated deep spatio temporal models to map rice\, SAR–optical fusion\, mixed-pixel decomposition\, temporal attention networks\, multi-GPU UNet architectures\, and phenology-based yield estimation\, none of them have an all-encompassing\, scalable framework. The study suggests a Multimodal Deep Spatio-Temporal Framework that involves multispectral alongside SAR images and phenological data\, which can be used to automatically map crops and predict yields. With CNN-LSTM encoders\, attention-based TCNs\, adaptive mixed-pixel processing\, multimodal fusion\, and multi-GPU segmentation\, the framework should help provide a powerful\, scalable agricultural intelligence system that can be used to monitor the region and country in real-time.
CATEGORIES:VIRTUAL ROOM 1D
LOCATION:Virtual Room D\, Bangkok\, Thailand
SEQUENCE:0
UID:ae04984a0bcd27e171d75491c23016bc
URL:http://11thictisthailand.sched.com/event/ae04984a0bcd27e171d75491c23016bc
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260417T125213Z
DTSTART:20260409T023000Z
DTEND:20260409T043000Z
SUMMARY:Artificial Intelligence-Enhanced Zero Trust Security Framework for Hybrid Cloud Enterprise Networks
DESCRIPTION:Authors - Emerson Joey Caro Abstract - Detecting brain tumors or Brain Tumor Detection(BTD) from MRI scans is an essential step in the assessing of the presence and characteristics of any tumors and formulating an appropriate clinical management plan. The manual interpretation of MRI images by radiolo gists is not time-efficient as well as susceptible to mistakes\, which drives the need for automated\, accurate and reliable computational methods. In this study we will compare the most advanced Deep Learning (DL) ar chitectures\, including traditional CNNs (VGG19\, ResNet50\, DenseNet)\, modernized CNNs inspired by transformer design (ConvNext) and Effi cientNet\, to tell apart between tumor and non-tumor categories in brain MRI scans. Each model is trained and evaluated on a standardized dataset relying on measurable data such as accuracy\, precision\, recall\, F1-score\, F1 score\, and confusion matrix. Our results demonstrate that modern CNN architectures such as ConvNext and EfficientNet outper form traditional CNNs\, which capture both local texture\, spatial patterns and the global spatial context\, leading to improved context\, resulting in enhanced classification performance. This benchmark is informative in evaluating the best models used in deep learning and adopt them to identify brain tumors\, and in turn may be used in optimizing the use of diagnostic decision-making to improve and reducing the burden on the diagnosis.
CATEGORIES:VIRTUAL ROOM 1D
LOCATION:Virtual Room D\, Bangkok\, Thailand
SEQUENCE:0
UID:2829e9346d7754bc267a8984b791006a
URL:http://11thictisthailand.sched.com/event/2829e9346d7754bc267a8984b791006a
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260417T125213Z
DTSTART:20260409T023000Z
DTEND:20260409T043000Z
SUMMARY:AutoNoteX: AI-Powered Multimodal Note Generation and Interactive Learning Assistant
DESCRIPTION:Authors - Vasavi Ravuri\, Indupriya Vempati\, Sai Anuradha Kappaganthula\, Pavani Muppalla\, Navya Taduri Abstract - In the shadow of overlooked safety violations\, different factories have lost thousands\, in terms of capital as well as lives. Which is especially harrowing as these were caused due to easily preventable work accidents or easily noticeable defective machinery. Our paper dives into how artificial intelligence based methodologies\, particularly\, would help in mitigating these risks based on past and present research. We also recommend a potential prototype system according to the findings from the literature we reviewed\, for Real-Time worker safety check and automated industrial machine quality inspection system. We have reviewed four major topics pertaining to our system: [1] Personal Protective Equipment (PPE) compliance detection through CCTV monitoring as opposed to manual monitoring\, [2] industrial machine quality inspection for automatic defect identification [3] evaluation of previously used object detection models and their performance for industry applications\, and [4] system level considerations for practical deployment of the said systems on a large scale. We have compared methods\, deployment strategies and results from existing studies to identify key criteria like scalable architectures as well as low latency processing. We are highlighting challenges such as insufficient annotated data for rare machinery defects\, good accuracy in harsh industrial conditions that might hinder detection of safety violations\, and ethical issues with worker monitoring as well in this paper.
CATEGORIES:VIRTUAL ROOM 1D
LOCATION:Virtual Room D\, Bangkok\, Thailand
SEQUENCE:0
UID:aae62fc1d6e466b35d30f645babeb457
URL:http://11thictisthailand.sched.com/event/aae62fc1d6e466b35d30f645babeb457
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260417T125213Z
DTSTART:20260409T023000Z
DTEND:20260409T043000Z
SUMMARY:Circular Shaped Wearable Patch Antennas for 6G Application
DESCRIPTION:Authors - Siddalingappagouda Biradar\, Vinod B Durdi\, Suganthi Neelagiri\, Devaraju Ramakrishna\, Preeti Khanwalkar\, Shashi Raj K Abstract - Phishing attacks continue to evolve in scale and sophistication\, working on weaknesses across infrastructure\, content\, and user behavior. Earlier studies demonstrated that hybrid feature representations combining URL\, HTML\, and infrastructure features significantly outperform single-source approaches\, with tree-based and deep learning models achieving detection accuracies exceeding 95%. However\, these studies also revealed limitations related to global feature selection\, cluster-agnostic learning\, and evaluation protocols that may lead to optimistic performance estimates. In this paper\, propose a multi-cluster phishing detection framework that organizes features into three complementary clusters: Cluster 0 (C0) for infrastructure and transport-layer characteristics\, Cluster 1 (C1) for URL and HTML content features\, and Cluster 2 (C2) for behavioral and campaign-level patterns. To address the limitations of traditional feature selection methods\, we introduce HC²FS (Heuristic-Constrained Class-Conditional Feature Selection)\, a cluster-aware and class-conditional approach that preserves low-variance yet highly discriminative phishing indicators. The proposed system is evaluated on large-scale datasets comprising over 600 combined features\, using a strict 80% training and 20% testing split enforced prior to feature selection and model training.
CATEGORIES:VIRTUAL ROOM 1D
LOCATION:Virtual Room D\, Bangkok\, Thailand
SEQUENCE:0
UID:1ce7b6f36fdba7fe478e072b7113a64c
URL:http://11thictisthailand.sched.com/event/1ce7b6f36fdba7fe478e072b7113a64c
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260417T125213Z
DTSTART:20260409T023000Z
DTEND:20260409T043000Z
SUMMARY:Extending Latency Models for Long-Sequence Inference: Nonlinear\, Adaptive\, and Empirical Enhancements
DESCRIPTION:Authors - Koutaro HACHIYA\, Ioannis PATIAS\n Abstract - Inference latency remains a critical bottleneck in deploying large language models\, for real-time and resource-constrained environments. Prior work has proposed latency formulations that express latency as a function of key parameters. However\, they often assume a linear dependence on sequence length\, which fails to generalize to tasks involving significantly longer sequences\, such as document-level language modeling\, long-context retrieval\, or time-series forecasting\, where latency scales nonlinearly and unpredictably. This paper addresses the limitations of existing latency formulations by proposing three complementary enhancements to improve generalization across varying sequence lengths. First\, we introduce a nonlinear term for sequence length\, capturing the superlinear growth in latency observed in transformer-based architectures due to quadratic attention mechanisms and memory overhead. Second\, we propose a sequence-length-dependent scaling factor for the sequence length parameter itself\, allowing the model to adaptively adjust its sensitivity based on empirical latency profiles across different tasks and hardware configurations. Third\, we incorporate an empirical correction term enabling calibration of the latency model to account for hardware-specific and implementation-level nuances. By explicitly modeling the nonlinear and context-sensitive behavior of sequence length\, our approach offers a more faithful representation of latency dynamics. This work lays the foundation for more adaptive and hardware-aware latency estimation frameworks\, with implications for model deployment\, scheduling\, and cost optimization in production systems. We conclude by discussing future directions for integrating dynamic profiling and reinforcement learning to further refine latency predictions in evolving runtime environments.
CATEGORIES:VIRTUAL ROOM 1D
LOCATION:Virtual Room D\, Bangkok\, Thailand
SEQUENCE:0
UID:d3433465b1f4df83450c62058edce557
URL:http://11thictisthailand.sched.com/event/d3433465b1f4df83450c62058edce557
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260417T125213Z
DTSTART:20260409T023000Z
DTEND:20260409T043000Z
SUMMARY:Immersive Virtual Reality for Awareness and Development of Emotional Self-Regulation Skills: The Case of the Dear Alfred Project
DESCRIPTION:Authors - Joao Paulo Sousa\, Tiago Lopes\, Tatiana Ferreira\, Tatiana Batista\, Pedro Malheiro\, Joao Vitorino\, Barbara Barroso\, Carlos Costa Abstract - Medical hyperspectral imaging (MHSI) represents a burgeoning paradigm in diagnostic visualization\, capable of capturing contiguous spectral signatures across hundreds of narrow wavelengths to delineate pathological structures invisible to the human eye. Despite its diagnostic richness\, the advancement of deep learning models in the MHSI domain is severely constrained by two primary challenges: the extreme scarcity of high-quality\, pixel-level annotated datasets and the overwhelming data redundancy inherent in high-dimensional hypercubes. Traditional self-supervised methods\, particularly masked image modeling\, often fail to prioritize discriminative tissue signatures\, while domain-agnostic transfer learning from natural images proves inappropriate due to structural and feature-level incongruities. This paper introduces a novel high-quality research methodology: Reinforced Spatio-Spectral In-Context Learning (RSS-ICL). This framework integrates an asynchronous advantage actor-critic (A3C) reinforcement learning agent with visual in-context learning (ICL). The proposed model employs the RL agent to dynamically learn adaptive masking strategies that prioritize high-entropy\, "hardto- reconstruct" spatio-spectral voxels\, thereby forcing the backbone architecture to capture intricate biochemical signatures during pre-training. By reformulating segmentation as a supportquery inpainting task\, RSS-ICL facilitates universal medical segmentation\, allowing the model to adapt to novel clinical tasks and unseen tissue types in a zero-shot or one-shot manner. Theoretical arguments suggest that this synergistic approach effectively bridges the gap between low-level signal recovery and high-level semantic understanding in hyperspectral analysis. Through rigorous methodological development and empirical support from existing selfsupervised benchmarks\, this paper outlines a path for accelerating the deployment of interpretable\, annotation-efficient clinical AI.
CATEGORIES:VIRTUAL ROOM 1D
LOCATION:Virtual Room D\, Bangkok\, Thailand
SEQUENCE:0
UID:e3f98c099f63bd4b1f9bf6c6d914992f
URL:http://11thictisthailand.sched.com/event/e3f98c099f63bd4b1f9bf6c6d914992f
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260417T125213Z
DTSTART:20260409T023000Z
DTEND:20260409T043000Z
SUMMARY:Multi-Secret Sharing Scheme with CSS Codes
DESCRIPTION:Authors - Sushmita Sarkar\, Sumit Kumar Debnath Abstract - Multi-angle image synthesis is highly important when it comes to the generation of 3D scenes. But the current methods are either ex pensive in terms of computational costs or lack photorealism in their outputs. We propose a novel sketch and text based multiview image generation approach that solves the above-mentioned problems by mak ing use of multimodal diffusion models efficiently. Our pipeline utilises DreamShaper v8 for converting the input sketch and text into a pho torealistic 2D image and then passes this 2D image into a fine-tuned Zero123plus model for the final generation of consistent multiview im ages\, showing a 43.69% improvement in the overall perceptual quality compared to baseline sketch-to-multiview models. Moreover\, our pipeline shows flexibility in scalability by generating anywhere from 6 to 64 consis tent multiview images according to the requirements of the downstream tasks. We demonstrate the success of our pipeline through extensive ex periments conducted using voxel-based grid approaches and Neural Ra diance Fields (NeRF). Our pipeline greatly reduces computational costs\, all while maintaining photorealism in the outputs\, confirming the poten tial of sketch and text based multimodal conditioning as an intuitive and efficient paradigm for controlled 3D content generation.
CATEGORIES:VIRTUAL ROOM 1D
LOCATION:Virtual Room D\, Bangkok\, Thailand
SEQUENCE:0
UID:37301353eaa9675529e316f2e3b0f308
URL:http://11thictisthailand.sched.com/event/37301353eaa9675529e316f2e3b0f308
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260417T125213Z
DTSTART:20260409T023000Z
DTEND:20260409T043000Z
SUMMARY:Persistent Authority in Agent Systems: Memory Poisoning\, Provenance Laundering\, and Remediation-Complete Containment
DESCRIPTION:Authors - Carl Kugblenu\, Petri Vuorimaa Abstract - Compressed-domain audio steganography poses a critical foren sic challenge in modern VoIP systems\, particularly within low-bitrate codecs. Traditional deep learning models often lack interpretability and struggle with low embedding rates. This paper introduces AUSPEX\, a lightweight forensic framework ( 170k parameters) optimized for uni versal compressed audio steganalysis. A novel three-channel tensoriza tion strategy is proposed\; incorporating raw bits\, temporal derivatives\, and bit stability to amplify subtle embedding perturbations. A non trainable high-pass residual stream further enhances sensitivity to first and second-order temporal noise. To ensure forensic transparency\, a dual level explainability framework integrates intrinsic spatial attention with post-hoc Integrated Gradients\, providing bit-level evidence attribution. Experiments demonstrate detection across CNV and PMS algorithms at low embedding rates. AUSPEX advances the field by unifying ef f icient\, edge-deployable detection with rigorous human-centric forensic interpretability.
CATEGORIES:VIRTUAL ROOM 1D
LOCATION:Virtual Room D\, Bangkok\, Thailand
SEQUENCE:0
UID:ca38b144cbf5afb6918cd8cafe0dff6c
URL:http://11thictisthailand.sched.com/event/ca38b144cbf5afb6918cd8cafe0dff6c
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260417T125213Z
DTSTART:20260409T023000Z
DTEND:20260409T043000Z
SUMMARY:Profanity Analysis in Hollywood Movies
DESCRIPTION:Authors - Nitika Gawande\, Pradnya Bapat\, Sanyukta Sasane\, Trupti Bankar\, Rakhi Dongaonkar\, Rashmi Apte\, Mangesh Bedekar\n Abstract - The abstract of the study emphasizes the thorough discussion of cussword usage in Hollywood films over a period of thirty five years\, from 1990 to 2025\, particularly in genres such as Action\, Comedies\, and Romances. On the basis of a carefully selected dataset of cusswords from Kaggle along with a considerable subtitle file dataset (.srt)\, the results have been obtained to determine whether profanity has been used over the years with an appropriate level of intensity in the respective genres of films.
CATEGORIES:VIRTUAL ROOM 1D
LOCATION:Virtual Room D\, Bangkok\, Thailand
SEQUENCE:0
UID:eab16223b25ea85ee1f75c72dc5d92fa
URL:http://11thictisthailand.sched.com/event/eab16223b25ea85ee1f75c72dc5d92fa
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260417T125213Z
DTSTART:20260409T023000Z
DTEND:20260409T043000Z
SUMMARY:Structured State Representation for Action-Masked Reinforcement Learning in Flexible Job Shop Scheduling
DESCRIPTION:Authors - Kostiantyn Hrishchenko\, Oleksii Pysarchuk\n Abstract - Flexible Job Shop Scheduling Problems (FJSP) involve large discrete decision spaces and strict feasibility constraints\, making them challenging for deep reinforcement learning methods. In this work\, we study how state represen tation and feature extraction architecture influence the performance of action masked Proximal Policy Optimization (PPO) in flexible scheduling. The scheduling task is formulated as a sequential assignment of operations to machines with a fixed discrete action space\, where infeasible actions are removed using a feasibility mask. The environment state is represented using three heter ogeneous feature blocks describing resource availability\, operation readiness\, and time-related attributes of assignment alternatives. We compare a baseline single-branch encoder with a multi-branch feature extraction architecture that processes these blocks separately before aggregation. Experiments were conducted on the Brandimarte MK benchmark suite (MK01 MK10). Under identical training conditions\, the multi-branch representation achieved lower makespan on 9 out of 10 instances\, with relative improvements ranging from 2.4% to 27.8% compared to the single-branch baseline. The largest reductions were observed on MK06 (−27.8%) and MK10 (−25.2%)\, while per formance remained comparable on MK08. Training results indicate improved stability and more consistent convergence for structured representations. These results demonstrate that structured state design and feature extraction ar chitecture are critical factors in action-masked reinforcement learning for flexible job shop scheduling.
CATEGORIES:VIRTUAL ROOM 1D
LOCATION:Virtual Room D\, Bangkok\, Thailand
SEQUENCE:0
UID:77ef2fb51624ada07d5b0f43a729ecdb
URL:http://11thictisthailand.sched.com/event/77ef2fb51624ada07d5b0f43a729ecdb
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260417T125213Z
DTSTART:20260409T023000Z
DTEND:20260409T043000Z
SUMMARY:A Compact Pairing-Based Signature Scheme from Isogeny Inspired Structures
DESCRIPTION:Authors - Stuti Kumari\, Kunal Dey Abstract - Teen suicide remains a significant public health concern in the Unit ed States\, with substantial geographic variation across counties. Understanding how socio-environmental and healthcare access factors relate to suicide risk can help identify communities that may benefit from targeted interventions. This study aims to support this effort by analyzing county-level teen suicide patterns using K-means clustering\, an unsupervised machine learning technique. A da taset of 248 U.S. counties with reported teen suicide data was constructed using five-year aggregated suicide crude rates (2019-2023) alongside multiple socio environmental and healthcare indicators\, including hospitalization rates\, mental health provider availability\, primary care provider rates\, social association rates\, uninsured population percentages\, poverty levels\, food insecurity\, and rural population share. K-means clustering was then applied to identify county-level risk profiles. The results reveal two distinct county groups: one characterized by lower suicide rates\, greater healthcare provider availability\, stronger social as sociations\, and lower socioeconomic disadvantage\; and another characterized by higher suicide rates\, reduced healthcare access\, higher poverty and food in security\, and greater rural residency. These findings highlight meaningful coun ty-level disparities and demonstrate the utility of machine learning approaches to identify regional risk profiles associated with teen suicide. The results may help inform public health strategies and policy efforts aimed at prioritizing re sources and expanding mental health services in high-risk communities.
CATEGORIES:VIRTUAL ROOM 1E
LOCATION:Virtual Room E\, Bangkok\, Thailand
SEQUENCE:0
UID:1544c6e9133deec73c21aa1216b18f70
URL:http://11thictisthailand.sched.com/event/1544c6e9133deec73c21aa1216b18f70
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260417T125213Z
DTSTART:20260409T023000Z
DTEND:20260409T043000Z
SUMMARY:A Comparative Study of Machine Learning Models for Early Diabetes Risk Prediction
DESCRIPTION:Authors - Sanjida Karim Peuly\, Sharmin Alam Mou\, Tamanna Hossain Badhon\n Abstract - Diabetes diagnosis at the early stages is an important factor in avoiding long-term complications. The existing body of literature tends to be based on small\, saturated datasets that are not very interpretable and externalized. This pa-per will suggest a powerful machine learning model to predict diseases at the first stage of diabetes on the basis of a symptom-based dataset of One thousand five hundred and sixty cases. Six classifiers\, including Logistic Regression\, Decision Tree\, Random Forest\, K-Nearest Neighbors\, Naive Bayes\, and XGBoost\, were considered on the stratified cross-validation and independent test sets. Systematic hyperparameter optimization using GridSearchCV was used to prevent overfit-ting and improve the generalization. Additionally\, a Stacking Ensemble model was provided\; the Logistic Regression\, Random Forest\, and XGBoost were com-bined to obtain a high level of predictive stability. Experimental evidence has shown that ensemble-based methods are more effective than single classifiers\, as XGBoost and Stacking Ensemble have the highest accuracy and ROC-AUC val-ues. The analysis of feature importance suggested polyuria and polydipsia as the most important clinical signs\, which is consistent with medical knowledge. This study offers a practical and interpretable decision support model in screening early diabetes\, which bridges the predictive performance and clinical utility gap.
CATEGORIES:VIRTUAL ROOM 1E
LOCATION:Virtual Room E\, Bangkok\, Thailand
SEQUENCE:0
UID:be825ece3a7e8f95eb756963258dde84
URL:http://11thictisthailand.sched.com/event/be825ece3a7e8f95eb756963258dde84
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260417T125213Z
DTSTART:20260409T023000Z
DTEND:20260409T043000Z
SUMMARY:A Probabilistic Pipeline for Forecasting Cryptographic Lifetimes Under Classical and Quantum Threats
DESCRIPTION:Authors - Jose R. Rosas-Bustos\, Mark Pecen\, Jesse Van Griensven The\, Roydon Andrew Fraser\, Nadeem Said\, Sebastian Ratto Valderrama\, Andy Thanos\n Abstract - Post-quantum migration is increasingly constrained by time: deployed cryptographic mechanisms may need to be retired\, hybridized\, or re-keyed before effective security margins fall below asset-specific pol icy thresholds. This timing problem is complicated by uncertainty in clas sical hardware acceleration\, algorithmic progress\, implementation ero sion\, and the arrival of cryptographically relevant quantum comput ers. This paper presents a compact probabilistic pipeline that translates evolving assumptions and evidence into decision-facing migration guid ance. The approach couples three layers: (i) a security-trajectory model that encodes expected margin erosion under scenario parameters\, (ii) a latent-regime model that represents partially observed risk states and updates them as evidence changes\, and (iii) an option-style timing layer that quantifies the diminishing value of delaying migration as thresholds approach. Outputs are conditional on stated assumptions and are in tended to be reported with sensitivity bands and lead-time constraints. In practice\, the pipeline is intended to be re-run as assumptions and evidence evolve\, preserving an auditable trail from scenario inputs to in termediate states and final decision artifacts. The primary deliverables are comparative rankings and conservative “start-by” windows under stated assumptions\, rather than single predicted break dates.
CATEGORIES:VIRTUAL ROOM 1E
LOCATION:Virtual Room E\, Bangkok\, Thailand
SEQUENCE:0
UID:8b0545ca2daa241696c20eede1c865d9
URL:http://11thictisthailand.sched.com/event/8b0545ca2daa241696c20eede1c865d9
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260417T125213Z
DTSTART:20260409T023000Z
DTEND:20260409T043000Z
SUMMARY:AI-Driven Sentiment Analysis Framework for Optimizing Digital Marketing Promotions
DESCRIPTION:Authors - Ronald S. Cordova\, Rowena O. Sibayan\, Hazel C. Tagalog\, Rolou Lyn R. Maata\n Abstract - Awareness regarding consumer sentiments will benefit a business entity and/or a company in making their marketing strategies more effective and engaging in the current digital marketing context. In traditional marketing scenarios\, since there is a lack of actual emotional aspect in expressing views in real-time contexts\, it has always been challenging for a business to perform a significant adjustment in their marketing campaigns and achieve a greater success rate. The proposed idea focuses on AI and ML-based approaches for sentiment analysis in digital marketing. &nbsp\;The framework is made up of seven core steps: data collection\, preprocessing and data cleaning\, sentiment analysis models\, feature extraction and model training\, sentiment classification and analysis\, insights and decision-making\, and application in digital marketing. From social media to e-commerce reviews to online discussions\, consumer sentiment data comes from many digital sources. The text for analysis is standardized\, and noise is cleaned in data preparation. Then\, apart from other artificial intelligence-based sentiment classification models\, sentiments are classified as positive\, negative\, or neutral using lexicon-based\, machine learning\, and deep learning approaches. The learned knowledge enables businesses to react dynamically to consumer sentiment\, target advertisements\, and adjust marketing strategies. &nbsp\;Businesses will be able to conduct more profitable promotions\, communicate with customers better\, and monitor real-time sentiment through this AI-driven sentiment analysis platform. The paper emphasizes the benefit of incorporating artificial intelligence in decision-making within digital marketing\, even in addressing issues like ambiguous sentiment expression management and multi-language data. This paper provides a strategic way towards maximum customer interaction and brand loyalty and also emphasizes the need for sentiment analysis that is sustained by available data in modern digital marketing.
CATEGORIES:VIRTUAL ROOM 1E
LOCATION:Virtual Room E\, Bangkok\, Thailand
SEQUENCE:0
UID:470eeea62a23004f70b3487bd7ff5c85
URL:http://11thictisthailand.sched.com/event/470eeea62a23004f70b3487bd7ff5c85
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260417T125213Z
DTSTART:20260409T023000Z
DTEND:20260409T043000Z
SUMMARY:Detecting Fake Products with Blockchain Technology
DESCRIPTION:Authors - Mandala Nagarjuna Naidu\, Bandi Hemalatha\, Kadavakallu Viswanath\, Kotapati Venkata Pavan\, Ms.Ragavarthini\n Abstract - Autonomous vehicles rely on powerful perception systems with real-time object detection and tracking capabilities. Our paper presents a unified deep learning framework based on YOLOv8n and ByteTrack for multi-class detection of vehicles\, pedestrians\, traffic signs and lights on roads. Our work maintains consistent tracking between frames without the limitations of previous works that rely on static images or single-object-type detection. The lightweight model\, with only 3.2 million parameters in YOLOv8n\, provides a good trade-off between accuracy and efficiency for embedded automotive hardware. Experiments conducted on the COCO validation dataset\, achieving 52.11% mAP @ 0.5\,with precision and recall values of 63.42% and 47.44% respectively.It runs real-time on traffic videos with an average frame rate of 62 FPS and a mean inference time of 10.10 ms.Results for tests on traffic videos show\, on average 10.15 objects detected with 68.29% confidence.These findings make this approach apt for both autonomous navigation and intelligent traffic monitoring.
CATEGORIES:VIRTUAL ROOM 1E
LOCATION:Virtual Room E\, Bangkok\, Thailand
SEQUENCE:0
UID:14d899b253f1399a69cb7e9ca369d78d
URL:http://11thictisthailand.sched.com/event/14d899b253f1399a69cb7e9ca369d78d
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260417T125213Z
DTSTART:20260409T023000Z
DTEND:20260409T043000Z
SUMMARY:Network Security Evolution Through Artificial Intelligence and Machine Learning: Architecture\, Models\, and Experimental Evaluation
DESCRIPTION:Authors - Mazdak Zamani\, Mohammad Naderi Dehkordi\, Riham Hilal\, Azizah Abdul Manaf\, Achyut Shankar\, Touraj Khodadadi Abstract - Access to formal financial services remains limited in many develop ing regions\, largely due to economic and infrastructural constraints. This study uses the ISO/IEC 25010 as the evaluation framework to present a software quality assessment of a lending automation system installed in a financial insti tution in Butuan City\, Philippines. The evaluation focuses on five essential as pects of software quality: usability\, reliability\, functional suitability\, perfor mance efficiency\, and security. Usability surveys using SUS and UMUX-Lite\, operational and performance testing\, and an evaluation of security and data pri vacy compliance were used to gather empirical data. According to the results\, the system achieved high performance with an average inference latency of 0.208 ms per record\, uptime reliability of ≥99.5%\, excellent usability with a mean SUS score of 82.5\, and full compliance with data privacy regulations. Predictive analytics\, specifically the Random Forest model with isotonic cali bration\, further enhanced the automated loan assessment’s interpretability and reliability. The system proved that it is appropriate for real-world applications and can encourage financial inclusion in resource-constrained environments\, as it exceeded the intended benchmarks for each quality model. To guarantee the long-term adoption of lending automation technologies\, the study emphasizes the significance of thorough software quality evaluation in addition to predic tive accuracy.
CATEGORIES:VIRTUAL ROOM 1E
LOCATION:Virtual Room E\, Bangkok\, Thailand
SEQUENCE:0
UID:1d472d48e35cf02fb6fae7ec8c18d624
URL:http://11thictisthailand.sched.com/event/1d472d48e35cf02fb6fae7ec8c18d624
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260417T125213Z
DTSTART:20260409T023000Z
DTEND:20260409T043000Z
SUMMARY:Novel spectral Jailbreak classifier system and a stability metric to assess and identify the complex non-linear prompt dynamics in Large Language model Jailbreak attempts
DESCRIPTION:Authors - Sai Sundarakrishna\, Vedant Maheshwari\n Abstract - Recent literature has posed LLMs as nonlinear dynamical systems. LLM safety\, in these modern LLMs is about the systematic and critical monitoring of logit based oscillations\, hidden state rotations and entropy fluctuations. Many of these important factors are spectral proxies for the generation of imaginary eigenvalues. These imaginary eigenvalues are\, in a way\, determinants of the latent oscillation energy. Though the system in its original state space is inherently nonlinear\, through the Koopman operator\, we can linearize the evolution in the lifted space of observables. We design a spectral jailbreak detector that has a Sparsely regularized koopman autoencoder as its backbone. We obtain the koopman operator through this SR-KAE\, and also obtain the imaginary component of the eigenvalues of that spectral operator\, A new risk score metric is proposed that is used to classify prompts as either jailbreak or safe. This becomes a physics-style stability classifier on prompts. We present several test cases\, while we discuss the strengths and limitations of this new system.
CATEGORIES:VIRTUAL ROOM 1E
LOCATION:Virtual Room E\, Bangkok\, Thailand
SEQUENCE:0
UID:6372366edaedb736dd6d022a3b9292cd
URL:http://11thictisthailand.sched.com/event/6372366edaedb736dd6d022a3b9292cd
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260417T125213Z
DTSTART:20260409T023000Z
DTEND:20260409T043000Z
SUMMARY:Opportunities and Emerging Risks of Network Security Evolution Through Artificial Intelligence (AI) and Machine Learning (ML)
DESCRIPTION:Authors - Mazdak Zamani\, Mohammad Naderi Dehkordi\, Riham Hilal\, Azizah Abdul Manaf\, Achyut Shankar\, Touraj Khodadadi Abstract - The rushed development of edge computers\, including Internet-of-things (IoT) nodes\, wearable similes\, and embedded cyber-physical systems has enhanced the necessity to deploy machine-learning (ML) models with a high diligence to function within harsh resource restraint conditions. Although traditional deep-learning models have high predictive accuracy\, they usually require significant computational resources\, memory and power which makes them infeasible in these settings. This paper provides a thorough proposal of accuracy-efficiency trade-off of lightweight ML models adapted to resource-constrained resource providers. We compare classical and modern lightweight methods of determining classification: linear frameworks\, tree-based learners\, shallow and compressed neural networks\, on various performance metrics of accuracy\, inference latency\, memory base\, and energy usage. Experimental outcomes based on commonly used benchmark datasets show that lightweight models can achieve competitive accuracy at significantly reduced overall computation overhead. The results also provide useful recommendations to select and design ML models in edge intelligence\, real-time decision-making\, and low-power AI models.
CATEGORIES:VIRTUAL ROOM 1E
LOCATION:Virtual Room E\, Bangkok\, Thailand
SEQUENCE:0
UID:4c33a78b578c546d5947a69c98b3b4a9
URL:http://11thictisthailand.sched.com/event/4c33a78b578c546d5947a69c98b3b4a9
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260417T125213Z
DTSTART:20260409T023000Z
DTEND:20260409T043000Z
SUMMARY:PricePulse: An AI-Enhanced Multi-Platform Price Comparison System for E-Commerce Decision Support
DESCRIPTION:Authors - Sri Kavya Swarna\, Varun Kumar Reddy Kola\, DS Bhupal Naik\, Dinesh Reddy Tiyyagura\, Lakshmi Charitha Bandaru\, Srinivasa Rao P.\n Abstract - This paper presents PricePulse\, a web-based price comparison system that supports consumers with real-time multi-platform price analysis and AI-powered shopping insights. The system aggregates product data from Amazon\, Flipkart\, and Meesho via SerpAPI’s Google Shopping API and enriches results with recommendations generated by Google’s Gemini AI. Built on Next.js and Flask\, PricePulse addresses gaps in the e-commerce ecosystem by eliminating manual price comparison across platforms. The system uses JWT-based authentication\, maintains search history in SQLite\, and provides an intuitive interface with React and Tailwind CSS. Evaluation shows average response times under 2 seconds and 95% accuracy in price extraction\, demonstrating significant potential to help consumers make informed purchasing decisions and save on purchases.
CATEGORIES:VIRTUAL ROOM 1E
LOCATION:Virtual Room E\, Bangkok\, Thailand
SEQUENCE:0
UID:f21cf3532eee85a9f07a82d8db44ae40
URL:http://11thictisthailand.sched.com/event/f21cf3532eee85a9f07a82d8db44ae40
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260417T125213Z
DTSTART:20260409T023000Z
DTEND:20260409T043000Z
SUMMARY:Risks and Opportunities of Using Artificial Intelligence and Machine Learning for Digital Forensics
DESCRIPTION:Authors - Mazdak Zamani\, Mohammad Naderi Dehkordi\, Riham Hilal\, Azizah Abdul Manaf\, Achyut Shankar\, Touraj Khodadadi Abstract - Nowadays\, small networks are commonly used by people at home\, in laboratories\, or by small offices. These networks are not secured and an attacker can easily attempt to intrude these networks. To prevent this we need to continue to monitor the network and detect wrong activity early. Our simple system is called NetSentinels\, and was developed in this project. It monitors the network traffic at all times and displays alerts message in case of a questionable event. We have used Snort which is free and open source tool. It assists in identifying attacks such as port scans\, ICMP floods and multiple attempts of logging in. This system does not require the use of sophisticated devices thus can be installed in ordinary computers. NetSentinels can be applied in small networks to remain safe against attackers and enhance general security practices. In addition to real-time monitoring\, the system also stores alert logs which can be used for later analysis and understanding attack patterns. The use of a virtual machine environment ensures safe deployment and easy portability across different systems. The system is designed to consume minimal CPU and memory\, making it suitable for continuous operation without affecting system performance. Overall\, NetSentinels provides a simple\, low- cost and practical approach for improving network visibility and security awareness in small-scale environments.
CATEGORIES:VIRTUAL ROOM 1E
LOCATION:Virtual Room E\, Bangkok\, Thailand
SEQUENCE:0
UID:f89e1929a7c11d8a6440abef1558fcbf
URL:http://11thictisthailand.sched.com/event/f89e1929a7c11d8a6440abef1558fcbf
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260417T125213Z
DTSTART:20260409T023000Z
DTEND:20260409T024500Z
SUMMARY:Exploring how Biomechatronics and CFD Simulations can Help Determine Health Risk Conditions
DESCRIPTION:Authors - Hector Rafael Morano Okuno\n Abstract - Mechatronics is an interdisciplinary field that draws on mechanics\, electronics\, and computer science. In recent years\, the term biomechatronic has been used with increasing frequency\; it is also a multidisciplinary field that in volves biological sciences and\, therefore\, bioinformatics. With the development of AI\, bioinformatics provides data to biomechatronic systems\, enabling appli cations ranging from agriculture to medicine. This article explores how bio mechatronics and CFD simulations can help monitor a person's health status. The objectives of this research were: 1) to determine whether\, using biomarkers such as hemoglobin\, fibrinogen\, and low-density lipoprotein (LDL)\, among others\, and CFD simulations\, it is possible to obtain blood flow velocity pro files\; and 2) to investigate whether the information from CFD simulations can be used to feed a biomechatronic system to monitor a person's health condi tions. Among the results\, it was found that it is necessary to have models that allow relating the main biomarkers to determine the state of health of a person\, as well as with suitable sensors to measure each variable according to the orien tation of the application that is to be developed\, for example\, for physical train ing or for the monitoring of nutrition.
CATEGORIES:VIRTUAL ROOM 1F
LOCATION:Virtual Room F\, Bangkok\, Thailand
SEQUENCE:0
UID:8cc17d7e0c67a339eeab350ef52ce4b8
URL:http://11thictisthailand.sched.com/event/8cc17d7e0c67a339eeab350ef52ce4b8
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260417T125213Z
DTSTART:20260409T024500Z
DTEND:20260409T030000Z
SUMMARY:A Hybrid IoT-AI Framework for Real-Time Predictive Monitoring in Intensive Care Units
DESCRIPTION:Authors - Radha Gawande\, Supriya Nara\n Abstract - Complicated nature of the intensive care unit (ICU)\, immediate and accurate decision-making is vital to the survival of the patient. The problems that healthcare providers are struggling with are the overload of information\, slowness of the decision making process\, and the human factor due to growing amount of various patient information. Recent development in artificial intelligence (AI) offers promising solutions since they facilitate effective analysis of data\, pattern detection and predictive modelling. This changes the provision of critical care. In this paper\, the changing application of AI in ICUs is discussed. It talks about its usage\, merits and demerits\, as well as technological basis. It also discusses AI methods such as machine learning (ML)\, deep learning (DL)\, natural language process (NLP)\, and expert system\, predictive analytics\, early sepsis detection\, clinical decision support system\, automated monitoring and insight-based treatments by documentation fueled by natural language processing\, are but a few of the practical methods of applying AI. The advantages of automation and robotics to enhance productivity and patient care are also discussed\, which are AI-based medication delivery system and robotics helper. Nonetheless\, a number of challenges to implement AI in critical care units are a lack of consensus\, algorithm bias\, understanding model decisions\, and various data\, personalized AI-driven care in the ICU\, integration of edge computing and internet of medical things (IoMT)\, reinforcement learning in adapting patient management are some of the future prospects[1].
CATEGORIES:VIRTUAL ROOM 1F
LOCATION:Virtual Room F\, Bangkok\, Thailand
SEQUENCE:0
UID:5a5111a5d98275e041d4b6472660a873
URL:http://11thictisthailand.sched.com/event/5a5111a5d98275e041d4b6472660a873
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260417T125213Z
DTSTART:20260409T030000Z
DTEND:20260409T031500Z
SUMMARY:A Survey on Climate Pattern Detection Using Data Analysis
DESCRIPTION:Authors - Priyanka Patel\, Ashvi Padshala\, Moxa Patel\n Abstract - This paper surveys recent advances in the application of data analysis\, machine learn ing\, artificial intelligence\, and big data techniques for climate pattern detection. It covers sources of climate data\, analytical methods\, computational architectures\, key challenges\, and emerging trends. The focus is on identifying how integrated data-driven methods enhance the understanding\, prediction\, and interpretability of climate phenomena.
CATEGORIES:VIRTUAL ROOM 1F
LOCATION:Virtual Room F\, Bangkok\, Thailand
SEQUENCE:0
UID:8bc2df6ab9944572a16829d2b36f0ab2
URL:http://11thictisthailand.sched.com/event/8bc2df6ab9944572a16829d2b36f0ab2
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260417T125213Z
DTSTART:20260409T031500Z
DTEND:20260409T033000Z
SUMMARY:NoSQL and SQL for Today's Data-Intensive Workloads
DESCRIPTION:Authors - Rohan Dafare\, Supriya Narad\n Abstract - The quick spread of big data and the rising need for instant analytics have shown the built-in limits of old-school relational database management systems (RDBMS). NoSQL ("Not SQL") databases give schema-less design\, side-to-side growth\, and adaptable data shaping making them a better fit for handling messy and semi-messy data on a big scale. This paper looks at the edge NoSQL has over SQL systems by checking out key traits like how flexible the data model is how well it works under high output how easy it is to grow sideways\, and how well it fits with cloud-native setups. Using a careful review of NoSQL teaching and use\, we boil down real-world findings and suggest ways to pick the right database tech based on what the app needs. Our talk ends with a plan to help pros and teachers get when and why to use NoSQL fixes instead of\, or along with classic SQL databases. Modern data intensive workloads driven by real time analytics\, large scale user interactions\, IoT streams\, and unstructured content. It demands storage system capable of delivering high throughput\, scalability and flexible data models. Traditional SQL databases continue to offer strong consistency\, ACID guarantees and structured schema support\, making them ideal for transactional applications and environments requiring strict data integrating. However\, as data volume\, variety and velocity increase\, NOSQL databases have emerged as powerful alternative\, providing horizontal scalability\, schema-less design and optimized performance for distributed and semi-structured data processing.
CATEGORIES:VIRTUAL ROOM 1F
LOCATION:Virtual Room F\, Bangkok\, Thailand
SEQUENCE:0
UID:0ae4fa2098d6453f14cf7ff84b55bbe4
URL:http://11thictisthailand.sched.com/event/0ae4fa2098d6453f14cf7ff84b55bbe4
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260417T125213Z
DTSTART:20260409T033000Z
DTEND:20260409T034500Z
SUMMARY:Transfer Learning-Based Facial Skin Analysis with Attention-Guided Feature Refinement
DESCRIPTION:Authors - Anshuman Prajapati\, Madhav Desai\, Priyanka Patel\n Abstract - Analysis of facial skin conditions is essential for both dermatological and cosmetic evaluation\; however\, inter-class similarity and localized texture variations make multi-label classification of characteristics like wrinkles\, dark circles\, enlarged pores\, hyperpigmentation\, pimples\, and fine lines difficult. The effectiveness of transfer learning for this task is examined in this paper\, and an attention-enhanced framework based on EfficientNet-B0 is proposed. In order to highlight the importance of pre-trained feature representations\, we first assess a bespoke convolutional neural network (CNN) as a baseline. Using the Convolu tional Block Attention Module (CBAM)\, which combines channel and spatial attention processes to enhance discriminative feature localization while maintain ing computational efficiency\, we build upon this by using EfficientNet-B0 as the transfer learning backbone. According to experimental data\, our CBAM augmented EfficientNet achieves better class-balanced performance in macro-F1 score than both the baseline EfficientNet and the bespoke CNN. Consistent in creases are confirmed by per-class analysis and confusion matrices\, even for dif ficult settings. Additionally\, Grad-CAM visualizations show that by concentrat ing activation on pertinent facial regions\, the attention mechanism improves in terpretability. These results imply that a promising avenue for multi-label derma tological image analysis is attention-guided transfer learning.
CATEGORIES:VIRTUAL ROOM 1F
LOCATION:Virtual Room F\, Bangkok\, Thailand
SEQUENCE:0
UID:a3be15487a93fe5a5b05d8321e3a429d
URL:http://11thictisthailand.sched.com/event/a3be15487a93fe5a5b05d8321e3a429d
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260417T125213Z
DTSTART:20260409T034500Z
DTEND:20260409T035000Z
SUMMARY:Session Chair Concluding Remarks
DESCRIPTION:\n
CATEGORIES:VIRTUAL ROOM 1F
LOCATION:Virtual Room F\, Bangkok\, Thailand
SEQUENCE:0
UID:7eb7285df162602c436e7cc0ce4d3f34
URL:http://11thictisthailand.sched.com/event/7eb7285df162602c436e7cc0ce4d3f34
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260417T125213Z
DTSTART:20260409T035000Z
DTEND:20260409T040000Z
SUMMARY:Session Closing and Information To Authors
DESCRIPTION:\n
CATEGORIES:VIRTUAL ROOM 1F
LOCATION:Virtual Room F\, Bangkok\, Thailand
SEQUENCE:0
UID:48b717f6d3c9e594f179ebad75b09e46
URL:http://11thictisthailand.sched.com/event/48b717f6d3c9e594f179ebad75b09e46
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260417T125213Z
DTSTART:20260409T043000Z
DTEND:20260409T043200Z
SUMMARY:Session Chair Concluding Remarks
DESCRIPTION:\n
CATEGORIES:VIRTUAL ROOM 1A
LOCATION:Virtual Room A\, Bangkok\, Thailand
SEQUENCE:0
UID:a979f43b0c22303cde6e3c3aee4b4560
URL:http://11thictisthailand.sched.com/event/a979f43b0c22303cde6e3c3aee4b4560
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260417T125213Z
DTSTART:20260409T043000Z
DTEND:20260409T043200Z
SUMMARY:Session Chair Concluding Remarks
DESCRIPTION:\n
CATEGORIES:VIRTUAL ROOM 1B
LOCATION:Virtual Room B\, Bangkok\, Thailand
SEQUENCE:0
UID:b733446cd07509141a1bd938a75e45be
URL:http://11thictisthailand.sched.com/event/b733446cd07509141a1bd938a75e45be
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260417T125213Z
DTSTART:20260409T043000Z
DTEND:20260409T043200Z
SUMMARY:Session Chair Concluding Remarks
DESCRIPTION:\n
CATEGORIES:VIRTUAL ROOM 1C
LOCATION:Virtual Room C\, Bangkok\, Thailand
SEQUENCE:0
UID:db18e0a5d93c80fe1f0a543e33baf61f
URL:http://11thictisthailand.sched.com/event/db18e0a5d93c80fe1f0a543e33baf61f
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260417T125213Z
DTSTART:20260409T043000Z
DTEND:20260409T043200Z
SUMMARY:Session Chair Concluding Remarks
DESCRIPTION:\n
CATEGORIES:VIRTUAL ROOM 1D
LOCATION:Virtual Room D\, Bangkok\, Thailand
SEQUENCE:0
UID:e64b53b0bc4e761b7a11f42b1ed232da
URL:http://11thictisthailand.sched.com/event/e64b53b0bc4e761b7a11f42b1ed232da
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260417T125213Z
DTSTART:20260409T043000Z
DTEND:20260409T043200Z
SUMMARY:Session Chair Concluding Remarks
DESCRIPTION:\n
CATEGORIES:VIRTUAL ROOM 1E
LOCATION:Virtual Room E\, Bangkok\, Thailand
SEQUENCE:0
UID:2ebbb61d3ccbad25138138d8086794c8
URL:http://11thictisthailand.sched.com/event/2ebbb61d3ccbad25138138d8086794c8
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260417T125213Z
DTSTART:20260409T043200Z
DTEND:20260409T043500Z
SUMMARY:Session Closing and Information To Authors
DESCRIPTION:\n
CATEGORIES:VIRTUAL ROOM 1A
LOCATION:Virtual Room A\, Bangkok\, Thailand
SEQUENCE:0
UID:d61c7a66d9bc5a7f47c9f53f16d9cc5f
URL:http://11thictisthailand.sched.com/event/d61c7a66d9bc5a7f47c9f53f16d9cc5f
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260417T125213Z
DTSTART:20260409T043200Z
DTEND:20260409T043500Z
SUMMARY:Session Closing and Information To Authors
DESCRIPTION:\n
CATEGORIES:VIRTUAL ROOM 1B
LOCATION:Virtual Room B\, Bangkok\, Thailand
SEQUENCE:0
UID:d73832cad45a7a99716665c384fc4af7
URL:http://11thictisthailand.sched.com/event/d73832cad45a7a99716665c384fc4af7
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260417T125213Z
DTSTART:20260409T043200Z
DTEND:20260409T043500Z
SUMMARY:Session Closing and Information To Authors
DESCRIPTION:\n
CATEGORIES:VIRTUAL ROOM 1C
LOCATION:Virtual Room C\, Bangkok\, Thailand
SEQUENCE:0
UID:c89eaca9032727684e3243c089b9d150
URL:http://11thictisthailand.sched.com/event/c89eaca9032727684e3243c089b9d150
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260417T125213Z
DTSTART:20260409T043200Z
DTEND:20260409T043500Z
SUMMARY:Session Closing and Information To Authors
DESCRIPTION:\n
CATEGORIES:VIRTUAL ROOM 1D
LOCATION:Virtual Room D\, Bangkok\, Thailand
SEQUENCE:0
UID:637c620cf46a3caceedd304e936d499e
URL:http://11thictisthailand.sched.com/event/637c620cf46a3caceedd304e936d499e
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260417T125213Z
DTSTART:20260409T043200Z
DTEND:20260409T043500Z
SUMMARY:Session Closing and Information To Authors
DESCRIPTION:\n
CATEGORIES:VIRTUAL ROOM 1E
LOCATION:Virtual Room E\, Bangkok\, Thailand
SEQUENCE:0
UID:579bd549aaae8bc03bbfaf73f9671fb8
URL:http://11thictisthailand.sched.com/event/579bd549aaae8bc03bbfaf73f9671fb8
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260417T125213Z
DTSTART:20260409T051300Z
DTEND:20260409T051500Z
SUMMARY:Opening Remarks
DESCRIPTION:\n
CATEGORIES:VIRTUAL ROOM 2A
LOCATION:Virtual Room A\, Bangkok\, Thailand
SEQUENCE:0
UID:78f9aeec0c8dd9b15b8902025cbb9244
URL:http://11thictisthailand.sched.com/event/78f9aeec0c8dd9b15b8902025cbb9244
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260417T125213Z
DTSTART:20260409T051300Z
DTEND:20260409T051500Z
SUMMARY:Opening Remarks
DESCRIPTION:\n
CATEGORIES:VIRTUAL ROOM 2B
LOCATION:Virtual Room B\, Bangkok\, Thailand
SEQUENCE:0
UID:adab0b707c7271a11cefa86cb68a2240
URL:http://11thictisthailand.sched.com/event/adab0b707c7271a11cefa86cb68a2240
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260417T125213Z
DTSTART:20260409T051300Z
DTEND:20260409T051500Z
SUMMARY:Opening Remarks
DESCRIPTION:\n
CATEGORIES:VIRTUAL ROOM 2C
LOCATION:Virtual Room C\, Bangkok\, Thailand
SEQUENCE:0
UID:af2dd587298aab5a3f7b96ac5ce492c8
URL:http://11thictisthailand.sched.com/event/af2dd587298aab5a3f7b96ac5ce492c8
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260417T125213Z
DTSTART:20260409T051300Z
DTEND:20260409T051500Z
SUMMARY:Opening Remarks
DESCRIPTION:\n
CATEGORIES:VIRTUAL ROOM 2D
LOCATION:Virtual Room D\, Bangkok\, Thailand
SEQUENCE:0
UID:f9d78538d11f5aadebae2e02f2788a6f
URL:http://11thictisthailand.sched.com/event/f9d78538d11f5aadebae2e02f2788a6f
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260417T125213Z
DTSTART:20260409T051300Z
DTEND:20260409T051500Z
SUMMARY:Opening Remarks
DESCRIPTION:\n
CATEGORIES:VIRTUAL ROOM 2E
LOCATION:Virtual Room E\, Bangkok\, Thailand
SEQUENCE:0
UID:0d746590a6e79ec104ef85f003d5c446
URL:http://11thictisthailand.sched.com/event/0d746590a6e79ec104ef85f003d5c446
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260417T125213Z
DTSTART:20260409T051300Z
DTEND:20260409T051500Z
SUMMARY:Opening Remarks
DESCRIPTION:\n
CATEGORIES:VIRTUAL ROOM 2F
LOCATION:Virtual Room F\, Bangkok\, Thailand
SEQUENCE:0
UID:e6090d39d565234ac3eb211845c42d5d
URL:http://11thictisthailand.sched.com/event/e6090d39d565234ac3eb211845c42d5d
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260417T125213Z
DTSTART:20260409T051300Z
DTEND:20260409T051500Z
SUMMARY:Opening Remarks
DESCRIPTION:\n
CATEGORIES:VIRTUAL ROOM 2G
LOCATION:Virtual Room G\, Bangkok\, Thailand
SEQUENCE:0
UID:4ae20dc4d44372e2e3a6c43329352cfe
URL:http://11thictisthailand.sched.com/event/4ae20dc4d44372e2e3a6c43329352cfe
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260417T125213Z
DTSTART:20260409T051500Z
DTEND:20260409T071500Z
SUMMARY:Adaptive Hybrid RIME Optimization for Reliable Feature Selection and Photovoltaic MPPT in Dynamic Conditions
DESCRIPTION:Authors - K.Surya Teja\, Immanuel Anupalli\, P.Sudheer Abstract - Maximum power point tracking (MPPT) is a vital module of photovoltaic (PV) systems. Traditional maximum power MPPT techniques struggle in a complex and ever-changing scenarios\, and the solar system's output characteristic curve shows multi-peak phenomena owing to dissimilarities in temperature and light concentration. This paper proposes an adaptive hybrid RIME optimization technique which enhances the exploratory capabilities of the method during the initialization phase by integrating tent mapping. The goal is to improve feature selection tasks and MPPT for PV systems under partial shading condition. It uses piecewise mapping to optimize the algorithm's parameters and attack a fair steadiness amongst global exploration and local exploitation. The search method is dynamically adjusted with an adaptive inertia weight introduced\, which further increases convergence speed\, search efficiency and algorithm's adaptability. In order to reduce computational costs and increase classification accuracy\, the hybrid method employs natural-inspired metaheuristics for feature selection\, resulting in optimal subsets. When it comes to tracking speed\, precision\, and stability in the PV MPPT environment\, the method beats PSO-BOA\, conventional RIME\, IRIME and HRIME approaches.
CATEGORIES:VIRTUAL ROOM 2A
LOCATION:Virtual Room A\, Bangkok\, Thailand
SEQUENCE:0
UID:45433db911619f7304269a74bf722f40
URL:http://11thictisthailand.sched.com/event/45433db911619f7304269a74bf722f40
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260417T125213Z
DTSTART:20260409T051500Z
DTEND:20260409T071500Z
SUMMARY:AI-Driven Health Risk Advisor: A Predictive Approach to Personalized Healthcare
DESCRIPTION:Authors - Gauree Prabhakar Sayam\, Supriya Narad Abstract - Chronic non-communicable diseases like diabetes\, heart disease\, and obesity continue to increase globally\, comprising 74% of all deaths\, even as noted by the World Health Organization in the 2025 progress monitor on non-communicable diseases. This work describes the design and deployment of Health Risk Advisor\, an AI (artificial intelligence) web application powered by machine learning that predicts early risks and provides personalized recommendations on disease prevention. The integration of ensemble models such as Random Forest and XG-Boost into a rule-based advisory engine allows the application to achieve more than 90% accuracy in making risk classifications\, addressing access barriers to healthcare in underserved regions\, such as rural India. From architecture and design\, healthcare applications and benefits\, to ethical AI challenges and considerations\, this work discusses every aspect of the new technology using diverse sets of datasets that inform practices as well as recommend ethical AI. Evaluations showed reductions of the burden from NCDs between 20-30% by engaging the application in a preventive healthcare intervention\, which is aligned to global health equity goals.
CATEGORIES:VIRTUAL ROOM 2A
LOCATION:Virtual Room A\, Bangkok\, Thailand
SEQUENCE:0
UID:dc08f354680fb7ba8d4a697f87003161
URL:http://11thictisthailand.sched.com/event/dc08f354680fb7ba8d4a697f87003161
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260417T125213Z
DTSTART:20260409T051500Z
DTEND:20260409T071500Z
SUMMARY:BREAST CANCER DETECTION IN ULTRASOUND IMAGING USING CLAHE AND ENSEMBLE DEEP LEARNING: A REPLICATION AND ENHANCEMENT STUDY
DESCRIPTION:Authors - Saurabh Nimje\, Reena Satpute\, Utkarsha Pacharaney\, Anup Bhitre Abstract - Breast cancer is considered as one of the top causes of mortality on women across the world making early and accurate diagnosis a key element in addressing patient outcomes. The work introduces artificial breast instances of cancer detection techniques in ultrasound imaging by means of Contrast Limited Adaptive Histogram Equalization (CLAHE) and ensemble deep learning framework. Data used was a balanced data set comprising of 200 ultrasound images that are made to be benign\, malignant\, and normal. The CLAHE preprocessing was quite useful in terms of image quality as it provided edge and local contrast enhancement and profited letting the lesions be seen more effectively. A number of the convolutional neural network (CNN) architectures were tuned collectively in an ensemble arrangement with soft voting and weighted averaging\, and this produced an improved classification performance. The proposed model returned an accuracy of 93.7%\, sensitivity of 92.5%\, specificity of 94.5% and AUC of 0.97 even better than the baseline general CNN models and the single CNN models with CLAHE. The findings are indicative of the fact that CLAHE-enhanced ensemble learning is a robust\, reproducible\, and promising tool in breast cancer detection within ultrasound imaging that holds a great promise in clinical.
CATEGORIES:VIRTUAL ROOM 2A
LOCATION:Virtual Room A\, Bangkok\, Thailand
SEQUENCE:0
UID:52c9d5852632f12659081786556697b2
URL:http://11thictisthailand.sched.com/event/52c9d5852632f12659081786556697b2
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260417T125213Z
DTSTART:20260409T051500Z
DTEND:20260409T071500Z
SUMMARY:ChatGPT-Enabled IoT at the Edge: A Quantitative Study of Latency\, Energy\, and Security Under Latest LLM Trends
DESCRIPTION:Authors - Naga Sujitha Vummaneni\, Srilakshmi Bharadwaj\, Himani Varshney Abstract - The global healthcare landscape is currently undergoing a radical transformation\, driven by the dual catalysts of the post-pandemic necessity for remote care and the rapid proliferation of digital infrastructure in developing economies. This research paper presents a comprehensive study on the design\, development\, and strategic positioning of a desktop-based "Healthcare Management System with Telemedicine." Developed using the Java ecosystem—specifically Java Swing for the graphical user interface (GUI) and Java Database Connectivity (JDBC) for persistence—the system integrates third-party WebRTC services via Jitsi Meet to facilitate real-time virtual consultations. Unlike purely administrative Hospital Management Systems (HMS)\, this solution integrates clinical workflows with administrative tasks\, offering a unified platform for patient authentication\, appointment scheduling\, and remote video consultation. This report goes beyond technical implementation to provide an exhaustive analysis of the Indian digital health market\, projected to reach USD 106.97 billion by 2033. It critically evaluates market leaders such as Practo\, Zocdoc\, and Teladoc to identify structural gaps in service delivery\, particularly regarding cost-barriers and infrastructure dependency in Tier-2 and Tier-3 cities. By adopting the Prototyping Model of software engineering\, the research iteratively addresses requirements for security\, usability\, and legacy hardware compatibility. The findings suggest that while cloud-native SaaS models dominate the current market\, lightweight Java-based desktop solutions offer distinct advantages in data sovereignty\, offline capability\, and operational stability for resource-constrained healthcare settings. The paper concludes with a roadmap for integrating Artificial Intelligence (AI) for predictive diagnostics and expanding into mobile ecosystems\, positioning the developed system as a viable component of the emerging Global Initiative on Digital Health (GIDH).
CATEGORIES:VIRTUAL ROOM 2A
LOCATION:Virtual Room A\, Bangkok\, Thailand
SEQUENCE:0
UID:874108527d367d71a2d18085b89124e7
URL:http://11thictisthailand.sched.com/event/874108527d367d71a2d18085b89124e7
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260417T125213Z
DTSTART:20260409T051500Z
DTEND:20260409T071500Z
SUMMARY:Design Principles for Regularized Meta-Learning: A Framework Proposal
DESCRIPTION:Authors - Nevil Dhinoja\, Shubh Patel\, Binal Kaka Abstract - Gradient conflicts\, computational complexity\, and optimization instability are some of the issues with model-agnostic meta-learning\, or MAML. We introduce a methodical methodology that integrates three improvement techniques: meta-level regularization\, adaptive optimization management\, and taskaware gradient. By combining three complimentary mechanisms—task-aware gradient modulation\, meta-level regularization\, and adaptive optimization management—this work suggests an organized design framework to increase the stability and robustness of MAML-based optimization. The paradigm provides a solid basis for the methodical creation of more reliable and scalable meta-learning systems\, even while empirical evaluation is saved for later research.
CATEGORIES:VIRTUAL ROOM 2A
LOCATION:Virtual Room A\, Bangkok\, Thailand
SEQUENCE:0
UID:8f8c4b7f625f9dfd975211bdad304430
URL:http://11thictisthailand.sched.com/event/8f8c4b7f625f9dfd975211bdad304430
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260417T125213Z
DTSTART:20260409T051500Z
DTEND:20260409T071500Z
SUMMARY:FaceIt: A Novel AI Framework for Preliminary Autism Screening Using Facial Imaging
DESCRIPTION:Authors - Liyan Grace Shaji\, Lakshmi K.S\, Shazil Mohammad Iqbal\, Don Basil Saj\, Tom Thomas Abstract - Autism Spectrum Disorder (ASD) is a neurodevelopmental condition that affects skills related to social interaction and communication. Of late\, this is estimated to be prevalent in 1 among 100 children\, across the world. Unfortunately\, our present\, diagnostic methods\, like ADI-R and ADOS\, rely on questionnaires\, which render them to be time-consuming\, expensive\, and skill-dependent. Hence\, to address these challenges\, FaceIt is developed\, which is a Deep Learning-based diagnostic tool that integrates real-time image capture and classification for rapid and accessible ASD screening. The tool efficiently processes facial images captured or uploaded by users\, by performing preprocessing steps like cropping and alignment. A Convolutional Neural Network (CNN) extracts facial features to detect ASD\, while a Bayesian CNN captures uncertainty in predictions. Its user-friendly interface allows self administration\, devoid of professional supervision. The faster and more accessible preliminary screening even facilitates timely follow-up diagnostics if needed\, thus making this an optimum solution for widespread use.
CATEGORIES:VIRTUAL ROOM 2A
LOCATION:Virtual Room A\, Bangkok\, Thailand
SEQUENCE:0
UID:135b21939c3e5eb6f71baaf49c571299
URL:http://11thictisthailand.sched.com/event/135b21939c3e5eb6f71baaf49c571299
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260417T125213Z
DTSTART:20260409T051500Z
DTEND:20260409T071500Z
SUMMARY:Feature Fusion based Enhanced Information Representation for Improved Accuracy of BCRP Inhibition Prediction in Drug Discovery
DESCRIPTION:Authors - Nabeela Kausar\, Ramiza Ashraf\, Naila Ashraf\, Romana Ali Abstract - The conventional way of preparing an advertisement is an elaborate process incorporating human subjectivity and human resources heavily dependent on creativity. Making advertisements by human effort can be regarded as an inefficient utilization of capital for small to medium-scale businesses due to increased cost of production. Even in current advancements in the development of generative techniques including LLM-based strategies for Advertisement Generation with Prompts\, creating apt prompts for the depiction of products requires human expertise\, making them less accessible. In order to overcome the challenges presented by the current models\, we introduce a fast\, affordable\, and scalable platform for the automation of advertisement generation for products leveraging the capabilities of pre-trained diffusion models. The proposed system requires no training or fine-tuning since everything is performed at the inference level. The AI-aware system for designing assists in the identification of color schemes and attributes from the images of the products\, whereas the descriptions and categories of the items help identify the theme and pattern recommendations for advertisements. These recommendations are channeled through a pre-trained Stable diffusion model guided by the LLaMA language model.
CATEGORIES:VIRTUAL ROOM 2A
LOCATION:Virtual Room A\, Bangkok\, Thailand
SEQUENCE:0
UID:4c86890b7e9f84ec7b5c0827a803895e
URL:http://11thictisthailand.sched.com/event/4c86890b7e9f84ec7b5c0827a803895e
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260417T125213Z
DTSTART:20260409T051500Z
DTEND:20260409T071500Z
SUMMARY:Full-Stack TinyML for Scalable IoT Sensing: A Quantitative Study of Quantization\, Sparsity\, and Compiler–Runtime Co-Design on Microcontrollers
DESCRIPTION:Authors - Naga Sujitha Vummaneni\, Ishan Kumar\, Adarsh Mittal Abstract - Digital evidence is now central to cyber investigations\, legal trials\, and organizational audits. However\, traditional evidence management systems rely heavily on centralized storage\, making them vulnerable to unauthorized modifications\, insider attacks\, and in complete audit trails. This research introduces a Blockchain-Based Evidence Management System designed to secure digital evidence through immutability\, transparent verification\, and tamper proof storage of evidence hashes. The proposed solution integrates Java FX as a user-friendly interface\, MongoDB for storing meta data\, SHA-256 for generating unique evidence fingerprints\, and the Polygon Mumbai blockchain for permanent registration of hash values. Users can upload evidence\, verify its authenticity\, and review all actions through a detailed activity log. Experimental results show that blockchain-backed verification reliably identifies tampered evidence and significantly strengthens the chain of custody. The system offers an efficient\, scalable\, and secure enhancement to traditional evidence-handling methods.
CATEGORIES:VIRTUAL ROOM 2A
LOCATION:Virtual Room A\, Bangkok\, Thailand
SEQUENCE:0
UID:baa453566c00a63b26423f1f58766904
URL:http://11thictisthailand.sched.com/event/baa453566c00a63b26423f1f58766904
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260417T125213Z
DTSTART:20260409T051500Z
DTEND:20260409T071500Z
SUMMARY:Raspberry Pi–Centric IoT in 2024–2026: A Quantitative Study of Edge Gateway Scaling\, Containerized Microservices\, and On-Device AI
DESCRIPTION:Authors - Naga Sujitha Vummaneni\, Adarsh Mittal\, Ishan Kumar Abstract - The rapid growth of digital platforms has transformed the way individuals buy and sell goods. However\, college students still largely depend on informal and unorganized methods for peer-to-peer trading. This paper presents UNIBID\, a Java-based online marketplace designed specifically for college students to enable secure\, reliable\, and efficient product trading within the campus community. The system allows users to register\, authenticate\, list products\, browse products\, search and filter items\, and perform secure purchase transactions. The backend is implemented using Java [1]\, while database operations are handled using a reliable database management system. The proposed system eliminates the drawbacks of manual trading such as lack of trust\, delay in communication\, and absence of product verification. Experimental results show that UNIBID significantly improves transaction speed\, transparency\, and user convenience compared to traditional methods. The system is scalable\, secure\, and suitable for deployment in real academic environments.
CATEGORIES:VIRTUAL ROOM 2A
LOCATION:Virtual Room A\, Bangkok\, Thailand
SEQUENCE:0
UID:c21f53f7e28997f5599c7f4a6b334be9
URL:http://11thictisthailand.sched.com/event/c21f53f7e28997f5599c7f4a6b334be9
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260417T125213Z
DTSTART:20260409T051500Z
DTEND:20260409T071500Z
SUMMARY:UNIBID COLLEGE STUDENT MARKETPLACE PLATFORM
DESCRIPTION:Authors - Nishu\, Kajal\, Pavitra Jangir\, Ayush Kumar Gupta\, Kiran Dikshit\, Ajay\, Vishal Shrivastava\, Akhil Pandey\, Ram Babu Buri\, Harveer Choudhary Abstract - Despite the availability of digital voting systems\, prior studies continue to identify gaps such as weak or voter authentication\, security vulnerabilities and insufficient fraud prevention mechanisms. This paper presents BotoSafe\, a secure and user-centered electronic voting (e-voting) platform developed for student government elections within educational institutions. The system implements multifactor authentication (MFA) using one-time password (OTP) verification and facial recognition with an anti-spoofing mechanism. To ensure the confidentiality and integrity of the voting process we employ the Advanced Encryption Standard in Galois/Counter Mode (AES-GCM). A developmental research design with a quantitative approach was used for the system development and evaluation. A mock election involving 84 students from Western Mindanao State University–Pagadian Campus was conducted\, followed by a post-assessment survey. Results from the System Usability Scale (SUS) yielded a score of 72.08\, indicating acceptable usability. User responses further showed that the system is easy to use\, safe\, and trustworthy for student elections. These findings indicate that BotoSafe is a viable e-voting solution for student government elections and may be further enhanced in future studies.
CATEGORIES:VIRTUAL ROOM 2A
LOCATION:Virtual Room A\, Bangkok\, Thailand
SEQUENCE:0
UID:f4b31113611cf228975e32407af53289
URL:http://11thictisthailand.sched.com/event/f4b31113611cf228975e32407af53289
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260417T125213Z
DTSTART:20260409T051500Z
DTEND:20260409T071500Z
SUMMARY:AI-Based Post-Event Surveillance System
DESCRIPTION:Authors - Deepali Newaskar\, Saurabh Parhad\, Anjali Yadav\, Siddhi Shinde\, Samika Karne\, Atharva Nangare\, Misbah Shaikh Abstract - This study investigates the effectiveness of a student-driven development (SDD) approach utilizing ChatGPT to create SQL-based inventory management systems for Micro\, Small\, and Medium Enterprises (MSMEs)\, with a focus on contributing to Sustainable Development Goal (SDG) 12 (Re-sponsible Consumption and Production). A mixed-method study involving 30 student-MSME collaborations was conducted to evaluate the resulting systems based on stock accuracy\, reporting efficiency\, and user satisfaction. The quantitative results demonstrate significant performance enhancements\, with systems achieving average scores of 7.7 for stock accuracy\, 7.63 for reporting efficiency\, and 8.17 for user satisfaction (on a 10-point scale). Technical analysis showed ChatGPT's pivotal role in input validation (15 cases)\, SQL query construction (8 cases)\, and report optimization (7 cases). The most frequent SQL commands were SELECT (14 instances)\, UPDATE (11 instances) and INSERT (5 instances)\, highlighting robust data handling. The findings confirm that integrating AI tools like ChatGPT within an SDD framework can deliver practical\, scalable\, and sustainable digital solutions for MSMEs\, advancing digital trans-formation while reinforcing the applied role of higher education in achieving global sustainability goals. These results highlight the potential of student-led AI-assisted development as a scalable model for MSME digital transformation aligned with SDG 12.
CATEGORIES:VIRTUAL ROOM 2B
LOCATION:Virtual Room B\, Bangkok\, Thailand
SEQUENCE:0
UID:b23a5b66bc8e37cfa63216049b95faa0
URL:http://11thictisthailand.sched.com/event/b23a5b66bc8e37cfa63216049b95faa0
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260417T125213Z
DTSTART:20260409T051500Z
DTEND:20260409T071500Z
SUMMARY:AN INTELLIGENT CYBER-ATTACK DETECTION AND MITIGATION FRAMEWORK USING DEEP CLOCKWORK RECURRENT NEURAL NETWORK AND DEEP Q-NETWORKS
DESCRIPTION:Authors - Lavanya K\, Srinidhi G A Abstract - The pace with which artificial intelligence (AI) has been adopted in decision-critical applications has\, in turn\, elevated the need to have more than merely accurate AI systems that are also transparent and comprehendible. Although the complex machine learning models can be highly predictive\, its black box strategy creates a question mark on the aspects of trust\, accountability\, and usability in real-world systems based on artificial intelligence. This paper examines the tradeoff between accuracy and transparency in interpretable machine intelligence and oranges by pointing to the trade-offs that exist between predictive accuracy and model explanation. There is a proposed structured framework which is used for comparing and investigating the black-box and interpretable models on the basis of quantitative performance measures and explainability measures. The article highlights the importance of explainable AI methods of post-hoc in improving the transparency of models without affecting the accuracy of the model significantly. Using a systematic assessment\, the paper shows that interpretable machine intelligence may be used to help make reliable decisions and maintain competitive predictive performance. The results help in the creation of credible AI-based systems as it provides information about the creation of models that are effective in balancing the accuracy and interpretability when applied to different application settings.
CATEGORIES:VIRTUAL ROOM 2B
LOCATION:Virtual Room B\, Bangkok\, Thailand
SEQUENCE:0
UID:c7bfe737d286f151c7fe830685804b1a
URL:http://11thictisthailand.sched.com/event/c7bfe737d286f151c7fe830685804b1a
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260417T125213Z
DTSTART:20260409T051500Z
DTEND:20260409T071500Z
SUMMARY:Blockchain-Based Evidence Management System
DESCRIPTION:Authors - Dev Kumar Prajapat\, Chakshum Mittal\, Abhishek Sharma\, Jatin Yadav\, Mohammad Shaad\, Vishal Shrivastava\, Ram Babu Buri\, Akhil Pandey Abstract - This paper presents Printify\, a real-time\, location-based service platform revolutionizing document printing workflows via a dual-interface architecture: a Flutter-based mobile app for end-users and a React/TypeScript Progressive Web Application (PWA) for shopkeepers. Addressing inefficiencies like delays\, security vulnerabilities\, and service discovery limitations\, Printify leverages Firebase for instantaneous cross-platform state synchronization. The PWA utilizes Service Workers for offline functionality and secure protocols enabling paymentconditional document release. Evaluations show a 73% reduction in processing latency\, 95% improvement in service discovery\, and Lighthouse scores exceeding 92. The platform achieves PCI-DSS compliance and end-to-end encryption\, establishing a novel hybrid mobile-web paradigm for location-based services.
CATEGORIES:VIRTUAL ROOM 2B
LOCATION:Virtual Room B\, Bangkok\, Thailand
SEQUENCE:0
UID:eb93fffa76d37d92f79ad72347a1ca41
URL:http://11thictisthailand.sched.com/event/eb93fffa76d37d92f79ad72347a1ca41
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260417T125213Z
DTSTART:20260409T051500Z
DTEND:20260409T071500Z
SUMMARY:Comparative Performance Analysis of VSI\, H5\, and H6 Transformerless Inverter Topologies for A 175-Kw Grid-Connected Photovoltaic System
DESCRIPTION:Authors - Suresh Reddy\, Immanuel Anupalli\, P.Sudheer Abstract - This paper presents a comparative framework for detecting knee and elbow form errors in overhead press videos using machine learning. Using more than 2\,000 videos from the Fitness-AQA dataset\, three models are evaluated: an Inception-based Long Short-Term Memory (LSTM) network with residual connections\, a custom stacked LSTM network\, and a feedforward neural network baseline. Human pose keypoints are extracted using MediaPipe\, and frame-to-frame differences are computed to encode motion dynamics. The dataset includes temporally localized annotations with explicit start and end timestamps for knee and elbow errors\, resulting in a class-imbalanced classification task. Model performance is evaluated using accuracy\, precision\, recall\, F1- score\, and confusion matrices. Experimental results demonstrate that the Inception-based LSTM consistently outperforms the alternative architectures\, followed by the custom LSTM\, while the feedforward baseline performs substantially worse. These findings highlight the importance of temporal modeling and multi-scale feature extraction for fine-grained Action Quality Assessment in weightlifting.
CATEGORIES:VIRTUAL ROOM 2B
LOCATION:Virtual Room B\, Bangkok\, Thailand
SEQUENCE:0
UID:75538bffbe212e3fa25a00c15bfc2f80
URL:http://11thictisthailand.sched.com/event/75538bffbe212e3fa25a00c15bfc2f80
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260417T125213Z
DTSTART:20260409T051500Z
DTEND:20260409T071500Z
SUMMARY:Design of a Compact Modified Inset-Fed Circular and Inverted U-Shaped Patch Antenna for Terahertz Applications
DESCRIPTION:Authors - Tanvir Ahmed Fahim\, Md. Sohel Rana\, Shamsul Arefin Bipul\, Tanvir Hasan\, Niyaz Mahmud MD. Mujahid\, Hridoy Datta Abstract - The rapid expansion of Information and Communication Technologies (ICT) has transformed financial inclusion from a policy objective centered on access into a data-driven process mediated by digital identity systems\, algorithmic credit assessment\, and fintech platforms. While ICT-enabled financial inclusion promises efficiency\, scalability\, and outreach to marginalized populations\, it simultaneously raises profound concerns relating to personality rights\, including identity\, dignity\, autonomy\, privacy\, and reputation. This paper advances a normative and conceptual analysis of Personality Rights–Based Financial Inclusion through ICT\, arguing that contemporary financial systems increasingly construct a digital economic identity that determines an individual’s financial opportunities and exclusions. Such identities\, often generated through opaque algorithms and data profiling\, risk reducing individuals to abstract data points\, thereby undermining human dignity and meaningful self-determination. The paper develops a conceptual framework that positions ICT as the mediating layer between individuals and financial inclusion outcomes\, with personality rights functioning as essential normative safeguards. Central to this framework is the articulation of the Right to Economic Self-Representation\, which recognizes the individual’s entitlement to access\, understand\, contest\, and contextualize their digital financial profile. By reframing financial inclusion as a rights-dependent process rather than a purely technological or developmental intervention\, the paper highlights the dangers of algorithmic exclusion\, permanent economic stigmatization\, and surveillance-based inclusion. The study contributes to interdisciplinary scholarship at the intersection of ICT law\, financial regulation\, and human rights by proposing a rights-compatible model of inclusive finance. It argues that embedding personality rights into the design and governance of financial technologies is crucial to ensuring that financial inclusion operates as a mechanism of empowerment rather than control. The paper concludes that sustainable and legitimate digital financial inclusion must balance technological innovation with the preservation of human dignity and agency.
CATEGORIES:VIRTUAL ROOM 2B
LOCATION:Virtual Room B\, Bangkok\, Thailand
SEQUENCE:0
UID:231069b52f8f172cc4a97d742a2135eb
URL:http://11thictisthailand.sched.com/event/231069b52f8f172cc4a97d742a2135eb
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260417T125213Z
DTSTART:20260409T051500Z
DTEND:20260409T071500Z
SUMMARY:DIGITAL PEDAGOGY IN THE AGE OF AI: OPPORTUNITIES\, DANGERS\, AND THEIR DEMAND FOR EVALUATION IN THE BROADER DOMAIN OF HIGHER EDUCATION PROVISION ONLINE
DESCRIPTION:Authors - Janina Odette S. Vidallon\, Apolinar P. Datu\, Dominic T. Urgelles\, Aljen B. Cabrera\, Erika Joy F. Lagos\, Lady Anne R. Logdat\, Shenclaire A. Galero\, Ericka Jean M. Amparo Abstract - Brain-computer interface systems can help people who are unable to communicate due to paralysis or severe motor disabilities. In this work\, we im plemented an EEG-based P300 speller that allows users to select characters by focusing on a visual stimulus.The system functions by means of the P300 signal that appears when the user identifies their target character. We developed a com plete pipeline that includes feature extraction\, machine learning model classifi cation\, and preprocessing of EEG data. The system was tested using the BNCI Horizon 2020 P300 dataset\, and the results showed that character selection accu racy ranged from 82% to 86%.Random Forest performed better compared to other classifiers in our implementation. The system was designed in a modular way so that future improvements can be added easily. This implementation shows that EEG-based communication systems can be developed using accessible tools and can support basic communication for people with severe motor impairments.
CATEGORIES:VIRTUAL ROOM 2B
LOCATION:Virtual Room B\, Bangkok\, Thailand
SEQUENCE:0
UID:5a0839a83f375fa8cb8e69a2cd52a315
URL:http://11thictisthailand.sched.com/event/5a0839a83f375fa8cb8e69a2cd52a315
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260417T125213Z
DTSTART:20260409T051500Z
DTEND:20260409T071500Z
SUMMARY:Emergency Evacuation Simulation Model
DESCRIPTION:Authors - Sejal Vaishnav\, Sanskrati Jain\, Suman Dikshit\, Vashvi Srivastava\, Shailendra Sharma\, Vaishnav Preeti Prakash\, Vishal Shrivastava\, Ram Babu Buri\, Mohit Mishra Abstract - Traditional object detection systems are limited in their ability to capture the complexity of urban scenes\, often overlooking critical spatial\, contextual\, and functional relationships required. This paper introduces Urban Scene Intelligence\, a Semantic Anchor-and-Expand (SAE) framework that integrates multi-modal perception\, structured scene graph construction\, and controlled narrative generation to produce grounded descriptions of urban environments. The proposed modular architecture incorporates OWL-ViT for open-vocabulary object detection\, SegFormer for semantic segmentation\, DepthAnything for spatial depth estimation\, Qwen2-VL for attribute enrichment\, and OCR for extracting textual context. Unlike end-to-end multimodal models\, the threestage pipeline explicitly separates visual perception\, symbolic reasoning\, and language generation\, thereby improving interpretability and factual grounding. By unifying heterogeneous visual cues into a symbolic representation and generating context-aware descriptions from this representation\, the SAE framework establishes a transparent and extensible approach to urban scene understanding in complex real-world environments.
CATEGORIES:VIRTUAL ROOM 2B
LOCATION:Virtual Room B\, Bangkok\, Thailand
SEQUENCE:0
UID:225f711a6a8a3b0449aced0e1fea0ddb
URL:http://11thictisthailand.sched.com/event/225f711a6a8a3b0449aced0e1fea0ddb
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260417T125213Z
DTSTART:20260409T051500Z
DTEND:20260409T071500Z
SUMMARY:Intelligent Continuous Improvement Framework for Ergonomic Risk Mitigation in Automotive Manufacturing Using CNN-Based Wrist Posture Recognition and Real-Time RULA Evaluation
DESCRIPTION:Authors - Sara OULED LAGHZAL\, Abdelmajid El Ouadi\n Abstract - Musculoskeletal disorders (MSDs) are a significant occupational health problem in the automotive industry [1].Manual and semiautomated assembly work often exposes workers to repetitive movements and non-neutral wrist positions. Conventional ergonomic assessments are often ad hoc and subjective\, limiting their ability to capture positional variations and cumulative strain over time. This article proposes a framework for continuous improvement using artificial intelligence that combines a convolutional neural network-based classification of wrist position (CNN) and a rapid upper limb assessment (RULA)[2] in real time. The convolutional neural network distinguishes between acceptable and unacceptable wrist postures during task execution\, and the RULA layer translates the posture data into standardised biomechanical risk indicators. Empirical tests in an industrial context have shown that the CNNRULA hybrid system reliably detects even subtle deviations in wrist position that are difficult to detect by visual observation. This enables comfortable\, data-driven proactive interventions in an Industry 4.0 environment.
CATEGORIES:VIRTUAL ROOM 2B
LOCATION:Virtual Room B\, Bangkok\, Thailand
SEQUENCE:0
UID:610acf6bb06c714809538c99d3296140
URL:http://11thictisthailand.sched.com/event/610acf6bb06c714809538c99d3296140
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260417T125213Z
DTSTART:20260409T051500Z
DTEND:20260409T071500Z
SUMMARY:Multi-Object Tracking in MOT15 Using YOLOv8x with Enhanced ByteTrack Integration
DESCRIPTION:Authors - Darshika Dudhat\, Riya Jagani\, Sarita Thummar Abstract - Plant diseases represent one of the major threats for the world's food security and agricultural productivity. In this paper\, we present a novel deep CNN model which is improved by the Squeeze-and-Excitation (SE) modules and the Attention Gates (AGs)\, for multi-class plant disease classification based on five crops including apple\, maize\, grape\, potato\, tomato. With large number of image data set and a well-designed training strategy\, the established model demonstrates good performance in all aspects including 99% accuracy\, 0.99 F1-score and excellent specificity. Exploratory studies are performed through feature visualization and Grad-CAM interpretability. The intense robustness and interpretability of the model give it high potential for practical agricultural applications. The main research methodologies of this paper have: • The proposed Method of Attention-based Deep CNN Model combines (SE) blocks and Attention Gates (AGs)\, which further improve the channel-wise spatial feature leaning for plant disease classification. • Proposes the Grad-CAM visualizations to show disease-specific regions on leaves and achieves the state-of-the-art performance on five representative crop disease classification tasks. •Introducing attention mechanisms greatly improved the model's ability to focus on disease-related features\, as evidenced by its strong generalization performances across a wide array of disease classes.
CATEGORIES:VIRTUAL ROOM 2B
LOCATION:Virtual Room B\, Bangkok\, Thailand
SEQUENCE:0
UID:2fcafe6d4c9d81129bc3976b7e1937ee
URL:http://11thictisthailand.sched.com/event/2fcafe6d4c9d81129bc3976b7e1937ee
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260417T125213Z
DTSTART:20260409T051500Z
DTEND:20260409T071500Z
SUMMARY:Predictive Optimization of Energy Consumption in Smart Building Through AI_Gen Scenarios
DESCRIPTION:Authors - Ekanand Mungra\, Roopesh Kevin Sungkur\n Abstract - Today's increasing energy demand\, particularly in developing regions\, supports both economic growth and the improvement of living conditions. However\, these regions experience power outages frequently\, due to the high energy consumption of commercial buildings. This research examines energy usage in smart commercial buildings by analyzing data from in-building sensors\, collected at ten-minute intervals for more than four months. The aim is to forecast the consumption of energy of these buildings while utilizing AI generated scenarios to generate simulations resembling real-life energy usage situations\, thereby improving our model’s predictions. In the era of smart buildings\, accurate predicting energy usage does not only facilitate cost savings for businesses\, but it also presents an opportunity for revenue generation\, particularly through the surplus energy supplied back to the grid from renewable sources such as solar panels. Unlike conventional approaches\, this research employs MLPRegressor\, a sophisticated model\, to analyze and predict intricate patterns of energy usage from the sensor data. This research is particularly significant for advancing energy management strategies in commercial sectors of developing countries\, promoting energy independence and efficiency.
CATEGORIES:VIRTUAL ROOM 2B
LOCATION:Virtual Room B\, Bangkok\, Thailand
SEQUENCE:0
UID:87c7df5b3dfd2868787396d06082645b
URL:http://11thictisthailand.sched.com/event/87c7df5b3dfd2868787396d06082645b
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260417T125213Z
DTSTART:20260409T051500Z
DTEND:20260409T071500Z
SUMMARY:A Case Study of TikTok’s AI-Driven Recommendations: Transparency\, Privacy\, and Mitigation Strategies
DESCRIPTION:Authors - Areej Almazroa\, Sara Albahlal\, Dalia Alswailem\, Dhay Altamimi\, Aljoharah Aldaej\, Heba Kurdi Abstract - Monitoring marine litter is essential for planetary and human survival. This study proposes a novel framework integrating satellite data and big data analytics to assess marine litter distribution in coastal and oceanic environments. Leveraging open-source imagery from COPERNICUS Sentinel-2 and LANDSAT\, the framework utilizes reflectance methodologies and image processing to identify and classify marine debris\, focusing on spectral bands from visible blue (490 nm) to short-wave infrared (1610 nm). A pilot case study in San Diego\, California\, demonstrates the approach’s feasibility. The study explores the potential of microwave radiometry and machine learning for material detection and contour analysis\, showing how satellite data can support dynamic and cross-platform monitoring systems. Results validate the use of remote sensing technologies to map plastic debris\, providing a replicable methodology that combines emergent (e.g.\, satellites\, drones) and traditional (e.g.\, sampling) techniques. This approach contributes to a deeper understanding of plastic pollution pathways\, sources\, and impacts across economic sectors. By generating harmonized data on mismanaged plastic waste\, the study informs sustainability strategies and circular economy practices\, helping redesign systemic plastic management and supporting local and global environmental governance.
CATEGORIES:VIRTUAL ROOM 2C
LOCATION:Virtual Room C\, Bangkok\, Thailand
SEQUENCE:0
UID:8d50db4a4279c363a162e4a344f46415
URL:http://11thictisthailand.sched.com/event/8d50db4a4279c363a162e4a344f46415
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260417T125213Z
DTSTART:20260409T051500Z
DTEND:20260409T071500Z
SUMMARY:A Robust and Efficient NLP Framework for Enterprise Ticket Classification under Domain Shift and Imbalance
DESCRIPTION:Authors - Sonali S. Gaikwad\, Jyotsna S. Gaikwad Abstract - In this semi-systematic literature review\, a detailed study of the role of Human-Computer Interaction (HCI) in creating game-based solutions for Attention-Deficit/Hyperactivity Disorder (ADHD) among children is conducted. Six peer-reviewed research studies were selected. The study demonstrates that HCI can serve as a major therapeutic mechanism by transforming digital platform-based cognitive training into engaging\, interactive experiences. These approaches not only improve focus but also enhance the overall effectiveness of interventions. Key findings from the analyzed studies are discussed\, and future research directions are proposed\, including multimodal hybrid systems with adaptive personalization and accessibility features to further improve outcomes for children with ADHD.
CATEGORIES:VIRTUAL ROOM 2C
LOCATION:Virtual Room C\, Bangkok\, Thailand
SEQUENCE:0
UID:452ea2ce54ece648aedb13bb22319214
URL:http://11thictisthailand.sched.com/event/452ea2ce54ece648aedb13bb22319214
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260417T125213Z
DTSTART:20260409T051500Z
DTEND:20260409T071500Z
SUMMARY:Clinical Risk Scores Demonstrate High Discrimination for Middle Cerebral Artery Aneurysm Rupture in a Single-Center Chinese Cohort: A Hypothesis-Generating Machine Learning Study
DESCRIPTION:Authors - Bharathi A\, Mohan Kumar P\, Subha B\n Abstract - Rupture of an intracranial aneurysm results in catastrophic subarachnoid hemorrhage with a 30–40% fatality rate. Although treatment decisions are guided by clinical risk scores (PHASES\, ELAPSS)\, recent research suggests that morphological analysis and computational fluid dynamics (CFD) may offer better rupture prediction. This study looked at 92 middle cerebral artery aneurysms from the CMHA dataset\, which included 71 that had ruptured and 21 that had not. We evaluated four feature sets: Clinical-Basic (13 variables)\, Clinical-Scores (adding PHASES and ELAPSS\; 15 variables)\, Scores and Morphology (24 variables)\, and Full (28 variables). We trained logistic regression models using 5- fold cross-validation with a 20% test set. We used bootstrap validation (1000 iterations) and Bonferroni-corrected feature importance analysis to reduce overfitting. The AUC for the Clinical-Basic set was 0.891±0.063. Performance was enhanced to a maximum AUC of 0.976±0.034 by adding PHASES and ELAPSS. The Full model achieved an AUC of 0.981±0.029\, with neither morphological nor hemodynamic variables giving much further improvement. Significant variance was revealed by bootstrap analysis (95% CI: 0.764-0.998). At 90% specificity\, the test set's AUC was 0.933\, but its sensitivity was only 14.3%. The primary contributors were ELAPSS (F=143.2\, p&lt\;10⁻¹) and PHASES (F=38.4\, p&lt\;10⁻¹)\, whereas morphological and hemodynamic characteristics did not exhibit any significant correlations. Clinical scores demonstrated strong discrimination\, but CFD-derived parameters offered minimal additional value in this small\, imbalanced\, single-center group. The wide confidence intervals and class imbalance limit clinical recommendations. Further validation in larger\, multicenter studies is necessary.
CATEGORIES:VIRTUAL ROOM 2C
LOCATION:Virtual Room C\, Bangkok\, Thailand
SEQUENCE:0
UID:9f5bb1576e03b544865b9dd68ec4dd12
URL:http://11thictisthailand.sched.com/event/9f5bb1576e03b544865b9dd68ec4dd12
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260417T125213Z
DTSTART:20260409T051500Z
DTEND:20260409T071500Z
SUMMARY:Data Governance for Sustainable Artificial Intelligence
DESCRIPTION:Authors - Tirupathi Rao Dockara\, Manisha Malhotra Abstract - AI and data platforms are increasingly expected to deliver end-to-end business automation under rapid market and regulatory change. However\, prevailing platform construction strategies remain predominantly top-down: teams standardize a generic capability stack and subsequently customize it for heterogeneous domains through code\, integration glue\, and service forks. This approach amplifies technical debt\, fragments governance\, and makes continuous adaptation expensive. This paper introduces the Inverse Vertex Pyramid (IVP)\, a design pattern that reverses the direction of platform derivation. IVP begins at the use-case vertex by conducting rigorous analysis of high-value specialized automation scenarios and generalizes them into explicit\, machine-actionable platform descriptors (metadata models\, domain ontologies\, policy/workflow specifications\, and capability contracts) that form a stable\, reusable core. Specialization is realized primarily via declarative configuration and policy changes\, rather than code rewrites. We formalize IVP as a pattern\, propose a reference architecture separating control and execution planes\, and provide a comparative analysis against layered architectures\, domain-driven design\, and microservice platforms. A proof-of-concept walkthrough in regulated claims automation illustrates the generalization mechanism and highlights how IVP can reduce re-engineering\, improve governance consistency\, and accelerate time-to-market. The paper concludes with limitations\, threats to validity\, and a research agenda for automated use-case mining\, formal verification of policies\, and quantitative evaluation of platform agility.
CATEGORIES:VIRTUAL ROOM 2C
LOCATION:Virtual Room C\, Bangkok\, Thailand
SEQUENCE:0
UID:105cf184eb885ff1ba8a928790b94c09
URL:http://11thictisthailand.sched.com/event/105cf184eb885ff1ba8a928790b94c09
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260417T125213Z
DTSTART:20260409T051500Z
DTEND:20260409T071500Z
SUMMARY:DRISHTI: An Edge-AI and IoT Multimodal Assistive Navigation System for the Visually Impaired
DESCRIPTION:Authors - Nishant Shah\, Ansh Bajpai\, Shrivaths S. Nair\, Manas Verma K\, Sabitha S Abstract - Digital accessibility in higher education is a key requirement to ensure the inclusion of students with hearing disabilities. However\, institutional plat-forms often present barriers that limit autonomy\, understanding of information\, and full participation. The objective of this study was to evaluate the user experience of students with hearing disabilities on the EVIRTUAL\, SGA\, and SIS platforms of the Technical University of Manabí\, identifying perceptions\, accessibility barriers\, and improvement proposals. A descriptive\, exploratory study with a mixed-methods approach was conducted. The population consisted of seventy-eight students with hearing disabilities registered in the Inclusion Unit\, from which an intentional subsample of ten participants was selected. A structured sur-vey with Likert-type scales and a participatory observation form were applied in real interaction situations with the platforms. Quantitative analysis was carried out using descriptive statistics\, while qualitative information was organized into thematic categories. The results show that half of the participants achieve full autonomy in the use of the platforms\, forty percent require intermittent support\, and the rest need constant assistance. Regarding clarity of information and con-tent comprehension\, intermediate responses predominate\, which reveals recur-rent difficulties. The main barriers identified were a confusing interface\, non-intuitive navigation\, insufficient visual supports\, and the need for external assistance. The study proposes improvements such as customizable subtitles\, step-by-step visual guides\, an accessibility button\, a sign language interpreter avatar\, and optimization for mobile devices\, aimed at strengthening autonomy and user experience.
CATEGORIES:VIRTUAL ROOM 2C
LOCATION:Virtual Room C\, Bangkok\, Thailand
SEQUENCE:0
UID:e5381fc9aa86f120eae691cbe56ff8a1
URL:http://11thictisthailand.sched.com/event/e5381fc9aa86f120eae691cbe56ff8a1
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260417T125213Z
DTSTART:20260409T051500Z
DTEND:20260409T071500Z
SUMMARY:Securing E-commerce with Blockchain and Smart Contracts
DESCRIPTION:Authors - Sabarishwaran V\, Gomathi K\, Andey Phani Vinay\, Jagadeeswaran V\, Ranjith Kumar M\n Abstract - The rapid expansion of digital commerce platforms has significantly transformed on- line transactional systems\; however\, conventional centralized architectures continue to face critical challenges related to security\, transparency\, data integrity\, and trust management. Traditional e-commerce systems rely heavily on centralized databases\, making them vulnerable to data tam- pering\, unauthorized access\, fraudulent transactions\, and single points of failure. To address these limitations\, this paper proposes a secure\, scalable\, and modular web-based e-commerce system that is architecturally designed for integration with blockchain technology and smart contracts. The proposed system is implemented using widely adopted web technologies\, with a responsive frontend and a robust backend to support essential functionalities such as user authentication\, product catalog management\, shopping cart operations\, order processing\, inventory management\, and administrative control. The architecture emphasizes separation of concerns\, enabling flexibility\, maintainability\, and future extensibility. A key contribution of this work lies in the incorporation of a blockchain-ready framework that enables immutable transaction recording and enhanced trace- ability across the entire transaction lifecycle. Smart contracts automate transaction validation and order execution. The system also introduces an AI-based anomaly detection mechanism using a Deep Q-Network to detect fraudulent behavior. Experimental validation demonstrates reliable per- formance and scalability.
CATEGORIES:VIRTUAL ROOM 2C
LOCATION:Virtual Room C\, Bangkok\, Thailand
SEQUENCE:0
UID:b6ce88870bd8c1753d5be3e97ad87c8e
URL:http://11thictisthailand.sched.com/event/b6ce88870bd8c1753d5be3e97ad87c8e
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260417T125213Z
DTSTART:20260409T051500Z
DTEND:20260409T071500Z
SUMMARY:Smart Handheld Oscilloscope With Integrated Processing For Real-Time Signal Analysis
DESCRIPTION:Authors - Sowmyashree N\, Madhu Sunkanur\, Impana M\, Suchithra B S\, Hemalatha P G Abstract - The existence of a growing social media has created complex cyber systems in which vast quantities of interactions constitute substantial issues regarding misinformation\, privacy invasion\, deception of identities\, and destructive behavioural tendencies. The regularity of involvement in this type of big systems requires sophisticated systems that are able to judge the motive of the user\, content validity and suspicious activities within real time. Overall interest will be to develop a universal trust calculation system that will be more secure and effective in ensuring privacy and increasing the accuracy of suspicious or malicious users in social sites. The proposed Multi-Layer Federated Trust Framework algorithm is a combination of peer-based user reputation scoring\, feature-based content authenticity detection\, federated trust indicators aggregation\, and anomaly detection with the help of behavioural anomalies. These approaches cooperate with secure aggregation and decentralized learning in removing the uncoded information exposure and enable the computation of trust at scale. The proposed algorithm is experimentally confirmed\, and the obtained results are 95.2\, 94.1\, 93.5\, and 93.8\, corresponding to a minimum latency of 65 ms and a privacy preservation score of 0.98. The general results indicate a viable and holistic response that adds to secure interactions\, blocks malicious acts and encourages trust in the actual social media settings.
CATEGORIES:VIRTUAL ROOM 2C
LOCATION:Virtual Room C\, Bangkok\, Thailand
SEQUENCE:0
UID:0435bccc87277dfecad8b213a9cd0336
URL:http://11thictisthailand.sched.com/event/0435bccc87277dfecad8b213a9cd0336
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260417T125213Z
DTSTART:20260409T051500Z
DTEND:20260409T071500Z
SUMMARY:Statistical Learning Frameworks for Automated Detection and Classification
DESCRIPTION:Authors - Md. Shahidul Islam\, Hasina Islam Abstract - Cross-domain recommendations are imperative in the growing tourism industry and with the increasing means of communication. Preference drift\, preference transfer\, and unfamiliarity with places have an overbearing impact on recommender systems. Most approaches do not address geometric misalignment across domains\, which is essential for cross-domain preference shift analysis in recommendation tasks. We propose Procrustes-Based Contextual Thompson Sampling (P-CTS) for Cross-Domain POI Recommendation\, integrating adversarial domain-invariant learning\, optimal geometric alignment via Procrustes transformation\, and adaptive Thompson Sampling with sleeping bandit management. First\, the embeddings are constructed to model the preference drift across the domains. Next\, the Procrustes transformation aligns source and target embedding spaces via optimal rotation\, scaling\, and translation. In the last phase\, we initialize Beta priors with similarity-weighted pseudo-counts derived from the aligned embeddings. The experiments on Gowalla and Foursquare across domains demonstrate 5.1% improvements in Precision@5 and 9.75% improvements in cold-start accuracy\, suggesting an adaptive exploration-exploitation trade-off.
CATEGORIES:VIRTUAL ROOM 2C
LOCATION:Virtual Room C\, Bangkok\, Thailand
SEQUENCE:0
UID:d0670a013b3d7244ada977d6f923ef18
URL:http://11thictisthailand.sched.com/event/d0670a013b3d7244ada977d6f923ef18
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260417T125213Z
DTSTART:20260409T051500Z
DTEND:20260409T071500Z
SUMMARY:The interaction of convolutional structure and KAN bottlenecks in U-KAN architectures
DESCRIPTION:Authors - Binh Pham Nguyen Thanh\, Long Duong Phi\, Phung Thi-Kim Nguyen\, Nhan Thi Cao Abstract - The rapid proliferation of Internet of Things (IoT) devices has significantly increased the digital attack surface\, which\, in turn\, has raised network vulnerability to sophisticated Distributed Denial of Service (DDoS) campaigns that could reduce the effectiveness of traditional signature-based Intrusion Detection System (IDS). Furthermore\, conventional Machine Learning (ML) approaches are often subject to manual feature engineering and lack the capture of complex spatial and temporal dependencies\, which are essential to detect subtle\, polymorphic threats. In this regard\, the present work proposes a lightweight hybrid Deep Learning (DL) architecture for reliable (DDoS) detection. The proposed approach integrates spatial feature extraction using a Convolutional Neural Network (CNN) with a Bidirectional Long Short-Term Memory (BiLSTM) network to capture temporal correlations\, further enhanced by an additive attention mechanism that underlines the importance of flow segments relevant to recognition. To mitigate issues with computational complexity\, a two-phase hybrid feature selection approach\, a combination of Information Gain (IG) and Dynamic Particle Swarm Optimization (PSO) would be utilized to select an optimal subset of features. The performance of the model was evaluated using the CICDDoS2019 benchmark dataset. The feature selection process was able to reduce the input space from 80 to 17 relevant features. The combined CNN-BiLSTM model\, along with threshold optimization\, was able to achieve an accuracy of 94.1%\, which indicates a significant improvement in the reduction of false negatives and validates the effectiveness of the proposed method in a secure IoT environment.
CATEGORIES:VIRTUAL ROOM 2C
LOCATION:Virtual Room C\, Bangkok\, Thailand
SEQUENCE:0
UID:e8037491f85a363c6fd6d47acf846efb
URL:http://11thictisthailand.sched.com/event/e8037491f85a363c6fd6d47acf846efb
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260417T125213Z
DTSTART:20260409T051500Z
DTEND:20260409T071500Z
SUMMARY:Weedy and Cultivated Rice Classification During Harvesting Stage Using YOLOv8
DESCRIPTION:Authors - Wani Zahidah Mohd Subari\, Shuzlina Abdul-Rahman\, Mohamad Faizal Ab Jabal\, Sharifalillah Nordin Abstract - Role-playing games (RPGs) allow the player to take on a specific role and complete different missions during gameplay. Their diversity enables a range of ap-plications beyond entertainment\, as they are often used in educational contexts. Learning content can be embedded in common components\, such as game fields\, tasks\, objects\, or non-playing characters (NPCs). The paper presents several educational RPGs with their features and characteristics\, and existing models of didactic video games. It proposes a two-level metamodel for describing an educational RPG. The metamodel is divided into five main components (world\, educational aspects\, quest\, playing character\, and NPCs)\, and their taxonomies are presented briefly. The authors propose a conceptual model that includes the interrelationships among the components mentioned. In addition\, their interpretations and significance for the development of RPG educational games are explained. An example of the metamodel is represented through a quest from a real educational RPG in the field of Chemistry. The presented RPG metamodel improves under-standing and helps to better design\, develop\, and integrate such games into various learning environments. The presented taxonomy can serve as a useful template for structuring design details.
CATEGORIES:VIRTUAL ROOM 2C
LOCATION:Virtual Room C\, Bangkok\, Thailand
SEQUENCE:0
UID:60a8a8020964d89388b4547470e12fc8
URL:http://11thictisthailand.sched.com/event/60a8a8020964d89388b4547470e12fc8
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260417T125213Z
DTSTART:20260409T051500Z
DTEND:20260409T071500Z
SUMMARY:An Accessible and Safety-Aware AI-Driven Digital Mental Health Platform for Visually Impaired and Multilingual Users
DESCRIPTION:Authors - Abhishek Chaudhari\, Mahalakshmi Bodireddy\, Aditya Bhor\, Onkar Dadas\, Prajakta Shinkar\, Chinmay Chougule\n Abstract - The growing mental health challenges around the globe need access to scalable\, available\, and safety conscious digital interventions. The paper describes a mental health support platform\, based on AI\, which combines conversational intelligence\, multi-therapeutic persona modeling\, structured mood analytics\, proactive crisis identification\, multi-lingual interaction\, and voice-based access in a secure full stack design. The system\, which runs on the Google Gemini AI\, provides context-sensitive therapeutic dialogue and performs four-dimensional mood analysis of anxiety\, stress\, depression\, and wellbeing\, allowing longitudinal assessment by providing interactive dashboards and automated reporting. A safety-first crisis override system offers validated emergency capacity in the high-risk situations. The platform also includes multilingual voice feedback to facilitate inclusion of the visually impaired users and non-English speaking communities in providing inclusive digital mental health care. The proposed system is capable of changing the prevalent perception that AI and its applications may never be responsible and scalable because it integrates therapeutic diversity\, structured analytics\, accessibility features\, and proactive safety controls into a single framework.
CATEGORIES:VIRTUAL ROOM 2D
LOCATION:Virtual Room D\, Bangkok\, Thailand
SEQUENCE:0
UID:b2efa1e418d66ff9fe85acec997cc693
URL:http://11thictisthailand.sched.com/event/b2efa1e418d66ff9fe85acec997cc693
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260417T125213Z
DTSTART:20260409T051500Z
DTEND:20260409T071500Z
SUMMARY:ARTIFICIAL INTELLIGENCE AS A TOOL FOR IMPROVING PARTICIPATORY BUDGETING IN THE CONTEXT OF THE DIGITAL TRANSFORMATION OF UZBEKISTAN ECONOMY
DESCRIPTION:Authors - Anvar Saidmakhmudovich Usmanov\, Mikhail Borisovich Khamidulin\, Shakhlo Rustamovna Abdullaeva\, Fazilat Dzhamoliddinovna Akhmedova\, Shoh-Jakhon Khamdаmov Abstract - This paper presents a data-driven forecasting and anomaly detection dashboard for live births in Surigao del Norte\, utilizing the Family Health Service Information System (FHSIS) data from 2021 and onwards. The research methodology is based on the CRISP-DM framework\, with business under-standing for the needs of maternal services planning in the provinces and municipalities\, data preparation for municipalities by quarters\, time aware modeling\, evaluation\, and deployment through the API and visualization layer. The research employs several machine learning techniques for forecasting\, such as ARIMA/SARIMA\, Exponential Smoothing (ETS and Holt-Winters)\, and the Prophet method\, along with a naïve method. The performance of the models is evaluated through the symmetric Mean Absolute Percentage Error (sMAPE)\, Root Mean Squared Error (RMSE)\, Mean Absolute Error (MAE)\, and Mean Absolute Scaled Error (MASE). A strict evaluation criterion for the deployment of the model is also implemented\, such as the availability of sufficient data points in the past for the model to be deployed (i.e.\, 12 data points in the past)\, the accuracy of the model (sMAPE < 20%)\, and the performance of the model in comparison with the naïve method (MASE < 1). A low confidence filter is also implemented for the series with intermittent data to prevent incorrect results. The results show high reliability of the forecasting model for the entire province and better interpretability for strategic planning. However\, the results also show that some of the municipalities with low population volumes and intermittent data points pose a challenge in the operation of the model.
CATEGORIES:VIRTUAL ROOM 2D
LOCATION:Virtual Room D\, Bangkok\, Thailand
SEQUENCE:0
UID:4278598bdfeecb50be509bd630496e60
URL:http://11thictisthailand.sched.com/event/4278598bdfeecb50be509bd630496e60
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260417T125213Z
DTSTART:20260409T051500Z
DTEND:20260409T071500Z
SUMMARY:Computational Reconstruction of Missing Notes in Polyphonic Music Using Bigram-Based Entropy Models
DESCRIPTION:Authors - Michele Della Ventura Abstract - Feature representations that are both high-dimensional and reduce redundancy often prove to be significant constraints on the performance of object detection. In this study\, we present the first hybrid metaheuristic feature selection framework that combines the enhanced grey wolf optimizer (EGWO) and firefly algorithm (FA) with a deep learning-based detection pipeline. The proposed EGWO-EFA method for identifying useful and compact feature subsets has been shown to reduce dimensionality by over 99.99% on the Pascal VOC and Brain Tumor M2PBP datasets. The experiments conducted demonstrate that\, compared to classical feature selection\, this method has an improved F1-score and precision\, by an average of 2%. In addition\, the overall pipeline execution time is considerably shorter. These results show that hybrid metaheuristic optimization is an effective approach to scalable and efficient object detection for high-dimensional feature representations.
CATEGORIES:VIRTUAL ROOM 2D
LOCATION:Virtual Room D\, Bangkok\, Thailand
SEQUENCE:0
UID:28ec95a6274309b32032d11c2abb65f8
URL:http://11thictisthailand.sched.com/event/28ec95a6274309b32032d11c2abb65f8
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260417T125213Z
DTSTART:20260409T051500Z
DTEND:20260409T071500Z
SUMMARY:CYBERSECURITY CHALLENGES IN INTELLIGENT RAILWAY SYSTEMS: A Review of ICT Architectures\, Threat Models\, and AI-Based Defense Approaches
DESCRIPTION:Authors - Roshna Dhakal\, Khanista Namee\n Abstract - Modern railway system increasingly rely on digital technologies such as Communication-Based Train Control (CBTC)\, European Train Control System (ETCS) and Supervisory Control and Data Acquisition (SCADA) systems\, raising significant cyber-security challenges. We have seen 220% increase in attacks over five years from opportunistic ransomware to sophisticated targeted threats. This paper provides an overview of railway cybersecurity and surveys the coverage area considering ICT architectures\, cyber threat models\, and AI-based defense approaches. 75% of cases employed Distributed Denial of Service (DDoS) tactics while ransomware had affected 54% of the OT environments. We describe a comparative taxonomy of Artificial Intelligence and Ma-chine Learning approaches including the methods based on supervised learning\, unsupervised learning\, and advanced deep learning practices with detection accuracy as high as 97.46%. However\, there exist several challenges: few available public data sets\, lack of validation in real-world scenarios\, demands for explain ability from that AI system and worries about adversarial robustness. We discuss eight potential research gaps\, and future directions focusing on federated learning\, digital twin development\, multimodal AI fusion and safety-security co-engineering frameworks.
CATEGORIES:VIRTUAL ROOM 2D
LOCATION:Virtual Room D\, Bangkok\, Thailand
SEQUENCE:0
UID:97b0890cc9c3866f6b4b5269fddb7a12
URL:http://11thictisthailand.sched.com/event/97b0890cc9c3866f6b4b5269fddb7a12
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260417T125213Z
DTSTART:20260409T051500Z
DTEND:20260409T071500Z
SUMMARY:Digital System Integration as a Procurement Strategy for Sustainable Urban Metro Systems: Evidence from Indian Case Studies
DESCRIPTION:Authors - Bhonsle Rashmi Ravindra\, Shankar Chaudhary\, Shivoham Singh\, Hemant Kothari\, Raj Kothari\n Abstract - Urban metro rail systems are the key to urban sustainable mobility\; however\, in spite of the developed technologies\, projects regularly experience delays and contractual disputes. These perceived challenges are highly attributed by prior scholarship to matters of the execution phase and restricted illumination is given on the institutional circumstances that form system performance in ICT intensive infrastructure. This paper examines procurement strategy as a govern ance tool that affects the results of digital system integration and sustainability in Indian metro rail projects. Based on statutory performance audit reports and com parative case studies\, the analysis indicates that fragmented procurement arrange ments fragment the integration functions to several contracts\, leading to coordi nation failure\, delayed commissioning\, and high claims. Instead\, the more coor dinated procurement models with consolidated interdependent systems and de fined integration roles have a better coordination structure and predictable deliv ery. The results indicate that the problem of metro project integration is more of an institutional than a technological problem. This research study adds to the body of knowledge on infrastructure governance by noting the design of procure ment to be one of the determinatives in the realization of effective and sustainable urban transit outcomes.
CATEGORIES:VIRTUAL ROOM 2D
LOCATION:Virtual Room D\, Bangkok\, Thailand
SEQUENCE:0
UID:c83827c208c570085220b3a573319bf0
URL:http://11thictisthailand.sched.com/event/c83827c208c570085220b3a573319bf0
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260417T125213Z
DTSTART:20260409T051500Z
DTEND:20260409T071500Z
SUMMARY:E-Health and Mental Well-Being: User Engagement with Mental Health Content on YouTube
DESCRIPTION:Authors - Irmawan Rahyadi\n Abstract -This research investigates the digital footprint of mental health infor mation as it circulates on YouTube. Using a qualitative content analysis ap proach\, the study examines 100 selected videos in conjunction with social media analytics to identify recurring patterns in the dissemination of mental health dis course. The findings reveal a mix of misleading or incomplete claims\, educa tional resources\, personal narratives\, and recovery-oriented content\, illustrating how mental health discussions shape and amplify user perspectives at both broad (macro) and specific (micro) levels within the evolving field of e-health. To in terpret these dynamics\, the analysis applies Gibson’s theory of transactional af fordances\, which illuminates key themes of risk\, relevance\, lived experience\, credibility\, and social support. By situating these themes within the broader con text of video-sharing platforms\, the study underscores the importance of YouTube as a platform for mental health communication. It underscores its role in broader public conversations about health in the digital age. The future re search should investigate mental health discourse from other social media users.
CATEGORIES:VIRTUAL ROOM 2D
LOCATION:Virtual Room D\, Bangkok\, Thailand
SEQUENCE:0
UID:8d114f1e4f78ffc11d2e1cd1c33527ec
URL:http://11thictisthailand.sched.com/event/8d114f1e4f78ffc11d2e1cd1c33527ec
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260417T125213Z
DTSTART:20260409T051500Z
DTEND:20260409T071500Z
SUMMARY:Early Mental Health Detection Using AI From Typing And Voice Patterns
DESCRIPTION:Authors - Ahir Jaimi\, Niyati Patel\, Nirav Bhatt Abstract - This research studied the economic impact and perceptions of air pollution\, particularly PM2.5\, in Chiang Mai Province\, Thailand\, using the Multiple Indicators Multiple Causes model (MIMIC model) and Mixed Data Sampling Regression (MIDAS model). The MIMIC model analyzed data from questionnaires administered to 5 0 7 respondents and examined factors influencing public perception of hotspots and PM2.5. The MIDAS model analyzed the impact of monthly PM2.5 levels and monthly hotspot counts on quarterly Gross Provincial Product (GPP)\, using data from 2019 to 2023.The MIMIC model analysis revealed that perception of burning or activities causing hotspots was the most influential factor in determining public perception of the impact of PM2.5. The effectiveness of government efforts to address the pollution problem had a negative correlation\, while demographic and socioeconomic characteristics showed no statistically significant impact. This indicates that public perception is more influenced by received information or education than by personal characteristics. The MIDAS model highlighted the economic impact of hotspots and air pollution. The analysis results indicate that When hotspots or burning occur\, these activities have a statistically significant positive impact on the province's GPP. A 1% increase in hotspots is correlated with an approximately 0 .14% increase in quarterly GPP\, suggesting that economic activity or agricultural burning may lead to increased economic activity and consequently a short-term increase in GPP. Conversely\, a decrease in PM2.5 concentration in the previous month resulted in an approximately 0.47% decrease in quarterly GPP\, demonstrating that the economic costs of air pollution occur with a delayed effect rather than simultaneously. Therefore\, this research highlights the importance of the correlation between short-term economic benefits and polluting activities\, as well as the delayed economic losses resulting from poor and toxic air quality. This research emphasizes the importance of air quality management\, risk communication and support\, and economic and environmental policies to address the long-term economic and social impacts of PM2.5 pollution.
CATEGORIES:VIRTUAL ROOM 2D
LOCATION:Virtual Room D\, Bangkok\, Thailand
SEQUENCE:0
UID:a745afc1c65c4cbd9983b15858871342
URL:http://11thictisthailand.sched.com/event/a745afc1c65c4cbd9983b15858871342
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260417T125213Z
DTSTART:20260409T051500Z
DTEND:20260409T071500Z
SUMMARY:How Omnichannel ICT-Enabled Marketing Shapes Customer Engagement and Loyalty in Culinary Hospitality Services
DESCRIPTION:Authors - Aditya Nova Putra\, Budi Riyanto\, Alda Chairani\, Sandy Dwiputra Yubianto Abstract - This study examines the determinants of continuance intention in YouTube live streaming consumption among Indonesian Generation Z\, focusing on social interaction\, entertainment\, passing time\, and enjoyment. Drawing upon Uses and Gratifications Theory and Computer-Mediated Communication\, this research situates live streaming as an interactive digital environment where audiences actively negotiate social and emotional experiences. A quantitative explanatory survey was conducted among 108 Generation Z subscribers of the Windah Basudara YouTube channel\, and the data were analyzed using Partial Least Squares Structural Equation Modeling (PLS-SEM). The findings reveal that social interaction and passing time significantly influence continuance intention\, whereas entertainment and enjoyment do not demonstrate significant effects. These results suggest that sustained engagement in live streaming environments is driven more by interactive and habitual gratifications than by purely hedonic motivations. By highlighting the contextual dynamics of Indonesian gaming live streaming\, this study extends the application of Uses and Gratifications Theory in synchronous digital media settings and offers practical implications for content creators seeking to strengthen audience retention strategies.
CATEGORIES:VIRTUAL ROOM 2D
LOCATION:Virtual Room D\, Bangkok\, Thailand
SEQUENCE:0
UID:102f9f6bd3d26450ddcd251aac97710c
URL:http://11thictisthailand.sched.com/event/102f9f6bd3d26450ddcd251aac97710c
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260417T125213Z
DTSTART:20260409T051500Z
DTEND:20260409T071500Z
SUMMARY:Prediction of Academic Performance of Postgraduate Students at the Technical University of Manabí using Big Data and Machine Learning Techniques
DESCRIPTION:Authors - Steveen Eduardo Pinzon Morales\, Yandry Jose Olarte Sancan\, Marely del Rosario Cruz Felipe\, Maricela Pinargote-Ortega Abstract - The recent decade has witnessed a more increase on the impact of applying and implementing green computing which mainly focuses in protecting the overall nature of the environment. Within the scope of this comprehensive assessment of the relevant literature\, the most recent advancements in energyefficient software design\, sustainable hardware design\, and improved algorithms are examined and compiled. A wide range of enterprises use cloud computing for its adaptability\, reliability\, speed\, and cost-effectiveness. The proliferation of cloud computing is affecting a shift in the manner in which we network. The application of these new technologies are mainly focused on the overall protection of the environmental aspects\, they are more targeting in reducing the emission of dangerous type of gases and substances\, use renewable mode of energy and thereby focusing in protecting the world for the future generations. The article is mainly involved in understanding the overall nature of implementing the green computing in realizing the overall development aspect.
CATEGORIES:VIRTUAL ROOM 2D
LOCATION:Virtual Room D\, Bangkok\, Thailand
SEQUENCE:0
UID:b1de650b643a91627d0fb211c474d1fe
URL:http://11thictisthailand.sched.com/event/b1de650b643a91627d0fb211c474d1fe
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260417T125213Z
DTSTART:20260409T051500Z
DTEND:20260409T071500Z
SUMMARY:StewardFM: An Optimized Association Management Solution Utilizing the Deflate Compression Algorithm for Efficient Cloud Storage
DESCRIPTION:Authors - Ain Geuel E. Escober\, Rosicar E. Escober\, Demelyn E. Monzon\n Abstract - This study presents the development of StewardFM\, an information management system designed to evaluate the effectiveness of the Deflate compression algorithm in optimizing storage for associations and small organizations with limited cloud VPS resources. By integrating membership\, event\, collection\, and budget management into one platform\, StewardFM reduces storage overhead while maintaining essential functionality\, offering a cost-efficient and scalable solution for resource-constrained organizational environments.
CATEGORIES:VIRTUAL ROOM 2D
LOCATION:Virtual Room D\, Bangkok\, Thailand
SEQUENCE:0
UID:80acb8009d3aa45288579d6a8dcee1b8
URL:http://11thictisthailand.sched.com/event/80acb8009d3aa45288579d6a8dcee1b8
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260417T125213Z
DTSTART:20260409T051500Z
DTEND:20260409T071500Z
SUMMARY:A Comparative Behavioral Analysis: False Positive–Optimized Ensemble Prediction of Cervical Cancer
DESCRIPTION:Authors - Shabnam Praveen\, Shubham Kumar\, Tulika Roy\, Sanskriti Sahu\, Subhangi Raj\, Ranjita Kumari Dash Abstract - The implementation and design of a covert communication channel that embeds hidden information within TCP/IP packet headers rather than within the actual payload of the packets is presented as a project. This is different than traditional embedding methods (steganography)\, which typically embed data into multime dia files\, in that steganography in this case utilizes header fields that are not cur rently in use or can be modified so that TCP/IP packets can transmit hidden data. The fields that are used to transmit hidden data are the IP Identification Field\, TCP Sequence Number\, TCP Acknowledgment Number\, and TCP Window Size. The sender module encodes and generates packets\, and the receiver retrieves packets\, extracts encoded bits\, and reassembles data from the encoded bits found in the packets. The integrity of the data is verified using a checksum (SHA-256) and packet loss is reported. The lack of a payload will further enhance the stealth various data transmission methods may enjoy as it will circumvent conventional intrusion detection techniques (which primarily examine the payload data within packets). This project will demonstrate the ability to use this or similar covert communication channels to implement covert communication systems. In addi tion\, covert communication channels can be used for different types of files and demonstrate the security and educational value of covert channel research in net work security.
CATEGORIES:VIRTUAL ROOM 2E
LOCATION:Virtual Room E\, Bangkok\, Thailand
SEQUENCE:0
UID:dd24372ca2c7edd66ca74beb2e74e854
URL:http://11thictisthailand.sched.com/event/dd24372ca2c7edd66ca74beb2e74e854
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260417T125213Z
DTSTART:20260409T051500Z
DTEND:20260409T071500Z
SUMMARY:AI-Powered Personal Assistant for Smart Task Planning and Productivity Optimization
DESCRIPTION:Authors - Prajakta Shinkar\, Madhuri Suryavanshi\, Sakshi Satav\, Mahima Thakre\, Saisha Chaudhary\n Abstract - The contemporary academic and professional world requires smart systems of productivity that are not limited to the old task managers. The paper introduces an intelligent personal productivity assistant powered by AI that consists of generative AI\, dynamical schedule\, behavioral analytics\, and gamification using a mobile-first structure. The system is based on a Flutter frontend and FastAPI backend and a hybrid AI architecture to create conversational tasks and understand their context. A burnout detection module is a behavioral module that analyzes workload trends\, tasks owed and completion trends to give early risk alerts. A smart scheduling system aggressively plans on a daily basis with priority-based model\, conflict resolution and Pomodoro-based segmentation. The proposed system combines conversational AI\, predictive analytics\, and motivational reinforcement to increase productivity and decrease cognitive load and help avoid burnout in managing tasks.
CATEGORIES:VIRTUAL ROOM 2E
LOCATION:Virtual Room E\, Bangkok\, Thailand
SEQUENCE:0
UID:dd725664cc9ea36451dbc6675c828520
URL:http://11thictisthailand.sched.com/event/dd725664cc9ea36451dbc6675c828520
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260417T125213Z
DTSTART:20260409T051500Z
DTEND:20260409T071500Z
SUMMARY:An Interpretable Multi-Cluster Phishing Detection Framework with Leakage-Free Feature Engineering
DESCRIPTION:Authors - Ashvini Jadhav\, Pankaj Chandre Abstract - The contemporary academic and professional world requires smart systems of productivity that are not limited to the old task managers. The paper introduces an intelligent personal productivity assistant powered by AI that consists of generative AI\, dynamical schedule\, behavioral analytics\, and gamification using a mobile-first structure. The system is based on a Flutter frontend and FastAPI backend and a hybrid AI architecture to create conversational tasks and understand their context. A burnout detection module is a behavioral module that analyzes workload trends\, tasks owed and completion trends to give early risk alerts. A smart scheduling system aggressively plans on a daily basis with priority-based model\, conflict resolution and Pomodoro-based segmentation. The proposed system combines conversational AI\, predictive analytics\, and motivational reinforcement to increase productivity and decrease cognitive load and help avoid burnout in managing tasks.
CATEGORIES:VIRTUAL ROOM 2E
LOCATION:Virtual Room E\, Bangkok\, Thailand
SEQUENCE:0
UID:016748c701b3ed709518367b971ebfe7
URL:http://11thictisthailand.sched.com/event/016748c701b3ed709518367b971ebfe7
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260417T125213Z
DTSTART:20260409T051500Z
DTEND:20260409T071500Z
SUMMARY:Development and Implementation of a Physical AI Based Material Control System
DESCRIPTION:Authors - NamUook\, Kim\, Gihwan Bong\, Yoon Seok\, Chang Abstract - The heterogeneity of data sources makes the design of traditional da ta ware-houses complex and time-consuming. Indeed\, the data warehouse system must process structured\, semi-structured\, and unstructured sources. To over-come this challenge\, we propose an interactive approach to data ware house design based on a federated ontology. The ontology serves as a unified conceptual layer that integrates heterogeneous data sources and facilitates the building of the data warehouse. Our approach allows decision-makers to in teractively select the subdomain of the federated ontology according to their needs and generate their data warehouse. The generation of the data ware-house in the constellation schema is automated using algorithms. It also ensures the maintenance of the data warehouse to take into account various changes in data and decision-makers' needs. The proposed methodology is summarized through architectures defined at each stage\, each addressing a specific challenge. At the ontology construction level\, it resolves issues related to data heterogeneity while enabling interoperability among multiple do-main ontologies. It also provides a complete scenario for the decision-maker to assist in the full construction of a data warehouse from an ontology. Finally\, it facilitates querying the constructed data warehouse using requests ex-pressed by the decision-maker in natural language.
CATEGORIES:VIRTUAL ROOM 2E
LOCATION:Virtual Room E\, Bangkok\, Thailand
SEQUENCE:0
UID:9ea04f916ab5dfffb9f87343ca8855de
URL:http://11thictisthailand.sched.com/event/9ea04f916ab5dfffb9f87343ca8855de
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260417T125213Z
DTSTART:20260409T051500Z
DTEND:20260409T071500Z
SUMMARY:DYNAMIC TRAFFIC FLOW PREDICTION IN SMART CITIES USING DEEP LEARNING
DESCRIPTION:Authors - C Nitheeshwaran\, M Saravanan\, S Mukesh\, K S Anuvarshini Abstract - The present study explores the online privacy concerns of young Indian consumers. Using the segmentation approach popularized by Dr Alan Wes-tin in the U.S.\, this study identifies the segments within Indian youth. This study is based on a survey conducted on a sample of Indian university students. Hierarchical and non-hierarchical cluster analysis techniques were applied to identify segments within young Indian consumers based on their privacy concerns. The study identified three consumer segments: highly concerned\, moderately concerned\, and less concerned based on online privacy concerns. The findings also reveal important differences among the three segments in terms of out-come variables such as perceived effectiveness of legal/regulatory policy\, fabricating personal information\, and software usage for protection. The results indicate an overall increased level of concern for online privacy among young Indian consumers. The results suggest similarities and dissimilarities with Westin’s approach. While previous research on online privacy has been chiefly based on the Western context\, this study offers a window to look at the Eastern context by examining the privacy concerns of young Indian consumers\, who have not been studied\, and hence provides an important contribution to the existing literature.
CATEGORIES:VIRTUAL ROOM 2E
LOCATION:Virtual Room E\, Bangkok\, Thailand
SEQUENCE:0
UID:61a46ad48149fde7e547b0d5ce00803a
URL:http://11thictisthailand.sched.com/event/61a46ad48149fde7e547b0d5ce00803a
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260417T125213Z
DTSTART:20260409T051500Z
DTEND:20260409T071500Z
SUMMARY:Graph-guided Hierarchical Multi-class Decomposition for Intuitionistic Fuzzy Weighted Least Squares Twin Support Vector Machine
DESCRIPTION:Authors - Quang-Thinh Bui\, Lan T.T. Tran Abstract - The digital transformation of the construction industry has intensified the demand for standardized methods of information exchange. Building Infor mation Modeling (BIM) has become a cornerstone of this transformation\, ena bling interdisciplinary collaboration and improving data quality. However\, recur ring challenges such as inconsistent data structures\, unclear contractual require ments\, and limited interoperability continue to hinder efficient project delivery. To address these issues\, the Information Delivery Specification (IDS) was devel oped within the buildingSMART ecosystem as a computer-interpretable standard for defining and validating information requirements. Officially approved in June 2024\, IDS bridges human-readable requirements with machine-interpretable val idation rules\, positioning itself as both a contractual instrument and a technical validation tool. This study synthesizes insights from official IDS documentation and academic literature to provide a comprehensive evaluation of IDS’s role in the construction sector. The systematic literature review categorizes contributions into five the matic domains: standardization\, application scenarios\, systematic reviews\, coun try and domain-specific studies\, and methodological innovations. Findings high light IDS’s versatility across diverse applications\, including acoustic assessment\, accessibility compliance\, railway projects\, and energy simulation. At the same time\, research gaps remain in areas such as national adaptation strategies\, auto mated compliance checking through CI/CD pipelines\, and methodological devel opment via linkage with the Level of Information Needs (LoIN). By integrating theoretical perspectives with practical case studies\, this research demonstrates how IDS functions as both a technical standard and a methodolog ical framework. The study concludes that IDS has the potential to become a cor nerstone of digital construction practices\, bridging regulatory requirements with automated validation in BIM workflows.
CATEGORIES:VIRTUAL ROOM 2E
LOCATION:Virtual Room E\, Bangkok\, Thailand
SEQUENCE:0
UID:cd57122c4398d40c8a230d0d7bcc3bef
URL:http://11thictisthailand.sched.com/event/cd57122c4398d40c8a230d0d7bcc3bef
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260417T125213Z
DTSTART:20260409T051500Z
DTEND:20260409T071500Z
SUMMARY:Lifestyle Influences on Menstrual Cramps: ML Analysis and EHG Monitoring with a Low-Cost Device
DESCRIPTION:Authors - Anuja Kelkar\, Pradnya Kardile\, Aditi Dudhe\, Prajakta Chaudhari\, Meenal Kamlakar Abstract - In this paper we derive a new estimate of the channel bit rate. The estimates is a special transformation of the main EVT theorem that is particularly designed for use in telecommunication automated systesm meaning it’s robust to noise\, computationally cheep\, needs very few data points and no manual validation. Due to the EVT methodology we can evaluate if the bit rate can keep dropping indefinitely or if it has a guaranteed minimum value. The method is relatively fast because it uses Newton’s interpolation instead of hypothesis testing or regression.
CATEGORIES:VIRTUAL ROOM 2E
LOCATION:Virtual Room E\, Bangkok\, Thailand
SEQUENCE:0
UID:3a991b4b99557fc7d9e99828be14a1cc
URL:http://11thictisthailand.sched.com/event/3a991b4b99557fc7d9e99828be14a1cc
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260417T125213Z
DTSTART:20260409T051500Z
DTEND:20260409T071500Z
SUMMARY:RCAN-RS: An Enhanced Residual Channel Attention Network for Remote Sensing Image Super-Resolution
DESCRIPTION:Authors - Ankur Maurya\, Shaurya Oberoi\, Madhav Malhotra\, Rakesh Chandra Joshi\, Garima Aggarwal\, Malay Kishore Dutta\n Abstract - Remote sensing imagery plays an important role in applications such as environmental monitoring\, disaster management\, urban planning and agricultural analysis. However\, the spatial resolution of such imagery is often limited by sensor constraints\, revisit frequency and acquisition cost. To address this challenge\, this paper presents RCAN-RS\, an enhanced Residual Channel Attention Network for remote sensing image super-resolution. The proposed model extends the RCAN framework through three targeted modifications: a dual-pooling channel attention mechanism\, a spectral attention module and an edge enhancement module. These components are designed to improve detail reconstruction while preserving inter-channel consistency and sharp structural boundaries in remote sensing imagery. The model was trained and evaluated on the DOTA dataset un-der a 2× super-resolution setting from 256 × 256 to 512 × 512 pixels. Quantitative evaluation using both conventional image-quality metrics and remote-sensing-oriented measures shows that RCAN-RS achieves a mean PSNR of 34.42 dB\, SSIM of 0.9398\, Edge Preservation Index of 0.9524\, ERGAS of 6.68 and UQI of 0.9846 on the test set. These results demonstrate the effectiveness of integrating attention-guided and edge-aware mechanisms for remote sensing image super-resolution.
CATEGORIES:VIRTUAL ROOM 2E
LOCATION:Virtual Room E\, Bangkok\, Thailand
SEQUENCE:0
UID:106b71ca21850e28c671d9b6a256901f
URL:http://11thictisthailand.sched.com/event/106b71ca21850e28c671d9b6a256901f
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260417T125213Z
DTSTART:20260409T051500Z
DTEND:20260409T071500Z
SUMMARY:Synergistic Spatio-Spectral Representation Learning via Deep Reinforced Adaptive Masking for Universal Medical Hyperspectral Image Segmentation
DESCRIPTION:Authors - Inuka Gajanayake\, Gagani Kulathilaka\, Guhanathan Poravi\, Saadh Jawwadh Abstract - The swift growth of digital interfaces has facilitated manipulative design practices called dark patterns\, which take advantage of cognitive biases to manipulate users and subvert informed decision-making. Though widespread across e-commerce\, social media\, and other areas\, automated identification and empirical knowledge of user vulnerability are still in their infancy. This work introduces an end-to-end framework integrating a GenAI-augmented browser add-on for real-time detection of dark patterns with systematic estimation of user awareness and behavioral reactions. A new Pattern Vulnerability Index (PVI) measures the threat from individual patterns according to frequency\, unawareness among users\, and potential damage. Cross-platform analysis identified high-risk patterns like Discount Anchoring\, Urgency\, and cost-related manipulations to be frequently overlooked by users. Clustering identifies scenarios in which several deceptive patterns occur in co-presence\, including checkout processes\, promotional displays\, and subscription pitfalls. The results highlight the moral significance of manipulative interface design and establish the capability of machine-based tools to promote user safeguard\, sensitize\, and guide regulation and design efforts. This study provides a basis for consumer-oriented solutions and future research towards more transparent and ethical online encounters.
CATEGORIES:VIRTUAL ROOM 2E
LOCATION:Virtual Room E\, Bangkok\, Thailand
SEQUENCE:0
UID:ecaa65d08abf1e22ebca798c34dab480
URL:http://11thictisthailand.sched.com/event/ecaa65d08abf1e22ebca798c34dab480
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260417T125213Z
DTSTART:20260409T051500Z
DTEND:20260409T071500Z
SUMMARY:Theory and Applications of Threshold and General Secret Sharing Schemes
DESCRIPTION:Authors - Hiep. L. Thi Abstract - As a core pillar industry in China's economic transformation toward a service-oriented economy\, the tourism industry plays an irreplaceable role in boosting domestic demand growth\, optimizing regional industrial structures\, and advancing high-quality economic development. The Dazu Rock Carvings in Chongqing\, holding the dual top-tier qualifications of a World Cultural Heritage site and a National 5A Scenic Area\, embody over 1\,300 years of historical accumulation. With their unique cultural core of ‘Confucian-Buddhist-Taoist Syncretism’ and top-tier high-relief artistic craftsmanship\, they stand as the pinnacle of Chinese stone carving art\, boasting remarkable cultural tourism economic value and cultural inheritance value. However\, for a long time\, the Dazu Rock Carvings have been trapped in the dilemma of ‘high cultural value but low market recognition’—acclaimed but underrecognized in the market. Their visibility enhancement relies excessively on short-term hotspots\, lacking a long-term support mechanism. Based on theories of culture-tourism integration\, brand communication\, and sustainable cultural heritage development\, this paper employs literature review\, data analysis\, case comparison\, and field research to accurately identify core pain points. It constructs a scientific and feasible new marketing path from six dimensions: innovative resource transformation\, precise audience cultivation\, diversified channel expansion\, upgraded cross-border linkage\, breakthrough international communication\, and long-term institutional safeguards. This path aims to help the Dazu Rock Carvings transition from traffic-dependent development to value-driven development and\, at the same time\, provide practical references for similar cultural heritage scenic spots in China.
CATEGORIES:VIRTUAL ROOM 2E
LOCATION:Virtual Room E\, Bangkok\, Thailand
SEQUENCE:0
UID:98ec06d3d8d2610e2f6d3d3332f00d43
URL:http://11thictisthailand.sched.com/event/98ec06d3d8d2610e2f6d3d3332f00d43
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260417T125213Z
DTSTART:20260409T051500Z
DTEND:20260409T071500Z
SUMMARY:A Data-Driven Framework for Identifying and Classifying Core Virtual Learning Environment Factors Using Systematic Review and iKeyCriteria
DESCRIPTION:Authors - Fredy Gavilanes-Sagnay\, Edison Loza-Aguirre\, Luis Castillo-Salinas\, Narcisa de Jesus Salazar Alvarez Abstract - Ayurveda\, India's ancient system of medicine\, is full of inter-connected knowledge about diseases\, their symptoms\, herb and formulation (compounds). However\, texts such as Charaka Samhita are mostly unstructured and cannot be readily analysed computationally. This work presents AyurKOSH which is a machine-readable\, high-quality Ayurvedic dataset that is designed as a Knowledge Graph (KG) in order to support Artificial Intelligence driven research. The dataset is represented as subject–predicate–object triplets\, which enables semantic interoperability\, graph traversal\, and multi-hop inferencing across entities. The dataset is designed by following schema-driven ontology which standardizes relationships between various nodes such as diseases\, symptoms\, pharmacological attributes\, and compound formulations. DB Schema ensures consistency and computational tractability. AyurKOSH has the structured data of diseases and related symptoms\, drug preparations\, herbs and the detailed pharmacological properties are Rasa\, Guna\, Virya\, Vipaka\, Karma. The graph structure shows real-world biomedical network characteristics such as high sparsity and low average degree\, which makes it suitable for embedding-based learning\, graph neural networks\, and explainable AI frameworks. Moreover\, there is botanical metadata and herb-substitution relationships added for the prediction of synergy and repurposing of drugs. The dataset facilitates applications in biomedical NLP\, and automated reasoning systems and clinical decision assistance\, and pedagogy in integrative medicine. AyurKOSH became available for academic and non-commercial research under CC BY-NC-SA 4.0 license.
CATEGORIES:VIRTUAL ROOM 2F
LOCATION:Virtual Room F\, Bangkok\, Thailand
SEQUENCE:0
UID:abd63802da9e19c3671c4e9cad9b450d
URL:http://11thictisthailand.sched.com/event/abd63802da9e19c3671c4e9cad9b450d
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260417T125213Z
DTSTART:20260409T051500Z
DTEND:20260409T071500Z
SUMMARY:An Adaptive Diglossia-Aware Framework for Controlled Sinhala Story and Quiz Generation Using Parameter-Efficient Fine-Tuning
DESCRIPTION:Authors - W M I T Warnasooriya\, T D Jayadeera\, A M G S Adhikari\, M A F Zumra\, A J Vidanaralage\, M Samaraweera\n Abstract - The integration of large language models (LLMs) into primary educa tion remains limited in low resource\, diglossic languages like Sinhala. General purpose models often produce grammatically inconsistent or cognitively over whelming output for young learners. This paper introduces a grade-adaptive\, con straint-driven framework for automated Sinhala story and quiz generation target ing Grades 1-5. Building upon an 8-billion-parameter Sinhala-adapted LLaMA 3 model\, we apply Quantized Low-Rank Adaptation (QLoRA) using a curated multi-task educational dataset. The system enforces tier-specific linguistic con straints separating conversational Sinhala for lower grades from formal written Sinhala for upper grades while embedding strict structural rules such as con trolled sentence counts (5-6 vs. 7-8) and validated multiple-choice formats (3 vs. 4 options). Evaluation on 100 structured prompts demonstrated substantial im provements over a zero-shot baseline: structural compliance increased from 64% to 93%\, and hallucination-related failures decreased from 31% to 8%. Further more\, evaluation against 50 unseen real-world classroom prompts yielded a 0.0% crash rate and 95% register adherence\, confirming robust qualitative perfor mance. Results demonstrate that diglossia-aware dataset engineering and con straint-aware fine-tuning enable reliable\, pedagogically aligned deployment of LLMs in low-resource primary learning environments.
CATEGORIES:VIRTUAL ROOM 2F
LOCATION:Virtual Room F\, Bangkok\, Thailand
SEQUENCE:0
UID:e608437eec002cf66f0de9134c7c1d4a
URL:http://11thictisthailand.sched.com/event/e608437eec002cf66f0de9134c7c1d4a
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260417T125213Z
DTSTART:20260409T051500Z
DTEND:20260409T071500Z
SUMMARY:Biomimetic Approach to Enhancing Anthropomorphism in Social Motions of Humanoid Robots Leveraging Machine Learning
DESCRIPTION:Authors - S. M. Mizanoor Rahman Abstract - Removable USB storage devices are widely used in day-to day computing\, but they also introduce risks such as unauthorized data transfer and misuse of external media. Understanding how these devices are used on a system is important during forensic investigations\, espe cially when analyzing potential data leakage incidents. On Windows sys tems\, traces of USB activity are not stored in a single location. Instead\, they are distributed across registry entries\, system logs\, and file system records. Examining these sources individually often makes it difficult to form a clear picture of events. This paper introduces a forensic frame work that brings together USB-related artifacts from multiple system components and analyzes them in a unified manner. The method gath ers data from sources such as registry entries\, Plug-and-Play logs\, and f ile system structures\, and then aligns them based on their timestamps. A Python-based implementation is used to automate this process and to relate device connection events with file operations. Experiments con ducted on a Windows setup show that the framework can identify device usage and reconstruct the sequence of related activities with clarity. By combining evidence into a single timeline\, the approach helps simplify analysis and supports consistent interpretation of results.
CATEGORIES:VIRTUAL ROOM 2F
LOCATION:Virtual Room F\, Bangkok\, Thailand
SEQUENCE:0
UID:9a7e9a31ae98d3a8f5209989cdab5e88
URL:http://11thictisthailand.sched.com/event/9a7e9a31ae98d3a8f5209989cdab5e88
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260417T125213Z
DTSTART:20260409T051500Z
DTEND:20260409T071500Z
SUMMARY:Detection of AI - Generated Images using ResNet18 and Robustness Analysis on the CIFAKE Dataset
DESCRIPTION:Authors - Shamita Jagarlamudi\, Soormayee Joshi\, Aman Aditya\, Anushka Gangwar\, Pratvina Talele Abstract - Federated Learning (FL) is a privacy-preserving\, distributed learning framework where models are trained locally on client devices\, and only the trained parameters are shared with a central server. Nevertheless\, FL encounters substantial obstacles in real-world applications due to data heterogeneity\, such as non-IID distributions leading to local inconsistencies and client drift thereby diminishing global model efficacy. To tackle these challenges\, we propose a Federated Prox Drift Correction (FedPDC)\, an effective and practical method designed to mitigate client drift and local overfitting through the use of drift correction and proximal terms. Comprehensive experiments conducted on public datasets demonstrate that FedPDC performance is superior compared to state-of-the-art methods.
CATEGORIES:VIRTUAL ROOM 2F
LOCATION:Virtual Room F\, Bangkok\, Thailand
SEQUENCE:0
UID:2c87aac835f8d98db8e36e3fdb763c7d
URL:http://11thictisthailand.sched.com/event/2c87aac835f8d98db8e36e3fdb763c7d
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260417T125213Z
DTSTART:20260409T051500Z
DTEND:20260409T071500Z
SUMMARY:Driver Monitoring and Vehicle Access Control: A Review
DESCRIPTION:Authors - U. A. Walke\, G. A. Kulkarni\, Pranav Mungankar\, Om Kale\, Tejas Kadam Abstract - Digitizing damaged historical texts requires multiple processing steps that can propagate semantic noise through the workflow. Efforts have been made to improve the recognition\, correction\, and normalization steps of the pipeline\, but few studies have quantified model-level effects in isolation under a controlled architecture setup. Here we present Probanza\, an extensible staged evaluation framework that decouples preprocessing normalization from semantic modeling to facilitate clean comparisons between LLMs. We perform super-resolution\, contextual correction\, and historical normalization before English translation. We selected 30 total degraded pages from the Florentine Codex and digitized them with three LLM configurations: GPT-5\, GPT-4o\, and Gemini 3 Flash. Co sine similarity was computed between model predictions and archival baseline translations to measure semantic accuracy. A one-way repeated-measures ANOVA was done to examined differences across configurations. The analysis revealed a significant main effect of LLM configuration. Gemini 3 Flash pro duced the highest mean similarity (M = .881\, SD = .075)\, while GPT-5 (M = .783\, SD = .147) and GPT-4o (M = .769\, SD = .135) which were not significantly dif ferent from one another. Our results demonstrate that significant differences exist between LLM configurations for the task of digitizing damaged historical texts when preprocessing is held constant. Probanza allows an isolating model-level effects comparison in LLM-based historical digitization workflows.
CATEGORIES:VIRTUAL ROOM 2F
LOCATION:Virtual Room F\, Bangkok\, Thailand
SEQUENCE:0
UID:682eebbc93dad50d04d8a6482e38409e
URL:http://11thictisthailand.sched.com/event/682eebbc93dad50d04d8a6482e38409e
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260417T125213Z
DTSTART:20260409T051500Z
DTEND:20260409T071500Z
SUMMARY:FedPDC: A Novel Approach to Improving Federated Learning via Proximal Drift Correction
DESCRIPTION:Authors - Kushall Pal Singh\, Vijay Kumar\, Monu Verma\, Dinesh Kumar Tyagi\, Santosh Kumar Vipparthi Abstract - Hybrid enterprise environments spanning on-premises systems and public cloud services increase exposure to credential abuse\, lateral movement\, and misconfiguration-driven attack paths\, motivating continuous verification and policy enforcement beyond perimeter assumptions. This paper presents an Azure-native\, AI-enhanced Zero Trust framework that integrates identity-first enforcement (Microsoft Entra Conditional Access\, Continuous Access Evaluation\, and Privileged Identity Management)\, telemetry centralization (Microsoft Sentinel with UEBA)\, and an Azure Machine Learning classifier that outputs a probability-derived 0–100 trust score. Because identity policy engines consume bounded native signals\, the framework binds external scoring to enforcement using SOAR automation that updates policy-targeted identity group membership via Microsoft Graph. A controlled A/B evaluation compares a static baseline (non-adaptive enforcement) with an adaptive mode (ML-in-the-loop scoring and automated score-to-policy binding) using MITRE ATT&CK-aligned scenarios: impossible travel sign-in\, privilege escalation attempts via privileged activation workflows\, and lateral movement via remote access/filesharing pathways. Quantitative outcomes are reported using median (P50) and tail (P95) time-to-detect\, decision latency\, and false-positive rate. To technically validate the adaptive control loop\, the paper also reports an instrumented latency decomposition (trigger delay\, playbook runtime\, ML scoring call duration\, and score-to-policy execution time) to show which components dominate end-to-end delay.
CATEGORIES:VIRTUAL ROOM 2F
LOCATION:Virtual Room F\, Bangkok\, Thailand
SEQUENCE:0
UID:43bd6432c7bf87aac0ed9dc20ce19b94
URL:http://11thictisthailand.sched.com/event/43bd6432c7bf87aac0ed9dc20ce19b94
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260417T125213Z
DTSTART:20260409T051500Z
DTEND:20260409T071500Z
SUMMARY:Hybrid Transformer Framework for High-Accuracy Misinformation Detection
DESCRIPTION:Authors - Karuppasamy E\, Krithika V\, Harish P\, Pravinbaalaa V\, Satheeskumar Abstract - The large online data consist of duplication and plagiarized contents. Due to Artificial Intelligence\, data generation has become very easy. But\, it may also lack an ethical data generation process. Hence\, there is a need of validating plagiarism free data for authentic usage. In this research work\, authors focus on word-level plagiarism detection methods in Natural Language Processing. The proposed method uses a comparative analysis of cosine similarity\, Euclidean distance and Manhattan distance methods for word-level plagiarism detection for different n-gram sizes. The inculcation of n-gram size improved the accuracy compared to unigram based methods. The experimental results of the cosine similarity method outperform Euclidean and Manhattan distance methods by achieving an average accuracy range of 88 % to 92 % and 75 % to 80 % for direct plagiarism and lightly paraphrased text respectively. The future work is to identify reused images and visual contents.
CATEGORIES:VIRTUAL ROOM 2F
LOCATION:Virtual Room F\, Bangkok\, Thailand
SEQUENCE:0
UID:b6a3b1b813e08160010ccbcd6d4609e3
URL:http://11thictisthailand.sched.com/event/b6a3b1b813e08160010ccbcd6d4609e3
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260417T125213Z
DTSTART:20260409T051500Z
DTEND:20260409T071500Z
SUMMARY:Ransomware Defence for Electronic Health Records
DESCRIPTION:Authors - Nagaraj.M\, V. Balamurugan\, Matam Veera Chandra Kundan\, M.J. Mathesh\, V. Vijairam Abstract - Academic credential fraud is a global issue that undermines institutional trust. Although blockchain solutions provide immutability\, they are generally reactive\, securing documents only after potential errors or fraud have already occurred. This paper proposes a proactive approach to prevent inconsistencies before degree issuance. We introduce a hybrid model that integrates Digital Twins as a preventive validation layer and Multichain as an immutable ledger. The Digital Twin operates as a virtual sensor during the degree creation process at Universidad El Bosque\, simulating and validating academic\, financial\, and national exam data (Saber Pro) in real time\; if inconsistencies are detected\, “red flags” are triggered prior to issuance. Once validated\, the degree’s hash is anchored to a Multichain network. A functional prototype developed in Python achieved a 100% detection rate of inconsistent records during testing. The pro-posed model transforms the academic certification process into a proactive\, se-cure\, and trustworthy ecosystem by combining preventive validation with block-chain immutability.
CATEGORIES:VIRTUAL ROOM 2F
LOCATION:Virtual Room F\, Bangkok\, Thailand
SEQUENCE:0
UID:0faa13d82095b34e78b04b651c887672
URL:http://11thictisthailand.sched.com/event/0faa13d82095b34e78b04b651c887672
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260417T125213Z
DTSTART:20260409T051500Z
DTEND:20260409T071500Z
SUMMARY:Reinforcement Learning Approach to Predicting Pick and Place Strategies in Robotic Object Manipulation
DESCRIPTION:Authors - S. M. Mizanoor Rahman Abstract - Driver fatigue is a major cause of accidents on the road that generates major safety issues for drivers as well as passengers. Real-time detection of driver fatigue can help avert accidents by warning the driver about impending lapses in his attention. This paper proposes a real-time automated system for the detection of driver fatigue through observation of eye blink and yawn\, which are major notifications for fatigue. The system uses a combination of deep learning models that give high accuracy levels in detecting a drowsy driver. Eye blink is detected by using a state-of-the-art object detection model that is trained to locate the open and closed states of the eyes accurately using correct coordinate mapping methods\, giving an accuracy level of 96 percent. Yawning is detected using a combination of CNN and LSTM models that allow it to analyze spatial information as well as temporal information obtained through videos\, giving an accuracy level of 98 percent. Both of these modules work on real-time camera inputs\, which makes it possible for a constant monitoring of the alertness of the driver. Whenever the driver is found dozing off due to either excessive blinking or yawning\, the system releases a real time auditory warning alert to caution the driver. The result of the experiments has justified that the capability of the combined system works well while operating reliability with low-latency responses in real time. This study has shown that the hybrid detection strategy with spatial and temporal analysis is quite effective in detecting a dozy driver on the road and developing such a system that can be helpful in increasing the safety of the road.
CATEGORIES:VIRTUAL ROOM 2F
LOCATION:Virtual Room F\, Bangkok\, Thailand
SEQUENCE:0
UID:0f7a0c1329a31c4cc414fb5af589d60f
URL:http://11thictisthailand.sched.com/event/0f7a0c1329a31c4cc414fb5af589d60f
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260417T125213Z
DTSTART:20260409T051500Z
DTEND:20260409T071500Z
SUMMARY:Retrieval-Grounded Multimodal Clinical Language Modeling
DESCRIPTION:Authors - Kaniska D\, Shreya J V\, Srinidhi K\, Sudhakar K S\, Bagavathi Sivakumar P\, Krishna Priya G\n Abstract - Language modeling of clinical text in healthcare pens down a necessitated context along with a high level of security measure for sensitive patient information. A few large language models have shown very good clinically related performance in documentation\, summarization\, and these models have been rolled out freely. Therefore\, these models generate hallucinated or non verifiable outputs. Retrieval augmented approaches thus fix the problem by limiting the answer to the evidences retrieved. However\, majority of the existing systems rely on the textual records only and the integration of the diagnostic imaging is not done systematically. In this paper\, we put forward a retrieval grounded multimodal clinical modeling framework that unifies structured clinical text with imaging-derived contextual features. A patient specific vector indexing approach is used for isolated retrieval and a modality aware visual analytics approach turn imaging outputs into structured signals\, hence language generation. The entire framework is performed fully offline\, thus supporting privacy preserving deployment in resource-limited clinical settings. Experimental results show steady multimodal integration as well as the semantic consistency alignment between the retrieved evidence and the generated output.
CATEGORIES:VIRTUAL ROOM 2F
LOCATION:Virtual Room F\, Bangkok\, Thailand
SEQUENCE:0
UID:e765e2f330c213475be2addea1d13f36
URL:http://11thictisthailand.sched.com/event/e765e2f330c213475be2addea1d13f36
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260417T125213Z
DTSTART:20260409T051500Z
DTEND:20260409T071500Z
SUMMARY:A Comparative and Hybrid Study of CNN and Transformer Models for Multi-Class Virus Classification in Transmission Electron Microscopy
DESCRIPTION:Authors - Md Mahmudul Hoque\, Md Kawser Islam\, Md. Mamunur Rahman Moon\, Abdullah Rakib Akand\, Md. Hadi Al-amin\, H.M. Azrof\n Abstract - The automatic recognition of virus particles in transmission electron microscopy (TEM) images remains a demanding task\, primarily owing to strong inter-class similarity\, scale variability\, and pronounced class imbalance. In this study\, several convolutional neural networks and transformer-based architectures were comparatively evaluated for the classification of 22 virus categories using the TEM virus dataset. All models were trained under identical preprocessing and optimization conditions\, and imbalance effects were mitigated through a weighted crossentropy formulation. Performance was quantified using overall accuracy together with macro-averaged precision\, recall\, and F1 score. Among standalone models\, the Swin Transformer achieved the highest accuracy (0.8831) and macro-F1 score (0.8444)\, followed by DeiT (accuracy 0.8669). Convolutional architectures exhibited comparatively lower balanced performance\, with ResNet50 demonstrating substantial degradation (accuracy 0.5887) under imbalanced conditions. To exploit complementary representational properties\, decision-level hybrid strategies were implemented. The performance-weighted hybrid attained an accuracy of 0.8831 and the highest macro-F1 score (0.8528)\, slightly surpassing the equal-weight hybrid configuration. These observations indicate that architectural heterogeneity contributes to improved inter-class balance without sacrificing overall predictive accuracy. Future work may explore scale-aware representations\, feature-level fusion mechanisms\, and expanded TEM datasets to further enhance robustness and generalization in virus identification tasks.
CATEGORIES:VIRTUAL ROOM 2G
LOCATION:Virtual Room G\, Bangkok\, Thailand
SEQUENCE:0
UID:8c918b7d780090f80c94df318acf48cc
URL:http://11thictisthailand.sched.com/event/8c918b7d780090f80c94df318acf48cc
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260417T125213Z
DTSTART:20260409T051500Z
DTEND:20260409T071500Z
SUMMARY:A Dual-Stream Deep Learning Framework for Independent Audio and Video Deepfake Detection
DESCRIPTION:Authors - SunilKumar Ketineni\, Preethi Kandukuri\, Hruthik Sreeramaneni\, Vivek Bojjagani Abstract - Phishing continues to pose a serious threat to digital security by ex ploiting human vulnerabilities to steal confidential data through deceptive online interactions. Traditional detection methods often fall short in identifying advanced phishing strategies. This survey presents a comprehensive overview of phishing detection techniques\, with a strong focus on modern\, multi-layered machine learning and deep learning-based solutions. The proposed layered framework includes four key stages: data collection and preparation\, model training\, detection and prediction\, and explainability. In the first layer\, email\, URL\, and metadata are collected and preprocessed for feature extraction. The second layer involves model training using both machine learning classifiers such as Random Forest\, SVM\, Naïve Bayes\, and KNN and deep learning archi tectures like CNN\, RNN\, and LSTM. These models feed into the third layer where phishing is detected and classified. Finally\, the fourth layer integrates Explainable AI (XAI) methods like LIME\, SHAP\, and Anchors to enhance model transparency and interpretability. This survey evaluates the effectiveness and limitations of each layer and highlights the need for explainable\, scalable\, and adaptive phishing detection systems.
CATEGORIES:VIRTUAL ROOM 2G
LOCATION:Virtual Room G\, Bangkok\, Thailand
SEQUENCE:0
UID:492401d2294123f0be7c5f01f5ac5ef3
URL:http://11thictisthailand.sched.com/event/492401d2294123f0be7c5f01f5ac5ef3
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260417T125213Z
DTSTART:20260409T051500Z
DTEND:20260409T071500Z
SUMMARY:A Hybrid Ensemble and Attention-LSTM Based Credit Card Fraud Detection Framework integrating Cost-Sensitive Learning and Explainability
DESCRIPTION:Authors - K.Poorani\, K Karan\, R Seenivasan\, V Ramkumar Abstract - Older email detection technologies have struggled to accurately iden tify malicious emails in the face of the latest techniques attackers use to compro mise victims. While modern solutions perform well in detecting malicious emails\, they are not completely foolproof. As a result\, malicious emails can still reach a user’s mailbox\, necessitating measures to reduce potential harm. This study suggests transforming the decision-making processes of recent algorithms into a white-box model\, enabling transparency in decision-making through Ex plainable AI. This is achieved by having the proposed model compute confidence level scores for each email\, which users can use to exercise caution if a malicious email slips into their inbox.
CATEGORIES:VIRTUAL ROOM 2G
LOCATION:Virtual Room G\, Bangkok\, Thailand
SEQUENCE:0
UID:eca0fab231dcb99771aa64c63280f0e1
URL:http://11thictisthailand.sched.com/event/eca0fab231dcb99771aa64c63280f0e1
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260417T125213Z
DTSTART:20260409T051500Z
DTEND:20260409T071500Z
SUMMARY:Cross-Document Fact Validation: A Transformer-Based Framework for Identifying Factual Inconsistencies
DESCRIPTION:Authors - Nazura Javed\, Rida Javed Kutty\, Muralidhara B L\n Abstract - The increasing availability of online information has made it easier to access diverse sources\, but it has also introduced challenges in verifying the reliability and consistency of content. Conflicting statements across different sources often contribute to misinformation and make it difficult to establish factual accuracy. This study focuses on the problem of cross-document contradiction and inconsistency detection as a step toward improving fact verification in textual data. A two-stage pipeline is proposed in which semantically related sentence pairs are first retrieved from documents discussing the same event and then analyzed using Natural Language Inference (NLI) techniques to determine whether they express contradictory information. In contrast to conventional sentence-level contradiction detection\, the proposed approach emphasizes document-level comparison to identify inconsistencies across independent sources. Two pre-trained transformer models\, DistilBERT (DistilBERT-base-uncased) and RoBERTa (RoBERTa-base)\, are used for contradiction classification. The approach is evaluated on the SNLI dataset and the PHEME Rumor Dataset\, which are widely used benchmarks for NLI and misinformation research. Experimental results show accuracies of 94.50% (F1 score 94.50%) on SNLI and 92.39% (F1-score 92.31%) on PHEME\, indicating that the proposed framework is effective in identifying contradictions and supporting cross-document fact validation.
CATEGORIES:VIRTUAL ROOM 2G
LOCATION:Virtual Room G\, Bangkok\, Thailand
SEQUENCE:0
UID:a8a801a7496ec374b7d98b147ec13f08
URL:http://11thictisthailand.sched.com/event/a8a801a7496ec374b7d98b147ec13f08
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260417T125213Z
DTSTART:20260409T051500Z
DTEND:20260409T071500Z
SUMMARY:Dynamic Consistency Drift Management in Distributed Systems
DESCRIPTION:Authors - B.Purnachandra Rao\, Gaurang Jinka\n Abstract - Distributed systems rely on data replication across multiple nodes to ensure high availability\, fault tolerance\, and scalability. While replication improves system reliability\, it also introduces temporary inconsistencies between primary and replica nodes during data propagation. This phenomenon\, commonly referred to as consistency drift\, occurs when distributed nodes maintain slightly different states before synchronization is completed. As distributed infrastructures grow in scale and complexity\, consistency drift becomes increasingly significant due to network latency\, workload variability\, and communication overhead between nodes. Traditional synchronization mechanisms typically rely on static replication intervals or fixed update propagation strategies that do not adapt effectively to dynamic system conditions. Such approaches may allow drift to accumulate before synchronization occurs\, resulting in delayed consistency and inefficient resource utilization. Managing consistency drift therefore becomes a critical challenge in distributed computing environments where maintaining accurate and synchronized data states is essential. This research addresses the problem of consistency drift in distributed systems by examining the factors that contribute to state divergence among nodes and exploring mechanisms for dynamic drift management. The proposed framework focuses on monitoring system behavior\, including workload intensity\, network latency\, and node communication patterns\, to regulate synchronization behavior more effectively. By enabling adaptive synchronization strategies that respond to real time system conditions\, the framework aims to reduce drift accumulation and improve overall data consistency across distributed clusters. Effective management of consistency drift ultimately enhances system reliability\, operational stability\, and performance in modern distributed computing platforms operating under dynamic workloads.
CATEGORIES:VIRTUAL ROOM 2G
LOCATION:Virtual Room G\, Bangkok\, Thailand
SEQUENCE:0
UID:10ccb8fc822f6620195106ed06c6e299
URL:http://11thictisthailand.sched.com/event/10ccb8fc822f6620195106ed06c6e299
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260417T125213Z
DTSTART:20260409T051500Z
DTEND:20260409T071500Z
SUMMARY:Feature Selection for Balanced CICIoT2023 dataset using Machine Learning in IDS
DESCRIPTION:Authors - Suganya Moorthy\, Jayakumar Kaliappan Abstract - Internet of Things (IoT) networks have grown really fast\, which has increased the attack surface of cyber attacks by a big mar gin. However\, the severely limited computational resources\, the hetero geneous architecture\, and incomplete or decentralized communications make the IoT environments very susceptible to intrusion attacks\, in cluding Distributed Denial of Service (DDoS)\, spoofing\, botnets\, and data exfiltration attacks. Older signature-based intrusion detection sys tem (IDS) is not effective in detecting zero-day and dynamic threats. The paper will present a new machine learning-based intrusion detection system\, which was developed with IoT networks in mind. The design proposed combines the characteristics of feature search\, feature detec tion\, and group classification model in order to increase the accuracy of detection as well as reduce the number of computations. Benchmark IoT intrusion datasets that have undergone experimental evaluations prove to be more effective in detection accuracy\, false positive rates and scaling than the traditional IDS frameworks. Practical constraints that include the computational overhead of resource-constrained IoT devices\, imbal ance of the dataset\, and interpretability of the model are addressed. The directions of future research are lightweight federated learning systems\, explainable AI system incorporations\, and real-time adaptive threat in telligence systems to build better resiliencies of IoT security.
CATEGORIES:VIRTUAL ROOM 2G
LOCATION:Virtual Room G\, Bangkok\, Thailand
SEQUENCE:0
UID:070e8a712ea1d04b10f3ccf9195959ce
URL:http://11thictisthailand.sched.com/event/070e8a712ea1d04b10f3ccf9195959ce
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260417T125213Z
DTSTART:20260409T051500Z
DTEND:20260409T071500Z
SUMMARY:Federated Learning for Battery State-of-Charge Estimation: A Comparative Study of LSTM Architectures and Aggregation Strategies
DESCRIPTION:Authors - Konstantina Karathanasopoulou\, Ioannis Vondikakis\, Dimitris Georgiadis\, George Dimitrakopoulos Abstract - Digital signatures are fundamental public-key cryptographic primitives used for message authentication and integrity. A message’s recipient must be able to validate that it comes from the reported sender and hasn’t been altered by anybody else. Pairing-based cryptography provides elegant and efficient mechanisms for constructing compact dig ital signature schemes. Inspired by isogeny structures on elliptic curves\, we present a pairing-based digital signature system in this study. Our construction targets classical security settings and is analyzed under standard computational hardness assumptions related to bilinear groups and isogeny-based mappings. We demonstrate that the proposed ap proach attains “existential unforgeability under adaptive chosen-message attacks (UF-CMA)” within the random oracle model and address the construction’s soundness and security. Moreover\, the scheme offers com pact public key and signature sizes\, making it suitable for lightweight cryptographic applications.
CATEGORIES:VIRTUAL ROOM 2G
LOCATION:Virtual Room G\, Bangkok\, Thailand
SEQUENCE:0
UID:85052b303c4620c130682c58b70e3f77
URL:http://11thictisthailand.sched.com/event/85052b303c4620c130682c58b70e3f77
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260417T125213Z
DTSTART:20260409T051500Z
DTEND:20260409T071500Z
SUMMARY:GreenOps: Energy Efficient Cloud Deployment
DESCRIPTION:Authors - Nirmaladevi J\, Kanishka R\, Kirthiga B\, Lathikasri T R\, Ranjani Shree R S\n Abstract - The vast implementation of cloud computing has uplifted the modern IT practices by improving scalability\, flexibility\, and budget efficiency. In contrast\, there has been an increase in energy consumption\, which results in carbon emissions. This happens because of overusage\, overconsumption\, overprovisioning\, unused capacity\, and inefficient data center management. These days\, data centers act as the sole contributor to global greenhouse gas (GHG) emissions\; therefore\, sustainable cloud operations are essential in addressing this challenge. GreenOps\, or green operations\, defines the cloud deployment and operational practices that take place but also considers the environmental impact\; it depicts energy-efficient infrastructure design\, optimized resource usage\, virtualization\, and the integration of renewable energy resources. This survey presents a summary of green cloud computing\, including the current trends\, challenges\, energy-aware scheduling algorithms\, and optimization techniques for obtaining energy-efficient cloud deployment.
CATEGORIES:VIRTUAL ROOM 2G
LOCATION:Virtual Room G\, Bangkok\, Thailand
SEQUENCE:0
UID:032eb144097b57cabdec710ea742c4c4
URL:http://11thictisthailand.sched.com/event/032eb144097b57cabdec710ea742c4c4
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260417T125213Z
DTSTART:20260409T051500Z
DTEND:20260409T071500Z
SUMMARY:Low Grade Glioma Segmentation from FLAIR MR Images using UNet Variants
DESCRIPTION:Authors - Pranaav Contractor\, Sanika Ajgaonkar\, Nishanth Ravichandran\, Satishkumar Chavan Abstract - This paper examines the interplay between demographic factors and a newly developed behav ioral construct—modern investment curiosity—and how these elements collectively shape finan cial behaviors among higher education faculty. Drawing from survey responses of 145 educators situated in Kollam District\, Kerala\, India\, the study applies descriptive statistical techniques alongside chi-square tests to evaluate four research hypotheses. The data reveals a predominantly risk-averse financial posture among participants\, with post-retirement security ranking as the foremost financial goal and bank deposits serving as the dominant investment channel. Statistical testing shows no meaningful relationships between saving patterns and either household size or disability status. A statistically significant positive association emerges between investment cu riosity and ownership of equity or mutual fund products (χ² = 8.40\, p < 0.01). Additionally\, mar ital status demonstrates a significant relationship with investment curiosity (χ² = 5.28\, p < 0.05)\, where unmarried faculty report higher curiosity levels. These observations are consistent with established frameworks including the Life-Cycle Hypothesis and the Theory of Planned Behav ior\, positioning investment curiosity as a relevant psychological factor in financial decision-mak ing. The paper offers practical suggestions for institutional programming and identifies avenues for subsequent scholarly inquiry.
CATEGORIES:VIRTUAL ROOM 2G
LOCATION:Virtual Room G\, Bangkok\, Thailand
SEQUENCE:0
UID:843c8c489552968f60c207f46c96a923
URL:http://11thictisthailand.sched.com/event/843c8c489552968f60c207f46c96a923
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260417T125213Z
DTSTART:20260409T051500Z
DTEND:20260409T071500Z
SUMMARY:Replication Delay Optimization in Distributed Systems
DESCRIPTION:Authors - B.Purnachandra Rao\, Gaurang Jinka\n Abstract - Distributed systems rely on data replication to ensure availability\, fault tolerance\, and scalability across multiple nodes in modern cloud environments. Replication enables systems to maintain continuity even when individual nodes fail or experience network disruptions. However\, replication often introduces synchronization delays between primary and replica nodes\, known as replication delay. These delays can cause temporary data inconsistency\, stale reads\, and increased response latency\, degrading application performance and user experience. As infrastructures scale to larger clusters\, communication overhead\, network latency\, and workload variability further amplify replication delays\, making efficient synchronization increasingly challenging. Traditional replication mechanisms typically rely on static synchronization intervals or sequential update propagation strategies. These approaches fail to adapt to dynamic network conditions and fluctuating workloads\, resulting in inefficient data propagation and delayed consistency across nodes. In large scale systems\, such limitations may cause bottlenecks\, reduced reliability\, and inconsistent states during high workload periods or network congestion. Addressing replication delay is critical for maintaining reliability and consistency in distributed environments. Recent research emphasizes intelligent synchronization mechanisms capable of adapting to changing conditions. Adaptive synchronization strategies that monitor network latency\, workload intensity\, and node communication patterns offer improvements in replication efficiency. By enabling replication decisions that respond dynamically to system behavior\, such approaches reduce synchronization delays and improve data consistency across clusters. Enhanced replication efficiency ultimately strengthens reliability\, scalability\, and operational performance in modern distributed computing platforms operating under variable workload conditions.
CATEGORIES:VIRTUAL ROOM 2G
LOCATION:Virtual Room G\, Bangkok\, Thailand
SEQUENCE:0
UID:36b8a027e61bc8cee46e02c11b2bc621
URL:http://11thictisthailand.sched.com/event/36b8a027e61bc8cee46e02c11b2bc621
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260417T125213Z
DTSTART:20260409T071500Z
DTEND:20260409T071700Z
SUMMARY:Session Chair Concluding Remarks
DESCRIPTION:\n
CATEGORIES:VIRTUAL ROOM 2A
LOCATION:Virtual Room A\, Bangkok\, Thailand
SEQUENCE:0
UID:d3c0d00604915822029c9c1ff31ffdd2
URL:http://11thictisthailand.sched.com/event/d3c0d00604915822029c9c1ff31ffdd2
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260417T125213Z
DTSTART:20260409T071500Z
DTEND:20260409T071700Z
SUMMARY:Session Chair Concluding Remarks
DESCRIPTION:\n
CATEGORIES:VIRTUAL ROOM 2B
LOCATION:Virtual Room B\, Bangkok\, Thailand
SEQUENCE:0
UID:fa5e5557975a8395e6020aacd6743e67
URL:http://11thictisthailand.sched.com/event/fa5e5557975a8395e6020aacd6743e67
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260417T125213Z
DTSTART:20260409T071500Z
DTEND:20260409T071700Z
SUMMARY:Session Chair Concluding Remarks
DESCRIPTION:\n
CATEGORIES:VIRTUAL ROOM 2C
LOCATION:Virtual Room C\, Bangkok\, Thailand
SEQUENCE:0
UID:7bfc459bb367e42337fe1e873ff1dd01
URL:http://11thictisthailand.sched.com/event/7bfc459bb367e42337fe1e873ff1dd01
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260417T125213Z
DTSTART:20260409T071500Z
DTEND:20260409T071700Z
SUMMARY:Session Chair Concluding Remarks
DESCRIPTION:\n
CATEGORIES:VIRTUAL ROOM 2D
LOCATION:Virtual Room D\, Bangkok\, Thailand
SEQUENCE:0
UID:ddd899b29720ce9b4d2f3890c68dfad5
URL:http://11thictisthailand.sched.com/event/ddd899b29720ce9b4d2f3890c68dfad5
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260417T125213Z
DTSTART:20260409T071500Z
DTEND:20260409T071700Z
SUMMARY:Session Chair Concluding Remarks
DESCRIPTION:\n
CATEGORIES:VIRTUAL ROOM 2E
LOCATION:Virtual Room E\, Bangkok\, Thailand
SEQUENCE:0
UID:f2174e80b9b003051e1b2492f98814f1
URL:http://11thictisthailand.sched.com/event/f2174e80b9b003051e1b2492f98814f1
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260417T125213Z
DTSTART:20260409T071500Z
DTEND:20260409T071700Z
SUMMARY:Session Chair Concluding Remarks
DESCRIPTION:\n
CATEGORIES:VIRTUAL ROOM 2F
LOCATION:Virtual Room F\, Bangkok\, Thailand
SEQUENCE:0
UID:61d059b0c053bdd7bdc8ad3c5b561702
URL:http://11thictisthailand.sched.com/event/61d059b0c053bdd7bdc8ad3c5b561702
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260417T125213Z
DTSTART:20260409T071500Z
DTEND:20260409T071700Z
SUMMARY:Session Chair Concluding Remarks
DESCRIPTION:\n
CATEGORIES:VIRTUAL ROOM 2G
LOCATION:Virtual Room G\, Bangkok\, Thailand
SEQUENCE:0
UID:bef559401b40c58eee87ec0e4a63a378
URL:http://11thictisthailand.sched.com/event/bef559401b40c58eee87ec0e4a63a378
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260417T125213Z
DTSTART:20260409T071700Z
DTEND:20260409T072000Z
SUMMARY:Session Closing and Information To Authors
DESCRIPTION:\n
CATEGORIES:VIRTUAL ROOM 2A
LOCATION:Virtual Room A\, Bangkok\, Thailand
SEQUENCE:0
UID:a525da110f5cdbef0ff9bde4c05c9d35
URL:http://11thictisthailand.sched.com/event/a525da110f5cdbef0ff9bde4c05c9d35
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260417T125213Z
DTSTART:20260409T071700Z
DTEND:20260409T072000Z
SUMMARY:Session Closing and Information To Authors
DESCRIPTION:\n
CATEGORIES:VIRTUAL ROOM 2B
LOCATION:Virtual Room B\, Bangkok\, Thailand
SEQUENCE:0
UID:4bbab462ea0f1a6c75b674030758b24e
URL:http://11thictisthailand.sched.com/event/4bbab462ea0f1a6c75b674030758b24e
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260417T125213Z
DTSTART:20260409T071700Z
DTEND:20260409T072000Z
SUMMARY:Session Closing and Information To Authors
DESCRIPTION:\n
CATEGORIES:VIRTUAL ROOM 2C
LOCATION:Virtual Room C\, Bangkok\, Thailand
SEQUENCE:0
UID:d8e9552560f1805e439aab6afb638a3f
URL:http://11thictisthailand.sched.com/event/d8e9552560f1805e439aab6afb638a3f
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260417T125213Z
DTSTART:20260409T071700Z
DTEND:20260409T072000Z
SUMMARY:Session Closing and Information To Authors
DESCRIPTION:\n
CATEGORIES:VIRTUAL ROOM 2D
LOCATION:Virtual Room D\, Bangkok\, Thailand
SEQUENCE:0
UID:1496783e2438e2b274c4d52a1d8a3eff
URL:http://11thictisthailand.sched.com/event/1496783e2438e2b274c4d52a1d8a3eff
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260417T125213Z
DTSTART:20260409T071700Z
DTEND:20260409T072000Z
SUMMARY:Session Closing and Information To Authors
DESCRIPTION:\n
CATEGORIES:VIRTUAL ROOM 2E
LOCATION:Virtual Room E\, Bangkok\, Thailand
SEQUENCE:0
UID:39c88ba990c54376e9d9b68d5d81e274
URL:http://11thictisthailand.sched.com/event/39c88ba990c54376e9d9b68d5d81e274
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260417T125213Z
DTSTART:20260409T071700Z
DTEND:20260409T072000Z
SUMMARY:Session Closing and Information To Authors
DESCRIPTION:\n
CATEGORIES:VIRTUAL ROOM 2F
LOCATION:Virtual Room F\, Bangkok\, Thailand
SEQUENCE:0
UID:f2c0e9984fd0bff20e2e63e8ad79f6da
URL:http://11thictisthailand.sched.com/event/f2c0e9984fd0bff20e2e63e8ad79f6da
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260417T125213Z
DTSTART:20260409T071700Z
DTEND:20260409T072000Z
SUMMARY:Session Closing and Information To Authors
DESCRIPTION:\n
CATEGORIES:VIRTUAL ROOM 2G
LOCATION:Virtual Room G\, Bangkok\, Thailand
SEQUENCE:0
UID:d01620a4612d6868dbd8203c9fc2149e
URL:http://11thictisthailand.sched.com/event/d01620a4612d6868dbd8203c9fc2149e
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260417T125213Z
DTSTART:20260409T075800Z
DTEND:20260409T080000Z
SUMMARY:Opening Remarks
DESCRIPTION:\n
CATEGORIES:VIRTUAL ROOM 3A
LOCATION:Virtual Room A\, Bangkok\, Thailand
SEQUENCE:0
UID:0ebb940456f3c04f68f304d4f476f224
URL:http://11thictisthailand.sched.com/event/0ebb940456f3c04f68f304d4f476f224
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260417T125213Z
DTSTART:20260409T075800Z
DTEND:20260409T080000Z
SUMMARY:Opening Remarks
DESCRIPTION:\n
CATEGORIES:VIRTUAL ROOM 3B
LOCATION:Virtual Room B\, Bangkok\, Thailand
SEQUENCE:0
UID:15f0a9dde71fb936a23db4a7e03884f8
URL:http://11thictisthailand.sched.com/event/15f0a9dde71fb936a23db4a7e03884f8
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260417T125213Z
DTSTART:20260409T075800Z
DTEND:20260409T080000Z
SUMMARY:Opening Remarks
DESCRIPTION:\n
CATEGORIES:VIRTUAL ROOM 3C
LOCATION:Virtual Room C\, Bangkok\, Thailand
SEQUENCE:0
UID:6b9676f26a8ccae1c257d38adb543ce5
URL:http://11thictisthailand.sched.com/event/6b9676f26a8ccae1c257d38adb543ce5
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260417T125213Z
DTSTART:20260409T075800Z
DTEND:20260409T080000Z
SUMMARY:Opening Remarks
DESCRIPTION:\n
CATEGORIES:VIRTUAL ROOM 3D
LOCATION:Virtual Room D\, Bangkok\, Thailand
SEQUENCE:0
UID:69ca8711b4eb2e163e1f96e98247d03e
URL:http://11thictisthailand.sched.com/event/69ca8711b4eb2e163e1f96e98247d03e
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260417T125213Z
DTSTART:20260409T075800Z
DTEND:20260409T080000Z
SUMMARY:Opening Remarks
DESCRIPTION:\n
CATEGORIES:VIRTUAL ROOM 3E
LOCATION:Virtual Room E\, Bangkok\, Thailand
SEQUENCE:0
UID:ca936052b8cd816541647c2cee1a4a84
URL:http://11thictisthailand.sched.com/event/ca936052b8cd816541647c2cee1a4a84
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260417T125213Z
DTSTART:20260409T075800Z
DTEND:20260409T080000Z
SUMMARY:Opening Remarks
DESCRIPTION:\n
CATEGORIES:VIRTUAL ROOM 3F
LOCATION:Virtual Room F\, Bangkok\, Thailand
SEQUENCE:0
UID:a1a2c4fa2d6c83238c51eae97a389f4f
URL:http://11thictisthailand.sched.com/event/a1a2c4fa2d6c83238c51eae97a389f4f
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260417T125213Z
DTSTART:20260409T075800Z
DTEND:20260409T080000Z
SUMMARY:Opening Remarks
DESCRIPTION:\n
CATEGORIES:VIRTUAL ROOM 3G
LOCATION:Virtual Room G\, Bangkok\, Thailand
SEQUENCE:0
UID:b50a38dbfb210c65919bc6b9c1cb8b29
URL:http://11thictisthailand.sched.com/event/b50a38dbfb210c65919bc6b9c1cb8b29
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260417T125213Z
DTSTART:20260409T080000Z
DTEND:20260409T100000Z
SUMMARY:A Hierarchical Deep Learning Framework for Ciphertext-Only Classification of Cryptographic Algorithms
DESCRIPTION:Authors - Banda Rithija\, MV Parth\, Haripriya L\, Skandan SS\, Manju Abstract - The task of identifying Cryptographic Algorithms from ciphertext is a challenge within digital forensics and security auditing\, when there is no knowledge of either the plaintext or the key used. As modern encryption algorithms increase in sophistication\, their output becomes indistinguishable from random noise\, rendering traditional pattern recognition techniques ineffective. This paper proposes a two-stage Hierarchical Cipher Classifiers\, the first stage discriminates among three major Cryptographic Families: Symmetric\, Asymmetric\, and Hash\; the second stage identifies the specific algorithm within those families in the context of six Modern Encryption Standards: Advanced Encryption Standard\, Triple Data Encryption Standard\, Blowfish\, Rivest–Shamir–Adleman\, ElGamal\, and Secure Hash Algorithm 256-bit. In order to achieve high accuracy\, we developed a hybrid feature space consisting of 167 attributes that included both Statistical and Transform- Domain Features.We incorporated SHapley Additive exPlanations (SHAP) into our classifiers to address the concern of the black-box nature of Deep Learning. Empirical Results indicate that the Hierarchical Classifier Structure has produced a substantial reduction in the rate of misclassifications compared to flat classifiers\, offering a transparent and effective tool for automated cryptanalysis.
CATEGORIES:VIRTUAL ROOM 3A
LOCATION:Virtual Room A\, Bangkok\, Thailand
SEQUENCE:0
UID:3fb16a595666f3a6d9eaf60407b2ac0b
URL:http://11thictisthailand.sched.com/event/3fb16a595666f3a6d9eaf60407b2ac0b
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260417T125213Z
DTSTART:20260409T080000Z
DTEND:20260409T100000Z
SUMMARY:AN ADVANCED GRAPH NEURAL NETWORK FRAMEWORK FOR MODELING DYNAMIC CYBER NETWORK TOPOLOGIES AND DETECTING ANOMALOUS BEHAVIORAL PATTERNS.
DESCRIPTION:Authors - Megha Potdar Abstract - This paper delineates a compact microstrip patch antenna that operates within the frequency range of 6.5 to 8.5 THz and exhibits a resonance frequency of 7.344 THz. The antenna maintains a flat\, compact shape that is well-suited for terahertz circuit integration and also incorporates circular and U-shaped patch modifications that enhance radiation efficiency\, gain\, and band-width. According to the simulation results\, the device has a gain of 7.042 dBi\, a VSWR of 1.1329\, a low return loss of –24.109 dB\, and a wide impedance bandwidth of 1.119 THz. It demonstrates consistent radiation patterns and effective impedance matching across the operating frequency range\, indicating that the proposed design outperforms conventional THz patch antennas and rep-resents a highly efficient solution for high-speed terahertz communication\, im-aging\, and sensing applications.
CATEGORIES:VIRTUAL ROOM 3A
LOCATION:Virtual Room A\, Bangkok\, Thailand
SEQUENCE:0
UID:b1d40e893ce3d65ccb66f8747bae81af
URL:http://11thictisthailand.sched.com/event/b1d40e893ce3d65ccb66f8747bae81af
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260417T125213Z
DTSTART:20260409T080000Z
DTEND:20260409T100000Z
SUMMARY:Enhancing E-Commerce Sentiment Analysis Using Fine-Tuned RoBERTa and BERT on Women’s Clothing Reviews
DESCRIPTION:Authors - Babatunde David Ikudehinbu\, Atefeh Khazaei\, Hamidreza Khaleghzadeh Abstract - In this paper\, we outline the design and implementation of a novel electronic voting kiosk\, dubbed BlockVote\, which helps counter identity-related fraud and data tampering via biometric and blockchainbased approaches. The proposed system is a standalone embedded system running on an ESP32-S3 SoC-based microcontroller. The system includes a touchscreen display for user input and an optical fingerprint sensor for identity checking. This collected bio-data and voting selection are then integrated in such a manner that a secure transaction is created through cryptography. This is then sent through the Node.js gateway\, which leads it to the secure Ethereum-based blockchain network. Such an application of physical verification technologies with blockchain technology ensures that the proposed voting system is more secure than the traditional e-voting machines or e-voting websites. Block-vote is a hybrid security system in which hardware-based verification techniques are combined with blockchain-based data management in a power-saving\, compact format. The prototype has shown proof of its functional viability\, its module-based construction\, and its reliability\, particularly in the field of embedded systems. The experimental results demonstrate the system’s high precision\, low latency\, and robustness against illegitimate use. The suggested framework demonstrates the practical feasibility of blockchain and biometric technology in the creation of trustworthy electronic voting systems that can be used in both urban and rural areas.
CATEGORIES:VIRTUAL ROOM 3A
LOCATION:Virtual Room A\, Bangkok\, Thailand
SEQUENCE:0
UID:9c925ea73ec1f92c7f39c014cb9b6e8d
URL:http://11thictisthailand.sched.com/event/9c925ea73ec1f92c7f39c014cb9b6e8d
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260417T125213Z
DTSTART:20260409T080000Z
DTEND:20260409T100000Z
SUMMARY:Enhancing Threat Detection in Cloud Environments Through Temporal Anomaly Modeling
DESCRIPTION:Authors - Deepika K M\, Girish Gowda J\, Ravi Honnalli\, Nikhil S G Abstract - Cloud computing environments face increasingly sophisticated cyber threats that demand advanced detection mechanisms capable of identifying anomalous behavior in real-time. This study introduces an innovative hybrid temporal anomaly modeling system that integrates Autoregressive Integrated Moving Average (ARIMA) with Long Short-Term Memory (LSTM) networks\, augmented by meta-learning fusion strategies. Our method solves the difficult problem of getting high recall rates (>95%) that are needed to keep operational efficiency while reducing missed critical threats. We tested five meta-learning architectures—Logistic Regression\, Random Forest\, XG-Boost\, Gradient Boosting\, and Neural Network—along with four rule-based fusion strategies on a large Cloud Anomaly Dataset with 249\,595 samples taken from 11 virtual machines over 30 days. The Hybrid-RF (Random Forest) model had the best balance\, with a recall of 95.75%\, an accuracy of 10.59%\, and an F1-score of 11.37%. This was much better than the average in the literature (75-85% recall). We set up the system as a production-ready Flask REST API on Google Cloud Platform\, with response times of less than 200 milliseconds. This shows that it is possible to use real-time cloud security monitoring. Our findings demonstrate that metalearning fusion of statistical and deep learning temporal models yields enhanced threat detection capabilities relative to single-model approaches\, achieving recall improvements of 10-20% over state-of- the-art methods while adhering to real-time performance constraints.
CATEGORIES:VIRTUAL ROOM 3A
LOCATION:Virtual Room A\, Bangkok\, Thailand
SEQUENCE:0
UID:72865c9234fd344311f5cd3d2df3b6fb
URL:http://11thictisthailand.sched.com/event/72865c9234fd344311f5cd3d2df3b6fb
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260417T125213Z
DTSTART:20260409T080000Z
DTEND:20260409T100000Z
SUMMARY:Evaluating AI-Based Diagnostic Models for Cervical Spinal Stenosis Detection Using Magnetic Resonance Imaging
DESCRIPTION:Authors - Anup Bhitre\, Saurabh Nimje\, Utkarsha Pacharaney\, K. T. Reddy Abstract - Cervical Spinal Stenosis (CSS) is a progressive spinal disorder caused by narrowing of the spinal canal in the neck\, potentially leading to severe neurological damage if undiagnosed. Due to rising CSS cases and the limitations of manual MRI analysis—such as subjectivity\, time consumption\, and inter-observer variation—there is a growing need for automated\, reliable diagnostic tools. This study evaluates and compares four AI models—CNN\, ResNet50\, SVM\, and Random Forest—using 1\,200 T2-weighted MRI images processed through normalization\, segmentation\, and augmentation. Performance was measured using accuracy\, precision\, recall\, F1-score\, and AUC-ROC. ResNet50 achieved the highest accuracy (93.6%) and AUC-ROC (0.97)\, demonstrating superior diagnostic performance. SHAP was used for interpretability\, highlighting spinal canal diameter and ligamentum flavum thickening as key diagnostic features. The findings confirm that deep learning\, especially ResNet50\, offers a scalable\, interpretable\, and clinically effective method for early CSS detection.
CATEGORIES:VIRTUAL ROOM 3A
LOCATION:Virtual Room A\, Bangkok\, Thailand
SEQUENCE:0
UID:8f1218dad9cc7fe94198083e6274e450
URL:http://11thictisthailand.sched.com/event/8f1218dad9cc7fe94198083e6274e450
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260417T125213Z
DTSTART:20260409T080000Z
DTEND:20260409T100000Z
SUMMARY:Image Inpainting with Restoration and Resolution Enhancement
DESCRIPTION:Authors - C Ashik Poojary\, Chirag B Jogi\, Sanath Shetty\, Sandhya P\, Mahitha G\n Abstract - Image inpainting plays an important role in restoring and reconstructing degraded or damaged images by filling in missing regions. This work proposes a gated convolutional neural network based on a U-Net architecture to achieve perceptually accurate and high-resolution restoration. The model was trained on a large-scale dataset of over 20\,000 images generated with the CelebA dataset along with extensive enhancement using artificial damages such as scratches\, cracks\, random patches\, blurring\, sepia-toning\, and grayscale degradation. The proposed method performs two phases of restoration: context-aware inpainting\, followed by resolution enhancement while preserving both global structure and local texture. Quantitative metrics such as PSNR and SSIM were evaluated\, and qualitative comparisons demonstrate faithful texture synthesis and tone-consistent fills across color\, grayscale\, and sepia domains.
CATEGORIES:VIRTUAL ROOM 3A
LOCATION:Virtual Room A\, Bangkok\, Thailand
SEQUENCE:0
UID:05e2541d758cae839cf368492a874bb0
URL:http://11thictisthailand.sched.com/event/05e2541d758cae839cf368492a874bb0
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260417T125213Z
DTSTART:20260409T080000Z
DTEND:20260409T100000Z
SUMMARY:Music Control Using Hand Gesture Recognition and Audio-Reactive Visualization
DESCRIPTION:Authors - D.Nagaraju\, Padinjaroot Monesh Raj\, G .Likith\, K .Kavitha\, Thella Muni Chandrika Abstract - This Gesture recognition technology is studied in this article as it pertains to controlling music wirelessly via a music controller de-vice. The gesture recognition system highlighted in this study is an innovative advancement in this area. In addition to providing the user with an easy-to-use interface for controlling the volume of music with hand motions\, This provides a no-contact way to play percussion instruments where users can play from anywhere\, either they have good eyesight or not! In addition to providing users with visual experience while using the application\, the application also provides users with 3D graphics and animations that dynamically reflect the user’s movement on the screen as they create percussion music through the application. The entire system is created using JavaScript and thus\, is completely platform-independent and will work on any recent web browser.
CATEGORIES:VIRTUAL ROOM 3A
LOCATION:Virtual Room A\, Bangkok\, Thailand
SEQUENCE:0
UID:1a79655c3dfa8fd68422e98548f58c77
URL:http://11thictisthailand.sched.com/event/1a79655c3dfa8fd68422e98548f58c77
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260417T125213Z
DTSTART:20260409T080000Z
DTEND:20260409T100000Z
SUMMARY:Printify: A Real-Time\, Location-Based Service Platform for Document Printing and Digitization\, featuring a Progressive Web Application for Shopkeeper Operations
DESCRIPTION:Authors - Aditya Ajitrao Kulkarni\, Mayuri Shelke\, Saurabh Babasaheb Gonte\, Kalpak Sanjay Kedari\, Parikshit Balasaheb Jadhav Abstract - Image inpainting is a basic problem in image restoration that focuses on recovering the missing or damaged areas of an image in a visually plausible and semantically consistent way. However\, in practical image restoration tasks like historical photo restoration\, images are often degraded by complex damages like cracks\, scratches\, fading\, stains\, and tone changes. Conventional image restoration methods relying on interpolation or diffusion have limitations in restoring high-frequency details and global semantic information. This paper presents a gated convolutional neural network with a U-Net structure for effective image inpainting and restoration with resolution enhancement. The proposed network is trained on a large-scale dataset of more than 20\,000 synthetically degraded images created from the CelebA dataset\, considering various damage patterns like scratches\, cracks\, random occlusions\, blurring\, grayscale conversion\, and sepia tone transformation. The image restoration process involves two steps: context-aware image inpainting and resolution refinement. The proposed framework is extensively evaluated using PSNR and SSIM metrics for its effectiveness in color\, grayscale\, and sepia image restoration.
CATEGORIES:VIRTUAL ROOM 3A
LOCATION:Virtual Room A\, Bangkok\, Thailand
SEQUENCE:0
UID:7f4a3dd1743578a8b189438e7b8a6963
URL:http://11thictisthailand.sched.com/event/7f4a3dd1743578a8b189438e7b8a6963
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260417T125213Z
DTSTART:20260409T080000Z
DTEND:20260409T100000Z
SUMMARY:Robust machine learning framework for accurate fault identification in solar photovoltaic systems
DESCRIPTION:Authors - G Venkata Suresh Reddy\, Immanuel Anupalli\, P.Sudheer Abstract - Solar photovoltaic (PV) systems require robust and intelligent problem detection systems to guarantee they continue producing energy effectively as they gain traction as a renewable energy source. In order to detect various defects in photovoltaic (PV) systems operating under nonlinear and noisy conditions\, this research presents a data-driven fault classification framework that employs machine learning techniques. Electrical data from photovoltaic (PV) panels\, including current-voltage (I-V) and power-voltage (P-V) curves recorded in three distinct operating circumstances (Healthy\, Shading\, and Open-Circuit)\, formed the basis of the dataset used for training and testing the model. For each condition\, crucial electrical characteristics have been used to characterize the system's electrical behavior\, including open-circuit voltage\, short-circuit current\, maximum power point voltage and current\, fill factor\, and a handful of statistical statistics. Logistic Regression\, Naïve Bayes\, and k-Nearest Neighbors (KNN) are the three supervised machine learning methods that were employed to detect various errors. Each model was fine-tuned using hyper parameter tweaking and k-fold cross-validation. The classification performance in the comparative performance analysis was greatest for Logistic Regression (96.09% accuracy\, 96.25% precision\, 96.49% recall\, and 96.36% F1-score). Second place went to the KNN model\, which had a 95.47% accuracy rate. In contrast\, the Naïve Bayes model maintained its reliability\, with an accuracy rate of 94.13%. This demonstrates that it is still effective when dealing with nonlinear data that contains noise. According to the overall results\, many machine learning algorithms\, especially Logistic Regression\, do a great job of finding PV problems in real time. The suggested framework is both efficient and useful for real-world PV monitoring systems because it just needs to measure electrical parameters that are easy to get (I-V and P-V data). Using this strategy for preventative maintenance makes solar systems more reliable and increases their production\, which in turn cuts down on power losses.
CATEGORIES:VIRTUAL ROOM 3A
LOCATION:Virtual Room A\, Bangkok\, Thailand
SEQUENCE:0
UID:1b6ddb10d591cd20bfc79cb849b5cf3c
URL:http://11thictisthailand.sched.com/event/1b6ddb10d591cd20bfc79cb849b5cf3c
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260417T125213Z
DTSTART:20260409T080000Z
DTEND:20260409T100000Z
SUMMARY:Stimulation of Secured Agent-to-Agent Communication Protocol with Secure Session Key Management with AI-Based Attack Detection
DESCRIPTION:Authors - Premanand Ghadekar\, Utkarsh Patil\, Niraj Ukare\, Vansh Bhatt\, Rohan Uplenchwar\, Shreya Sidnale Abstract - Traditional multi-agent communication systems rely on fixed security protocols and static message processing pipelines\, leaving them vulnerable to advancing cyberattacks and dependent on expensive infrastructure. This paper introduces a Secure Multi-Agent Communicational Protocol designed as a lightweight\, affordable framework for small and medium-scale systems to communicate safely without enterpriselevel costs. The current setup depends heavily on predictable session keys\, making systems prone to impersonation\, replay attacks\, token alterations\, and man-in-the-middle interceptions. This framework stimulates agentto- agent interactions through three primary components: a predictive security model\, a dual-token authentication mechanism\, and a protocolaware attack engine. The infrastructure utilizes WebSocket connections integrated with Redis Pub/Sub for real-time messaging. A dynamic session key generation process works alongside a rotating refresh-token system\, ensuring that even if a session key is compromised\, attackers still require a valid refresh token. The predictive component features a Protocol-Aware XLNet model with a dual-thread structure to examine message sequences and statistical irregularities. A fusion layer integrates these analyses\, reporting a Dual-Thread Consistency Score of 0.87 and a 31% gain in early-warning capability. Experimental evaluations demonstrate 93.5% violation sensitivity\, 91.7% replay detection accuracy\, and 89.3% attack-type classification accuracy. This approach enables timely identification of replay incidents\, interceptions\, and protocol tampering. Additionally\, an independent XGBoost model filters fraudulent links. These enhancements provide substantial gains in early warning capabilities and consistent classification accuracy across various attack categories.
CATEGORIES:VIRTUAL ROOM 3A
LOCATION:Virtual Room A\, Bangkok\, Thailand
SEQUENCE:0
UID:64470332af49e6db7074076e95c74e9f
URL:http://11thictisthailand.sched.com/event/64470332af49e6db7074076e95c74e9f
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260417T125213Z
DTSTART:20260409T080000Z
DTEND:20260409T100000Z
SUMMARY:A Dual-Governance Blockchain-IoT Framework for Scalable Halal Poultry Traceability
DESCRIPTION:Authors - Md. Mijanur Rahman\, Mst. Tasnia Fahmida\, Shithi Bhowmick\, Md Tanzid\, Zubaed Hossain\, Zaid Bin Sajid\n Abstract - Halal industry has become a major foundation of the Islamic global economy\, where religious adherence\, consumer trust\, and supply chain transparency are becoming increasingly critical. In spite of the existing systems\, halal poultry supply chains are persistently confronted by the problem of fragmented stores\, dependence on centralized databases and a restricted real-time traceability system. These limitations present the greatest risks of mislabeling and non-compliance of regulations. The performance of existing blockchain-IoT traceability systems becomes increasingly doubtful as they grow more complex because of scalability issues and lack of integration with halal regulatory systems\, as well as automatic compliance monitoring. This paper suggests a solution to these issues: a Blockchain-IoT Integrated Halal Poultry Traceability System (BIHPTS) implemented on Hyperledger Fabric. The Proposed system Integrates IoT telemetry for constant data gathering\, off-chain storage using InterPlanetary File System (IPFS) to counteract the expansion of on-chain storage\, and a dual-governance\, rule-based structure based on smart contracts. This framework ensures that distributed\, immutable\, and secure records are accessible together with the supply chain. The system validates the use of halal feed\, authorized slaughtering processes\, transportation constraints\, and environmentally acceptable threshold limits.
CATEGORIES:VIRTUAL ROOM 3B
LOCATION:Virtual Room B\, Bangkok\, Thailand
SEQUENCE:0
UID:1487d612e8946d6df5ed462976b75f51
URL:http://11thictisthailand.sched.com/event/1487d612e8946d6df5ed462976b75f51
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260417T125213Z
DTSTART:20260409T080000Z
DTEND:20260409T100000Z
SUMMARY:A Hybrid AI-Based Generative Design and Topology Optimization Framework for Sustainable Lightweight Machine Structures
DESCRIPTION:Authors - Indrajitsinh J. Jadeja\, Nirav P. Maniar Abstract - Agriculture plays a vital role in ensuring food security\, yet traditional crop selection and yield estimation practices often fail to account for complex interactions among soil\, climatic\, and environmental factors. Recent advances in machine learning (ML) have shown significant potential in addressing these challenges by enabling data-driven decision support for farmers. This paper presents a comprehensive review of machine learning–based crop recommendation and yield prediction techniques\, focusing on their effectiveness in improving agricultural productivity and sustainability. The study analyzes various supervised and ensemble learning models applied to soil quality parameters such as nitrogen\, phosphorus\, potassium\, pH\, moisture\, and climatic variables. Emphasis is placed on multimodal data integration\, highlighting how the fusion of soil\, weather\, and remote sensing data enhances prediction accuracy. The review also discusses current limitations\, including data scarcity\, model generalization\, and real-time deployment challenges\, particularly in resource-con-strained farming environments. Finally\, the paper identifies key research gaps and future directions toward developing robust\, scalable\, and intelligent agricultural decision-support systems.
CATEGORIES:VIRTUAL ROOM 3B
LOCATION:Virtual Room B\, Bangkok\, Thailand
SEQUENCE:0
UID:f4335ebd78d4b09c3c57698d605ff3a5
URL:http://11thictisthailand.sched.com/event/f4335ebd78d4b09c3c57698d605ff3a5
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260417T125213Z
DTSTART:20260409T080000Z
DTEND:20260409T100000Z
SUMMARY:Action Quality Assessment to Perform Automated Shoulder and Elbow Error Detection in Overhead Press Weightlifting Videos
DESCRIPTION:Authors - Thony Enechi\, Tevin Moodley Abstract - The legal profession is in a transformative era\, driven by technological advancement and global shifts in businesses. This study aims to explore key factors influencing legal sustainability performance in Indian Law firms\, with A focus on Environmental\, Social\, and Governance (ESG) practices through an Artificial Intelligence (AI)-Enabled computational intelligence perspective. While ESG frameworks are widely adopted across industries\, their application in the legal sector remains limited due to overreliance on qualitative assessment and the absence of a computational decision mechanism. Considering legal infrastructure as a complex socio-technical system\, this research adopts digitization and AI to enhance ESG-based accountability and governance. The pro-posed framework applied the Fuzzy Delphi Method to aggregate 30 legal experts’ knowledge and the Fuzzy DEMATEL to computationally model interdependencies among ESG performance factors. This enables systematic identification of critical sustainability drivers and their causal relationships. The study contributes a computational intelligence-driven sustainability framework de-signed for the legal industry\, offering both theoretical and practical insights for technology-enabled ESG implementation. The proposed intelligent system sup-ports informed decision-making and strengthens environmental law enforcement and accountability within Indian law firms. Future research guidelines are also outlined
CATEGORIES:VIRTUAL ROOM 3B
LOCATION:Virtual Room B\, Bangkok\, Thailand
SEQUENCE:0
UID:9a63eae0fca316edeff0d6f8aaca6a97
URL:http://11thictisthailand.sched.com/event/9a63eae0fca316edeff0d6f8aaca6a97
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260417T125213Z
DTSTART:20260409T080000Z
DTEND:20260409T100000Z
SUMMARY:Comparative Evaluation of Model-Based and Data-Driven Methods for SOC\, SOH\, and SOP Estimation of PV-Battery Systems under Ultra-Challenging Operating Profiles
DESCRIPTION:Authors - P.Srikanth\, Immanuel Anupalli\, P.Sudheer Abstract - Halal industry has become a major foundation of the Islamic global economy\, where religious adherence\, consumer trust\, and supply chain transparency are becoming increasingly critical. In spite of the existing systems\, halal poultry supply chains are persistently confronted by the problem of fragmented stores\, dependence on centralized databases and a restricted real-time traceability system. These limitations present the greatest risks of mislabeling and non-compliance of regulations. The performance of existing blockchain-IoT traceability systems becomes increasingly doubtful as they grow more complex because of scalability issues and lack of integration with halal regulatory systems\, as well as automatic compliance monitoring. This paper suggests a solution to these issues: a Blockchain-IoT Integrated Halal Poultry Traceability System (BIHPTS) implemented on Hyperledger Fabric. The Proposed system Integrates IoT telemetry for constant data gathering\, off-chain storage using InterPlanetary File System (IPFS) to counteract the expansion of on-chain storage\, and a dual-governance\, rule-based structure based on smart contracts. This framework ensures that distributed\, immutable\, and secure records are accessible together with the supply chain. The system validates the use of halal feed\, authorized slaughtering processes\, transportation constraints\, and environmentally acceptable threshold limits.
CATEGORIES:VIRTUAL ROOM 3B
LOCATION:Virtual Room B\, Bangkok\, Thailand
SEQUENCE:0
UID:b12e94e11216ed77954c3e97843f4d61
URL:http://11thictisthailand.sched.com/event/b12e94e11216ed77954c3e97843f4d61
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260417T125213Z
DTSTART:20260409T080000Z
DTEND:20260409T100000Z
SUMMARY:EEG-Based Brain Signal Decoding for Word Prediction: A P300 Speller Approach for Paralyzed Patient Communication
DESCRIPTION:Authors - Om Sarvaiya\, Maulik Shah\n Abstract - Brain-computer interface systems can help people who are unable to communicate due to paralysis or severe motor disabilities. In this work\, we im plemented an EEG-based P300 speller that allows users to select characters by focusing on a visual stimulus.The system functions by means of the P300 signal that appears when the user identifies their target character. We developed a com plete pipeline that includes feature extraction\, machine learning model classifi cation\, and preprocessing of EEG data. The system was tested using the BNCI Horizon 2020 P300 dataset\, and the results showed that character selection accu racy ranged from 82% to 86%.Random Forest performed better compared to other classifiers in our implementation. The system was designed in a modular way so that future improvements can be added easily. This implementation shows that EEG-based communication systems can be developed using accessible tools and can support basic communication for people with severe motor impairments.
CATEGORIES:VIRTUAL ROOM 3B
LOCATION:Virtual Room B\, Bangkok\, Thailand
SEQUENCE:0
UID:3fea68953d3386c2de16d18c777050ac
URL:http://11thictisthailand.sched.com/event/3fea68953d3386c2de16d18c777050ac
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260417T125213Z
DTSTART:20260409T080000Z
DTEND:20260409T100000Z
SUMMARY:Efficient Hybrid Intrusion Detection for IoT networks With LLM using Gaussian Mixture Clustering
DESCRIPTION:Authors - Anita Anand\, Shivangi Surati Abstract - Artificial intelligence has transformed the predictive analysis of electoral processes by enabling a deeper understanding of candidates' preferences and behaviors through digital data. This study aimed to develop and compare deep learning models for sentiment analysis based on aspects of Ecuadorian electoral opinions. The Cross-Industry Standard Process for Machine Learning methodology was adopted. A dataset of Spanish-language comments collected from YouTube and Twitter\, associated with presidential candidates\, was constructed. Three classification architectures were implemented: BETO\, BETO with Long Short-Term Memory (LSTM)\, and BETO with Bidirectional LSTM (BiLSTM). The results show that the hybrid architecture BETO with BiLSTM achieves the best performance\, with an F1-score of 84.51% and precision of 85.09%\, surpassing the other architectures and reaching levels comparable to international studies that employ BERT and hybrid models in political analysis. This model was integrated into an interactive dashboard that allows users to visualize the distribution of positive\, neutral\, and negative sentiment by candidate\, making it a valuable tool for analyzing digital public opinion trends in Ecuador. Future work includes incorporating data balancing techniques\, expanding the volume and time frame of comments\, integrating demographic and geographic variables\, and exploring more advanced models based on transformers and Large Language Models.
CATEGORIES:VIRTUAL ROOM 3B
LOCATION:Virtual Room B\, Bangkok\, Thailand
SEQUENCE:0
UID:e25daed14678131a56a2e5f0d123dbb4
URL:http://11thictisthailand.sched.com/event/e25daed14678131a56a2e5f0d123dbb4
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260417T125213Z
DTSTART:20260409T080000Z
DTEND:20260409T100000Z
SUMMARY:Intelligent Ticket Routing via LLM-Based Multi-Agent Systems: A Case Study in a University Environment
DESCRIPTION:Authors - Valeria Alexandra Yunga Manzanillas\, Pablo Andres Figueroa Juca\, Nelson Oswaldo Piedra Pullaguari Abstract - In the digital era\, the global emergence of COVID-19 has necessitated the development of transformative technology to redefine how we interact with and manage public health crises. To effectively slow mortality rates\, this work emphasizes the critical requirement for accurate and rapid diagnostic methods that enable early-stage disease detection. Drawing on the necessity for more efficient systems\, this paper proposes a high-fidelity diagnostic framework utilizing Convolutional Neural Networks (CNN)\, Deep Neural Networks (DNN)\, and Transfer Learning algorithms. Implemented through a TensorFlow-based 3-class classification strategy\, the system was evaluated using a dataset of 817 chest X-ray images (comprising COVID-19\, pneumonia-affected\, and normal images). The experimental results yielded accuracies of 93.29% for the CNN\, 92.68% for the DNN\, and a superior 97.56% for the Transfer Learning approach\, which outperforms the state of the art methods. These results demonstrate that such high-fidelity computational models provide the conceptual clarity and robustness needed to revolutionize traditional diagnostic methods. By providing instant feedback and a meaningful interpretation of complex medical imagery\, the proposed system allows clinical practitioners to achieve precise detections in significantly less time.
CATEGORIES:VIRTUAL ROOM 3B
LOCATION:Virtual Room B\, Bangkok\, Thailand
SEQUENCE:0
UID:eb57bdce403bf878d95ce1f28c91ec2f
URL:http://11thictisthailand.sched.com/event/eb57bdce403bf878d95ce1f28c91ec2f
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260417T125213Z
DTSTART:20260409T080000Z
DTEND:20260409T100000Z
SUMMARY:Interpretable Machine Intelligence: Balancing Accuracy and Transparency in AI-Driven Systems
DESCRIPTION:Authors - Shahin Makubhai\, Ganesh R Pathak\, Pankaj R Chandre\, Raju Gurav Abstract - Artificial intelligence (AI)–driven personalization is increasingly embedded in digital customer journeys to enhance relevance and efficiency. However\, such systems simultaneously raise concerns related to surveillance\, autonomy\, and trust\, particularly in data-intensive service environments. This study investigates how AI personalization intensity and recommendation quality influence perceived surveillance\, perceived autonomy\, trust\, customer experience\, and loyalty within AI-enabled hotel journeys. Using a quantitative approach\, survey data were collected from 200 hotel guests who interacted with AI-based personalization features. The proposed model was tested using Partial Least Squares Structural Equation Modeling (PLS-SEM). The findings reveal that AI personalization in-tensity and recommendation quality significantly increase perceived surveillance and perceived autonomy\, while perceived surveillance plays a central role in trust formation. In contrast\, customer experience and loyalty are weakly explained by AI personalization alone. The study contributes to ICT research by demonstrating that AI-driven systems primarily shape cognitive and perceptual mechanisms rather than directly driving behavioral outcomes\, highlighting the importance of human-centered and ethically designed AI personalization in digital service con-texts.
CATEGORIES:VIRTUAL ROOM 3B
LOCATION:Virtual Room B\, Bangkok\, Thailand
SEQUENCE:0
UID:c3eead236494886c1a7bd373b30405c2
URL:http://11thictisthailand.sched.com/event/c3eead236494886c1a7bd373b30405c2
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260417T125213Z
DTSTART:20260409T080000Z
DTEND:20260409T100000Z
SUMMARY:Meta-heuristic Feature Selection for XGBoost Histogram-Based Cardiovascular Disease Risk Modeling
DESCRIPTION:Authors - My-Phuong Ngo\, Hoang-Thanh Ngo\, Loan T.T. Nguyen Abstract - Automated classification of enterprise support tickets is a foundational natural language processing (NLP) task for intelligent service management systems. While trans-former-based models have achieved strong performance on benchmark datasets\, their behavior under real-world enterprise constraints—such as class imbalance\, do-main shift\, calibration reliability\, and retraining cost—remains insufficiently under-stood. This paper presents a comprehensive and reproducible NLP framework for enterprise ticket classification\, systematically evaluating classical machine learning baselines\, full fine-tuning of transformer encoders\, and parameter-efficient fine-tuning using Low-Rank Adaptation (LoRA). Extensive experiments are conducted on a large enterprise-style ticket corpus using time-based splits\, out-of-domain testing\, imbalance stress\, calibration analysis\, inference latency\, and ablation studies. Results show that transformer-based models substantially outperform classical baselines\, while LoRA achieves comparable performance to full fine-tuning with significantly reduced training overhead. The proposed evaluation protocol and findings provide practical guidance for deploying robust and efficient NLP systems in enterprise environments.
CATEGORIES:VIRTUAL ROOM 3B
LOCATION:Virtual Room B\, Bangkok\, Thailand
SEQUENCE:0
UID:2366559e91d4a8c26ddf3e043a2ef3a5
URL:http://11thictisthailand.sched.com/event/2366559e91d4a8c26ddf3e043a2ef3a5
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260417T125213Z
DTSTART:20260409T080000Z
DTEND:20260409T100000Z
SUMMARY:TrustChain: Decentralized Identity Verification for Secure Access
DESCRIPTION:Authors - Hiren Darji\, Devarsh Chandiwade\, Tushar Panchal\, Meenakshi Chandra\, Swapnil Gharat Abstract - Strategic decision making in time dependent systems often involves complex trade-offs between short-term performance gains and long-term degradation effects. Designing effective strategies in such environments requires accurate modelling of performance evolution and careful evaluation of discrete intervention decisions. This paper presents an intelligent strategy simulation framework that integrates data-driven modelling and predictive analytics to evaluate decision strategies under progressive performance degradation. Using high-frequency Formula 1 telemetry data as a representative case study\, the proposed framework models lap-time evolution as a function of degradation age and operational context. Both regression-based models and neural network predictors are employed to estimate performance trends\, enabling comparison between linear baselines and nonlinear learning approaches. A simulation engine is then used to evaluate multiple strategic scenarios by incorporating degradation dynamics and discrete intervention penalties\, allowing quantitative assessment of alternative decision policies. The framework enables direct comparison of strategy outcomes through cumulative performance metrics and visual race progress analysis\, providing interpretable decision support. Experimental results demonstrate that both degradation rate and decision timing have a signi􀏐icant impact on overall system performance. Furthermore\, neural network models consistently outperform linear regression in capturing non-linear degradation behaviour\, particularly during extended operational phases. Although demonstrated using motorsport telemetry data\, the proposed approach is generalizable to a wide range of real world optimization and decision-support problems involving degradation\, uncertainty\, and staged decision points.
CATEGORIES:VIRTUAL ROOM 3B
LOCATION:Virtual Room B\, Bangkok\, Thailand
SEQUENCE:0
UID:414c168cc06bb69901ac593e6500de8f
URL:http://11thictisthailand.sched.com/event/414c168cc06bb69901ac593e6500de8f
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260417T125213Z
DTSTART:20260409T080000Z
DTEND:20260409T100000Z
SUMMARY:A Hierarchical Latent Retrieval Model for Constant Time Semantic Query Processing
DESCRIPTION:Authors - Prabhat Kumar Gupta\, Perumal T\, Karthick Pannerselvam\n Abstract - Generation Large language models\, as well as retrieval-augmented generation (RAG)\, are highly performing on semantic queries\, but with considerable latency as they require embedding computation\, a vector similarity search\, and generation at inference time. Such delays make them inappropriate in time-sensitive and domain-specific retrieval activities. In this paper\, the Hierarchy Latent Retrieval Model (HLRM) which is a deterministic architecture will be introduced and able to answer semantic queries in O(1) constant time. HLRM unites hierarchical semantic routing and semantic hashing so that pre-validated units of knowledge can be directly illuminated without the need to search methods or language model informing of their existence at run time. All computationally expensive processes are done offline\, which means that embedding processes or vector databases are not needed to run a query. Milliseconds-response time with very high exact-match accuracy is proved under experimental assessment on an orderly institutional knowledge environment. The findings suggest that HLRM offers an alternative of fast\, interpretable\, and reliable systems to the generative retrieval systems in non-random settings where precision and response latency is paramount.
CATEGORIES:VIRTUAL ROOM 3C
LOCATION:Virtual Room C\, Bangkok\, Thailand
SEQUENCE:0
UID:0104f79f876a19da6765b60daf10c6fc
URL:http://11thictisthailand.sched.com/event/0104f79f876a19da6765b60daf10c6fc
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260417T125213Z
DTSTART:20260409T080000Z
DTEND:20260409T100000Z
SUMMARY:An Efficient Near Collision Attack for Lightweight Stream Cipher – A5/1
DESCRIPTION:Authors - Khedkar Aboli Audumbar\, Uday Pandit Khot\, Balaji G. Hogade Abstract - Malicious or compromised internal users can act like normal users with valid login credentials and thus become difficult to detect. As a result of their similarity to normal users\, traditional methods of detecting intrusions\, have difficulty identifying the subtle and changing behaviors of malicious insiders. This paper introduces a comprehensive User and Entity Behavior Analytics (UEBA) framework to help detect malicious insiders. It works by analyzing activity logs generated by the enterprise. Further it performs data cleaning and feature engineering\; creating behavioral profiles for each user based upon the attributes of time\, environment\, and behavior. These profiles are used to model normal interaction patterns and with the DBLOF algorithm\, an outlier score for each profile is created. The outlier score indicates whether or not a given user’s behavior has deviated from normal. In order to make the proposed system adaptable to changing environments over time\, it utilizes deep learning algorithms to detect changes in behavior and to increase the accuracy of anomalous behavior detection. The proposed system also enables the ingestion of real-time data\, the evaluation of risk\, and the display of alerts in a visual format. Thus\, providing the scalability and operational performance required to support large-scale organizations. Overall\, the proposed system represents a reliable\, modular\, and understandable UEBA framework. It is capable of accurately detecting malicious insider threats and representing an efficient method for proactively mitigating risks through security operations within enterprises.
CATEGORIES:VIRTUAL ROOM 3C
LOCATION:Virtual Room C\, Bangkok\, Thailand
SEQUENCE:0
UID:88c3bf77767e8e4e9cadb8e66277c7a9
URL:http://11thictisthailand.sched.com/event/88c3bf77767e8e4e9cadb8e66277c7a9
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260417T125213Z
DTSTART:20260409T080000Z
DTEND:20260409T100000Z
SUMMARY:Assessing the Performance of Quantum Machine Learning for Motor Imagery Brain-Computer Interfaces: Consumer Perspective of Wearable Electronics
DESCRIPTION:Authors - Poonam Chaudhary\, Rita Chhikara\, Nupur Prakash Abstract - This work addresses the challenge of Isolated Sign Language Recognition (ISLR) on mobile and edge devices\, where computational resources\, memory\, and energy budgets are severely constrained. Existing approaches based on pixel-level three-dimensional convolutional neural networks are computationally expensive and sensitive to background variations\, while recurrent models such as Long Short-Term Memory networks suffer from a sequential processing bottleneck that limits parallel execution on modern hardware accelerators. To overcome these limitations\, this paper proposes a hybrid Adaptive Graph Convolutional Network (A-GCN) and Transformer architecture that decouples spatial and temporal modeling of skeletal sign representations. The A-GCN employs a learnable adjacency matrix to capture dynamic and semantically meaningful spatial relationships between skeletal landmarks\, while the Transformer encoder leverages parallel self-attention to model long-range temporal dependencies without recurrence. Experimental evaluation on the 250-class Google Isolated Sign Language Recognition dataset demonstrates a Top-1 accuracy of 78.90%\, outperforming a Bi-LSTM baseline by 6.96%. In addition\, the proposed model achieves a throughput of 400.55 frames per second with a latency of 2.50 ms on accelerator hardware and maintained real-time performance on consumer-grade devices. These results demonstrate that landmark-based\, parallel architectures enable accurate\, real-time\, and privacy-preserving sign language recognition suitable for deployment on standard mobile devices.
CATEGORIES:VIRTUAL ROOM 3C
LOCATION:Virtual Room C\, Bangkok\, Thailand
SEQUENCE:0
UID:b220863240415c183d8d0c3a70bf4439
URL:http://11thictisthailand.sched.com/event/b220863240415c183d8d0c3a70bf4439
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260417T125213Z
DTSTART:20260409T080000Z
DTEND:20260409T100000Z
SUMMARY:Autoregressive Mamba Based Structured State Space Model for Regional Monsoon Rainfall Severity Prediction in Coimbatore
DESCRIPTION:Authors - Subin Simon\, Prathilothamai M Abstract - Deep learning has shown significant potential in medical image classification\; however\, a systematic comparison of deep feature extraction strategies for multi class diabetic eye disease assessment remains limited. This study presents a comprehensive comparative analysis of seven deep learning architectures\, including conventional CNN\, pretrained VGG16\, Vision Transformer (ViT)\, Conformer\, hybrid CNN ViT\, and attention-augmented variants incorporating Squeeze-and-Excitation (SE) and Convolutional Block Attention Module (CBAM). All models are evaluated under a unified preprocessing and training framework to ensure fair performance comparison.The investigation focuses on analyzing how different architectural paradigms capture discriminative local and global retinal features relevant to disease classification. Extensive experiments are conducted on public fundus image datasets using standard evaluation metrics\, including accuracy\, precision\, recall\, and F1-score. Experimental results demonstrate that hybrid and attention-integrated architectures outperform standalone CNN and transformer models. In particular\, the Conformer architecture achieves the best overall performance\, reaching approximately 91% classification accuracy in the four class setting (Diabetic Retinopathy\, Glaucoma\, Cataract\, and Normal)\, while the CNN ViT model attains approximately 89% accuracy.These findings highlight the effectiveness of combining convolutional operations with global self-attention mechanisms for robust and discriminative feature extraction in automated diabetic eye disease classification.
CATEGORIES:VIRTUAL ROOM 3C
LOCATION:Virtual Room C\, Bangkok\, Thailand
SEQUENCE:0
UID:7e22dd0e0e147214c96e4381d4a80bc9
URL:http://11thictisthailand.sched.com/event/7e22dd0e0e147214c96e4381d4a80bc9
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260417T125213Z
DTSTART:20260409T080000Z
DTEND:20260409T100000Z
SUMMARY:CALIBRATION-WEIGHTED ENSEMBLE WITH MCC-OPTIMIZED THRESHOLD FOR LIVER DISEASE PREDICTION
DESCRIPTION:Authors - M.Murugesen\, Priyanka P Abstract - Deep learning–based medical image models have achieved expert level performance in GPU-based research environments [1–3]. However\, relia ble deployment in real clinical systems remains challenging due to constraints related to power consumption\, hardware stability\, and long-term operation. While prior studies have focused on improving model architectures or hardware accelerators [4\,5]\, relatively limited attention has been devoted to systematical ly managing the transition from GPU-based development to NPU-based de ployment environments. This study formulates the GPU-to-NPU transition as an independent deployment research problem. Rather than proposing a new model architecture\, we focus on preserving functional equivalence when trans ferring a validated GPU-trained medical vision model to an NPU-based infer ence environment. The proposed framework consists of reference model fixa tion\, intermediate representation (IR)-based conversion [13–15]\, operator com patibility management\, inference pipeline alignment\, and output-level function al equivalence validation. The framework is evaluated through deployment of a ResNet-50–based pa thology classification model on a commercial ATOM NPU platform. Experi mental results demonstrate a 99.1% agreement rate (991/1\,000 samples) be tween GPU-based and NPU-based inference outputs\, confirming consistent de cision behavior despite architectural differences. These findings indicate that deployment reliability depends more on execution environment control and preprocessing alignment than on model architecture modification. By redefining deployment as a structured research problem\, this work pro vides a reproducible methodology for translating research-grade medical AI models into energy-efficient NPU inference systems under practical clinical constraints.
CATEGORIES:VIRTUAL ROOM 3C
LOCATION:Virtual Room C\, Bangkok\, Thailand
SEQUENCE:0
UID:bcd55c98e44cd9dacf8fdae2cf098b74
URL:http://11thictisthailand.sched.com/event/bcd55c98e44cd9dacf8fdae2cf098b74
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260417T125213Z
DTSTART:20260409T080000Z
DTEND:20260409T100000Z
SUMMARY:Decoding Tamil Heritage through Segmentation of Stone Inscriptions
DESCRIPTION:Authors - Jayanthi J\, P.Uma Maheswari\, S.Uma Maheswari\, Arun Kumar\, Karishma V R Abstract - The rapid migration of artificial intelligence from cloud platforms to edge-based Internet of Things environments has intensified the demand for transparent and trustworthy decision-making under severe resource constraints. While edge intelligence enables low-latency and privacy-preserving analytics\, the opacity of deployed models limits user trust\, accountability\, and regulatory acceptance. Existing explainability techniques largely assume cloud-level resources\, making them unsuitable for real-time and energy-limited edge deployments. In order to close this gap\, this work develops an interpretable intelligence framework that is resource-aware and adaptable\, specifically designed for limited IoT systems. The suggested approach integrates interpretability directly into the decision-making process\, allowing for the generation of faithful\, lightweight explanations in addition to predictions while dynamically adjusting to operational context and runtime restrictions. Further balancing local responsiveness with system- level insight aggregation and secure governance is achieved through hierarchical explanation control. Transparency\, efficiency\, and scalability are all in line with the framework's treatment of explainability as a fundamental system capacity. The study shows that adaptive\, deployment-aware explainability can greatly improve edge intelligence's operational reliability and trustworthiness. These insights establish a foundation for building accountable and interpretable AI systems in real-world IoT environments.
CATEGORIES:VIRTUAL ROOM 3C
LOCATION:Virtual Room C\, Bangkok\, Thailand
SEQUENCE:0
UID:a85f70070f36b7b6971140fd09623c38
URL:http://11thictisthailand.sched.com/event/a85f70070f36b7b6971140fd09623c38
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260417T125213Z
DTSTART:20260409T080000Z
DTEND:20260409T100000Z
SUMMARY:DeepEye: Interpretable Deep Ensemble Framework for Eye Disease Detection with Grad-CAM Visualization Using Eye Disease Image Dataset
DESCRIPTION:Authors - Rimon Kumer Roy\, Jannatul Ferdous\, Kazi Lutfur Nahar Mithila\, Sabbir Islam\, Mohammad Zahid Hassan\, Sadah Anjum Shanto\n Abstract - Early identification of ophthalmic disease is critical to pre serve eyesight. We present DeepEye\, a stacking-ensemble framework for multi-disease classification on the Eye Disease Image Dataset (EDID\, Mendeley Data). After standardized preprocessing and augmentation\, f ive architectures ResNet50\, VGG16\, DenseNet121\, EfficientNet-B4\, and Vision Transformer were trained and evaluated. The final ensemble in tegrates the top base models with a logistic regression meta-learner op timized via hyperparameter tuning. On a held-out test set\, DeepEye achieves 91.34% accuracy and AUC of 0.9965\, outperforming all con stituent models and exhibiting stable gains across cross validation folds. Model transparency is supported with Grad-CAM visualizations that lo calize disease-relevant regions\, enhancing clinical interpretability. These results indicate that combining convolutional and transformer backbones within a tuned stacking framework yields a high-accuracy\, explainable approach for automated eye disease detection in healthcare settings.
CATEGORIES:VIRTUAL ROOM 3C
LOCATION:Virtual Room C\, Bangkok\, Thailand
SEQUENCE:0
UID:0f14f3ccc236b832b37ac22a9a50a41a
URL:http://11thictisthailand.sched.com/event/0f14f3ccc236b832b37ac22a9a50a41a
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260417T125213Z
DTSTART:20260409T080000Z
DTEND:20260409T100000Z
SUMMARY:Drivers and Barriers to Implementing the Internet of Things in the Healthcare Supply Chain in Jordanian Hospitals
DESCRIPTION:\n
CATEGORIES:VIRTUAL ROOM 3C
LOCATION:Virtual Room C\, Bangkok\, Thailand
SEQUENCE:0
UID:08915958758e9b7092f668aeea33f61e
URL:http://11thictisthailand.sched.com/event/08915958758e9b7092f668aeea33f61e
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260417T125213Z
DTSTART:20260409T080000Z
DTEND:20260409T100000Z
SUMMARY:Fraud Detection in E-Wallet Transactions: A Comparative Analysis of XGBoost and Random Forest
DESCRIPTION:Authors - Nguyen Thi Hoi\, Vu Thi Anh Hong\, Dang Thi Anh Tho\, Dang Thuy Linh\, Nguyen Khanh Linh Abstract - The increasing use of renewable energy sources has made the integra tion of Flexible AC transmission system (FACTS) devices into contemporary power systems\, an important area of research. The function and effectiveness of FACTS devices in enhancing power quality and preserving stability in traditional power systems and those that significantly count on renewable energy source are comprehensively examined in this study. Variability and unpredictability brought about by renewable energy sources can negatively impact the voltage profile\, particularly at high penetration levels. Devices from the Flexible AC Transmis sion System\, like the Thyristor-Controlled Series Capacitor (TCSC) & Static Var Compensator (SVC)\, provide efficient ways to improve system stability and dy namically regulate voltage. This paper investigates a coordinated control strategy of SVC and TCSC for improving voltage profiles in a transmission network with high renewable energy integration. Using an IEEE-14 bus test system\, various scenarios of renewable penetration are simulated to analyze the performance of coordinated FACTS operation. The findings show that the suggested coordinated control improves overall system dependability and power transfer capabilities in addition to reducing voltage variations and reactive power imbalances. The study highlights the importance of optimal placement and coordinated tuning of FACTS devices as a cost effective solution for enabling secure and stable opera tion of renewable-rich power grids.
CATEGORIES:VIRTUAL ROOM 3C
LOCATION:Virtual Room C\, Bangkok\, Thailand
SEQUENCE:0
UID:e818ed1c3ddcea587f7913449b748963
URL:http://11thictisthailand.sched.com/event/e818ed1c3ddcea587f7913449b748963
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260417T125213Z
DTSTART:20260409T080000Z
DTEND:20260409T100000Z
SUMMARY:SUEMas: A Secure Multi-Agent Ecosystem based on LLMs for Integrated University Services using Dynamic Tool Registries
DESCRIPTION:Authors - Josue Piedra\, Nelson Piedra Abstract - Accurate crop production forecasting is essential for sustainable agricultural planning\, effective resource management\, and long-term food security. Conventional statistical and regression-based models often fail to capture the complex\, nonlinear relationships that exist among agro-climatic variables\, soil characteristics\, and crop yield [1]. To address these limitations\, this paper proposes an agentic artificial intelligence (AI)–based framework for crop production analysis that integrates autonomous decision-making with machine learning and deep learning techniques. The proposed framework utilizes agro-climatic and soil parameters such as temperature\, humidity\, soil moisture\, cultivated area\, and seasonal information to model crop production behaviour. Three predictive approaches— Linear Regression\, Random Forest\, and CNN–LSTM—are implemented and evaluated within the agentic framework using Root Mean Square Error (RMSE)\, Mean Absolute Error (MAE)\, and the Coefficient of Determination (R2) as performance metrics. Experimental results demonstrate that the Random Forest model significantly outperforms the other models\, achieving an RMSE of 0.56\, MAE of 0.31\, and R2 value of 0.96. These findings highlight the effectiveness of agent-driven machine learning systems in accurately modelling agricultural data and supporting intelligent decision-making for crop yield optimization.
CATEGORIES:VIRTUAL ROOM 3C
LOCATION:Virtual Room C\, Bangkok\, Thailand
SEQUENCE:0
UID:5e260a94ce17acbdf4c1dfa20f1fad5a
URL:http://11thictisthailand.sched.com/event/5e260a94ce17acbdf4c1dfa20f1fad5a
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260417T125213Z
DTSTART:20260409T080000Z
DTEND:20260409T100000Z
SUMMARY:Topology-Aware Botnet Traffic Detection Using Spatiotemporal Graph Neural Networks with Gated Feature Fusion
DESCRIPTION:Authors - Priyanka Halder\, Anupam Sinha Abstract - This study analyzes the extent to which credibility from influencers impacts consumers' buying behavior. The focus will be on how the intention to buy impacts this relationship as the problem is being analyzed in the context of social commerce on TikTok. The study is developed within the framework of Source Credibility Theory which suggests that consumers’ perception and consequent behavior are influenced by the perceived degree of the spokesperson’s Attractiveness\, Trustworthiness\, and Expertise. The study employs a quantitative explanatory methodology. A purposive sampling technique was used to collect data from a sample of 100 active TikTok users who follow the provided influencer. The analyzed relationships will be quantified using Structural Equation Modelling with Partial Least Squares (SEM-PLS). The research results concluded that influencer credibility increases the intention to buy\, but does not increase the purchasing decision. The intention to buy completely mediates the relationship between influencer credibility and purchasing decision. This demonstrates that influencer credibility is a significant factor in the intention to buy behavior\, but it is the intention that is essential in order to convert the persuasive influence into actual buying behavior. The study contributes to digital marketing communication research by extending Source Credibility Theory to the context of short-video social commerce platforms.
CATEGORIES:VIRTUAL ROOM 3C
LOCATION:Virtual Room C\, Bangkok\, Thailand
SEQUENCE:0
UID:ac08a1f1bb57da91cd3dfb80e42873ec
URL:http://11thictisthailand.sched.com/event/ac08a1f1bb57da91cd3dfb80e42873ec
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260417T125213Z
DTSTART:20260409T080000Z
DTEND:20260409T100000Z
SUMMARY:A Hybrid AI–Terminology Microservice for Dual-Coded AYUSH EMRs
DESCRIPTION:Authors - Sarthak\, Utkarsh Kumar Singh\, Ankur Yadav\, Aarushi Sharma\, Samarth Saxena\, Vaishnavi Kumari Singh\, Anisha Kumari Abstract - Cervical cancer prediction using machine learning is often limited by class imbalance\, dataset variability\, and insufficient control of false positive rates. While many existing models report high accuracy\, they frequently fail to maintain a clinically appropriate balance between sensitivity and specificity\, particularly across datasets with different sizes and feature structures [1]. Models trained on large clinical risk-factor datasets may not generalize well to smaller behavioral datasets\, and recall-oriented optimization can significantly increase false positives. This study proposes a false positive–optimized ensemble framework combining behavioral and clinical risk factors and analyzes its performance across two heterogeneous datasets. Threshold tuning and ensemble techniques\, including soft voting and stacking\, are employed to increase minority-class detection while retaining specificity. Results indicate that independent classifiers show dataset-dependent instability\, with trade-offs between recall and false positive control. However\, ensemble methods provide more consistent accuracy\, precision\, recall\, and F1-score across datasets. The findings demonstrate that threshold optimization combined with ensemble learning improves cross-dataset robustness and supports more clinically reliable cervical cancer prediction.
CATEGORIES:VIRTUAL ROOM 3D
LOCATION:Virtual Room D\, Bangkok\, Thailand
SEQUENCE:0
UID:4d50b660ff9b30461d35d47e0609aa8f
URL:http://11thictisthailand.sched.com/event/4d50b660ff9b30461d35d47e0609aa8f
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260417T125213Z
DTSTART:20260409T080000Z
DTEND:20260409T100000Z
SUMMARY:Adaptive Hybrid PSO-GD with Stagnation Detection for Robust and Efficient Multimodal Optimization
DESCRIPTION:Authors - Sowmini Devi Veeramachaneni\, Yaswanth Gavini\, Arun K Pujari\n Abstract - Combining Particle Swarm Optimization (PSO) with gradientbased local search enhances efficiency in solving complex optimization problems. Existing hybrids often use fixed switching rules\, causing premature convergence orwastedcomputation.We present an adaptive PSO–gradient descent method where stagnation detection triggers local refinement only when needed. Adam is employed for local search without extra parameters. Tests on seven benchmark functions show the approach achieves strong or competitive results on challenging cases while ensuring robust convergence on simpler ones.
CATEGORIES:VIRTUAL ROOM 3D
LOCATION:Virtual Room D\, Bangkok\, Thailand
SEQUENCE:0
UID:5450d36b5e9c7b45a1fb5c40a343a55a
URL:http://11thictisthailand.sched.com/event/5450d36b5e9c7b45a1fb5c40a343a55a
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260417T125213Z
DTSTART:20260409T080000Z
DTEND:20260409T100000Z
SUMMARY:Agentic AI in Supply Chain Management Systems: A Systematic Review
DESCRIPTION:Authors - Amna Ali\, Rida Hijab Basit\n Abstract - With the advent of agentic Artificial Intelligence\, systems have demonstrated significant ability to understand data and respond to changing business environments without human assistance. Agentic AI is largely being used in supply chain management (SCM) systems for automating the supply chain tasks - demand forecasting and planning\, logistics and transportation optimization\, supplier management and risk reduction\, and warehouse management. Use of agentic AI in SCM represents a drastic shift from traditional rule-based systems to automated goal-driven systems that operate without human intervention. Such systems are supported by Natural Language Processing and deep learning models which have made the supply chain processes much easier\, efficient and less prone to error. The organizations that have incorporated agentic AI in their business processes have reported operational efficiency and cost effectiveness. However\, such advancements in technology have raised concerns related to privacy ethics and data security. In this paper\, we have conducted the systematic review of the existing research on the usage of Agentic AI in Supply Chain Management. The paper discusses characteristics of agents in SCM\, different types of architectures and analyses the limitations and challenges related to the usage of AI agents in supply chain management.
CATEGORIES:VIRTUAL ROOM 3D
LOCATION:Virtual Room D\, Bangkok\, Thailand
SEQUENCE:0
UID:0425152928a27e280966dd13cd71e296
URL:http://11thictisthailand.sched.com/event/0425152928a27e280966dd13cd71e296
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260417T125213Z
DTSTART:20260409T080000Z
DTEND:20260409T100000Z
SUMMARY:An Enhance Approach to Prevent Man-in-the-Middle Attack in Diffie-Hellman Key Exchange Protocol
DESCRIPTION:Authors - Bikram Bikash Das\, Chukhu Chunka\, Pantha Kanti Nath\, Nippu Kumar Abstract - Credit card transaction analysis is challenged by severe class imbalance with evolving spending behavior and large-scale financial data. Many existing fraud detection approaches rely on supervised learning and assume stable fraud labels\, limiting robustness under changing fraud prevalence. This study presents a large-scale\, multi-year credit card trans action dataset stored in partitioned Parquet format and conducts a systematic comparison of classical machine learning\, supervised deep learning\, and unsupervised deep learning models for customer spend ing behavior analysis. An exploratory behavioral analysis characterizes spending heterogeneity\, temporal regularities\, and channel and category variations. Supervised sequence models based on LSTM and CNN ar chitectures are evaluated alongside unsupervised sequence autoencoders and hybrid detection pipelines across fraud rates ranging from 2-12%. To ensure fair evaluation under extreme imbalance\, models are assessed using ranking-based metrics under fixed alert budgets\, including pre cision–recall area under the curve and recall-at-K. A hybrid of Autoen coder and LSTM architectures achieves the highest performance for large systems. An integrated XAI module is introduced to derive important features providing interpretable insights.
CATEGORIES:VIRTUAL ROOM 3D
LOCATION:Virtual Room D\, Bangkok\, Thailand
SEQUENCE:0
UID:d0f752ae4527ab118c00e18d7d9c42f0
URL:http://11thictisthailand.sched.com/event/d0f752ae4527ab118c00e18d7d9c42f0
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260417T125213Z
DTSTART:20260409T080000Z
DTEND:20260409T100000Z
SUMMARY:Cross-Lingual Sentiment Analysis for Low-Resource Languages: A Case Study on Thai Railway Services
DESCRIPTION:Authors - Dao Khanh Duy\, Nguyen Hoang Hieu\, Karn Nasritha\, Khanista Namee\n Abstract - This research examines the effectiveness of four state-of-the art transformer-based models (LaBSE\, mBERT\, XLM-RoBERTa\, and mT5) for cross-lingual sentiment analysis of railway passenger feedback. We focus on transferring knowledge from high-resource languages (En glish\, French\, Vietnamese\, and Korean) to Thai\, a low-resource language in this domain. To address data imbalance and scarcity\, the study inves tigates transfer learning strategies ranging from zero-shot to "ultra-shot" (using only 60 labeled samples) and high-shot paradigms. Experimental results demonstrate that while generative models like mT5 perform well in zero-shot settings\, the LaBSE model achieves a superior accuracy of 94.65% under high-shot fine-tuning. Notably\, our proposed ultra-shot strategy enables LaBSE to reach 90.42% accuracy with minimal data\, effectively bridging the performance gap without extensive annotation. These findings suggest a strategic approach for AI systems in railway op erations: rather than investing in large-scale datasets or computationally heavy models\, operators can implement the ultra-shot strategy by fine tuning robust sentence-embedding models like LaBSE with a small set of gold-standard data to achieve optimal performance and cost-efficiency.
CATEGORIES:VIRTUAL ROOM 3D
LOCATION:Virtual Room D\, Bangkok\, Thailand
SEQUENCE:0
UID:5432e0a34e6261b60942f5e32302e349
URL:http://11thictisthailand.sched.com/event/5432e0a34e6261b60942f5e32302e349
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260417T125213Z
DTSTART:20260409T080000Z
DTEND:20260409T100000Z
SUMMARY:Ensemble Learning for High-Precision Prediction of Rare Critical Events
DESCRIPTION:Authors - Nazar Melnyk\, Oleksandr Korochkin\n Abstract - Reliable prediction of rare critical events is a key enabler for modern risk management\, civil protection\, and decision support sys tems\, yet it remains challenging due to extreme class imbalance and strict requirements on false alarm rates. We present an ensemble learn ing framework that combines a deep feed-forward neural network with a Random Forest classifier\, complemented by temporal feature engineering and precision-oriented optimization. The approach addresses three ob jectives: extracting informative temporal and regional patterns from raw event logs\, learning calibrated probabilistic scores under severe imbalance using focal loss\, and tuning per-region decision thresholds to achieve high precision while preserving acceptable recall. As a case study we apply the framework to air alert prediction over 25 administrative regions across 38 months\, totalling 774\,125 hourly observations. The system attains 96.13% accuracy\, 75.1% precision\, and 77.9% recall\, demonstrating that high-precision early warning is feasible in strongly imbalanced settings. The framework is applicable to a wide range of safety-critical rare event prediction tasks.
CATEGORIES:VIRTUAL ROOM 3D
LOCATION:Virtual Room D\, Bangkok\, Thailand
SEQUENCE:0
UID:d9a6aedc7fc0fa69ce960ca70a70b1f2
URL:http://11thictisthailand.sched.com/event/d9a6aedc7fc0fa69ce960ca70a70b1f2
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260417T125213Z
DTSTART:20260409T080000Z
DTEND:20260409T100000Z
SUMMARY:IMPLEMENTING DIGITAL TWIN ARCHITECTURE THROUGH BIM AND IOT INTEGRATION FOR SUSTAINABLE MUSEUMS: MACA LIVING LAB APPLICATION
DESCRIPTION:Authors - Lavinia Chiara Tagliabue\, Silvia Meschini\, Viviana Vaccaro\, Hira Ovais\, Silvana Dalmazzone\, Gianluca Torta\, Ferruccio Damiani\, Stefano Rinaldi Abstract - Named Entity Recognition (NER) is an essential task for sequence labelling and information extraction that plays a fundamental role in subsequent Natural Language Processing (NLP) applications\, such as information retrieval\, question answering\, knowledge graph development\, and machine translation. Although significant advancements have been made in NER for high resource languages\, achieving effective entity recognition in Indian languages continues to be an unresolved research challenge because of linguistic diversity\, complex morphology\, typological differences\, flexible word order\, script differences\, and prevalent codemixing. The scarce presence of annotated datasets and the lack of standardized evaluation metrics further limit supervised and transfer learning methods in these low resource environments. This document introduces a multilingual NER framework rooted in Sentence embeddings derived from Large Language Models (LLMs) and inference guided by prompts. The suggested method employs contextual\; language independent embeddings obtained from pretrained multilingual LLMs to encode semantic representations of Indian and foreign languages within a common embedding space. Rather than using traditional token level classification\, entity recognition and classification are achieved via structured prompting\, allowing for zero-shot and few-shot generalization without the need for task specific finetuning. The system guarantees that entity identification and retrieval take place in the same language as the input text\, maintaining linguistic accuracy and reducing error propagation caused by translation. To tackle domain variability and informal writing\, constraints/guardrails for prompts and simple rule-based normalization are utilized to manage orthographic differences\, script inconsistencies\, and codemixed phrases often found in user generated content and social media. Experimental assessment across various Indian languages shows reliable enhancements in precision\, recall\, and F1score compared to traditional neural and transformer-based benchmarks\, especially in low resource conditions. The findings suggest that embeddings powered by LLMs along with prompt-based reasoning provide a scalable and data efficient option for multilingual NER. This project advances the development of resilient\, inclusive\, and language adaptive systems for extracting information in linguistically varied settings.
CATEGORIES:VIRTUAL ROOM 3D
LOCATION:Virtual Room D\, Bangkok\, Thailand
SEQUENCE:0
UID:42e6127e5c600a2c6de29cd8cdd7f592
URL:http://11thictisthailand.sched.com/event/42e6127e5c600a2c6de29cd8cdd7f592
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260417T125213Z
DTSTART:20260409T080000Z
DTEND:20260409T100000Z
SUMMARY:Information Delivery Specification (IDS) in the Construction Sector: A Systematic Literature Review
DESCRIPTION:Authors - Murat Aydın Abstract - Combining Particle Swarm Optimization (PSO) with gradientbased local search enhances efficiency in solving complex optimization problems. Existing hybrids often use fixed switching rules\, causing premature convergence orwastedcomputation.We present an adaptive PSO–gradient descent method where stagnation detection triggers local refinement only when needed. Adam is employed for local search without extra parameters. Tests on seven benchmark functions show the approach achieves strong or competitive results on challenging cases while ensuring robust convergence on simpler ones.
CATEGORIES:VIRTUAL ROOM 3D
LOCATION:Virtual Room D\, Bangkok\, Thailand
SEQUENCE:0
UID:e2dd5f75a219de276e2de1028d1751df
URL:http://11thictisthailand.sched.com/event/e2dd5f75a219de276e2de1028d1751df
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260417T125213Z
DTSTART:20260409T080000Z
DTEND:20260409T100000Z
SUMMARY:Recent Advances in Nonconvex Optimization for Machine Learning
DESCRIPTION:Authors - Hiep. L. Thi Abstract - Flexible Job Shop Scheduling Problems (FJSP) involve large discrete decision spaces and strict feasibility constraints\, making them challenging for deep reinforcement learning methods. In this work\, we study how state represen tation and feature extraction architecture influence the performance of action masked Proximal Policy Optimization (PPO) in flexible scheduling. The scheduling task is formulated as a sequential assignment of operations to machines with a fixed discrete action space\, where infeasible actions are removed using a feasibility mask. The environment state is represented using three heter ogeneous feature blocks describing resource availability\, operation readiness\, and time-related attributes of assignment alternatives. We compare a baseline single-branch encoder with a multi-branch feature extraction architecture that processes these blocks separately before aggregation. Experiments were conducted on the Brandimarte MK benchmark suite (MK01 MK10). Under identical training conditions\, the multi-branch representation achieved lower makespan on 9 out of 10 instances\, with relative improvements ranging from 2.4% to 27.8% compared to the single-branch baseline. The largest reductions were observed on MK06 (−27.8%) and MK10 (−25.2%)\, while per formance remained comparable on MK08. Training results indicate improved stability and more consistent convergence for structured representations. These results demonstrate that structured state design and feature extraction ar chitecture are critical factors in action-masked reinforcement learning for flexible job shop scheduling.
CATEGORIES:VIRTUAL ROOM 3D
LOCATION:Virtual Room D\, Bangkok\, Thailand
SEQUENCE:0
UID:26c4c4af402950b8f3877a80697599b3
URL:http://11thictisthailand.sched.com/event/26c4c4af402950b8f3877a80697599b3
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260417T125213Z
DTSTART:20260409T080000Z
DTEND:20260409T100000Z
SUMMARY:Reliability-Aware Late Fusion for Robust Multimodal Emotion Recognition under Modality Imbalance and Domain Shift
DESCRIPTION:Authors - Karn Na Sritha\, Khang Tran Chi Nguyen\, Dao Khanh Duy\, Khanista Namee\n Abstract - Multimodal affective computing system (MACS) aims to improve the affect prediction performance by fusing the complementary cues in visual and audio channels. While late fusion approaches are modular and can be flexibly deployed\, they often rely on static modality weights which pre-assumes fixed reliability among modalities. In practical situation\, visual stream can be corrupted by occlusion\, variation of illumination and motion artifact while audio stream could be interfered by noise and reverberation or channel mismatch. Moreover\, domain shifts between different datasets further contribute to the problem of in consistent calibration across modalities\, which results in inaccurate fused predic tion. In this paper\, a reliability-aware late fusion model is proposed to enhance ro bustness for multimodal emotion recognition. Based on the independently trained branches of FER and SER\, we conduct an analytical process for theoretical var iance-covariance stability analysis of linear late fusion with respect to a modality imbalance condition. We further investigate entropy-driven reliability estima tion and calibration-aware weighting schemes. Experiment results from original test report are incorporated into the theoretical framework\, it makes evidence that one modality’s dominance is more related to entropy stable and calibration char acteristics than raw unimodal accuracy. Our results also indicate that reliability aware weighting increases robustness under simulated degradation and missing modalities\, without the need for retraining unimodal models.
CATEGORIES:VIRTUAL ROOM 3D
LOCATION:Virtual Room D\, Bangkok\, Thailand
SEQUENCE:0
UID:e13842b53704a85e54690c2824607154
URL:http://11thictisthailand.sched.com/event/e13842b53704a85e54690c2824607154
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260417T125213Z
DTSTART:20260409T080000Z
DTEND:20260409T100000Z
SUMMARY:A Bibliometric Analysis of Research Trends in Finance: Mapping Intellectual Structure and Emerging Themes
DESCRIPTION:Authors - Deepak sharma\, Pankajkumar Anawade\, Anurag Luharia\, Gaurav Mishra Abstract - The rapid digital transformation of modern society has significantly increased the complexity of network infrastructures and the sophistication of cyber threats. Traditional rule-based and signature-based security systems are increasingly ineffective against advanced persistent threats\, zero-day vulnera bilities\, and AI-driven cyberattacks. Artificial Intelligence (AI) and Machine Learning (ML) have emerged as transformative technologies that enhance net work security through intelligent threat detection\, automated response\, and pre dictive analytics. However\, the integration of AI and ML also introduces new vulnerabilities\, including adversarial attacks\, model poisoning\, privacy con cerns\, and algorithmic bias. This paper critically examines the evolution of net work security through AI and ML\, analyzing both the technological advance ments and the emerging risks associated with their deployment. The study ar gues that while AI-driven security systems represent a significant improvement over traditional mechanisms\, careful governance\, transparency\, and robust model protection are essential to mitigate new threats introduced by intelligent systems.
CATEGORIES:VIRTUAL ROOM 3E
LOCATION:Virtual Room E\, Bangkok\, Thailand
SEQUENCE:0
UID:a9ae76efe87306cedb6d467e59e5e8fb
URL:http://11thictisthailand.sched.com/event/a9ae76efe87306cedb6d467e59e5e8fb
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260417T125213Z
DTSTART:20260409T080000Z
DTEND:20260409T100000Z
SUMMARY:A Review of Worker Safety Assurance and Automated Industrial Quality Inspection and the Proposed AI-Powered System
DESCRIPTION:Authors - Isha Bhagat\, Rishita Chourey\, Anjali Kurhade\, Vedika Desai\, Meenal Kamalakar\, Vishal Goswami\, Nayan Wagh\n Abstract - In the shadow of overlooked safety violations\, different factories have lost thousands\, in terms of capital as well as lives. Which is especially harrowing as these were caused due to easily preventable work accidents or easily noticeable defective machinery. Our paper dives into how artificial intelligence based methodologies\, particularly\, would help in mitigating these risks based on past and present research. We also recommend a potential prototype system according to the findings from the literature we reviewed\, for Real-Time worker safety check and automated industrial machine quality inspection system. We have reviewed four major topics pertaining to our system: [1] Personal Protective Equipment (PPE) compliance detection through CCTV monitoring as opposed to manual monitoring\, [2] industrial machine quality inspection for automatic defect identification [3] evaluation of previously used object detection models and their performance for industry applications\, and [4] system level considerations for practical deployment of the said systems on a large scale. We have compared methods\, deployment strategies and results from existing studies to identify key criteria like scalable architectures as well as low latency processing. We are highlighting challenges such as insufficient annotated data for rare machinery defects\, good accuracy in harsh industrial conditions that might hinder detection of safety violations\, and ethical issues with worker monitoring as well in this paper.
CATEGORIES:VIRTUAL ROOM 3E
LOCATION:Virtual Room E\, Bangkok\, Thailand
SEQUENCE:0
UID:6bf9806f588dac2804d7df0618eabbbc
URL:http://11thictisthailand.sched.com/event/6bf9806f588dac2804d7df0618eabbbc
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260417T125213Z
DTSTART:20260409T080000Z
DTEND:20260409T100000Z
SUMMARY:ClaimWatcheR: A Smart Healthcare Insurance Fraud Prevention using Privacy-Preserving Decentralized Intelligent Framework
DESCRIPTION:Authors - P Subhash\, P. Abhi Varshini\, V. Udai Sree\, P. Praneeth Reddy\, Sai Mahitha Abstract - The recognition of transaction fraud in credit cards is a major problem that is still faced. It is mainly because of the gap between real and fraud transaction. In traditional methods\, evaluations are mainly done with the main eye on accuracy\, but it is sometimes inadequate and indecisive because the fraud occurrence is only 1% of all the data. Many studies in this field that have been done lately have focused on deep learning and machine learning structures. A very less number of works really stress on relatively simpler structures that can go well with imbalance and variance in class without the need of any complicated frameworks. A dataset that is publicly accessible has been used here for comparative study and has 284\,807 transaction data. For classification\, three learning algorithms like Logistic Regression\, Random Forest\, and XGBoost have been used. Precision-Recall AUC (PR-AUC)\, Matthews Correlation Coefficient (MCC)\, precision\, and recall have been used to assess the model performance and not just accuracy. Random forest shows a steady outcome with a strong variance between false positive control and detection capability. The analysis also reveals that naive class-weighting strategies can significantly increase recall while producing impractically high false positive rates. Feature importance analysis further enhances interpretability and provides insight into influential transaction components.
CATEGORIES:VIRTUAL ROOM 3E
LOCATION:Virtual Room E\, Bangkok\, Thailand
SEQUENCE:0
UID:28df25364ee0fe4607226b408e0c38e4
URL:http://11thictisthailand.sched.com/event/28df25364ee0fe4607226b408e0c38e4
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260417T125213Z
DTSTART:20260409T080000Z
DTEND:20260409T100000Z
SUMMARY:EV2EV:A Fuzzy Controlled Dual Active Bridge for Intelligent Charging
DESCRIPTION:Authors - Tintu Pious\, Adon Hale J Payyapilly\, Akshit Charan\, Amal Suresh\, Ashwin Babu Mampilly\n Abstract - The shift toward decentralized energy grids has established Vehicle-to-Vehicle (V2V) power transfer as a cornerstone of modern EV infrastructure. Central to this exchange is the Dual Active Bridge (DAB) converter\, a bidirectional DC-DC topology prized for its high power density and galvanic isolation. The DAB utilizes two symmetrical H-bridges linked by a high-frequency transformer\; one bridge acts as an inverter while the other performs synchronous rectification\, depending on the power flow direction. Managing energy between independent batteries is challenging due to fluctuating voltage levels that create "moving targets" for control systems. Traditional PID loops often struggle with the instability caused by sudden voltage shifts in dynamic V2V scenarios. This project implements a Fuzzy Logic Controller (FLC) based on a voltage mapping principle. By comparing real-time voltage profiles of donor and receiver batteries\, the FLC automatically determines the current direction and optimal phase shift angle without requiring complex mathematical modelling. Beyond emergency charging\, this technology enables EVs to function as a mobile\, distributed energy storage system within Smart Grids. It optimizes microgrid management in commercial hubs by sharing power autonomously\, preventing transformer overload during peak demand. This approach ensures that decentralized energy sharing is both reliable and commercially viable.
CATEGORIES:VIRTUAL ROOM 3E
LOCATION:Virtual Room E\, Bangkok\, Thailand
SEQUENCE:0
UID:5e8414df552aaf3c2b47d6e6e5c522ce
URL:http://11thictisthailand.sched.com/event/5e8414df552aaf3c2b47d6e6e5c522ce
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260417T125213Z
DTSTART:20260409T080000Z
DTEND:20260409T100000Z
SUMMARY:Fast Channel Bit Rate Estimation Through a Novel Approximate EVT Estimate
DESCRIPTION:Authors - Vladislav Vasilev\, Georgi Iliev\n Abstract - In this paper we derive a new estimate of the channel bit rate. The estimates is a special transformation of the main EVT theorem that is particularly designed for use in telecommunication automated systesm meaning it’s robust to noise\, computationally cheep\, needs very few data points and no manual validation. Due to the EVT methodology we can evaluate if the bit rate can keep dropping indefinitely or if it has a guaranteed minimum value. The method is relatively fast because it uses Newton’s interpolation instead of hypothesis testing or regression.
CATEGORIES:VIRTUAL ROOM 3E
LOCATION:Virtual Room E\, Bangkok\, Thailand
SEQUENCE:0
UID:a668053577587b4616c01ae47ff448b8
URL:http://11thictisthailand.sched.com/event/a668053577587b4616c01ae47ff448b8
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260417T125213Z
DTSTART:20260409T080000Z
DTEND:20260409T100000Z
SUMMARY:GST Frameworks Across Borders – A Comparative Study of India and Global Models
DESCRIPTION:Authors - Deepak sharma\, Pankajkumar Anawade\, Anurag Luharia\, Gaurav Mishra\, Akshit Yadav Abstract - The exponential growth of cybercrime\, cloud-native infrastructures\, Internet of Things (IoT) ecosystems\, encrypted communications\, and AI enabled adversarial techniques has fundamentally challenged traditional digital forensic methodologies. Conventional forensic frameworks developed for static systems cannot scale to high-velocity\, heterogeneous data environments. This study proposes and empirically evaluates a lifecycle-oriented AI-enhanced digi tal forensic architecture integrating machine learning (ML)\, deep learning (DL)\, graph analytics\, and explainable AI (XAI). Across benchmark datasets in intru sion detection\, malware classification\, multimedia authentication\, and textual intelligence extraction\, AI-enhanced systems significantly improved detection accuracy (up to 98.3%) and reduced analyst workload (40–60%). However\, ad versarial robustness testing and explainability evaluation reveal governance and admissibility challenges. The findings demonstrate that while AI enhances scalability and zero-day detection\, its responsible adoption requires reproduci bility controls\, interpretability safeguards\, and alignment with legal standards such as Daubert.
CATEGORIES:VIRTUAL ROOM 3E
LOCATION:Virtual Room E\, Bangkok\, Thailand
SEQUENCE:0
UID:115d81c0a191c72bd792551898838623
URL:http://11thictisthailand.sched.com/event/115d81c0a191c72bd792551898838623
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260417T125213Z
DTSTART:20260409T080000Z
DTEND:20260409T100000Z
SUMMARY:Interactive Visual Analytics for Fidelity Assessment of Synthetic Tabular Data
DESCRIPTION:Authors - Netochukwu Onyiaji\, Lukas Cironis\, Leonid Bogachev\, Liqun Liu\, Janos Gyarmati-Szabo\, Roy A. Ruddle Abstract - This study examines the adoption of AI-enabled hotel chatbots by investigating the role of technology readiness and consumer perceptions in shaping guests’ attitudes and behavioral intentions. Drawing upon the Technology Acceptance Model (TAM) and the Technology Readiness Index (TRI 2.0)\, the research integrates technological and psychological determinants of AI service adoption in hospitality settings. Data were collected from 270 hotel guests who had previously interacted with chatbots in four-star hotels in Jakarta and analyzed using Partial Least Squares Structural Equation Modeling (PLS-SEM). The results indicate that technology readiness\, perceived convenience\, and perceived information quality significantly influence guests’ attitudes toward AI hotel chatbots. However\, attitude and perceived convenience do not directly translate into adoption intention\, revealing an attitude–intention gap. The model explains 61% of the variance in attitude and 38% in behavioral intention. These findings extend technology adoption literature by highlighting the role of psychological readiness and service perceptions in shaping guest adoption of AI-enabled hospitality technologies.
CATEGORIES:VIRTUAL ROOM 3E
LOCATION:Virtual Room E\, Bangkok\, Thailand
SEQUENCE:0
UID:50f16523e435e306d0a1e6c81f9e78f6
URL:http://11thictisthailand.sched.com/event/50f16523e435e306d0a1e6c81f9e78f6
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260417T125213Z
DTSTART:20260409T080000Z
DTEND:20260409T100000Z
SUMMARY:Ollama-Based Retrieval-Augmented Generation for PCOS Diagnosis Support System: A Locally Deployed Conversational AI Approach
DESCRIPTION:Authors - Aung Nyein Chan Paing\, Sudhir Kumar Sharma Abstract - This paper presents a semantic video search system that supports natural lan guage querying over video content using vision–language models and vector similarity search. The proposed system processes videos offline by extract ing representative frames through similarity-based filtering\, generating textual descriptions using a pre-trained BLIP (Bootstrapping Language–Image Pre training) image captioning model\, and encoding the captions into dense vector embeddings. These embeddings are indexed in a vector database to enable effi cient retrieval of relevant video segments based on textual queries. The system architecture comprises a Python-based backend with GPU acceleration for video processing and a web-based interface for query interaction. Experimental obser vations indicate that similarity-based frame filtering reduces redundant frames by approximately 50–70% while preserving semantic information. Qualitative eval uation demonstrates that the system effectively retrieves semantically relevant video timestamps in response to natural language queries. The proposed frame work serves as a modular prototype for content-based video retrieval and semantic video analysis applications.
CATEGORIES:VIRTUAL ROOM 3E
LOCATION:Virtual Room E\, Bangkok\, Thailand
SEQUENCE:0
UID:9040c895b42415f2a4924425147cbfee
URL:http://11thictisthailand.sched.com/event/9040c895b42415f2a4924425147cbfee
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260417T125213Z
DTSTART:20260409T080000Z
DTEND:20260409T100000Z
SUMMARY:On estimating the infinite value of circumference ratio
DESCRIPTION:Authors - Qing Li Abstract - Intrusion Detection Systems (IDS) are critical for cybersecurity\, yet conventional approaches based on machine learning often suffer from limited explainability\, high computational cost\, and scalability issues. We introduce Recommendation-Driven IDS (RD-IDS)\, a novel framework that models security events and detection rules as a hypergraph\, reformulating intrusion detection as a structured recommendation problem. Detection is achieved through the computation of minimal transversals\, identifying minimal and actionable sets of security measures. RD-IDS is formally defined with hypergraph representations\, recommendation semantics\, and UML-based architecture\, ensuring traceability and modularity. Algorithmically\, we leverage minimal transversal enumeration\, including the Fredman–Khachiyan dualization method\, and analyze temporal and spatial complexity\, demonstrating that structural reductions and active set optimizations mitigate overhead. RD-IDS offers deterministic\, explainable\, and scalable detection by construction\, providing a principled alternative to machine learning-centric IDS. This work establishes the formal and algorithmic foundations of RD-IDS\, laying the groundwork for practical implementation and experimental validation in a companion study.
CATEGORIES:VIRTUAL ROOM 3E
LOCATION:Virtual Room E\, Bangkok\, Thailand
SEQUENCE:0
UID:be399174eef012f4359f667f7e422a8a
URL:http://11thictisthailand.sched.com/event/be399174eef012f4359f667f7e422a8a
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260417T125213Z
DTSTART:20260409T080000Z
DTEND:20260409T100000Z
SUMMARY:Severity-Aware Weighted Loss for Arabic Medical Text Generation
DESCRIPTION:Authors - Ahmed Alansary\, Molham Mohamed\, Ali Hamdi Abstract - Quantum secret sharing (QSS) scheme is a cryptographic protocol for sharing a secret among parties in a secure way\, such that only the set of all authorized parties can reconstruct the secret using the quantum information. In this manuscript\, a multi-secret sharing scheme (namely\, qMSS) is proposed and analyzed utilizing a quantum error-correcting code (CSS code) for generating and reconstructing shares. qMSS generates n quantum shares of an m(≤ k)-bit classical secret using [[n\,k\,d]]q CSS code and distributes shares among n participants. This work generalizes the sharing of one-bit classical secret\, utilizing CSS codes\, proposed by Sarvepalli and Klappenecker. The set of all authorized parties is identified by minimal codewords associated with the classical code underlying the CSS code. The proposed qMSS is a perfect multi-secret sharing scheme due to the set of all unauthorized parties is unable to obtain any information about the secret.
CATEGORIES:VIRTUAL ROOM 3E
LOCATION:Virtual Room E\, Bangkok\, Thailand
SEQUENCE:0
UID:1b63c7f1c2de29ce2519f1473fe2a297
URL:http://11thictisthailand.sched.com/event/1b63c7f1c2de29ce2519f1473fe2a297
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260417T125213Z
DTSTART:20260409T080000Z
DTEND:20260409T100000Z
SUMMARY:Strategic Management Framework for Scaling Telemedi- cine in Rare Chronic Disease Care: A Rapid Review of Rural India’s Digital Health Ecosystem
DESCRIPTION:\n
CATEGORIES:VIRTUAL ROOM 3E
LOCATION:Virtual Room E\, Bangkok\, Thailand
SEQUENCE:0
UID:c95a100d654cfc4702175434f6429557
URL:http://11thictisthailand.sched.com/event/c95a100d654cfc4702175434f6429557
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260417T125213Z
DTSTART:20260409T080000Z
DTEND:20260409T100000Z
SUMMARY:Artificial Intelligence Reliant Cybersecurity Compliance Automation and Threat Response
DESCRIPTION:Authors - Pratham Vasa\, Amishi Desai\, Chahel Gupta\, Avani Bhuva\, Mohini Reddy Abstract - Content Delivery Networks (CDNs) play an essential role in enhancing the content delivery speed by caching frequently requested data in edge servers distributed across geographical regions. Traditional CDNs utilize rule-based pol icy and machine learning approaches for optimizing the cache. Machine learning is performed centrally\, and the cache optimization is performed using the traffic logs collected by the central server. Although the use of central learning ap proaches is beneficial\, it poses certain limitations\, including data privacy and high communication cost. The central learning approach aggregates raw data\, which poses data privacy issues. This paper proposes an architecture for secure federated learning\, which is utilized for cache hit prediction in CDNs. The proposed archi tecture is evaluated using a synthetic dataset containing 1\,30\,548 records\, and the features include temporal and network features. The proposed architecture is com pared with the traditional central learning approach\, and the results reveal that the secure federated learning model achieves an accuracy of 70.15%\, which is com parable to the central learning approach. The proposed architecture is found to reduce data privacy exposure by 30%.
CATEGORIES:VIRTUAL ROOM 3F
LOCATION:Virtual Room F\, Bangkok\, Thailand
SEQUENCE:0
UID:2270d5cae8b476894c5d3f2c7ffa42ca
URL:http://11thictisthailand.sched.com/event/2270d5cae8b476894c5d3f2c7ffa42ca
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260417T125213Z
DTSTART:20260409T080000Z
DTEND:20260409T100000Z
SUMMARY:Comprehensive USB Device Forensic Framework for Artifact Correlation and Timeline Reconstruction on Windows Systems
DESCRIPTION:Authors - Syed Shanika Zaida\, Kamineni Leela Tapaswi\, Kilari Dhana Malikarjuna Rao\, Adarapu Sandeep\, Amar Jukuntla\n Abstract - Removable USB storage devices are widely used in day-to day computing\, but they also introduce risks such as unauthorized data transfer and misuse of external media. Understanding how these devices are used on a system is important during forensic investigations\, espe cially when analyzing potential data leakage incidents. On Windows sys tems\, traces of USB activity are not stored in a single location. Instead\, they are distributed across registry entries\, system logs\, and file system records. Examining these sources individually often makes it difficult to form a clear picture of events. This paper introduces a forensic frame work that brings together USB-related artifacts from multiple system components and analyzes them in a unified manner. The method gath ers data from sources such as registry entries\, Plug-and-Play logs\, and f ile system structures\, and then aligns them based on their timestamps. A Python-based implementation is used to automate this process and to relate device connection events with file operations. Experiments con ducted on a Windows setup show that the framework can identify device usage and reconstruct the sequence of related activities with clarity. By combining evidence into a single timeline\, the approach helps simplify analysis and supports consistent interpretation of results.
CATEGORIES:VIRTUAL ROOM 3F
LOCATION:Virtual Room F\, Bangkok\, Thailand
SEQUENCE:0
UID:9f859c0be58b0da9bb14ec610f3799d5
URL:http://11thictisthailand.sched.com/event/9f859c0be58b0da9bb14ec610f3799d5
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260417T125213Z
DTSTART:20260409T080000Z
DTEND:20260409T100000Z
SUMMARY:DataMCP: Guidelines\, Guardrails\, Prompts and Tools for Secure Natural-Language Database Access
DESCRIPTION:Authors - Sanchi Mahajan\, Nandini Jain\, Evangelin G\, Jansi K R\, Shivam Shivam Abstract - The issue of efficient work planning in heterogeneous multi-cloud in frastructures is still an open issue due to scalability limitations\, data privacy\, and latency sensitivity. The conventional centralized scheduling approach requires data aggregation\, which is associated with critical privacy challenges and com munication cost. The proposed work aims to design a privacy-preserving feder ated multi-cloud task scheduling framework for smart mobility applications to overcome the limitations of conventional approaches. The proposed framework employs a decentralized scheduler for separate cloud regions. The proposed framework employs a novel task abstraction approach to transform real-time traffic data into task-scheduling forms. The proposed framework eliminates the requirement to communicate raw traffic data by employing a federated learning based aggregation approach. The proposed framework employs a federated ag gregation approach\, which is associated with scalability\, routing\, and multi cloud coordination while ensuring data locality. The proposed framework is evaluated by conducting experiments on Random\, Rule-Based\, Local-ML ap proaches using a Smart Mobility dataset. As can be observed from the results\, considerable reductions in communication overhead and privacy leakage are achieved with the preservation of competitive execution latency and SLA com pliance. The strategy has been observed to scale well with an increase in cloud regions\, as the communication scalability results indicate. It is the ability to sup port federated\, scalable\, and privacy-aware job scheduling for smart traffic sys tems without central data sharing that makes this work interesting.
CATEGORIES:VIRTUAL ROOM 3F
LOCATION:Virtual Room F\, Bangkok\, Thailand
SEQUENCE:0
UID:d2ead1280f9dc5f1f9787689785750c7
URL:http://11thictisthailand.sched.com/event/d2ead1280f9dc5f1f9787689785750c7
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260417T125213Z
DTSTART:20260409T080000Z
DTEND:20260409T100000Z
SUMMARY:Deepfakes: A Review of Creation and Research Trends
DESCRIPTION:Authors - Thota Neha\, Napa. Sai Gopi\, R. Aarthi Abstract - The increasing realism of deepfake media has raised signifi cant concerns regarding the authenticity of digital content. Most existing detection methods rely on audio–visual fusion\, which often introduces ad ditional complexity and may degrade performance when one modality is unavailable or unreliable. This work presents a dual-stream deep learning framework that pro cesses audio and video independently\, avoiding explicit fusion. The au dio stream employs a CNN–BiLSTM model on log-Mel spectrograms to capture temporal and spectral artifacts\, while the video stream uses EfficientNet-B0 with BiLSTM to model spatial inconsistencies and tem poral variations in facial sequences. Experiments conducted on multiple benchmark datasets\, including ASVspoof 2019\, WaveFake\, LJSpeech\, FaceForensics++\, and Celeb-DF (v2)\, demon strate that the proposed approach achieves competitive detection perfor mance. In addition\, the framework maintains robustness under missing modality conditions and offers improved interpretability compared to fusion-based methods. These results indicate that independent modality-specific learning pro vides a practical and effective alternative for deepfake detection in real world scenarios.
CATEGORIES:VIRTUAL ROOM 3F
LOCATION:Virtual Room F\, Bangkok\, Thailand
SEQUENCE:0
UID:eb51f8f41748508ccc10f9511550da90
URL:http://11thictisthailand.sched.com/event/eb51f8f41748508ccc10f9511550da90
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260417T125213Z
DTSTART:20260409T080000Z
DTEND:20260409T100000Z
SUMMARY:Neuro-Behavioral Analytics and Network Threat Detection: A Real-Time AI Fusion Platform for Intrusion Monitoring
DESCRIPTION:Authors - Ankit Podder\, Piyush Ranjan Das\, Soham Acharya\, Ayushmaan Singh\, Soumitra Sasmal\, Partho Mallick\n Abstract - Static perimeter-based security architectures are now inef fective in the current threat scenario. The ability of attackers to obtain legitimate credentials and the presence of zero-day exploits often cause real-time breaches of the network perimeter. An area of concern is the real-time monitoring of these systems. In the current scenario\, security monitoring is performed in a segregated manner\, where network analysts analyze time-stamped network logs and identity analysts analyze time stamped login attempts\, without cross-referencing in real time between these two domains. The proposed solution is a fusion platform capable of ingestion of raw network transport data and real-time human element monitoring data. This is achieved through the integration of two dif ferent threat detection mechanisms using a FastAPI backend. The first threat detection system will be the Network Threat Detector (NTD)\, im plemented in Python and using the Scapy library to parse deep packet data in real time for flow analysis. The second threat detection system will be a JavaScript tracker designed for monitoring digital behavioral indicators and calculating real-time metrics such as mouse velocities\, ac celerations\, kinematic jerk\, and typing speeds. Real-time monitoring will be achieved through a machine learning framework with three different modules for inferring user intent using the Random Forest algorithm\, detecting anomalous statistical patterns using the Isolation Forest algo rithm\, and detecting malicious plaintext syntax using Logistic Regres sion. The system has been tested in a lab scenario and has been able to classify user session states into four different states: Engaged\, Con fused\, Frustrated and Suspicious with accuracy exceeding 95%. These digital behavioral indicators will be fed into the Network Transport Data (NTD)\, allowing the computation of a real-time risk score.
CATEGORIES:VIRTUAL ROOM 3F
LOCATION:Virtual Room F\, Bangkok\, Thailand
SEQUENCE:0
UID:60948702a0221bf191a7dcd40a069106
URL:http://11thictisthailand.sched.com/event/60948702a0221bf191a7dcd40a069106
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260417T125213Z
DTSTART:20260409T080000Z
DTEND:20260409T100000Z
SUMMARY:OS-Level Private Browsing Forensics
DESCRIPTION:Authors - Lavu Uha Saranya\, T.V.S.S. Reddy\, I.V.M.K. Sarma\, Dipesh Kumar Kushwaha\, T.N.V.D. Sai Krishna\n Abstract - Digital Forensic investigations have typically focused on the identification of private browsing at the application layer using artifacts from memory and disk\, as well as the fact that modern browsers rely extensively on the operating system for fundamental capabilities such as rendering\, input processing\, and networking. This paper extends the forensic scope by demonstrating that session Data related to private Sessions remain in shared Subsystems of the OS in Volatile Memory. In particular\, This paper examines the three primary components of the linux desktop environment: the display compositor (GNOME shell)\; the Input Pipeline (IBus Daemon)\; and the network resolver (systemdresolved). utilizing physical memory acquisitions via LiME on an ubuntu 25.04 System\, This paper monitored the migration of high entropy inputs across these subsystems. The results of this research indicate that critical session data including: Window metadata associated with wayland sessions\; Plaintext keystroke data received through D-Bus\; and fallback queries made via DNS-over-HTTPS were found to remain in OS Managed Memory for extended periods of time after the conclusion of the private browsing session. The author provides a reproducible framework for analysis of memory associated with the OS level and demonstrates that browser based privacy controls are structurally insufficient to fully sanitize volatile memory.
CATEGORIES:VIRTUAL ROOM 3F
LOCATION:Virtual Room F\, Bangkok\, Thailand
SEQUENCE:0
UID:bb05eca0f6d4775e1bd1da0d8bfb842a
URL:http://11thictisthailand.sched.com/event/bb05eca0f6d4775e1bd1da0d8bfb842a
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260417T125213Z
DTSTART:20260409T080000Z
DTEND:20260409T100000Z
SUMMARY:Privacy-preserving behavioral intelligence for distributed cloud systems via personalized federated autoencoders
DESCRIPTION:Authors - Venkata Saikumar Thalupuru\, Shubham Kumar\, Santhoshini Pranathi Singaraju\, Vishal Gupta Abstract - As the use of online banking and digital payments grew faster\, that has also left the institution at risk of becoming the victims of credit card fraud\, which has become a major challenge for traditional banks and other financial institutions. This huge discrepancy in transaction datasets is one of the greatest challenges in fraud analytics wherein only the rare fraudulent activity takes up a tiny fraction of the total transaction. Traditional machine learning models are often quite accurate but not great at detecting occasional frauds. To overcome this limitation\, this study proposes a cost-aware hybrid framework comprising Attention-based Long Short-Term Memory (Attention-LSTM) and ensemble-based machine learning. This method will take care to preprocess the data\, maintain balance among classes using SMOTE\, select features based on mutual information by leveraging a soft-voting ensemble of the Logistic Regression\, Random Forest\, and the XGBoost models. Cost-aware learning is coupled with decision threshold enhancement to minimize false negative predictions. Additionally\, SHAP-based explainability is added on top for enhanced transparency and interpretability of the model. The experimental results show 99.3% accuracy\, 0.905 precision\, 0.892 recall\, 0.898 F1-score\, and 0.98 ROC-AUC\, indicating that our new framework is effective in detecting genuine financial fraud.
CATEGORIES:VIRTUAL ROOM 3F
LOCATION:Virtual Room F\, Bangkok\, Thailand
SEQUENCE:0
UID:b4a13cabc540b4ec1998ac8ecb2b1c32
URL:http://11thictisthailand.sched.com/event/b4a13cabc540b4ec1998ac8ecb2b1c32
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260417T125213Z
DTSTART:20260409T080000Z
DTEND:20260409T100000Z
SUMMARY:SAMA: Spectral-Aware Minimal Adaptation for Parameter-Efficient Fine-Tuning
DESCRIPTION:Authors - Ismail Suleiman\, Dinesh Reddy Vemula\, Abhaya Kumar Pradhan Abstract - This paper presents the evaluation and demonstration phases of a Design Science Research Methodology (DSRM) study that produced the Organisational Security Culture Framework (OSCF) for Namibian Public Enterprises. An empirical needs assessment established a three-tier security culture maturity deficit: a 40% policy awareness gap\; a widespread misconception among non-IT staff that cybersecurity is solely an IT responsibility\; and a training gap in which 25% of staff had received no formal security training in the preceding year. The OSCF comprises five interrelated components: Risk Assessment\, Security Policy and Enforcement\, Security Compliance\, Training and Awareness\, and Ethical Conduct. Demonstration was executed across four staged phases: baseline assessment\, component testing\, pilot integration\, and full-scale deployment. Evaluation employed a dual approach: expert panel review against eight criteria and Key Performance Indicator (KPI) measurement across five strategic objectives. Results confirm that the OSCF closed the 40% policy awareness gap\, achieving 95% staff awareness post-implementation\, and significantly reduced phishing susceptibility. Seven evidence based refinements evolved the OSCF from a static policy model into a continuous security culture maturity loop. The framework’s modular\, tiered architecture supports long-term sustainability of behavioural change and scalable deployment across organisations of varying cybersecurity maturity\, including federated multi-institutional environments.
CATEGORIES:VIRTUAL ROOM 3F
LOCATION:Virtual Room F\, Bangkok\, Thailand
SEQUENCE:0
UID:63c18b3d8412876c62e0d39a4282d610
URL:http://11thictisthailand.sched.com/event/63c18b3d8412876c62e0d39a4282d610
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260417T125213Z
DTSTART:20260409T080000Z
DTEND:20260409T100000Z
SUMMARY:AI Explainability Framework in Legal Decision Support Systems
DESCRIPTION:Authors - Konstantina Rigou\, George Dimitrakopoulos\n Abstract - The rapid adoption of Artificial Intelligence (AI) in high-impact domains (healthcare\, finance\, justice) creates an urgent need for sys tems that are legally compliant\, explainable\, ethical and transparent. Decision Support Systems (DSS) aim to assist managerial and professional decision-making\, yet few works translate legal and ethical principles into concrete technical design constraints for explainable AI (XAI). This paper proposes a Legal Explainability Framework (LEF) that maps legal obligations (General Data Protection Regulation\, European Union Artificial Intelligence Act) and ethical principles to measurable XAI requirements and implementation steps\, and demonstrates the approach with a prototype using an open legal dataset derived from judgments of the European Court of Human Rights (ECtHR). The results show that legally compliant XAI is not merely a normative aspiration\, but a technically feasible and practically implementable design paradigm.
CATEGORIES:VIRTUAL ROOM 3G
LOCATION:Virtual Room G\, Bangkok\, Thailand
SEQUENCE:0
UID:50ceaae4e78158e97c9e3a068915e204
URL:http://11thictisthailand.sched.com/event/50ceaae4e78158e97c9e3a068915e204
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260417T125213Z
DTSTART:20260409T080000Z
DTEND:20260409T100000Z
SUMMARY:An Intelligent RAG Based Chatbot for Enhanced and Context Aware User Interaction
DESCRIPTION:\nAuthors -&nbsp\;P.Pandiaraja\, N.Shiva Kumar\, B.Vishnu Vardhan\, C.Sevarathi\, Charles Prabu V\, S.JaganAbstract - Retrieval-Augmented Generation (RAG) chatbots represent a significant advancement in intelligent conversational systems\, grounded in the prin-ciples of natural communication\, accuracy\, and reliability. Traditional chatbots are constrained by pre-trained knowledge or rule-based responses\, limiting their effectiveness in dynamic and complex real-world scenarios. RAG-based systems integrate information retrieval mechanisms with sophisticated language generation models to identify relevant knowledge in real time and produce contextually appropriate responses. The proposed system employs sentence-transformers (all-MiniLM-L6-v2) for dense vector embeddings and FAISS as the vector data-base backend\, enabling fast and semantically accurate document retrieval. Ex-perimen- tal results demonstrate a mean retrieval accuracy of 87.4%\, an average response latency of 1.3 s\, and a user satisfaction score of 4.2 out of 5\, confirm-ing the system’s readiness for real-world deployment.
CATEGORIES:VIRTUAL ROOM 3G
LOCATION:Virtual Room G\, Bangkok\, Thailand
SEQUENCE:0
UID:0c06ecdb2cf2e93ff81916bd313b16fd
URL:http://11thictisthailand.sched.com/event/0c06ecdb2cf2e93ff81916bd313b16fd
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260417T125213Z
DTSTART:20260409T080000Z
DTEND:20260409T100000Z
SUMMARY:Analysis of Decomposition Levels and Vanishing Moments in Wavelet-Based Processing Of Ultrasonic TOFD A-Scan Signals from Austenitic Stainless Steel Weld Pad
DESCRIPTION:Authors - Manjula K\, Vijayarekha K\, Venkatraman B\n Abstract - The fabrication of components across various industries is accom plished through welding. Although welding has been practiced for more than a hundred years\, defects may still occur during the welding process. Thus\, indus trial standards require welded joints to be inspected and evaluated to ensure their quality and reliability. Conventional ultrasonic testing (UT) has long been widely used in industry for detecting and evaluating defects in weld specimens. Over the last few decades\, advances in sensor technology and signal analysis techniques have significantly advanced ultrasonic testing methods. Advanced methods\, such as Time Of Flight Diffraction (TOFD)\, are more likely to detect linear defects. However\, one of the major challenges in applying TOFD to the inspection of austenitic stainless steel (ASS) weldments is noise in the signals. Various signal processing approaches have been developed to suppress such noise\, each with its own advantages and limitations. In this work\, the focus is placed on the applica tion of multi-level discrete wavelet transform (DWT) decompositions with ‘n’- order wavelet filters for de-noising ultrasonic TOFD A-scan signals. The results show that this approach achieves greater improvement in signal-to-noise ratio (SNR) while requiring less computational time.
CATEGORIES:VIRTUAL ROOM 3G
LOCATION:Virtual Room G\, Bangkok\, Thailand
SEQUENCE:0
UID:10359885669476708d67f512e03345c6
URL:http://11thictisthailand.sched.com/event/10359885669476708d67f512e03345c6
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260417T125213Z
DTSTART:20260409T080000Z
DTEND:20260409T100000Z
SUMMARY:Error Rate Analysis over α-Beaulieu-Xie and α-Beaulieu-Xie Extreme Fading Channels in Additive White Generalized Gaussian Noise
DESCRIPTION:Authors - Likhitha Ragha Ramya Nakka\, Anuradha Andra\, Appalaswami Ravada\, Vinay Kumar Pamula\n Abstract - This study uses Roland Barthes' semiotic approach to analyze how meaning is represented in HMNS' Untitled Humans ad on Instagram Reels. Understanding how storytelling campaigns create and communicate meaning has become crucial for successful digital marketing as social media plays a big-ger role in brand communication strategies. This study examines a selection of Instagram Reels content from the official Instagram @hmns account using a qualitative-descriptive methodology\, emphasizing how text\, sound\, and visual components interact to provide multiple interpretations. The study methodically sign how everyday occurrences\, human relationships\, and nature scenery are turned into symbolic representations of authenticity\, freedom\, and personal identity using Roland Barthes' three-level semiotic framework: denotation\, connotation\, and myth. Direct observation and content documentation of Reels recordings are used for data gathering\, and triangulation is used for analysis to guarantee validity and thoroughness. Results show that by creating an existential story that prioritizes closeness\, introspection\, and human connection\, the campaign goes beyond traditional product advertising. Authentic\, unconstructed life imagery is presented at the denotative level\, visual and musical elements evoke emotion and personal memory at the connotative level\, and perfume\, rather than being a commercial product\, becomes a symbol of emotional intimacy and identity exploration at the mythic level.
CATEGORIES:VIRTUAL ROOM 3G
LOCATION:Virtual Room G\, Bangkok\, Thailand
SEQUENCE:0
UID:499d637d498079075123e4f1021c0007
URL:http://11thictisthailand.sched.com/event/499d637d498079075123e4f1021c0007
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260417T125213Z
DTSTART:20260409T080000Z
DTEND:20260409T100000Z
SUMMARY:Machine Learning based Automated Subjective Answers Evaluation System
DESCRIPTION:Authors - Deepak Mane\, Siddhi Dhamal\, Shivam Devkar\, Divit Maheshwari\, Riddhi Kaulage\, Diya Nair\, Deepak R. More Abstract - The evaluation of handwritten answers sheet has so many challenges since from many years due to variability in handwriting\, linguistic barrier and personal bias. This is very time-consuming method and inconsistent method which highlights the need for automated subjective answers evaluation. Here\, proposed automated handwritten answers evaluation system uses TrOCR based handwritten answer detection\, NLTK tokenization\, WordNet lemmatization and semantic similarity check between teacher’s and student’s answer based on meaning. This advanced multi-model system overcomes traditional keyword matching technique and improves contextual accuracy. This system also overcomes traditional manual checking and results in fast evaluation. The system promotes the fairness\, fast and accurate processing. Moreover\, the suggested framework removes human fatigue\, encourages fair grading\, and offers a solution that can be used for large-scale academic tests. The results show that this automated method not only works like a human brain but also makes the evaluation process more fair and open.0
CATEGORIES:VIRTUAL ROOM 3G
LOCATION:Virtual Room G\, Bangkok\, Thailand
SEQUENCE:0
UID:ada3e04dafdd1e672c90b35d45454c75
URL:http://11thictisthailand.sched.com/event/ada3e04dafdd1e672c90b35d45454c75
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260417T125213Z
DTSTART:20260409T080000Z
DTEND:20260409T100000Z
SUMMARY:Multi-Leaf Crop Disease Detection Using EfficientNet-B0 with Transfer Learning
DESCRIPTION:Authors - Deepak Mane\, Deepak R. More\, Arya Kale\, Ravina Jagtap \, Soumya Dubewar \, Diya Nair\n Abstract - Timely detection of crop diseases is essential to ensuring high agricultural produc- tivity\; thus\, early and accurate detection has always been a priority for the farmers. So we pro- posed a deep learning based framework that classifies the condition of basil leaves in three cat- egories - wilting\, infection by mildew and healthy - through an EfficientNet-B0 convolutional neural network fine-tuned using transfer learning. We leverage a curated dataset of 1\,442 plant images available at the Roboflow platform\, splitting the dataset into 70% training\, 20% valida- tion and 10% testing. Transfer learning was used where we started EfficientNet-B0 with weights learned on large scale ImageNet pretraining. Training was done in two stages: first the whole model was trained with the backbone frozen and only the newly added classification head being trained\, followed by unfreeze the last 100 layers and perform fine-tuning to the domain. Leaf orientation and illumination variability were treated by a group of data augmentation methods including random horizontal flipping\, rotational transforms\, zoom perturbations\, and contrast adjustments. The proposed system achieved a remarkable result with high generalization of 96.6% training accuracy and 97.8% test accuracy. The detailed analysis of the confusion matrix and the ROC-AUC curves corroborate faithful multi-class discrimination. A Streamlit-based web interface was also developed to facilitate live inference\, farmers and agronomists are now able to make immediate predictions of the disease with confidence estimates. The results showed that the well optimized EfficientNet-B0 model can be a feasible and scalable solution for automated monitoring of crop diseases in the context of smart agriculture.0
CATEGORIES:VIRTUAL ROOM 3G
LOCATION:Virtual Room G\, Bangkok\, Thailand
SEQUENCE:0
UID:ab28477a7962add25fac56951697f247
URL:http://11thictisthailand.sched.com/event/ab28477a7962add25fac56951697f247
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260417T125213Z
DTSTART:20260409T080000Z
DTEND:20260409T100000Z
SUMMARY:Night Patrolling System Using CCTV and Real- Time Violence Detection with IoT and AI
DESCRIPTION:Authors - Vinodkumar Bhutnal\, Prajwal Vijay Sonawane\, Om Vinod Chaudhari\, Avinash Golande\, Mohit Ashok Tajane\, Sujal Kishor Papdeja\n Abstract - There is no more pressing issue in modern cities\, industries\, and public venues than nighttime security\, as the conventional approach of patrolling in-person only works well until fatigue and coverage become challenges\, when humanity and human error become a finite issue that requires short delay interruptions. Urbanization\, increased crime rates\, and the inadequacy of current traditional patrolling to provide a sufficient security posture have led to the proposal of an Intelligent Night Patrolling System that uses edge-cloud frameworks\, IoT-enabled CCTV camera technology\, and artificial intelligence video analytics to significantly reduce the presence gap. This system will provide continuous\, real-time proactive surveillance of locations and even be equipped with advanced deep learning models like Cummings Neural Networks (CNNs) and Long Short term Memory (LSTM) to detect suspicious activity\, anomalies\, intrusions\, and violent types of activities. This research introduces the concept of Night Patrolling System designed to assist security personnel during night surveillance.The proposed system achieves an estimated accuraxy of over 90% with a reduced latency \, demonstarting it’s effectiveness for a real time survillence applications.
CATEGORIES:VIRTUAL ROOM 3G
LOCATION:Virtual Room G\, Bangkok\, Thailand
SEQUENCE:0
UID:90798e3ddc70f0ae72f537a3ed1a560d
URL:http://11thictisthailand.sched.com/event/90798e3ddc70f0ae72f537a3ed1a560d
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260417T125213Z
DTSTART:20260409T080000Z
DTEND:20260409T100000Z
SUMMARY:Real-Time Multimodal Vehicle Type Classification System using Deep learning
DESCRIPTION:Authors - Deepak T. Mane\, Deepak R. More\, Gopal D. Upadhye\, Rucha C. Samant\, Hemlata U. Karne\, Suraksha Suryawanshi\, Prem Borse Abstract - Efficient vehicle type classification is vital for intelligent transportation systems\, traffic monitoring\, and urban mobility planning. This paper presents a Real-time Multimodal Vehicle Type Classification System that leverages both visual and acoustic data to identify and categorize vehicles such as cars\, buses\, trucks\, and motorcycles from live video streams. The proposed system integrates CNN-based and Transformer- based models for feature extraction across modalities\, enhancing detection robustness under diverse lighting\, weather\, and traffic conditions. A lightweight preprocessing pipeline performs synchronized frame extraction\, audio segmentation\, and feature fusion while ensuring minimal latency in real-time environments. The proposed multimodal architecture combines late fusion of visual and audio features to enhance the reliability of classification when either modality is suffering from low visibility or occlusion. Experimental evaluations demonstrate that the proposed framework achieves a classification accuracy of 96.2% at 28 fps\, outperforming unimodal baselines with real-time efficiency. This system is deployable for intelligent traffic surveillance\, automated tolling\, and urban safety analytics.
CATEGORIES:VIRTUAL ROOM 3G
LOCATION:Virtual Room G\, Bangkok\, Thailand
SEQUENCE:0
UID:b2c2b684356f7f43694f0bf927dad60f
URL:http://11thictisthailand.sched.com/event/b2c2b684356f7f43694f0bf927dad60f
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260417T125213Z
DTSTART:20260409T080000Z
DTEND:20260409T100000Z
SUMMARY:Reducing Methane Emissions in Rice Cultivation Using Federated Learning: A Privacy‑Preserving Framework for Climate‑Smart Agriculture
DESCRIPTION:Authors - Shwetha Ramadas\, Krutthika Hirebasur Krishnappa\, Sudhir Trivedi\n Abstract - Methane (CH4) emission from rice paddies is a significant source of greenhouse gas emissions from agriculture. Currently\, most models for methane prediction from rice paddies depend on collecting field data and sending it to a server. In this new paradigm\, several privacy concerns arise\, model scalability is restricted\, and a large number of data points are exposed to the attacker. This paper addresses all privacy con cerns by providing an edge-based solution for modeling methane emis sions from rice paddies that leverages data from edge sensors at respec tive locations\, while keeping individual sensor data private. The method employs different machine learning (ML) algorithms\, including Linear Regression\, Random Forest\, XGBoost\, and a Feedforward Neural Net work (FNN)\, implemented using TensorFlow Federated (TFF) in both centralized and federated learning (FL) frameworks. The FL-based FNN achieved an R2 score of 0.91\, which was superior to both centralized classical and centralized FL models\, especially for highly non-IID client side data distributions in sensor datasets. In summary\, this paper extends the current literature on modeling methane emissions from rice paddies and provides a comprehensive evaluation of our proposed FL system ar chitecture\, an in-depth discussion of the communication resources re quired for FL implementation\, and an examination of the effects of abla tion studies on clients’ data heterogeneity. Therefore\, the proposed FL approach is efficient and scalable\, enabling safe\, privacy-preserving modeling of methane emissions from rice paddies to effectively imple ment Climate Smart Agriculture (CSA) and mitigate global warming while supporting sustainable rice cultivation.
CATEGORIES:VIRTUAL ROOM 3G
LOCATION:Virtual Room G\, Bangkok\, Thailand
SEQUENCE:0
UID:7bf7890232eefa40d68908bb4d10e802
URL:http://11thictisthailand.sched.com/event/7bf7890232eefa40d68908bb4d10e802
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260417T125213Z
DTSTART:20260409T080000Z
DTEND:20260409T100000Z
SUMMARY:Secure Knowledge Based Financial Assistant Using RAG
DESCRIPTION:\nAuthors -&nbsp\;P. Pandiaraja\, P.Krishna Kishore\, E. Ganesh\, C. Selvarathi\, Charles Prabu V\, S. JaganAbstract -&nbsp\; Large Language Models have facilitated the development of sophist i-cated smart platforms that are actively leveraged in the provision of financialservices to various classes of customers. This advancement has enabled peopleto obtain individual financial advice. This paper presents a framework for buil d-ing a financial chatbot that incorporates Retrieval Augmented Generation(RAG) technology and several SQL agents to improve reliability. The proposedapproach addresses five fundamental challenges in financial artificial inte ll igence: eradicating hallucinations\, obtaining up to date information\, utilising u s-er facts to tailor individual suggestions\, safeguarding user privacy\, and provi d-ing clear explanations. RAG is used to retrieve verified financial knowledge\,while SQL agen ts query databases to produce accurate outputs. The solutionprovides advisory responses that are relevant to users and protect sensitive i n-formation through a zero trust security architecture. The system architecture i n-corporates multiple validation check points and is dynamically configured tomeet individual user requirements. Experimental results demonstrate a 96.2%accuracy rate in handling financial queries with a 3.8% error rate and a mean r e-sponse time of 1.5 seconds\, outperforming comparable solutio ns. The proposedarchitecture establishes a reliable baseline for financial professionals seekingdependable advisory services.
CATEGORIES:VIRTUAL ROOM 3G
LOCATION:Virtual Room G\, Bangkok\, Thailand
SEQUENCE:0
UID:9928c08b26353db283ca44cd8b786755
URL:http://11thictisthailand.sched.com/event/9928c08b26353db283ca44cd8b786755
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260417T125213Z
DTSTART:20260409T100000Z
DTEND:20260409T100200Z
SUMMARY:Session Chair Concluding Remarks
DESCRIPTION:\n
CATEGORIES:VIRTUAL ROOM 3A
LOCATION:Virtual Room A\, Bangkok\, Thailand
SEQUENCE:0
UID:f4847a4d42b137edb0cf7c0431a42d1c
URL:http://11thictisthailand.sched.com/event/f4847a4d42b137edb0cf7c0431a42d1c
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260417T125213Z
DTSTART:20260409T100000Z
DTEND:20260409T100200Z
SUMMARY:Session Chair Concluding Remarks
DESCRIPTION:\n
CATEGORIES:VIRTUAL ROOM 3B
LOCATION:Virtual Room B\, Bangkok\, Thailand
SEQUENCE:0
UID:1f6481c423759b36c40f3e12de426f6e
URL:http://11thictisthailand.sched.com/event/1f6481c423759b36c40f3e12de426f6e
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260417T125213Z
DTSTART:20260409T100000Z
DTEND:20260409T100200Z
SUMMARY:Session Chair Concluding Remarks
DESCRIPTION:\n
CATEGORIES:VIRTUAL ROOM 3C
LOCATION:Virtual Room C\, Bangkok\, Thailand
SEQUENCE:0
UID:38f66c01cca8b1ce47f5fd56f861c651
URL:http://11thictisthailand.sched.com/event/38f66c01cca8b1ce47f5fd56f861c651
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260417T125213Z
DTSTART:20260409T100000Z
DTEND:20260409T100200Z
SUMMARY:Session Chair Concluding Remarks
DESCRIPTION:\n
CATEGORIES:VIRTUAL ROOM 3D
LOCATION:Virtual Room D\, Bangkok\, Thailand
SEQUENCE:0
UID:45dd8896824154031ab5cec9e160bfc8
URL:http://11thictisthailand.sched.com/event/45dd8896824154031ab5cec9e160bfc8
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260417T125213Z
DTSTART:20260409T100000Z
DTEND:20260409T100200Z
SUMMARY:Session Chair Concluding Remarks
DESCRIPTION:\n
CATEGORIES:VIRTUAL ROOM 3E
LOCATION:Virtual Room E\, Bangkok\, Thailand
SEQUENCE:0
UID:2112b966840f4efa33732e8cb084baa0
URL:http://11thictisthailand.sched.com/event/2112b966840f4efa33732e8cb084baa0
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260417T125213Z
DTSTART:20260409T100000Z
DTEND:20260409T100200Z
SUMMARY:Session Chair Concluding Remarks
DESCRIPTION:\n
CATEGORIES:VIRTUAL ROOM 3F
LOCATION:Virtual Room F\, Bangkok\, Thailand
SEQUENCE:0
UID:b9ef40c81563e4ccfbdcde35b8193b4a
URL:http://11thictisthailand.sched.com/event/b9ef40c81563e4ccfbdcde35b8193b4a
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260417T125213Z
DTSTART:20260409T100000Z
DTEND:20260409T100200Z
SUMMARY:Session Chair Concluding Remarks
DESCRIPTION:\n
CATEGORIES:VIRTUAL ROOM 3G
LOCATION:Virtual Room G\, Bangkok\, Thailand
SEQUENCE:0
UID:3c79742b83c28a5bec5315579eb3cae6
URL:http://11thictisthailand.sched.com/event/3c79742b83c28a5bec5315579eb3cae6
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260417T125213Z
DTSTART:20260409T100200Z
DTEND:20260409T100500Z
SUMMARY:Session Closing and Information To Authors
DESCRIPTION:\n
CATEGORIES:VIRTUAL ROOM 3A
LOCATION:Virtual Room A\, Bangkok\, Thailand
SEQUENCE:0
UID:e95344b2a4d9703b9c33dffeae351ee2
URL:http://11thictisthailand.sched.com/event/e95344b2a4d9703b9c33dffeae351ee2
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260417T125213Z
DTSTART:20260409T100200Z
DTEND:20260409T100500Z
SUMMARY:Session Closing and Information To Authors
DESCRIPTION:\n
CATEGORIES:VIRTUAL ROOM 3B
LOCATION:Virtual Room B\, Bangkok\, Thailand
SEQUENCE:0
UID:68469385530991560e6d97e6780aa2ad
URL:http://11thictisthailand.sched.com/event/68469385530991560e6d97e6780aa2ad
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260417T125213Z
DTSTART:20260409T100200Z
DTEND:20260409T100500Z
SUMMARY:Session Closing and Information To Authors
DESCRIPTION:\n
CATEGORIES:VIRTUAL ROOM 3C
LOCATION:Virtual Room C\, Bangkok\, Thailand
SEQUENCE:0
UID:c723d7ab73bbd49be1575b434c7644c8
URL:http://11thictisthailand.sched.com/event/c723d7ab73bbd49be1575b434c7644c8
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260417T125213Z
DTSTART:20260409T100200Z
DTEND:20260409T100500Z
SUMMARY:Session Closing and Information To Authors
DESCRIPTION:\n
CATEGORIES:VIRTUAL ROOM 3D
LOCATION:Virtual Room D\, Bangkok\, Thailand
SEQUENCE:0
UID:ed18981c266c6d8059402ad7edb8569a
URL:http://11thictisthailand.sched.com/event/ed18981c266c6d8059402ad7edb8569a
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260417T125213Z
DTSTART:20260409T100200Z
DTEND:20260409T100500Z
SUMMARY:Session Closing and Information To Authors
DESCRIPTION:\n
CATEGORIES:VIRTUAL ROOM 3E
LOCATION:Virtual Room E\, Bangkok\, Thailand
SEQUENCE:0
UID:e93a5a67fbe0fcc52f11f196e6af51e3
URL:http://11thictisthailand.sched.com/event/e93a5a67fbe0fcc52f11f196e6af51e3
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260417T125213Z
DTSTART:20260409T100200Z
DTEND:20260409T100500Z
SUMMARY:Session Closing and Information To Authors
DESCRIPTION:\n
CATEGORIES:VIRTUAL ROOM 3F
LOCATION:Virtual Room F\, Bangkok\, Thailand
SEQUENCE:0
UID:8a335b6f0b27debfb5758121825afff9
URL:http://11thictisthailand.sched.com/event/8a335b6f0b27debfb5758121825afff9
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260417T125213Z
DTSTART:20260409T100200Z
DTEND:20260409T100500Z
SUMMARY:Session Closing and Information To Authors
DESCRIPTION:\n
CATEGORIES:VIRTUAL ROOM 3G
LOCATION:Virtual Room G\, Bangkok\, Thailand
SEQUENCE:0
UID:a507181a3cc94118900c66de42d5b4d6
URL:http://11thictisthailand.sched.com/event/a507181a3cc94118900c66de42d5b4d6
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260417T125213Z
DTSTART:20260410T013000Z
DTEND:20260410T023000Z
SUMMARY:Registration with Networking Tea / Coffee & Cookies
DESCRIPTION:
CATEGORIES:INAUGURAL SESSION
LOCATION:Benchasiri 1\, Bangkok Marriott Hotel Sukhumvit\, Thailand
SEQUENCE:0
UID:763c18f68691bddd80ee738aa01ce436
URL:http://11thictisthailand.sched.com/event/763c18f68691bddd80ee738aa01ce436
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260417T125213Z
DTSTART:20260410T022800Z
DTEND:20260410T023000Z
SUMMARY:Opening Remarks
DESCRIPTION:\n
CATEGORIES:VIRTUAL ROOM 7A
LOCATION:Virtual Room A\, Bangkok\, Thailand
SEQUENCE:0
UID:bc03f558a9d30d26698a9ca0a73ae2c7
URL:http://11thictisthailand.sched.com/event/bc03f558a9d30d26698a9ca0a73ae2c7
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260417T125213Z
DTSTART:20260410T022800Z
DTEND:20260410T023000Z
SUMMARY:Opening Remarks
DESCRIPTION:\n
CATEGORIES:VIRTUAL ROOM 7B
LOCATION:Virtual Room B\, Bangkok\, Thailand
SEQUENCE:0
UID:16f08db892e43334822863772ca560f1
URL:http://11thictisthailand.sched.com/event/16f08db892e43334822863772ca560f1
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260417T125213Z
DTSTART:20260410T022800Z
DTEND:20260410T023000Z
SUMMARY:Opening Remarks
DESCRIPTION:\n
CATEGORIES:VIRTUAL ROOM 7C
LOCATION:Virtual Room C\, Bangkok\, Thailand
SEQUENCE:0
UID:0dd47fa11a9f1f0e5c4d264b58839a07
URL:http://11thictisthailand.sched.com/event/0dd47fa11a9f1f0e5c4d264b58839a07
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260417T125213Z
DTSTART:20260410T022800Z
DTEND:20260410T023000Z
SUMMARY:Opening Remarks
DESCRIPTION:\n
CATEGORIES:VIRTUAL ROOM 7D
LOCATION:Virtual Room D\, Bangkok\, Thailand
SEQUENCE:0
UID:30dc822fd0906e8e34fc74338a6bdc51
URL:http://11thictisthailand.sched.com/event/30dc822fd0906e8e34fc74338a6bdc51
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260417T125213Z
DTSTART:20260410T022800Z
DTEND:20260410T023000Z
SUMMARY:Opening Remarks
DESCRIPTION:\n
CATEGORIES:VIRTUAL ROOM 7E
LOCATION:Virtual Room E\, Bangkok\, Thailand
SEQUENCE:0
UID:458c43f727c75a1751e7d3d8a4da10ef
URL:http://11thictisthailand.sched.com/event/458c43f727c75a1751e7d3d8a4da10ef
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260417T125213Z
DTSTART:20260410T022800Z
DTEND:20260410T023000Z
SUMMARY:Opening Remarks
DESCRIPTION:\n
CATEGORIES:VIRTUAL ROOM 7F
LOCATION:Virtual Room F\, Bangkok\, Thailand
SEQUENCE:0
UID:fe0caae92762936b8765ded2d36b1f97
URL:http://11thictisthailand.sched.com/event/fe0caae92762936b8765ded2d36b1f97
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260417T125213Z
DTSTART:20260410T022800Z
DTEND:20260410T023000Z
SUMMARY:Opening Remarks
DESCRIPTION:\n
CATEGORIES:VIRTUAL ROOM 7G
LOCATION:Virtual Room G\, Bangkok\, Thailand
SEQUENCE:0
UID:47cc7c3c00f234f7b34edc8b6c1a0342
URL:http://11thictisthailand.sched.com/event/47cc7c3c00f234f7b34edc8b6c1a0342
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260417T125213Z
DTSTART:20260410T023000Z
DTEND:20260410T024000Z
SUMMARY:Welcome Remarks By
DESCRIPTION:
CATEGORIES:INAUGURAL SESSION
LOCATION:Benchasiri 1\, Bangkok Marriott Hotel Sukhumvit\, Thailand
SEQUENCE:0
UID:ff40d16584f14ba7af596d518b1ac9ac
URL:http://11thictisthailand.sched.com/event/ff40d16584f14ba7af596d518b1ac9ac
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260417T125213Z
DTSTART:20260410T023000Z
DTEND:20260410T043000Z
SUMMARY:A Design and Study of a DTMF Technology Enabled Water Surface Cleaning Robot
DESCRIPTION:Authors - Senthilkumar Selvaraj\, Suresh kumar Chiluka\, Swetha D Abstract - This paper presents the design and construction of a robot that cleans rivers. The robot is designed to be used in situations when it is necessary to remove floating rubbish from bodies of water. Conventional waste-collection techniques\, like trash skimmers\, boats\, and hand cleaning\, are usually used close to the edges of rivers\, lakes\, or ponds. These methods are frequently dangerous\, time-consuming\, and inconvenient. A water-surface cleaning robot has been created to overcome these constraints and remove trash more effectively\, securely\, and easily. Using commands sent from a cell phone\, the robot moves in different directions while operating on the water's surface. When a call is placed to the phone that is attached to the robot's DTMF decoder\, the controller processes the tone signals it receives and then adjusts the motors. A filter mechanism installed on roller belts is used to catch floating material. Waste particles are lifted and collected by the filter setup as the chain assembly moves in response to the motor's rotation. After that\, the gathered material is placed in a special storage tank\, allowing the water's surface to be continuously and successfully cleaned.
CATEGORIES:VIRTUAL ROOM 7A
LOCATION:Virtual Room A\, Bangkok\, Thailand
SEQUENCE:0
UID:687701df70fd1b6e9ab877b9898a3672
URL:http://11thictisthailand.sched.com/event/687701df70fd1b6e9ab877b9898a3672
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260417T125213Z
DTSTART:20260410T023000Z
DTEND:20260410T043000Z
SUMMARY:A Lightweight Zero-Trust Security Framework for IoT Systems Using ECC Authentication\, Trust Scoring\, and Machine Learning–Based Attack Detection
DESCRIPTION:Authors - Reena Pal\, Premal Patel Abstract - The quantum computing potential to transform the conventional public key cryptosystems\, specifically when they are being implemented on the structure of a cloud\, and are operating on sensitive data is especially problematic. In the current paper\, we introduce quantumresistant\, fully homomorphic encryption (FHE) new homomorphic encryption scheme\, to offer secure and scalable cloud data encryption. We solve Learning With Errors (LWE) problems and Ring-LWE problems and include dynamic key management and re encryption protocols to improve better security of multi-users [14]. The results of our Largescale simulations on a cloud testbed show that our design actually has the desired throughput and resource efficiency under horizontal scaling [10] although 60-70% addition of additional latency is noticeable as compared to non-BFT systems. Key to Practicality The paper will offer the much-needed trade-offs of high performance and high security assurances [12].
CATEGORIES:VIRTUAL ROOM 7A
LOCATION:Virtual Room A\, Bangkok\, Thailand
SEQUENCE:0
UID:6a06f64b2ed0c91cfc31d5a13a73bfcb
URL:http://11thictisthailand.sched.com/event/6a06f64b2ed0c91cfc31d5a13a73bfcb
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260417T125213Z
DTSTART:20260410T023000Z
DTEND:20260410T043000Z
SUMMARY:A SIFT-Based Classification Method for Traditional Japanese Stencil Images
DESCRIPTION:Authors - Yuuki Ario\, Yuyu Araki\, Hiroshi Sakamoto Abstract - Crowd analysis has become a critical component of modern urban and smart surveillance systems\, where effective monitoring of densely populated public areas is essential for resource management\, emergency response\, and public safety. YOLO-based models are popular for detecting a person or an object. In this study\, we present a comprehensive objective evaluation analysis of state-of-the-art object detection architectures—YOLOv5\, YOLOv8\, and YOLOv11. We have implemented YOLO models for detecting groups of people as integrated entities to enable crowd classification based on group size\, including individuals\, small groups\, and large crowds. The evaluation was con-ducted using four diverse benchmark datasets: VSCrowd\, Crowd Mall\, Crowd11\, and NWPU-Crowd\, with all images annotated using LabelImg. Each model was rigorously trained and tested under consistent conditions. Experimental results reveal that on the VSCrowd dataset\, YOLOv5s achieved an mAP@0.5 of 0.454\, while YOLOv5l slightly improved this to 0.459. YOLOv8m demonstrated high performance with an mAP@0.5 of 0.530. On Crowd Dataset\, YOLOv5m achieved an mAP@0.5 of 0.300\, YOLOv8m obtained 0.306\, and YOLOv11m achieved 0.302. These results indicate that newer YOLO architectures provide enhanced detection capabilities in highly crowded scenes\, exhibiting better generalization\, robustness\, and adaptability for real-world crowd analysis applications.
CATEGORIES:VIRTUAL ROOM 7A
LOCATION:Virtual Room A\, Bangkok\, Thailand
SEQUENCE:0
UID:745bd6d2e7abbcc94f76b4ca2da12b3b
URL:http://11thictisthailand.sched.com/event/745bd6d2e7abbcc94f76b4ca2da12b3b
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260417T125213Z
DTSTART:20260410T023000Z
DTEND:20260410T043000Z
SUMMARY:Comparative Overview of Deep Learning Architectures for Disease Detection in Medicinal Plants
DESCRIPTION:Authors - Sakthi Saranya.S\, W.Rose Varuna Abstract - Agriculture is one of the most important industries that provides for human basic need. To identify medicinal plant diseases using traditional methods\, it will take long time. Medicinal Plants such as\, Tulsi\, Aloe vera\, Mint and Ashwagandha play a crucial role in both ancient and modern systems. These plant images were taken into this work. Early identification of diseases in these plants is most important to maintain their medicinal benefit as well as economic value. This study investigates and compares five deep learning architectures\, namely ResNet50\, DenseNet121\, EfficientNet-B0\, InceptionV3 and CNN-for classifying leaf diseases in medicinal plants. Moreover\, several performance metrics are used for the evaluation of these architectures. This work mainly focuses on determining the most suitable deep learning model for detecting the diseases in medicinal plants.
CATEGORIES:VIRTUAL ROOM 7A
LOCATION:Virtual Room A\, Bangkok\, Thailand
SEQUENCE:0
UID:fc110699d1481a806b012f5cf0498bda
URL:http://11thictisthailand.sched.com/event/fc110699d1481a806b012f5cf0498bda
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260417T125213Z
DTSTART:20260410T023000Z
DTEND:20260410T043000Z
SUMMARY:Evaluating Explanation Consistency of Explainable Machine Learning Models for Heart Disease Risk Prediction
DESCRIPTION:Authors - Kari Sai Vardhan\, Maram Ramakrishna Reddy\, Kore Akhil\, Modugula Pavan Kumar Reddy\, Aaskaran Bishnoi Abstract - This study evaluates the effectiveness of student-led mobile-first web design implementation for Small and Medium-sized Enterprises (SMEs) using the Bootstrap framework. By applying a project-based information system development model\, this study analyzes the technical performance of websites\, particularly in terms of layout responsiveness and system metrics such as PageSpeed. A mixed-methods approach was used to collect quantitative data from technical evaluations and qualitative data from student and client feedback. The results indicate that these student-led projects successfully produced highly responsive and high performing websites\, significantly enhancing the digital presence of SMEs. The findings underscore the pedagogical efficacy of project-based learning in equipping students with industry-relevant competencies and practical skills in information system development\, while simultaneously sup-porting the digital transformation of SMEs.
CATEGORIES:VIRTUAL ROOM 7A
LOCATION:Virtual Room A\, Bangkok\, Thailand
SEQUENCE:0
UID:b155ae73a2a4cac77954ddea5c0594f0
URL:http://11thictisthailand.sched.com/event/b155ae73a2a4cac77954ddea5c0594f0
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260417T125213Z
DTSTART:20260410T023000Z
DTEND:20260410T043000Z
SUMMARY:Hybrid AI Framework for Smart Energy Grids: DRL-Based Control with Solar Fault Detection
DESCRIPTION:Authors - Kalyani Ghuge\, Dhruv Battawar\, Om Bhoye\, Suhani Buche\, Adithiya Anantharaman\, Anvay Bavdhankar\n Abstract - For the integration of solar systems within the power grid\, there is the requirement for smarter systems that are capable of not only detecting faults but also optimizing their performance. The current paper introduces an innovative hybrid method that focuses on the detection of solar thermal faults and adaptive grid control\, where the challenge had existed in the separation of the two aspects. This is achieved through the use of a deep learning U-Net model\, where different kinds of solar panel fault types\, such as single and multi hotspots\, are detected from grayscale thermal images. The different kinds of fault types identified are used as a reinforcement learning approach (PPO)\, where decisions regarding safe and efficient use of the grid are made while considering fault awareness. Higher priority is granted to critical fault types through rewards that use penalties. It also comes with an immediate safety function to isolate faulty panels with zero delay for smooth and efficient function of the solar energy grid.
CATEGORIES:VIRTUAL ROOM 7A
LOCATION:Virtual Room A\, Bangkok\, Thailand
SEQUENCE:0
UID:2521e285babb7c346fc4dbd23a78b951
URL:http://11thictisthailand.sched.com/event/2521e285babb7c346fc4dbd23a78b951
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260417T125213Z
DTSTART:20260410T023000Z
DTEND:20260410T043000Z
SUMMARY:SELF-HEALING REAL-TIME OPERATING SYSTEMS USING REINFORCEMENT LEARNING-BASED RECOVERY POLICIES
DESCRIPTION:Authors - Azad Mohammed Shaik\n Abstract -&nbsp\;A real-time operating system (RTOS) should be able to recover from interruptions. Since RTOS systems are used in safety-critical environments\, this function is essential for ensuring system availability and reliability. However\, while many of the current anomaly detection techniques can detect faults\, they do not provide any means for recovery. Therefore\, in this paper\, I propose a self-repairing RTOS framework that utilizes reinforcement learning (RL) to automatically select the best course of action to take when an anomalous event arises. I propose a Q-Learning agent that learns to recover from six types of common faults\, including: sensor degradation\, stuck sensor\, priority inversion\, memory leaks\, sporadic overloads\, and task starvation. The framework is built on FreeRTOS\, and the agent utilizes an 8-dimensional state space and the six different types of recovery options available for each fault. The overall success rate of the system was 99.2 % after 5\,000 training episodes\, with average success rates of 98.0 % and 99.9 % when handling individual faults. The RL agent completely prevented system crashes and returned the system to normal operation within an average of 0.06 ms after an interruption occurred. The training results provide strong evidence that the model learned to operate effectively and consistently\, with its success rate improving from 97.0 % during early training stages to 100 % after training was completed. Therefore\, this study demonstrates a practical\, production-ready method to implement autonomous fault recoveries in RTOSs in automotive applications. To our knowledge\, this is the first successful implementation of RL for autonomous\, self-repairing behaviors in this area.
CATEGORIES:VIRTUAL ROOM 7A
LOCATION:Virtual Room A\, Bangkok\, Thailand
SEQUENCE:0
UID:807801fc3b18487a95e94c01bde62488
URL:http://11thictisthailand.sched.com/event/807801fc3b18487a95e94c01bde62488
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260417T125213Z
DTSTART:20260410T023000Z
DTEND:20260410T043000Z
SUMMARY:The EPIC-E Framework: A Multi-Dimensional Model for Evaluating the Effectiveness of Dynamic Infographics in Digital News Visualization
DESCRIPTION:Authors - Muchlis Almubaraq\, Mohd Norasri Ismail\, Norhalina Senan\, Larisang\, Mutiara Ayu Mawaddah Abstract - Conventional recipe formats interrupt cooking workflows by requiring repeated attention shifts to external devices. This paper presents Beyond the Cookbook\, a Mixed Reality (MR) cooking assistant developed for Meta Quest headsets. The system delivers spatially anchored\, context-aware instructions using persistent holographic overlays\, synchronized narration\, and multimodal interaction including voice commands\, controller input\, and hand-tracking gestures. By integrating passthrough MR and spatial mapping\, the assistant enables hands-free and hygienic guidance directly within the user’s kitchen environment. A usability study with twenty-one participants demonstrates high interaction reliability\, instructional clarity\, and user confidence. The results validate the feasibility of MR-based procedural learning support in domestic settings.
CATEGORIES:VIRTUAL ROOM 7A
LOCATION:Virtual Room A\, Bangkok\, Thailand
SEQUENCE:0
UID:197186b2dd0223ce693842fab99fdef7
URL:http://11thictisthailand.sched.com/event/197186b2dd0223ce693842fab99fdef7
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260417T125213Z
DTSTART:20260410T023000Z
DTEND:20260410T043000Z
SUMMARY:Usability and Accessibility on the Website of the Inclusion\, Social Equity and Gender Unit of the Technical University of Manabí
DESCRIPTION:Authors - Maricela Pinargote-Ortega\, Marely del Rosario Cruz Felipe\, Carlos Manuel Lucas Aragundi\, Iter Alexander Posligua Solorzano Abstract - Open data is often associated with objectives linked to fostering innovation and economic growth\, political accountability and democratic participation\, and public sector efficiency. However\, data privacy has been frequently cited as a challenge for open data publication and processing. This paper uses a 9780-row dataset from the 2025 community engagement survey of the Philippine National Police Regional Office 5 to synthesize a privacy-preserving dataset using natural language processing and the Laplace mechanism with a total Privacy Loss Budget (PLB) value of 1. The text dataset fields with the highest privacy risk were replaced with generated topic models and corresponding overall sentiment values. The dataset fields were then categorized into four blocks\, grouping variables that require correlations to be preserved. Noise was added to the four blocks using the Laplace mechanism\, generating a privacy-preserved synthetic query robust to de-anonymization attacks. The synthesized dataset shows minimal distortion from the original dataset\, with mean shifts of less than 0.25\, and preserving key variable correlations\, while significantly increasing data subject privacy. End-user validation confirmed that the synthetic dataset is suitable for both data sharing and joint processing without sacrificing the accuracy of analysis results. This study demonstrated that a differentially private synthetic data generation pipeline combining natural language processing and the Laplace mechanism (ε = 1) can substantially enhance data subject privacy while preserving the analytical utility of a real-world public sector survey dataset.
CATEGORIES:VIRTUAL ROOM 7A
LOCATION:Virtual Room A\, Bangkok\, Thailand
SEQUENCE:0
UID:3280da0f406196b81ece5156e3cb9252
URL:http://11thictisthailand.sched.com/event/3280da0f406196b81ece5156e3cb9252
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260417T125213Z
DTSTART:20260410T023000Z
DTEND:20260410T043000Z
SUMMARY:WaveTrust: Trust-Based Reinforced Routing Protocol against Malicious Node Influence in Underwater Sensor Environments
DESCRIPTION:Authors - Sona Ravindran\, K Nattar Kannan Abstract - This research examines the transfer learning deep learning models in multimodal human activity recognition based on wearable sensor data. Raw IMU signals are converted to Gramian Angular Field (GAF) images to improve the feature representation and tested on WISDM and PAMAP2 datasets of 18 activity classes. Five CNN models\, namely VGG16\, MobileNetV2\, ResNet50\, DenseNet121\, and EfficientNetB0\, are trained and evaluated in the same conditions and measured by classification accuracy\, statistical significance\, and computation efficiency. GAF representations are always better than raw signals. DenseNet121 and ResNet50 have 99% accuracy\, VGG16 and MobileNetV2 perform competitively and EfficientNetB0 performs worse. Most of the differences in performance are statistically significant (p < 0.05).
CATEGORIES:VIRTUAL ROOM 7A
LOCATION:Virtual Room A\, Bangkok\, Thailand
SEQUENCE:0
UID:2a63c3e7c7c074962907b58ccbea22f1
URL:http://11thictisthailand.sched.com/event/2a63c3e7c7c074962907b58ccbea22f1
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260417T125213Z
DTSTART:20260410T023000Z
DTEND:20260410T043000Z
SUMMARY:A Multi-Layer Federated Trust Framework for Comprehensive Security in Social Media Networks
DESCRIPTION:Authors - Aiswarya Rajan K K\, K Nattar Kannan Abstract - This study presents a systematic literature review on the emergence\, adoption\, and challenges of AI-driven Human Resource Management (AI-HRM). Thematic synthesis and bibliometric insights were used to analyze eighteen Scopus-indexed studies published between 2019 and 2024 using the PRISMA framework. Using the Technology Acceptance Model (TAM/UTAUT)\, Socio-Technical Systems (STS) Theory\, and Responsible AI principles\, the review shows how AI improves HRM by automating repetitive tasks\, facilitating data-driven decision-making\, and allowing for individualized employee development. However\, ethical risks like algorithmic bias\, lack of transparency\, privacy issues\, and employee resistance continue to be major obstacles. The results imply that only when technological capabilities are in line with human judgment\, organizational culture\, and ethical governance can AI pro-vide long-term value in HRM.
CATEGORIES:VIRTUAL ROOM 7B
LOCATION:Virtual Room B\, Bangkok\, Thailand
SEQUENCE:0
UID:c0d8ec53d62b27e25dbdf9a977e8ebb1
URL:http://11thictisthailand.sched.com/event/c0d8ec53d62b27e25dbdf9a977e8ebb1
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260417T125213Z
DTSTART:20260410T023000Z
DTEND:20260410T043000Z
SUMMARY:AI-Enhanced Smart Monitoring and Recommendation Framework for Groundnut plant Disease Management
DESCRIPTION:Authors - Padma Lakshmi G\, Swetha V\, Monik Raj Murugan S\, Srinivasa Perumal R\, Lakshmi Priya G G Abstract - We have proposed ”Haze to vision: Pipeline for Underwater Image Restoration\, Enhancement and Object detection”.The images captured underwater suffer from bluish tint\,greenish tint\,haze\,color distortion. As light travels in water it will undergo scattering\, refraction and absorption\, the higher the wavelength will be observed first\, and the lower wavelength will be absorbed later. This phenomenon affects the bluish/greenish color in the captured images . To study underwater species\, underwater environments\, we need good quality images and videos. The images captured underwater are poor quality. There have been several researches yet they have many drawacks.We have proposed pipeline.Our model consists of restoration\,enhancement\,object detection. Restoration process built from deep convolutional neural network called autoencoder .Which has been trained by 5000 synthetic images. The second model is the self-supervised enhancement model. The selfsupervised model is trained for 10\,000 epochs of 5\,000 datasets.We have used the customized gan model to obtain the best results.We have also used transfer learning and residual network for the improvement of the model.We have reached the PSNR value of 38.33 . CIQUE value 0.82 and UIQM 0.5.Our third model is object detection model. We have used the latest version of YOLOv5 for the betterment and the best object detection model.
CATEGORIES:VIRTUAL ROOM 7B
LOCATION:Virtual Room B\, Bangkok\, Thailand
SEQUENCE:0
UID:59d38e7f108de63dcdc27ce419416728
URL:http://11thictisthailand.sched.com/event/59d38e7f108de63dcdc27ce419416728
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260417T125213Z
DTSTART:20260410T023000Z
DTEND:20260410T043000Z
SUMMARY:An Exclusive-Embedding Cluster-Driven Lightweight Synonym Replacement Paraphrasing Model
DESCRIPTION:Authors - Kishore S\, Jeganathan L\, Janaki Meena M\, Ummity Srinivasa Rao\, Jayaram Balabaskaran Abstract - Finding movies from an enormous number of movies that fit our interests and preferences becomes a challenging endeavor. Because recommendation systems address information overload by recommending the most appropriate products to users\, they have become widely used in today’s world. The majority of recommendation systems disregard the constraints of the user such as not suggesting certain exceptional movies to them because they aren’t as popular as others. Furthermore\, the lack of transparency about how these recommendation algorithms operate creates concerns regarding accountability. In this work\, we propose an improved ALS-based recommendation framework that is implemented on Apache Spark and uses HDFS for processing and storing data. In order to address the long tail bias problem\, we utilize the ALSbased framework that enhances exposure to low-frequency items through strong interaction filtering. This study employs SHAP to improve transparency and facilitate fairness analysis by explaining the elements generating recommendations to overcome this limitation. Root Mean Square Error (RMSE) and Top-K long-tail exposure metrics are used to assess the model’s performance on a large movie interaction dataset.
CATEGORIES:VIRTUAL ROOM 7B
LOCATION:Virtual Room B\, Bangkok\, Thailand
SEQUENCE:0
UID:8be6b373e6eb52661c6ac1a0acc64ca9
URL:http://11thictisthailand.sched.com/event/8be6b373e6eb52661c6ac1a0acc64ca9
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260417T125213Z
DTSTART:20260410T023000Z
DTEND:20260410T043000Z
SUMMARY:Analysis of Transformer Based Models for Answer Identification in small sized Dataset
DESCRIPTION:Authors - Pradnya Gotmare\, Aryan Halkude\, Manish Potey Abstract - The high pace of the data-driven applications growth in the distributed settings has enhanced the pressure to ensure that the data sharing infrastructure remains secure\, efficient\, and privacy-sensitive. The classic centralized data sharing architectures have the intrinsic limitations of being single-point-of-failure\, untransparent\, and unauthorized access to data\, and prone to data corruption. To curb these hurdles\, this paper proposes a decentralized approach of sharing secure data with the use of blockchain technology. The suggested system also uses the decentralized and unalterable features of blockchain to provide data integrity\, transparency\, and confidence among the involved parties without involving third-party intermediaries. Access control policies are the policies implemented using smart contracts to allow only trusted users to access the shared data. The solution is to keep sensitive information in off-chain repositories\, where blockchain limitations of storage and scalability do not exist\, yet cryptographic hash values and access control measures (ACMs) are stored in the blockchain registry. This design makes sure that the data transactions are confidential and data verifiability and auditability maintained.
CATEGORIES:VIRTUAL ROOM 7B
LOCATION:Virtual Room B\, Bangkok\, Thailand
SEQUENCE:0
UID:4e865bff0cdc85b072c3bd65d67e3a83
URL:http://11thictisthailand.sched.com/event/4e865bff0cdc85b072c3bd65d67e3a83
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260417T125213Z
DTSTART:20260410T023000Z
DTEND:20260410T043000Z
SUMMARY:Bridging Accessibility Gaps in Higher Education: A Multi-Stakeholder Validated Framework for Academic Website Design
DESCRIPTION:Authors - Mutiara Ayu Mawaddah\, Norhalina Senan\, Mohd Norasri Ismail\, Larisang\, Muchlis Almubaraq Abstract - With the growing use of smart meters\, massive amounts of electricity consumption data are being generated every day. Managing and analyzing this data efficiently is a big challenge. In this study\, we generated a smart meter dataset of 10 million records\, adding realistic anomalies such as missing values\, noise\, and unusual spikes to reflect real-world conditions. The data was stored in Hadoop Distributed File System (HDFS) on a single-node virtual machine running on Kali Linux for distributed processing . Using Apache PySpark\, we cleaned the data\, filled in missing values\, identified outliers\, and normalized features. For predicting electricity consumption\, we trained a linear regression model which achieved a Root Mean Squared Error (RMSE) of 0.0141 and a R2 score of 0.9891\, showing that the model predicts consumption very accurately. Overall\, this study demonstrates a practical end-to-end approach that combines big data tools and machine learning for smart meter analytics. In the future\, this workflow could be extended to multi-node clusters to improve fault tolerance and handle even larger datasets.
CATEGORIES:VIRTUAL ROOM 7B
LOCATION:Virtual Room B\, Bangkok\, Thailand
SEQUENCE:0
UID:91258bafc9d969ffc2e817f464e16845
URL:http://11thictisthailand.sched.com/event/91258bafc9d969ffc2e817f464e16845
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260417T125213Z
DTSTART:20260410T023000Z
DTEND:20260410T043000Z
SUMMARY:Deep Learning–Based Food Portion Estimation Using Mask R-CNN and Geometric Analysis
DESCRIPTION:Authors - Shilpa Dhopte\, Lalit Damahe\n Abstract - The food portion estimation is a critical component of automated dietary assessment systems\, enabling better monitoring of nutritional intake and supporting healthcare\, weight management\, and public health applications. Traditional self-reporting methods are often inaccurate and time-consuming\, motivating the need for computer vision–based approaches that can reliably estimate food portions from images captured in real-world conditions. This paper presents deep learning pipeline for food portion estimation that integrates image preprocessing\, deep learning–based segmentation\, and geometric volume computation. The data preprocessing with Mask R-CNN used for precise food seg-mentation\, providing pixel-level masks and bounding boxes that isolate individual food items from complex backgrounds. The segmented mask is used to estimate the pixel area of the food region. Experimental evaluation demonstrates that the proposed method achieves high segmentation accuracy\, with a segmentation IoU of 87.6%\, precision of 90.3%\, recall of 88.9%\, and an F1-score of 89.6%. The pixel area estimation error is limited to 6.8%\, resulting in an overall portion estimation accuracy of 89.1%\, indicating reliable and consistent performance across different food images. The proposed framework highlights the effectiveness of combining deep instance segmentation with geometric volume estimation for accurate food portion assessment. Future work will focus on multi-view image integration and real-time deployment in mobile dietary monitoring systems to enhance robustness and scalability.
CATEGORIES:VIRTUAL ROOM 7B
LOCATION:Virtual Room B\, Bangkok\, Thailand
SEQUENCE:0
UID:f8acdc59fe9eb63703d036facf63130e
URL:http://11thictisthailand.sched.com/event/f8acdc59fe9eb63703d036facf63130e
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260417T125213Z
DTSTART:20260410T023000Z
DTEND:20260410T043000Z
SUMMARY:Early Warning of Frequency Fluctuations in Time Series Data
DESCRIPTION:Authors - Md. Shahidul Islam\, Md. Murad Hossain\, Omar Faruck Ansari\n Abstract - Time series prediction plays a critical role in monitoring and control of electrical power systems\, particularly for detecting frequency fluctuations caused by imbalances between generation and demand. This study proposes an early warning framework for frequency fluctuation events using a hybrid k-Nearest Neighbour (KNN) and Dynamic Time Warping (DTW) approach combined with a global confidence interval based decision mechanism. Electricity frequency data collected from the New Zealand power grid over a six-month period were segmented into training\, validation\, and testing sequences. Alignment distances between historical and incoming sequences were used to identify precursor patterns indicative of impending frequency disturbances. Experimental results show that the proposed method achieves high warning accuracy with a very low false negative rate\, outperforming baseline models such as ARIMA and LSTM. The findings demonstrate that KNN–DTW provides an effective and practical solution for early warning of frequency fluctuations\, supporting improved operational reliability in modern power systems.
CATEGORIES:VIRTUAL ROOM 7B
LOCATION:Virtual Room B\, Bangkok\, Thailand
SEQUENCE:0
UID:8aa2cb7ca5069b4cfca7d7fdce6cc71f
URL:http://11thictisthailand.sched.com/event/8aa2cb7ca5069b4cfca7d7fdce6cc71f
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260417T125213Z
DTSTART:20260410T023000Z
DTEND:20260410T043000Z
SUMMARY:Instant Messaging Mobile Application with Quantum-Safe Key Establishment
DESCRIPTION:Authors - Gina Gallegos-Garcıa\, Nidia A. Cortez Duarte\, Jose A. Arellano Munguıa\, Humberto A. Ortega Alcocer\n Abstract - "Communication has been a topic as ancient as man and at the same time so important that\, over time\, various forms have been cre- ated to facilitate it\, among which stand out: mail\, telephony\, telegrams\, and fax\, to name a few. Nowadays many people use instant messaging applications to communicate with each other by feeling that their con- versations are protected. However\, that feeling could not be further from reality and should not be taken lightly\, since there are always groups focused on taking advantage of the vulnerability of this kind of applica- tions\, resulting in users’ privacy being compromised. In this paper\, we present the development of an instant messaging application that inte- grates a novel key establishment protocol based on a quantum-resistant algorithm. Our application employs cutting-edge lattice-based crypto- graphic techniques\, ensuring robust security against quantum attacks while maintaining operational efficiency. Obtained results show the ap- plication’s viability by offering a practical solution to safeguard mobile communication in the impending quantum era."
CATEGORIES:VIRTUAL ROOM 7B
LOCATION:Virtual Room B\, Bangkok\, Thailand
SEQUENCE:0
UID:c5477825976ec5f6e7db06d90664ac4e
URL:http://11thictisthailand.sched.com/event/c5477825976ec5f6e7db06d90664ac4e
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260417T125213Z
DTSTART:20260410T023000Z
DTEND:20260410T043000Z
SUMMARY:Multi‑Modal Satellite Data Fusion for AI‑Based Crop Field Identification
DESCRIPTION:Authors - Soumen Halder\, Subhamoy Bhaduri\, Binayak Mukherjee Abstract - Paraphrasing is significant in applications that require controlled lexical variation to original text with semantic equivalence\, especially in educational assessment systems where student answers should be scored on more than surface level matching. Recent transformer-based paraphrasing models do not exhibit regulated structural changes but instead generate uncontrolled changes\, are costly in terms of computation\, and are not feasible in low-resource or real-time implementations. These limitations are overcome by this work with a lightweight synonymreplacement paraphrasing framework on the basis of exclusive embedding clustering. The proposed EEC-SRP model groups semantically similar words into local embedding clouds and limits the search of synonyms to the tiny areas\, which lowers the complexity of search considerably. An embedding augmentation algorithm involves perturbation to form embedding clusters and a neural network is trained to output contextually favorable synonym embeddings in those clusters. Strict semantic fidelity and controlled lexical substitution is ensured by the model by maintaining word count and sentence structure. Experimental analysis of standard paraphrasing tasks show that the suggested methodology attains high levels of semantic similarity\, competitive levels of BLEU and ROUGE\, and significantly quicker inference than conventional embedding-based and transformer-based models. The proposed model can be effectively implemented in automated assessment systems\, controlled text rewriting and resource-constrained applications of natural language processing due to its low memory footprint and computational efficiency.
CATEGORIES:VIRTUAL ROOM 7B
LOCATION:Virtual Room B\, Bangkok\, Thailand
SEQUENCE:0
UID:730f2f7053df8c5c078346f7cf1983c1
URL:http://11thictisthailand.sched.com/event/730f2f7053df8c5c078346f7cf1983c1
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260417T125213Z
DTSTART:20260410T023000Z
DTEND:20260410T043000Z
SUMMARY:Personalized OER Recommendation Through a Graph-Based Multi-Agent System
DESCRIPTION:Authors - Pablo Ramon\, Josue Piedra\, Nelson Piedra Abstract - The increasing numbers of deepfakes and AI tools have made it difficult to trust digital images these days. Images can be altered and ownership can be established without revealing private information. Current systems have many limitations\, and systems that either rely on easyto change metadata or on cryptographic methods that are too costly like ZKSNARKs. To overcome these limitations\, an authentication verification model has been presented named ZKP-Guard based on a Dual- Lock architecture framework. The detection system verifies an image is a real image by using ECDSA signatures and a custom ownership in the Schnorr-based Zero-Knowledge Proof for the protocol. This framework was tested on a dataset with significant number of images and produced desired results.
CATEGORIES:VIRTUAL ROOM 7B
LOCATION:Virtual Room B\, Bangkok\, Thailand
SEQUENCE:0
UID:e29312c0c106db6601c30aa00a8dc77c
URL:http://11thictisthailand.sched.com/event/e29312c0c106db6601c30aa00a8dc77c
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260417T125213Z
DTSTART:20260410T023000Z
DTEND:20260410T043000Z
SUMMARY:AI-Powered Augmented Reality System for Real-Scale Furniture Visualization and decor guidance
DESCRIPTION:Authors - Swasti Shinde\, Ishita Rajarshi\, Shravani Mote\, Abhilasha Gandhi\, Megha Dhotay Abstract - The use of artificial intelligence (AI)\, especially deep learning\, has brought major changes in the study of histopathology images for cancer diagnosis for the doctors. This review compares the latest deep learning methods used to identify whether the tissues are Benign. (noncancerous) or malignant (cancerous). This paper discusses different technologies used for the study of histopathology images. Convolutional neural network (CNNs) is effective in capturing small local details in the images. Several studies report that Transformers (ViTs) can outperform CNNs in complex classification tasks [18\, 22]. Along with the discussion about newer hybrid models and large foundation models\, the paper specifies the strength of combining the strength of both. Although these models are developed focusing on achieving high accuracy on good data sets\, there are few challenges in their practical use\, such as i. Models often fail to generalize data from different hospitals due to domain shift [1\, 28] ii. Model interpretability remains a significant challenge in clinical development [34]. iii. There is a lack of proper methods to measure the uncertainty in the decisions [40\,41]. This paper highlights the research gaps in real clinical use and focuses on the need to develop models that are robust\, interpretable\, and suitable for practical healthcare applications.
CATEGORIES:VIRTUAL ROOM 7C
LOCATION:Virtual Room C\, Bangkok\, Thailand
SEQUENCE:0
UID:c7907d108eeb7b4312b2fc672800c1fa
URL:http://11thictisthailand.sched.com/event/c7907d108eeb7b4312b2fc672800c1fa
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260417T125213Z
DTSTART:20260410T023000Z
DTEND:20260410T043000Z
SUMMARY:Assessing the Adoption of Online Proctoring Solutions at the National University of Samoa: A Diffusion of Innovation Perspective
DESCRIPTION:Authors - Ioana Chan Mow\, Fiafaitupe Lafaele\, Sarai Faleupolu-Tevita\, Vensel Chan\, Soonalote Eti\, Fiti Tolai\n Abstract - The need to move to online proctored exams urged the National University of Samoa (NUS) to trial and evaluate a variety of online proctoring systems to ensure the offering of safe and secure exams online. The aim of the 4-phase research was to answer the following question: “What are some feasible options for online proctoring systems (OPSs) for offering online exams for NUS?” This paper is based on the last phase of this 4-phase study conducted at NUS to evaluate the feasibility of two proctoring systems\, Integrity Advocate and Proctorio\, for online exams\, particularly during lockdown. Specifically\, the objectives were to i) trial and evaluate the suitability of each OPS as well as the type of exam mode (two options: in the laboratory or from home) using a diffusion of innovation framework and\, from the evaluation\, recommend a suitable OPS for NUS. Both between-subjects and within-subjects analyses revealed highly positive responses for both OPS and exam mode across the 5 variables of the diffusion of innovation model of relative advantage\, compatibility\, ease of use\, observability\, and trialability. Most of the findings did not show any differences by OPS type\, exam mode\, or gender\, as most responses across the 5 variables of diffusion of innovation were highly positive and very similar\, indicating positive and high rates of adoption of the two OPS. An in-depth investigation into the features of the two OPS also revealed that Proctorio had a wider scope of features than Integrity Advocate.
CATEGORIES:VIRTUAL ROOM 7C
LOCATION:Virtual Room C\, Bangkok\, Thailand
SEQUENCE:0
UID:00f05a85a1fb4f5adc8f0e1fed019148
URL:http://11thictisthailand.sched.com/event/00f05a85a1fb4f5adc8f0e1fed019148
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260417T125213Z
DTSTART:20260410T023000Z
DTEND:20260410T043000Z
SUMMARY:Collaborative Intelligence in Digital Design: A Phenomenological Study of Human-AI Interaction within Generative Design Ecosystems
DESCRIPTION:Authors - Syammas Pinasthika Syarbini\, Irmawan Rahyadi\, Muhammad Aras\, La Mani Abstract - The need to move to online proctored exams urged the National University of Samoa (NUS) to trial and evaluate a variety of online proctoring systems to ensure the offering of safe and secure exams online. The aim of the 4-phase research was to answer the following question: “What are some feasible options for online proctoring systems (OPSs) for offering online exams for NUS?” This paper is based on the last phase of this 4-phase study conducted at NUS to evaluate the feasibility of two proctoring systems\, Integrity Advocate and Proctorio\, for online exams\, particularly during lockdown. Specifically\, the objectives were to i) trial and evaluate the suitability of each OPS as well as the type of exam mode (two options: in the laboratory or from home) using a diffusion of innovation framework and\, from the evaluation\, recommend a suitable OPS for NUS. Both between-subjects and within-subjects analyses revealed highly positive responses for both OPS and exam mode across the 5 variables of the diffusion of innovation model of relative advantage\, compatibility\, ease of use\, observability\, and trialability. Most of the findings did not show any differences by OPS type\, exam mode\, or gender\, as most responses across the 5 variables of diffusion of innovation were highly positive and very similar\, indicating positive and high rates of adoption of the two OPS. An in-depth investigation into the features of the two OPS also revealed that Proctorio had a wider scope of features than Integrity Advocate.
CATEGORIES:VIRTUAL ROOM 7C
LOCATION:Virtual Room C\, Bangkok\, Thailand
SEQUENCE:0
UID:7db08ad2dde4fed11de26200fe612e8e
URL:http://11thictisthailand.sched.com/event/7db08ad2dde4fed11de26200fe612e8e
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260417T125213Z
DTSTART:20260410T023000Z
DTEND:20260410T043000Z
SUMMARY:Coordinated Control of SVC and TCSC with renewable energy penetration for voltage profile improvement
DESCRIPTION:Authors - Maulikkumar Pandya Abstract - Skin lesion segmentation is essential for computer-aided dermatological diagnosis\, but reliable pixel-level annotations are costly and require experts. To reduce dependence on manual labeling\, pseudolabeling combined with foundation models such as the Segment Anything Model (SAM) has been explored\; however\, most pipelines rely on a single pseudo-label per image\, which can introduce boundary bias when pseudo-labels are noisy. In this paper\, we compare two U-Net training pipelines built on pseudo-labels generated using U²-Net and SAM. The first pipeline follows a single pseudo-label inheritance strategy as a strong annotation-free baseline. The second pipeline synthesizes multi-style pseudo-labels (tight/moderate/loose) and applies agreement-based learning to supervise only high-confidence consensus regions while suppressing uncertain boundary pixels. No ground-truth masks are used during training\; manual annotations\, when available\, are used only for offline evaluation. Experiments on ISIC 2018 under a pseudo-reference protocol show improved boundary behavior (higher Boundary F-score) and more coherent contours\, especially in ambiguous border regions.
CATEGORIES:VIRTUAL ROOM 7C
LOCATION:Virtual Room C\, Bangkok\, Thailand
SEQUENCE:0
UID:911bc01f3d6a12877b904769978ea48a
URL:http://11thictisthailand.sched.com/event/911bc01f3d6a12877b904769978ea48a
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260417T125213Z
DTSTART:20260410T023000Z
DTEND:20260410T043000Z
SUMMARY:Design and Development of an Explainable Transfer Learning and Deep Learning Framework to Address Data Scarcity and Improve Trustworthiness in Liver Cancer Diagnosis
DESCRIPTION:Authors - Satyendra Sharma\, Pradeep Laxkar Abstract - Reconstructing polyphonic musical sequences represents a significant challenge in computational music analysis. This study presents a method based on empirical entropy and the analysis of multi-voice bigrams to identify and re-construct missing notes in polyphonic sequences. The approach combines statistical modeling of transitions between simultaneous voices in a musical piece\, represented as tuples duration:interval|duration:interval|... depending on the number of voices\, with techniques for generating and ranking possible segments according to probability and entropy. Results show that considering multi-voice bigrams effectively captures the polyphonic structure and improves the accuracy of missing note prediction. This work opens new perspectives for the application of probabilistic models to polyphonic music and AI-assisted music generation.
CATEGORIES:VIRTUAL ROOM 7C
LOCATION:Virtual Room C\, Bangkok\, Thailand
SEQUENCE:0
UID:83080a6d08d8d7af63fb71b01bcdbaf8
URL:http://11thictisthailand.sched.com/event/83080a6d08d8d7af63fb71b01bcdbaf8
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260417T125213Z
DTSTART:20260410T023000Z
DTEND:20260410T043000Z
SUMMARY:Effect of Number of Hotspots\, PM2.5\, and Other Factors on Economy\, and Public Health in Chiang Mai
DESCRIPTION:Authors - Paponsun Eakkapun\, Sulak Sumitsawan\, Chukiat Chaiboonsri Abstract - Cloud cover\, shadows\, haze\, illumination variation\, and atmospheric noise severely degrade the reliability of satellite image change detection. This paper proposes an atmosphere-aware\, physics-driven preprocessing framework that performs cloud\, shadow\, haze\, and illumination compensation before change analysis\, without relying on convolutional or transformer-based networks. Two multi-temporal satellite images are processed through unified cloud and shadow handling\, haze correction\, illumination normalization\, and residual atmospheric noise suppression\, followed by a spectrally invariant change detector with structural consistency validation. The system also generates semantic multi-class change maps and geo-contextual text explanations to enhance interpretability. Experiments on diverse multi-temporal datasets demonstrate a change detection accuracy of 98.9% with high precision and recall\, significantly outperforming conventional and deep learning baselines
CATEGORIES:VIRTUAL ROOM 7C
LOCATION:Virtual Room C\, Bangkok\, Thailand
SEQUENCE:0
UID:47ebf8140779ba8a20c971d160b5de9f
URL:http://11thictisthailand.sched.com/event/47ebf8140779ba8a20c971d160b5de9f
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260417T125213Z
DTSTART:20260410T023000Z
DTEND:20260410T043000Z
SUMMARY:EthSure: A Blockchain-Based Decentralized Framework for Transparent Life Insurance Claim Management
DESCRIPTION:Authors - C. R. Patil\, Arundhati Sarvadnya\, Diksha Shejwal\, Sakshi Nehe\, Sobiya Shaikh Abstract - The rapid expansion of the Internet\, together with the pervasive diffusion of mobile technologies\, has fundamentally reshaped contemporary socio-economic activities\, positioning e-commerce as a core pillar of the digital economy. In response to increasing competitive pressures and the growing demand for personalized consumer experiences\, enterprises have progressively adopted advanced analytical technologies\, among which machine learning has emerged as a key strategic instrument. This study develops and empirically evaluates a machine learning–based product recommendation framework that integrates historical transaction data with sentiment information extracted from user-generated reviews. Data were collected from multiple e-commerce platforms and assessed using widely adopted evaluation metrics\, including Accuracy\, Recall\, and F1-score. The experimental findings demonstrate that the XGBoost algorithm consistently outperforms alternative models\, exhibiting superior capability in identifying latent consumer preferences and behavioral patterns. Overall\, the results provide robust empirical evidence supporting the effectiveness of the proposed approach and underscore its practical potential for enhancing personalization quality and improving recommendation performance in large-scale e-commerce environments.
CATEGORIES:VIRTUAL ROOM 7C
LOCATION:Virtual Room C\, Bangkok\, Thailand
SEQUENCE:0
UID:0cef7bff247161975e236bb518bad83f
URL:http://11thictisthailand.sched.com/event/0cef7bff247161975e236bb518bad83f
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260417T125213Z
DTSTART:20260410T023000Z
DTEND:20260410T043000Z
SUMMARY:Measuring Robustness of Teacher–Student Network Using Relative Reconstruction Loss for Hyper-spectral Image Classification
DESCRIPTION:Authors - Upendra Pratap Singh\, Akshay Anand Abstract - The rapid proliferation of Internet of Things (IoT) systems has led to the widespread adoption of artificial intelligence for autonomous sensing\, prediction\, and decision-making across critical application domains. While these AIdriven IoT systems achieve high operational efficiency\, their increasing reliance on complex and opaque models raises serious concerns regarding transparency\, trust\, accountability\, and regulatory compliance. These concerns are particularly acute in distributed IoT environments\, where decisions are made across heterogeneous devices under resource constraints. Existing explainable artificial intelligence (XAI) approaches largely focus on centralized or standalone machine learning models and fail to address the unique challenges of IoT systems\, including deployment heterogeneity\, dynamic data distributions\, privacy requirements\, and real-time decision-making. As a result\, explanations are often disconnected from system behavior\, lack consistency across layers\, and provide limited support for trust assessment and human oversight. This paper presents a comprehensive survey of explainable AI techniques for trustworthy IoT systems and introduces a deployment-aware reference architecture that integrates explainability\, trust evaluation\, privacy preservation\, and human-in-the-loop feedback across edge\, fog\, and cloud intelligence layers. The architecture emphasizes localized explanation generation\, context-aware refinement\, explanation validation\, and multi-metric trust assessment\, enabling explanations to evolve alongside system behavior. By explicitly coupling explanation quality with trust monitoring and adaptive feedback\, the proposed framework bridges the gap between predictive performance and operational trustworthiness in distributed IoT environments. The survey highlights key research trends\, identifies critical gaps in current methodologies\, and outlines future directions for scalable\, reliable\, and human-centered explainable IoT systems. By positioning explainability as a core system property rather than a post-hoc add-on\, this work provides a foundation for designing AI-enabled IoT systems that are transparent\, accountable\, and trustworthy by design.
CATEGORIES:VIRTUAL ROOM 7C
LOCATION:Virtual Room C\, Bangkok\, Thailand
SEQUENCE:0
UID:05b981366716fc7bad8f05152b5a462e
URL:http://11thictisthailand.sched.com/event/05b981366716fc7bad8f05152b5a462e
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260417T125213Z
DTSTART:20260410T023000Z
DTEND:20260410T043000Z
SUMMARY:The Impact of AI-Generated Content on Instagram on Political Trust Among Youth in India
DESCRIPTION:Authors - Sreebala V S\, Arun Kumar V N\, Agna.S. Nath Abstract - The Commercial Territory Design Problem (CTDP) plays an important role in sales and marketing management. The problem focuses on partitioning some basic units into territories to optimize compactness while ensuring workload balance and connectivity constraints. Due to the NP-hard property of the problem\, exact approaches often have limitations in scalability across large datasets. This study proposes a combination of the classical ALNS algorithm framework and an ActorCritic Deep Reinforcement Learning architecture to deal with the large CTDP instances. Our proposed algorithm can automatically select destroy and repair operators\, and dynamically fine-tune hyperparameters such as destruction level and acceptance criteria based on the actual state of the search process. Experimental results on benchmark instances with various sizes show that our algorithm not only achieves superior quality solutions compared to traditional ALNS but also surpasses exact solutions in terms of convergence speed within the same runtime limit. It can achieve high-quality solutions within a reasonable execution time and has the potential for real-world applications.
CATEGORIES:VIRTUAL ROOM 7C
LOCATION:Virtual Room C\, Bangkok\, Thailand
SEQUENCE:0
UID:d998ba28e1222ac8bfac9e77b8249763
URL:http://11thictisthailand.sched.com/event/d998ba28e1222ac8bfac9e77b8249763
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260417T125213Z
DTSTART:20260410T023000Z
DTEND:20260410T043000Z
SUMMARY:Travelers Adoption of AI Voice Assistants as Decision-Support Systems
DESCRIPTION:Authors - Tri Wiyana\, Roberto Tomahuw Abstract - Mental health disorders are among the major global health problems\, and early diagnosis is the key for effective management. Conventional methods are based on self-reported or clinical scales\, for which intervention comes late. In this paper\, we propose a multimodal AI framework for the detection of early mental health detection from typing and voice behaviors. We extract BERT-based linguistic embeddings of text transcripts and spectral features of the speech signals from the audio data using the DAIC-WOZ dataset for capturing verbal cues. These features are then combined by machine learning algorithms to classify depression. The proposed framework prioritizes non invasive\, privacyconscious detection with explainability techniques used to foster clinical confidence. We further present experimental results to show that the multimodal fusion also provides classification gain over unimodal baselines. This study demonstrates the capability of AI-based\, real-time methods for proactive mental health monitoring and provides a stepping stone towards healthcare deployment.
CATEGORIES:VIRTUAL ROOM 7C
LOCATION:Virtual Room C\, Bangkok\, Thailand
SEQUENCE:0
UID:aec684c6aefd9a921c0e820760a51032
URL:http://11thictisthailand.sched.com/event/aec684c6aefd9a921c0e820760a51032
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260417T125213Z
DTSTART:20260410T023000Z
DTEND:20260410T043000Z
SUMMARY:A Governed Forecasting and Anomaly Detection Framework for Live Birth Planning in Provincial Health Systems
DESCRIPTION:Authors - Britt Kristoff B. Montalvo\, Vicente Pitogo\n Abstract - This paper presents a data-driven forecasting and anomaly detection dashboard for live births in Surigao del Norte\, utilizing the Family Health Service Information System (FHSIS) data from 2021 and onwards. The research methodology is based on the CRISP-DM framework\, with business under-standing for the needs of maternal services planning in the provinces and municipalities\, data preparation for municipalities by quarters\, time aware modeling\, evaluation\, and deployment through the API and visualization layer. The research employs several machine learning techniques for forecasting\, such as ARIMA/SARIMA\, Exponential Smoothing (ETS and Holt-Winters)\, and the Prophet method\, along with a naïve method. The performance of the models is evaluated through the symmetric Mean Absolute Percentage Error (sMAPE)\, Root Mean Squared Error (RMSE)\, Mean Absolute Error (MAE)\, and Mean Absolute Scaled Error (MASE). A strict evaluation criterion for the deployment of the model is also implemented\, such as the availability of sufficient data points in the past for the model to be deployed (i.e.\, 12 data points in the past)\, the accuracy of the model (sMAPE &lt\; 20%)\, and the performance of the model in comparison with the naïve method (MASE &lt\; 1). A low confidence filter is also implemented for the series with intermittent data to prevent incorrect results. The results show high reliability of the forecasting model for the entire province and better interpretability for strategic planning. However\, the results also show that some of the municipalities with low population volumes and intermittent data points pose a challenge in the operation of the model.
CATEGORIES:VIRTUAL ROOM 7D
LOCATION:Virtual Room D\, Bangkok\, Thailand
SEQUENCE:0
UID:a8681948aa500ac1bcd79f862adc8e8a
URL:http://11thictisthailand.sched.com/event/a8681948aa500ac1bcd79f862adc8e8a
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260417T125213Z
DTSTART:20260410T023000Z
DTEND:20260410T043000Z
SUMMARY:A Hybrid Wavelet CNN Vision Transformer Framework with Explainable AI for Medical Image Classification
DESCRIPTION:Authors - Shriram Dange\, Namdeo. M. Sawant\, Sumeet S Ingole\, Somnath A. Zambare\n Abstract - Medical image classification is of immense importance in the context of early-stage diagnosis of various neurological diseases\, including Alzheimer’s disease and brain tumours. However\, it remains infeasible for conventional deep learning architectures to efficiently encode frequency domain information and long-range spatial dependencies found in medical images. In this paper\, a novel Hybrid Wavelet CNN Vision Trans-former\, coupled with Explainable Artificial Intelligence\, has been proposed for efficient and accurate medical image classification. In the proposed architecture\, the application of discrete wavelet transform\, convolutional neural networks\, and Vision transformers for medical image classification has been presented. Additionally\, explainability aspects have been addressed using the Grad-CAM technique. The proposed model was experimented with using two datasets: one for Alzheimer’s disease MRI and another for brain tumours. The experimental results reveal that the proposed deep learning architecture achieves an accuracy of 96.8%\, precision of 0.96\, and recall of 0.97\, F1score of 0.97 for the brain tumours dataset\, which beats conventional CNN\, vision Transformer\, and Wavelet CNN architectures. The integration of explainable AI further enhances model transparency and clinical reliability\, making the proposed framework suitable for real-world medical diagnostic applications.
CATEGORIES:VIRTUAL ROOM 7D
LOCATION:Virtual Room D\, Bangkok\, Thailand
SEQUENCE:0
UID:c8b850f568108475b27da152e8c4e761
URL:http://11thictisthailand.sched.com/event/c8b850f568108475b27da152e8c4e761
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260417T125213Z
DTSTART:20260410T023000Z
DTEND:20260410T043000Z
SUMMARY:AI-Powered Investment Assistant
DESCRIPTION:Authors - Sherly K.K.\, Merin Jose\, Aleena Gerard Nidhiry\, Amit Shibu Kadambamoodan\, Alfahad Shahi\n Abstract - This paper introduces an AI-based investment assistant that helps users to understand the fundamental principles of the financial markets. This work is mainly focused on stock market data to provide accurate insights and helps in various decision-making purposes. The rising volatility in the financial markets\, massive data set\, and the complexity of financial instruments\, makes decision-making in financial sectors more difficult to individual investors.In order to cope with this problem\, our model integrates time series forecasts\, large language model intelligence with real-time financial information with interactive visualizations and personalized insights. The suggested system will interpret user queries in natural language with the help of a Large Language Model (Gemini 2.5 Flash) and extracts the corresponding stock tickers and financial objects and transforms them into structured inputs to be used in predictive analysis. Past and current stock market data are retrieved with the help of yfinance API and fed into an LSTM-based time-series predictive model that predicts future price fluctuations.The results predicted are presented in interactive charts created with Plotly\, which users can analyze trends easily and compare several stocks. The system can also give personalized recommendations\, textual summaries of stock movements (moving up or down)\, multi-turn chatbot conversations\, portfolio\, wishlist and real time price moves besides forecasting. The proposed investment assistant improves the gap between complicated financial information and practical results by incorporating natural language comprehension\, deep learningbased prediction\, and intuitive visualization etc. The system promotes user knowledge and helps them in effective decision making .
CATEGORIES:VIRTUAL ROOM 7D
LOCATION:Virtual Room D\, Bangkok\, Thailand
SEQUENCE:0
UID:e9a636ceab8c1c792226350c1af70da1
URL:http://11thictisthailand.sched.com/event/e9a636ceab8c1c792226350c1af70da1
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260417T125213Z
DTSTART:20260410T023000Z
DTEND:20260410T043000Z
SUMMARY:Beyond Continuity: Modeling Discontinuous Risk in Altcoin Portfolios via Merton Jump-Diffusion and EWMA Covariance
DESCRIPTION:Authors - Ekleen Kaur\n Abstract - Traditional risk frameworks\, including the Geometric Brownian Motion (GBM) and stationary GARCH models\, fail to account for the "volatility bursts" and "flash crashes" endemic to the altcoin market. This study the third in a series on cryptoeconomic risk introduces a multi-asset Merton Jump-Diffusion (MJD) model integrated with an Exponentially Weighted Moving Average (EWMA) covariance matrix to model portfolio risk in altcoin-only environments. By focusing exclusively on high-beta altcoins (XRP\, SOL\, ADA) and we address a critical gap by excluding market-anchor assets to isolate long-tail volatility dynamics neglected in existing literature. We implement a dual-model approach: a baseline MJD simulation and a "Capped Return" MJD model designed to mitigate unrealistic exponential price paths in long-horizon forecasts. Our results using Monte Carlo Value-at-Risk simulations demonstrate that incorporating a Poisson-driven jump component (j = 2.0) significantly improves λthe capture of tail risk compared to continuous models indicating pathological exponential growth without suppressing crash dynamics. Our work provides a technically rigorous framework for managing portfolios in decentralized\, high-liquidity-shock environments. Backtesting via Kupiec’s Proportion of Failures test indicates that jump-based\, non-stationary models achieve statistically consistent risk coverage. These findings suggest discontinuous modeling as a prerequisite for regulatory-grade risk estimation in high-beta crypto assets.
CATEGORIES:VIRTUAL ROOM 7D
LOCATION:Virtual Room D\, Bangkok\, Thailand
SEQUENCE:0
UID:0a172c6021aa6c95df2e2e3050448747
URL:http://11thictisthailand.sched.com/event/0a172c6021aa6c95df2e2e3050448747
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260417T125213Z
DTSTART:20260410T023000Z
DTEND:20260410T043000Z
SUMMARY:Bridging Linguistic Diversity: Enhancing NER Performance through Large Language Models on Indian & Foreign Languages
DESCRIPTION:Authors - Makrand Dhanokar\, Anirban Sarkar\, Prajakta Dange Sant\, Shivakarthik S\, Krishnanjan Bhattacharjee\, Swati Mehta\n Abstract - Named Entity Recognition (NER) is an essential task for sequence labelling and information extraction that plays a fundamental role in subsequent Natural Language Processing (NLP) applications\, such as information retrieval\, question answering\, knowledge graph development\, and machine translation. Although significant advancements have been made in NER for high resource languages\, achieving effective entity recognition in Indian languages continues to be an unresolved research challenge because of linguistic diversity\, complex morphology\, typological differences\, flexible word order\, script differences\, and prevalent codemixing. The scarce presence of annotated datasets and the lack of standardized evaluation metrics further limit supervised and transfer learning methods in these low resource environments. This document introduces a multilingual NER framework rooted in Sentence embeddings derived from Large Language Models (LLMs) and inference guided by prompts. The suggested method employs contextual\; language independent embeddings obtained from pretrained multilingual LLMs to encode semantic representations of Indian and foreign languages within a common embedding space. Rather than using traditional token level classification\, entity recognition and classification are achieved via structured prompting\, allowing for zero-shot and few-shot generalization without the need for task specific finetuning. The system guarantees that entity identification and retrieval take place in the same language as the input text\, maintaining linguistic accuracy and reducing error propagation caused by translation. To tackle domain variability and informal writing\, constraints/guardrails for prompts and simple rule-based normalization are utilized to manage orthographic differences\, script inconsistencies\, and codemixed phrases often found in user generated content and social media. Experimental assessment across various Indian languages shows reliable enhancements in precision\, recall\, and F1score compared to traditional neural and transformer-based benchmarks\, especially in low resource conditions. The findings suggest that embeddings powered by LLMs along with prompt-based reasoning provide a scalable and data efficient option for multilingual NER. This project advances the development of resilient\, inclusive\, and language adaptive systems for extracting information in linguistically varied settings.
CATEGORIES:VIRTUAL ROOM 7D
LOCATION:Virtual Room D\, Bangkok\, Thailand
SEQUENCE:0
UID:112d9bfd4c72c529aefc09faf4d1776a
URL:http://11thictisthailand.sched.com/event/112d9bfd4c72c529aefc09faf4d1776a
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260417T125213Z
DTSTART:20260410T023000Z
DTEND:20260410T043000Z
SUMMARY:Explainable Deep Learning Driven Transaction-level Customer Spending Behavior Analysis for Fraud Detection in a Big Data Framework
DESCRIPTION:Authors - Asmaul Hosna Sadika\, M. M. Musharaf Hussain\, Mohammad Shamsul Arefin\n Abstract - Credit card transaction analysis is challenged by severe class imbalance with evolving spending behavior and large-scale financial data. Many existing fraud detection approaches rely on supervised learning and assume stable fraud labels\, limiting robustness under changing fraud prevalence. This study presents a large-scale\, multi-year credit card trans action dataset stored in partitioned Parquet format and conducts a systematic comparison of classical machine learning\, supervised deep learning\, and unsupervised deep learning models for customer spend ing behavior analysis. An exploratory behavioral analysis characterizes spending heterogeneity\, temporal regularities\, and channel and category variations. Supervised sequence models based on LSTM and CNN ar chitectures are evaluated alongside unsupervised sequence autoencoders and hybrid detection pipelines across fraud rates ranging from 2-12%. To ensure fair evaluation under extreme imbalance\, models are assessed using ranking-based metrics under fixed alert budgets\, including pre cision–recall area under the curve and recall-at-K. A hybrid of Autoen coder and LSTM architectures achieves the highest performance for large systems. An integrated XAI module is introduced to derive important features providing interpretable insights.
CATEGORIES:VIRTUAL ROOM 7D
LOCATION:Virtual Room D\, Bangkok\, Thailand
SEQUENCE:0
UID:e5f3a34d624827e80f1a68acf507de2e
URL:http://11thictisthailand.sched.com/event/e5f3a34d624827e80f1a68acf507de2e
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260417T125213Z
DTSTART:20260410T023000Z
DTEND:20260410T043000Z
SUMMARY:Eye Movements and Their Influence on Cognitive Processing
DESCRIPTION:Authors - Christian Vera\, Christian Torres-Moran\n Abstract - This study examines how students distribute visual attention and coordinate gaze with response selection when solving image-supported multiple-choice questions in a Google Forms interface. Twenty-five students participated\, selected through convenience sampling under explicit inclusion and exclusion criteria\, while both fixations and click events were recorded. Oculomotor signals were processed using clustering algorithms to derive participant-specific gaze AOIs and click AOIs\, complemented by a 3×3 grid-based spatial analysis to quantify global space utilization. Metrics were computed including time to first fixation\, total fixation duration and fixation counts per area\, transitions between areas\, and the proportion of pre-response fixations within the region where the click was executed. Results show a systematic concentration of fixations in the central band of the interface\, where the image and response options are located\, with one or two dominant areas accounting for most fixation time. The optimal number of gaze clusters ranged from two to eight across participants\, reflecting more focused versus more exploratory strategies. A high level of attention–action coupling was observed\, with 80% to 95% of clicks occurring within the same area that concentrated most fixations. These findings support the use of eye track-ing as a tool for cognitive validation of item design and inform principles for more efficient and transparent digital assessments.
CATEGORIES:VIRTUAL ROOM 7D
LOCATION:Virtual Room D\, Bangkok\, Thailand
SEQUENCE:0
UID:451069cd6c6f15c64d1e3a0d26deecf8
URL:http://11thictisthailand.sched.com/event/451069cd6c6f15c64d1e3a0d26deecf8
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260417T125213Z
DTSTART:20260410T023000Z
DTEND:20260410T043000Z
SUMMARY:Hybrid Deep Learning and Quantum Approach for Multimodal Deepfake Detection
DESCRIPTION:Authors - V. Abarna\, R. Shyamala\n Abstract - The rapid advancement of artificial intelligence has significantly enhanced deepfake generation techniques\, posing serious challenges to digital media authenticity\, cybersecurity\, and misinformation control. Conventional detection approaches often rely on single-modality analysis\, limiting their effective-ness against sophisticated synthetic media. This paper proposes a multimodal deepfake detection framework that integrates visual\, audio\, textual\, and behavioral biometric information using a hybrid deep learning architecture combined with a variational quantum learning approach. Deep neural models are employed for feature extraction across modalities\, including convolutional networks for visual artifacts\, transformer-based models for speech and text analysis\, and bio-metric behavioral assessment such as eye movement\, lip synchronization\, and motion consistency. A hierarchical fusion mechanism aggregates modality-specific representations\, while a variational quantum classifier enhances classification robustness through hybrid quantum–classical learning. An explainability module provides insight into modality contributions and prediction confidence\, supported by a web-based dashboard for real-time interaction. The proposed framework aims to improve detection reliability\, interpretability\, and practical deployment in applications such as digital forensics\, social media verification\, and cybersecurity. This work presents a conceptual architecture and implementation roadmap to support future research in multimodal deepfake detection.
CATEGORIES:VIRTUAL ROOM 7D
LOCATION:Virtual Room D\, Bangkok\, Thailand
SEQUENCE:0
UID:1dc0d098d46905342ad8e561cbeb3eec
URL:http://11thictisthailand.sched.com/event/1dc0d098d46905342ad8e561cbeb3eec
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260417T125213Z
DTSTART:20260410T023000Z
DTEND:20260410T043000Z
SUMMARY:Is Common Hardening Methods Really Sufficient? A Risk Analysis on Current ICS Vulnerabilities
DESCRIPTION:Authors - Emine YAZICI\, Alper UGUR\n Abstract - Critical infrastructures are of strategic importance to the security of societies\, economic stability\, and the continuity of public services. However\, with digitalization\, these infrastructures are facing progressively complex cyber threats such as supply chain exploitation\, ransomware\, and AI-assisted targeted attacks. Traditional hardening methods are becoming insufficient in the face of these developments. This study examines the types of attacks and threat trends that have emerged in the literature in recent years\; and evaluates the effectiveness of hardening methods applied against them at the software\, physical\, and organ izational levels. The findings indicate that\, due to the dynamic nature of threat vectors\, utilized common risk analysis and hardening strategies are insufficient to deliver the expected security outcomes. However\, the literature lacks a risk analysis score and hardening guide for decision-makers regarding current threat models and attack techniques. In this study\, risk scores based on CVSS were cre ated for up-to-date threats in the ICS field\, and hardening mechanisms were also proposed according to the mechanisms behind the related threats and their ef fects. &nbsp\;We aim to address existing shortcomings to some extent by calculating the risk scores of new attacks and to make ICS more secure through proposed hard ening mechanisms against these risks. The sustainability of security can be achieved through holistic security policies that include multi-layered approaches\, continuous monitoring\, adaptive response mechanisms and advanced approaches such as Zero Trust architecture\, AI-based anomaly detection\, and hybrid defense systems in the domain where traditional measures fall short.
CATEGORIES:VIRTUAL ROOM 7D
LOCATION:Virtual Room D\, Bangkok\, Thailand
SEQUENCE:0
UID:a449ad328d70168182349516fcbb651e
URL:http://11thictisthailand.sched.com/event/a449ad328d70168182349516fcbb651e
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260417T125213Z
DTSTART:20260410T023000Z
DTEND:20260410T043000Z
SUMMARY:Time-Synchronized Industrial Data Analytics for Current Unbalance Mitigation in HVJ Electric Boilers: An FMEA-Guided Approach
DESCRIPTION:Authors - Nurkholis\, Katherin Indriawati\n Abstract -This paper presents a case study on a High Voltage Jet (HVJ) electric boiler\, focusing on current unbalance (CU) risk identification and mitigation us ing a combined data-analytics and Failure Mode and Effects Analysis (FMEA) framework. Power-quality assessment follows IEC 61000-4-30 for voltage un balance (VU)\, while CU interpretation refers to NEMA MG-1 and IEEE recom mendations. The proposed workflow integrates (i) instrument classification (Class A for voltage)\, (ii) time synchronization across logger/PLC/power-quality analyzer to avoid timestamp drift\, and (iii) historian-based data pre-processing (outlier cleaning\, scaling\, and missing-data handling) prior to statistical analysis. Results show an average CU of 6.85% with a standard deviation of 0.48% and a maximum of 15.92%\, indicating operational periods exceeding common industry limits. FMEA highlights electrode aging/damage\, loose/corroded cable connec tions\, and supply power-quality issues as the dominant contributors. Recom mended actions include online phase-current monitoring\, improved water-chem istry and blowdown management\, and control optimization of the VFD-driven boiler circulation pump (BCP).
CATEGORIES:VIRTUAL ROOM 7D
LOCATION:Virtual Room D\, Bangkok\, Thailand
SEQUENCE:0
UID:36c9a1d38b2d09257580744b1452b801
URL:http://11thictisthailand.sched.com/event/36c9a1d38b2d09257580744b1452b801
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260417T125213Z
DTSTART:20260410T023000Z
DTEND:20260410T043000Z
SUMMARY:A Literature Review on Fog Computing in Supply Chain Management: Enhancing Efficiency\, Security\, and Scalability
DESCRIPTION:Authors - Hasan Ahmed\, Ram Singh Abstract - The growth of digital media platforms has resulted in more disseminated falsehoods which now include elaborate AI-generated syn thetic text instead of manually created false information. The develop ments create major obstacles which disrupt both information trustwor thiness and public confidence. The research presents a High-Accuracy Misinformation Detection Hybrid Transformer Framework which uses BERT and RoBERTa models within an ensemble learning system. The system undergoes initial training on WELFake dataset which serves as a standard benchmark collection that contains equal proportions of au thentic and fraudulent news articles derived from both verified and un verified sources. The framework achieves adaptability through its in cremental updating process which incorporates contemporary headlines and machine-generated content. The weighted fusion mechanism merges probability results from both transformer models to decrease model spe cific bias while strengthening the system’s classification ability. The sys tem shows better results than single transformer setups and operates through a web-based system which provides immediate misinformation assessment. The study results show that using ensemble modeling to gether with scheduled model updates creates an efficient method for tackling the ongoing emergence of synthetic misinformation.
CATEGORIES:VIRTUAL ROOM 7E
LOCATION:Virtual Room E\, Bangkok\, Thailand
SEQUENCE:0
UID:6def0b798fa0bd053e01ebff31737e88
URL:http://11thictisthailand.sched.com/event/6def0b798fa0bd053e01ebff31737e88
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260417T125213Z
DTSTART:20260410T023000Z
DTEND:20260410T043000Z
SUMMARY:A Zero-Shot Cross-Patient Transfer Framework for Seizure Forecasting via the Strict Discipline Protocol
DESCRIPTION:Authors - Gagani Kulathilaka\, Inuka Gajanayake\, Guhanathan Poravi\, Saadh Jawwadh Abstract - In modern digital environments\, organizations require intelligent sys tems to manage complex workflows and decision-making. Unlike most of the task management systems that are manual and give no feedback and even lack competence\; this leads to poor prioritization\, deadline been missed and poor com munication between teams. Thus\, IntelliTask is an intelligent system of dealing with tasks\, which is AI-powered and\, consequently\, is context-aware\, giving it an edge to enhance the quality of the working processes of the people using the system (both individuals and businesses)\, enhancing the prioritization\, and im proving the productivity. The IntelliTask platform is machine-learning models\, predictive analytics\, and dynamic scheduling based on identifying key tasks to balance the workloads and the cognitive load on users without the user having to engage in the task. The solution will enhance the rate at which the tasks are ac complished\, making informed decisions and will bring flexibility on what task management systems will be established in the future in enterprises.
CATEGORIES:VIRTUAL ROOM 7E
LOCATION:Virtual Room E\, Bangkok\, Thailand
SEQUENCE:0
UID:12f74a95bc817c3de072d36d0090dbd0
URL:http://11thictisthailand.sched.com/event/12f74a95bc817c3de072d36d0090dbd0
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260417T125213Z
DTSTART:20260410T023000Z
DTEND:20260410T043000Z
SUMMARY:AI-Driven Multi Disease Prediction System Using Random Forest Algorithm
DESCRIPTION:Authors - Umar Ali R\, Payas Khan H\, Nouriensha N\, Nithish Kumar S\, Nisha M Abstract - An effort to calculate the infinite value of circumference ratio is made in this paper. Instead of being made of countless infinitesimals\, a given circle is parts of an new defined infinity that is single magnitude continuum derived from the change in direction that indicates that there is a jumping from finiteness to infinity .This single magnitude continuum is the accumulations of infinitely many finite magnitudes and can never be achieved by forever extending continuously finite magnitudes.The change in direction implies that infinite length (i.e. infinite distance) can be defined as two parallel lines that never intersect \,which denotes that only the terminal end of the first straight line is meaningful when extending towards infinite distance\, and this terminal end is defined as infinite length\, which is a magnitude that cannot be discussed any magnitudes outside of it. When the first straight line extends to infinite distance\, its one-dimensional feature will be lost and become an infinite dimensional magnitude\, which is determined by the change in direction.The infinite value of circumference ratio is this new defined infinity.
CATEGORIES:VIRTUAL ROOM 7E
LOCATION:Virtual Room E\, Bangkok\, Thailand
SEQUENCE:0
UID:3c667a1ee6731c17b26c22cb729d1536
URL:http://11thictisthailand.sched.com/event/3c667a1ee6731c17b26c22cb729d1536
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260417T125213Z
DTSTART:20260410T023000Z
DTEND:20260410T043000Z
SUMMARY:AUSPEX: A Lightweight Multi-View Forensic Framework for Low-Payload Compressed Audio Steganalysis with Dual-Level Explainability
DESCRIPTION:Authors - Sarah Rahim\, Guhanathan Poravi Abstract - In mobile networks without fixed base stations (MANETs)\, finding the best path for data is difficult when devices are constantly moving. Traditional methods often lead to dropped data and wasted battery. This study introduces a smarter approach by combining the standard routing protocol with a "Dolphin Partner Optimization" (DPO) algorithm. Much like how dolphins coordinate\, this system picks the best path by looking at battery life\, connection stability\, and speed all at once. Testing shows this new method keeps the network running longer and sends data much more reliably than older systems.
CATEGORIES:VIRTUAL ROOM 7E
LOCATION:Virtual Room E\, Bangkok\, Thailand
SEQUENCE:0
UID:e71aca605f9da5928f00e69fcbe3a5b2
URL:http://11thictisthailand.sched.com/event/e71aca605f9da5928f00e69fcbe3a5b2
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260417T125213Z
DTSTART:20260410T023000Z
DTEND:20260410T043000Z
SUMMARY:Development of a comprehensive fake image detection dataset from social media with DCT-based evaluation
DESCRIPTION:Authors - Md. Mehedi Rahman Rana\, Md. Anisur Rahman\, Kamrul Hasan Talukder\, Syed Md. Galib Abstract - The adoption of AI in the law sphere on a larger scale has left new opportunities of case analysis and verdict prediction as well as legal texts interpretation with the help of the robot. However\, the existing Legal Judgment Prediction (LJP) systems are submissible to implicit data bias\, which contains adult information on such delicate aspects as gender\, caste\, occupation\, and socio-economic status. These biases may result in ethically unsound and unreliable forecasting\, which is a vital issue in high stakes judicial settings. This work provides a Bias-Aware Legal Case Classification and Judgment Interpretation architecture that enables improved levels of fairness\, interpretability and contextual reliability in legal decision support systems. The bias-sensitive preprocessing pipeline proposed combines the Named Entity Recognition and zero-shot and legal-specific bias-tagging. These two types of vocabularies are used with a dual-encoder framework based on LegalBERT on bias-masked data and BERT on unmasked data in order to trade-off legal reasoning with controlled demographic awareness. Representations in a gating-based fusion mechanism are combined in advance to make final classification. The system is set to work on the real case documents of the Indian laws based on the publicly available repositories. Instead of substituting the jurisdictional powers\, the framework is intended to deliver ethical\, transparent\, and contextually sensitive support to the legal practitioners. The research is relevant in the history of responsible AI\, as it focuses on the issues of fairness and interpretability in the field of automated legal analytics.
CATEGORIES:VIRTUAL ROOM 7E
LOCATION:Virtual Room E\, Bangkok\, Thailand
SEQUENCE:0
UID:2a8cd2f7dafc8ed962c61327f3198a4d
URL:http://11thictisthailand.sched.com/event/2a8cd2f7dafc8ed962c61327f3198a4d
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260417T125213Z
DTSTART:20260410T023000Z
DTEND:20260410T043000Z
SUMMARY:Digital Twins and Multichain for preventive academic degree fraud: A Study Case
DESCRIPTION:Authors - Leonardo Juan Ramirez Lopez\, Cristian Santiago Cruz Jimenez\, Johan Sebastian Ayala Gaitan Abstract - Ongoing technological progress has significantly increased global energy demand\, particularly in rapidly developing economies\, a trend further intensified by continuous population growth. Although improving energy efficiency is a universal objective\, it remains an unresolved challenge. Advances in science and engineering have enabled the creation of diverse energy-harvesting technologies that utilize established non-conventional sources— such as solar\, wind\, thermal\, hydro\, piezoelectric\, electromagnetic\, and bio-battery systems—as well as emerging concepts like rectenna-based collection. This study aims to present a comprehensive evaluation and comparison of these technologies by examining their energy sources\, availability\, conversion principles\, infrastructure needs\, production costs\, performance outputs\, application domains\, overall efficiency\, harvesting capacity\, constraints\, resource characteristics\, and commercial feasibility. By offering a systematic comparison\, the authors seek to clarify the strengths of each approach while also highlighting the practical challenges involved in applying them to meet present and future global energy demands through both existing and prospective alternative energy solutions. The main objective of this paper is to systematically evaluate and compare a wide range of energy harvesting technologies—spanning established non-conventional sources and emerging concepts—by analyzing their operating principles\, resource availability\, infrastructure requirements\, cost\, efficiency\, performance\, limitations\, and practical applicability\, with the aim of identifying their strengths\, challenges\, and potential contributions toward meeting current and future global energy demands through sustainable alternative solutions.
CATEGORIES:VIRTUAL ROOM 7E
LOCATION:Virtual Room E\, Bangkok\, Thailand
SEQUENCE:0
UID:931a5d1ae87582d6e14a951f518b9f97
URL:http://11thictisthailand.sched.com/event/931a5d1ae87582d6e14a951f518b9f97
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260417T125213Z
DTSTART:20260410T023000Z
DTEND:20260410T043000Z
SUMMARY:GreenSec-DBO: A Trust-Aware\, Carbon-Aware and Post-Quantum Secure Multi-Objective Task Scheduling Framework Using Dung Beetle Optimization for Sustainable Cloud Computing
DESCRIPTION:Authors - Asmit U. Patil\, Sneha Jadhav Mane\, Swati Suryawanshi\, Prerana Mahajan\, Priya Sharma\, Smita Shedbale\, Dhanaraj S. Jadhav\, Supriya Mane Abstract - Inference latency remains a critical bottleneck in deploying large language models\, for real-time and resource-constrained environments. Prior work has proposed latency formulations that express latency as a function of key parameters. However\, they often assume a linear dependence on sequence length\, which fails to generalize to tasks involving significantly longer sequences\, such as document-level language modeling\, long-context retrieval\, or time-series forecasting\, where latency scales nonlinearly and unpredictably. This paper addresses the limitations of existing latency formulations by proposing three complementary enhancements to improve generalization across varying sequence lengths. First\, we introduce a nonlinear term for sequence length\, capturing the superlinear growth in latency observed in transformer-based architectures due to quadratic attention mechanisms and memory overhead. Second\, we propose a sequence-length-dependent scaling factor for the sequence length parameter itself\, allowing the model to adaptively adjust its sensitivity based on empirical latency profiles across different tasks and hardware configurations. Third\, we incorporate an empirical correction term enabling calibration of the latency model to account for hardware-specific and implementation-level nuances. By explicitly modeling the nonlinear and context-sensitive behavior of sequence length\, our approach offers a more faithful representation of latency dynamics. This work lays the foundation for more adaptive and hardware-aware latency estimation frameworks\, with implications for model deployment\, scheduling\, and cost optimization in production systems. We conclude by discussing future directions for integrating dynamic profiling and reinforcement learning to further refine latency predictions in evolving runtime environments.
CATEGORIES:VIRTUAL ROOM 7E
LOCATION:Virtual Room E\, Bangkok\, Thailand
SEQUENCE:0
UID:b5a6cd6af86dfee9ccff7b3af20d723c
URL:http://11thictisthailand.sched.com/event/b5a6cd6af86dfee9ccff7b3af20d723c
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260417T125213Z
DTSTART:20260410T023000Z
DTEND:20260410T043000Z
SUMMARY:Hybrid System of Deep Learning and Neuromorphic Computing for Energy Prediction in 5G NSA Networks
DESCRIPTION:Authors - Felipe M. Coelho\, Margarida N. P. dos Santos\, Jeziel M. Pessoa\, William A. P. de Melo\, Joel C. do Nascimento\, Carlos A. O. de Freitas \, Debora R. Raimundo\, Vandermi J. da Silva\n Abstract - The transition from 4G to 5G networks\, particularly in Non Standalone (NSA) deployments\, introduces new challenges for the energy effi ciency of mobile devices\, as they must maintain simultaneous connectivity with LTE for signaling while using 5G NR for high-speed data transmission. To ad dress this issue\, this work proposes a hybrid artificial intelligence approach for predicting current consumption that combines conventional deep learning with neuromorphic computing principles. Real-world telemetry data are first pro cessed using convolutional layers and bidirectional LSTM units to capture spa tial and temporal patterns\, and the resulting representations are then converted through rate coding and provided to a Spiking Neural Network (SNN). The model is trained using a hybrid strategy that integrates Spike-Timing Dependent Plasticity (STDP) with surrogate gradients\, together with a custom loss function designed to emphasize prediction accuracy during high-demand periods. Experimental results show that the proposed model achieves an RMSE of 0.1164 mA\, representing a 6.3% improvement compared to standard Recur rent Spiking Neural Network (RSNN) approaches\, indicating its ability to cap ture abrupt variations in power consumption typical of 5G NSA environments.
CATEGORIES:VIRTUAL ROOM 7E
LOCATION:Virtual Room E\, Bangkok\, Thailand
SEQUENCE:0
UID:6820a4cce4c6fa953be8021bd480f338
URL:http://11thictisthailand.sched.com/event/6820a4cce4c6fa953be8021bd480f338
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260417T125213Z
DTSTART:20260410T023000Z
DTEND:20260410T043000Z
SUMMARY:Improved Rotor Position Estimation and Estimated Error Convergence using Sliding Window in Extended Kalman Filtering in BLDC motor for Dual Axis Solar Panel Tracking System.
DESCRIPTION:Authors - Udayamoorthy Venkateshkumar\n Abstract - This paper focus on dual axis solar panel tracking system using Brushless Direct Current motor (BLDC)\, in which rotor position estimation along azimuthal angle and elevation angle is predicted using incremental en coder. The physical kinematics and dynamics parameters which are non-linear in nature is converted to linear form and processed in conventional estimated kalman filter (EKF) algorithm. The physical process noise covariance value Qk and measured noise covariance value Rk is estimated from conventional EKF predicted value\, using sliding window method. Smoothing factor λ is used for quick convergence and tuning factor &nbsp\; to estimate the process noise covariance. The simulation is performed using Python and results shows rotor position es timation along azimuthal angle is improved by 50% and 55% along elevation angle. Dual axis estimation error convergence during dynamic tracking along azimuthal angle is reduced by 66% and along elevation angle is reduced by 70% when compared to conventional EKF algorithm.
CATEGORIES:VIRTUAL ROOM 7E
LOCATION:Virtual Room E\, Bangkok\, Thailand
SEQUENCE:0
UID:d31b4ff08ae70f9fa467f0ba69baaf74
URL:http://11thictisthailand.sched.com/event/d31b4ff08ae70f9fa467f0ba69baaf74
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260417T125213Z
DTSTART:20260410T023000Z
DTEND:20260410T043000Z
SUMMARY:Singer Identification via ECAPA-TDNN and Classical Machine Learning Models
DESCRIPTION:Authors - Ananya Kale\, Aditi Jaikar\, Shravika Hamjade\, Neeta Maitre\, Rashmi Apte\, Mangesh Bedekar\n Abstract - Singer identification is a challenging task because of pitch and me lodic variations\, tempo\, vibrato\, and adaptive singing styles. This paper propos es a novel approach towards singer identification and classification by adapting a model originally meant for speaker recognition. Specifically\, this work utiliz es vector representations extracted from a pretrained Speech Brain Emphasized Channel Attention\, Propagation and Aggregation in Time Delay Neural Net work (ECAPA-TDNN) model. The research pipeline processes a custom curated dataset of four prominent Indian playback singers into fixed\, 8 second audio clips\, with mono channel sampled at 16 kHz and exported as wav files. The Speech Brain Emphasized Channel Attention\, Propagation and Aggrega tion (ECAPA) encoder transforms these labelled clips into fixed embeddings which are unique vector representations of voice characteristics of each audio clips. A suite of classical machine learning classifiers is trained on these em beddings. The study evaluates four of them namely\, Logistic Regression\, Sup port Vector Machines\, Random Forests\, and a Multi-Layer Perceptron (MLP). The MLP achieved the highest accuracy of 99.38% on held-out test data. Sup porting this result\, both confusion matrix analysis and t-SNE projection clearly demonstrate clear cluster separation based on individual singer identities. These findings thus collectively validate that ECAPA embeddings contain sufficient identity-bearing structure on a singing voice. This analysis thus concludes that adaptation of speaker recognition models with appropriate classifiers is a great ly effective and efficient approach for singer identification.
CATEGORIES:VIRTUAL ROOM 7E
LOCATION:Virtual Room E\, Bangkok\, Thailand
SEQUENCE:0
UID:1989e6c05222bb4808c1b1ba53c997f4
URL:http://11thictisthailand.sched.com/event/1989e6c05222bb4808c1b1ba53c997f4
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260417T125213Z
DTSTART:20260410T023000Z
DTEND:20260410T043000Z
SUMMARY:Advanced Sensor Less Field-Oriented Control of PMSM Using Super-Twisting Sliding Mode Observers for Electric Vehicle Applications
DESCRIPTION:Authors - Thomas K P\, Sherly K K\n Abstract - Permanent Magnet Synchronous Motors (PMSMs) are commonly utilized in electric vehicle (EV) traction systems because of its high efficiency\, power density\, and reliability. Conventional field-oriented control (FOC) schemes require accurate rotor position and speed information\, typically obtained from mechanical sensors\, which increase cost and reduce system reliability. Sensor less control techniques based on observer theory have therefore gained significant attention. Among them\, sliding mode observers (SMOs) offer strong robustness against parameter variations and external disturbances but suffer from chattering and noise sensitivity. This paper presents an advanced sensor less FOC strategy for PMSM drives using a super-twisting SMO (ST-SMO) for rotor position sensing and estimation of speed. The proposed approach employs a ST-SMO algorithm to achieve the convergence in finite-time while significantly reducing chattering effects. The observer is integrated into a standard FOC framework and evaluated under EV-relevant operating conditions\, including low-speed operation and load transients. Comparative performance discussion demonstrates the suitability and the effectiveness of the proposed method for high-efficiency EV traction.
CATEGORIES:VIRTUAL ROOM 7F
LOCATION:Virtual Room F\, Bangkok\, Thailand
SEQUENCE:0
UID:7f6eca63e107b7bb2c1143acb9b1aaf0
URL:http://11thictisthailand.sched.com/event/7f6eca63e107b7bb2c1143acb9b1aaf0
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260417T125213Z
DTSTART:20260410T023000Z
DTEND:20260410T043000Z
SUMMARY:An Ensemble Voting-Based Model for Reliable Sleep Disorder Classification
DESCRIPTION:Authors - N. V. Naik\, Raga Madhuri Dhulipudi\, Marisetti Sandhya\, Jadda Anjan Kumar Abstract - Distributed systems rely on data replication to ensure availability\, fault tolerance\, and scalability across multiple nodes in modern cloud environments. Replication enables systems to maintain continuity even when individual nodes fail or experience network disruptions. However\, replication often introduces synchronization delays between primary and replica nodes\, known as replication delay. These delays can cause temporary data inconsistency\, stale reads\, and increased response latency\, degrading application performance and user experience. As infrastructures scale to larger clusters\, communication overhead\, network latency\, and workload variability further amplify replication delays\, making efficient synchronization increasingly challenging. Traditional replication mechanisms typically rely on static synchronization intervals or sequential update propagation strategies. These approaches fail to adapt to dynamic network conditions and fluctuating workloads\, resulting in inefficient data propagation and delayed consistency across nodes. In large scale systems\, such limitations may cause bottlenecks\, reduced reliability\, and inconsistent states during high workload periods or network congestion. Addressing replication delay is critical for maintaining reliability and consistency in distributed environments. Recent research emphasizes intelligent synchronization mechanisms capable of adapting to changing conditions. Adaptive synchronization strategies that monitor network latency\, workload intensity\, and node communication patterns offer improvements in replication efficiency. By enabling replication decisions that respond dynamically to system behavior\, such approaches reduce synchronization delays and improve data consistency across clusters. Enhanced replication efficiency ultimately strengthens reliability\, scalability\, and operational performance in modern distributed computing platforms operating under variable workload conditions.
CATEGORIES:VIRTUAL ROOM 7F
LOCATION:Virtual Room F\, Bangkok\, Thailand
SEQUENCE:0
UID:2faa3d2df8960a396034311d794c53d6
URL:http://11thictisthailand.sched.com/event/2faa3d2df8960a396034311d794c53d6
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260417T125213Z
DTSTART:20260410T023000Z
DTEND:20260410T043000Z
SUMMARY:An Intelligent System for Vehicle Ignition Access and Real-Time Alerting for Theft Prevention in Smart Cities
DESCRIPTION:Authors - Shaik Shafi\, C Santhoshi\n Abstract - In the recent past\, vehicle theft in India has increasing nearly 2.5 times\, with more than 2 lakh vehicles stolen annually. The Delhi NCR region alone accounts for over 30% of reported cases\, and in Delhi\, a vehicle is reportedly stolen approximately every 14 minutes. These alarming trends highlight the ur-gent need for stronger and smarter vehicle security mechanisms. Traditionally\, vehicle anti-theft technologies have relied largely on non-biometric approaches such as GPS–GSM tracking modules. Thus\, biometric authentication is an emerging security approach that limits vehicle access to authorized individuals by verifying unique biological traits such as fingerprints\, facial features\, iris pat-terns\, or voice. Although this technology significantly strengthens vehicle security\, its widespread deployment still faces certain technical and social constraints. Thus in this paper\, an IoT enabled biometric ignition system with security alerts is proposed. The proposed model makes use of an ESP32 micro controller and fingerprint sensor to replace traditional keys. The system operates in two stages: first secure door access and secondly engine ignition authorization. Any unauthorized attempts trigger real-time alerts with GPS location via IoT protocols like MQTT or HTTP. Further\, cloud integration enables remote monitoring\, data storage\, and scalability\, making suitable for modern intelligent transport systems. In the same way\, the fingerprint-based vehicle starter grants the privilege of starting the vehicle only to the registered users\, thus deterring theft and ensuring safety. Over all\, biometric vehicle ignition is a dependable\, economical\, and hassle-free solution to access control as well as theft prevention.
CATEGORIES:VIRTUAL ROOM 7F
LOCATION:Virtual Room F\, Bangkok\, Thailand
SEQUENCE:0
UID:a5c92ef61c0d2ed86c40ccc0b00f66b4
URL:http://11thictisthailand.sched.com/event/a5c92ef61c0d2ed86c40ccc0b00f66b4
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260417T125213Z
DTSTART:20260410T023000Z
DTEND:20260410T043000Z
SUMMARY:Automated Question Paper Generator using NLP
DESCRIPTION:Authors - A.Sree Rama Chandra Murthy\, T.Gamya Sri\, B.Harshitha\, G.Vincent Paul Abstract - Accurate forecasting of drug demand is one of the challenging areas in the healthcare service to reduce waste as well as shortages. Some recent studies focused only on predicting drug use demand for regions and hospitals\, missing an overall way to combine these forecasts. In this study\, a multilevel machine learning framework is presented that merges regional tender demand predictions with monthly and seasonal order forecasting in hospitals and pharmacies. With historical drug usage\, the system captures time-based changes\, seasonal demands\, and also location specific behaviors . Models for regional tenders predict yearly procurement\, but models at hospitals and pharmacies try to tell the need of each month\, allowing better resource distribution. The rigorous experimental process showed better estimates and forecasting with less error than just making a single-level prediction. This framework helps to make better purchasing decisions and ensures a stable drug supply across healthcare systems. Health departments\, hospital chains\, and pharmacy groups can benefit from using a model.
CATEGORIES:VIRTUAL ROOM 7F
LOCATION:Virtual Room F\, Bangkok\, Thailand
SEQUENCE:0
UID:1bb20194834b900f3276591fd2a80d40
URL:http://11thictisthailand.sched.com/event/1bb20194834b900f3276591fd2a80d40
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260417T125213Z
DTSTART:20260410T023000Z
DTEND:20260410T043000Z
SUMMARY:Comparing Evaluation Metric Sensitivity to Identify Errors in Thai-English Machine Translation
DESCRIPTION:Authors - Seamus Lyons Abstract - Methane (CH4) emission from rice paddies is a significant source of greenhouse gas emissions from agriculture. Currently\, most models for methane prediction from rice paddies depend on collecting field data and sending it to a server. In this new paradigm\, several privacy concerns arise\, model scalability is restricted\, and a large number of data points are exposed to the attacker. This paper addresses all privacy con cerns by providing an edge-based solution for modeling methane emis sions from rice paddies that leverages data from edge sensors at respec tive locations\, while keeping individual sensor data private. The method employs different machine learning (ML) algorithms\, including Linear Regression\, Random Forest\, XGBoost\, and a Feedforward Neural Net work (FNN)\, implemented using TensorFlow Federated (TFF) in both centralized and federated learning (FL) frameworks. The FL-based FNN achieved an R2 score of 0.91\, which was superior to both centralized classical and centralized FL models\, especially for highly non-IID client side data distributions in sensor datasets. In summary\, this paper extends the current literature on modeling methane emissions from rice paddies and provides a comprehensive evaluation of our proposed FL system ar chitecture\, an in-depth discussion of the communication resources re quired for FL implementation\, and an examination of the effects of abla tion studies on clients’ data heterogeneity. Therefore\, the proposed FL approach is efficient and scalable\, enabling safe\, privacy-preserving modeling of methane emissions from rice paddies to effectively imple ment Climate Smart Agriculture (CSA) and mitigate global warming while supporting sustainable rice cultivation.
CATEGORIES:VIRTUAL ROOM 7F
LOCATION:Virtual Room F\, Bangkok\, Thailand
SEQUENCE:0
UID:f3a2625b0c29da019eeedc077ed63f85
URL:http://11thictisthailand.sched.com/event/f3a2625b0c29da019eeedc077ed63f85
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260417T125213Z
DTSTART:20260410T023000Z
DTEND:20260410T043000Z
SUMMARY:Hybrid Training for Single-Turn Medical Diagnosis with Knowledge Graph
DESCRIPTION:Authors - Gia Nghi Thoi\, My An Tran\, Tram Thi Tuyet Le\, Nhat Van Hoang Nguyen\, Long Hong Buu Nguyen\, Dien Dinh\n Abstract - Medical diagnosis using Small Language Models (SLMs) of ten suffers from hallucinations and knowledge inconsistency. While re inforcement learning (RL) from knowledge graph feedback offers a po tential solution\, pure reinforcement learning strategies often encounter challenges related to sample inefficiency and poor exploration. To address this\, a hybrid training pipeline that combines supervised alignment with structural reinforcement is proposed. The method applies knowledge guided supervised fine-tuning (SFT) with hard negatives to refine deci sion boundaries and employs a bipartite-specific reward model to capture interactions between symptoms and diseases. Experiments on multiple medical datasets\, including DXY\, GMD\, and MED-D\, demonstrate that this hybrid approach outperforms pure RL methods. By incorporating knowledge graph (KG) information as a structural regularizer\, the model achieves improved accuracy\, stronger cross-dataset generalization\, and reduced overfitting while maintaining strict adherence to diagnostic out put constraints
CATEGORIES:VIRTUAL ROOM 7F
LOCATION:Virtual Room F\, Bangkok\, Thailand
SEQUENCE:0
UID:192d0659d9435c0c362cc262a9374dce
URL:http://11thictisthailand.sched.com/event/192d0659d9435c0c362cc262a9374dce
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260417T125213Z
DTSTART:20260410T023000Z
DTEND:20260410T043000Z
SUMMARY:Knowledge Management in Artificial Intelligence Driven Adaptive Learning
DESCRIPTION:Authors - Mustafa Icel\, Ochilbek Rakhmanov\, Ergul Gunerhan\, Muhammad Qasim\n Abstract - Artificial intelligence driven adaptive learning systems progressively operate as knowledge management platforms by collecting\, refining\, and using learner knowledge to personalize instruction. However\, empirical evidence demonstrating how managed knowledge translates into measurable student achievement remains as a question to answer. This study examines the effective ness of AI driven adaptive learning as a knowledge management system in a high school setting. Using de-identified archival data from 182 students across three academic years\, the study explores relationships among AI-managed knowledge mastery\, engagement\, course performance\, and standardized assessment out comes. Learning analytics techniques\, including descriptive statistics and Pear son correlation analysis\, were employed to examine knowledge–performance re lationships. Predictive modeling using multivariable linear regression and Ran dom Forest classification was performed to assess the extent to which knowledge management indicators predict end-of-course achievement and performance lev els. Results indicate that final knowledge mastery is moderately associated with standardized assessment outcomes and is a stronger predictor of achievement than time-on-task alone. While predictive models demonstrate modest accuracy\, findings suggest that AI driven knowledge management supports student achievement when integrated within instructional contexts.
CATEGORIES:VIRTUAL ROOM 7F
LOCATION:Virtual Room F\, Bangkok\, Thailand
SEQUENCE:0
UID:f499b1865b6b78a0cfd89e19a43e64e5
URL:http://11thictisthailand.sched.com/event/f499b1865b6b78a0cfd89e19a43e64e5
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260417T125213Z
DTSTART:20260410T023000Z
DTEND:20260410T043000Z
SUMMARY:Modern Approaches to Crop Monitoring: Enhancing Productivity and Sustainability
DESCRIPTION:Authors - Akshay Kumar\, Reena Satpute\, Kumar Gaurav\, Sanjit Kumar\, Edidiong Akpabio\, Sudhir Agarmore Abstract - Recent literature has posed LLMs as nonlinear dynamical systems. LLM safety\, in these modern LLMs is about the systematic and critical monitoring of logit based oscillations\, hidden state rotations and entropy fluctuations. Many of these important factors are spectral proxies for the generation of imaginary eigenvalues. These imaginary eigenvalues are\, in a way\, determinants of the latent oscillation energy. Though the system in its original state space is inherently nonlinear\, through the Koopman operator\, we can linearize the evolution in the lifted space of observables. We design a spectral jailbreak detector that has a Sparsely regularized koopman autoencoder as its backbone. We obtain the koopman operator through this SR-KAE\, and also obtain the imaginary component of the eigenvalues of that spectral operator\, A new risk score metric is proposed that is used to classify prompts as either jailbreak or safe. This becomes a physics-style stability classifier on prompts. We present several test cases\, while we discuss the strengths and limitations of this new system.
CATEGORIES:VIRTUAL ROOM 7F
LOCATION:Virtual Room F\, Bangkok\, Thailand
SEQUENCE:0
UID:5d7b04182200ece8809de328813b9891
URL:http://11thictisthailand.sched.com/event/5d7b04182200ece8809de328813b9891
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260417T125213Z
DTSTART:20260410T023000Z
DTEND:20260410T043000Z
SUMMARY:Optimized GAN-Based Data Augmentation for Enhanced Lung Cancer CT Classification
DESCRIPTION:Authors - Kamala L\, Mohan K G Abstract - This paper presents the error performance of digital commu nication systems operating over α-Beaulieu-Xie (α-BX) and its extreme variant\, the α-BXe fading channel. A generalized noise model\, additive white generalized Gaussian noise (AWGGN)\, is adopted to account for various practical scenarios including impulsive and Laplacian environ ments. We derive closed-form average bit error rate (ABER) expressions utilizing the Fox-H function. The mathematical expressions derived are validated through numerical integration for binary phase shift keying (BPSK) and binary frequency shift keying (BFSK) modulation schemes. Our results demonstrate the degradation caused by Laplacian noise and characterize the irreducible error floors inherent in the α-BXe model\, providing a robust tool for system designers in complex fading environ ments.
CATEGORIES:VIRTUAL ROOM 7F
LOCATION:Virtual Room F\, Bangkok\, Thailand
SEQUENCE:0
UID:ffab078fb69dc70591b7c452eaebc648
URL:http://11thictisthailand.sched.com/event/ffab078fb69dc70591b7c452eaebc648
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260417T125213Z
DTSTART:20260410T023000Z
DTEND:20260410T043000Z
SUMMARY:PRIVACY-AWARE MULTI-CLOUD TASK SCHEDULING USING FEDERATED LEARNING
DESCRIPTION:Authors - Akshay Kumar\, Deepa Thilak\n Abstract - Smart city apps are growing quickly\, which means that there are more real-time\, latency-sensitive\, and privacy-critical workloads that are hard for traditional single-cloud computing models to handle. In particular\, smart mobility and traffic management systems generate large volumes of geographically distributed data that require efficient processing with minimal delay and high reliability. This project proposes a multi-cloud task scheduling framework that protects privacy and uses federated learning to solve these problems. The suggested system turns real-time smart mobility traffic data into abstract scheduling tasks and sends them to different cloud regions using a lightweight\, decision-free task broker. Each cloud region has its own local federated scheduler that uses only data that is available in that region to schedule tasks based on latency and congestion. Federated learning is used to work together to improve scheduling policies by safely combining local model updates without sharing raw data. This keeps data private and meets data sovereignty requirements. The system enables improved scalability\, reduced response time\, fault tolerance\, and avoidance of vendor lock-in compared to centralized scheduling approaches. Using a smart mobility dataset to test the proposed method shows that it works well for scheduling tasks quickly and with privacy in mind in multi-cloud settings.
CATEGORIES:VIRTUAL ROOM 7F
LOCATION:Virtual Room F\, Bangkok\, Thailand
SEQUENCE:0
UID:bd160238d62d7e20599e63872bd6e35f
URL:http://11thictisthailand.sched.com/event/bd160238d62d7e20599e63872bd6e35f
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260417T125213Z
DTSTART:20260410T023000Z
DTEND:20260410T043000Z
SUMMARY:A Methodology for LTO Decision Support in Military Aviation using Rule-Based Modeling and Synthetic Data
DESCRIPTION:Authors - Mohd. Zuhaib Ahmed\, Akash Priya\, Deepti Chopra\, Pankaj Kumar\n Abstract - Effective landing and take-off (LTO) decision-making in mil itary aviation is critically dependent on airfield serviceability and pre vailing weather conditions. A fundamental challenge is the absence of structured expert pilot decision logs\, as such data are operationally sen sitive and access-restricted. This work presents a replicable methodolog ical framework for developing machine learning-based decision support systems in domains where operational data are scarce or classified. The pipeline encompasses synthetic data forged using correlated Monte Carlo sampling\, constrained by location-specific geographic\, seasonal\, and ter rain parameters across ten Indian Air Force (IAF) stations\, yielding ap proximately 60\,000 simulated operational scenarios. The dataset is gen erated within domain-constrained operational bounds to ensure physi cal plausibility. A rule-based expert classification system assigns opera tional status as Green (Safe)\, Orange (Caution)\, or Red (Unsafe)\; four ML algorithms are subsequently evaluated: Logistic Regression\, Naïve Bayes\, Support Vector Machines\, and Decision Trees. The Decision Tree achieves the highest performance\, with an accuracy of 0.983\, an F1 score of 0.983\, and a ROC-AUC of 0.984. The proposed framework supports two deployment pathways: the rule engine as a deterministic automa tion tool for standard clearances\, and the ML model as the inference core of a real-time Human-in-Loop (HIL) expert system requiring opera tor authorisation at every decision. As expert pilot decision logs become available\, the system may be progressively elevated to a fully adaptive expert system.
CATEGORIES:VIRTUAL ROOM 7G
LOCATION:Virtual Room G\, Bangkok\, Thailand
SEQUENCE:0
UID:e7103a3f517f1e11b52679faf9455b71
URL:http://11thictisthailand.sched.com/event/e7103a3f517f1e11b52679faf9455b71
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260417T125213Z
DTSTART:20260410T023000Z
DTEND:20260410T043000Z
SUMMARY:An Adaptive Retrieval-Augmented Customer Support Agent with Learning-to-Rank Using Azure ML and OpenAI
DESCRIPTION:Authors - Ritesh Kumar Verma\, Preethiya T\n Abstract - Contemporary customer support systems require processing a massive number of user queries with low latency and high semantic relevance. Rule-based systems fail to capture context\, while fully LLM-based systems are computation ally expensive and suffer from high latency. This paper introduces an adaptive AI-assisted customer support automation system using an optimized Retrieval Augmented Generation (RAG) model. The proposed system combines Azure OpenAI embeddings\, FAISS-based vector search\, selective Cross-Encoder re ranking\, and a Learning-to-Rank (LambdaMART) model for adaptive score fu sion. Unlike vanilla RAG models\, the proposed system adaptively re-ranks only the top-k retrieved candidates\, trading off ranking precision and latency. Experi ments were carried out on a 1\,30\,000-sample e-commerce customer support da taset with query-response pairs annotated with intent labels. Compared to rule based retrieval\, embedding+FAISS\, and vanilla RAG models\, the proposed hybrid system showed improved top-1 retrieval precision with a concurrent reduc tion in end-to-end latency from 0.414s to 0.365s (≈11.8% relative improvement). The LambdaMART model adaptively learned weights from FAISS and Cross Encoder scores\, improving ranking robustness and eliminating misranked top re sponses. The system was implemented on Azure Machine Learning with a cloud scale pipeline and interactive Streamlit web interface\, showcasing the cost-effec tive inference capabilities of the proposed system via selective re-ranking. &nbsp\;
CATEGORIES:VIRTUAL ROOM 7G
LOCATION:Virtual Room G\, Bangkok\, Thailand
SEQUENCE:0
UID:e8b005a045424dfe506e563f1c77e356
URL:http://11thictisthailand.sched.com/event/e8b005a045424dfe506e563f1c77e356
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260417T125213Z
DTSTART:20260410T023000Z
DTEND:20260410T043000Z
SUMMARY:BALANCE: A Dual-Judge Framework for Fine-Grained Hallucination Detection in Arabic LLM Outputs
DESCRIPTION:Authors - Abdelrahman El Antably\, Ali Hamdi\, Ammar Mohamed\n Abstract - Large Language Models (LLMs) frequently generate plausi ble but incorrect information\, known as hallucinations. Detecting these errors at a fine-grained level is crucial\, especially for morphologically rich languages like Arabic with limited resources. We introduce BAL ANCE:Bi-perspective Analysis for LLM Accuracy via coNsensus ChEck ing\, a novel dual-judge framework for token-level hallucination detection in Arabic LLM outputs. Our six-module pipeline features context filtra tion\, argument decomposition\, and distinct strict and lenient LLM-based judges. A consensus coordinator then synthesizes their verdicts\, and a span annotator precisely localizes errors. Evaluated on the Arabic sub set of the SemEval-2025 MuSHROOM benchmark\, BALANCE achieved an Intersection over Union (IoU) score of 72.87%. This significantly outperforms the task’s winning system by approximately 8.76% rela tive improvement and consistently surpasses zero-shot baselines across various LLMs by up to 39.80 percentage points.
CATEGORIES:VIRTUAL ROOM 7G
LOCATION:Virtual Room G\, Bangkok\, Thailand
SEQUENCE:0
UID:d3cb43664e85ec6b9da6f8751da0967e
URL:http://11thictisthailand.sched.com/event/d3cb43664e85ec6b9da6f8751da0967e
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260417T125213Z
DTSTART:20260410T023000Z
DTEND:20260410T043000Z
SUMMARY:DBWiki-VN15K: Vietnamese Multimodal Knowledge Graphs for Entity Alignment
DESCRIPTION:Authors - Duy Pham\, Tung-Duong Le-Duc\, Anh-Tai Pham-Nguyen\, Trung Nguyen Mai\, Long Nguyen\, Dien Dinh\n Abstract - Multimodal knowledge graphs improve structured knowledge representation and tasks such as cross-graph entity alignment. However\, most benchmarks focus on resource-rich languages and assume dense relational structures and balanced attributes. Low-resource languages like Vietnamese pose additional challenges\, including structural sparsity\, attribute asymmetry\, and modality noise. To address this gap\, we in troduce DBWiki-VN15K\, the first large-scale Vietnamese multimodal knowledge graph dataset for entity alignment. Built from Wikidata and DBpedia\, it contains 15\,000 aligned entity pairs with relational triples\, lo calized numerical attributes\, and visual modalities. The dataset provides both word-segmented and unsegmented text to support different linguis tic processing approaches. Experiments with state-of-the-art multimodal entity alignment models reveal that structure-guided multimodal fusion and dynamic modality weighting are more robust to sparse and noisy features. Additionally\, unsegmented subword tokenization better han dles cross-graph translation inconsistencies than strict Vietnamese word segmentation. DBWiki-VN15K offers a realistic benchmark for studying multilingual and multimodal knowledge fusion. Our dataset is available at: https://github.com/Tim50c/DBWiki-VN15K.
CATEGORIES:VIRTUAL ROOM 7G
LOCATION:Virtual Room G\, Bangkok\, Thailand
SEQUENCE:0
UID:08599d72e041ee2905d2e116057f0cdd
URL:http://11thictisthailand.sched.com/event/08599d72e041ee2905d2e116057f0cdd
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260417T125213Z
DTSTART:20260410T023000Z
DTEND:20260410T043000Z
SUMMARY:Framework for Querying Database Using Natural Language
DESCRIPTION:Authors - Ritesh Jawarkar\, Reena Satpute\, Sudhir Agarmore\n Abstract - Because sleep problems can influence the health of a person and his/her quality of life\, such diagnosis and treatment relies on specific classification. Even though single deep learning and machine learning models have shown their potential\, they are limited by overfitting and bias in the model. In order to solve these issues\, the current research proposes the expansion of the ensemble learning-based sleep disorder classification through the inclusion of machine learning model predictions. A voting classifier enhances the optimization base classifier outputs in terms of robustness and classification accuracy. According to Sleep Health and Lifestyle Dataset\, the ensemble method has 97.3 percent accuracy with individual models. The interface is designed as a Flask-based web interface that allows user authentication to increase user interaction and usage of the system on a real-time basis. Suggested extension ensures the reliable\, accurate and easy-to-use automated sleep problem diagnosis.
CATEGORIES:VIRTUAL ROOM 7G
LOCATION:Virtual Room G\, Bangkok\, Thailand
SEQUENCE:0
UID:e75aee98996110a56f1badf038e75813
URL:http://11thictisthailand.sched.com/event/e75aee98996110a56f1badf038e75813
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260417T125213Z
DTSTART:20260410T023000Z
DTEND:20260410T043000Z
SUMMARY:HIERARCHICAL FEDERATED LEARNING FOR PRIVACY-PRESERVING INTELLIGENT CONTENT DELIVERY NETWORKS
DESCRIPTION:Authors - Aman Kumar\, Mary Subaja christo\n Abstract - Content Delivery Networks (CDNs) play an essential role in enhancing the content delivery speed by caching frequently requested data in edge servers distributed across geographical regions. Traditional CDNs utilize rule-based policy and machine learning approaches for optimizing the cache. Machine learning is performed centrally\, and the cache optimization is performed using the traffic logs collected by the central server. Although the use of central learning approaches is beneficial\, it poses certain limitations\, including data privacy and high communication cost. The central learning approach aggregates raw data\, which poses data privacy issues. This paper proposes an architecture for secure federated learning\, which is utilized for cache hit prediction in CDNs. The proposed architecture is evaluated using a synthetic dataset containing 1\,30\,548 records\, and the features include temporal and network features. The proposed architecture is compared with the traditional central learning approach\, and the results reveal that the secure federated learning model achieves an accuracy of 70.15%\, which is comparable to the central learning approach. The proposed architecture is found to reduce data privacy exposure by 30%.
CATEGORIES:VIRTUAL ROOM 7G
LOCATION:Virtual Room G\, Bangkok\, Thailand
SEQUENCE:0
UID:314ff8f1ae6b612b60572c83a9978306
URL:http://11thictisthailand.sched.com/event/314ff8f1ae6b612b60572c83a9978306
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260417T125213Z
DTSTART:20260410T023000Z
DTEND:20260410T043000Z
SUMMARY:IDA* Search for Event Reconstruction in Falsified Forensic Timelines
DESCRIPTION:Authors - Bambang Marsudi Salim\, Hudan Studiawan\, Baskoro Adi Pratomo\n Abstract - Digital forensic investigations face a growing threat from sophisticated log tampering\, in which adversaries delete or modify computer event logs to conceal evidence of criminal activity. This paper presents an empirical comparison of A Search and Iterative Deepening A* (IDA*) for reconstructing falsified computer event logs\, extending the previous bipartite graph framework. Three log artefacts were constructed from the public forensic timeline dataset: an original computer log\, a trusted ISP log\, and a deliberately falsified log containing 15 strategically deleted events. To address timestamp heterogeneity arising from different system and ISP browser log parsers\, a window-based matching strategy is introduced. Experiments conducted across maximal consecutive event sequences (MCES) demonstrate that IDA* consistently explores fewer nodes than A*. Anomaly detection identified 60.7% of browser events as uncorroborated by ISP records\, achieving 60.0% recall on the 15 deliberately deleted events.
CATEGORIES:VIRTUAL ROOM 7G
LOCATION:Virtual Room G\, Bangkok\, Thailand
SEQUENCE:0
UID:c825b9010808103a2c614cce09c2857f
URL:http://11thictisthailand.sched.com/event/c825b9010808103a2c614cce09c2857f
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260417T125213Z
DTSTART:20260410T023000Z
DTEND:20260410T043000Z
SUMMARY:Intelligent Auto-Reply System for Twitter using Kafka\, Spark & LSTM
DESCRIPTION:Authors - Akshay Ladha\, Supraja P\n Abstract -&nbsp\;Twitter social media platforms have become the primary means of communication for customer support\, requiring rapid\, accurate\, and scalable response solutions. Conventional customer support mechanisms are primarily manual and inefficient in handling large volumes of real-time interactions. This paper presents an AI-Assisted Twitter Support System that combines deep learning with distributed streaming engines to automate real-time customer interactions. The system design utilizes Apache Kafka for tweet streaming\, Apache Spark Streaming for distributed processing\, and Long Short-Term Memory (LSTM) networks for sentiment analysis and multi-class complaint classification. A confidence-aware decision-making module is used to ensure that automated responses are produced only when the prediction confidence level exceeds certain thresholds\, thus avoiding potential miscommunications. The system was trained and tested on the Kaggle Airline Sentiment dataset (1\,46\,400 tweets) with three sentiment classes and eight complaint categories. The sentiment analysis model attained an accuracy of 85.2% (F1-score of 0.846)\, and the complaint classification model attained an accuracy of 80.5% (F1-score of 0.792). The complete pipeline maintained an average latency of 2.9 seconds with a maximum processing rate of 2500 tweets per minute.
CATEGORIES:VIRTUAL ROOM 7G
LOCATION:Virtual Room G\, Bangkok\, Thailand
SEQUENCE:0
UID:39d0749793510d904ba9193021e83670
URL:http://11thictisthailand.sched.com/event/39d0749793510d904ba9193021e83670
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260417T125213Z
DTSTART:20260410T023000Z
DTEND:20260410T043000Z
SUMMARY:MODERN INVESTMENT CURIOSITY AND FINANCIAL DECISION-MAKING: AN EMPIRICAL STUDY OF COLLEGE TEACHERS IN KERALA\, INDIA
DESCRIPTION:Authors - Pravitha N R\, Sreelakshmi S R\, Valsalachandran K\, Savithri S Abstract - The rapid expansion of digital services has significantly increased the collection and processing of personal data through online platforms such as e-commerce systems\, social media applications\, and digital payment services. To regulate the use of personal information\, governments worldwide have introduced data protection regulations such as the General Data Protection Regulation (GDPR)\, the Digital Personal Data Protection Act (DPDPA)\, and the California Consumer Privacy Act (CCPA). Organizations publish privacy policies to inform users about their data practices\; however\, these policies are often lengthy\, complex\, and difficult for users to understand. Consequently\, users frequently accept privacy policies without fully reviewing how their personal data is collected\, processed\, and shared. Recent research has explored automated approaches for privacy policy analysis using artificial intelligence techniques\, including machine learning\, natural language processing\, and large language models. Retrieval-Augmented Generation (RAG) has further enhanced compliance evaluation by linking policy statements with relevant regulatory clauses. Despite these advancements\, challenges remain\, such as the lack of standardised datasets\, limited explainability of AI decisions\, dependence on prompt design\, and insufficient validation with regulatory experts. This paper discusses future research directions in AI-driven privacy policy compliance analysis and highlights emerging opportunities for improving regulatory compliance assessment\, user privacy protection\, and transparent privacy governance in digital ecosystems.
CATEGORIES:VIRTUAL ROOM 7G
LOCATION:Virtual Room G\, Bangkok\, Thailand
SEQUENCE:0
UID:64e8bff3eea0fd97d57e7f43745cd533
URL:http://11thictisthailand.sched.com/event/64e8bff3eea0fd97d57e7f43745cd533
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260417T125213Z
DTSTART:20260410T023000Z
DTEND:20260410T043000Z
SUMMARY:Risk-Adaptive and Change Aware Backup Optimization for Sensitive Data Using Reinforcement Learning
DESCRIPTION:Authors - Ayushi Raj\, Malathy C\n Abstract - The rapid growth of sensitive data requires backup systems that are both storage-efficient and risk-aware. Traditional backup approaches rely on static policies that ignore temporal changes\, data sensitivity\, and redundancy\, leading to inefficient storage use and higher risk exposure. This work proposes a risk-adaptive backup optimization framework integrating temporal modelling\, sensitivity-aware deduplication\, and online learning. The system reconstructs data evolution using intrinsic timestamps and quantifies data criticality through continuous sensitivity scoring. A unified risk model combines sensitivity\, change intensity\, and exposure over time to determine backup urgency. An online rein forcement learning agent dynamically optimizes backup decisions based on evolving data patterns. The framework applies secure\, sensitivity-based dedupli cation to reduce redundancy while preserving privacy. Operating in a read-only\, metadata-driven manner\, it ensures compliance with strict data governance re quirements. By decoupling decision logic from storage\, the system supports hy brid cloud environments. Experimental results show reduced storage costs and controlled risk\, demonstrating its effectiveness for scalable\, intelligent data pro tection.
CATEGORIES:VIRTUAL ROOM 7G
LOCATION:Virtual Room G\, Bangkok\, Thailand
SEQUENCE:0
UID:2f7eabbbce076ec1286f8678e89062a1
URL:http://11thictisthailand.sched.com/event/2f7eabbbce076ec1286f8678e89062a1
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260417T125213Z
DTSTART:20260410T024000Z
DTEND:20260410T025000Z
SUMMARY:Address By Local Conference Chair
DESCRIPTION:
CATEGORIES:INAUGURAL SESSION
LOCATION:Benchasiri 1\, Bangkok Marriott Hotel Sukhumvit\, Thailand
SEQUENCE:0
UID:636f646e0a35dc179d9260f435581b8c
URL:http://11thictisthailand.sched.com/event/636f646e0a35dc179d9260f435581b8c
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260417T125213Z
DTSTART:20260410T025000Z
DTEND:20260410T030000Z
SUMMARY:Address By Invited Guest & Speaker
DESCRIPTION:
CATEGORIES:INAUGURAL SESSION
LOCATION:Benchasiri 1\, Bangkok Marriott Hotel Sukhumvit\, Thailand
SEQUENCE:0
UID:ead61b9825fe10a0595b4d53281d520d
URL:http://11thictisthailand.sched.com/event/ead61b9825fe10a0595b4d53281d520d
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260417T125213Z
DTSTART:20260410T030000Z
DTEND:20260410T031000Z
SUMMARY:Address By Invited Guest & Speaker
DESCRIPTION:
CATEGORIES:INAUGURAL SESSION
LOCATION:Benchasiri 1\, Bangkok Marriott Hotel Sukhumvit\, Thailand
SEQUENCE:0
UID:a4065d04c6ae95f924bb5c1c9a1ea15c
URL:http://11thictisthailand.sched.com/event/a4065d04c6ae95f924bb5c1c9a1ea15c
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260417T125213Z
DTSTART:20260410T031000Z
DTEND:20260410T032000Z
SUMMARY:Address By Invited Guest & Speaker
DESCRIPTION:
CATEGORIES:INAUGURAL SESSION
LOCATION:Benchasiri 1\, Bangkok Marriott Hotel Sukhumvit\, Thailand
SEQUENCE:0
UID:49a18357e1eba04c49e0518f16b81763
URL:http://11thictisthailand.sched.com/event/49a18357e1eba04c49e0518f16b81763
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260417T125213Z
DTSTART:20260410T032000Z
DTEND:20260410T033000Z
SUMMARY:Address By Invited Guest & Speaker
DESCRIPTION:
CATEGORIES:INAUGURAL SESSION
LOCATION:Benchasiri 1\, Bangkok Marriott Hotel Sukhumvit\, Thailand
SEQUENCE:0
UID:a4a62d78353c26ddc7669c1af0092286
URL:http://11thictisthailand.sched.com/event/a4a62d78353c26ddc7669c1af0092286
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260417T125213Z
DTSTART:20260410T033000Z
DTEND:20260410T034000Z
SUMMARY:Address By Invited Guest & Speaker
DESCRIPTION:
CATEGORIES:INAUGURAL SESSION
LOCATION:Benchasiri 1\, Bangkok Marriott Hotel Sukhumvit\, Thailand
SEQUENCE:0
UID:a35076de9728f08839d3f669edf3a909
URL:http://11thictisthailand.sched.com/event/a35076de9728f08839d3f669edf3a909
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260417T125213Z
DTSTART:20260410T034000Z
DTEND:20260410T035000Z
SUMMARY:Address By Invited Guest & Speaker
DESCRIPTION:
CATEGORIES:INAUGURAL SESSION
LOCATION:Benchasiri 1\, Bangkok Marriott Hotel Sukhumvit\, Thailand
SEQUENCE:0
UID:7fc8abd2ae54982bc3c82c6e079a7a3b
URL:http://11thictisthailand.sched.com/event/7fc8abd2ae54982bc3c82c6e079a7a3b
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260417T125213Z
DTSTART:20260410T035000Z
DTEND:20260410T040000Z
SUMMARY:Address By Invited Guest & Speaker
DESCRIPTION:
CATEGORIES:INAUGURAL SESSION
LOCATION:Benchasiri 1\, Bangkok Marriott Hotel Sukhumvit\, Thailand
SEQUENCE:0
UID:df861b3df669d8e8779272d0e4711f41
URL:http://11thictisthailand.sched.com/event/df861b3df669d8e8779272d0e4711f41
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260417T125213Z
DTSTART:20260410T040000Z
DTEND:20260410T041000Z
SUMMARY:Address By Invited Guest & Speaker
DESCRIPTION:
CATEGORIES:INAUGURAL SESSION
LOCATION:Benchasiri 1\, Bangkok Marriott Hotel Sukhumvit\, Thailand
SEQUENCE:0
UID:95afcabeb7dd56b66ae60d733b4750d0
URL:http://11thictisthailand.sched.com/event/95afcabeb7dd56b66ae60d733b4750d0
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260417T125213Z
DTSTART:20260410T041000Z
DTEND:20260410T041500Z
SUMMARY:Felicitations and Conference Group Photograph
DESCRIPTION:
CATEGORIES:INAUGURAL SESSION
LOCATION:Benchasiri 1\, Bangkok Marriott Hotel Sukhumvit\, Thailand
SEQUENCE:0
UID:ec09cbe0f6034a5cc1a1aac241557065
URL:http://11thictisthailand.sched.com/event/ec09cbe0f6034a5cc1a1aac241557065
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260417T125213Z
DTSTART:20260410T041500Z
DTEND:20260410T044500Z
SUMMARY:Networking Tea & Coffee
DESCRIPTION:
CATEGORIES:INAUGURAL SESSION
LOCATION:Benchasiri 1\, Bangkok Marriott Hotel Sukhumvit\, Thailand
SEQUENCE:0
UID:9e320f2dc2596ccebfd272c236b08ebb
URL:http://11thictisthailand.sched.com/event/9e320f2dc2596ccebfd272c236b08ebb
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260417T125213Z
DTSTART:20260410T043000Z
DTEND:20260410T043200Z
SUMMARY:Session Chair Concluding Remarks
DESCRIPTION:\n
CATEGORIES:VIRTUAL ROOM 7A
LOCATION:Virtual Room A\, Bangkok\, Thailand
SEQUENCE:0
UID:44b9d8b57ed4e643612d457ab33b781c
URL:http://11thictisthailand.sched.com/event/44b9d8b57ed4e643612d457ab33b781c
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260417T125213Z
DTSTART:20260410T043000Z
DTEND:20260410T043200Z
SUMMARY:Session Chair Concluding Remarks
DESCRIPTION:\n
CATEGORIES:VIRTUAL ROOM 7B
LOCATION:Virtual Room B\, Bangkok\, Thailand
SEQUENCE:0
UID:ecfa47349e3e2a63a554e9d0e8ee51b9
URL:http://11thictisthailand.sched.com/event/ecfa47349e3e2a63a554e9d0e8ee51b9
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260417T125213Z
DTSTART:20260410T043000Z
DTEND:20260410T043200Z
SUMMARY:Session Chair Concluding Remarks
DESCRIPTION:\n
CATEGORIES:VIRTUAL ROOM 7C
LOCATION:Virtual Room C\, Bangkok\, Thailand
SEQUENCE:0
UID:cd97bd2b51a373df9de21630acbd137b
URL:http://11thictisthailand.sched.com/event/cd97bd2b51a373df9de21630acbd137b
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260417T125213Z
DTSTART:20260410T043000Z
DTEND:20260410T043200Z
SUMMARY:Session Chair Concluding Remarks
DESCRIPTION:\n
CATEGORIES:VIRTUAL ROOM 7D
LOCATION:Virtual Room D\, Bangkok\, Thailand
SEQUENCE:0
UID:910147e76615832c094b6a0d2759c9c9
URL:http://11thictisthailand.sched.com/event/910147e76615832c094b6a0d2759c9c9
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260417T125213Z
DTSTART:20260410T043000Z
DTEND:20260410T043200Z
SUMMARY:Session Chair Concluding Remarks
DESCRIPTION:\n
CATEGORIES:VIRTUAL ROOM 7E
LOCATION:Virtual Room E\, Bangkok\, Thailand
SEQUENCE:0
UID:cfba9a7f42c7d2e95fb0125eb4e11e85
URL:http://11thictisthailand.sched.com/event/cfba9a7f42c7d2e95fb0125eb4e11e85
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260417T125213Z
DTSTART:20260410T043000Z
DTEND:20260410T043200Z
SUMMARY:Session Chair Concluding Remarks
DESCRIPTION:\n
CATEGORIES:VIRTUAL ROOM 7F
LOCATION:Virtual Room F\, Bangkok\, Thailand
SEQUENCE:0
UID:cf8f48a2bb59ba6a9c05d68520c6cbd7
URL:http://11thictisthailand.sched.com/event/cf8f48a2bb59ba6a9c05d68520c6cbd7
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260417T125213Z
DTSTART:20260410T043000Z
DTEND:20260410T043200Z
SUMMARY:Session Chair Concluding Remarks
DESCRIPTION:\n
CATEGORIES:VIRTUAL ROOM 7G
LOCATION:Virtual Room G\, Bangkok\, Thailand
SEQUENCE:0
UID:589d1f83cd04f04251c094b8b4cc64c0
URL:http://11thictisthailand.sched.com/event/589d1f83cd04f04251c094b8b4cc64c0
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260417T125213Z
DTSTART:20260410T043200Z
DTEND:20260410T043500Z
SUMMARY:Session Closing and Information To Authors
DESCRIPTION:\n
CATEGORIES:VIRTUAL ROOM 7A
LOCATION:Virtual Room A\, Bangkok\, Thailand
SEQUENCE:0
UID:54b001c292b538ef3250ad0d345ca48f
URL:http://11thictisthailand.sched.com/event/54b001c292b538ef3250ad0d345ca48f
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260417T125213Z
DTSTART:20260410T043200Z
DTEND:20260410T043500Z
SUMMARY:Session Closing and Information To Authors
DESCRIPTION:\n
CATEGORIES:VIRTUAL ROOM 7B
LOCATION:Virtual Room B\, Bangkok\, Thailand
SEQUENCE:0
UID:47f6a2da9259348ff1cc3d43c27abb85
URL:http://11thictisthailand.sched.com/event/47f6a2da9259348ff1cc3d43c27abb85
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260417T125213Z
DTSTART:20260410T043200Z
DTEND:20260410T043500Z
SUMMARY:Session Closing and Information To Authors
DESCRIPTION:\n
CATEGORIES:VIRTUAL ROOM 7C
LOCATION:Virtual Room C\, Bangkok\, Thailand
SEQUENCE:0
UID:2dd19e119b0a509925e71d573c3eac5a
URL:http://11thictisthailand.sched.com/event/2dd19e119b0a509925e71d573c3eac5a
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260417T125213Z
DTSTART:20260410T043200Z
DTEND:20260410T043500Z
SUMMARY:Session Closing and Information To Authors
DESCRIPTION:\n
CATEGORIES:VIRTUAL ROOM 7D
LOCATION:Virtual Room D\, Bangkok\, Thailand
SEQUENCE:0
UID:319fba9d536a65601fc33defa1b4734e
URL:http://11thictisthailand.sched.com/event/319fba9d536a65601fc33defa1b4734e
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260417T125213Z
DTSTART:20260410T043200Z
DTEND:20260410T043500Z
SUMMARY:Session Closing and Information To Authors
DESCRIPTION:\n
CATEGORIES:VIRTUAL ROOM 7E
LOCATION:Virtual Room E\, Bangkok\, Thailand
SEQUENCE:0
UID:7e1361f9f5d2b8b34f2b6e6fd2cee826
URL:http://11thictisthailand.sched.com/event/7e1361f9f5d2b8b34f2b6e6fd2cee826
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260417T125213Z
DTSTART:20260410T043200Z
DTEND:20260410T043500Z
SUMMARY:Session Closing and Information To Authors
DESCRIPTION:\n
CATEGORIES:VIRTUAL ROOM 7F
LOCATION:Virtual Room F\, Bangkok\, Thailand
SEQUENCE:0
UID:d8e6460f5d10a90d28d12626e127fb4a
URL:http://11thictisthailand.sched.com/event/d8e6460f5d10a90d28d12626e127fb4a
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260417T125213Z
DTSTART:20260410T043200Z
DTEND:20260410T043500Z
SUMMARY:Session Closing and Information To Authors
DESCRIPTION:\n
CATEGORIES:VIRTUAL ROOM 7G
LOCATION:Virtual Room G\, Bangkok\, Thailand
SEQUENCE:0
UID:2dbaee5c8786bd542c8866f64785bf27
URL:http://11thictisthailand.sched.com/event/2dbaee5c8786bd542c8866f64785bf27
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260417T125213Z
DTSTART:20260410T044500Z
DTEND:20260410T050000Z
SUMMARY:Spatio-Temporal Deep Learning for Cellular Traffic Prediction
DESCRIPTION:Authors - Sunakshi Singh\, Abhay Kumar Agrahari\, Raghav Abstract - As cellular networks move toward 6G\, traffic behavior becomes increasingly complex\, shaped by user mobility and diverse service demands that vary across time and location. Accurate traffic prediction is therefore critical for efficient resource allocation and intelligent network operation. However\, traditional statistical and conventional machine learning approaches rely on simplifying assumptions and struggle to capture the rich spatio-temporal interactions observed in large urban networks. Although recurrent models such as LSTM are effective at learning temporal patterns\, they offer limited insight into how traffic evolves across geographically distributed regions. To address these limitations\, this work frames cellular traffic prediction as a spatio-temporal learning problem and introduces a deep learning framework that jointly models temporal dynamics and spatial correlations using historical CDR data. The proposed approach is evaluated on real-world urban datasets and benchmarked against statistical and deep learning baselines\, demonstrating superior prediction accuracy\, faster convergence\, and greater robustness under limited training data.
CATEGORIES:PHYSICAL TECHNICAL SESSION 1A
LOCATION:Benchasiri 1\, Bangkok Marriott Hotel Sukhumvit\, Thailand
SEQUENCE:0
UID:dbf7c8f1bc674910130b88f1892ddd06
URL:http://11thictisthailand.sched.com/event/dbf7c8f1bc674910130b88f1892ddd06
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260417T125213Z
DTSTART:20260410T044500Z
DTEND:20260410T050000Z
SUMMARY:CNN-Based Automated Detection of Mango Leaf Diseases Using Transfer Learning
DESCRIPTION:Authors - Md. Nadimul Islam\, Sajid-Ul Islam\, Tahsina Islam Afra\, Mohammad Shidujaman Abstract - Foliar diseases impact negatively on the health and productivity of mango trees\, hence it is essential to manage them effectively. The proposed research is an automated approach to diagnosing popular in common mango leaf diseases\, such as Anthracnose\, Bacterial Canker\, and Powdery Mildew\, utilizing high-throughput imagery. The suggested methodology deploys a Transfer Learning model which employs MobileNetV2 framework which is already trained using ImageNet to guarantee successful and precise classification on battery limited devices such as Raspberry Pi. With the combination of target feature detection and a specialized classification head\, the system offers real-time detection that can be used in spraying mechanisms using the IoT. Through experimental analysis\, it is shown that the proposed CNN-based framework is highly accurate in terms of classification when the experiment is conducted under controlled conditions and as such\, the framework has potential to be used in automated mango leaf disease detection.
CATEGORIES:PHYSICAL TECHNICAL SESSION 1B
LOCATION:Benchasiri 2\, Bangkok Marriott Hotel Sukhumvit\, Thailand
SEQUENCE:0
UID:99ee5569d4dd0b28816a2f107dc110e1
URL:http://11thictisthailand.sched.com/event/99ee5569d4dd0b28816a2f107dc110e1
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260417T125213Z
DTSTART:20260410T044500Z
DTEND:20260410T050000Z
SUMMARY:Performance Prediction of Free Space Optical Communication systems using Neural Networks
DESCRIPTION:Authors - Shreepreet Sahu\, Prasant Kumar Sahu Abstract - Free-space optical (FSO) communication is a promising technology for B5G and 6G communication systems due to its security\, reliability\, high data rates\, low latency and electromagnetic immunity. However\, its performance is limited by atmospheric turbulence\, weather conditions\, beam divergence\, misalignment errors and link range variations. Existing analytical or simulation-based methods become too complex or computationally expansive as number of impairments considered simultaneously increases introducing a gap in fast and precise system-level performance estimation. This limitation motivates the use of intelligent data-driven approaches capable of capturing highly nonlinear interrelations. This paper proposes an artificial neural network (ANN) for predicting Q-factor values of the modelled system. The ANN-based model is trained by an extensive dataset generated under varying FSO link ranges and other scenarios. Model legitimacy specification starts with error histograms proceeding through mean squared error (MSE) convergence finding concluding regression analysis before eye pattern evaluation takes place. As shown by the results the high prediction accuracy\, generalization capability and closeness of forecasted Q-value to the actual one ensures noticeable improvement over existing framework satisfactorily addressing the above issues. The proposed approach provides an efficient alternative to conventional analytical methods\, making it suitable for real-time performance evaluation and optimization practical FSO systems.
CATEGORIES:PHYSICAL TECHNICAL SESSION 1C
LOCATION:Benchasiri 3\, Bangkok Marriott Hotel Sukhumvit\, Thailand
SEQUENCE:0
UID:30641eea5c5d41541d3b75530efe3bb1
URL:http://11thictisthailand.sched.com/event/30641eea5c5d41541d3b75530efe3bb1
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260417T125213Z
DTSTART:20260410T044500Z
DTEND:20260410T050000Z
SUMMARY:A Low-Cost Smartphone-Based System for Detecting Falls from an Altitude
DESCRIPTION:Authors - Nikhil Kumar\, Anurag Barthwal\, Shakti Kundu Abstract - Falls from an altitude are among the most common causes of both fatal and non-fatal injuries in the global community and second only to road traffic accidents in accidental mortality. One of the primary problems in alleviating the effects of such incidents is the late detection and reporting of falls\, especially in the cases where witnesses are not present\, which exposes the victim to a high risk of severe injuries\, or even death\, because of the lack of medical care. To curb this problem\, this paper proposes an effective and affordable smartphone-based solution towards automated detection of human falls off heights. The suggested solution uses built-in smartphone sensors namely accelerators and barometers to record motion dynamics and changes in altitude which are linked to falls. The primary characteristics\, such as the absolute linear acceleration\, change in altitude\, are acquired and applied to train and test a Support Vector Machine (SVM)-based classification model\, which shows strong performance\, with the F1-score of 0.94\, which\, in turn\, proves the high reliability of the model in differentiating between fall and non-fall events. The results indicate the success of the multi-sensor data fusion with machine learning methods and emphasize the possible relevance of the given system to practical applications in the field of fall detection in real-time\, early emergency response\, and the overall occupational and population safety schemes.
CATEGORIES:PHYSICAL TECHNICAL SESSION 1D
LOCATION:Benchasiri 4\, Bangkok Marriott Hotel Sukhumvit\, Thailand
SEQUENCE:0
UID:c7e6a8cdcdda0da61a04dd4ecba78d52
URL:http://11thictisthailand.sched.com/event/c7e6a8cdcdda0da61a04dd4ecba78d52
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260417T125213Z
DTSTART:20260410T050000Z
DTEND:20260410T051500Z
SUMMARY:Optimal Personal Study Plan Generation using Meta-Heuristic Algorithms
DESCRIPTION:Authors - G.L.H.B. Gaweshika\, T.G.I. Fernando Abstract - Optimization has become an active research area nowadays in every field majoring in Computer Science. This research focuses on developing an Optimal Personal Study Plan (PSP) generation system utilizing Metaheuristic Algorithms\, considering the specific requirements of an individual student for a degree program. The PSP generation problem can be considered as an NP-hard problem\, highlighting the need for efficient meta-heuristic algorithms to tackle this optimization challenge. The novel contribution of this work lies in the de-sign of a Genetic Algorithm (GA) and a Hybridized Genetic Algorithm-based Firefly Algorithm (GA-FA) for the PSP generation. The developed metaheuristic-based approach presents a promising avenue for enhancing the personalized study plan concept for students and academic support systems.
CATEGORIES:PHYSICAL TECHNICAL SESSION 1A
LOCATION:Benchasiri 1\, Bangkok Marriott Hotel Sukhumvit\, Thailand
SEQUENCE:0
UID:16971819c05489fc15bf70a0415ec7ed
URL:http://11thictisthailand.sched.com/event/16971819c05489fc15bf70a0415ec7ed
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260417T125213Z
DTSTART:20260410T050000Z
DTEND:20260410T051500Z
SUMMARY:Green IT Capital as a Catalyst: How Green Innovation and Green Finance Index enhance Sustainable Business Performance
DESCRIPTION:Authors - Kazi Saiful Islam\, Sadman Kabir\, Abir Sen Gupta\, Sayra Islam Saki\, Md. Tafshir Jaman Takib\, S.M. Sayem\n Abstract - This paper explores the critical role of Green Innovation and Green Finance Index in influencing Sustainable Business Performance with a specific focus on Green IT Capital as mediator. For primary data collection\, questionnaire was distributed among Bangladeshi employees appointed in several industries and 407 responses were obtained. The Partial Least Square Structural Equation Modelling (PLS-SEM) approach was used for the data analysis. The findings demonstrate that Green Innovation (consisted of Green Product Innovation\, Green Process Innovation and Green Technology Innovation) as well as Green Finance Index (consisted of Green Bond and Green Investment) positively influence Sustainable Business Performance. Moreover\, Green IT Capital directly impacts Sustainable Business Performance. Additionally\, Green IT Capital significantly mediates the relationship of Green Finance Index and Sustainable Business Performance\, however\, significant mediation between the relationship of Green Innovation and Sustainable Business Performance was not found\, which is a central finding of this study. The results infer several insights for firms to utilize the funds to integrate Green IT Capital in their core activities to attain sustainable outcomes. The findings clarify the need to arrange policies to incentivize Green IT Capital adoption across industries. These factors may enhance Green Communication strategies and accelerate the nation to attain SDG 9 and SDG 12.
CATEGORIES:PHYSICAL TECHNICAL SESSION 1B
LOCATION:Benchasiri 2\, Bangkok Marriott Hotel Sukhumvit\, Thailand
SEQUENCE:0
UID:1cdd0805d519ead7f02b838547ce3d2f
URL:http://11thictisthailand.sched.com/event/1cdd0805d519ead7f02b838547ce3d2f
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260417T125213Z
DTSTART:20260410T050000Z
DTEND:20260410T051500Z
SUMMARY:Mind2Video: Generating Video Using EEG
DESCRIPTION:Authors - Poorna Pragnya H\, Neha V Malage\, Pranav Muppuru\, Sanya Vashist\, Surabhi Narayan\n Abstract - This work introduces a novel Sequence-to-Sequence (Seq2Seq) framework that converts Electroencephalography (EEG) signals and related metadata into coherent natural language descriptions. The key innovation is a spatio-temporal EEG encoder built using Dense Graph Convolutional Networks (GCNs)\, which effectively model spatial relationships among electrodes as well as their temporal dynamics in multi-channel EEG data. This encoder is coupled with an attentiondriven Gated Recurrent Unit (GRU) decoder to generate textual sequences. To strengthen learning\, the model adopts a multi-task objective that simultaneously predicts scene-level attributes\, such as colors and objects\, alongside caption generation\, promoting better alignment between EEG features and language outputs. Experiments on a large-scale dataset demonstrate competitive results\, achieving a BLEU score of 0.21\, ROUGE-1 of 0.4519\, and ROUGE-L of 0.4447. The generated captions are further used as inputs to a text-to-video generation module. While precise pixel-level matching remains difficult\, evaluation shows strong semantic alignment between generated and reference videos\, with an SSIM of 0.19 and a CLIP-based semantic similarity score of 0.746. Overall\, the results highlight the promise of GCN-based EEG representations for complex language decoding and downstream video generation tasks.
CATEGORIES:PHYSICAL TECHNICAL SESSION 1C
LOCATION:Benchasiri 3\, Bangkok Marriott Hotel Sukhumvit\, Thailand
SEQUENCE:0
UID:b67c78bc43ef5100c1a9b08be11e9688
URL:http://11thictisthailand.sched.com/event/b67c78bc43ef5100c1a9b08be11e9688
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260417T125213Z
DTSTART:20260410T050000Z
DTEND:20260410T051500Z
SUMMARY:Investigating the Potential Correlation between Harralick Texture-Derived Surface Roughness and Rice Yield Using UAV Imagery: A Pilot Study
DESCRIPTION:Authors - Van-Cuong Nguyen\, Huu-Cuong Nguyen\, Quang-Hieu Ngo\, Trong-Hieu Luu\, Thanh-Tam Nguyen Abstract - This paper aims to introduce the relationship between surface roughness and rice yield on paddy field using camera mounted on UAV. Unlike other studies where people focus on genes and rice varieties\, we think that the surface roughness also has a big impact on rice yield. We surveyed paddy by using successive aerial images\, generated the ortho-photos before conducted the surface roughness by using Harralick texture extracting. From the resulting mapping photo\, we chose three distinct local areas for sample data collection based on the surface differences. Three different treatments were applied across these areas\, with agronomic traits and yield components meticulously documented. As the crop season progressed\, discernible disparities in crop vitality emerged\, observable both in the field and through analysis using the Normalized Difference Vegetation Index (NDVI). Furthermore\, our rigorous evaluation of agronomic traits and yield components revealed statistically significant disparities among treatments\, reaching the remarkable 1% significance level. These findings hold considerable promise for farmers\, facilitating informed decisions in land use planning for subsequent crop seasons.
CATEGORIES:PHYSICAL TECHNICAL SESSION 1D
LOCATION:Benchasiri 4\, Bangkok Marriott Hotel Sukhumvit\, Thailand
SEQUENCE:0
UID:f86602599864c4ceecff02afeb34e0cd
URL:http://11thictisthailand.sched.com/event/f86602599864c4ceecff02afeb34e0cd
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260417T125213Z
DTSTART:20260410T051300Z
DTEND:20260410T051500Z
SUMMARY:Opening Remarks
DESCRIPTION:\n
CATEGORIES:VIRTUAL ROOM 8A
LOCATION:Virtual Room A\, Bangkok\, Thailand
SEQUENCE:0
UID:651ddf44f41b107de82e5f0000966a74
URL:http://11thictisthailand.sched.com/event/651ddf44f41b107de82e5f0000966a74
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260417T125213Z
DTSTART:20260410T051300Z
DTEND:20260410T051500Z
SUMMARY:Opening Remarks
DESCRIPTION:\n
CATEGORIES:VIRTUAL ROOM 8B
LOCATION:Virtual Room B\, Bangkok\, Thailand
SEQUENCE:0
UID:4a0828c5c48ab26a974a31b61ed2ef1f
URL:http://11thictisthailand.sched.com/event/4a0828c5c48ab26a974a31b61ed2ef1f
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260417T125213Z
DTSTART:20260410T051300Z
DTEND:20260410T051500Z
SUMMARY:Opening Remarks
DESCRIPTION:\n
CATEGORIES:VIRTUAL ROOM 8C
LOCATION:Virtual Room C\, Bangkok\, Thailand
SEQUENCE:0
UID:541b27e398299b95e112ffc24c468c50
URL:http://11thictisthailand.sched.com/event/541b27e398299b95e112ffc24c468c50
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260417T125213Z
DTSTART:20260410T051300Z
DTEND:20260410T051500Z
SUMMARY:Opening Remarks
DESCRIPTION:\n
CATEGORIES:VIRTUAL ROOM 8D
LOCATION:Virtual Room D\, Bangkok\, Thailand
SEQUENCE:0
UID:c893841c4d91bb146927b3ff85e2153b
URL:http://11thictisthailand.sched.com/event/c893841c4d91bb146927b3ff85e2153b
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260417T125213Z
DTSTART:20260410T051300Z
DTEND:20260410T051500Z
SUMMARY:Opening Remarks
DESCRIPTION:\n
CATEGORIES:VIRTUAL ROOM 8E
LOCATION:Virtual Room E\, Bangkok\, Thailand
SEQUENCE:0
UID:f686ba96ed6b3c847f1c448872dc9e57
URL:http://11thictisthailand.sched.com/event/f686ba96ed6b3c847f1c448872dc9e57
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260417T125213Z
DTSTART:20260410T051300Z
DTEND:20260410T051500Z
SUMMARY:Opening Remarks
DESCRIPTION:\n
CATEGORIES:VIRTUAL ROOM 8F
LOCATION:Virtual Room F\, Bangkok\, Thailand
SEQUENCE:0
UID:b9f62996430b8a651a734ca0579ea62b
URL:http://11thictisthailand.sched.com/event/b9f62996430b8a651a734ca0579ea62b
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260417T125213Z
DTSTART:20260410T051300Z
DTEND:20260410T051500Z
SUMMARY:Opening Remarks
DESCRIPTION:\n
CATEGORIES:VIRTUAL ROOM 8G
LOCATION:Virtual Room G\, Bangkok\, Thailand
SEQUENCE:0
UID:a3158802b296b8bd1edbe1fc54b7dd0a
URL:http://11thictisthailand.sched.com/event/a3158802b296b8bd1edbe1fc54b7dd0a
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260417T125213Z
DTSTART:20260410T051500Z
DTEND:20260410T053000Z
SUMMARY:Security and Privacy in Edge Computing: A Bibliometric Analysis of Recent Developments (2023-2025)
DESCRIPTION:Authors - Kalpesh Popat\, Divyakant Meva Abstract - Context Edge computing allows for processing data in real-time closer to its sources\, which helps in applications like IoT\, smart cities\, healthcare\, and industrial systems. However\, security and privacy concerns hinder its mass adoption. This bibliometric analysis deals with security and privacy research in edge computing from 2023 – 2025. In compliance with the PRISMA guidelines\, we con-ducted a bibliometric analysis on 643 peer-reviewed journal articles obtained from Scopus\, employing methods such as analysis of publication trends\, key-word co-occurrence\, technology mapping\, and domain analysis using VOSviewer and Biblioshiny software. Number of publications also grew exponentially (165 in 2023\, 402 in 2024\, 76 in early 2025 alone). The dataset provides h-index of 18 and g-index of 32. Security technologies such as blockchain\, federated learning\, and machine learning are prevalent. Primary domains include IoT networks\, healthcare\, and vehicular computing. All the publications are open access. Output in publications is led by China\, India and the United States. This field shows fast maturing with the focus on lightweight cryptography\, privacy-preserving mechanisms\, and integration of the emerging technologies. Future research needs to focus on scalability\, energy efficiency\, and standardization to support mainstream adoption.
CATEGORIES:PHYSICAL TECHNICAL SESSION 1A
LOCATION:Benchasiri 1\, Bangkok Marriott Hotel Sukhumvit\, Thailand
SEQUENCE:0
UID:8429e3b25eb9945b8d0c032589a3b1e1
URL:http://11thictisthailand.sched.com/event/8429e3b25eb9945b8d0c032589a3b1e1
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260417T125213Z
DTSTART:20260410T051500Z
DTEND:20260410T053000Z
SUMMARY:LLM-enabled Disease Diagnosis for Patients at Admission Using Multimodal Data
DESCRIPTION:Authors - Thinh Truong\, Chau Vo\, Anh Duong Abstract - The early diagnosis of patient conditions at the hospital admission stage is crucial for optimizing medical resource allocation\, reducing overcrowding\, and improving patient outcomes. Traditional diagnostic approaches at admission rely on limited initial information and expert assessment\, which can lead to misclassification and delayed treatment. This paper proposes a multimodal data-driven approach that integrates Large Language Model (LLM) to predict patient conditions using structured and unstructured medical data. In particular\, we propose a classification model that leverages LLM for multimodal data processing and generates feature representation based on demographics\, biometrics\, vital signs\, lab values and electrocardiogram (ECG) data for 78-disease diagnoses. Compared to the existing models\, our model decides a better data fusion with semantics-preserving. Indeed\, evaluated through experiments on the constructed dataset from MIMIC-IV using standard metrics such as Area Under the Receiver Operating Characteristic (AUROC)\, Precision\, Recall\, and F1-score\, the proposed model outperforms traditional ones. Experimental results also highlight the potential of integrating multiple data sources for automated patient triage at the admission stage.
CATEGORIES:PHYSICAL TECHNICAL SESSION 1B
LOCATION:Benchasiri 2\, Bangkok Marriott Hotel Sukhumvit\, Thailand
SEQUENCE:0
UID:f7c35b0a53096dfa8f3b32185cb33395
URL:http://11thictisthailand.sched.com/event/f7c35b0a53096dfa8f3b32185cb33395
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260417T125213Z
DTSTART:20260410T051500Z
DTEND:20260410T053000Z
SUMMARY:A Causal-Chain Transformer with Structured Latent Stress Evolution for Drought Forecasting
DESCRIPTION:Authors - Barsa Priyadarshani Behera\, Monalisa Jena\, Ranjan Kumar Behera\, Sung-Bae Cho Abstract - Drought prediction remains challenging due to complex physical interactions and limited observability of land-atmosphere processes. This study proposes a Causal-Chain Transformer that explicitly employs drought evolution through three sequential latent representations corresponding to heat stress\, evaporation stress\, and soil moisture stress. Using only past temperature and evaporation data over a xed historical window\, the model predicts future drought occurrence at a predened lead time\, while excluding current soil moisture to avoid target leak- age. Experiments on region-averaged NASA POWER and ERA5-Land datasets over Odisha\, a state of India\, show that the proposed model achieves the highest F1-scores (0.709 on NASA POWER and 0.467 on ERA5-Land)\, outperforming logistic regression\, Long Short-Term Memory (LSTM)\, and standard Transformer baselines. The learned latent stress signals provide intrinsic interpretability\, with early increases in heat and evaporation stress frequently preceding observed drought events\, supporting its applicability for early-warning systems in agriculture- dependent regions.
CATEGORIES:PHYSICAL TECHNICAL SESSION 1C
LOCATION:Benchasiri 3\, Bangkok Marriott Hotel Sukhumvit\, Thailand
SEQUENCE:0
UID:693b22b0ac38fe56e2aeb51aa12dfd10
URL:http://11thictisthailand.sched.com/event/693b22b0ac38fe56e2aeb51aa12dfd10
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260417T125213Z
DTSTART:20260410T051500Z
DTEND:20260410T053000Z
SUMMARY:PUF-based authentication protocol for VANETs system
DESCRIPTION:Authors - Abhay Kumar Agrahari\, Snehal Rajput\, Omji\, Akhil Pandey\, Chiluka Varshith Reddy Abstract - In today’s era\, reliable and safe communication has become a major requirement in smart vehicle networks. In this research work\, we present a specific method for authentication between the vehicle’s on-board unit (OBU) and roadside unit (RSU)\, which uses Physical Unclonable Function (PUF). This technology provides an identity for each vehicle unit that cannot be repeated. In this process\, both units are registered with a reliable authority\, which is the basis of certification. The process of mutual certification not only pays attention to safety\, but has also been made faster with minimal resources. The validation of the protocol is checked via the ROR model and the AVISPA tool\, which shows that this model is protected from common security threats. In addition\, we will compare our proposed protocol with predefined algorithms on the basis of communication cost and also do the security analysis. This study offers a general description of the VANET authentication system that is practical\, safe and skilled.
CATEGORIES:PHYSICAL TECHNICAL SESSION 1D
LOCATION:Benchasiri 4\, Bangkok Marriott Hotel Sukhumvit\, Thailand
SEQUENCE:0
UID:47bbd958ba6fba042a7efc8bfed6f726
URL:http://11thictisthailand.sched.com/event/47bbd958ba6fba042a7efc8bfed6f726
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260417T125213Z
DTSTART:20260410T051500Z
DTEND:20260410T071500Z
SUMMARY:A REVIEW ON FRAMEWORK FOR DETECTION AND PREVENTION OF EMAIL PHISHING ATTACKS
DESCRIPTION:Authors - Morveen Bamania\, Anilkumar Patel\, Yassir Farooqui Abstract - Digital image manipulation has become sophisticated day by day with the help of advanced editing tools. This posing significant challenges to image authenticity verification and raising a critical concern in the field of legal proceedings\, social harmony\, scientific publications\, forensic and law enforcement\, healthcare and journalism. In this paper we implement a unique and novel approach for the detection of image forgery. We use Convolutional Autoencoder (CAE) combined with Error Level Analysis (ELA). Our proposed preprocessing pipeline follows the sequence: resize the input image and pass through ELA apply denoise method. Where Gaussian denoising is strategically applied to the ELA output rather than the original image to preserve forgery artifacts while reducing noise. The CAE architecture consists of a four-block encoder that compresses input images into a 128- dimensional latent space\, a symmetric decoder for reconstruction\, and a fully connected classifier for binary forgery detection. The model is trained using a combined loss function. One is Mean Squared Error (MSE). It helps for reconstruction. The other one is Binary Cross- Entropy (BCE). It improves its ability to correctly classify. Experimental evaluation on the CASIA v2.0 dataset demonstrates the effectiveness of our approach. It is achieving competitive accuracy\, precision\, recall\, and F1-score metrics. The proposed method successfully identifies both copy-move and splicing forgeries. It identifies the forgeries by analyzing compression artifact inconsistencies revealed through ELA.
CATEGORIES:VIRTUAL ROOM 8A
LOCATION:Virtual Room A\, Bangkok\, Thailand
SEQUENCE:0
UID:c961844e9e8a17b2eacde90fdfb2f394
URL:http://11thictisthailand.sched.com/event/c961844e9e8a17b2eacde90fdfb2f394
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260417T125213Z
DTSTART:20260410T051500Z
DTEND:20260410T071500Z
SUMMARY:AI-Driven Penalty Performance Analysis System: A Multi-Modal Explainable AI Approach for Football Strategy
DESCRIPTION:Authors - Albert Manamela\, Tevin Moodley\n Abstract - Student retention is critical for academic quality and institutional effectiveness\, especially in programs where foundational natural science courses such as mathematics\, physics\, and chemistry strongly influence progression and pose significant challenges. Early dropout identification in these contexts requires predictive models that are both accurate and interpretable. This study proposes an interpretable machine learning framework for student dropout prediction using academic\, financial\, and demographic data. It combines cost-sensitive XGBoost with Shapley Additive exPlanations (SHAP)\, addressing class imbalance without synthetic oversampling to preserve authentic performance patterns. Using a benchmark dataset from the Polytechnic Institute of Portalegre\, the model achieved strong performance (Accuracy = 89.6%\, F1 = 0.834\, AUC-ROC = 0.934). SHAP analyses identified academic engagement\, tuition payment status\, and scholarship access as key predictors. The findings support transparent early-warning systems and inform policies to improve retention\, strengthen support in science-based learning environments\, and promote equitable student outcomes.
CATEGORIES:VIRTUAL ROOM 8A
LOCATION:Virtual Room A\, Bangkok\, Thailand
SEQUENCE:0
UID:36be7eaa59499bad0cb88f51bd1582e2
URL:http://11thictisthailand.sched.com/event/36be7eaa59499bad0cb88f51bd1582e2
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260417T125213Z
DTSTART:20260410T051500Z
DTEND:20260410T071500Z
SUMMARY:Automated Generation of High-Level Architectural Diagrams from Embedded System Code using Explainable AI
DESCRIPTION:Authors - Aqdas Hassan\, Farooque Azam\, and Muhammad Waseem Anwar Abstract - The RISC-V Vector Extension (RVV) enables scalable data-parallel processing through a flexible vector length architecture\, offers a standardized and scalable approach to vector computing. Derived from an analysis of existing RVV architectures\, this paper presents a focused architectural study and implementation of a basic RVV-based vector extension. Unlike complex\, high-performance designs\, the proposed architecture prioritizes simplicity and clarity\, implementing only essential vector arithmetic and memory instructions. The vector extension is integrated with a single-cycle scalar RISC-V core\, and instruction decoding is implemented and verified at RTL level. Functional simulation confirms correctness of RVV instruction decoding. This work bridges the gap between theoretical RVV studies and practical step-by-step hardware implementation.
CATEGORIES:VIRTUAL ROOM 8A
LOCATION:Virtual Room A\, Bangkok\, Thailand
SEQUENCE:0
UID:cdbe7eb96eadf80986fe0d9d2e186b3b
URL:http://11thictisthailand.sched.com/event/cdbe7eb96eadf80986fe0d9d2e186b3b
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260417T125213Z
DTSTART:20260410T051500Z
DTEND:20260410T071500Z
SUMMARY:Development and Strategic Analysis of a Java-Based Healthcare Management System with Integrated WebRTC Telemedicine: Bridging the Digital Divide in Emerging Markets
DESCRIPTION:Authors - Harshwardhan Singh Rathore\, Dev Krishan\, Amit\, Abhinav Vyas\, Harshit Choudhary\, Kunal Chittora\, Vishal Shrivastava\, Ram Babu Buri\, Akhil Pandey\, Mukesh Mishra Abstract - Predicting protein–ligand binding affinity is an essential step in early drug discovery. We present Alchemy\, a ligand-centric Graph Neural Network (GNN) framework for predicting binding affinities (pKd/pKi) from molecular graphs and a production-ready web interface for easy inference. Using a curated subset of the PDBbind dataset for prototyping and RDKit for cheminformatics preprocessing [6]\, we implement a message-passing GCN model with global pooling and train it using MSE regression. We evaluate model performance using RMSE\, MAE\, Pearson and Spearman correlations\, and Concordance Index\, and compare against docking scores and classical ML baselines. On the demo subset our model achieves an RMSE of X (±Y) and Pearson r of Z (±W) — results that highlight the potential and limitations of ligand-only approaches. We discuss data-scaling\, protein incorporation strategies\, ablation studies\, and provide reproducible code and a web app to facilitate adoption.
CATEGORIES:VIRTUAL ROOM 8A
LOCATION:Virtual Room A\, Bangkok\, Thailand
SEQUENCE:0
UID:a2e24f4e23a5d90f2a0bee858abe521e
URL:http://11thictisthailand.sched.com/event/a2e24f4e23a5d90f2a0bee858abe521e
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260417T125213Z
DTSTART:20260410T051500Z
DTEND:20260410T071500Z
SUMMARY:HyperGNNs for Multi-Modal Classification and Severity Analysis of Neurodegenerative Disorders
DESCRIPTION:Authors - K Bhavish Raju\, K Musadiq Pasha\, Mohammed Saqlain\, Nishaan Padanthaya\, Jayashree R Abstract - Neuro-degenerative disorders\, particularly Alzheimer’s Disease (AD)\, pose a significant challenge in early diagnosis and severity assessment due to overlapping symptoms with conditions such as Mild Cognitive Impairment (MCI) and Cognitively Normal (CN) conditions. Accurate differentiation between these stages is essential for timely intervention but remains difficult due to the progressive and heterogeneous nature of these disorders. Traditional machine learning models struggle to effectively integrate diverse data modalities\, such as medical imaging (MRI) and clinical tabular data. This study proposes Hypergraph Neural Networks (HyperGNNs) based framework to enhance multi-modal classification and disease severity modeling. By representing complex patient relationships as hypergraphs\, our approach aims to improve diagnostic accuracy\, reduce misdiagnosis\, and provide an interpretable framework for understanding disease progression. To ensure clinical transparency\, we incorporate explainability techniques such as SHAP and Grad-CAM to ensure model transparency\, enabling clinicians to understand key features influencing predictions. The model will be evaluated on standard neuro-imaging datasets and clinical records\, offering potential applications in personalized medicine and early intervention strategies.
CATEGORIES:VIRTUAL ROOM 8A
LOCATION:Virtual Room A\, Bangkok\, Thailand
SEQUENCE:0
UID:9bef170ad86ffa7a6a2f6e088133951a
URL:http://11thictisthailand.sched.com/event/9bef170ad86ffa7a6a2f6e088133951a
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260417T125213Z
DTSTART:20260410T051500Z
DTEND:20260410T071500Z
SUMMARY:Research Status and Challenges of Electronic Waste Small Component Detection Based on Improved YOLOv8
DESCRIPTION:Authors - Zhou Xu\, Shuzlina Abdul Rahman\, Norlina Mohd Sabri\, Rogayah Abdul Majid Abstract - For the integration of solar systems within the power grid\, there is the requirement for smarter systems that are capable of not only detecting faults but also optimizing their performance. The current paper introduces an innovative hybrid method that focuses on the detection of solar thermal faults and adaptive grid control\, where the challenge had existed in the separation of the two aspects. This is achieved through the use of a deep learning U-Net model\, where different kinds of solar panel fault types\, such as single and multi hotspots\, are detected from grayscale thermal images. The different kinds of fault types identified are used as a reinforcement learning approach (PPO)\, where decisions regarding safe and efficient use of the grid are made while considering fault awareness. Higher priority is granted to critical fault types through rewards that use penalties. It also comes with an immediate safety function to isolate faulty panels with zero delay for smooth and efficient function of the solar energy grid.
CATEGORIES:VIRTUAL ROOM 8A
LOCATION:Virtual Room A\, Bangkok\, Thailand
SEQUENCE:0
UID:8e9e310aa51f22266b6e21d495044f99
URL:http://11thictisthailand.sched.com/event/8e9e310aa51f22266b6e21d495044f99
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260417T125213Z
DTSTART:20260410T051500Z
DTEND:20260410T071500Z
SUMMARY:SMART EXPENSES TRACKER: MANAGE EXPENSE SMARTLY
DESCRIPTION:Authors - Shubhrat Chaursiya\, Toshif Mohammed Shaikh\, Snehlata\, Sangam Kumari\, Vishal Shriastava\, Ram Babu Buri\, Vibhakar Pathak Abstract - Computational modeling is essential for studying complex pedestrian dynamics under emergency conditions. This paper presents the design and implementation of an Emergency Evacuation Simulator\, a robust grid-based modeling tool developed in Java. The system integrates two core components: an Agent-Based Model (ABM) for pedestrian behavior and Cellular Automata (CA) for modeling dynamic hazard propagation (Fire and Smoke spread). A key innovation is the use of an Optimized Breadth-First Search (BFS) algorithm coupled with 8directional pathfinding (Chebyshev distance)\, which significantly improves path efficiency and movement realism compared to traditional 4-directional methods. The simulator incorporates heterogeneous agents with varying vulnerability levels and features local collision avoidance. Experimental analysis confirms the efficiency of the 8-directional path finding and provides quantitative metrics on evacuation time\, rate\, and fatality statistics\, offering a valuable platform for enhancing building safety protocols and emergency response strategies.
CATEGORIES:VIRTUAL ROOM 8A
LOCATION:Virtual Room A\, Bangkok\, Thailand
SEQUENCE:0
UID:b8cd1fa5c78617ebc48a3978b18375a7
URL:http://11thictisthailand.sched.com/event/b8cd1fa5c78617ebc48a3978b18375a7
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260417T125213Z
DTSTART:20260410T051500Z
DTEND:20260410T071500Z
SUMMARY:The Development and Effect of AI-Powered Farmer Support Chatbots.
DESCRIPTION:Authors - Vedant Khade\, Supriya Narad Abstract - The world agricultural industry is increasingly becoming more complex due to the variability of climate\, increasing shortage of resources\, and the demand to obtain real-time and localized information. The conventional agricultural extension services that have been hindered by operational limited costs and low ratios of the farmers to experts tend to fail to provide the required advice at the right time and in a more personalized way especially to the smallholder farmers in the remote and resource-limited locations. The present paper examines the new and disruptive position of the AI-based farmer support chatbots as a scalable\, effective\, and ubiquitous response to this issue. They offer 24/7\, multi-lingual\, and highly context-sensitive advice on a wide range of issues\, including complicated crop management protocols\, early pest and disease detection\, live market price tracking\, and navigation of complicated government subsidy programs\, using their sophistication in Natural Language Processing (NLP)\, advanced Machine Learning (ML) algorithms and Computer Vision (CV). The study conducts a synthesis of the existing technological practices and provides important quantitative evidence\, including these findings\; (a) large-scale changes in the profitability of farmers\, yield maximization\, and efficient resource use\; (b) the critical analysis of the technical and socio-ethical issues\, including the bias of the data\, the lack of digital literacy\, and the accountability systems. The paper concludes by offering an assumption that although rigorous\, responsible\, and ethical development is the most important\, farmer support chatbots are not merely the instruments of the incremental change\, but should be the ones that will radically transform agricultural knowledge dissemination\, which will subsequently result in more resilient\, productive\, and sustainable global food systems.
CATEGORIES:VIRTUAL ROOM 8A
LOCATION:Virtual Room A\, Bangkok\, Thailand
SEQUENCE:0
UID:b96d15f5e17c64a18df02fd5aedc2a95
URL:http://11thictisthailand.sched.com/event/b96d15f5e17c64a18df02fd5aedc2a95
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260417T125213Z
DTSTART:20260410T051500Z
DTEND:20260410T071500Z
SUMMARY:The Next Generation of Code Quality Assurance: AI- Accelerated Code Review Platforms
DESCRIPTION:Authors - Aditya Kasture\, Supriya Narad\n Abstract - The pace of change of the software development industry is unprecedented as the introduction of AI code generation tools has not only doubled the productivity of developers by up to 55 percent but also introduced the industry with a new problem of exponential growth in the complexity of the code and technical debt. The former techniques of code review are monotonous\, infrequent and time consuming. Such an approach cannot validate the mammoth amounts of gains that are evident in an AI-oriented development cycle. The structure\, performance\, and service of AI-Accelerated Code Review (AACR) Platforms\, which we discuss in this paper\, would be the last mile of quality control that would be the solution to this so-called paradoxical situation of such engineering productivity. We propose an AACR system\, which is built on a Multi-Agent Architecture with Large Language Models (LLM) to accomplish contextual and reasoning problems\, custom machine learning (ML) models to evaluate security and performance\, and a code graph analysis to obtain a good composition of the codebase. We conclude that median code review time is an option to decrease by 40-60 per- cent with the AACR platforms. Besides\, the accuracy of the detection of the defects can also rise in comparison with the old method of analyzing and reviewing of the data manually. The article relies on the primary argument presented in the description above and the debates concerning the unlawful use of AI generated data and the in- creased use of AI.
CATEGORIES:VIRTUAL ROOM 8A
LOCATION:Virtual Room A\, Bangkok\, Thailand
SEQUENCE:0
UID:20485d3ad4aaca7d0e3b7870622222b3
URL:http://11thictisthailand.sched.com/event/20485d3ad4aaca7d0e3b7870622222b3
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260417T125213Z
DTSTART:20260410T051500Z
DTEND:20260410T071500Z
SUMMARY:Urban Scene Intelligence: A Semantic Anchor-and-Expand Framework for Grounded Scene Understanding
DESCRIPTION:Authors - V. R. Badri Prasad\, Shrujana Patil\, Shreeraksha\, Prathik S. Hanji\, S Vikas Vathsal\n Abstract - Traditional object detection systems are limited in their ability to capture the complexity of urban scenes\, often overlooking critical spatial\, contextual\, and functional relationships required. This paper introduces Urban Scene Intelligence\, a Semantic Anchor-and-Expand (SAE) framework that integrates multi-modal perception\, structured scene graph construction\, and controlled narrative generation to produce grounded descriptions of urban environments. The proposed modular architecture incorporates OWL-ViT for open-vocabulary object detection\, SegFormer for semantic segmentation\, DepthAnything for spatial depth estimation\, Qwen2-VL for attribute enrichment\, and OCR for extracting textual context. Unlike end-to-end multimodal models\, the threestage pipeline explicitly separates visual perception\, symbolic reasoning\, and language generation\, thereby improving interpretability and factual grounding. By unifying heterogeneous visual cues into a symbolic representation and generating context-aware descriptions from this representation\, the SAE framework establishes a transparent and extensible approach to urban scene understanding in complex real-world environments.
CATEGORIES:VIRTUAL ROOM 8A
LOCATION:Virtual Room A\, Bangkok\, Thailand
SEQUENCE:0
UID:35e24acd77f5c7891f4488fd962bc772
URL:http://11thictisthailand.sched.com/event/35e24acd77f5c7891f4488fd962bc772
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260417T125213Z
DTSTART:20260410T051500Z
DTEND:20260410T071500Z
SUMMARY:A Dual Reactive-Proactive Multi-Agent System for Personalized University Tutoring using LLMs and RAG
DESCRIPTION:Authors - Pablo Figueroa\, Valeria Yunga\, Pablo Ramon\, Nelson Piedra Abstract - Traditional airport meet-and-greet operations are often characterized by a sea of physical placards and manual\, paper-based logging systems. This manual approach not only creates logistical clutter in arrival halls but also leads to significant information lag and frequent data entry errors during the administrative reconciliation process. This paper presents the design and implementation of a centralized digital platform developed to streamline the coordination be-tween airport authorities\, hotel representatives\, and arriving passengers. Utilizing a responsive web-based architecture\, the system eliminates the requirement for native application installations\, thereby ensuring immediate accessibility for international travelers and hotel staff through their mobile devices. The platform integrates a multi-tier interface that facilitates real-time booking\, automated digital check-ins\, and instantaneous data synchronization. By replacing error-prone manual key-in tasks with an automated data pipeline\, the system provides airport management with real-time operational visibility and analytics. Preliminary results from the implementation demonstrate a substantial reduction in guest waiting times and a marked improvement in data accuracy. Ultimately\, this digital transition enhances terminal space management and provides a more seamless\, professional experience for international arrivals\, establishing a scalable model for modern airport ground handling services.
CATEGORIES:VIRTUAL ROOM 8B
LOCATION:Virtual Room B\, Bangkok\, Thailand
SEQUENCE:0
UID:8b731a0a3ef4b48c7aa07ad6c43f9c38
URL:http://11thictisthailand.sched.com/event/8b731a0a3ef4b48c7aa07ad6c43f9c38
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260417T125213Z
DTSTART:20260410T051500Z
DTEND:20260410T071500Z
SUMMARY:AI-Powered Early Dyslexia Detection Using Webcam-Based Eye Tracking\, Speech Analysis\, and Adaptive Learning: A Multimodal Review and System Framework
DESCRIPTION:Authors - Monali Deshmukh\, Payal Shete\, Tanvi Pakhale\, Pranjal Alhat\, Krutika Salve Abstract - Because of their expensive price\, large size\, and reliance on lab settings\, conventional oscilloscopes are inconvenient tools for signal analysis. They have made it necessary to have small\, inexpensive\, portable devices that can see waveforms outside of typical lab settings. The creation of a portable digital oscilloscope utilizing a 2.8-inch TFT display and an ESP32 microprocessor is detailed in this paper. Because of its autonomous operation\, the gadget can record data in real time and display analog signals. Because it runs on batteries\, the oscilloscope is affordable\, lightweight\, and portable. The ESP32 samples analog signals and displays them with user-controlled time-base settings. This oscilloscope has features including a grid display\, waveform zooming\, and freeze for convenience and readability. Both AC and DC signals can be monitored with an oscilloscope. According to tests\, the device accurately displays common waveforms including sine\, square\, and sawtooth signals\, which makes it ideal for embedded system development\, simple troubleshooting\, and instructional purposes.
CATEGORIES:VIRTUAL ROOM 8B
LOCATION:Virtual Room B\, Bangkok\, Thailand
SEQUENCE:0
UID:18e18f50379855d26525bf020f489f33
URL:http://11thictisthailand.sched.com/event/18e18f50379855d26525bf020f489f33
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260417T125213Z
DTSTART:20260410T051500Z
DTEND:20260410T071500Z
SUMMARY:Artificial Intelligence in Predictive Analysis of Electoral Processes in Ecuador
DESCRIPTION:Authors - Luis Anthony Hidalgo Ponce\, Maricela Pinargote-Ortega Abstract - Technical support management in university environments often faces a high manual operational load due to the constant increase in digital service requests. This paper presents a multi-agent system based on Large Language Models (LLMs) designed to automate the ticket lifecycle\, including classification\, urgency-based prioritization\, and intelligent routing. The proposed solution is built upon a modular architecture coordinated by an orchestrator agent and integrated with Retrieval-Augmented Generation (RAG) techniques to resolve frequent queries without human intervention. The system’s performance was evaluated through a controlled dataset\, achieving a classification accuracy of 85.7% and a 100% effectiveness rate in user intent detection. The results demonstrate a significant reduction in response times compared to manual processes\, validating the efficacy of generative artificial intelligence to optimize efficiency and user experience within university technology service desks.
CATEGORIES:VIRTUAL ROOM 8B
LOCATION:Virtual Room B\, Bangkok\, Thailand
SEQUENCE:0
UID:379b8c070faa7c7794be6b9f0801beb7
URL:http://11thictisthailand.sched.com/event/379b8c070faa7c7794be6b9f0801beb7
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260417T125213Z
DTSTART:20260410T051500Z
DTEND:20260410T071500Z
SUMMARY:Comparative Evaluation of Commercial ASR APIs for Specialized Domains: Performance Analysis\, Limitations\, and Future Directions
DESCRIPTION:Authors - Madhuri Surwase\, Trupti Bansode\, Jyoti Pawar\, Smita Katkar\, Vaishali Kalsgonda\, Prakash Bansode\, Namdev Falake\n Abstract - Automatic Speech Recognition (ASR) systems have achieved remarkable progress through deep learning and Transformer-based architectures\, demonstrating near-human accuracy on clean audio. However\, their performance degrades significantly under challenging conditions and specialized domains. This comprehensive study evaluates leading commercial ASR APIs—Google Cloud Speech-to-Text\, Microsoft Azure Speech Service\, AssemblyAI\, Deepgram\, OpenAI Whisper\, Speechmatics\, and others—across multiple dimensions: general speech recognition\, low-quality forensic-like audio\, domain-specific mathematical notation\, and personalized speaker adaptation. Results demonstrate 100% accuracy on clean audio for leading systems (Deepgram\, Speechmatics\, Webkit SpeechRecognition)\, but dramatic performance degradation to 10− 81% word error rates on forensic-like audio. Analysis of domain-specific challenges reveals that none of the tested commercial ASR systems natively support direct transcription of mathematical symbols and Greek letters into structured symbolic output (e.g.\, LaTeX). The study identifies critical limitations in robustness\, modularity\, and domain adaptation\, while highlighting promising customization mechanisms including custom vocabularies\, language models\, and post-processing integration. Performance improvements through speaker personalization ranged from 3% for natural voices to 10% for synthetic voices. Despite notable advances in end-to-end and Transformer-based approaches\, ASR systems remain unsuitable for forensic applications and specialized domains without substantial customization and post-processing. Future research must address low-resource performance\, linguistic diversity\, robustness in extreme noise\, and the integration of Large Language Models for semantic understanding. This paper synthesizes recent advances and critical gaps\, providing a roadmap for advancing ASR technology in specialized and challenging acoustic environments.
CATEGORIES:VIRTUAL ROOM 8B
LOCATION:Virtual Room B\, Bangkok\, Thailand
SEQUENCE:0
UID:bfef3039e95800b16bbff8660f7582ec
URL:http://11thictisthailand.sched.com/event/bfef3039e95800b16bbff8660f7582ec
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260417T125213Z
DTSTART:20260410T051500Z
DTEND:20260410T071500Z
SUMMARY:Developing Augmented Reality Using Assemblr Edu to Introduce the Alphabet to Dyslexic Children in Elementary School
DESCRIPTION:Authors - Nurul Istiq faroh\, Nur Asitah\, Amiruddin Hadi Wibowo\, Ricky Setiawan\, Abdur-Razaq Aliyy Abolaji\, Hendratno Abstract - Detecting structural breaks and anticipating volatility regimes in foreign exchange markets remain challenging due to the non-stationary and nonlinear nature of exchange rate dynamics. This study proposes a non-parametric framework for identifying structural breaks in the NZD/ USD exchange rate by integrating sliding-window volatility estimation\, concentration bound based change point detection\, and wavelet-based time frequency analysis. Volatility is first quantified using a movingwindow approach and compared against a Hoeffding bound to detect extraordinary events. The resulting change points are used to segment the exchange rate series into statistically reliable sequences\, which are subsequently analyzed using wavelet scalograms. Empirical results reveal a consistent three-regime structure in the wavelet domain\, comprising post-event reaction\, stable market behavior\, and pre-event escalation phases. Non-parametric statistical tests confirm significant differences in volatility distributions across these regimes\, with the pre-event regime exhibiting markedly higher variability and acting as a precursor to structural breaks. The findings demonstrate that wavelet coefficients contain informative signatures of impending market instability. Overall\, the proposed framework provides an interpretable and robust approach for analyzing regime-dependent volatility dynamics and offers valuable insights for early warning and risk management in currency markets.
CATEGORIES:VIRTUAL ROOM 8B
LOCATION:Virtual Room B\, Bangkok\, Thailand
SEQUENCE:0
UID:bc1db13ab8e598daf295d716abeada25
URL:http://11thictisthailand.sched.com/event/bc1db13ab8e598daf295d716abeada25
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260417T125213Z
DTSTART:20260410T051500Z
DTEND:20260410T071500Z
SUMMARY:Energy Consumption Trend Analysis from Smart Meter Data under Big Data Tools
DESCRIPTION:Authors - Syeda Zaina Rohana Sneha\, Mohammad Shamsul Arefin\, M. M. Musharaf Hussain Abstract - This study details the development and evaluation of a web-based digital health platform that uses Optical Character Recognition (OCR) and Artificial Intelligence (AI) to automate the reading of medication labels and manage appointments. Users photograph medication labels and appointment slips\, and the system automatically extracts and organizes relevant data to generate medication schedules\, appointment calendars\, and reminders with minimal manual effort. Designed with a user-centered approach to lessen cognitive load\, the platform was tested with 35 users. Three experts verified the content validity of the assessment tool via the Item Objective Congruence (IOC) index. User satisfaction analysis indicates high approval\, particularly for reducing the memory burden associated with medication routines and appointments. The results indicate that integrating OCR and AI can support continuous care\, enhance usability\, and increase patient engagement in the sustainable management of chronic diseases.
CATEGORIES:VIRTUAL ROOM 8B
LOCATION:Virtual Room B\, Bangkok\, Thailand
SEQUENCE:0
UID:c7d4d8668789e738a23f73cae3d42f24
URL:http://11thictisthailand.sched.com/event/c7d4d8668789e738a23f73cae3d42f24
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260417T125213Z
DTSTART:20260410T051500Z
DTEND:20260410T071500Z
SUMMARY:HARMONIA: A Pluggable\, Risk-Aware Data Sharing Framework with Continuous Compliance\, Provenance\, and Machine Unlearning — Design and Proof-of-Concept Blueprint
DESCRIPTION:Authors - Tirupathi Rao Dockara\, Manisha Malhotra Abstract - The prediction of cardiovascular disease (CVD) risk by machine learning is frequently impeded by duplicated and associated clinical characteristics\, leading to complex and less robust models. Feature selection is therefore essential to improve model compactness while maintaining predictive performance. This study presents a systematic evaluation of meta-heuristic-based feature selection for CVD risk modeling under a standardized experimental setting. Feature selection is formulated as a wrapper-based optimization problem and evaluated using representative population-based meta-heuristic algorithms from multiple families. All methods are assessed using the XGBoost Histogram classifier on a public cardiovascular dataset comprising approximately 70\,000 records with 13 clinical features. Experimental results show that meta-heuristic feature selection consistently reduces the number of input features by more than 60% while achieving comparable predictive performance across different algorithmic families. In addition\, SHAP analysis is employed to examine the contributions of the selected features and support model interpretability.
CATEGORIES:VIRTUAL ROOM 8B
LOCATION:Virtual Room B\, Bangkok\, Thailand
SEQUENCE:0
UID:e6cd047a586fbf1f46747878d3e519d9
URL:http://11thictisthailand.sched.com/event/e6cd047a586fbf1f46747878d3e519d9
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260417T125213Z
DTSTART:20260410T051500Z
DTEND:20260410T071500Z
SUMMARY:Machine Learning for Causal Inference on AI Adoption
DESCRIPTION:Authors - Md. Shahidul Islam\, Ronobir Chandra Sarker\n Abstract - The widespread adoption of artificial intelligence (AI) and automation is emerging as a central driver of productivity growth in European firms. Yet identifying the causal impact of AI adoption on firm productivity is complicated by endogeneity\, selection bias\, and heterogeneous treatment effects. This paper analyzes the productivity effects of AI and automation adoption using a unified framework that combines traditional econometric techniques with causal machine learning methods. Using firm-level data from Orbis merged with industry-level productivity and ICT capital measures from EU KLEMS for the period 2010–2023\, we estimate both average and heterogeneous treatment effects. Double Machine Learning yields a robust average productivity gain of approximately 4.5 percent\, while Causal Forests reveal substantial heterogeneity across industries\, firm size\, human capital\, and digital maturity. The results provide credible causal evidence that AI adoption enhances firm productivity and highlight the importance of complementary capabilities in realizing its economic benefits.
CATEGORIES:VIRTUAL ROOM 8B
LOCATION:Virtual Room B\, Bangkok\, Thailand
SEQUENCE:0
UID:7728979e70af990ac37d28f5f27f685f
URL:http://11thictisthailand.sched.com/event/7728979e70af990ac37d28f5f27f685f
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260417T125213Z
DTSTART:20260410T051500Z
DTEND:20260410T071500Z
SUMMARY:SkillBizz: A Social Media App for Local Businesses and Skilled Services
DESCRIPTION:Authors - Sonia Kuwelkar\, Veena Gauns\, Rohit Sopan\, Sonia Shetkar\, Dinanath Usgaonkar Abstract - Prompt engineering has emerged as an essential paradigm in leveraging desired behaviors from large language models (LLMs) without altering their parameters. Although the majority of the current literature has revolved around the introduction of novel prompt engineering strategies\, there has been comparatively less emphasis on the contribution of the evaluation and optimization of prompts in concrete systems. In this paper\, we offer a specialized review of prompt engineering from an evaluation/optimization centric viewpoint with a larger nod to conceptual developments and illumination rather than detailing the comparisons of approaches. Furthermore\, we attempt to establish the concrete importance of prompt engineering via a real-life application\, which resulted in improved performances in tasks through the process of prompt refinement and informal evaluations without the need to change the architecture and weights of the models. The paper will also introduce the deficiencies in prompt engineering in the realms of re-producibility\, robustness\, and the unavailability of standardized approaches in the aspect of concrete evaluations.
CATEGORIES:VIRTUAL ROOM 8B
LOCATION:Virtual Room B\, Bangkok\, Thailand
SEQUENCE:0
UID:e61d3f287341cad208e30b5532c1a10d
URL:http://11thictisthailand.sched.com/event/e61d3f287341cad208e30b5532c1a10d
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260417T125213Z
DTSTART:20260410T051500Z
DTEND:20260410T071500Z
SUMMARY:Spatial Geo-Informatics and Big Data Analytics on Marine Litter Monitoring
DESCRIPTION:Authors - Domenico Vito\, Carol Maione\, Gabriela Fernandez\, Catia Algieri\, Sudip Chakraborty Abstract - The demand for long-endurance\, intelligent drone systems is growing across diverse domains including defense\, sports analytics\, and industrial inspection. This paper presents the design and implementation of a solar-powered drone platform equipped with an autonomous\, image-based range scoring system. Leveraging high-efficiency monocrystalline photovoltaic panels and Silicon- Carbide (SiC)-based lithium-ion batteries\, the drone achieves extended flight durations while maintaining energy reliability. A centralized Energy Management System (EMS)\, featuring Maximum Power Point Tracking (MPPT) control\, optimizes real-time energy harvesting and distribution. The platform also integrates an AI-enhanced thermal imaging module for precise target impact detection and scoring\, with results computed using a multi-parameter range scoring model. An interactive Ground Control Station (GCS) interface enables intuitive mission planning\, telemetry visualization\, and data export. Experimental evaluations demonstrate significant gains in energy efficiency and scoring precision\, underscoring the system’s potential for sustainable\, autonomous aerial operations in real-world conditions.
CATEGORIES:VIRTUAL ROOM 8B
LOCATION:Virtual Room B\, Bangkok\, Thailand
SEQUENCE:0
UID:82a89f23e95e39616ae30ba256dbd062
URL:http://11thictisthailand.sched.com/event/82a89f23e95e39616ae30ba256dbd062
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260417T125213Z
DTSTART:20260410T051500Z
DTEND:20260410T071500Z
SUMMARY:A Deep Learning Framework Using CNN\, LSTM\, and Transfer Learning for Multi-Class Detection of COVID-19 and Pneumonia from Chest X-ray Images
DESCRIPTION:Authors - Shylaja P\, Jayasudha J S Abstract - The Question Answering system (QA) is one of the popular and widely used ap-plications of NLP. It is an information retrieval system that attempts to find the correct answer for a question based on the given paragraph text. Transformers have been widely used for QA tasks\, due to their contextual embedding\, attention mechanism\, and transfer learning for specialized tasks. Transformer-based models can be easily fine-tuned with large datasets. Such models provide state-of-the-art performance over large datasets for question-answering tasks. The proposed approach compares performance of transformer based model over a small sized dataset. We incorporated an answer formation layer along with transformers to comprehend contextual\, syntactical\, and semantic information from small-sized datasets. We developed a set of rules according to question categories to generate semantically and syntactically coherent option sentences based on the questions. These rules transformed option phrases into contextually appropriate sentences. We evaluated SBERT transformer models namely all-mpnet-base-v2\, all-MiniLM-L6-v2\, all-distilroberta-v1 over answer formatted data and it showed in-crease in accuracy. Answer formation rules over noun phrases of small-sized datasets can help transformers to learn contextual knowledge about the options in the QA sample\, Addition of answer formation stage on samples of SciQ data resulted in a rise in accuracy from 86 % to 92 % when using all-MiniLM-L6-v2 model.
CATEGORIES:VIRTUAL ROOM 8C
LOCATION:Virtual Room C\, Bangkok\, Thailand
SEQUENCE:0
UID:cf822fea91b57093e9abff33ec4baf9f
URL:http://11thictisthailand.sched.com/event/cf822fea91b57093e9abff33ec4baf9f
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260417T125213Z
DTSTART:20260410T051500Z
DTEND:20260410T071500Z
SUMMARY:A Secure and Decentralized Framework for Threshold-Based Encrypted Image Sharing Using Blockchain and IPFS
DESCRIPTION:Authors - Asritha Paruchuri\, Gudivada Krishna Prakash\, Mulla Junaid Rahman\, Lambu Damarukanath\, Guttikonda Prashanti\n Abstract - The sharing of images in decentralized settings needs high assurances of secrecy\, integrity and controlled access. The fast development of cloud-based services and online communication tools have multiplied the communication of sensitive images\, and the traditional centralized storage and single-layer security systems are susceptible to cyber-attacks\, unauthorized access\, and data leakage. The presented paper outlines a safe and decentralized image-sharing system based on Advanced Encryption Standard (AES)\, the Secret Sharing scheme by Shamir\, blockchain authentication\, and decentralized storage with the Interplanetary File System (IPFS). First\, the input image is encrypted with the help of AES to provide high cryptographic confidentiality. The ciphertext image is further split into shares with secret sharing scheme that avoids unauthorized disclosure and only allows the reconstruction of the encrypted image when the necessary number of valid shares is received. The encrypted shares that are generated are stored in a decentralized way using IPFS\, which is highly available\, fault tolerant\, and does not have a single point of failure. Decentralized access control\, participant authentication and integrity verification that is tamper-resistant are enforced using blockchain technology. In the reconstruction process\, the encrypted image is reconstructed with the help of Lagrange interpolation and then decrypted with the help of AES\, which guarantees safe and lossless recovery of the original im-age. The suggested framework offers multi-layer security\, increases confidentiality and data integrity\, removes centralized vulnerabilities\, and is highly resistant to unauthorized access and data-alteration.
CATEGORIES:VIRTUAL ROOM 8C
LOCATION:Virtual Room C\, Bangkok\, Thailand
SEQUENCE:0
UID:e43d2f4e34e13b46a5a263919d54b78e
URL:http://11thictisthailand.sched.com/event/e43d2f4e34e13b46a5a263919d54b78e
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260417T125213Z
DTSTART:20260410T051500Z
DTEND:20260410T071500Z
SUMMARY:A Study on Deep Learning for Welding Surface Inspection
DESCRIPTION:Authors - Mach Thai Loc\, Nguyen Hong Phuc\, Huu-Cuong Nguyen Abstract - With the development of e-commerce and global supply chains\, there is a growing concern about fake or counterfeit products. Current methods for verifying product authenticity are often cumbersome \,time consuming\, and vulnerable to tampering. In order to address these issues\, for the purpose of this project\, a QR code based "Fake Product Detection System" is introduced. In this system\, the manufacturer creates an exclusive QR code for each product. The manufacturer then keeps the QR code in a database. If the QR code is scanned through the web-based application\, the code is instantly verified. If the code is unique and has not been used be-fore\, the product is genuine. But if the code is duplicated or used multiple times\, the product is deemed counterfeit. The system is implemented using the Flask web development framework\, SQLite database\, and web interfaces using the HTML/CSS duo\, which is lightweight and easy to use. Other notable features of the system are user authentication\, history logging\, suspicious image upload for the QR code\, and detection of counterfeit items. Overall\, this solution would offer a simple\, economical\, and efficient means to uncover Trojan products while fostering trust amongst consumers and aiding manufacturers to track counterfeit practices.
CATEGORIES:VIRTUAL ROOM 8C
LOCATION:Virtual Room C\, Bangkok\, Thailand
SEQUENCE:0
UID:6a1907cb96c9bc1e80423a9d61b2c623
URL:http://11thictisthailand.sched.com/event/6a1907cb96c9bc1e80423a9d61b2c623
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260417T125213Z
DTSTART:20260410T051500Z
DTEND:20260410T071500Z
SUMMARY:An Efficient Hybrid LSTM–GRU Stacking Model for Acoustic Vehicle Classification in Smart City Traffic Systems
DESCRIPTION:Authors - K. Thirupathi Reddy\, K. Venkata Ajay Kumar\, M. Kaveri Abstract - This study explores female creators’ subjective lived experiences navigating human–AI interaction (HAI) within generative design ecosystems. It examines how creators engage with intelligent systems during collaborative creation processes and how they negotiate creative agency between algorithmic outputs and personal meaning-making. Drawing on an Interpretative Phenomenological Analysis (IPA) approach\, the study involves seven women who actively utilize Canva’s AI-enabled capabilities to produce professional digital content. Data were collected through in-depth semi-structured interviews and digital observation of design outputs distributed on Instagram. The findings indicate that participants interpret Canva AI as a collaborative creative partner that supports iterative dialogue\, experimentation\, and reflective decision-making. Rather than replacing human authorship\, AI interaction functions as a mediated process in which creators provide prompts\, reinterpret generated results\, and refine instructions to align outcomes with their subjective intentions. This interaction fosters a sense of psychological safety\, particularly among non-professional designers\, enabling them to explore creative practices with greater confidence. Through this ongoing negotiation between human agency and algorithmic assistance\, participants describe pathways toward professional identity formation and increased participation in contemporary digital creative cultures. Overall\, the study highlights how intelligent design systems can shape meaning-making processes\, reinforce creative self-efficacy\, and support women’s evolving roles within AI-assisted visual communication practices.
CATEGORIES:VIRTUAL ROOM 8C
LOCATION:Virtual Room C\, Bangkok\, Thailand
SEQUENCE:0
UID:0c1ef74c1c21ef272bd4035ed9887348
URL:http://11thictisthailand.sched.com/event/0c1ef74c1c21ef272bd4035ed9887348
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260417T125213Z
DTSTART:20260410T051500Z
DTEND:20260410T071500Z
SUMMARY:Bone Fracture Detection in X-ray: A Comparative Evaluation of YOLOv8 Variants
DESCRIPTION:Authors - Dudi Gnana Prasoona\, Zeenathunnisa\, Yamuna V\, Pushyami B\, Ramandeep Kaur\, Navjot Kaur\n Abstract - In the global health sector\, Diabetes is a major concern which needs accurate and effective models for early prediction. This work is quantitative re-search work. The dataset was collected from CDC Diabetes Health Indicators\, and we used Light Gradient Boosting Machine (LightGBM) model for predicting diabetes. Since this research work is binary classification-based work\, in our data preprocessing stage\, we used Synthetic Minority Oversampling Technique (SMOTE) for controlling class imbalance and for feature selection we used Chi-square test to improve the model performance. The proposed LightGBM model showed its ability to recognize complex correlation between diabetes-related health indicators with the training accuracy of 92% and a ROC-AUC score of 0.97 on the test dataset. Overall\, the findings highlight that predictive accuracy is significantly improved after applying both imbalance data controlling and most correlated feature selection techniques.
CATEGORIES:VIRTUAL ROOM 8C
LOCATION:Virtual Room C\, Bangkok\, Thailand
SEQUENCE:0
UID:7e0897fcbe477ccee9888ebfb427412a
URL:http://11thictisthailand.sched.com/event/7e0897fcbe477ccee9888ebfb427412a
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260417T125213Z
DTSTART:20260410T051500Z
DTEND:20260410T071500Z
SUMMARY:Comparative Analysis of Efficient Deep Feature Extraction Strategies for Diabetic Eye Disease Classification
DESCRIPTION:Authors - Lekshmipriya Vijayan\, Bindu V R Abstract - In the present paper\, a model on an EOQ policy for deteriorated inventory items with stock-sensitive demand pattern under inflation when the deterioration rate is considered to be a linear function of time. Partially backlogged shortages form is allowed to occur in this system. The required conditions are stated to validate the optimal solutions of the present model. Furthermore\, the average cost function and decision variables such as shortages time-point and replenishment cycle have been computed with the help of a step-by-step solution procedure and Mathematica software 12.3. Finally\, a numerical example as well as its post-optimal analysis for theoretical model is presented to illustrate the pro-posed work.
CATEGORIES:VIRTUAL ROOM 8C
LOCATION:Virtual Room C\, Bangkok\, Thailand
SEQUENCE:0
UID:99269833d597ea03d55d5e48f29823c7
URL:http://11thictisthailand.sched.com/event/99269833d597ea03d55d5e48f29823c7
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260417T125213Z
DTSTART:20260410T051500Z
DTEND:20260410T071500Z
SUMMARY:Development and Validation of an AI-Driven Digital Audit Maturity Index: The Moderating Role of Internal Control Maturity in Advancing SDG 9
DESCRIPTION:Authors - Windy Permata Suyono\, Marsellisa Nindito\, Dwi Handarini\, Ratna Anggraini\, Eka Septariana Puspa\, Surya Anugrah\, Sabo Hermawan\, Rio Firnanda\, Irima Rahmadani Abstract - Gas leaks pose a threat to safety because they can cause fires and damage to property\, and they are sometimes fatal. Traditional detection methods are manually dependent or delayed in response\, which means they are not always reliable and timely. This paper presents Safe- Gas system for gas leak detection\, monitoring and automatic shut-off. The system uses low-cost gas sensor\, flame sensors\, load cell and an ESP32 microcontroller for local processing. The system is connected to the cloud via Firebase to send alerts\, and it has a battery backup to keep it running when the internet or power goes off. The app supports both remote and autonomous valve shutoff. SafeGas is a name that stands for resilience and accuracy. The designers and developers of the device have tested it in the laboratory and in the field to ensure it meets the set standards. First\, the system aims to reduce the number of false alarms. Second\, it can operate without an internet connection. Third\, it can take safety measures independently. The embedded system and cloud integration aspects of the project demonstrate how they can be combined to achieve the desired results.
CATEGORIES:VIRTUAL ROOM 8C
LOCATION:Virtual Room C\, Bangkok\, Thailand
SEQUENCE:0
UID:c0f122e49d02947d9380b157261dfae6
URL:http://11thictisthailand.sched.com/event/c0f122e49d02947d9380b157261dfae6
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260417T125213Z
DTSTART:20260410T051500Z
DTEND:20260410T071500Z
SUMMARY:Explainable Artificial Intelligence for Trustworthy Internet of Things Systems: Models\, Methods\, and Challenges
DESCRIPTION:Authors - Sachin Ratnaparkhi\, Parikshit Mahalle\, Pankaj Chandre Abstract - Spatial judgment\, incorrect furniture size\, and poor personalized decor advice are common issues in most interior design planning. The aim of this paper is to introduce an AI-powered Augmented Reality Interior Design Assistant that makes it possible for users to visualize furniture and decor in real spaces using accurate real-world measurements. Spatial mapping using SLAM based AR core plane detection and depth sensing allows for more accurate estimations in room sizes\, identifies objects in the scene\, and makes AI-driven suggestions on furniture size and styles. .A hybrid AI engine is built using K-nearest neighbours\, collaborative filtering and feature extraction methods. The AR rendering process takes care of depth by modifying 3D assets to expected sizes to make sure everything is placed correctly. The AR application is based on Unity 3D with AR Foundation and ARCore\, the backend services are provided by python(flask) connected through RESTful APIs\, for user profile and catalog management Firebase/PostgreSQL is used. Scikit is used for building machine learning models which is supported with Numpy and Panda for data handling. The assistant will also provide design tips through a conversational AI feature that makes it accessible to everyone. Tests show a significant reduction in spatial errors\, much faster design decisions\, and better relevance of recommendations. These results indicate that real-scale visualization with AI suggestions tremendously increases design confidence and at the same time reduces the need for redesigns. This system connects AR visualization with AI interior support for a smooth and smart design experience.
CATEGORIES:VIRTUAL ROOM 8C
LOCATION:Virtual Room C\, Bangkok\, Thailand
SEQUENCE:0
UID:a8e42dc794123f9cdcfb6384c244a107
URL:http://11thictisthailand.sched.com/event/a8e42dc794123f9cdcfb6384c244a107
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260417T125213Z
DTSTART:20260410T051500Z
DTEND:20260410T071500Z
SUMMARY:NETWORK-BASED MULTI-OMICS DRUG REPURPOSING FOR HUNTINGTON’S DISEASE
DESCRIPTION:Authors - Deepikaa R Ra\, Sriram S\, Sudhanthira G\n Abstract - Huntington’s disease is a devastating brain disorder. It gradually destroys nerve cells due to mutations in the HTT gene that disrupt gene functions. Years of research have not led to effective treatments that can slow or stop the disease. Clearly\, we need faster ways to find new drugs. This paper introduced an AI-powered systems biology framework that examines both transcriptomic and clinical data to identify drugs that could be repurposed for Huntington’s disease. First\, it uses ordinary least squares regression to remove any unusual variables followed by creating gene co-expression networks to closely examine the specific molecular disorder in the disease. Next\, they conduct differential network analysis to identify pathways and transcriptional regulators that go awry and compare known drug effects with Huntington’s molecular signatures\, rating each drug based on its ability to reverse those harmful gene changes. This helps them quickly focus on drugs that might actually be effective. The entire setup allows researchers to filter\, rank\, and test potential treatments efficiently\, improving the process's reproducibility and reliance on real data.
CATEGORIES:VIRTUAL ROOM 8C
LOCATION:Virtual Room C\, Bangkok\, Thailand
SEQUENCE:0
UID:ef1aab37a39aebd72767ad8a246cfb5e
URL:http://11thictisthailand.sched.com/event/ef1aab37a39aebd72767ad8a246cfb5e
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260417T125213Z
DTSTART:20260410T051500Z
DTEND:20260410T071500Z
SUMMARY:Prediction of attention deficit hyperactivity disorder in children using multimodel approach
DESCRIPTION:Authors - Vani K S\, Nanditha B\, R Bharadhwaj\, Rishika Ghai\n Abstract - Internet of Things (IoT) applications have experienced fast development resulting in massive interconnectivity of devices\, and IoT networks have become susceptible to security risks of Sybil\, flooding\, and masquerading attacks. Conventional centralized security schemes lack flagella\, lack the dynamism of trust evaluation\, and are vulnerable to single-point failures\, whereas the current blockchain-based systems impose too much extra computational and energy load to be applicable in resource-constrained IoT applications. These issues underscore the necessity to have a lightweight\, decentralized\, and trust-conscious security system that can be used to guarantee secure IoT communication in adversarial environments. The paper presents a lightweight framework of blockchain-based trust that can be exploited to provide security to IoT communication against network-level attacks. The suggested architecture combines a decentralized blockchain architecture and dynamic trust assessment operation to distinguish trustful nodes and isolate bad actors. It uses a trust-sensitive Proof-of-Work (PoW) architecture to verify block authenticity\, in which a node trust score is calculated following communication behavior and history of interaction. Technique of order of preference similarity to Ideal solution (TOPSIS) is used to choose the high trust nodes to validate the transaction securely\, which minimizes the amount of computation wasted and increases the network reliability.
CATEGORIES:VIRTUAL ROOM 8C
LOCATION:Virtual Room C\, Bangkok\, Thailand
SEQUENCE:0
UID:94415c6194474d3e2b5c72d2e575755c
URL:http://11thictisthailand.sched.com/event/94415c6194474d3e2b5c72d2e575755c
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260417T125213Z
DTSTART:20260410T051500Z
DTEND:20260410T071500Z
SUMMARY:AI-Driven Secure Intelligent Systems for Next-Generation Cyber-Physical Applications
DESCRIPTION:Authors - Gabriel Wilson\, Krutthika Hirebasur Krishnappa\, Aliaa Salim\, Nigel Gwee\, Sudhir Trivedi\, Shizhong Yang\, Tapan Sarkar\, Mathieu Kokoly Kourouma Abstract - This paper presents the design and generation of a novel high-fidelity intrusion detection dataset specifically targeting 5G core control-plane attacks. The dataset is constructed using an Open5GS based testbed integrated with my5G-RANTester\, enabling realistic sim ulation of benign UE registration and advanced authentication-layer attacks\, including MAC failure\, SQN desynchronization\, replay\, brute force\, NAS message manipulation\, and denial-of-service scenarios. From raw packet captures\, 25 protocol-aware features are engineered\, com bining flow-level statistics with entropy-based and sequence-consistency indicators that reflect 5G-AKA signaling logic. To validate the dataset’s effectiveness\, multiple machine learning models—ranging from Decision Trees to ensemble methods such as Random Forest and XGBoost—are evaluated using Accuracy\, F1-score\, and cross-validation metrics un der class imbalance conditions. Experimental results demonstrate that ensemble models achieve near-perfect classification performance with strong generalization capability\, highlighting the discriminative power of semantic-aware features. The findings confirm that context-aware fea ture engineering is essential for reliable intrusion detection in virtualized 5G core infrastructures.
CATEGORIES:VIRTUAL ROOM 8D
LOCATION:Virtual Room D\, Bangkok\, Thailand
SEQUENCE:0
UID:0815812251c83909700789e4cf0a7ede
URL:http://11thictisthailand.sched.com/event/0815812251c83909700789e4cf0a7ede
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260417T125213Z
DTSTART:20260410T051500Z
DTEND:20260410T071500Z
SUMMARY:An Ensemble Learning Approach to Cardiac Catheterization
DESCRIPTION:Authors - Tahani Muftah Abdulsalam\, Amina Abdo\, Kaled milad\, Nouri Bader Mahjoub\, Suad Mohammed\n Abstract - The Solana Blockchain has found a good change around the world by allowing decentralized applications (Dapps) to be built on its high transaction speeds and low fees. This will open up a whole new level of scalability for de velopers\, giving them more ways to create and innovate in a wide range of mar kets\, including the DeFi (Decentralized Finance) market\, Non- fungible Tokens (NFTs)\, Gaming\, Cryptocurrencies\, Social Networks\, and more. The Solana eco system is growing at an unprecedented rate. New users and developers are having trouble finding projects that interest them\, and developers are having trouble get ting their projects in front of potential users. As a result\, many potential projects with high potential have gone unnoticed because of the overwhelming amount of obsolete and conflicting information as well as only partial information being available. The end result has led to confusion\, frustration and poor project man agement for many users and developers within the Solana ecosystem. To solve these issues for Solana developers we are creating a community of Solana devel opers through a web based platform which allows Solana developers to showcase their works that are associated with Solana Blockchain.
CATEGORIES:VIRTUAL ROOM 8D
LOCATION:Virtual Room D\, Bangkok\, Thailand
SEQUENCE:0
UID:23f11d7defa7640cfe89239759824fbf
URL:http://11thictisthailand.sched.com/event/23f11d7defa7640cfe89239759824fbf
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260417T125213Z
DTSTART:20260410T051500Z
DTEND:20260410T071500Z
SUMMARY:An EOQ replenishment policy with varying deterioration\, stock-sensitive demand and money inflation
DESCRIPTION:Authors - Sai Jagnyaseni Rana\, Trailokyanath Singh\, Pallavi Joshi\, Sudhansu Sekhar Routray\n Abstract - This paper presents a data-driven closed-loop (CL) identification and controller reconstruction framework for interacting multivariable processes\, validated on the benchmark Wood-Berry (WB) distillation column. CL reaction curve data are employed to identify process dynamics without interrupting operation. The measured step responses of diagonal and interaction channels are modeled using secondorder plus time-delay (SOPTD) structures\, whose parameters are estimated through a hybrid particle swarm optimization (PSO) and nonlinear least-square fitting (NLSF) refinement scheme. The identified models are reduced to first-order plus time-delay (FOPTD) form using Skogestad’s approximation and further refined for improved accuracy. Based on the optimized FOPTD models and measured CL responses\, decentralized PID controller are reconstructed using both PSO and reinforcement learning (RL) via a proximal policy optimization (PPO) agent. Simulation studies demonstrate that while PSO achieved reliable controller recovery\, the RL-based approach provides superior transient matching and reduced tracking error. The results validate the effectiveness of the proposed framework for CL identification and data- driven controller reconstruction in interacting multivariable systems.
CATEGORIES:VIRTUAL ROOM 8D
LOCATION:Virtual Room D\, Bangkok\, Thailand
SEQUENCE:0
UID:62798d739b1da3b70087183f3fe66c20
URL:http://11thictisthailand.sched.com/event/62798d739b1da3b70087183f3fe66c20
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260417T125213Z
DTSTART:20260410T051500Z
DTEND:20260410T071500Z
SUMMARY:Analysis of Financial Market Dynamics using Neurocomputing for COVID 19 Regime Transitions
DESCRIPTION:Authors - N. Ahana Priyanka\, R. Harishkanna\, R. Sneka Nandhini\n Abstract - Financial markets are widely modeled as rational systems. However\, practical evidence suggests that collective decision-making is influenced by interacting emotionally\, risk-based\, and control mechanisms. To capture this intricacy\, this study introduces the Financial Connectome\, a neuroscience-inspired pipeline that models the market as a collective cognitive network. This work investigates the long-standing disconnect between neuroscience and finance by mutually analyzing value\, risk\, sentiment\, and control processes at the market level. Building on neurobiological theories of decision-making\, a Neuro-Decision Systems (NDS) framework is suggested to examine the market dynamics reorganization under systemic stress. The framework is applied to 1\,516 trading days of the NIFTY Bank Index spanning 2017–2023\, encompassing the COVID-19 crisis period. The results indicate a significant structural reconfiguration of market states. The Neuro-Decision Score (NDS) exhibits a statistically significant post-COVID shift toward risk dominance\, with Kolmogorov–Smirnov\, permutation\, and Mann–Whitney U tests all rejecting the null hypothesis (p &lt\; 0.001). In addition\, average state persistence increases by approximately 24%\, indicating greater temporal rigidity in market dynamics. The ML-generalized NDS further strengthens the distributional separation\, increasing the observed effect size from small to medium magnitude. Post-pandemic markets exhibit heightened sensitivity\, reflected by higher activation frequencies across all cognitive systems. These findings suggest that market behavior undergoes measurable cognitive reorganization during periods of extreme uncertainty. The framework provides a structured approach for analyzing regime reconfiguration under sustained uncertainty.
CATEGORIES:VIRTUAL ROOM 8D
LOCATION:Virtual Room D\, Bangkok\, Thailand
SEQUENCE:0
UID:57fccca351c1b2a67c0eac26e0978cc4
URL:http://11thictisthailand.sched.com/event/57fccca351c1b2a67c0eac26e0978cc4
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260417T125213Z
DTSTART:20260410T051500Z
DTEND:20260410T071500Z
SUMMARY:Comparative Analysis of Machine Learning and Transformer-Based Models for News Topic Classification in Low-Resource Myanmar
DESCRIPTION:Authors - Ei Sandar Myint\, Khin Mar Soe Abstract - Hallucination occurs when large language models (LLMs) produce information that is incorrect or not supported by facts\, posing a significant challenge to the safe and reliable use of these models. Recent research on hallucination detection and prevention is summarized\, and important directions for future work are identified. The need for detailed detection methods that can pinpoint exactly where errors occur\, as well as techniques for handling hallucinations in long and complex responses\, is emphasized. Analysis of model internal states is highlighted as a key approach to understanding the causes of hallucinations. Emerging chal lenges in multi-modal models that process both text and images are dis cussed\, along with the growing focus on preventing hallucinations rather than only detecting them after generation. Additionally\, the importance of addressing hallucination issues in multilingual and low-resource lan guage settings is underscored. This review aims to support the develop ment of more trustworthy and inclusive language technologies.
CATEGORIES:VIRTUAL ROOM 8D
LOCATION:Virtual Room D\, Bangkok\, Thailand
SEQUENCE:0
UID:ecff601826365c402a666989f0ac2240
URL:http://11thictisthailand.sched.com/event/ecff601826365c402a666989f0ac2240
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260417T125213Z
DTSTART:20260410T051500Z
DTEND:20260410T071500Z
SUMMARY:Integrating Storytelling and Game-Based Learning in Primary English Education: A Teacher-Based Needs Analysis
DESCRIPTION:Authors - Min Wai Yan Oo\, Jirarat Sitthiworachart Abstract - Plant diseases pose a major threat to agricultural productivity\, food security\, and the preservation of medicinal plant species. Early and accurate disease identification is essential to minimize crop losses\; however\, traditional diagnostic methods rely on manual inspection and expert knowledge\, which are often time-consuming\, expensive\, and not easily accessible to farmers in rural areas. To overcome these limitations\, this paper proposes a Smart System for Identifying Leaf Disease Detection using Artificial Intelligence (AI) and Computer Vision techniques. The primary objective of the proposed system is to develop an automated\, scalable\, and web-based solution capable of identifying plant species and detecting leaf diseases through image analysis. The system utilizes Computer Vision algorithms to extract critical visual features such as color variations\, texture patterns\, and morphological characteristics from uploaded leaf images. A deep learning–based classification model processes these features to determine whether the leaf is healthy or diseased. The frontend interface is developed using React and TypeScript\, ensuring an interactive and responsive user experience\, while backend AI processing is integrated through secure API services. Experimental evaluation demonstrates high classification accuracy and reliable confidence scores under varying environmental conditions. The system also provides treatment recommendations to promote sustainable agricultural practices. By integrating AI driven analytics with modern web technologies\, the proposed system enhances early disease detection\, reduces dependency on expert consultation\, and contributes to sustainable farming\, improved crop management\, and digital preservation of medicinal plant knowledge.
CATEGORIES:VIRTUAL ROOM 8D
LOCATION:Virtual Room D\, Bangkok\, Thailand
SEQUENCE:0
UID:1337827fe87e376b097a5f7c8525b287
URL:http://11thictisthailand.sched.com/event/1337827fe87e376b097a5f7c8525b287
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260417T125213Z
DTSTART:20260410T051500Z
DTEND:20260410T071500Z
SUMMARY:Predictive Modelling approaches for Detection of Skin Disease - Future Research Directives
DESCRIPTION:Authors - Maharajpet Sheela\, Roy Ratnakirti\, Thakur Manish Kumar Abstract - The swift expansion of networked vehicles and city traffic has presented major challenges to the management of traffic in smart cities and therefore solutions that are intelligent and privacy-protecting are needed. In this paper\, a Drift-Aware Edge-Federated Spatio-Temporal Intelligence (EF-STI) model that utilizes Long Short-Term Memory (LSTM) networks to predict traffic flowing predictively and accurately is offered. Instead of using a traditional centralized or cloud-based model\, EF-STI allows individual vehicle or roadside edge units to locally-train a lightweight LSTM model\, which is only encrypted model parameters are shared with an aggregator located globally. In order to deal with the non-static and dynamic traffic\, a drift-aware federated optimization plan is implemented\, which enables the system to adjust to the sudden change and different traffic patterns. The framework uses Vehicle-to-Vehicle (V2V) and Vehicle-to-Infrastructure (V2I) communication to predict traffic density and flow with minimal latency enabling proactive interventions to traffic management problems including dynamic signal control\, route recommendations\, and congestion warnings. It is proved by experimental analysis that EF-STI has better prediction accuracy\, lesser communication overhead\, and better adaptability than traditional methodology. The article demonstrates a special intersection of edge computing\, privacy-sensitive federated learning\, spatio-temporal LSTM modeling\, and vehicular networking\, building intelligent transportation systems to be scalable\, secure\, and autonomous in traffic management.
CATEGORIES:VIRTUAL ROOM 8D
LOCATION:Virtual Room D\, Bangkok\, Thailand
SEQUENCE:0
UID:359fdb0485fbe3f74d73213b8d35de25
URL:http://11thictisthailand.sched.com/event/359fdb0485fbe3f74d73213b8d35de25
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260417T125213Z
DTSTART:20260410T051500Z
DTEND:20260410T071500Z
SUMMARY:Sentiment Analysis of Uzbek Social Media Posts: Methods and Research
DESCRIPTION:Authors - Botir Boltayevich Elov\, Guzal Tursunpulatovna Malikova\, Malika Suyunova Odil qizi\, Feruzakhon Mukhiddinovna Bobokhonova\, Shamsiddin Mukhiddinovich Primov Abstract - The Internet of Things (IoT) has spread rapidly\, significantly increasing several secu-rity vulnerabilities\, as traditional detection systems are becoming insufficient to manage the vol-ume and diversity of traffic that characterizes modern networks. The review provides a compre-hensive analysis of recent advances in learning-based intrusion detection systems (IDS)\, focusing primarily on deep learning\, traditional learning\, machine learning\, and hybrid frameworks. Through critically evaluating a diverse range of state-of-the-art studies\, the review explores dif-ferent methodological solutions\, data\, and performance measurement in the field. The available empirical results show that\, although deep learning models are better at identifying complex pat-terns in the data\, traditional machine learning algorithms require less computational power. In addition\, hybrid and ensemble models often outperform single-method options\, but often with high computational cost. The review outlines a number of important challenges\, including the issue of class imbalance and the fact that models are not very interpretable. It argues that light-weight and interpretable AI systems should be a priority in future studies\, and the gap between theoretical academic frameworks and practical IoT applications would be minimized.
CATEGORIES:VIRTUAL ROOM 8D
LOCATION:Virtual Room D\, Bangkok\, Thailand
SEQUENCE:0
UID:8c918d5added7465f3c6cb0a517828e9
URL:http://11thictisthailand.sched.com/event/8c918d5added7465f3c6cb0a517828e9
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260417T125213Z
DTSTART:20260410T051500Z
DTEND:20260410T071500Z
SUMMARY:Smart Campus Surveillance and Guidance System
DESCRIPTION:Authors - Sriram V.A\, Rajkumar P.N\, Babu M Abstract - Imagine smart glasses as the ultimate wearable sidekick— poised to change everything from daily navigation to factory work—but they’re stuck in neutral thanks to tech glitches\, user frustrations\, and market messiness. Picture powerhouse AI like YOLOv8 smashing object detection for the visually impaired at 92.7% mAP@0.5\, with 94% precision\, 91% recall\, and 0.93 F1-scores\, or DeepLabv3+ delivering sharp segmentation at 89% accuracy\, 93% precision\, 0.82 IoU\, and 0.18 RMSE\; yet real-world hits like Meta Ray-Bans limp on 85-160 mAh batteries for just 30 minutes of action\, eye-tracking wobbles at 1.2◦ RMSE (dreaming of sub-0.5◦)\, and custom CNNs nail 96% navigation accuracy with 0.12m MAE but guzzle 40% more power in slim designs. Folks love gestures that cut task times 35-40% over voice (gaze hitting 88% precision\, just 12% error)\, but older users battle 25% extra mental strain dropping acceptance to 47%\, 60% report gaze-control fatigue\, and even Wang’s health- care apps with 95% diagnostic recall lose 30% usability sans standard interfaces—add 1.2 million Ray-Ban Metas sold by 2025 via Llama-3.1’s zippy 87% query accuracy under 2s latency\, but 80% privacy jitters\, 70% interoperability woes from 101 studies\, and Yoo’s stellar 91% industrial boosts (15s/task MAE) all yell for beefier 400+ mAh batteries\, ethical AI under 5% false positives\, and shared standards to grab that $31.5B market.
CATEGORIES:VIRTUAL ROOM 8D
LOCATION:Virtual Room D\, Bangkok\, Thailand
SEQUENCE:0
UID:94c79baf035a66248fc687b6decff9d1
URL:http://11thictisthailand.sched.com/event/94c79baf035a66248fc687b6decff9d1
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260417T125213Z
DTSTART:20260410T051500Z
DTEND:20260410T071500Z
SUMMARY:Unmasking Dark Patterns: A Data-Driven Framework for Detection and User Awareness
DESCRIPTION:Authors - Mugdha Kulkarni\, Diya Oswal\, Rudra Kadam\, Sachin Pande\, Gargi Meshram\n Abstract - The swift growth of digital interfaces has facilitated manipulative design practices called dark patterns\, which take advantage of cognitive biases to manipulate users and subvert informed decision-making.\nThough widespread across e-commerce\, social media\, and other areas\, automated identification and empirical knowledge of user vulnerability are still in their infancy. This work introduces an end-to-end framework integrating a GenAI-augmented browser add-on for real-time detection of dark patterns with systematic estimation of user awareness and behavioural reactions. A new Pattern Vulnerability Index (PVI) measures the threat from individual patterns according to frequency\, unawareness among users\, and potential damage. Cross-platform analysis identified high-risk patterns like Discount Anchoring\, Urgency\, and cost-related manipulations to be frequently overlooked by users. Clustering identifies scenarios in which several deceptive patterns occur in co-presence\, including checkout processes\, promotional displays\, and subscription pitfalls. \nThe results highlight the moral significance of manipulative interface design and establish the capability of machine-based tools to promote user safeguard\, sensitize\, and guide regulation and design efforts. This study provides a basis for consumer-oriented solutions and future research towards more transparent and ethical online encounters.
CATEGORIES:VIRTUAL ROOM 8D
LOCATION:Virtual Room D\, Bangkok\, Thailand
SEQUENCE:0
UID:cb78f0555f8cbcc53873968d7e933419
URL:http://11thictisthailand.sched.com/event/cb78f0555f8cbcc53873968d7e933419
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260417T125213Z
DTSTART:20260410T051500Z
DTEND:20260410T071500Z
SUMMARY:Autonomous AI Agents for Offensive Cybersecurity: Capabilities\, Ethics\, and Defense Implications
DESCRIPTION:Authors - Mohammad Kaif\, Anshika Banyal\, Rohitashwa Dey\, Shashi Mehrotra Abstract - A Natural Language Interface (NLI) lets users ask questions to get data from a database without having to learn a new language like Structured Query Language. Structured data with text is needed for many applications in many fields\, such as search engines\, customer service\, and healthcare. There are many problems that have been studied\, such as the popularity of relational databases\, the complexity of configuration\, and the processing needs of algorithms. Translating plain language into database queries is only one of these problems. The resurgence of natural language to database queries research is driven by the increasing prevalence of querying systems and speech-enabled interfaces. The last poll on this topic was done six years ago\, in 2013. As far as we know\, there hasn't been any recent research that looks at the best natural language translation frameworks for both structured and unstructured query languages. We examined 47 frameworks from 2008 to 2018 in this report. 35 of the 47 were very useful for what we do. There are three kinds of SQL-based frameworks: connectionist\, symbolic\, and statistical. There are two types of NoSQL-based frameworks: semantic matching and pattern matching. After that\, these frameworks are judged based on their language support\, heuristic rule sys-tem\, interoperability support\, dataset scope\, and overall performance. The results showed that 70% of the work to make natural language queries work with databases has been done for SQL. NoSQL languages like SPAROL\, CYPHER\, and GREMLIN only account for 15%\, 10%\, and 5% of the work\, respectively. It has also been found that most of the frame-works only work with English.
CATEGORIES:VIRTUAL ROOM 8E
LOCATION:Virtual Room E\, Bangkok\, Thailand
SEQUENCE:0
UID:b87008e9e7744acad829976382b57bac
URL:http://11thictisthailand.sched.com/event/b87008e9e7744acad829976382b57bac
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260417T125213Z
DTSTART:20260410T051500Z
DTEND:20260410T071500Z
SUMMARY:Backtracking-Free Autonomous Navigation in GPS-Denied Environment
DESCRIPTION:Authors - Avisek Sharma\, Arpita Dey\, Buddhadeb Sau Abstract - The increasing adoption of intelligent transportation systems has high lighted the importance of preventive vehicle safety mechanisms that address critical human factors such as unauthorized access\, alcohol impairment\, and driver fatigue. This review presents a structured analysis of recent research on automated vehicle access and driver alert systems that integrate biometric au thentication\, alcohol sensing\, and vision-based drowsiness detection. Embedded platforms\, particularly Raspberry Pi– based implementations\, are examined alongside computer vision techniques for facial and eye-state analysis and MQ series sensors for alcohol detection. The study reviews and compares commonly used algorithms\, including classical feature-based methods and deep learning ap proaches\, in terms of detection accuracy\, computational requirements\, and real time suitability for embedded environments. Communication strategies for alert generation and remote notification are also discussed. The review identifies key challenges related to multi-module system integration\, robustness under varying illumination conditions\, and long-term sensor calibration. It concludes that an integrated\, low-cost\, and real-time embedded framework offers a practical and scalable approach to improving vehicular security and reducing road accidents by ensuring that only authorized\, sober\, and alert drivers operate vehicles.
CATEGORIES:VIRTUAL ROOM 8E
LOCATION:Virtual Room E\, Bangkok\, Thailand
SEQUENCE:0
UID:7acdaeb29f5be1eaeb64e7394c2ec731
URL:http://11thictisthailand.sched.com/event/7acdaeb29f5be1eaeb64e7394c2ec731
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260417T125213Z
DTSTART:20260410T051500Z
DTEND:20260410T071500Z
SUMMARY:Brand crisis on social media: Opinion cumulation and automation of crisis analytics
DESCRIPTION:Authors - Alena Rodicheva\, Svetlana S. Bodrunova\, Zaeem Yasin\, Ivan S. Blekanov\, Nikita Tarasov Abstract - Polycystic ovary syndrome (PCOS) is a complex of symptoms that affects many women and is estimated to affect 6 to 12% of women of childbearing age. This commonality makes it hard for healthcare professionals to give an accurate diagnosis of PCOS and thereby received adequate treatment. We created a computer system that converses with users and guides their understanding of PCOS. This system uses a language model called Ollama\, which is similar to the LLaMA model. We also added a vast detailed database about PCOS categorized into 12 sections. It analyzes user questions to ensure that the responses are relevant and correct. The system was trialed with positive performance. It accurately detected PCOS related queries and formulated appropriate responses. Well\, the system is very smart and can go through a huge amount of data to find for each question three most relevant answers. The most common application is augmenting LLM with scraping & performing other programming operations over the LLM to give more accurate answers than just a language model. We developed a computer program that can help PCOS patients without compromising their privacy. This system even has benefits for healthcare providers as it provides information that aids them in such treatments for women with PCOS. This project is a great example of using computer programs to help humans with PCOS and other similar things.
CATEGORIES:VIRTUAL ROOM 8E
LOCATION:Virtual Room E\, Bangkok\, Thailand
SEQUENCE:0
UID:610d24fbd68167b5c6c322c90545e621
URL:http://11thictisthailand.sched.com/event/610d24fbd68167b5c6c322c90545e621
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260417T125213Z
DTSTART:20260410T051500Z
DTEND:20260410T071500Z
SUMMARY:Design and Development of a Multimodal AI Framework for Real-Time Nutrient Deficiency Detection and Fertilizer Recommendation in Sugarcane Farming
DESCRIPTION:Authors - Lakshmi Priya G G\, Gokulakrishnan. V\, Nithin Joel. J\, Padmalakshmi Govindarajan Abstract - Potatoes are among the most widely farmed crops globally. Healthy potato plants are crucial for the large-scale production of potato-derived foods. However\, a vari ety of leaf diseases can harm potato plants\, with Early Blight and Late Blight being the most prevalent. In this investigation\, we employed a dataset of 1500 photos comprising healthy\, early\, and late blight leaves. For the diagnosis of leaf diseases\, we used a transfer learning-based Ensemble Modeling. We selected Effi cientNetB0\, ResNet50\, MobileNetv2\, and VGG16 as transfer learning models\, integrating logistic regression as a meta-classifier within the Ensemble Model. We have attained 99.4% accuracy in distinguishing disease-affected leaves from healthy potato leaves\, which is better than most of the recent works. For the per formance measurements\, we employed accuracy\, precision\, recall\, and F1-score. To ensure the credibility of our technique\, we have integrated explainable AI (Grad-CAM) for our models\, which indicates which parts of the image play a vital role in our model’s performance.
CATEGORIES:VIRTUAL ROOM 8E
LOCATION:Virtual Room E\, Bangkok\, Thailand
SEQUENCE:0
UID:6489d93234530d91f538a51df689df40
URL:http://11thictisthailand.sched.com/event/6489d93234530d91f538a51df689df40
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260417T125213Z
DTSTART:20260410T051500Z
DTEND:20260410T071500Z
SUMMARY:Detecting High-Latency Low-Resource Anomalies in Kubernetes Microservices Based on Application Log and Infrastructure Metrics
DESCRIPTION:Authors - Muhamad Surya Nugraha\, Dedy Rahman Wijaya\, Tuntun Aditara Maharta\n Abstract - The widespread adoption of Kubernetes for orchestrating micro services has heightened monitoring complexity if we focus on identifying per formance degradation not visible at the level of infrastructure resource utiliza tion. In this paper\, we present an application-centric AIOps framework that can be leveraged to detect “high-latency\, low-resource” anomalies in Kubernetes microservices. Traditional autoscaling mechanisms that only rely on resource metrics (CPU and memory) fail to provide optimum response time with the emergence of reactive applications. The model for anomaly detection is trained using machine learning classifiers such as Random Forest\, LightGBM\, and Lo gistic Regression. This approach leads to a weak supervision-based approach to label datasets\, with respect to Service Level Objective (SLO) violations. A course registration system is proposed to validate the application of this frame work under conditions of high concurrency and parallel simulation traffic. Ex perimental results show that the established machine learning model exhibits marked performance compared with normal threshold methods\, leading to im proved operational steadiness and service robustness.
CATEGORIES:VIRTUAL ROOM 8E
LOCATION:Virtual Room E\, Bangkok\, Thailand
SEQUENCE:0
UID:bc1b2dd1532e77d641d0c93f1511a8a5
URL:http://11thictisthailand.sched.com/event/bc1b2dd1532e77d641d0c93f1511a8a5
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260417T125213Z
DTSTART:20260410T051500Z
DTEND:20260410T071500Z
SUMMARY:HiSS-Fuse: Linear-Time Hierarchical State-Space Fusion for Efficient Histopathology Image Classification
DESCRIPTION:Authors - Y. Rama Devi\, Panigrahi Srikanth\, Devansh Makam Abstract - Large language models have shown strong potential for Arabic medical text generation\; however\, traditional fine-tuning objectives treat all medical cases uniformly\, ignoring differences in clinical severity. This limitation is particularly critical in healthcare settings\, where errors in severe cases contain higher clinical risk. In this work\, we propose a severity-aware weighted loss for fine-tuning Arabic language models on medical complaint–response data. The method depends on soft severity probabilities to dynamically scale token-level loss contributions during optimization\, thereby prioritizing clinically critical interactions without modifying model architectures. Experiments are conducted using the MAQA dataset\, which provides Arabic medical complaints and trusted human responses. Severity labels and probabilistic scores are automatically derived using a fine-tuned AraBERT-based classifier and incorporated exclusively at the loss level. The proposed approach is evaluated across ten Arabic large language models of varying architectures and parameter scales. While standard cross-entropy fine-tuning yields only modest improvements\, severity-aware optimization consistently achieves larger gains. Using a balanced weighting configuration\, performance improves from 54.04% to 66.14% for AraGPT2-Base\, from 59.16% to 67.18% for AraGPT2-Medium\, and from 57.83% to 66.86% for Qwen2.5-0.5B\, with peak performance reaching 67.18%. Overall\, severityaware fine-tuning delivers improvements of up to 12.10% over non-finetuned baselines\, demonstrating robust and architecture-consistent gains.
CATEGORIES:VIRTUAL ROOM 8E
LOCATION:Virtual Room E\, Bangkok\, Thailand
SEQUENCE:0
UID:0781c56637b2e84ea32847e75bc6f919
URL:http://11thictisthailand.sched.com/event/0781c56637b2e84ea32847e75bc6f919
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260417T125213Z
DTSTART:20260410T051500Z
DTEND:20260410T071500Z
SUMMARY:Lapis Whale - A Framework for Continual Learning in Transformers through Selective Memory Replay
DESCRIPTION:Authors - Siddharth Jha\, Mayur Jaiswal\, Ajay Deshmukh\, Kajal Joseph Abstract - The importance of agriculture for the survival of humans and the economic stability of the world continues to grow\; however\, at the same time\, it has also come to face many severe problems due to increasing population figures\, climate change\, and the loss of natural resources. The traditional techniques for crop monitoring are mostly based on manual surveys and the use of vision for inspecting crops\; thus\, they are regarded as too labor-intensive\, time-consuming\, and passive in nature\, thereby becoming ineffective for managing modern large-scale farming techniques. The avail-ability of the latest technological features\, such as remote sensing\, Internet of Things (IoT) devices\, unmanned aerial vehicles (UAVs)\, artificial intelligence (AI) devices\, and block chain technology\, has transformed crop monitoring techniques into an intelligent and proactive process for farmers to monitor crops in an efficient and precise manner. Drones play an important role in crop monitoring by using high-resolution imaging devices for detecting early crop problems\, such as crop stress\, pest infestations\, or nutrient deficiencies\, whereas IoT devices are utilized for real-time monitoring of fluctuating environment parameters\, such as soil\, in an intelligent manner. All these innovations help towards a high and efficient agricultural system within a sustainable environment. Hence\, there are still certain limitations and hindrances faced by these advanced techniques\, including high initial cost\, complexity\, infrastructural constraints\, and data privacy\, limiting these techniques for small and marginal farmers. Hence\, in this review paper\, a detailed review of advanced crop monitoring techniques used in agriculture is discussed\; further\, a critical analysis of these techniques for achieving these requirements with efficiency and standards\, and an understanding of these techniques for achieving a sustainable and robust ecosystem in an agricultural system is discussed.
CATEGORIES:VIRTUAL ROOM 8E
LOCATION:Virtual Room E\, Bangkok\, Thailand
SEQUENCE:0
UID:12b07d47c46063d789544dd01e467a91
URL:http://11thictisthailand.sched.com/event/12b07d47c46063d789544dd01e467a91
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260417T125213Z
DTSTART:20260410T051500Z
DTEND:20260410T071500Z
SUMMARY:Probanza: A Stabilized Multi-Stage LLM Evaluation Architecture for Semantic Fidelity in Historical Manuscript Digitization
DESCRIPTION:Authors - Neha Kriti\, Arti Devi\, Sarthak Srivastava\, Varun Dutt Abstract - Localization in Autonomous Underwater Vehicles (AUVs) continues to be a major challenge in GPS-denied environments\, where inertial drift\, low visibility and uncertain motion models frequently un dermine state estimation. In this paper\, we present our visual-inertial odometry framework A-KIT VIO specifically designed for resilient pose tracking underwater. The system employs tightly coupled monocular camera observations with IMU data using an Extended Kalman Filter to maintain high-rate inertial propagation along with feature-based vi sual updates to avoid drift. To address the frequent covariance mismatch during non-stationary maneuvers\, we added a transformer-based module to adaptively adjust IMU process noise based on the vehicle’s immediate motion context. This method of uncertainty modeling ensures filter sta bility in scenarios where standard\, fixed-noise configurations typically diverge. Evaluated within a Gazebo-based underwater simulation\, the framework demonstrated consistent state estimation and bounded drift over long-range trajectories\, highlighting the efficacy of adaptive covari ance for reliable underwater localization.
CATEGORIES:VIRTUAL ROOM 8E
LOCATION:Virtual Room E\, Bangkok\, Thailand
SEQUENCE:0
UID:04d00c3b6c2e2cb21b43e2a5025f77dc
URL:http://11thictisthailand.sched.com/event/04d00c3b6c2e2cb21b43e2a5025f77dc
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260417T125213Z
DTSTART:20260410T051500Z
DTEND:20260410T071500Z
SUMMARY:SHAAZ: A Digital Intervention for Psycho-Social Skills Development and Behavior Training of Autistic Children
DESCRIPTION:Authors - Fatima Batool\, Farzana Jabeen\, Tahira Anwar Lashari\, Mehvish Rashid\n Abstract -&nbsp\;Autism Spectrum Disorder (ASD) is an invisible disorder that is of ten misdiagnosed in Pakistan due to unawareness and social stigma. There ex ist multiple technological digital interventions for children with autism designed to target their social\, emotional or cognitive skills. However\, recent studies have overlooked on the intervention integrating the psycho-social and behavioral skills of children with autism. This mixed-method study evaluates the effectiveness of a multi-modal learning framework\, SHAAZ\, for psycho-social and behavioral skills enhancement of children with ASD. Employing the proposed research design\, the 7 week intervention was tested on autistic children with different severity level of disorder\, aged 4 to 12 years. The results revealed that across observations\, there is an improvement in users performance scores. The System Usability Scale (SUS)andAppQualityandImpactEvaluationbasedonMobileAppRatingScale (MARS) scores for the designed product was 89.16 and 4.27 respectively\, imply ing high usability\, user engagement and a positive impact on the targeted skills of the users.
CATEGORIES:VIRTUAL ROOM 8E
LOCATION:Virtual Room E\, Bangkok\, Thailand
SEQUENCE:0
UID:cd1de67da1fe181ba950fdb8babd8959
URL:http://11thictisthailand.sched.com/event/cd1de67da1fe181ba950fdb8babd8959
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260417T125213Z
DTSTART:20260410T051500Z
DTEND:20260410T071500Z
SUMMARY:Stock price forecasting using Time Domain Filtering\, STFT\, and Deep Learning
DESCRIPTION:Authors - Onkar Yende\, Nayan Bhutada\, Mohit Thakre\, Sai Khadse\, Mridula Korde\n Abstract -Reliable stock price forecasting remains challenging due to the noisy\, nonlinear\, and non-stationary characteristics of financial time-series data. Traditional statistical methods and deep learning models that rely solely on raw price data often struggle to capture short-term fluctuations and evolving market dynamics. To address these limitations\, this study proposes a hybrid forecasting framework that integrates causal time-domain filtering\, time–frequency feature extraction\, and deep learning–based temporal modeling. The proposed approach employs Savitzky–Golay and Kalman filters to sup press high-frequency market noise while preserving important price trends in a causality-aware manner suitable for real-time forecasting. Localized spectral fea tures representing transient and time-varying market behavior are then extracted using the Short-Time Fourier Transform (STFT). These enhanced time-domain and frequency-domain features are combined and modeled using a Long Short Term Memory (LSTM) network\, which effectively captures long-range depend encies and nonlinear temporal patterns in financial data. The framework is evaluated using standard performance metrics\, including RMSE\, MAPE\, and R². Experimental results demonstrate that integrating causal filtering with STFT-based features significantly improves forecasting accuracy and robustness compared to baseline models\, providing a reliable and practical solution for short-term and multi-step stock price prediction.
CATEGORIES:VIRTUAL ROOM 8E
LOCATION:Virtual Room E\, Bangkok\, Thailand
SEQUENCE:0
UID:3f9a1e70397fdbebcbc6486c6e201c4b
URL:http://11thictisthailand.sched.com/event/3f9a1e70397fdbebcbc6486c6e201c4b
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260417T125213Z
DTSTART:20260410T051500Z
DTEND:20260410T071500Z
SUMMARY:A Low-Distortion Reversible Data Hiding Method for Dual Images
DESCRIPTION:Authors - Thanh-Phuong Ngo\, Van-Thanh Huynh\, Thai-Son Nguyen\n Abstract - This paper presents a novel Reversible Data Hiding (RDH) method for dual images. First\, secret data is converted into a binary sequence of equal length and then divided into shorter segments to control the amount of data embedded into each pixel. The embedding process uses two copies of the original image to distribute the data\, reducing the impact on each image while maintaining overall image quality. During recovery\, the original image is restored by averaging the pixel values at corresponding locations in the two stego images\, while the embedded data is recovered through a reverse process. Experimental results on grayscale images demonstrate that the method maintains good image quality\, achieving a high Peak Signal-to-Noise Ratio (PSNR) across different embedding levels while ensuring accurate recovery of both the secret data and the original image.
CATEGORIES:VIRTUAL ROOM 8F
LOCATION:Virtual Room F\, Bangkok\, Thailand
SEQUENCE:0
UID:02d14c6ce69b291a1b77c8d89934590f
URL:http://11thictisthailand.sched.com/event/02d14c6ce69b291a1b77c8d89934590f
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260417T125213Z
DTSTART:20260410T051500Z
DTEND:20260410T071500Z
SUMMARY:A Robust Deep Learning Framework for Automated Diabetic Retinopathy Detection and Severity Grading
DESCRIPTION:Authors - Yasir Abdullah R\, Lakshmana Kumar T\, Vijaykumar M\, Thirunavukkarasu C\, Saravanagukhan P\, Hariharasuthan M Abstract - In the recent past\, vehicle theft in India has increasing nearly 2.5 times\, with more than 2 lakh vehicles stolen annually. The Delhi NCR region alone accounts for over 30% of reported cases\, and in Delhi\, a vehicle is reportedly stolen approximately every 14 minutes. These alarming trends highlight the ur-gent need for stronger and smarter vehicle security mechanisms. Traditionally\, vehicle anti-theft technologies have relied largely on non-biometric approaches such as GPS–GSM tracking modules. Thus\, biometric authentication is an emerging security approach that limits vehicle access to authorized individuals by verifying unique biological traits such as fingerprints\, facial features\, iris pat-terns\, or voice. Although this technology significantly strengthens vehicle security\, its widespread deployment still faces certain technical and social constraints. Thus in this paper\, an IoT enabled biometric ignition system with security alerts is proposed. The proposed model makes use of an ESP32 micro controller and fingerprint sensor to replace traditional keys. The system operates in two stages: first secure door access and secondly engine ignition authorization. Any unauthorized attempts trigger real-time alerts with GPS location via IoT protocols like MQTT or HTTP. Further\, cloud integration enables remote monitoring\, data storage\, and scalability\, making suitable for modern intelligent transport systems. In the same way\, the fingerprint-based vehicle starter grants the privilege of starting the vehicle only to the registered users\, thus deterring theft and ensuring safety. Over all\, biometric vehicle ignition is a dependable\, economical\, and hassle-free solution to access control as well as theft prevention.
CATEGORIES:VIRTUAL ROOM 8F
LOCATION:Virtual Room F\, Bangkok\, Thailand
SEQUENCE:0
UID:a078684355462ab876bdef59b014233e
URL:http://11thictisthailand.sched.com/event/a078684355462ab876bdef59b014233e
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260417T125213Z
DTSTART:20260410T051500Z
DTEND:20260410T071500Z
SUMMARY:Addax-Optimized Deep Convolutional Neural Network for Automated Detection of Age-Related Macular Degeneration from Retinal Fundus Images
DESCRIPTION:Authors - Amol Dhumane\, Jitendra Chavan\, Arijit Dutta\, Priyanka Paygude\, Aditi Sharma\, Datta Takale\, Yashwant Dongre\n Abstract - Depression is a psychiatric condition that is largely common all over the world and greatly influences the emotional stability\, cognitive performance and behavior functioning. Computational techniques that can detect the condition early can help to prevent psychological dangers in the long term and ensure timely treatment of the disease. This paper refers to a complete machine learning architecture of automated depression recognition of textual information based on hybrid feature engineering and ensemble learning approaches. The suggested methodology is a combination of text preprocessing\, Term Frequency / Inverse Document Frequency (TF -IDF) vectorization\, unigram and bigram features\, hand-crafted statistics and sentiment-based indicators\, and several classification models such as Logistic Regression\, Random Forest\, XGBoost\, and LightGBM. The issue of class imbalance is tackled using Synthetic Minority Over-sampling Technique (SMOTE) and compared. The original dataset of 7\,489 samples was cleaned and narrowed down to 7\,486 valid cases. Accuracy\, Precision\, Recall\, F1 score\, ROC-AUC and 5-fold cross-validation were used to evaluate the performance. There are experimental results to show that ensemble models are more effective compared to traditional linear classifiers. XGBoost performed best in the overall performance of 94.59% accuracy and F1-score of 0.8323. The hybrid-based feature fusion technique has a considerable improvement on the classification performance and does not sacrifice the level of interpretability and computational efficiency\, which is why the framework is applicable to scalable mental health analytics services.
CATEGORIES:VIRTUAL ROOM 8F
LOCATION:Virtual Room F\, Bangkok\, Thailand
SEQUENCE:0
UID:fb5b2558822e649671c51e2bf353da91
URL:http://11thictisthailand.sched.com/event/fb5b2558822e649671c51e2bf353da91
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260417T125213Z
DTSTART:20260410T051500Z
DTEND:20260410T071500Z
SUMMARY:Classification of Tree Species in the Philippines using LiDAR-UAV Data
DESCRIPTION:Authors - Armie E. Pakzad\, Nathanael Adrian T. Cua\, Louie T. Que\, Alvin Josh T. Valenciano\, Jana Johannes Valenzuela\, Abbasali Pakzad Abstract - Emotional Support Conversation (ESC) seeks to lessen users’ emotional dis tress through sympathetic communication. Current approaches concentrate on comprehending present emotional states and combining support techniques to generate responses. But they fail to take into account an important factor: emotional trajectories (how users’ feelings change over time). Two people expe riencing the same feeling may need essentially different answers depending on whether they are in a therapeutic window (gradually improving)\, a depressed spiral (continuous hopelessness)\, or a crisis escalation (rapidly worsening). We propose TRAGEDY (TRAjectory-Guided Emotional Dialogue System)\, a sys tem that explicitly models clinical patterns and emotional trajectories in order to direct response creation. We present: (1) a trajectory encoder that records the temporal dynamics of emotion and intensity sequences\; (2) a clinical pat tern detector that recognizes five psychologically grounded patterns (normal progression\, therapeutic window\, resistance pattern\, depressed spiral\, and crisis escalation)\; and (3) pattern-aware generation that bases responses on trajectories found. Experiments on the ESConv benchmark show that TRAGEDY provides interpretable trajectory insights while outperforming robust baselines\, across standard generation metrics. Our approach opens new avenues for trajectory aware conversational AI and emphasizes the significance of temporal dynamics in emotional support.
CATEGORIES:VIRTUAL ROOM 8F
LOCATION:Virtual Room F\, Bangkok\, Thailand
SEQUENCE:0
UID:313bdc065bb7cd7eaa642b423da5b8c2
URL:http://11thictisthailand.sched.com/event/313bdc065bb7cd7eaa642b423da5b8c2
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260417T125213Z
DTSTART:20260410T051500Z
DTEND:20260410T071500Z
SUMMARY:Clinical-Guided Region-Level Graph Transformer for Alzheimer’s Disease Stage Classification
DESCRIPTION:Authors - Akhil P\, Mallikharjuna Rao K. Abstract - Cloud storage platforms support diverse multimedia and col laborative workloads across organizations\, yet conventional methods ne glect user behavior’s role in shaping access patterns. Privacy regulations prohibit centralized aggregation of interaction traces\, while standard fed erated learning algorithms like FedAvg fail under statistical heterogene ity from varied user roles. This paper introduces FedPAE (Federated Per sonalized AutoEncoder)\, an unsupervised framework for behavior-aware user profiling in federated settings. FedPAE employs a shared global encoder for common patterns and private local decoders for individual adaptation\, augmented by an Adaptive Fine-Tuning (AF) mechanism to mitigate encoder drift and preserve global semantics\, without sharing any raw user data with the server. Evaluated on the CMU CERT benchmark and anonymized cloud storage logs\, FedPAE surpasses FedAvg\, FedProx\, and FedPer in anomaly detection accuracy across all thresholds (e.g.\, F1 gains of 5–13% points over FedAvg across all precision thresholds)\, con f irming that the approach holds across heterogeneous client populations.
CATEGORIES:VIRTUAL ROOM 8F
LOCATION:Virtual Room F\, Bangkok\, Thailand
SEQUENCE:0
UID:87ee568d1c0ceb4f50788aa96c520bba
URL:http://11thictisthailand.sched.com/event/87ee568d1c0ceb4f50788aa96c520bba
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260417T125213Z
DTSTART:20260410T051500Z
DTEND:20260410T071500Z
SUMMARY:Driver Drowsiness Detection using YOLOv5\,CNN\, and LSTM
DESCRIPTION:Authors - Yarragunta Babu \, Challa Yuva Prasanthi\, Vadapalli Sparjan\, Sanagapati Venkata Siva Naga Sai Jayanth Abstract - Distributed systems rely on data replication across multiple nodes to ensure high availability\, fault tolerance\, and scalability. While replication improves system reliability\, it also introduces temporary inconsistencies between primary and replica nodes during data propagation. This phenomenon\, commonly referred to as consistency drift\, occurs when distributed nodes maintain slightly different states before synchronization is completed. As distributed infrastructures grow in scale and complexity\, consistency drift becomes increasingly significant due to network latency\, workload variability\, and communication overhead between nodes. Traditional synchronization mechanisms typically rely on static replication intervals or fixed update propagation strategies that do not adapt effectively to dynamic system conditions. Such approaches may allow drift to accumulate before synchronization occurs\, resulting in delayed consistency and inefficient resource utilization. Managing consistency drift therefore becomes a critical challenge in distributed computing environments where maintaining accurate and synchronized data states is essential. This research addresses the problem of consistency drift in distributed systems by examining the factors that contribute to state divergence among nodes and exploring mechanisms for dynamic drift management. The proposed framework focuses on monitoring system behavior\, including workload intensity\, network latency\, and node communication patterns\, to regulate synchronization behavior more effectively. By enabling adaptive synchronization strategies that respond to real time system conditions\, the framework aims to reduce drift accumulation and improve overall data consistency across distributed clusters. Effective management of consistency drift ultimately enhances system reliability\, operational stability\, and performance in modern distributed computing platforms operating under dynamic workloads.
CATEGORIES:VIRTUAL ROOM 8F
LOCATION:Virtual Room F\, Bangkok\, Thailand
SEQUENCE:0
UID:ac98a0aba175d4c5268617782d549b1d
URL:http://11thictisthailand.sched.com/event/ac98a0aba175d4c5268617782d549b1d
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260417T125213Z
DTSTART:20260410T051500Z
DTEND:20260410T071500Z
SUMMARY:EVALUATING ONLINE SHOPPERS’ BEHAVIOR AND ENGAGEMENT USING SENTIMENT ANALYSIS
DESCRIPTION:Authors - Olutayo V. A.\, Agbele K. K.\, Ogundimu O. E.\, Dudu M. T.\n Abstract - As online shopping has become increasingly popular\, companies must utilize social media to develop and improve customer experience. This study examined customer interaction sentiment regarding online shopping through automated systems to classify comments on social media sites like Twitter\, Facebook\, and Instagram. This research study compared three machine learning and natural language processing (NLP) techniques: Bidirectional Gated Recurrent Units (GRUs)\, Random Forests\, and Naïve Bayes. Customer reviews were classified as positive\, negative\, and neutral\, as well as analyzed for time-related patterns. The classification framework was constructed by using sentiment analysis\, feature extraction\, and data preprocessing techniques. Furthermore\, model training and performance assessment were executed through Naïve Bayes and Support Vector Machines. Of all the models studied\, the Bidirectional GRU had the best performance with an accuracy of 88.08 %. The results of this study help companies understand customer preferences better\, and thereby refine their products\, services\, and marketing techniques.
CATEGORIES:VIRTUAL ROOM 8F
LOCATION:Virtual Room F\, Bangkok\, Thailand
SEQUENCE:0
UID:011178ea143416269babe082590e7c3b
URL:http://11thictisthailand.sched.com/event/011178ea143416269babe082590e7c3b
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260417T125213Z
DTSTART:20260410T051500Z
DTEND:20260410T071500Z
SUMMARY:Melanoma Cancer Detection in the Artificial Intelligence Era: Existing CAD Systems\, Challenges and Future Research Paths
DESCRIPTION:Authors - Akbar Kushanoor\, Sanjay K. Sahay Abstract - Traditional tree classification methods are inefficient\, requiring tremendous effort\, time\, and labor. To address this\, the primary objective of this research was to develop and implement a machine learning model that utilizes 3D Light Detection and Ranging (LiDAR) data\, acquired via an unmanned aerial vehicle (UAV)\, for the accurate classification of tree species in the Philippines. Then\, the collected data was pre-processed in preparation for the next portions of the methodology. Once completed\, the features used in preparation for machine learning were extracted for the creation and training of the model. Ground truth data\, validated by two licensed foresters\, were used to ensure species accuracy\, focusing on the five most abundant tree species in the dataset. Several machine learning algorithms were evaluated\, with the XGBoost model achieving the best performance\, reaching an overall accuracy of 85.63%\, a mean class accuracy of 84.98%\, and a Kappa accuracy of 81.57%. All producers’ accuracy exceeded 70%\, indicating robust model reliability. Additionally\, a user interface was developed to visualize the LiDAR data\, tree attributes\, and classification results. The findings demonstrate that LiDAR data obtained from UAVs can effectively be used for tree species classification in the Philippines\, supporting forest inventory initiatives and reforestation efforts. Future work may include expanding the dataset\, incorporating more species\, and testing additional machine learning algorithms to further enhance classification accuracy.
CATEGORIES:VIRTUAL ROOM 8F
LOCATION:Virtual Room F\, Bangkok\, Thailand
SEQUENCE:0
UID:c12817a6f6ba2cbb497628de2ba3af76
URL:http://11thictisthailand.sched.com/event/c12817a6f6ba2cbb497628de2ba3af76
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260417T125213Z
DTSTART:20260410T051500Z
DTEND:20260410T071500Z
SUMMARY:Recommendation-driven IDS : A Hypergraph – based\, Formal and Algorithmic Framework
DESCRIPTION:Authors - Monir El Mounaoui\, Kunale Kudagba\, Mohamed Yassin Chkouri Abstract - This paper presents PricePulse\, a web-based price comparison system that supports consumers with real-time multi-platform price analysis and AI-powered shopping insights. The system aggregates product data from Amazon\, Flipkart\, and Meesho via SerpAPI’s Google Shopping API and enriches results with recommendations generated by Google’s Gemini AI. Built on Next.js and Flask\, PricePulse addresses gaps in the e-commerce ecosystem by eliminating manual price comparison across platforms. The system uses JWT-based authentication\, maintains search history in SQLite\, and provides an intuitive interface with React and Tailwind CSS. Evaluation shows average response times under 2 seconds and 95% accuracy in price extraction\, demonstrating significant potential to help consumers make informed purchasing decisions and save on purchases.
CATEGORIES:VIRTUAL ROOM 8F
LOCATION:Virtual Room F\, Bangkok\, Thailand
SEQUENCE:0
UID:325d7082a15cdec956f327adcdc6cae2
URL:http://11thictisthailand.sched.com/event/325d7082a15cdec956f327adcdc6cae2
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260417T125213Z
DTSTART:20260410T051500Z
DTEND:20260410T071500Z
SUMMARY:Understanding the Adoption of AI Hotel Chatbots: The Role of Technology Readiness and Consumer Perceptions
DESCRIPTION:Authors - Tiurida Lily Anita\, Siti Nahdiah\, Muslikhin Muslikhin\, Mohd. Nor Shahizan Ali Abstract - Despite the importance of Allied Healthcare professionals in healthcare service delivery\, low professional development opportunities\, a high turnover rate\, and a shortage of workers in India are some of the challenges that are affecting Allied Healthcare professionals’ retention. The purpose of this research is to explore the po tential of Internet of Things (IoT) solutions and Big Data analytics\, coupled with infor mation and communication technology (ICT) as a solution to Allied Healthcare profes sionals’ retention strategies. The purpose of this paper is to propose a conceptual frame work that can be achieved by utilizing Internet of Things solutions coupled with Big Data analytics as a solution to Allied Healthcare professionals’ retention strategies by utilizing theories such as Technology Acceptance Model theory\, Job Demands-Re sources theory\, Social Exchange Theory\, among others. The paper concludes that ICT is a resource that can be utilized to reduce job stress\, enhance effective communication\, and provide career opportunities for Allied Healthcare professionals\; whereas Big Data analytics coupled with Internet of Things solutions can be utilized to predict potential risks that may affect Allied Healthcare professionals’ retention. The proposed concep tual framework offers a theoretical understanding of the digital revolution of human resource management practices in healthcare services.
CATEGORIES:VIRTUAL ROOM 8F
LOCATION:Virtual Room F\, Bangkok\, Thailand
SEQUENCE:0
UID:9dd6f4d72449a010bbed239abc2398af
URL:http://11thictisthailand.sched.com/event/9dd6f4d72449a010bbed239abc2398af
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260417T125213Z
DTSTART:20260410T051500Z
DTEND:20260410T071500Z
SUMMARY:An Intelligent IoT and AI Based Irrigation System for Efficient Water Management in Hilly Agricultural Areas
DESCRIPTION:Authors - Nimisha K\, Sridharan G\, Kathiresh kumar K\, Lohit S\, Shyam Ganesh K Abstract - The rapid growth of sensitive data requires backup systems that are both storage-efficient and risk-aware. Traditional backup approaches rely on static policies that ignore temporal changes\, data sensitivity\, and redundancy\, leading to inefficient storage use and higher risk exposure. This work proposes a risk-adaptive backup optimization framework integrating temporal modelling\, sensitivity-aware deduplication\, and online learning. The system reconstructs data evolution using intrinsic timestamps and quantifies data criticality through continuous sensitivity scoring. A unified risk model combines sensitivity\, change intensity\, and exposure over time to determine backup urgency. An online rein forcement learning agent dynamically optimizes backup decisions based on evolving data patterns. The framework applies secure\, sensitivity-based dedupli cation to reduce redundancy while preserving privacy. Operating in a read-only\, metadata-driven manner\, it ensures compliance with strict data governance re quirements. By decoupling decision logic from storage\, the system supports hy brid cloud environments. Experimental results show reduced storage costs and controlled risk\, demonstrating its effectiveness for scalable\, intelligent data pro tection.
CATEGORIES:VIRTUAL ROOM 8G
LOCATION:Virtual Room G\, Bangkok\, Thailand
SEQUENCE:0
UID:c70f8471ed3ad100a507ba6b667e6b38
URL:http://11thictisthailand.sched.com/event/c70f8471ed3ad100a507ba6b667e6b38
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260417T125213Z
DTSTART:20260410T051500Z
DTEND:20260410T071500Z
SUMMARY:Analysis and Evaluation of Static Noise Margin in SRAM Cells with Multiple Defects
DESCRIPTION:Authors - Anirudh P\, Nimisha K\, Princy P\n Abstract - As technology advances\, circuit complexity increases\, integrated cir cuits become more prone to defects during manufacturing and operation. Conse quently\, in order to ensure reliable operation\, effective testing and stability eval uation of memory cells are essential. Static random-access memory plays a major role in modern digital systems due to its high-speed data access and efficient per formance. However\, its reliable functioning is strongly influenced by device level parameters and supply voltage variations. In critical applications\, even single fault occurrence may pose serious reliability issues\, highlighting the need for ef ficient test methods. Extensive research has been carried out to investigate the static noise margin of SRAM cells. However\, the influence of multiple defects has received relatively limited attention in existing literature. This study empha sizes the analysis of multiple defects because their occurrence becomes more fre quent in nano-meter technology regimes. Moreover\, these defects can cause sig nificant fault behavior\, potentially reducing the stability and reliability of SRAM cells. Multiple defects (Df3-Df3c) and (Df4-Df4c) are selected for analysis as they produce strong fault effects as they occur in the power supply and ground paths of the SRAM cell\, which are critical for proper circuit operation. Any dis turbance along these conduction paths alters the effective operating voltage of the cross-coupled inverters and consequently affect the drive capability of the associated transistors. Moreover\, the behavior of these defects is examined under various temperature conditions\, supply voltages\, and process corners in order to assess their overall effect on SRAM cell stability.
CATEGORIES:VIRTUAL ROOM 8G
LOCATION:Virtual Room G\, Bangkok\, Thailand
SEQUENCE:0
UID:f70b37121f0d47dc443df34b04798551
URL:http://11thictisthailand.sched.com/event/f70b37121f0d47dc443df34b04798551
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260417T125213Z
DTSTART:20260410T051500Z
DTEND:20260410T071500Z
SUMMARY:Catch Fish Optimization Algorithm Based EfficientNetV2-M for Anomaly Detection in Sustainable Industrial IoT
DESCRIPTION:Authors - Sunil Jagannath Panchal\, Gajanan Madhavrao Malwatkar Abstract - This research deals with the persistent challenges of document man agement in higher education institutions which focuses on the development of a digital support tool for Mariano Marcos State University (MMSU). Traditional paper-based systems and fragmented repositories often result in inefficiencies\, duplication of work\, and risks of data loss. The project adopted the Agile Devel opment methodology with emphasis on flexibility\, collaboration\, and iterative improvement. The d-T.R.A.I.L. system was built using JavaScript\, PHP Laravel\, HighCharts\, and MySQL\, integrating features such as tagging\, repository man agement\, granular access control\, and collaborative modules like Teams. These functionalities were designed to streamline document organization\, retrieval\, and secure sharing across diverse academic and administrative units of the Univer sity. A User Acceptance Test (UAT) was conducted involving 70 participants from different MMSU offices that utilizes a Likert scale to measure satisfaction. Re sults yielded an overall mean score of 4.36 which was interpreted as Very Satis factory. High ratings were recorded for productivity\, user-friendliness\, and doc ument organization\, while scalability received the lowest score which indicates an area for future enhancement of the system. The findings demonstrate that the system effectively improves workflow efficiency\, accessibility\, and accountabil ity\, while aligning with national digital transformation policies.
CATEGORIES:VIRTUAL ROOM 8G
LOCATION:Virtual Room G\, Bangkok\, Thailand
SEQUENCE:0
UID:5b44dfff322e91391d24d584c90cf517
URL:http://11thictisthailand.sched.com/event/5b44dfff322e91391d24d584c90cf517
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260417T125213Z
DTSTART:20260410T051500Z
DTEND:20260410T071500Z
SUMMARY:Evaluation and Demonstration of the Organisational Security Culture Framework for a Namibian Public Enterprise
DESCRIPTION:Authors - Hileni Ihambo\, Fungai Bhunu Shava\, Gabriel Tuhafeni Nhinda Abstract - Fine-tuning large language models remains costly\, and Parameter- Efficient Fine-Tuning (PEFT) techniques have emerged to make this process feasible on limited hardware. Among them\, IA3 stands out for its extreme simplicity—it tunes only element-wise scaling vectors—but this design restricts the model to re-weighting features it already knows\; it cannot form new ones. In this paper\, we present SAMA (Spectral- Aware Minimal Adaptation)\, an extension of IA3 that introduces a single rank-1 update whose direction is derived from the pre-trained weights through Singular Value Decomposition. Each adapted layer gains only 4d extra parameters (3\,072 for d=768)\, which is roughly one quarter of what LoRA requires at rank 8. We benchmark SAMA against five baselines—LoRA\, DoRA\, AdaLoRA\, QLoRA\, and IA3—across BERT\, GPT-2\, and Flan-T5 on twelve diverse NLP tasks under a low-resource constraint of 1\,000 training samples per task. On the decoder-only GPT- 2\, SAMA lowers perplexity by 26–34% relative to IA3 on both WikiText- 2 and Penn Treebank. On BERT’s RTE task\, SAMA reaches 53.7% accuracy\, surpassing IA3 (47.2%) and LoRA (52.6%) despite using 63% fewer trainable parameters than LoRA. We investigate this architecture dependence in detail and distil practical guidelines to help practitioners choose the right PEFT method for their setting.
CATEGORIES:VIRTUAL ROOM 8G
LOCATION:Virtual Room G\, Bangkok\, Thailand
SEQUENCE:0
UID:b4373d6cc07a444ddd2aedc989816d01
URL:http://11thictisthailand.sched.com/event/b4373d6cc07a444ddd2aedc989816d01
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260417T125213Z
DTSTART:20260410T051500Z
DTEND:20260410T071500Z
SUMMARY:Federated vs Centralized Learning for Pneumonia Detection: A Cross-Architecture Comparison of SVM\, CNN\, and LSTM on Chest X-Ray Images
DESCRIPTION:Authors - M SANTHIYA\, V KALAICHELVI\n Abstract - The wide use of machine learning in the field of medical imaging has caused concern with regard to patient information security\, especially when mod els are being trained over multiple health care systems in a distributed manner. Centralized learning requires transferring raw patient data to a central server where there is an extreme risk of data breach and unauthorized access to patients' personal information. Violations of health care regulations (HIPAA and GDPR) can occur in a centralized system because of the transfer of patients' data. Feder ated Learning (FL) addresses these issues by allowing collaborative model de velopment on individual client devices. Therefore\, the sensitive patient data will remain at its source institution. This paper provides a thorough comparative study of centralized learning and federated learning methods for detecting pneumonia utilizing chest X-rays from the publicly available Kaggle Chest X-Ray Pneumo nia dataset. Three architecture types (Support Vector Machine (SVM)\, Convolu tional Neural Network (CNN) and Long Short-Term Memory (LSTM)) were tested in both centralized and federated environments utilizing the FedAvg ag gregation method. Only the model weights were shared between the clients and the central server\; therefore\, patient data was maintained private through the en tire model training process. Experimental results demonstrated that federated learning produced superior performance than centralized learning for all three architectures (81.1%\, 84.6%\, and 82.7% for SVM\, CNN and LSTM respec tively). The performance metrics for centralized learning were 76.6%\, 76.3%\, and 81.6%. This superior performance of FL is attributed to the inherent regular ization effect of local class-balancing within the federated clients that reduces the inherent class imbalance in the dataset. Overall\, our research demonstrates that FL is not only a viable privacy-preserving solution to centralized training but offers improved generalization in the medical imaging domain with imbalanced classes and is a suitable solution for application in distributed health care envi ronments.
CATEGORIES:VIRTUAL ROOM 8G
LOCATION:Virtual Room G\, Bangkok\, Thailand
SEQUENCE:0
UID:2b05cc8c506a7395784bdcb99541af83
URL:http://11thictisthailand.sched.com/event/2b05cc8c506a7395784bdcb99541af83
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260417T125213Z
DTSTART:20260410T051500Z
DTEND:20260410T071500Z
SUMMARY:From Administrative Data to Policy Intelligence: An Explainable and Accountable Governance Framework
DESCRIPTION:Authors - Vishruth B. Gowda\, Sowmya T\, Shreyas K\, Megha J\, Shreenidhi B S\, Pranav Srinivas\n Abstract - Public administrations generate extensive administrative data through routine governance processes yet it is weakly based on verifiable evidence. This paper introduces a human-centric policy intelligence system based on execution-level administrative data for provision of accountable and evidence-based policy-making. The framework brings together governance-conscious data ingestion\, cryptographic hash-based verification including permissioned blockchain systems to control the integrity of data\, cross-domain data harmonisation to overcome administrative silos\, and explainable machine learning models to create interpretable supporting insights. The framework is specifically meant as a human-in-the-loop system\, maximizing policy foresight\, administrative discretion\, and accountability to the law. The validation with actual Mahatma Gandhi National Rural Employment Guarantee Act administrative data of the year 2022–2023 proves that the framework can be used to stress the implementation issues and regional inequalities without computerising policy-related decisions. The suggested solution is lightweight\, scaled down to fit in the existing open-sector digital infrastructure.
CATEGORIES:VIRTUAL ROOM 8G
LOCATION:Virtual Room G\, Bangkok\, Thailand
SEQUENCE:0
UID:f3ac734fa1937368f2682dac8410ffa4
URL:http://11thictisthailand.sched.com/event/f3ac734fa1937368f2682dac8410ffa4
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260417T125213Z
DTSTART:20260410T051500Z
DTEND:20260410T071500Z
SUMMARY:Machine Learning-Based Depression Detection from Text Using Hybrid Feature Engineering and Ensemble Models
DESCRIPTION:Authors - Aprna Tripathi\, Akhilesh Kumar Sharma\, Avisikta Pal\, Srikanth Prabhu\, Ramakrishna Mundugar\, Reet Ginotra Abstract - This paper presents a novel approach to identifying translation errors in Thai-English machine translation through the comparative analysis of multiple automatic evaluation metrics. Using a rank deviation methodology\, we evaluate 350 Thai-English translations produced by seven contemporary systems provid ing online translations — including dedicated Machine Translation systems and large language models — across nine automatic evaluation metrics. By ranking translations within each metric and comparing individual metric rankings against the mean average rank\, we identify translations that receive solitary punishment from a single metric\, isolating these as candidates for manual error analysis. Our results demonstrate that individual metrics exhibit distinct sensitivity to specific error types\, and that surface-level metrics retain diagnostic value along side advanced neural metrics. Neural metrics effectively identify meaning and adequacy errors\, but surface-level metrics uniquely identify morphological vari ation\, word order errors\, preposition choice\, and number formatting issues that neural metrics fail to penalize. The diversity of metric sensitivity is therefore an asset rather than an inconvenience\, enabling a more complete characterization of translation error than any single metric can provide. This research supports the development of high-quality training data for MT fine-tuning by identifying the specific error types that individual metrics can and cannot detect and provides a repeatable diagnostic methodology applicable to other language pairs.
CATEGORIES:VIRTUAL ROOM 8G
LOCATION:Virtual Room G\, Bangkok\, Thailand
SEQUENCE:0
UID:8ec296e99c7f67bac86e9d57285c9182
URL:http://11thictisthailand.sched.com/event/8ec296e99c7f67bac86e9d57285c9182
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260417T125213Z
DTSTART:20260410T051500Z
DTEND:20260410T071500Z
SUMMARY:MMSU d-TRAIL: Development of Document Tagging\, Repository and Information Locator for the Records Office of Mariano Marcos State University
DESCRIPTION:Authors - Bobby A. Eclarin\, Mark Justine S. Cudapas\n Abstract - This research deals with the persistent challenges of document man agement in higher education institutions which focuses on the development of a digital support tool for Mariano Marcos State University (MMSU). Traditional paper-based systems and fragmented repositories often result in inefficiencies\, duplication of work\, and risks of data loss. The project adopted the Agile Devel opment methodology with emphasis on flexibility\, collaboration\, and iterative improvement. The d-T.R.A.I.L. system was built using JavaScript\, PHP Laravel\, HighCharts\, and MySQL\, integrating features such as tagging\, repository man agement\, granular access control\, and collaborative modules like Teams. These functionalities were designed to streamline document organization\, retrieval\, and secure sharing across diverse academic and administrative units of the Univer sity. A User Acceptance Test (UAT) was conducted involving 70 participants from different MMSU offices that utilizes a Likert scale to measure satisfaction. Re sults yielded an overall mean score of 4.36 which was interpreted as Very Satis factory. High ratings were recorded for productivity\, user-friendliness\, and doc ument organization\, while scalability received the lowest score which indicates an area for future enhancement of the system. The findings demonstrate that the system effectively improves workflow efficiency\, accessibility\, and accountabil ity\, while aligning with national digital transformation policies.
CATEGORIES:VIRTUAL ROOM 8G
LOCATION:Virtual Room G\, Bangkok\, Thailand
SEQUENCE:0
UID:27e5b9e74d624da3e44c97071b2ac024
URL:http://11thictisthailand.sched.com/event/27e5b9e74d624da3e44c97071b2ac024
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260417T125213Z
DTSTART:20260410T051500Z
DTEND:20260410T071500Z
SUMMARY:NFC-Enabled AI-Driven Pharmaceutical Supply Chain Framework for Circular Economy Sustainability in India
DESCRIPTION:Authors - Gauthaman S P\, Paneer Thanu Swaroop C\, Bagavathi Sivakumar P\, Anantha Narayanan V Abstract - Psoriasis is a long-term inflammatory skin disease commonly identi fied by red plaques\, scaling\, and abnormal thickening of the epidermis. Reliable evaluation of disease severity is important for determining appropriate treatment options and for tracking patient response to therapy. In clinical practice\, severity is often assessed using the Psoriasis Area and Severity Index (PASI). Although widely adopted\, this method largely depends on visual examination and clinician judgment\, which may lead to inconsistencies and observer-dependent variations. Recent developments in artificial intelligence and non-invasive dermatological imaging technologies provide opportunities for more objective and automated assessment of skin disorders. In this study\, a novel framework is proposed for psoriasis severity evaluation that integrates skin biomechanical characteristics with deep hybrid learning mod els. Biomechanical attributes of the skin\, including elasticity\, stiffness\, and vis coelastic behavior\, are obtained through non-invasive measurement techniques and combined with visual information derived from dermatological images. The proposed system employs a hybrid deep learning architecture that incorporates convolutional neural networks (CNN) for image feature extraction along with machine learning classifiers for severity prediction. By jointly analyzing biome chanical and visual features\, the framework aims to enhance the precision\, con sistency\, and reproducibility of psoriasis severity assessment. Experimental anal ysis indicates that the inclusion of biomechanical biomarkers alongside deep learning significantly improves prediction performance when compared with tra ditional image-based models. The proposed approach can support dermatologists in clinical decision-making and may also facilitate applications in tele-dermatol ogy and personalized disease monitoring.
CATEGORIES:VIRTUAL ROOM 8G
LOCATION:Virtual Room G\, Bangkok\, Thailand
SEQUENCE:0
UID:095e415345530ce503f9894454977725
URL:http://11thictisthailand.sched.com/event/095e415345530ce503f9894454977725
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260417T125213Z
DTSTART:20260410T051500Z
DTEND:20260410T071500Z
SUMMARY:Psoriasis Severity Assessment Using Skin Biomechanics: A Novel Approach Using Deep Hybrid Models
DESCRIPTION:Authors - Vijayanirmala Baddala\, Jolakula Asoka Smitha\, Bichagal Shadaksharappa Abstract - Accurate State-of-Charge (SoC) estimation is critical for ensuring the reliability\, safety\, and operational efficiency of lithium-ion batteries in electric vehicles and energy storage systems. While data-driven models offer high precision\, centralized approaches are increasingly limited by data privacy concerns\, high communi- cation overhead\, and poor scalability. This paper addresses these challenges by proposing a comprehensive deep learning and federated learning (FL) frame- work for decentralized SoC prediction using the OSF battery dataset. We use four LSTM architectures: Stacked LSTM\, Bidirectional LSTM\, Attention-based LSTM\, and Stateful LSTM\, which are integrated into a federated model to sys- tematically evaluate their performance. These include FedAvg\, FedProx\, and adaptive methods such as FedAdam and FedYogi. To our knowledge\, this is the first study to evaluate these architectures in the context of a federated battery management system (BMS). Results show that The comparative analysis inves- tigates the interplay between model complexity and federated optimization\, with a specific focus on predictive accuracy\, convergence behavior\, and robustness to non-IID data distributions stemming from heterogeneous battery capacities and usage patterns. By benchmarking these combinations\, this research identifies optimal strategies for implementing privacy-preserving\, communication-efficient\, and scalable Battery Management Systems (BMS) at the edge.
CATEGORIES:VIRTUAL ROOM 8G
LOCATION:Virtual Room G\, Bangkok\, Thailand
SEQUENCE:0
UID:12b224a618e01efe9c496d533ede325e
URL:http://11thictisthailand.sched.com/event/12b224a618e01efe9c496d533ede325e
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260417T125213Z
DTSTART:20260410T053000Z
DTEND:20260410T054500Z
SUMMARY:From Generative AI to Agentic AI: A Bibliometric Study of Emerging Paradigms in AI Research
DESCRIPTION:Authors - Divyakant Meva\, Kalpesh Popat Abstract - Based on the total of 274 publications from 2023-2025\, this bibliometric analysis reveals the evolving trend for agentic AI. This research utilized the PRISMA protocol and the Scopus database. From the results\, there has been an exponential increase\, which indicates that there has been a massive jump in the number of publications\, specifically that there has been a “342% increase in 2025 from 2024.” Key results indicate that there has been intensive application in the fields of healthcare\, education\, and manufacturing\, which comprise 18.2%\, 14.6%\, and 12.4% respectively. The United States has published the most\, specifically at 38.7%\, followed by China and European countries\, which comprise 22.3% and 24.1% respectively. Thematic analysis Six major clusters emerged from the thematic analysis: autonomous systems\, human-AI collaboration\, ethical frameworks\, multi-agent architectures\, application in various domains\, and evaluation methods. The study has demonstrated the shift from generative passive AI to autonomous agentic systems\, identified important research gaps and presented future research directions.
CATEGORIES:PHYSICAL TECHNICAL SESSION 1A
LOCATION:Benchasiri 1\, Bangkok Marriott Hotel Sukhumvit\, Thailand
SEQUENCE:0
UID:3568418166d4ba1eb8d317096a9ed890
URL:http://11thictisthailand.sched.com/event/3568418166d4ba1eb8d317096a9ed890
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260417T125213Z
DTSTART:20260410T053000Z
DTEND:20260410T054500Z
SUMMARY:Electronic Medical Percussion Using Safeguarded Musical Signals for Noninvasive Monitoring
DESCRIPTION:Authors - Kiwa Matsui\, Teruki Toya\, Kenji Ozawa Abstract - Medical percussion estimates internal body conditions from acoustic responses generated by tapping the body. To enable portable and comfortable health monitoring\, this study proposes a music-based electronic percussion system using ordinary musical signals as test sig nals. The system improves portability by introducing a compact piezo speaker exciter and a vibration pickup fixed to an abdominal band. In ad dition\, signal safeguarding is applied so that musical signals can be used for impulse-response measurements with sufficient spectral power. Exper iments measuring stomach responses before and after meals showed that the safeguarded musical signals produced results comparable to sweep signals and enabled detection of state changes. These results demon strate the feasibility of portable\, noninvasive health monitoring using music-based electronic percussion. Furthermore\, arbitrary music signals can be converted into reliable excitation signals through signal safeguard ing while preserving perceptual musical quality.
CATEGORIES:PHYSICAL TECHNICAL SESSION 1B
LOCATION:Benchasiri 2\, Bangkok Marriott Hotel Sukhumvit\, Thailand
SEQUENCE:0
UID:fdad53475b014594776470a379348440
URL:http://11thictisthailand.sched.com/event/fdad53475b014594776470a379348440
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260417T125213Z
DTSTART:20260410T053000Z
DTEND:20260410T054500Z
SUMMARY:An Optimized Multi-View Tire 3D Reconstruction for Industrial Applications
DESCRIPTION:Authors - Aman Kamboj\, Vishal Kumar\, Abhishek Mishra\, Kalpesh Patil Abstract - Accurate 3D reconstruction of tire tread geometry is essential for industrial applications such as automated wear estimation\, defect detection\, and quality assurance. However\, 3D reconstruction of tires from multi-view RGB images remains challenging due to low-texture rubber surfaces\, repetitive groove patterns\, and sensitivity to lighting variations. These factors often lead to incomplete or noisy reconstructions when using standard photogrammetry. This paper presents an optimized multi-view tire reconstruction framework tailored specifically for tire tread surfaces. The resulting 3D tire model was compared with the reference 3D model obtained from a laser scanner. The comparison showed a mean point-to-point distance of 0.05mm between the two models\, indicating a high level of geometric accuracy and close agreement with the ground-truth laser-scanned model. Experimental evaluations further demonstrate that the our optimized method is fast and achieves higher completeness\, depth information\, better preservation of tread grooves. Overall\, the proposed framework provides an accurate tire 3D reconstruction solution capable of delivering the precision required for modern tire inspection and analysis.
CATEGORIES:PHYSICAL TECHNICAL SESSION 1C
LOCATION:Benchasiri 3\, Bangkok Marriott Hotel Sukhumvit\, Thailand
SEQUENCE:0
UID:a72e5a9a3048fccaefe53149d4cb4b3a
URL:http://11thictisthailand.sched.com/event/a72e5a9a3048fccaefe53149d4cb4b3a
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260417T125213Z
DTSTART:20260410T053000Z
DTEND:20260410T054500Z
SUMMARY:A Cost-Effective Intelligent End-to-End Fall Detection System for Elderly Care Using IMU Sensors and Machine Learning
DESCRIPTION:Authors - Mohd Mansoor Khan Abstract - An exclusive action dataset\, termed the ImuFall\, was created using gyroscope data from the MPU6050 IMU sensor. An end-to-end posture and fall detection system was developed and evaluated on this dataset. A threshold-based mean slope algorithm was implemented and compared with machine learning methods\, namely ν-SVM for posture classification and random forest classifier (RFC) for fall detection. The ν-SVM was chosen to reduce overfitting\, while RFC was used for its effectiveness with time-series data. The cascaded framework achieves 100% best-case accuracy\, with 95.8% average posture accuracy and 100% fall detection accuracy. This is the first reported implementation of a cascaded ν-SVM–RFC end-to-end fall detection system.
CATEGORIES:PHYSICAL TECHNICAL SESSION 1D
LOCATION:Benchasiri 4\, Bangkok Marriott Hotel Sukhumvit\, Thailand
SEQUENCE:0
UID:b054d2424c37ca657c196d8e09c96937
URL:http://11thictisthailand.sched.com/event/b054d2424c37ca657c196d8e09c96937
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260417T125213Z
DTSTART:20260410T054500Z
DTEND:20260410T060000Z
SUMMARY:Container-based AI/ML Parallel Workloads in Multi-GPU Cluster System
DESCRIPTION:Authors - Seungmin Lee\, Ju-Won Park Abstract - The module-based static operating environment\, which is widely used in domestic and international supercomputer operating centers\, encounters numerous problems in supporting artificial intelligence / machine learning (AI/ML) parallel workloads because the variety of platforms and packages used make it difficult to build all execution environments. To address these issues and dynamically provide diverse execution environments\, container-based cloud technologies are being widely utilized in high-performance computing (HPC) cluster systems. However\, container runtime toolkits like Shifter and Singularity\, which are widely used in the HPC field\, present problems\, such as the need for image format conversion\, writing scheduler job script files\, environmental setup\, and direct management of the container lifecycle. This study proposes a solution to these problems by utilizing Kubernetes\, which has become the de facto standard for container orchestration as it supports AI/ML parallel workloads even in HPC environments. Supporting Kubernetes-native parallel workload execution offers several advantages. First\, image conversion is unnecessary because it directly uses Docker images. Second\, human errors are minimized because the operator automatically handles the environment setup required for parallel execution. Third\, in case of failures\, automatic recovery and re-execution are possible by leveraging Kubernetes’ powerful container lifecycle management capabilities. In addition\, this study introduces the distributed learning function of the KISTI Supercomputer web portal (MyKSC)\, which has been implemented using the proposed method.
CATEGORIES:PHYSICAL TECHNICAL SESSION 1A
LOCATION:Benchasiri 1\, Bangkok Marriott Hotel Sukhumvit\, Thailand
SEQUENCE:0
UID:a4fb36a078bb79c65a70bca7c3b9c6d0
URL:http://11thictisthailand.sched.com/event/a4fb36a078bb79c65a70bca7c3b9c6d0
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260417T125213Z
DTSTART:20260410T054500Z
DTEND:20260410T060000Z
SUMMARY:Holo-Agentic GraphRAG: Synergizing Spatial Computing and Hierarchical Knowledge Graphs for Dynamic Theme Detection
DESCRIPTION:Authors - Ajinkya Chavan Abstract - The pursuit of intelligent systems capable of parsing human intent and navigating complex information landscapes has evolved from rigid\, rule-based architectures to sophisticated\, agentic frameworks. Early prototypes\, such as the "Artificially Talented Architecture" (ATA)\, demonstrated the foundational utility of theme detection coupled with rudimentary holographic interfaces\; however\, these systems were constrained by the independence assumptions of Vector Space Models (VSM)\, limited context windows\, and a lack of semantic relationship modeling. In the current era of Generative AI\, while Large Language Models (LLMs) have solved fluency\, they continue to struggle with "Global Sensemaking"—the ability to synthesize highlevel themes across vast corpora without succumbing to hallucination or context fragmentation. This paper introduces Holo-Agentic GraphRAG\, a novel architecture that integrates Agentic Retrieval-Augmented Generation (Agentic RAG) with spatial computing to redefine state-ofthe- art theme detection. Unlike traditional methods relying on flat retrieval\, the proposed approach employs a hierarchical knowledge graph constructed via LLM extraction and refined through the Leiden community detection algorithm. This structure allows for dynamic graph traversal and multi-level summarization. Furthermore\, user interaction is formalized as a Partially Observable Markov Decision Process (POMDP) within a mixed-reality environment\, fusing gaze tracking and voice prosody to resolve communicative ambiguity. Experimental results on the GraphRAG-Bench and a proprietary spatial interaction dataset demonstrate that Holo- Agentic GraphRAG outperforms standard RAG and static GraphRAG baselines by 18.4% in multi-hop reasoning accuracy and 22% in theme detection coherence\, while significantly reducing token overhead.
CATEGORIES:PHYSICAL TECHNICAL SESSION 1B
LOCATION:Benchasiri 2\, Bangkok Marriott Hotel Sukhumvit\, Thailand
SEQUENCE:0
UID:65a5fdad6983e325511011871b8069a3
URL:http://11thictisthailand.sched.com/event/65a5fdad6983e325511011871b8069a3
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260417T125213Z
DTSTART:20260410T054500Z
DTEND:20260410T060000Z
SUMMARY:Markerless Flapping Pose Estimation and Phase Classification of High-speed Bat Flight Recordings
DESCRIPTION:Authors - D. P. Jayathung\, M. Ramashini\, Juliana Zaini\, R. Muller\, Liyanage C. De Silva Abstract - The primary objective of this research is to explore and interpret the complex flight kinematics of bats in order to deepen aerodynamic understanding and inspire future technological innovation. To achieve this\, the study adopts a hybrid approach for estimating flapping pose phases in high-speed bat flight recordings. Accurately distinguishing between the upstroke and downstroke phases is essential for examining the subtle dynamics and movement patterns of bats’ uniquely flexible wing structures. The methodology followed a structured work-flow\, beginning with video acquisition using an array of 50 high-speed cameras that recorded bat flights at 1000 frames per second within a controlled tunnel environment. An enhanced YOLOv5L model was then employed to remove un-necessary frames\, achieving a mean Average Precision (mAP) of 99.3% and successfully filtering out more than 85% of unwanted footage. For the pose estimation\, this work used DeepLabCut to define 20 anatomical keypoints. After com-paring five backbone architectures\, this study selected ResNet50 as the most suit-able model\, as it yielded the lowest test RMSE (3.98) and the highest test mAP (97.62%). A rule-based geometric method was developed to classify bat wing-beat phases using elbow–wrist–wingtip angles derived from DeepLabCut key-points. By analyzing the smoothed angle trajectory and its temporal derivative\, the rule-based approach reliably identified upstroke and downstroke cycles\, which were validated using test videos. The extracted phase information supports a deeper biomechanical understanding of bat flight while also enabling applications in bio-inspired robotics\, real-time flight monitoring\, and automated analysis of complex animal motion.
CATEGORIES:PHYSICAL TECHNICAL SESSION 1C
LOCATION:Benchasiri 3\, Bangkok Marriott Hotel Sukhumvit\, Thailand
SEQUENCE:0
UID:4ba0f42b18371f142226240b6bc07ed0
URL:http://11thictisthailand.sched.com/event/4ba0f42b18371f142226240b6bc07ed0
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260417T125213Z
DTSTART:20260410T054500Z
DTEND:20260410T060000Z
SUMMARY:B-Leaf Scanner: A Deep Learning-Based Mobile Application for Health Condition Scanning of Banana Leaves
DESCRIPTION:Authors - Maya Fitria\, Muhammad Hafiz Rinaldi\, Khairun Saddami\, Isack Farady\, Kahlil Muchtar\, Sayed Muchallil Abstract - As the most consumed commodity worldwide\, banana requires careful and proper growth management to maintain its production\, including maintaining its leaf health. Commonly\, farmers identify the disease in banana leaves by inspecting its appearance. However\, this conventional method is considered subjective to one person to another\, and this could lead to delayed treatment\, and may impact the fruit development and production. To address this issue\, this re-search proposed B-Leaf Scanner\, a mobile-based application integrating a deep learning approach for banana leaf disease detection. The application integrated the YOLOv5-based model to detect and classify the disease in banana leaf which is conducted by capturing image from a camera or by inputting from the device gallery. The proposed application was designed aligned with the findings from field observations and interviews with local farmers to ensure usability and related to real-world settings. The findings show that the detection model yielded an mAP of 80.1%\, following with 86.8% and 72.4% of precision and recall value\, respectively. These results indicate the reliability of the model in performing the detection process. Moreover\, the usability testing of the application was con-ducted to ten local farmers through task-based testing\, and System Usability Scale (SUS). Based on usability results\, the B-Leaf Scanner application achieved excellent usability with a SUS score of 88%\, indicating the application can effectively support local banana farmers in identifying leaf diseases.
CATEGORIES:PHYSICAL TECHNICAL SESSION 1D
LOCATION:Benchasiri 4\, Bangkok Marriott Hotel Sukhumvit\, Thailand
SEQUENCE:0
UID:9648b8339ad3a9ae57f13ece2b0809ff
URL:http://11thictisthailand.sched.com/event/9648b8339ad3a9ae57f13ece2b0809ff
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260417T125213Z
DTSTART:20260410T060000Z
DTEND:20260410T061500Z
SUMMARY:A Hybrid Trust Management System for real time IoT networks
DESCRIPTION:Authors - Satish Kamble\, Surendra Mahajan\, Lalit Patil\n Abstract - In today’s world\, IoT devices interact with each other for a specific purpose. IoT de-vices are used in every aspect of our lives. In IoT networks\, devices can act as malicious nodes and can perform attacks affecting the IoT network's performance. A trust management system can play a major role in these IoT networks. This paper suggests a trust management system that is based on quality of services (QoS) and implemented on a real Raspberry Pi and ESP32 IoT testbed. The model uses direct trust and indirect trust parameters. The model uses memory efficiently by using sliding-window mechanism. This system implements a threshold-based mechanism for detecting untrustworthy devices and further blocking them for future communication. A recency-weight is used for stabilizing the system. This system is capable of detecting attacks such as the grayhole attack and RTT inflation attack.
CATEGORIES:PHYSICAL TECHNICAL SESSION 1A
LOCATION:Benchasiri 1\, Bangkok Marriott Hotel Sukhumvit\, Thailand
SEQUENCE:0
UID:ea876155ad8feec5375c18f0afdc3c88
URL:http://11thictisthailand.sched.com/event/ea876155ad8feec5375c18f0afdc3c88
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260417T125213Z
DTSTART:20260410T060000Z
DTEND:20260410T061500Z
SUMMARY:First-B/M: A Smartphone-Based System for Home Fetal Heart Rate Monitoring
DESCRIPTION:Authors - Saki Matsudo\, Yurika Obata\, Koichiro Kido\, Kenji Ozawa Abstract - We present First-B/M\, a smartphone-based system that enables pregnant women to measure fetal heart rate (FHR) at home\, analogous to auscultation. The system integrates two external microphones embedded in short stethoscope tubes\, connected to an iOS device. The smartphone performs low-pass ltering (250 Hz)\, harmonic/percussive sound separation (HPSS) to extract fetal heart sounds\, and FHR es- timation based on frame-wise amplitude increases. To improve robust- ness in non-clinical environments\, a median-based temporal aggregation method is applied. A user-centered application supports recording\, FHR visualization\, and data sharing with clinicians. Usability was assessed with 10 participants through task completion and ve-point rating evaluations. System performance was evaluated using 20 recordings from pregnant women\, in which fetal heart sounds were identiable in 12 cases. Supplementary pseudo-fetal data\, generated by time-scaling adult heart sounds\, were used to examine algorithm behavior under ideal con- ditions. When fetal heart sounds were captured\, estimated FHR values agreed with human reference measurements within 3%. One outlier occurred under strong mid-recording noise\, indicating the need for auto- matic re-measurement support. These results demonstrate the feasibility of smartphone-based auscultation for home FHR monitoring and provide a practical foundation for non-clinical FHR measurement systems.
CATEGORIES:PHYSICAL TECHNICAL SESSION 1B
LOCATION:Benchasiri 2\, Bangkok Marriott Hotel Sukhumvit\, Thailand
SEQUENCE:0
UID:09a96f63bee10f11a69f1384915a4ea2
URL:http://11thictisthailand.sched.com/event/09a96f63bee10f11a69f1384915a4ea2
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260417T125213Z
DTSTART:20260410T060000Z
DTEND:20260410T061500Z
SUMMARY:Exploring an Agentic AI-Based Framework for Introductory Programming Education
DESCRIPTION:Authors - Frances Ysabelle D. Rebollido\, Jaime D.L. Caro Abstract - The rapid development of artificial intelligence (AI) creates new opportunities and challenges in introductory programming education. Existing AI tools provide immediate support and feedback to students\, but they have the tendency to generate inaccurate\, biased\, or pedagogically unsuitable responses. To address this\, we introduce the Agentic Learning & Adaptation System (ALAS)\, an Agentic AI-based system designed to deliver tailored and educationally grounded support for students. Hence\, with this process\, ALAS generates responses that are adaptive\, and pedagogically appropriate. This enables ALAS to provide personalized support to students. Its modular design provides a scalable foundation for integrating additional agents and functions. We present the conceptual design and early-stage prototype of ALAS to demonstrate its potential in enhancing students’ learning experiences and supporting the responsible use of AI in computing education. Future work will focus on implementing and evaluating ALAS in a classroom setting.
CATEGORIES:PHYSICAL TECHNICAL SESSION 1C
LOCATION:Benchasiri 3\, Bangkok Marriott Hotel Sukhumvit\, Thailand
SEQUENCE:0
UID:ec0e7243902e842d06c2cd508b03759c
URL:http://11thictisthailand.sched.com/event/ec0e7243902e842d06c2cd508b03759c
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260417T125213Z
DTSTART:20260410T060000Z
DTEND:20260410T061500Z
SUMMARY:Incorporating Distraction Mining (PFNet) for Improved Polyp Image Segmentation
DESCRIPTION:Authors - Sanjeeb Prasad Panday\, Ujawal Thapa\, Basanta Joshi\, Aman Shakya\, Anunaya Pandey\n Abstract - Early diagnosis of colorectal diseases depends upon the detection of polyps in colonoscopy images. These polyps often blend into their surrounding which often poses a challenge in detecting them. In this regard\, we introduce a new approach that improves polyp segmentation using distraction mining. Our method is based on the enhancement of Positioning and Focus Network (PFNet) which was originally designed for camouflaged object segmentation. The PFNet first identifies potential polyp regions using the Positioning Module (PM) and then refines the detection by focusing on hard-to-distinguish areas using the Focus Module (FM). We integrate a distraction mining technique into FM which helps the model differentiate polyps from misleading background details and further improved the accuracy. The comparison of the PFNet model with other models like SINet and PRANet. The PFNet models and other models like SINet and PRANet are evaluated on a different polyp datasets like Colon DB\, Laribpolyp DB\, and CVC-300. The result shows that the distraction mining enhance the segmentation performance on a complex datasets like laribpolyp DB with 0.8046 for S-measure\, 0.6651 for weighted F-measure\,0.0202 for MAE\,0.8590 for adaptive E-measure\, and CVC-300 with 0.8220 for S-measure\, 0.7317 for weighted F-measure\, 0.0299 for MAE and 0.8735 for adaptive Emeasures. There are slightly low accuracy in the colon DB datasets.
CATEGORIES:PHYSICAL TECHNICAL SESSION 1D
LOCATION:Benchasiri 4\, Bangkok Marriott Hotel Sukhumvit\, Thailand
SEQUENCE:0
UID:e05d6334c5141ecef5c2095f8ba6ca75
URL:http://11thictisthailand.sched.com/event/e05d6334c5141ecef5c2095f8ba6ca75
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260417T125213Z
DTSTART:20260410T061500Z
DTEND:20260410T063000Z
SUMMARY:Pixel-Level Multi-Level Chalkiness Analysis of Thai Hom Mali Rice Using U-Net with ResNet34 and Background Label Comparison
DESCRIPTION:Authors - Pitchayapatchaya Srikram\, Thanapak Khattiya\, Pathompong Charoansrimuang\, Chayanit Yoosri\, Nachirat Rachburee Abstract - Chalkiness in Thai Hom Mali rice is not only an important quality attribute for their market value and consumer acceptance\, but also for rice grain breeding. However\, conventional chalkiness evaluation relies on manual inspection\, which is subjective and time-consuming. This study proposes an automatic multi-level chalkiness analysis framework based on semantic segmentation using a U-Net architecture with a ResNet34 encoder to segment rice grains and chalky regions from digital images. Then it estimates the grain counts for pixel-level segmented rice regions and chalky regions to classify chalkiness levels. We compare experimental results across datasets with and without the black background label. Both results are not significantly different in loss value\, Mean IoU\, Dice score\, and F1 score. From a practical perspective\, the segmentation of both datasets differs between rice and chalky regions due to illumination. The dataset\, including the black background label\, shows clearer chalky-grain segmentation regions and is closer to the ground truth. In contrast\, the dataset excluding the background label shows chalky-grain segmentation regions and is closer to the original image.
CATEGORIES:PHYSICAL TECHNICAL SESSION 1A
LOCATION:Benchasiri 1\, Bangkok Marriott Hotel Sukhumvit\, Thailand
SEQUENCE:0
UID:ff0a9a51981a4db074831da8c48118dd
URL:http://11thictisthailand.sched.com/event/ff0a9a51981a4db074831da8c48118dd
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260417T125213Z
DTSTART:20260410T061500Z
DTEND:20260410T063000Z
SUMMARY:Neuro-Linguistic and Behavioral Modeling for Social Media-Based Depression Prediction Across Developed and Developing Countries
DESCRIPTION:Authors - Ranjan Kumar Behera\, S. Dinesh Naveen Kumar Abstract - Social networking platforms such as Twitter have become inuential spaces where users routinely express opinions\, emotions\, and personal experiences providing valuable signals for understanding mental health conditions. This study leverages such user generated con- tent to investigate depression indicators and analyze their prevalence across countries classied as developed and developing. Unlike traditional sentiment analysis approaches\, this work introduces a novel attention enhanced BiLSTM architecture combined with a hybrid ensemble framework specically tailored for depression detection in short\, informal social-media text. The proposed model integrates contextual attention with bidirectional sequence learning to capture subtle linguistic cues\, while the ensemble mechanism enhances robustness against noise and linguistic variability across regions. The proposed methodology involves a comprehensive preprocessing pipeline\, depression-lexicon construction\, machine-learning baselines\, and the proposed deep model. Experimental evaluation demonstrates a signicant improvement in detection accuracy and generalization\, out performing existing benchmark methods. The study also presents a unique cross-country comparative analysis of de- pression trends\, o ering insights into how socio-economic environments in uence online emotional expression.
CATEGORIES:PHYSICAL TECHNICAL SESSION 1B
LOCATION:Benchasiri 2\, Bangkok Marriott Hotel Sukhumvit\, Thailand
SEQUENCE:0
UID:93a0aa7b66cfb1b87872fafed6e7393f
URL:http://11thictisthailand.sched.com/event/93a0aa7b66cfb1b87872fafed6e7393f
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260417T125213Z
DTSTART:20260410T061500Z
DTEND:20260410T063000Z
SUMMARY:Enhancing Security in Swarm Learning Environment using Behavior and Trust Evaluation
DESCRIPTION:Authors - Yazhiniyan Tamizhnambi\, Senthil Prakash P.N Abstract - Having trustworthy systems in a decentralized systems remains a challenge\, especially in adversarial conditions that include model poisoning\, sigil attacks and unauthorized re-entries. Despite the fact that federated learning and swarm learning can achieve collaborative model training without sharing raw data\, existing methodologies largely use fixed identities\, self-reported accuracy\, or direct weight comparison\, which in an open or semi-trust environment is likely to be weak. This work presents a blockchain-based trust system in swarm learning\, based on behavioral fingerprinting instead of identity-based accountability. In the suggested system\, all involved nodes produce a behavioral fingerprint at every training round\, which contains an accuracy of the challenge-sets\, deviation of updating the model\, and the distribution of features importance. The fingerprints are then stored on chain with the help of Merkle root structures\, ensuring transparent behavioral tracking across rounds. To address early-time poisoning and delayed attacks\, the system will utilize trust-weighted round-gated aggregation where the model updates will be verified before affecting other participants. Trust is measured through short-term and long-term consistency of behavior supported by Round Performance Score (RPS) which measures inconsistency with peer consensus during a round. The framework further resists Sybil and reentry attacks by matching behavioral fingerprints across identities\, ensuring that malicious models cannot bypass detection by resetting node credentials. Behavioral fingerprints are matched across identities to stop further Sybil and re-entry attacks. This ensures credential resetting by nodes to bypass detection\, since the behavior of the model will more or less be the same. The experimental analysis of heterogeneous hospital data sets shows improved universal accuracy\, adequate poisoned updates mitigation\, and dependable detection of malicious re-entry strategies.
CATEGORIES:PHYSICAL TECHNICAL SESSION 1C
LOCATION:Benchasiri 3\, Bangkok Marriott Hotel Sukhumvit\, Thailand
SEQUENCE:0
UID:74d0daba071e396d14677c378494acbf
URL:http://11thictisthailand.sched.com/event/74d0daba071e396d14677c378494acbf
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260417T125213Z
DTSTART:20260410T061500Z
DTEND:20260410T063000Z
SUMMARY:Machine Learning-Based Vehicle Arrival Time Prediction in Urban Logistics “Just in time”: A Geospatial Clustering Approach
DESCRIPTION:Authors - Jose Alejandro Ascencio-Laguna\, Armida Gonzalez-Lorence\, Ana Lilia Mondragon-Solis\, Victor Alberto Gomez-Perez\n Abstract - Machine Learning (ML) and geospatial clustering have traditionally been applied as independent approaches to urban freight transportation chal lenges\, particularly arrival time prediction under "just-in-time" constraints. De spite their complementary nature\, their integration remains underexplored\, while distance-based methods relying on Euclidean metrics yield error margins of 18 35 minutes\, insufficient for operational logistics. This study proposes a hierarchical framework combining geographic k-means clustering (k=14) as a spatial segmentation layer with an enhanced Random For est regressor incorporating temporal feature engineering. The architecture is com putationally efficient and robust to real-world uncertainty after training. The framework was validated across three metropolitan areas in Mexico using 306\,847 records from June 2024\, benchmarked against five algorithms through stratified temporal validation and Wilcoxon tests with Bonferroni correction. The proposed model achieved a Mean Absolute Error of 347.2 seconds (5.79 min)\, representing a 68.1% reduction relative to historical baselines (MAE: 1\,089 s) and a 19.9% improvement over standalone Random Forest (MAE: 433 s). Eu clidean distance was the dominant predictor (43.7%)\, followed by geographic coordinates (32.8%). All improvements were statistically significant (p
CATEGORIES:PHYSICAL TECHNICAL SESSION 1D
LOCATION:Benchasiri 4\, Bangkok Marriott Hotel Sukhumvit\, Thailand
SEQUENCE:0
UID:b95ed5c538cb33eaef145770a46609c5
URL:http://11thictisthailand.sched.com/event/b95ed5c538cb33eaef145770a46609c5
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260417T125213Z
DTSTART:20260410T071500Z
DTEND:20260410T071700Z
SUMMARY:Session Chair Concluding Remarks
DESCRIPTION:\n
CATEGORIES:VIRTUAL ROOM 8A
LOCATION:Virtual Room A\, Bangkok\, Thailand
SEQUENCE:0
UID:49ee38b776f13b00daee33815bcde5ea
URL:http://11thictisthailand.sched.com/event/49ee38b776f13b00daee33815bcde5ea
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260417T125213Z
DTSTART:20260410T071500Z
DTEND:20260410T071700Z
SUMMARY:Session Chair Concluding Remarks
DESCRIPTION:\n
CATEGORIES:VIRTUAL ROOM 8B
LOCATION:Virtual Room B\, Bangkok\, Thailand
SEQUENCE:0
UID:b68dc1b5652fdcf4f8858f8c80aa57c1
URL:http://11thictisthailand.sched.com/event/b68dc1b5652fdcf4f8858f8c80aa57c1
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260417T125213Z
DTSTART:20260410T071500Z
DTEND:20260410T071700Z
SUMMARY:Session Chair Concluding Remarks
DESCRIPTION:\n
CATEGORIES:VIRTUAL ROOM 8C
LOCATION:Virtual Room C\, Bangkok\, Thailand
SEQUENCE:0
UID:5357d16371e896b325211cb2b322b548
URL:http://11thictisthailand.sched.com/event/5357d16371e896b325211cb2b322b548
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260417T125213Z
DTSTART:20260410T071500Z
DTEND:20260410T071700Z
SUMMARY:Session Chair Concluding Remarks
DESCRIPTION:\n
CATEGORIES:VIRTUAL ROOM 8D
LOCATION:Virtual Room D\, Bangkok\, Thailand
SEQUENCE:0
UID:789592ba829a70f3f37d9f6e310787a8
URL:http://11thictisthailand.sched.com/event/789592ba829a70f3f37d9f6e310787a8
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260417T125213Z
DTSTART:20260410T071500Z
DTEND:20260410T071700Z
SUMMARY:Session Chair Concluding Remarks
DESCRIPTION:\n
CATEGORIES:VIRTUAL ROOM 8E
LOCATION:Virtual Room E\, Bangkok\, Thailand
SEQUENCE:0
UID:cd41364c9f630a000fd19a9d2ad9c487
URL:http://11thictisthailand.sched.com/event/cd41364c9f630a000fd19a9d2ad9c487
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260417T125213Z
DTSTART:20260410T071500Z
DTEND:20260410T071700Z
SUMMARY:Session Chair Concluding Remarks
DESCRIPTION:\n
CATEGORIES:VIRTUAL ROOM 8F
LOCATION:Virtual Room F\, Bangkok\, Thailand
SEQUENCE:0
UID:fd79729161e08ab2dfc34c931f1102f6
URL:http://11thictisthailand.sched.com/event/fd79729161e08ab2dfc34c931f1102f6
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260417T125213Z
DTSTART:20260410T071500Z
DTEND:20260410T071700Z
SUMMARY:Session Chair Concluding Remarks
DESCRIPTION:\n
CATEGORIES:VIRTUAL ROOM 8G
LOCATION:Virtual Room G\, Bangkok\, Thailand
SEQUENCE:0
UID:f938bad45d843488f4f4a3857fa54fdd
URL:http://11thictisthailand.sched.com/event/f938bad45d843488f4f4a3857fa54fdd
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260417T125213Z
DTSTART:20260410T071700Z
DTEND:20260410T072000Z
SUMMARY:Session Closing and Information To Authors
DESCRIPTION:\n
CATEGORIES:VIRTUAL ROOM 8A
LOCATION:Virtual Room A\, Bangkok\, Thailand
SEQUENCE:0
UID:f559b4c29f20f74ed015b371579201a7
URL:http://11thictisthailand.sched.com/event/f559b4c29f20f74ed015b371579201a7
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260417T125213Z
DTSTART:20260410T071700Z
DTEND:20260410T072000Z
SUMMARY:Session Closing and Information To Authors
DESCRIPTION:\n
CATEGORIES:VIRTUAL ROOM 8B
LOCATION:Virtual Room B\, Bangkok\, Thailand
SEQUENCE:0
UID:d7026c9985a0fdc0a8446f6f5f9917fc
URL:http://11thictisthailand.sched.com/event/d7026c9985a0fdc0a8446f6f5f9917fc
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260417T125213Z
DTSTART:20260410T071700Z
DTEND:20260410T072000Z
SUMMARY:Session Closing and Information To Authors
DESCRIPTION:\n
CATEGORIES:VIRTUAL ROOM 8C
LOCATION:Virtual Room C\, Bangkok\, Thailand
SEQUENCE:0
UID:fb54ca79ab1b571140817004452482d8
URL:http://11thictisthailand.sched.com/event/fb54ca79ab1b571140817004452482d8
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260417T125213Z
DTSTART:20260410T071700Z
DTEND:20260410T072000Z
SUMMARY:Session Closing and Information To Authors
DESCRIPTION:\n
CATEGORIES:VIRTUAL ROOM 8D
LOCATION:Virtual Room D\, Bangkok\, Thailand
SEQUENCE:0
UID:85dd574477d06b769307d319fe9fcc9f
URL:http://11thictisthailand.sched.com/event/85dd574477d06b769307d319fe9fcc9f
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260417T125213Z
DTSTART:20260410T071700Z
DTEND:20260410T072000Z
SUMMARY:Session Closing and Information To Authors
DESCRIPTION:\n
CATEGORIES:VIRTUAL ROOM 8E
LOCATION:Virtual Room E\, Bangkok\, Thailand
SEQUENCE:0
UID:1e065e749a6734468278ce26e9f321b4
URL:http://11thictisthailand.sched.com/event/1e065e749a6734468278ce26e9f321b4
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260417T125213Z
DTSTART:20260410T071700Z
DTEND:20260410T072000Z
SUMMARY:Session Closing and Information To Authors
DESCRIPTION:\n
CATEGORIES:VIRTUAL ROOM 8F
LOCATION:Virtual Room F\, Bangkok\, Thailand
SEQUENCE:0
UID:766d5f46b7b2967b4be3fe2a29360603
URL:http://11thictisthailand.sched.com/event/766d5f46b7b2967b4be3fe2a29360603
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260417T125213Z
DTSTART:20260410T071700Z
DTEND:20260410T072000Z
SUMMARY:Session Closing and Information To Authors
DESCRIPTION:\n
CATEGORIES:VIRTUAL ROOM 8G
LOCATION:Virtual Room G\, Bangkok\, Thailand
SEQUENCE:0
UID:84f3a0deec4d3a3f57d946de3432ddce
URL:http://11thictisthailand.sched.com/event/84f3a0deec4d3a3f57d946de3432ddce
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260417T125213Z
DTSTART:20260410T073000Z
DTEND:20260410T074500Z
SUMMARY:Performance Enhancement of Robotic Arms Using CFGWO-Optimized Fuzzy Control
DESCRIPTION:Authors - Prashant Gaidhane Abstract - The control of robotic arms presents signicant engineering challenges due to their multi-input multi-output characteristics\, strong coupling e ects\, and inherent nonlinearities. The optimization landscape for controller parameter tuning exhibits multiple local optima\, complicating the search for globally optimal solutions. Achieving precise end e ector path prole following in robotic systems demands sophisticated control methodologies tailored to handle these complexities. This re- search introduces an innovative cooperative foraging-based Grey Wolf Optimizer (CFGWO) algorithm to address these control challenges. The proposed methodology employs CFGWO to optimize the parameters of a PI D-based fuzzy regulator\, targeting enhanced end e ector path prole performance in a Planar dual-link robotic arm with terminal load. The PI D-based fuzzy regulator incorporates additional design parameters beyond conventional PID structures\, o ering expanded exibility in controller synthesis. The optimization performance of CFGWO is bench- marked against established algorithms including standard GWO\, GWO- ABC hybrid\, and LGWO variants. Performance evaluation focuses on minimizing the Integral of Time-weighted Absolute Error (ITAE) criterion. Results indicate that CFGWO achieves superior optimization con- vergence rates and delivers the lowest ITAE values among tested algorithms. Comprehensive experimental validation and performance analysis conrm the enhanced e ectiveness of the CFGWO approach\, demonstrating its capability to balance exploration and exploitation mechanisms for robust global optimization in engineering applications.
CATEGORIES:PHYSICAL TECHNICAL SESSION 2A
LOCATION:Benchasiri 1\, Bangkok Marriott Hotel Sukhumvit\, Thailand
SEQUENCE:0
UID:58ab84fa1035df73b2ac112866648d0e
URL:http://11thictisthailand.sched.com/event/58ab84fa1035df73b2ac112866648d0e
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260417T125213Z
DTSTART:20260410T073000Z
DTEND:20260410T074500Z
SUMMARY:Automated Psoriasis Classification using Deep-Learning and Transfer-Learning Approaches
DESCRIPTION:Authors - Sumet Jirattisak\, Tanatorn Tanantong\, Nittaya Chemkomnerd Abstract - Psoriasis is a chronic autoimmune skin disease\, and accurate diagnosis remains challenging due to the shortage of dermatologists and the subjective na ture of visual assessment. To address this challenge\, this study developed an au tomated classification system using three deep learning architectures\, Efficient Net-B4\, MobileNetV3\, and Vision Transformer\, within a transfer learning frame work to classify Psoriasis\, Healthy Skin\, and Psoriasis-like Disorder images. The models were fine-tuned and evaluated using 5-fold cross-validation on three da tasets: the Thammasat University Hospital dataset\, the Kaggle dataset\, and a combined dataset derived from DermNet and a previously published study in volving Indian patients. EfficientNet-B4 achieved the highest accuracy on the TUH dataset (99.68%) and the Dermnet-India dataset (94.40%)\, while Mo bileNetV3 performed best on the Kaggle dataset (96.88%) and required the short est training time. Overall\, the results show that EfficientNet-B4 offers superior predictive performance\, whereas MobileNetV3 provides a better balance be tween accuracy and computational efficiency. The findings confirm that transfer learning is a time-efficient approach for psoriasis classification\, reducing training time and computational cost while maintaining acceptable performance\, particu larly under limited clinical data conditions.
CATEGORIES:PHYSICAL TECHNICAL SESSION 2B
LOCATION:Benchasiri 2\, Bangkok Marriott Hotel Sukhumvit\, Thailand
SEQUENCE:0
UID:8af3ee0052429fa73c17ba450d7f289c
URL:http://11thictisthailand.sched.com/event/8af3ee0052429fa73c17ba450d7f289c
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260417T125213Z
DTSTART:20260410T073000Z
DTEND:20260410T074500Z
SUMMARY:EEG-based Alcohol Addiction using Spectral Feature Engineering: A comparative study
DESCRIPTION:Authors - Shipra Swati\, Sunita Kumari\, Santwana Sneha\n Abstract - The significant changes in brain dynamics caused by alcohol addiction can be captured by electroencephalography (EEG). Automated alcoholism detection using EEG has gained attention as a non-invasive\, objective replace traditional clinical assessments. This study provides a detailed comparison between conventional machine learning models and deep learning architectures for the EEG-based classification of alcoholism. It uses a publicly available multichannel EEG dataset containing recordings of both control and alcoholic subjects. Preprocessing and feature extraction in the time\, frequency\, and time-frequency domains are done before the assessment of traditional classifiers like k-Nearest Neighbors (KNN)\, Decision Tree (DT)\, Random Forest (RF)\, Logistic Regression (LR)\, and Support Vector Machine (SVM). Furthermore\, image-like EEG representations were used to adapt deep convolutional neural networks (ResNet and GoogleLeNet) for classification. According to experimental results\, KNN achieves competitive accuracy with little training time\, while ensemble methods and deep residual networks perform better than simpler classifiers. The results demonstrate the relative benefits and drawbacks of deep learning and statistical learning paradigms for EEG-based alcoholism detection.
CATEGORIES:PHYSICAL TECHNICAL SESSION 2C
LOCATION:Benchasiri 3\, Bangkok Marriott Hotel Sukhumvit\, Thailand
SEQUENCE:0
UID:0f2438d980614c8367f5225a09affc11
URL:http://11thictisthailand.sched.com/event/0f2438d980614c8367f5225a09affc11
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260417T125213Z
DTSTART:20260410T073000Z
DTEND:20260410T074500Z
SUMMARY:Energy-Efficient NTT Sampler for Kyber Benchmarked on FPGA
DESCRIPTION:Authors - Paresh Baidya\, Rourab Paul\, Vikas Srivastava\, Sumit Kumar Debnath Abstract - Kyber is a lattice-based key encapsulation mechanism se lected for standardization by the NIST Post-Quantum Cryptography (PQC) project. A critical component of Kyber’s key generation process is the sampling of matrix elements from a uniform distribution over the ring Rq. This step is computationally intensive and significantly impact ing task in the performance of low-power embedded systems such as Internet of Things (IoT)\, wireless sensor networks (WSNs)\, smart cards\, etc. Existing approaches like SampleNTT and Parse-SPDM3 rely on rejec tion sampling\, need at least three SHAKE-128 squeezing steps per poly nomial. As a result\, it causes significant amount of latency and energy. In this work\, we propose a novel and efficient sampling algorithm\, namely Modified SampleNTT\, which substantially reduces the average number of bits required from SHAKE-128 to generate elements in Rq—achieving approximately a 33% reduction compared to conventional SampleNTT. Modified SampleNTT achieves 99.16% success in generating a complete polynomial using only two SHAKE-128 squeezes. Furthermore\, our algo rithm maintains the same average rejection rate as existing techniques and passes all standard statistical tests for randomness quality. FPGA implementation on Artix-7 demonstrates a 33.14% reduction in energy\, 33.32% lower latency\, and 0.28% fewer slices compared to SampleNTT.
CATEGORIES:PHYSICAL TECHNICAL SESSION 2D
LOCATION:Benchasiri 4\, Bangkok Marriott Hotel Sukhumvit\, Thailand
SEQUENCE:0
UID:9364e4e035046d6e0b18602ee7a73b13
URL:http://11thictisthailand.sched.com/event/9364e4e035046d6e0b18602ee7a73b13
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260417T125213Z
DTSTART:20260410T074500Z
DTEND:20260410T080000Z
SUMMARY:Interpretable Skin Cancer Classification Using EfficientNetB3 and Saliency Maps
DESCRIPTION:Authors - Nandini Babbar\, Anshika Shreshth\, Saswati Gogoi\, Sunil Kumar Abstract - Early and precise detection of skin cancer is very necessary\, as it is one of the most aggressive diseases in the world\, and its effective treatment is required. Because many skin cancer types appear visually similar and the available datasets are imbalanced\, accurate diagnosis of skin lesions remains difficult using current medical technologies. Melanoma\, one of the most severe skin cancer diseases\, has a very low survival rate. In this paper\, a multimodal is developed for classifying skin cancer by combining saliency maps with EfficientNetB3.This research work uses PAD-UFES-20 dataset to access and train the model. The clinicians can understand the lesion better through saliency maps\, as they provide insightful information about the model’s decision-making process. This work concludes how deep learning models can be useful in improving skin cancer classification using an efficient approach for early detection clinically.
CATEGORIES:PHYSICAL TECHNICAL SESSION 2A
LOCATION:Benchasiri 1\, Bangkok Marriott Hotel Sukhumvit\, Thailand
SEQUENCE:0
UID:3cc551c4877c38e6b1913ac1d64eeb50
URL:http://11thictisthailand.sched.com/event/3cc551c4877c38e6b1913ac1d64eeb50
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260417T125213Z
DTSTART:20260410T074500Z
DTEND:20260410T080000Z
SUMMARY:Breast Cancer Detection using Ultra-Wide Band Antenna SAR and ResNeXt with Spatial Attention Module
DESCRIPTION:Authors - Sangeeta Singha\, Lalhriatpuii\, Banani Basu\, Arnab Nandi Abstract - This research presents a microwave-based breast cancer detection framework that leverages the Specific Absorption Rate (SAR) of an Ultra-Wideband (UWB) patch antenna\, operating between 3.1 and 10.6 GHz. By positioning an antenna array on opposite sides of a breast phantom and rotating it\, the system records SAR distributions as 2D input images. To isolate pathological features\, image segmentation is performed on these 2D data samples to distinguish between healthy\, benign and malignant tissue. These processed images are then classified using a ResNeXt architecture integrated with a Spatial Attention Module (SAM) to enhance tumor detection. Experimental results demonstrate the efficacy of this attention-driven approach\, as the integration of the SAM improved classification accuracy to 98.44%.
CATEGORIES:PHYSICAL TECHNICAL SESSION 2B
LOCATION:Benchasiri 2\, Bangkok Marriott Hotel Sukhumvit\, Thailand
SEQUENCE:0
UID:3a1b76ecba6c5082a87bcdd79f5a063e
URL:http://11thictisthailand.sched.com/event/3a1b76ecba6c5082a87bcdd79f5a063e
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260417T125213Z
DTSTART:20260410T074500Z
DTEND:20260410T080000Z
SUMMARY:PP-OW-ACE: A Privacy-Preserving One-Way Access Control Encryption Scheme for Smart Home Systems
DESCRIPTION:Authors - Raghav\, Chanchal Maurya\, Sunakshi Singh\n Abstract - Smart home ecosystems consist of resource-constrained IoT devices that continuously generate sensitive data\, making privacy protection\, access control\, and resilience to device compromise critical challenges. This paper proposes a privacy-preserving one-way access-control encryption scheme for cloud-assisted smart home environments\, designed to enforce a strict separation between data generation and data access. In the proposed scheme\, devices are granted encryption capability only\, while decryption authority remains exclusively with the device owner\, thereby preventing unauthorized data disclosure and eliminating key escrow risks. To protect identity privacy\, devices employ periodically refreshed pseudonymous identifiers derived from ephemeral secrets\, ensuring unlinkability and resistance to tracking and profiling attacks. The scheme further limits the impact of device compromise and prevents adversarial data injection. Performance evaluation demonstrates that the proposed scheme incurs lower computational and communication overhead than existing encryption schemes\, making it lightweight and well suited for resource-constrained smart home IoT deployments.
CATEGORIES:PHYSICAL TECHNICAL SESSION 2C
LOCATION:Benchasiri 3\, Bangkok Marriott Hotel Sukhumvit\, Thailand
SEQUENCE:0
UID:653c8cf81c03731f46d3f81572b51b8a
URL:http://11thictisthailand.sched.com/event/653c8cf81c03731f46d3f81572b51b8a
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260417T125213Z
DTSTART:20260410T074500Z
DTEND:20260410T080000Z
SUMMARY:Trust-Aware Multi-Agent AI for Validating Bilingual (Tamil-Malay) AI-Generated Educational Content
DESCRIPTION:Authors - Kingston Pal Thamburaj\, Ramesh Mercedes Premalatha\, Mukhlis Abu Bakar\n Abstract - Large language models are increasingly used to generate educational explanations\, but hallucinations\, uneven language quality\, and untraceable confidence can introduce misconceptions. These risks are amplified in bilingual classrooms\, where meaning must remain aligned across languages and low-resource language support is limited. This paper introduces a trust-aware multi-agent validation architecture for bilingual Tamil-Malay AI-generated educational content. The architecture decomposes validation into specialized agents that verify factual claims via evidence-grounded retrieval\, assess linguistic well-formedness and terminological consistency\, estimate pedagogical suitability for a target grade level\, detect hallucination and bias risk\, and measure cross-lingual semantic consistency to identify drift between Tamil and Malay explanations. Agent outputs are combined through a transparent aggregation mechanism to produce an overall bilingual trust score and an interpretable validation report with actionable revision cues. A benchmark construction protocol and evaluation methodology are presented to quantify claim-level correctness\, cross-lingual agreement\, and trust-score calibration against expert annotations. The proposed approach supports human-AI collaborative content authoring and intelligent tutoring workflows\, improving the reliability and inclusiveness of bilingual education systems in Southeast Asian contexts.
CATEGORIES:PHYSICAL TECHNICAL SESSION 2D
LOCATION:Benchasiri 4\, Bangkok Marriott Hotel Sukhumvit\, Thailand
SEQUENCE:0
UID:afaca89d4a1fe277fd447733abc50d00
URL:http://11thictisthailand.sched.com/event/afaca89d4a1fe277fd447733abc50d00
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260417T125213Z
DTSTART:20260410T075800Z
DTEND:20260410T080000Z
SUMMARY:Opening Remarks
DESCRIPTION:\n
CATEGORIES:VIRTUAL ROOM 9A
LOCATION:Virtual Room A\, Bangkok\, Thailand
SEQUENCE:0
UID:95fb7b02aadee6c498a4cb2be7125401
URL:http://11thictisthailand.sched.com/event/95fb7b02aadee6c498a4cb2be7125401
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260417T125213Z
DTSTART:20260410T075800Z
DTEND:20260410T080000Z
SUMMARY:Opening Remarks
DESCRIPTION:\n
CATEGORIES:VIRTUAL ROOM 9B
LOCATION:Virtual Room B\, Bangkok\, Thailand
SEQUENCE:0
UID:1990b2aff15af74f3a931d19e3f36ad9
URL:http://11thictisthailand.sched.com/event/1990b2aff15af74f3a931d19e3f36ad9
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260417T125213Z
DTSTART:20260410T075800Z
DTEND:20260410T080000Z
SUMMARY:Opening Remarks
DESCRIPTION:\n
CATEGORIES:VIRTUAL ROOM 9C
LOCATION:Virtual Room C\, Bangkok\, Thailand
SEQUENCE:0
UID:e55755f866f155d5a4be356dd6041298
URL:http://11thictisthailand.sched.com/event/e55755f866f155d5a4be356dd6041298
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260417T125213Z
DTSTART:20260410T075800Z
DTEND:20260410T080000Z
SUMMARY:Opening Remarks
DESCRIPTION:\n
CATEGORIES:VIRTUAL ROOM 9D
LOCATION:Virtual Room D\, Bangkok\, Thailand
SEQUENCE:0
UID:3f009c47c102f4adc7da86aae263da24
URL:http://11thictisthailand.sched.com/event/3f009c47c102f4adc7da86aae263da24
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260417T125213Z
DTSTART:20260410T075800Z
DTEND:20260410T080000Z
SUMMARY:Opening Remarks
DESCRIPTION:\n
CATEGORIES:VIRTUAL ROOM 9E
LOCATION:Virtual Room E\, Bangkok\, Thailand
SEQUENCE:0
UID:c020efd5107842226383a7d71a856b30
URL:http://11thictisthailand.sched.com/event/c020efd5107842226383a7d71a856b30
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260417T125213Z
DTSTART:20260410T075800Z
DTEND:20260410T080000Z
SUMMARY:Opening Remarks
DESCRIPTION:\n
CATEGORIES:VIRTUAL ROOM 9F
LOCATION:Virtual Room F\, Bangkok\, Thailand
SEQUENCE:0
UID:31478703784e25739fdcf8c5c3a5980e
URL:http://11thictisthailand.sched.com/event/31478703784e25739fdcf8c5c3a5980e
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260417T125213Z
DTSTART:20260410T075800Z
DTEND:20260410T080000Z
SUMMARY:Opening Remarks
DESCRIPTION:\n
CATEGORIES:VIRTUAL ROOM 9G
LOCATION:Virtual Room G\, Bangkok\, Thailand
SEQUENCE:0
UID:6f6ddf146f6e42723f1ee2df72015b7e
URL:http://11thictisthailand.sched.com/event/6f6ddf146f6e42723f1ee2df72015b7e
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260417T125213Z
DTSTART:20260410T080000Z
DTEND:20260410T081500Z
SUMMARY:Evaluating Guest Experience and Usability of Biometric Smart Room Access in Hotels: An HCI Perspective
DESCRIPTION:Authors - Vittorio Kuonadi Karimun Lie\, Farrell Prema Tody\, Gabriel Rinaldy Sudarmawan\, Tiurida Lily Anita Abstract - The integration of biometric authentication technologies into smart hospitality environments introduces new challenges related to usability\, privacy\, and trust. This study evaluates biometric room access systems from a Human–Computer Interaction (HCI) perspective\, focusing on how perceived security\, perceived utility\, perceived privacy\, and perceived ease of use influence guest experience through trust. A study design that is quantitative was employed\, and data were collected from 150 hotel guests who had previously used biometric room access. Partial Least Squares Structural Equation Modeling (PLS-SEM) was used to assess the suggested model. The results indicate that the model ex-plains 63.8% of the variance in guest experience and 59.1% of the variance in trust. Trust emerges as the strongest predictor of guest experience\, while perceived privacy and perceived security significantly influence experience indirectly through trust mediation. In contrast\, usability-related factors demonstrate comparatively smaller effects once baseline functionality is achieved. These findings suggest that biometric authentication in smart environments operates as a trust-sensitive socio-technical system\, where perceived data governance and psychological assurance are critical determinants of experiential evaluation. The study contributes to intelligent systems research by demonstrating that authentication technologies embedded in physical access control contexts must integrate technical robustness with perceptual trust-building mechanisms to achieve sustainable user acceptance.
CATEGORIES:PHYSICAL TECHNICAL SESSION 2A
LOCATION:Benchasiri 1\, Bangkok Marriott Hotel Sukhumvit\, Thailand
SEQUENCE:0
UID:ed1daaaed12cc698dcc82125ce10e066
URL:http://11thictisthailand.sched.com/event/ed1daaaed12cc698dcc82125ce10e066
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260417T125213Z
DTSTART:20260410T080000Z
DTEND:20260410T081500Z
SUMMARY:Automated conductive charging of passenger cars – System design and functional safety
DESCRIPTION:Authors - Stefan Lippitsch\, Mario Hirz Abstract - Automated conductive charging of electric vehicles using robotics can increase availability and user convenience\, especially in depot and fleet applica tions. At the same time\, new safety-critical situations arise from close human robot-vehicle interaction\, changing environmental conditions and the coupling between charging infrastructure and electric passenger cars. This paper presents a camera-based robotic system for automated conductive charging with standard ized connectors\, including the overall system architecture\, perception for detect ing the charging flap and standardized charging inlet\, robust pose estimation and a state-based process control. The second part introduces a framework developed to perform a hazard anal ysis and risk assessment specifically tailored to automated charging processes. The approach includes a discussion of relevant (functional) safety standards from machinery and robotics domains and their applicability to automated charging\, linking functional safety with general machine and collaborative robotics safety. Additionally\, an evaluation method is introduced\, enabling a traceable deriva tion of safety goals for this use case. Finally\, a comparison is made to the auto motive equivalent functional safety standard using performance parameters. The presented methodology supports consistent risk reasoning across disci plines and provides a practical foundation for developing scalable\, compliant\, and risk-optimized automated charging systems.
CATEGORIES:PHYSICAL TECHNICAL SESSION 2B
LOCATION:Benchasiri 2\, Bangkok Marriott Hotel Sukhumvit\, Thailand
SEQUENCE:0
UID:0a1c41283595aa8d04d3d1a7bd95cadd
URL:http://11thictisthailand.sched.com/event/0a1c41283595aa8d04d3d1a7bd95cadd
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260417T125213Z
DTSTART:20260410T080000Z
DTEND:20260410T081500Z
SUMMARY:Artificial Intelligence for Trust and Fraud Prevention in Modern E-Commerce Ecosystems
DESCRIPTION:Authors - Anudeep Arora\, Minal Maheshwari\, Abha Pandey\, Neha Chabra\, Prashant Vats\, Surbhi Sharma\n Abstract - The rapid expansion of e-commerce platforms has intensified exposure to sophisticated digital threats\, including deepfake-driven identity manipulation\, financial fraud\, and large-scale automated attacks that undermine consumer trust. Traditional rule-based and signature-driven security mechanisms are increasingly inadequate against adaptive and AI-generated adversarial behaviors. This paper investigates the role of artificial intelligence in enabling proactive threat detection and sustained trust preservation within modern e-commerce ecosystems. We present an AI-enabled security framework that integrates deep learning-based anomaly detection\, behavioral analytics\, and multimodal content verifi cation to identify fraudulent transactions\, synthetic media attacks\, and coordinated threat patterns in real time. The proposed approach leverages temporal user behavior modeling\, transaction graph analysis\, and fea ture-level risk aggregation to enhance detection accuracy while minimiz ing false positives. Additionally\, explainable AI components are incor porated to support transparency and regulatory compliance\, thereby re inforcing user confidence and platform accountability.
CATEGORIES:PHYSICAL TECHNICAL SESSION 2C
LOCATION:Benchasiri 3\, Bangkok Marriott Hotel Sukhumvit\, Thailand
SEQUENCE:0
UID:e782d9f06e1f185954b6caa44cef689a
URL:http://11thictisthailand.sched.com/event/e782d9f06e1f185954b6caa44cef689a
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260417T125213Z
DTSTART:20260410T080000Z
DTEND:20260410T081500Z
SUMMARY:Nonlinear Effects of Text Complexity in Corporate Disclosures: Evidence from a New CCTI Index and Machine Learning Models
DESCRIPTION:Authors - Komendra Sahu\, Mallikharjuna Rao K.\, Sonali Agarwal Abstract - This study examines whether textual complexity in corporate disclosures predicts stock excess returns. Building on prior research using Loughran–McDonald (LM) tone variables\, the baseline ordinary least squares (OLS) results are replicated and the analysis is extended in three directions. a novel Corporate Communication Text Complexity Index (CCTI) is developed using structural and linguistic features of SEC 10-K and 10-Q filings. market-based controls\, including volatility and momentum\, are incorporated. machine learning models are applied to capture potential nonlinear dependencies. Analysis of a large sample of filings from 2009 to 2024 demonstrates that OLS models have near-zero explanatory power\, consistent with previous findings. In contrast\, Random Forest models significantly improve predictive performance (R2 = 0.19944)\, indicating that excess returns are influenced by nonlinear patterns in textual complexity. Polynomial regression also reveals a convex relationship\, with extreme textual complexity associated with negative excess returns. Analysis of a large sample of filings from 2009 to 2024 confirms that OLS exhibits near-zero explanatory power. This finding is consistent with prior research. In contrast\, Random Forest models substantially improve predictive performance (R2 = 0.19944)\, indicating that excess returns respond to nonlinear patterns in textual complexity. Polynomial regression reveals a convex relationship\, where extreme textual complexity is associated with negative excess returns. Overall\, these results indicate that market reactions to complexity are inherently nonlinear and cannot be adequately captured by traditional tone-based linear models.
CATEGORIES:PHYSICAL TECHNICAL SESSION 2D
LOCATION:Benchasiri 4\, Bangkok Marriott Hotel Sukhumvit\, Thailand
SEQUENCE:0
UID:58dead155540dd96b3f55c368472e133
URL:http://11thictisthailand.sched.com/event/58dead155540dd96b3f55c368472e133
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260417T125213Z
DTSTART:20260410T080000Z
DTEND:20260410T100000Z
SUMMARY:A Comprehensive Survey on Machine Learning and Deep Learning Methods for Vehicle Detection and Classification
DESCRIPTION:Authors - Kashyap Patel\, Urvashi Chaudhari\, Chirag Patel\, Nirav Bhatt Abstract - Automatic traffic surveillance has a hard time finding and classifying vehicles that are trying to get in the way. To keep an eye on things in real time\, you need to be able to tell the difference between cars\, trucks\, buses\, and other types of vehicles. Traffic management systems need to be able to accurately identify vehicles as the number of cars on the road grows. This paper examines various machine learning (ML) and deep learning (DL) techniques employed to identify and categorize vehicles in images and videos. The authors emphasize the significance of algorithms\, such as CNNs\, YOLO\, and AdaBoost\, in enhancing detection accuracy and efficiency. This paper examines various published re-search studies to discern methodologies\, datasets\, and future research directions in vehicle detection and classification\, offering insights into the existing techno-logical landscape and its prospective developments.
CATEGORIES:VIRTUAL ROOM 9A
LOCATION:Virtual Room A\, Bangkok\, Thailand
SEQUENCE:0
UID:7da0158a68104c9c938f4fac1ada4ca7
URL:http://11thictisthailand.sched.com/event/7da0158a68104c9c938f4fac1ada4ca7
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260417T125213Z
DTSTART:20260410T080000Z
DTEND:20260410T100000Z
SUMMARY:A Study on the Integration of Sensor Innovations for Monitoring Brake Pad Wear in Vehicles
DESCRIPTION:Authors - Renukaradya V\, Kumar P K Abstract - Ethylene and vinyl acetate or EVA is a co-polymer used as a substitute for a lot of materials. EVA is a versatile material and it has a lot of applications ranging from electronics\, healthcare\, footwear\, building applications etc. It is mainly used in sport shoes due to its property to absorb shock impact and insulation properties. In addition\, EVA is very cost-friendly\, produces no odor\, and light in weight material. But with overuse of it\, the cellular structure chang-es and can affect the shoes' quality and insulation properties. In addition to the cellular structure\, the air molecules present in it also collapse. This paper focus-es on the bonding properties of EVA at different temperatures and its dielectric properties under different operating and manufacturing conditions. The upper\, bottom\, and sides of EVA shoes are exposed to high voltage till the breakdown. The experimentation was done at Electrical HV laboratory on the university campus where a 100kV HVAC testing system is available. This paper presents the tabulated results on the dielectric strength of EVA shoes under varying operating conditions. Additionally\, it examines the bonding properties of EVA shoes at different manufacturing temperatures\, aiming to predict their lifespan\, quality\, and finish. The results of these studies are thoroughly discussed within the document.
CATEGORIES:VIRTUAL ROOM 9A
LOCATION:Virtual Room A\, Bangkok\, Thailand
SEQUENCE:0
UID:e55a5d8cfef22c70c9f257f971e0f6db
URL:http://11thictisthailand.sched.com/event/e55a5d8cfef22c70c9f257f971e0f6db
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260417T125213Z
DTSTART:20260410T080000Z
DTEND:20260410T100000Z
SUMMARY:Detecting Cybersecurity Threats by Integrating Explainable AI with SHAP Interpretability and Strategic Data Sampling
DESCRIPTION:Authors - Norrakith Srisumrith\, Sunantha Sodsee\n Abstract - The critical need for transparent and trustworthy machine learning in cybersecurity operations drives the development of this integrated Explainable AI (XAI) framework. Our methodology addresses three fundamental challenges in deploying AI for threat detection: handling massive datasets through Strategic Sampling Methodology that preserves class distributions while enabling efficient model development\; ensuring experimental rigor via Automated Data Leakage Prevention that systematically identifies and removes contaminated features\; and providing operational transparency through Integrated XAI Implementation using SHAP analysis for model-agnostic interpretability across algorithms. Applied to the CIC-IDS2017 dataset\, our approach maintains detection efficacy while reducing computational overhead and delivering actionable explanations for security analysts. The framework demonstrates that explainability\, computational efficiency\, and experimental integrity can be simultaneously achieved\, providing a robust foundation for deploying trustworthy AI systems in security operations centers where decision transparency is paramount.
CATEGORIES:VIRTUAL ROOM 9A
LOCATION:Virtual Room A\, Bangkok\, Thailand
SEQUENCE:0
UID:092f0d13b9795e11b808891af1f37680
URL:http://11thictisthailand.sched.com/event/092f0d13b9795e11b808891af1f37680
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260417T125213Z
DTSTART:20260410T080000Z
DTEND:20260410T100000Z
SUMMARY:Federated Learning for Fraud Detection Across Financial Institutions
DESCRIPTION:Authors - Amelia Santosh\, Bhavika Pradeep\, Dhanuvarsha S S\, Harisurya Reddy S\, Shruthi L Abstract - Real-time analysis\, high accuracy\, and robust privacy protection across several institutions are necessary for financial fraud detection. Restrictions on data sharing and non-IID transaction patterns cause traditional centralized models to fail. Graph Neural Networks (GNNs) for anomaly detection and a structured fraud reporting mechanism are integrated in this paper’s federated learning-based fraud detection framework. While GNNs capture intricate relationships between accounts\, devices\, and transactions\, the system allows institutions to jointly train a global model without exchanging raw data. The feasibility of implementing collaborative fraud detection across financial institutions is demonstrated by the experimental results\, which show improved fraud detection performance\, enhanced recall on minority fraud cases\, and effective privacy preservation.
CATEGORIES:VIRTUAL ROOM 9A
LOCATION:Virtual Room A\, Bangkok\, Thailand
SEQUENCE:0
UID:81d257cfb8cf7a2f3c893dd4fb9c3006
URL:http://11thictisthailand.sched.com/event/81d257cfb8cf7a2f3c893dd4fb9c3006
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260417T125213Z
DTSTART:20260410T080000Z
DTEND:20260410T100000Z
SUMMARY:Highly Isolated Dual Port MIMO UWB antenna Development for Wireless Applications
DESCRIPTION:Authors - Killol Pandya\, Aneri Pandya\, Trushit Upadhyaya\, Upesh Patel\, Poonam Thanki\, Kanwarpreet Kaur Abstract - The proposed Multiple Input Multiple Output dual-port antenna radiates for Ultra Wide-Band (UWB) applications. The engineered structure exhibits between the 2.10 GHz to 9.5 GHz frequency. The structure consists dual radiating elements which are positioned at certain distance in order to minimize the effect of inter element interference. The radiator is planar and having triangular shape at the upper side to disturb the current path which eventually creates better radiation. A couple of up arrow shaped slots have been created to improve the current distribution. The microstrip feed line is utilized to excite the antenna structure. A partial ground plane with isolating technique was created to receive the UWB response. The middle layer between the radiators and the ground plane is having the FR4 material which is a cost effective for the bulk production. The physical antenna has been developed from the prototype and the results were measured. The simulated results are aligned with the measured results which shows the antenna potential. The primary diversity parameters such as Diversity Gain\, Envelope Correlation Coefficient\, Channel Capacity Loss and Mean effective gain were also measure and their simulated values fall under the expected span. The developed antenna is well suitable for UWB wireless applications.
CATEGORIES:VIRTUAL ROOM 9A
LOCATION:Virtual Room A\, Bangkok\, Thailand
SEQUENCE:0
UID:adbdfcbfdb43570384898889520f4911
URL:http://11thictisthailand.sched.com/event/adbdfcbfdb43570384898889520f4911
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260417T125213Z
DTSTART:20260410T080000Z
DTEND:20260410T100000Z
SUMMARY:Human Perceptions of AI-Driven Personalization: Surveillance\, Autonomy\, and Trust in Digital Customer Journeys
DESCRIPTION:Authors - Tiurida Lily Anita\, Dino Gustaf Leonandri\, Mohd. Nor Shahizan Ali Abstract - In this paper\, we address the problem of rainy condition classification in order to allow autonomous systems to ensure safe operation in different weather conditions of rain\, especially for drones. The earlier weather condition classification methods are inclined towards using big and computationally costly models and cannot thus be employed in real-time on resource-constrained platforms such as drones and edge devices. The motivation behind this work is to introduce a light-weight\, efficient deep model which would be able to classify various rain conditions with low computational cost so that it may be deployed efficiently on low-resource devices. We present a novel CNN architecture and evaluate its performance on a collection of seven distinct rain conditions. The models are bench marked against some of the state-of-the-art pretrained models to demonstrate the compromise between efficiency and accuracy. Performance is evaluated using accuracy\, inference time\, and model size. The model has accuracy 95.93% with least model size 89.09 KB with inference time of 32.664 ms bridging the gap in lightweight and real-time classification.
CATEGORIES:VIRTUAL ROOM 9A
LOCATION:Virtual Room A\, Bangkok\, Thailand
SEQUENCE:0
UID:9b234eed452cac8711a89e36f553f576
URL:http://11thictisthailand.sched.com/event/9b234eed452cac8711a89e36f553f576
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260417T125213Z
DTSTART:20260410T080000Z
DTEND:20260410T100000Z
SUMMARY:Hybrid BERT-LSTM Model with XAI Integration for Reliable Fake News Detection
DESCRIPTION:Authors - Mouniesh V\, Sona S\, Mariya Ashile K\, Karthick Panneerselvam Abstract - This paper presents the implementation experience of indigenously developed Data Concentrator Units (DCUs) for Advanced Metering Infrastructure (AMI) system. The DCU functions as the last-mile communication bridge between field devices and the Head-End System (HES)\, enabling efficient data collection\, aggregation\, and management. By performing local processing and aggregation\, it reduces data traffic over the Wide Area Network (WAN)\, there-by improving communication efficiency\, scalability\, and reliability. The DCU firmware is designed for flexible communication and secure data handling\, sup-porting pluggable WAN and Neighbourhood Area Network (NAN) communication modules compliant with proprietary BHARAT IoT standards\, that can be upgraded or replaced without requiring complete system replacement or rede-sign. It also ensures robust data security through AES-GCM-GMAC encryption. The NAN module is implemented using LoRa technology\, enhanced through the LoRaPro communication module\, which increases payload capacity from the standard 256 bytes to 1 KB using an advanced packet stitching and slicing algorithm that ensures reliable reconstruction of larger messages. The paper discusses the major design and development challenges encountered and the methodologies adopted to address them.
CATEGORIES:VIRTUAL ROOM 9A
LOCATION:Virtual Room A\, Bangkok\, Thailand
SEQUENCE:0
UID:879f64ded0cea71cc33c8b57c2b53eaa
URL:http://11thictisthailand.sched.com/event/879f64ded0cea71cc33c8b57c2b53eaa
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260417T125213Z
DTSTART:20260410T080000Z
DTEND:20260410T100000Z
SUMMARY:Indigenous Development of Data Concentrator Units (DCUs) for Advanced Metering Infrastructure (AMI)
DESCRIPTION:Authors - Devika K S\, Jiju K\, Dinesh Kumar R\, Ashish Murikingal\, Anoop V G\n Abstract -This paper presents the implementation experience of indigenously developed Data Concentrator Units (DCUs) for Advanced Metering Infrastructure (AMI) system. The DCU functions as the last-mile communication bridge between field devices and the Head-End System (HES)\, enabling efficient data collection\, aggregation\, and management. By performing local processing and aggregation\, it reduces data traffic over the Wide Area Network (WAN)\, there-by improving communication efficiency\, scalability\, and reliability. The DCU firmware is designed for flexible communication and secure data handling\, sup-porting pluggable WAN and Neighbourhood Area Network (NAN) communication modules compliant with proprietary BHARAT IoT standards\, that can be upgraded or replaced without requiring complete system replacement or rede-sign. It also ensures robust data security through AES-GCM-GMAC encryption. The NAN module is implemented using LoRa technology\, enhanced through the LoRaPro communication module\, which increases payload capacity from the standard 256 bytes to 1 KB using an advanced packet stitching and slicing algorithm that ensures reliable reconstruction of larger messages. The paper discusses the major design and development challenges encountered and the methodologies adopted to address them.
CATEGORIES:VIRTUAL ROOM 9A
LOCATION:Virtual Room A\, Bangkok\, Thailand
SEQUENCE:0
UID:38ae87e917ad7d4b2098a0ff4cfa3431
URL:http://11thictisthailand.sched.com/event/38ae87e917ad7d4b2098a0ff4cfa3431
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260417T125213Z
DTSTART:20260410T080000Z
DTEND:20260410T100000Z
SUMMARY:INTRUSION DETECTION USING UNRAVELLED SPATIAL FEATURES IN MULTILAYER PERCEPTRON WITH GRADIENT JACOBIAN ANALYSIS
DESCRIPTION:Authors - Gaurav Kulkarni\, Maya Rathore\n Abstract -In digital world\, cyber-attacks are becoming more sophisticated and popular. The conventional intrusion detection models are not adequate in challenging threat escapes. Importantly\, the major reason for increasing demand in the networks\, unauthorized access is increasing their interests in these areas. Various network environments and organizations are tackling numerous of attacks on their network at frequent times. Traditionally\, various manual methods are used for intrusion detection such as packet and flow analysis\, traffic log reviewers and monitoring the security. Nevertheless\, the manual techniques for such type of the detections takes too much time and also the result obtained is not up to the mark\, so due to this it is difficult to predict all types of attacks and intrusions for network security. To overcome these issues\, several conventional researches have concentrated on intrusion detection models to offer effective security to the networks. Conversely\, it results with accuracy and speed lacks. For enhancing the intrusion detection\, research make use of a Deep Learning (DL) Unravelled Spatial Features in Multilayer Perceptron with Gradient Jacobian Matrix. Gaussian Activation is used to enhance the Intrusion detection system for an effective classification. In the proposed research work we are using the RT-IoT dataset and the final efficiency has been analyzed by using various parameters like overall correctness\, actually correct\, correctly identified by the model\,and the balance between the both values of recall and precision (Harmonic Mean). Furthermore\, the current work and the proposed model is developed to contribute to avoid the different cyber threats by timely identifying such type of intrusion in the networks.
CATEGORIES:VIRTUAL ROOM 9A
LOCATION:Virtual Room A\, Bangkok\, Thailand
SEQUENCE:0
UID:cbff15797057073f8f7d90983cf27fdc
URL:http://11thictisthailand.sched.com/event/cbff15797057073f8f7d90983cf27fdc
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260417T125213Z
DTSTART:20260410T080000Z
DTEND:20260410T100000Z
SUMMARY:Sustained Adoption of QR-Code Payments in Mobile Banking: Evidence from QRIS Users
DESCRIPTION:Authors - Tiurida Lily Anita\, Ali Faik\, Muhammad Zilal Hamzah\, Hainnuraqma Rahim\n Abstract - Web accessibility and usability are fundamental pillars for ensuring effective digital inclusion\, especially in higher education institutions committed to equity in access to information. This study aimed to evaluate the usability and accessibility of the website of the Inclusion\, Social Equity\, and Gender Unit at the Technical University of Manabí\, using the WCAG 2.0 guidelines. A mixed methodology with a qualitative and applied approach was employed. Initial results revealed a low level of compliance with accessibility standards\, highlighting deficiencies in the principles of perceptibility and operability\, such as the absence of alternative descriptions for images and insufficient contrast. After implementing improvements\, the website achieved 76% compliance according to a manual review\, with notable progress in responsive design and the incorporation of an accessibility toolbar. However\, challenges remain regarding the principle of robustness\, underscoring the importance of combining automated tools with thorough manual evaluations. Future work will adopt WCAG 2.1 guidelines and integrate advanced assistive technologies to overcome current limitations\, promoting a more inclusive and accessible digital environment for all users.
CATEGORIES:VIRTUAL ROOM 9A
LOCATION:Virtual Room A\, Bangkok\, Thailand
SEQUENCE:0
UID:0382e637f8eded85a5d0c42dd53d786a
URL:http://11thictisthailand.sched.com/event/0382e637f8eded85a5d0c42dd53d786a
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260417T125213Z
DTSTART:20260410T080000Z
DTEND:20260410T100000Z
SUMMARY:An integrated machine learning and blockchain-based framework for enhancing fraud detection in digital financial services
DESCRIPTION:Authors - Felix Kabwe\, Jackson Phiri Abstract - The growth of Open Educational Resources (OER) has created a paradox of abundance\, causing “academic infoxication” where students struggle to find content aligned with their competency levels. Traditional recommender systems often fail to interpret pedagogical context effectively. This paper presents the implementation and empirical validation of OPMAS\, a multi-agent architecture orchestrated with LangGraph that utilizes Large Language Models (LLMs) to automate the curation and adaptation of educational resources. Unlike linear chatbots\, OPMAS employs a state-graph of specialized agents (Router\, Query\, Search\, Adaptation) to map user queries to European competency frameworks like DigComp. The system\, built using Gemini 2.5 Flash and a hybrid retrieval strategy\, was validated through a Minimum Viable Product (MVP). Results demonstrate a functional success rate of 95% in complex reasoning flows and a semantic precision of 0.77. Although the deep reasoning process introduces an average latency of 96 seconds\, the system successfully prioritizes pedagogical relevance and content adaptation over immediate retrieval\, proving the technical viability of agentic architectures for personalized education.
CATEGORIES:VIRTUAL ROOM 9B
LOCATION:Virtual Room B\, Bangkok\, Thailand
SEQUENCE:0
UID:1558dc82b44f80cd7ae62d3413f3df79
URL:http://11thictisthailand.sched.com/event/1558dc82b44f80cd7ae62d3413f3df79
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260417T125213Z
DTSTART:20260410T080000Z
DTEND:20260410T100000Z
SUMMARY:BlockVote- Blockchain-Backed IoT Voting Kiosk with Biometric Authentication and Offline Resilience for Electoral Integrity
DESCRIPTION:Authors - Minal Deshmukh\, Aakash Dabhade\, Daksh Jethwa\, Siddhi Jadhav\, Ketki Khirsagar\n Abstract - In this paper\, we outline the design and implementation of a novel electronic voting kiosk\, dubbed BlockVote\, which helps counter identity-related fraud and data tampering via biometric and blockchainbased approaches. The proposed system is a standalone embedded system running on an ESP32-S3 SoC-based microcontroller. The system includes a touchscreen display for user input and an optical fingerprint sensor for identity checking. This collected bio-data and voting selection are then integrated in such a manner that a secure transaction is created through cryptography. This is then sent through the Node.js gateway\, which leads it to the secure Ethereum-based blockchain network. Such an application of physical verification technologies with blockchain technology ensures that the proposed voting system is more secure than the traditional e-voting machines or e-voting websites. Block-vote is a hybrid security system in which hardware-based verification techniques are combined with blockchain-based data management in a power-saving\, compact format. The prototype has shown proof of its functional viability\, its module-based construction\, and its reliability\, particularly in the field of embedded systems. The experimental results demonstrate the system’s high precision\, low latency\, and robustness against illegitimate use. The suggested framework demonstrates the practical feasibility of blockchain and biometric technology in the creation of trustworthy electronic voting systems that can be used in both urban and rural areas.
CATEGORIES:VIRTUAL ROOM 9B
LOCATION:Virtual Room B\, Bangkok\, Thailand
SEQUENCE:0
UID:9f12f08797bba9192f088889762b96d5
URL:http://11thictisthailand.sched.com/event/9f12f08797bba9192f088889762b96d5
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260417T125213Z
DTSTART:20260410T080000Z
DTEND:20260410T100000Z
SUMMARY:Bug Severity and Priority Prediction using Semi-supervised Expert guided Labelling
DESCRIPTION:Authors - S.D.P. Abeysekara\, J.A.D.N. Jayakody\, K.A. Dilini T. Kulawansa\n Abstract - Breast cancer is the second most prevalent cancer globally and a leading cause of death among women. According to the World Health Organization\, over 2.3 million new cases are diagnosed annu ally\, emphasizing the need for early and accurate detection.In this work\, Wavelet-Driven Intelligent Model for Multi-Class Breast Cancer Diagno sis is proposed. In this proposed work\, three level wavelet decomposition is used on BreakHis data to extract wavelet based features. These fea tures were fed to Artificial Neural Network Classifiers such as Multi-Layer Perceptron (MLP)\, Radial Basis Function (RBF) and Machine Learning Classifier Random Forest (RF). Multi-class classification (binary \, be nign sub-types\, 4 malignant sub-types) of breast tumour has been done. The experimental results show that RF achieved high accuracy of 94% for benign and malignant\, 97% for benign sub- type and 92% for malig nant subtype classification compared to RBF and MLP. Deep learning models such as Convolutional Neural Networks (CNNs) and Recurrent Neural Networks (RNNs) are more effective when trained on large-scale datasets but for small datasets and limited resource environments\, the proposed framework ensures efficient and consistent diagnostic approach. In future\, a prototype breast cancer alert system can be developed using raspberry pie for real time application.
CATEGORIES:VIRTUAL ROOM 9B
LOCATION:Virtual Room B\, Bangkok\, Thailand
SEQUENCE:0
UID:e9a93f824289ea0c4f5bb6fd9f19acf4
URL:http://11thictisthailand.sched.com/event/e9a93f824289ea0c4f5bb6fd9f19acf4
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260417T125213Z
DTSTART:20260410T080000Z
DTEND:20260410T100000Z
SUMMARY:Data Driven Insights into Climate Change Risk Assessment
DESCRIPTION:Authors - Md. Shahidul Islam\, Atiqur Rahman\, Md. Murad Hossain\n Abstract - This study examines the influence of both demographic and natural factors on climate change risk perception in New Zealand. Using data from a nationally representative survey\, the analysis applies exploratory factor analysis to construct a composite measure of risk perception\, followed by correlation and regression modeling to evaluate the relative contribution of environmental exposure and human characteristics. The findings indicate that while natural factors such as temperature anomalies and extreme weather exposure significantly shape perceived risk\, demographic variables including prior disaster experience\, trust in scientific institutions\, and media exposure exert a stronger overall influence. These results underscore the importance of incorporating social and behavioral dimensions into climate risk assessments and policy development to enhance public engagement and adaptive capacity.
CATEGORIES:VIRTUAL ROOM 9B
LOCATION:Virtual Room B\, Bangkok\, Thailand
SEQUENCE:0
UID:8e37be8b9c1f519d6ffa2ea7d48a65ea
URL:http://11thictisthailand.sched.com/event/8e37be8b9c1f519d6ffa2ea7d48a65ea
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260417T125213Z
DTSTART:20260410T080000Z
DTEND:20260410T100000Z
SUMMARY:Design and Analysis of Photonic Crystal Nano-Cavities-Based Force\, Pressure\, Bio\, Chemical and Temperature Sensors Using Cantilever Beam and Diaphragms on SOI Platform
DESCRIPTION:Authors - Shreyas M S\, Kumar P K\, Venkateswara Rao Kolli Abstract - The Newborns mostly use infant crying as their main form of communication and it represents a great variety of physiological and emotional conditions. Despite the high potential of automated infant cry analysis in early diagnosis and support of caregivers\, the application in real-life still has low usage rates because of environmental noise\, imbalance of classes\, low interpretability\, and high computational cost. This paper is a compilation of an effective\, interpretable\, and real-time infant cry classification system using a two-step hierarchical methodology. The first stage involves a distinction of cry and non-cry sounds to reduce the rate of false alarms due to background noise. The second stage involves categorizing detected cries into a particular intent. An adaptive feature fusion strategy based on reinforcement learning\, gives the cepstral and prosodic and qualitative acoustic features dynamic weights\, and SHAP-based explainability offers explicit feature interpretations. Data augmentation\, SMOTE-Audio\, and model pruning are used to find solutions to the issues of class imbalance\, noise robustness\, and deployment constraints. Experimental evidence shows that the proposed approach outperforms single feature base-lines\, it is also stable in noisy environments and also attains significant parameter reduction without significant loss in performance\, making it possible to run in resource-constrained devices in real time. The system is tested on a publicly available infant cry dataset which contains 889 audio samples of cry and non-cry signals in five categories of cry intent and was recorded in realistic conditions.
CATEGORIES:VIRTUAL ROOM 9B
LOCATION:Virtual Room B\, Bangkok\, Thailand
SEQUENCE:0
UID:6e9e7c5fd15b538c9a17691959e8dba1
URL:http://11thictisthailand.sched.com/event/6e9e7c5fd15b538c9a17691959e8dba1
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260417T125213Z
DTSTART:20260410T080000Z
DTEND:20260410T100000Z
SUMMARY:Early Structural Break Detection Using Volatility Signature Mining
DESCRIPTION:Authors - Md. Shahidul Islam\, Md. Raihan Habib\n Abstract - Detecting structural breaks and anticipating volatility regimes in foreign exchange markets remain challenging due to the non-stationary and nonlinear nature of exchange rate dynamics. This study proposes a non-parametric framework for identifying structural breaks in the NZD/ USD exchange rate by integrating sliding-window volatility estimation\, concentration bound based change point detection\, and wavelet-based time frequency analysis. Volatility is first quantified using a movingwindow approach and compared against a Hoeffding bound to detect extraordinary events. The resulting change points are used to segment the exchange rate series into statistically reliable sequences\, which are subsequently analyzed using wavelet scalograms. Empirical results reveal a consistent three-regime structure in the wavelet domain\, comprising post-event reaction\, stable market behavior\, and pre-event escalation phases. Non-parametric statistical tests confirm significant differences in volatility distributions across these regimes\, with the pre-event regime exhibiting markedly higher variability and acting as a precursor to structural breaks. The findings demonstrate that wavelet coefficients contain informative signatures of impending market instability. Overall\, the proposed framework provides an interpretable and robust approach for analyzing regime-dependent volatility dynamics and offers valuable insights for early warning and risk management in currency markets.
CATEGORIES:VIRTUAL ROOM 9B
LOCATION:Virtual Room B\, Bangkok\, Thailand
SEQUENCE:0
UID:50542dc42614bbe59134b92fc57f671a
URL:http://11thictisthailand.sched.com/event/50542dc42614bbe59134b92fc57f671a
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260417T125213Z
DTSTART:20260410T080000Z
DTEND:20260410T100000Z
SUMMARY:Genetic Programming applied to Matrix Factorization
DESCRIPTION:Authors - Diego Perez-Lopez\, Rodolfo Bojorque\, Jorge Duenas-Lerin\, Raul Lara-Cabrera Abstract - Accurate early detection of liver cancer remains a significant clinical challenge\, primarily due to scarce annotated imaging data\, inconsistencies in radiological interpretation\, and the inherent opacity of deep learning models. To address these limitations\, this study proposes a clinically informed\, explainable deep learning framework designed specifically for low-annotation settings. The framework combines transfer learning with advanced visualization techniques\, enabling both high diagnostic accuracy and medically meaningful outputs that integrate seamlessly into clinical workflows. Three pre-trained CNN architectures — ResNet-50\, DenseNet-121\, and EfficientNet-B4 — were adapted to liver cancer imaging through domain-specific fine-tuning. Model generalizability was reinforced by combining geometric data transformations with StyleGAN2-derived synthetic lesion generation. Model transparency was facilitated through Gradient-weighted Class Activation Mapping (Grad-CAM) and SHapley Additive exPlanations (SHAP)\, while clinical trustworthiness was evaluated via predictive uncertainty quantification\, subgroup bias analysis\, and resistance to adversarial perturbations. The proposed framework was evaluated on the LiTS and TCGA-LIHC datasets\, demonstrating a 15–20% improvement in accuracy over baseline models that consisted of standard convolutional neural networks trained from scratch without transfer learning or data augmentation. EfficientNet-B4 achieved 94.2% accuracy\, 0.96 specificity\, and an AUC-ROC of 0.978. Grad-CAM accurately highlighted tumor regions in 89.4% of cases\, and Bayesian dropout identified 7.3% of predictions as uncertain. These findings demonstrate the framework’s potential for clinical deployment by balancing performance\, transparency\, and reliability.
CATEGORIES:VIRTUAL ROOM 9B
LOCATION:Virtual Room B\, Bangkok\, Thailand
SEQUENCE:0
UID:15c975f73b02d6b137e2e59b93eba86c
URL:http://11thictisthailand.sched.com/event/15c975f73b02d6b137e2e59b93eba86c
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260417T125213Z
DTSTART:20260410T080000Z
DTEND:20260410T100000Z
SUMMARY:Graph Signal Processing for Multichannel EEG Signals Integrating Structural and Functional Connectivity
DESCRIPTION:Authors - Jutika Borah\, Debarun Chakraborty\, Bhabesh Deka\, Rosy Sarmah\, Siddeswara Bargur Linganna\, Diptadhi Mukherjee\, Ram Bilas Pachori\, Mohit Khamele\n Abstract - Electroencephalogram (EEG) signal modeling for downstream tasks\, such as classifying neurological states and identifying biomarkers\, is essential for designing effective brain-computer interfaces. Conventional methods often treat EEG channels independently\, overlooking inter-channel dependencies\, while existing graph-based approaches address this limitation either through fixed electrode geometry or entirely data-driven connectivity. In this paper\, we propose a graph representation framework that combines coherence-based spectral connectivity with domain-informed priors\, such as anatomical structure and regional proximity\, based on graph signal processing (GSP). The resulting representation embeds multichannel EEG signals as attributed graphs through graph convolutional networks (GCNN) to learn discriminative embeddings. Experimental results demonstrate that the hybrid framework enhances classification performance\, with the proposed GCNN-deep model achieving the highest area under the receiver operating characteristic curve (AUC) across all datasets and reaching 93% on Dataset 1. These EEG datasets correspond to three independent populations and include recordings from both healthy individuals and patients with neurological disorders such as major depressive disorder (MDD) and epilepsy.
CATEGORIES:VIRTUAL ROOM 9B
LOCATION:Virtual Room B\, Bangkok\, Thailand
SEQUENCE:0
UID:06b94f4ded936e6d8e9bbfa38ae839d0
URL:http://11thictisthailand.sched.com/event/06b94f4ded936e6d8e9bbfa38ae839d0
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260417T125213Z
DTSTART:20260410T080000Z
DTEND:20260410T100000Z
SUMMARY:Multilingual AI Health Assistant with Edge Device
DESCRIPTION:Authors - Samiksha Chougule\, Kirti Satpute\, Krishnraj Patil\, Om Kumbhardare\, Sumedha Patil\n Abstract - Rural communities face significant challenges in accessing essential healthcare services due to language barriers\, limited health literacy\, and insufficient medical support. Difficulties in understanding medical information\, communicating symptoms\, and interpreting diagnostic reports further restrict effective healthcare delivery. Moreover\, unreliable internet connectivity limits the reach of conventional digital health platforms. This paper presents a Multilingual AI Health Assistant designed to operate on low-cost edge devices\, enabling offline functionality to ensure continuous access and data privacy in low-connectivity areas. The proposed system integrates AI\, ML\, NLP\, OCR\, and speech recognition to allow users to interact in their native languages through text or voice. It analyzes user-reported symptoms to predict probable health conditions\, translates complex medical reports and prescriptions into simplified\, localized explanations\, and provides recommendations for nearby healthcare facilities. Unlike internet-dependent telemedicine systems\, this edge-based solution processes data directly on the device\, safeguarding sensitive health information while maintaining reliability. By bridging linguistic and literacy gaps\, the proposed assistant empowers rural populations with accessible and actionable healthcare insights\, ultimately improving health outcomes in underserved regions.
CATEGORIES:VIRTUAL ROOM 9B
LOCATION:Virtual Room B\, Bangkok\, Thailand
SEQUENCE:0
UID:3954a15755c8af1f1ab93f28726160a2
URL:http://11thictisthailand.sched.com/event/3954a15755c8af1f1ab93f28726160a2
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260417T125213Z
DTSTART:20260410T080000Z
DTEND:20260410T100000Z
SUMMARY:ZKP-Guard: A Lightweight Framework for Verifying Digital Image Authenticity and Ownership
DESCRIPTION:Authors - Noor\, Soumya Mukherjee\, Shivraj Singh Yadav\n Abstract - The increasing numbers of deepfakes and AI tools have made it difficult to trust digital images these days. Images can be altered and ownership can be established without revealing private information. Current systems have many limitations\, and systems that either rely on easyto change metadata or on cryptographic methods that are too costly like ZKSNARKs. To overcome these limitations\, an authentication verification model has been presented named ZKP-Guard based on a Dual- Lock architecture framework. The detection system verifies an image is a real image by using ECDSA signatures and a custom ownership in the Schnorr-based Zero-Knowledge Proof for the protocol. This framework was tested on a dataset with significant number of images and produced desired results.
CATEGORIES:VIRTUAL ROOM 9B
LOCATION:Virtual Room B\, Bangkok\, Thailand
SEQUENCE:0
UID:2c6bb3bc59aa6d4ecd2d99410720b612
URL:http://11thictisthailand.sched.com/event/2c6bb3bc59aa6d4ecd2d99410720b612
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260417T125213Z
DTSTART:20260410T080000Z
DTEND:20260410T100000Z
SUMMARY:A review on CRYSTALS-KYBER for Post Quantum Cryptography
DESCRIPTION:Authors - Palungbam Roji Chanu\, Venkata Sathish Kumar Badithala\, Nepholar Chongtham\, Arambam Neelima\, Gulshan Gupta\, Rohita Tyagi\n Abstract - Quantum computers are a major threat to the existing encryption mechanisms. In terms of security\, the traditional encryption algorithm depends on complex problems like discrete logarithm as well as factorization of integer. Shor’s algorithm is believed to break the current Public Key Encryption algorithms such as Advanced Encryption Standard (AES). Therefore\, several research are carried out in the area of PQC (Post Quantum Cryptography). PQC are based on very complex mathematical problems like Learning with error (LWE) which are robust against quantum computers. The National Institute of Standard and Technology (NIST) has initiated several rounds of standardization process for PQC algorithms\, among which NTRU\, SABER\, CRYSTAL-KYBER are the leading candidates. CRYSTALS-KYBER (Kyber) is the first chosen PQC for standardization. This works explores the recent development in Crystals Kyber implementation and its optimization. Researchers can approach for new research challenges and target for improvement thereby increasing efficiency.
CATEGORIES:VIRTUAL ROOM 9C
LOCATION:Virtual Room C\, Bangkok\, Thailand
SEQUENCE:0
UID:817727ee6ca436bddae9397c05b9951b
URL:http://11thictisthailand.sched.com/event/817727ee6ca436bddae9397c05b9951b
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260417T125213Z
DTSTART:20260410T080000Z
DTEND:20260410T100000Z
SUMMARY:A Two-Stage Explainable Framework for Infant Cry Classification with RL-Based Feature Fusion
DESCRIPTION:Authors - Taslima Ferdous Supty\, Fahima Hossain\, Era Aich\, Ananna Datta\, MD Sahadat Hossen Tanim\n Abstract - The Newborns mostly use infant crying as their main form of communication and it represents a great variety of physiological and emotional conditions. Despite the high potential of automated infant cry analysis in early diagnosis and support of caregivers\, the application in real-life still has low usage rates because of environmental noise\, imbalance of classes\, low interpretability\, and high computational cost. This paper is a compilation of an effective\, interpretable\, and real-time infant cry classification system using a two-step hierarchical methodology. The first stage involves a distinction of cry and non-cry sounds to reduce the rate of false alarms due to background noise. The second stage involves categorizing detected cries into a particular intent. An adaptive feature fusion strategy based on reinforcement learning\, gives the cepstral and prosodic and qualitative acoustic features dynamic weights\, and SHAP-based explainability offers explicit feature interpretations. Data augmentation\, SMOTE-Audio\, and model pruning are used to find solutions to the issues of class imbalance\, noise robustness\, and deployment constraints. Experimental evidence shows that the proposed approach outperforms single feature base-lines\, it is also stable in noisy environments and also attains significant parameter reduction without significant loss in performance\, making it possible to run in resource-constrained devices in real time. The system is tested on a publicly available infant cry dataset which contains 889 audio samples of cry and non-cry signals in five categories of cry intent and was recorded in realistic conditions.
CATEGORIES:VIRTUAL ROOM 9C
LOCATION:Virtual Room C\, Bangkok\, Thailand
SEQUENCE:0
UID:0a2d1d7539c7dba762dfa20d90e00d09
URL:http://11thictisthailand.sched.com/event/0a2d1d7539c7dba762dfa20d90e00d09
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260417T125213Z
DTSTART:20260410T080000Z
DTEND:20260410T100000Z
SUMMARY:Atmospheric Noise-Aware Preprocessing for accurate Change Detection in Satellite Imagery
DESCRIPTION:Authors - S.Nagarjuna Reddy\, B.Lakshmi Priyanka\, E.Vamsi\, G.Raja Shekar Reddy\n Abstract - Cloud cover\, shadows\, haze\, illumination variation\, and atmospheric noise severely degrade the reliability of satellite image change detection. This paper proposes an atmosphere-aware\, physics-driven preprocessing framework that performs cloud\, shadow\, haze\, and illumination compensation before change analysis\, without relying on convolutional or transformer-based networks. Two multi-temporal satellite images are processed through unified cloud and shadow handling\, haze correction\, illumination normalization\, and residual atmospheric noise suppression\, followed by a spectrally invariant change detector with structural consistency validation. The system also generates semantic multi-class change maps and geo-contextual text explanations to enhance interpretability. Experiments on diverse multi-temporal datasets demonstrate a change detection accuracy of 98.9% with high precision and recall\, significantly outperforming conventional and deep learning baselines
CATEGORIES:VIRTUAL ROOM 9C
LOCATION:Virtual Room C\, Bangkok\, Thailand
SEQUENCE:0
UID:51a36db9765e89ca1da8fb3b2d89f459
URL:http://11thictisthailand.sched.com/event/51a36db9765e89ca1da8fb3b2d89f459
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260417T125213Z
DTSTART:20260410T080000Z
DTEND:20260410T100000Z
SUMMARY:Comparative review on Benign and Malignant Stage Classification Benign and Malignant Stage Classification using Histopathology Images
DESCRIPTION:Authors - Shweta B. Barshe\, Garima B. Shukla\n Abstract - The use of artificial intelligence (AI)\, especially deep learning\, has brought major changes in the study of histopathology images for cancer diagnosis for the doctors. This review compares the latest deep learning methods used to identify whether the tissues are Benign. (noncancerous) or malignant (cancerous). This paper discusses different technologies used for the study of histopathology images. Convolutional neural network (CNNs) is effective in capturing small local details in the images. Several studies report that Transformers (ViTs) can outperform CNNs in complex classification tasks [18\, 22]. Along with the discussion about newer hybrid models and large foundation models\, the paper specifies the strength of combining the strength of both. Although these models are developed focusing on achieving high accuracy on good data sets\, there are few challenges in their practical use\, such as i. Models often fail to generalize data from different hospitals due to domain shift [1\, 28] ii. Model interpretability remains a significant challenge in clinical development [34]. iii. There is a lack of proper methods to measure the uncertainty in the decisions [40\,41]. This paper highlights the research gaps in real clinical use and focuses on the need to develop models that are robust\, interpretable\, and suitable for practical healthcare applications.
CATEGORIES:VIRTUAL ROOM 9C
LOCATION:Virtual Room C\, Bangkok\, Thailand
SEQUENCE:0
UID:66c69099771c622bb5871dbef77834a1
URL:http://11thictisthailand.sched.com/event/66c69099771c622bb5871dbef77834a1
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260417T125213Z
DTSTART:20260410T080000Z
DTEND:20260410T100000Z
SUMMARY:Image-Based Food Detection and Calorie Estimation
DESCRIPTION:Authors - Ch.Venkata Narayana\, T.Jhansi\, D.Charan\, K.Priskilla\, D.Tejaaswani\n Abstract - This work proposes an intelligent system for automatic food-image-based recognition and calorie estimation to meet the emerging demand for accurate dietary monitoring and personalized nutrition recommendations. Conventional food-logging methods are cumbersome\, prone to errors\, and mostly fail to capture portion sizes\, hence motivating an end-to-end computer vision and depth-based approach. The proposed system utilizes a custom-curated Indian food image dataset of eighty classes\, collected\, labeled\, and preprocessed to make it robust enough to present various variations in lighting\, background\, etc. A deep learning model was then trained for detecting and classifying food with high precision. The overall classification accuracy achieved by the proposed system is ninety-seven percent. The depth understanding of the detected food regions will provide an approximation of volume and weight\, leading to relatively better calorie calculations. Nutritional analysis gets integrated into the system by relating the type and estimated weight of food to the standard nutritional information for detailed insights in terms of calories\, proteins\, fats\, car-bohydrates\, fiber\, and micronutrient content. The results for evaluation reveal strong detection\, minimum estimation error\, and efficient real-time processing\, which clearly show its applications. In this paper\, an approach that combines recognition by image\, depth estimation by portion\, and nutrition logic capable of leading to a strong solution for diet determination has been introduced.
CATEGORIES:VIRTUAL ROOM 9C
LOCATION:Virtual Room C\, Bangkok\, Thailand
SEQUENCE:0
UID:8c6c07f0ff33401270b5697c643b09e6
URL:http://11thictisthailand.sched.com/event/8c6c07f0ff33401270b5697c643b09e6
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260417T125213Z
DTSTART:20260410T080000Z
DTEND:20260410T100000Z
SUMMARY:MediMitra: Voice Enabled Medicine Alert System
DESCRIPTION:Authors - Dipti Varpe\, Gouri Kulkarni\, Anish Sontakke\, Anuj Patil\, Prasanna Kekare\n Abstract - Inconsistent medication intake is a major issue\, especially for elderly individuals and patients with memory problems [1]. The MediMitra: Voice Enabled Medicine Alert System seeks to tackle this problem by offering an automated\, low-cost and user-friendly medication reminder solution. The system combines Raspberry Pi with Optical Character Recognition (OCR) technology to pull medicine names\, dosage details and intake times directly from scanned prescriptions. This reduces manual input and user reliance. The information is stored in a central database and connected to a scheduler that sends timely voice alerts through smart speakers or Bluetooth devices. This ensures users receive reliable and easy-to-access reminders. The OCR module is designed for high accuracy in processing printed prescription images by using image preprocessing techniques like noise reduction and thresholding\, which helps in effectively extracting key medication details [2]. The system focuses on accessibility\, affordability and ease of use in home or clinical settings. Overall\, MediMitra provides a useful technological solution to improve medication adherence and supports independent living. It also has potential for future integration with health-monitoring systems.
CATEGORIES:VIRTUAL ROOM 9C
LOCATION:Virtual Room C\, Bangkok\, Thailand
SEQUENCE:0
UID:cda0651f05287a2ea2cda63859a37b4a
URL:http://11thictisthailand.sched.com/event/cda0651f05287a2ea2cda63859a37b4a
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260417T125213Z
DTSTART:20260410T080000Z
DTEND:20260410T100000Z
SUMMARY:Modelling organisational sensitivity in sports clubs: A neuro-symbolic agent-based analysis of engagement dynamics
DESCRIPTION:Authors - Mamy Haja Rakotobe\, Remy Courdier\n Abstract - This article presents a neuro-symbolic modelling approach grounded in qualitative data collected from 25 sports clubs located in R´eunion. The study develops a methodological chain linking structured semantic extraction\, ontological formalisation in OWL\, and agent-based simulation implemented in NetLogo. Rather than modifying structural scenarios across experiments\, the design introduces two contrasting organisational sensitivity profiles derived from field observations: a damped profile and a high-gain profile. The structural configurations remain identical between profiles\; only the coefficients of the commitment update function vary\, ensuring strict experimental comparability. Results indicate that identical structural conditions produce differentiated collective trajectories depending on internal sensitivity parameters. In highgain configurations\, dominance-weighted interactions increase variance and generate polarised engagement distributions\, whereas damped configurations maintain relative stability across scenarios. These findings suggest that modelling organisational sensitivity parameters is critical for understanding the robustness of digitally mediated collaboration in volunteer-based organisations.
CATEGORIES:VIRTUAL ROOM 9C
LOCATION:Virtual Room C\, Bangkok\, Thailand
SEQUENCE:0
UID:9ca210c198e9f58d9a045822a278a062
URL:http://11thictisthailand.sched.com/event/9ca210c198e9f58d9a045822a278a062
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260417T125213Z
DTSTART:20260410T080000Z
DTEND:20260410T100000Z
SUMMARY:Physics-Guided Domain-Robust Open-Set Diagnosis for an Engine Air-Path Benchmark
DESCRIPTION:Authors - Silvio Simani\n Abstract -&nbsp\;This paper presents a hybrid diagnostic approach for an engine air-path benchmark characterised by environmental variability\, limited labelled faults\, and the need for reliable online decisions. The proposed method combines physics-guided residual features with datadriven temporal representation learning. Residuals derived from grey-box relations capture physically meaningful deviations\, while a lightweight encoder extracts temporal patterns across operating regimes. To enhance robustness under changing ambient conditions\, the model is explicitly conditioned on measured environmental variables and trained to favour stable representations across sessions. An open-set decision policy with calibrated rejection is incorporated to reduce misclassification when encountering unseen fault magnitudes or insufficient evidence. The method is evaluated under the official benchmark protocol using online processing constraints and standard metrics\, including false alarm rate\, detection rate\, isolation rate\, detection delay\, and computational cost. Results show improved reliability compared to competitive baselines\, with lower false alarms\, higher detection and isolation performance\, and stable behaviour across sessions. The approach remains computationally efficient and suitable for real-time deployment in practical diagnostic pipelines.
CATEGORIES:VIRTUAL ROOM 9C
LOCATION:Virtual Room C\, Bangkok\, Thailand
SEQUENCE:0
UID:f4a2930ee0f22d2cc83eecd020304039
URL:http://11thictisthailand.sched.com/event/f4a2930ee0f22d2cc83eecd020304039
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260417T125213Z
DTSTART:20260410T080000Z
DTEND:20260410T100000Z
SUMMARY:SafeGas: A Smart IoT-Based Gas Leak Detection\, Monitoring\, and Automated Shut-Off System
DESCRIPTION:Authors - Zubayer Bin Ahamed\, Umair Hossain\, Umara Binte Masud\, Abdullah Al Mamun\, Md. Rohan Islam\, Sadah Anjum Shanto\n Abstract - Gas leaks pose a threat to safety because they can cause fires and damage to property\, and they are sometimes fatal. Traditional detection methods are manually dependent or delayed in response\, which means they are not always reliable and timely. This paper presents Safe- Gas system for gas leak detection\, monitoring and automatic shut-off. The system uses low-cost gas sensor\, flame sensors\, load cell and an ESP32 microcontroller for local processing. The system is connected to the cloud via Firebase to send alerts\, and it has a battery backup to keep it running when the internet or power goes off. The app supports both remote and autonomous valve shutoff. SafeGas is a name that stands for resilience and accuracy. The designers and developers of the device have tested it in the laboratory and in the field to ensure it meets the set standards. First\, the system aims to reduce the number of false alarms. Second\, it can operate without an internet connection. Third\, it can take safety measures independently. The embedded system and cloud integration aspects of the project demonstrate how they can be combined to achieve the desired results.
CATEGORIES:VIRTUAL ROOM 9C
LOCATION:Virtual Room C\, Bangkok\, Thailand
SEQUENCE:0
UID:cabd4e505b4d2ae76f67230bca175402
URL:http://11thictisthailand.sched.com/event/cabd4e505b4d2ae76f67230bca175402
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260417T125213Z
DTSTART:20260410T080000Z
DTEND:20260410T100000Z
SUMMARY:Social Interaction\, Entertainment\, Pass Time\, and Enjoyment: YouTube Uses and Gratification Among Indonesian Gen Z
DESCRIPTION:Authors - Shafa Salsabila Risfi Febrian\, Ricardo Indra\, Aura Meivia Safira Arsya\, Aurellia Arthamevia Aisyah\n Abstract - This study examines the determinants of continuance intention in YouTube live streaming consumption among Indonesian Generation Z\, focusing on social interaction\, entertainment\, passing time\, and enjoyment. Drawing upon Uses and Gratifications Theory and Computer-Mediated Communication\, this research situates live streaming as an interactive digital environment where audiences actively negotiate social and emotional experiences. A quantitative explanatory survey was conducted among 108 Generation Z subscribers of the Windah Basudara YouTube channel\, and the data were analyzed using Partial Least Squares Structural Equation Modeling (PLS-SEM). The findings reveal that social interaction and passing time significantly influence continuance intention\, whereas entertainment and enjoyment do not demonstrate significant effects. These results suggest that sustained engagement in live streaming environments is driven more by interactive and habitual gratifications than by purely hedonic motivations. By highlighting the contextual dynamics of Indonesian gaming live streaming\, this study extends the application of Uses and Gratifications Theory in synchronous digital media settings and offers practical implications for content creators seeking to strengthen audience retention strategies.
CATEGORIES:VIRTUAL ROOM 9C
LOCATION:Virtual Room C\, Bangkok\, Thailand
SEQUENCE:0
UID:70dd6e21964e4f906f47ae88c83600f8
URL:http://11thictisthailand.sched.com/event/70dd6e21964e4f906f47ae88c83600f8
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260417T125213Z
DTSTART:20260410T080000Z
DTEND:20260410T100000Z
SUMMARY:A Hybrid RegNetX-Hierarchical Bidirectional Linear Cross Attention Framework for Medicinal Plant Leaf Identification
DESCRIPTION:Authors - Madhusmita Chakraborty\, Vijay Kumar Nath\, Deepika Hazarika\n Abstract - Due to morphological similarities between species\, environmental variability\, and the requirement for specialized knowledge\, accurate identification of medicinal plants is still difficult\, despite their critical role in primary healthcare systems around the world. A Hybrid RegNetX-Hierarchical Bidirectional Linear Cross Attention framework referred to as HR-HBCA framework for identifying medicinal plants from leaf photos is presented in this work. Multi-scale features are extracted using a RegNetX backbone\, and computationally efficient linear crossattention is used in Hierarchical Bidirectional Linear Cross-Attentive Fusion (HBLCAF) blocks to integrate shallow spatial and deep semantic representations. Balanced contextual exchange across scales is achieved by bidirectional cross-attentive fusion. The HR-HBCA framework shows strong performance under notable intra-class variability\, with accuracies ranging from 93.79% to 99.73% when tested on five diverse public datasets.
CATEGORIES:VIRTUAL ROOM 9D
LOCATION:Virtual Room D\, Bangkok\, Thailand
SEQUENCE:0
UID:3ae6a7e0c979d25027b019e646a44234
URL:http://11thictisthailand.sched.com/event/3ae6a7e0c979d25027b019e646a44234
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260417T125213Z
DTSTART:20260410T080000Z
DTEND:20260410T100000Z
SUMMARY:Adult Learners’ Preferences for Pedagogical Interface Agents: An Analysis Based on Noticeable Features
DESCRIPTION:Authors - Ntima Mabanza\n Abstract - Research that examines the use of Pedagogical interface agents (PIAs) in digital learning environments has demonstrated that PIAs can increase learner engagement\, motivation\, knowledge retention\, and improve the learning outcomes. Despite that\, there is limited empirical understanding of which PIA’s particular features are very noticeable and preferred by learners. A mixed-methods approach was used in this study\, combining initial training\, task completion\, and feature rating questionnaires with 62 adult participants. This approach was used to examine adult learner preferences for PIAs’ noticeable features\, such as appearance\, voice\, and movement. The study findings indicate that adult learners prioritize PIAs’ movement\, followed by their appearance\, and lastly their voice. The findings of this study provide very useful design guidelines for developing effective learner-centered PIA systems that maximize engagement and satisfaction.
CATEGORIES:VIRTUAL ROOM 9D
LOCATION:Virtual Room D\, Bangkok\, Thailand
SEQUENCE:0
UID:b4371e9592c2b0deade27376bec1526f
URL:http://11thictisthailand.sched.com/event/b4371e9592c2b0deade27376bec1526f
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260417T125213Z
DTSTART:20260410T080000Z
DTEND:20260410T100000Z
SUMMARY:Convolutional Neural Network Model Ablation for Accurate Single MRI Super-Resolution
DESCRIPTION:Authors - Imene Kichah\, Amir Aieb\, Antonio Liotta\, Muhammad Azfar Yaqub Abstract - The rapid growth of Information and Communication Technologies (ICT) has profoundly altered educational systems by redefining teaching practices\, institutional processes\, and professional expectations. Within the broader context of sustainable development and smart education\, ICT has emerged as an important facilitator of efficiency\, accessibility\, and innovation. This paper presents a conceptual analysis of how ICT can contribute to sustainable development through its influence on teachers’ work–life balance and job satisfaction in ICT-enabled learning environments. While ICT adoption has the potential to enhance instructional flexibility\, autonomy\, and efficiency\, excessive digital connectivity\, intensified workload\, and blurred work–life boundaries may adversely affect teachers’ well-being. The paper identifies work life balance as a key mediating factor linking ICT use to job satisfaction and long term professional sustainability. Furthermore\, the study situates teachers’ well being within the broader framework of sustainable development\, emphasizing its relevance to Sustainable Development Goals such as SDG 3 (Good Health and Well-Being)\, SDG 4 (Quality Education)\, and SDG 8 (Decent Work and Economic Growth). The analysis underscores the need for human-centred\, policy-driven\, and ethically oriented ICT integration strategies that prioritize teacher well-being alongside technological advancement. The paper contributes to the discourse on sustainable and intelligent education systems by highlighting that the long-term effectiveness of ICT-driven educational transformation depends on balanced digital practices that support teachers’ work–life balance and job satisfaction.
CATEGORIES:VIRTUAL ROOM 9D
LOCATION:Virtual Room D\, Bangkok\, Thailand
SEQUENCE:0
UID:2252c09b4014c207d78a649534282111
URL:http://11thictisthailand.sched.com/event/2252c09b4014c207d78a649534282111
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260417T125213Z
DTSTART:20260410T080000Z
DTEND:20260410T100000Z
SUMMARY:Drivers of Gen Z Impulsive Buying: Host\, Emotion\, and Quality in TikTok Live
DESCRIPTION:Authors - Aleah Prameswari Kalyana Merkadea Purnomo\, Muhammad Aras\n Abstract - TikTok Live Shopping has been rapidly growing and the way consumers and brands interact has changed\, with emotional and communicative engagement leading the way to driving purchases. However\, there is minimal literature to understand the impact of how host performance\, emotional euphoria\, and perceived quality value combine to affect impulse buying\, specifically in reference to preloved fashion and the Generation Z cohort. This study aims to fill the gap in literature by examining the impact of these three components on impulse buying behavior from the perspective of Integrated Marketing Communication (IMC). In this study\, a quantitative method was used by conducting an online survey with 136 respondents from Generation Z who have bought items through TikTok Live Shopping. The data was analyzed using Partial Least Squares–Structural Equation Modeling (SEM-PLS). Emotional euphoria is the only antecedent with a statistically significant positive relationship with impulsive buying behavior. Host performance and quality value have a positive relationship but are statistically insignificant. Moderately\, the model explains 57% of the variance in impulsive buying (R² = 0.570) showing moderate predictive power. Emotional stimulation is the largest driver of im-pulsive buying\, while cognitive evaluation centered around quality is merely justifying a post purchase rationale. This paper illustrates that in live commerce\, emotional irrationality is more dominant than communicative rationality\, offering a new dimension to the IMC paradigm in the context of real-time social commerce and underlining the criticality of emotional engagement in live sessions for improving customer conversion.
CATEGORIES:VIRTUAL ROOM 9D
LOCATION:Virtual Room D\, Bangkok\, Thailand
SEQUENCE:0
UID:c2ba0fbc00d54cc6462cd82f1ca527fc
URL:http://11thictisthailand.sched.com/event/c2ba0fbc00d54cc6462cd82f1ca527fc
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260417T125213Z
DTSTART:20260410T080000Z
DTEND:20260410T100000Z
SUMMARY:How Word of Mouth\, Branding\, and Exclusivity Shape Consumer Visit Intention
DESCRIPTION:Authors - Matthew Abrham Kristanto\, La Mani\, Cindy Magdalena\, Maudi Aulia Saraswati\, Annisa Atha Hanifah Abstract - Digital Twins (DTs) are increasingly explored for integrating BIM and IoT in facility management\, yet many implementations remain fragmented\, weakly governed semantically\, and difficult to scale. This paper presents a BIM-centric DT framework for the MaCA museum Living Lab in Turin\, combining indoor–outdoor environmental sensing\, automated BIM synchronization\, IFC-based interoperability\, and a prototype temporal analytics layer. The methodology links shared-parameter modeling\, Dynamo–Python synchronization\, and room-/zone-level identifier logic to validate end-to-end snapshot-to-BIM integration on a one-week monitoring dataset. Results confirm robust parameter mapping\, successful serialization of custom space-level IFC property sets\, and the feasibility of a dual-layer DT strategy in which BIM/IFC supports semantic-spatial contextualization while external temporal platforms support analytics and dashboard visualization. The core contribution lies in defining a scalable and standards-aligned workflow for cultural facilities based on identifier persistence\, modular synchronization\, interoperability\, and data-quality-aware DT deployment.
CATEGORIES:VIRTUAL ROOM 9D
LOCATION:Virtual Room D\, Bangkok\, Thailand
SEQUENCE:0
UID:1f78d80a453d2d9d09334870205663b4
URL:http://11thictisthailand.sched.com/event/1f78d80a453d2d9d09334870205663b4
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260417T125213Z
DTSTART:20260410T080000Z
DTEND:20260410T100000Z
SUMMARY:Hybrid AI-Enabled IoT Imaging Framework for Early-Stage Multi-Label Tomato Leaf Disease Detection
DESCRIPTION:Authors - Md. Abdul Malek Sobuj\, Md. Faruk Abdullah Al Sohan\, Afroza Nahar\, Saeeda Sharmeen Rahman\n Abstract - Tomato leaf diseases lead to significant losses in yield and quality\, especially in developing areas where timely diagnosis and expert help are scarce. Early and accurate disease detection is vital for sus tainable crop protection and better agricultural productivity. This pa per proposed a hybrid AI-IoT imaging framework for early-stage multi label tomato leaf disease detection in real-field agricultural settings. The proposed hybrid framework combines camera-based IoT sensing\, edge and cloud computing\, and a lightweight hybrid CNN\, the Transformer model\, to allow continuous monitoring\, timely diagnosis\, and decision support. The proposed hybrid framework merges local feature extrac tion with global context modeling to enable accurate multi-label clas sification while being suitable for deployment on devices with limited resources. A conceptual performance comparison and case study show the practical feasibility and benefits of this approach regarding diagnos tic reliability\, scalability\, and cost-effective deployment. The framework aims to improve early disease identification\, reduce crop losses\, and sup port precision agriculture practices. This study offers a practical and scalable solution for intelligent tomato disease management and aids the development of sustainable AI-IoT-based smart agriculture systems.
CATEGORIES:VIRTUAL ROOM 9D
LOCATION:Virtual Room D\, Bangkok\, Thailand
SEQUENCE:0
UID:cf9913684ea32da2100a79a1e074800c
URL:http://11thictisthailand.sched.com/event/cf9913684ea32da2100a79a1e074800c
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260417T125213Z
DTSTART:20260410T080000Z
DTEND:20260410T100000Z
SUMMARY:IoT-Based Smart Railway Crossing System Using Sensors for Real-Time Train Detection and Safety Enhancement
DESCRIPTION:Authors - Fahima Sultana Smrity\, Md. Ibrahim Tanjim\, Md. Faruk Abdullah Al Sohan\, Afroza Nahar\, Saeeda Sharmeen Rahman\n Abstract - Solar-powered systems in railway crossing safety are an effi cient approach for ensuring continuous monitoring and accident preven tion in risky and less supervised areas. Solar energy ensures the reliability of the system\, while the components connected to it are optimized for en ergy efficiency and long-range communication. In the transportation sec tor\, IoT-enabled safety devices are gaining importance\, and railway cross ings are a key example. This paper proposes a simplified solar-powered model\, called Smart Railway Crossing Protection (SRCP)\, for railway au tomation using IoT-based sensing and communication. This model intro duces an energy-efficient design with LiFePO4 battery backup\, MPPT based solar adaptation\, and wireless communication of the LoRa model\, focusing on reducing functional costs and dependence on manual su pervision compared to traditional railway safety systems. The proposed system aims to increase real-time responsiveness\, ensure stability in re mote places\, and improve the overall security of the passenger and vehi cle. Moreover\, the SRCP model emphasizes scalability and adaptability\, underlining its importance for various railway infrastructures.
CATEGORIES:VIRTUAL ROOM 9D
LOCATION:Virtual Room D\, Bangkok\, Thailand
SEQUENCE:0
UID:a680d05d4ec972ee455efa8ca70ddbf5
URL:http://11thictisthailand.sched.com/event/a680d05d4ec972ee455efa8ca70ddbf5
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260417T125213Z
DTSTART:20260410T080000Z
DTEND:20260410T100000Z
SUMMARY:Performance Analysis of UAV Assisted Free Space Optical Communication Link
DESCRIPTION:Authors - Subhrajyoti Sunani\, Prasant Kumar Sahu\, Debalina Ghosh Abstract - Topic detection is an essential task in Natural Language Processing (NLP) that enables the automatic classification of text into predefined categories. However\, research challenges in the Myanmar language remain limited due to the lack of annotated corpora and linguistic challenges. In this study\, word-level segmentation is employed to capture more semantically meaningful units for topic detection\, such as အနုပညာ (art)\, ဥပဒေ (law)\, အာားကစာား (sports)\, and နည ားပညာ (technology). The study trains and evaluates the system on a dataset of News articles categorized into 12 predefined topics: agriculture\, art\, crime\, disaster\, economy\, education\, foreign affairs\, health\, politics\, religion\, sports\, and technology. A variety of models was examined\, covering traditional machine-learning baselines\, a deep learning sequence model\, and transformer-based architectures. Logistic Regression and Naïve Bayes were tested and achieving accuracies of 0.73 and 0.63\, respectively\, with Logistic Regression outperforming Naïve Bayes as a stronger linear baseline. The LSTM model\, which incorporates sequential dependencies\, improves performance further with an accuracy of 0.85. Transformer based approaches deliver the best results: DistilBERT achieves 0.87 accuracy\, while word level mBERT achieves 0.95 accuracy at its peak\, demonstrating the effectiveness of word-level approaches for Myanmar topic detection. Overall\, the findings demonstrate that while traditional models offer useful baselines\, deep learning and especially transformer-based architectures provide substantial gains in accuracy and reliability for Myanmar topic detection. This research highlights the effectiveness of modern transformer-based methods for low resource language applications and sets a benchmark for future work in Myanmar NLP.
CATEGORIES:VIRTUAL ROOM 9D
LOCATION:Virtual Room D\, Bangkok\, Thailand
SEQUENCE:0
UID:5fb45d87ced597e339c71bd83fcbda06
URL:http://11thictisthailand.sched.com/event/5fb45d87ced597e339c71bd83fcbda06
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260417T125213Z
DTSTART:20260410T080000Z
DTEND:20260410T100000Z
SUMMARY:RadVision: Topological Data Analysis and Vision Transformers for Automated Radiology Report Generation
DESCRIPTION:Authors - Ayana Soman\, Diya P. Varghese\, Elizabeth Anna Liju\, Ethel Jimmy\, Liyan Grace Shaji\, P R Neethu\n Abstract - Radiology report generation is a vital and time-consuming part of medical imaging workflows. It is often shaped by heavy workloads and differences in opinions among observers. This paper presents RadVi sion\, an AI-driven platform designed to automatically generate prelimi nary radiology reports from medical imaging data\, with a specific focus on MRI scans. The framework uses Vision Transformers (ViT) for global feature extraction and Topological Data Analysis (TDA) to identify structural and shape-based abnormalities that traditional deep learning methods might miss. To improve understanding and clinical reliability\, RadVision includes explainability tools like Grad-CAM heatmaps and persistence diagrams from TDA. A transformer-based language model creates structured\, editable diagnostic reports with confidence scores\, allowing for effective validation by humans. The system is accessible through a secure web dashboard\, facilitating collaborative annotation\, feedback-based model improvement\, and smoother workflow integration. Experimental tests across various radiological cases show better diagnos tic support\, greater transparency\, and less reporting effort. These results position RadVision as a scalable and clear AI tool to assist radiologists and promote efficient and reliable medical reporting.
CATEGORIES:VIRTUAL ROOM 9D
LOCATION:Virtual Room D\, Bangkok\, Thailand
SEQUENCE:0
UID:320413727a331b6701d649e378d0a6a1
URL:http://11thictisthailand.sched.com/event/320413727a331b6701d649e378d0a6a1
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260417T125213Z
DTSTART:20260410T080000Z
DTEND:20260410T100000Z
SUMMARY:When Credibility Goes Viral: Influencer Impact on TikTok Purchase Behavior
DESCRIPTION:Authors - Aura Meivia Safira Arsya\, Ricardo Indra\, Shafa Salsabila Risfi Febrian\, Benedicta Kalyca Kyatimanyari\n Abstract - This study analyzes the extent to which credibility from influencers impacts consumers' buying behavior. The focus will be on how the intention to buy impacts this relationship as the problem is being analyzed in the context of social commerce on TikTok. The study is developed within the framework of Source Credibility Theory which suggests that consumers’ perception and consequent behavior are influenced by the perceived degree of the spokesperson’s Attractiveness\, Trustworthiness\, and Expertise. The study employs a quantitative explanatory methodology. A purposive sampling technique was used to collect data from a sample of 100 active TikTok users who follow the provided influencer. The analyzed relationships will be quantified using Structural Equation Modelling with Partial Least Squares (SEM-PLS). The research results concluded that influencer credibility increases the intention to buy\, but does not increase the purchasing decision. The intention to buy completely mediates the relationship between influencer credibility and purchasing decision. This demonstrates that influencer credibility is a significant factor in the intention to buy behavior\, but it is the intention that is essential in order to convert the persuasive influence into actual buying behavior. The study contributes to digital marketing communication research by extending Source Credibility Theory to the context of short-video social commerce platforms.
CATEGORIES:VIRTUAL ROOM 9D
LOCATION:Virtual Room D\, Bangkok\, Thailand
SEQUENCE:0
UID:2ba16643e580e141ccab5c610a4323f3
URL:http://11thictisthailand.sched.com/event/2ba16643e580e141ccab5c610a4323f3
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260417T125213Z
DTSTART:20260410T080000Z
DTEND:20260410T100000Z
SUMMARY:Assessment and Optimization Strategy of Thailand's Fruit Export Competitiveness to China
DESCRIPTION:Authors - Meixin Hu\, Chuanchen BI Abstract - Speech synthesis is an important tool for improving human-computer interac tion\, accessibility\, and other multimedia applications. Traditional Text-to-Speech (TTS) systems have issues related to robotic tone\, slow inference and lack of expressiveness. This current study presented a realization of the effectiveness of the neural TTS system using Fast Speech 2 as the underlying neural TTS sys tem. The system used in the current study was a combination of Fast Speech 2 as the underlying neural system in generating high-quality utterances and HiFi-GAN as the underlying neural vocoder. The process involves reconstructing natural-sounding text utterances in terms of mel-spectrograms by Fast Speech 2 that incorporate the use of variance adaptation in terms of pitch\, duration\, and energy. The implementation of natural-sounding utterances in terms of mel spectrograms is done in real-time using HiFi-GAN. The implementation of the available studies provided insights into Fast Speech 2’s effectiveness in generating mel-spectrograms in real-time and faster. The use of HiFi-GAN provided insights in generating natural-sounding utterances in real-time. The effectiveness of Fast Speech 2 in generating high-quality utterances has further stretched the poten tial use of Fast Speech 2 in virtual assistant applications\, audiobooks\, accessible text services\, further highlighting its significance in advanced human–computer interaction systems.
CATEGORIES:VIRTUAL ROOM 9E
LOCATION:Virtual Room E\, Bangkok\, Thailand
SEQUENCE:0
UID:2ab8dc0aa421bef9c50c969bf17dd665
URL:http://11thictisthailand.sched.com/event/2ab8dc0aa421bef9c50c969bf17dd665
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260417T125213Z
DTSTART:20260410T080000Z
DTEND:20260410T100000Z
SUMMARY:Breaking Through in Visibility: A New Sustainable Marketing Path for Culture-Tourism Integration at the Dazu Rock Carvings
DESCRIPTION:Authors - Cheng Cheng\, Chuanchen BI Abstract - In recent years\, there has been an increase in AI - generated images. This poses a major challenge in distinguishing fabricated images from real ones. This distinction is valuable for discovering misinformation and preserving digital trust. Some deep learning models\, particularly large Convolutional Neu ral Networks (CNNs)\, have demonstrated high accuracy on benchmark datasets like CIFAKE\, but their computational requirements often in clude specialised hardware like powerful Graphics Processing Units (GPUs)\, which ultimately limit practical deployment. This paper explores an alternative approach that focuses on efficiency and interpretability. The CIFAKE dataset is used\, but a significantly lighter CNN architecture\, ResNet18 is deployed which does not require high end local GPU hardware. Furthermore\, the paper applied Gradient - weighted Class Activation Mapping (Grad - CAM) not just for visu alization\, but also to validate that the model learns meaningful visual features that are relevant to the classification task. This work highlights a practical method to interpret AI - generated images.
CATEGORIES:VIRTUAL ROOM 9E
LOCATION:Virtual Room E\, Bangkok\, Thailand
SEQUENCE:0
UID:5afaf1eb1c8e4553351d8fd5873ff4d7
URL:http://11thictisthailand.sched.com/event/5afaf1eb1c8e4553351d8fd5873ff4d7
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260417T125213Z
DTSTART:20260410T080000Z
DTEND:20260410T100000Z
SUMMARY:Crisis Management and Strategic Failure: A Case Study of Evergrande’s Survival Struggles under China’s Regulatory Shift
DESCRIPTION:Authors - Jiayan Peng\, Chuanchen Bi Abstract - With the continued growth of digital education (and multiple platforms providing education/courses)\, students have many things to deal with in terms of finding useful content (e.g.\, Lecture videos\; audio files\; PDF's\; slides\, etc) and as a result\, it may be difficult to efficiently scan and gather all of this information. AutoNoteX is a tool that automatically creates notes from your spoken word using speech-to-text technology (e.g. Whisper)\, Natural Language Processing\, and various AI agents. AutoNoteX will provide accurate transcriptions\, along with structured summaries that highlight key points and provide diagrams when appropriate in order to create good\, clear notes for students. AutoNoteX can support collaborative and independent learning by allowing the user to merge their notes with Google Docs or download them as PDF's. AutoNoteX also includes interactive knowledge checks that have multiple levels of difficulty (easy\, medium\, difficult) when answering questions and also provide a means for the student to receive instant feedback on their progress. AutoNoteX was developed using React.js for the front end and Python Flask for the backend\, and is cloud-enabled (scalable\; accessible via many devices\; and easy to integrate into a variety of subjects) giving students the tools they need to create better notes. Overall\, AutoNoteX provides a new avenue for multi-modal\, AI-assisted\, and personalized digital note-taking\, while reducing the amount of time needed to make notes and improving student comprehension by encouraging students to participate in their learning process actively.
CATEGORIES:VIRTUAL ROOM 9E
LOCATION:Virtual Room E\, Bangkok\, Thailand
SEQUENCE:0
UID:de99e8115a6d7094143dd41ebf6476df
URL:http://11thictisthailand.sched.com/event/de99e8115a6d7094143dd41ebf6476df
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260417T125213Z
DTSTART:20260410T080000Z
DTEND:20260410T100000Z
SUMMARY:Cultural Positioning and Strategic Sustainability: A Case Study of a Regional New Style Milk Tea Brand in China
DESCRIPTION:Authors - Qixuan Geng\, Chuanchen BI Abstract - Efficient nutrient management is vital in a sugarcane cultivation to sustain the crop yields. But\, the conventional practices are still reactive and imprecise often leading to improper nutrient management and yield loss. To overcome this issue\, the study utilizes a multimodal AI driven framework by integrating drone-based canopy imaging and in-field soil sensors to aid in real-time nutrient deficiency detection and precise recommendation of fertilizers. UAV images are analysed using a transfer learning based Convolutional Neural Network (CNN) to locate visible deficiency symptoms and determine its severity. In order to forecast impending nutrient deficiencies\, significant soil parameters (NPK\, moisture\, pH\, electrical conductivity and temperature) are monitored continuously and processed using GRU/ LSTM- based models. The data and information from sensor networks\, images and environmental context are then integrated through a fusion architecture to produce a nutrient deficiency label\, severity score\, and confidence measure. To ensure interpretability and agronomic safety\, predictions are incorporated with crop growth stage- specific nutrient gap model that convert deficiencies into dosages of fertilizers\, with alerts given on high-risk conditions and optionally permissioned fertigation control. The proposed system allows proactive\, data-driven nutrient management\, mitigates the risk of over fertilization\, and supports scalable precision agriculture.
CATEGORIES:VIRTUAL ROOM 9E
LOCATION:Virtual Room E\, Bangkok\, Thailand
SEQUENCE:0
UID:714864b2889ec7c58a2b8a0155861211
URL:http://11thictisthailand.sched.com/event/714864b2889ec7c58a2b8a0155861211
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260417T125213Z
DTSTART:20260410T080000Z
DTEND:20260410T100000Z
SUMMARY:Ensemble Transfer Learning with Logistic Regression Metaclassifier and Explainable AI for Detecting Potato Leaves Diseases
DESCRIPTION:Authors - Md. Riaz Mahmud\, Kazi Asif Ahmed\, Md. Rafiqul Islam\, Kabya Guha Abstract - Modeling multi-scale spatial dependencies is essential in histopathology image analysis\, where diagnostically relevant patterns span cellular textures and tissue-level structures. While convolutional neural networks effectively capture local features\, they struggle to model long-range interactions\, and transformer-based approaches address this limitation at the cost of quadratic computational complexity with respect to spatial resolution. In this work\, we propose HiSS-Fuse\, a linear-time hierarchical state-space fusion framework that integrates multi-scale fea ture representations using Mamba-based selective state-space modules. The proposed architecture progressively fuses local and global contex tual information across network depths while maintaining O(L) com putational complexity\, where L denotes the number of spatial tokens. Experimental evaluation on the PathMNIST benchmark demonstrates that HiSS-Fuse achieves 97.0% classification accuracy with an AUC of 0.997 while maintaining strong computational efficiency. Ablation stud ies further confirm that hierarchical fusion systematically enhances rep resentation learning. Overall\, HiSS-Fuse provides a scalable and compu tationally efficient alternative to quadratic attention-based architectures for multi-scale histopathology image analysis.
CATEGORIES:VIRTUAL ROOM 9E
LOCATION:Virtual Room E\, Bangkok\, Thailand
SEQUENCE:0
UID:e09b66c8af929a9dde204e5af11faf36
URL:http://11thictisthailand.sched.com/event/e09b66c8af929a9dde204e5af11faf36
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260417T125213Z
DTSTART:20260410T080000Z
DTEND:20260410T100000Z
SUMMARY:Marketing Plans for Dazu Rock Carvings via the Perspective of Cultural and Tourism Integration
DESCRIPTION:Authors - Cheng Cheng\, Chuanchen BI\n Abstract - The increasing reliance on Information and Communication Technology (ICT)-driven intelligent systems has transformed organizational decision-making processes\, enabling more efficient\, data-driven\, and adaptive strategies. These systems\, which encompass artificial intelligence\, machine learning\, and decision support tools\, have revolutionized how businesses process and analyze vast amounts of data to inform strategic decisions (Cheng et al.\, 2017\; Yoo & Lee\, 2020). This paper presents a strategic framework for integrating ICT-driven intelligent systems into organizational decision-making\, addressing key challenges such as technological compatibility\, organizational resistance\, and alignment with strategic goals (Patel & Sharma\, 2019\; López et al.\, 2019). The main objective of this study is to develop a comprehensive and practical framework that organizations can adopt for successfully integrating intelligent systems into their decision-making processes. The research aims to bridge the gap between existing theoretical models and practical applications by proposing a step-by-step process that involves assessing organizational readiness\, selecting appropriate systems\, ensuring seamless integration\, and fostering continuous improvement (Ahmad et al.\, 2021\; Pereira et al.\, 2021). The methodology employed includes qualitative case studies from diverse industries\, supplemented with a review of relevant literature and theoretical models such as the Technology-Organization-Environment (TOE) framework (Tor-natzky & Fleischer\, 1990) and the Resource-Based View (Barney\, 1991). The findings suggest that successful ICT integration is contingent upon a well-planned\, strategic approach that aligns technological capabilities with organizational goals and promotes an adaptive organizational culture (Brinkman & Möller\, 2018). The implications of this study are far-reaching\, offering valuable insights for managers and policymakers to overcome integration barriers and optimize decision-making using intelligent systems (Hossain & Kaur\, 2021). This research contributes to the growing body of knowledge on ICT integration in decision-making\, offering both theoretical advancements and practical guidelines for successful implementation.
CATEGORIES:VIRTUAL ROOM 9E
LOCATION:Virtual Room E\, Bangkok\, Thailand
SEQUENCE:0
UID:09931d64cc1916cdef1c94cbfd03f536
URL:http://11thictisthailand.sched.com/event/09931d64cc1916cdef1c94cbfd03f536
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260417T125213Z
DTSTART:20260410T080000Z
DTEND:20260410T100000Z
SUMMARY:Online privacy concern segments: A cluster analysis of young Indian consumers
DESCRIPTION:Authors - Tajamul Islam\, Ruby Chanda\n Abstract - The present study explores the online privacy concerns of young Indian consumers. Using the segmentation approach popularized by Dr Alan Wes-tin in the U.S.\, this study identifies the segments within Indian youth. This study is based on a survey conducted on a sample of Indian university students. Hierarchical and non-hierarchical cluster analysis techniques were applied to identify segments within young Indian consumers based on their privacy concerns. The study identified three consumer segments: highly concerned\, moderately concerned\, and less concerned based on online privacy concerns. The findings also reveal important differences among the three segments in terms of out-come variables such as perceived effectiveness of legal/regulatory policy\, fabricating personal information\, and software usage for protection. The results indicate an overall increased level of concern for online privacy among young Indian consumers. The results suggest similarities and dissimilarities with Westin’s approach. While previous research on online privacy has been chiefly based on the Western context\, this study offers a window to look at the Eastern context by examining the privacy concerns of young Indian consumers\, who have not been studied\, and hence provides an important contribution to the existing literature.
CATEGORIES:VIRTUAL ROOM 9E
LOCATION:Virtual Room E\, Bangkok\, Thailand
SEQUENCE:0
UID:a0d8d984a936e3453e753066b02223a1
URL:http://11thictisthailand.sched.com/event/a0d8d984a936e3453e753066b02223a1
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260417T125213Z
DTSTART:20260410T080000Z
DTEND:20260410T100000Z
SUMMARY:Perception of the Tourism Destination Image of Nong Khai Border Region\, Thailand
DESCRIPTION:Authors - Meixin Hu\, Chuanchen BI Abstract - Secret-sharing schemes are fundamental cryptographic primitives en- abling secure distribution of sensitive information among multiple parties. Orig- inally introduced to protect cryptographic keys\, they have evolved into power- ful tools underpinning modern secure multiparty computation\, distributed stor- age\, blockchain systems\, and privacy-preserving machine learning. This review presents a systematic overview of threshold secret-sharing schemes\, ramp con- structions\, and secret-sharing schemes for arbitrary access structures. We discuss information-theoretic foundations\, lower bounds\, structural generalizations\, and recent advances. Furthermore\, we highlight emerging applications in distributed computing\, post-quantum cryptography\, and secure AI systems.
CATEGORIES:VIRTUAL ROOM 9E
LOCATION:Virtual Room E\, Bangkok\, Thailand
SEQUENCE:0
UID:90ed4cc13b281b1e653ea8130f072da9
URL:http://11thictisthailand.sched.com/event/90ed4cc13b281b1e653ea8130f072da9
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260417T125213Z
DTSTART:20260410T080000Z
DTEND:20260410T100000Z
SUMMARY:Research on the Current Situation of Tourism Marketing of The Song Dynasty Of Kungfu City
DESCRIPTION:Authors - Ying Tang\, Chuanchen BI Abstract - This article presents a comprehensive analysis of methods and recent research in the sentiment analysis of Uzbek-language social media posts. A balanced corpus of 100\,000 posts from Telegram\, Instagram\, Twitter\, and Facebook was constructed as the object of study\, in which positive\, neutral\, and negative classes are equally represented. The data were subjected to thorough preprocessing steps including cleaning\, normalization\, tokenization\, removal of stop words\, stemming\, and lemmatization. The evaluated models include Naive Bayes\, Support Vector Machines (SVM)\, Conditional Random Fields (CRF)\, Long Short-Term Memory networks (LSTM)\, and transformer-based architectures such as BERT and RoBERTa. The accuracy\, F1-score\, and runtime performance of each model were compared. Experimental results indicate that transformer-based models achieved the highest accuracy (~92%)\, followed by LSTM (~90%) and SVM (~88%). Despite being a simple method\, Naive Bayes served as a baseline (~78% accuracy). The literature review highlights prior research conducted in Uzbek sentiment analysis\, emphasizing the importance of corpus creation and accounting for language-specific features. The results indicate that transformer models provide the highest accuracy\, whereas classical methods remain competitive even in low-resource settings. The article concludes with a discussion of promising directions and potential practical applications in the field of Uzbek-language sentiment analysis.
CATEGORIES:VIRTUAL ROOM 9E
LOCATION:Virtual Room E\, Bangkok\, Thailand
SEQUENCE:0
UID:c6e8be87d5c28b035c97dd565568d351
URL:http://11thictisthailand.sched.com/event/c6e8be87d5c28b035c97dd565568d351
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260417T125213Z
DTSTART:20260410T080000Z
DTEND:20260410T100000Z
SUMMARY:Thematic Evolution of Generative and Agentic AI in Operations and Supply Chain Systems (2015–2025): A Bibliometric Analysis
DESCRIPTION:Authors - Lankalapalli Vamsi Krishna\, Santanu Mandal\n Abstract - The rapid advancement of generative and agentic artificial intelligence (AI) is significantly transforming research in operations management and supply chain systems. Despite the substantial increase in scholarly output in recent years\, the structural evolution and thematic consolidation of this interdisciplinary field remain insufficiently mapped. This study presents a bibliometric analysis of 116 Scopus-indexed articles published between 2015 and 2025 to examine publication trends\, knowledge concentration\, intellectual structure\, and longitudinal thematic transitions. Utilizing the Bibliometrix R package\, the analysis employs performance metrics\, Bradford’s Law\, keyword co-occurrence mapping\, thematic centrality–density analysis\, and temporal evolution modeling. The results indicate accelerating research growth and increasing consolidation within core engineering-oriented journals. Intellectual clustering reveals strong integration between computational modeling\, reinforcement learning\, and supply chain decision systems. Thematic mapping identifies computational methods and autonomous agents as central themes\, while generative AI emerges as a developing yet increasingly interconnected trajectory. Longitudinal analysis reveals a clear shift from agent-based simulation frameworks toward adaptive\, autonomous\, and AI-integrated operational ecosystems. The findings suggest that generative and agentic AI are becoming foundational elements of next-generation operational intelligence systems. This study provides structured insights into the maturation of AI-enabled operational research and offers guidance for future interdisciplinary investigations in autonomous supply chain intelligence.
CATEGORIES:VIRTUAL ROOM 9E
LOCATION:Virtual Room E\, Bangkok\, Thailand
SEQUENCE:0
UID:5ecdcb281a3fe6e26af3137070847c08
URL:http://11thictisthailand.sched.com/event/5ecdcb281a3fe6e26af3137070847c08
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260417T125213Z
DTSTART:20260410T080000Z
DTEND:20260410T100000Z
SUMMARY:A Hybrid Unsupervised Approach for Fetal Brain Anomaly Detection Based on Image Quality Analysis and Convolutional Autoencoders
DESCRIPTION:Authors - Soji Binu Mathew\, A. Hepzibah Christinal Abstract - Permanent Magnet Synchronous Motors (PMSMs) are commonly utilized in electric vehicle (EV) traction systems because of its high efficiency\, power density\, and reliability. Conventional field-oriented control (FOC) schemes require accurate rotor position and speed information\, typically obtained from mechanical sensors\, which increase cost and reduce system reliability. Sensor less control techniques based on observer theory have therefore gained significant attention. Among them\, sliding mode observers (SMOs) offer strong robustness against parameter variations and external disturbances but suffer from chattering and noise sensitivity. This paper presents an advanced sensor less FOC strategy for PMSM drives using a super-twisting SMO (ST-SMO) for rotor position sensing and estimation of speed. The proposed approach employs a ST-SMO algorithm to achieve the convergence in finite-time while significantly reducing chattering effects. The observer is integrated into a standard FOC framework and evaluated under EV-relevant operating conditions\, including low-speed operation and load transients. Comparative performance discussion demonstrates the suitability and the effectiveness of the proposed method for high-efficiency EV traction.
CATEGORIES:VIRTUAL ROOM 9F
LOCATION:Virtual Room F\, Bangkok\, Thailand
SEQUENCE:0
UID:0598199dbf19d02189d9145fa2037c8d
URL:http://11thictisthailand.sched.com/event/0598199dbf19d02189d9145fa2037c8d
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260417T125213Z
DTSTART:20260410T080000Z
DTEND:20260410T100000Z
SUMMARY:A Platform to Digitally Sign and Verify 3D Graphic Models
DESCRIPTION:Authors - Mohammed Mudassir\, Irene Joseph\, Jyothi Mandala\, Sandeep J Abstract - This study introduces a Bidirectional Long Short-term Memory based multichannel speech enhancement framework that operates in the short-time Fourier transform domain using time-varying complex spectral masking. The pro-posed approach predicts channel-specific complex masks\, allowing adaptive frame-wise suppression of noise in reverberant and multi-noise environments. A comprehensive dataset was created using multiple noise sources\, and experiments were carried out at different signal-to-noise ratios. The proposed method outperformed the Relative Transfer Matrix and Deep Multichannel Active Noise Control techniques in perceptual speech quality and intelligibility across all test conditions\, indicating its potential for real-world speech enhancement applications.
CATEGORIES:VIRTUAL ROOM 9F
LOCATION:Virtual Room F\, Bangkok\, Thailand
SEQUENCE:0
UID:27ceb5df5b8359deec7b39718497e071
URL:http://11thictisthailand.sched.com/event/27ceb5df5b8359deec7b39718497e071
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260417T125213Z
DTSTART:20260410T080000Z
DTEND:20260410T100000Z
SUMMARY:A-KIT Based Visual-Inertial Odometry Framework for Autonomous Underwater Vehicle Positioning
DESCRIPTION:Authors - Gauri P Nair\, Vinaya V\, Dona Sebastian\, Kavitha K V Abstract - Reliable stock price forecasting remains challenging due to the noisy\, nonlinear\, and non-stationary characteristics of financial time-series data. Traditional statistical methods and deep learning models that rely solely on raw price data often struggle to capture short-term fluctuations and evolving market dynamics. To address these limitations\, this study proposes a hybrid forecasting framework that integrates causal time-domain filtering\, time–frequency feature extraction\, and deep learning–based temporal modeling. The proposed approach employs Savitzky–Golay and Kalman filters to sup press high-frequency market noise while preserving important price trends in a causality-aware manner suitable for real-time forecasting. Localized spectral fea tures representing transient and time-varying market behavior are then extracted using the Short-Time Fourier Transform (STFT). These enhanced time-domain and frequency-domain features are combined and modeled using a Long Short Term Memory (LSTM) network\, which effectively captures long-range depend encies and nonlinear temporal patterns in financial data. The framework is evaluated using standard performance metrics\, including RMSE\, MAPE\, and R². Experimental results demonstrate that integrating causal filtering with STFT-based features significantly improves forecasting accuracy and robustness compared to baseline models\, providing a reliable and practical solution for short-term and multi-step stock price prediction.
CATEGORIES:VIRTUAL ROOM 9F
LOCATION:Virtual Room F\, Bangkok\, Thailand
SEQUENCE:0
UID:bc7728100c814e968b58d46c21c89f21
URL:http://11thictisthailand.sched.com/event/bc7728100c814e968b58d46c21c89f21
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260417T125213Z
DTSTART:20260410T080000Z
DTEND:20260410T100000Z
SUMMARY:Bi-LSTM-based T-F Complex Masking for Multi-Source Noise Suppression
DESCRIPTION:Authors - Zubair Zaland\, Mumtaz Begum Mustafa\, Miss Laiha Mat Kiah\, Hua-Nong Ting\, Zuraidah M Don\, Saravanan Muthaiyah Abstract - As digital marketing expands in Oman\, many organizations struggle to transform large volumes of customer data into actionable insights. This study presents an AI-driven marketing intelligence framework designed for non-technical users\, combining automated customer segmentation\, sentiment analysis\, and personalized recommendations. The framework employs an autoencoder-based feature extraction approach to capture key behavioral patterns\, followed by K-Means clustering to define meaningful customer segments (Berahmand et al.\, 2024). A fine-tuned BERT model analyzes multilingual feedback in Arabic and English to assess customer sentiment (Manias et al.\, 2023). The framework was evaluated using 12 months of campaign data from 450 customers across multiple Omani businesses. Analysis revealed four distinct customer groups and an overall positive sentiment of +0.55. Controlled A/B experiments demonstrated that AI-guided campaigns outperformed traditional methods\, increasing conversion rates by 27%\, improving retention by 15%\, and generating a threefold return on marketing spend. These results indicate that accessible AI tools can deliver measurable marketing benefits in emerging markets and provide a scalable solution for Gulf-region businesses.
CATEGORIES:VIRTUAL ROOM 9F
LOCATION:Virtual Room F\, Bangkok\, Thailand
SEQUENCE:0
UID:2b7682402e2f2613a269f94395dda16d
URL:http://11thictisthailand.sched.com/event/2b7682402e2f2613a269f94395dda16d
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260417T125213Z
DTSTART:20260410T080000Z
DTEND:20260410T100000Z
SUMMARY:Edge-Enabled Federated Learning for Privacy Preserving Healthcare Analytics
DESCRIPTION:Authors - B.Usha Rani\, M.Sudhakar\, A.Srivani\, Y.Surya Praveen Abstract - The purpose of Diabetic Retinopathy Prediction is to use computer technology to identify early stages of retinal damage caused by diabetes. Since diabetic retinopathy can lead to blindness or permanent vision impairment if not treated in a timely manner\, accurate and rapid diagnosis is vital. Recent tech niques for diagnosing diabetic retinopathy require an ophthalmologist to perform a manual examination of the eye’s retina with the use of fundus photography. The diagnostic process can be costly\, time-consuming\, and vary significantly from one person to another. A large percentage of diabetes patients live in rural areas\, where it is difficult or impossible for them to have periodic screening by a diabetic specialist or receive healthcare services. There is a need to develop a solution to these problems\, and the Diabetic Retinopathy Prediction System uses deep learning based techniques to analyze retinal fundus images and produce pre dictions regarding diabetic retinopathy. Analysis of the retinal fundus images will include preprocessing\, feature extraction using CNNs\, and automated classifica tion into diabetic retinopathy by degree and severity. This approach increases the accuracy and consistency of diabetic retinopathy diagnosis while minimizing the need for human input. The proposed system will allow for early identification of diabetic retinopathy in resource poor environments\, support large scale screening programs and aid in clinical decision making by ophthalmologists. Additionally\, the system has potential integration into mobile health systems and tele-ophthal mology networks. Experimental results indicate the proposed system is capable of accurately detecting diabetic retinopathy with high levels of specificity and sensitivity.
CATEGORIES:VIRTUAL ROOM 9F
LOCATION:Virtual Room F\, Bangkok\, Thailand
SEQUENCE:0
UID:207a41b1d650d49112e0ee5f16b4ba03
URL:http://11thictisthailand.sched.com/event/207a41b1d650d49112e0ee5f16b4ba03
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260417T125213Z
DTSTART:20260410T080000Z
DTEND:20260410T100000Z
SUMMARY:Federated Learning (FL) and Multimodal Federated Learning (MFL) a review in healthcare domain
DESCRIPTION:Authors - Bai B Mathura\, Narra Dhanalakshmi Abstract - This paper presents a novel Reversible Data Hiding (RDH) method for dual images. First\, secret data is converted into a binary sequence of equal length and then divided into shorter segments to control the amount of data embedded into each pixel. The embedding process uses two copies of the original image to distribute the data\, reducing the impact on each image while maintaining overall image quality. During recovery\, the original image is restored by averaging the pixel values at corresponding locations in the two stego images\, while the embedded data is recovered through a reverse process. Experimental results on grayscale images demonstrate that the method maintains good image quality\, achieving a high Peak Signal-to-Noise Ratio (PSNR) across different embedding levels while ensuring accurate recovery of both the secret data and the original image.
CATEGORIES:VIRTUAL ROOM 9F
LOCATION:Virtual Room F\, Bangkok\, Thailand
SEQUENCE:0
UID:199b08ca52efd343d1298d60b9af29a7
URL:http://11thictisthailand.sched.com/event/199b08ca52efd343d1298d60b9af29a7
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260417T125213Z
DTSTART:20260410T080000Z
DTEND:20260410T100000Z
SUMMARY:Lightweight Machine Learning Models for Resource-Constrained Environments: Accuracy–Efficiency Trade-Off Analysis
DESCRIPTION:Authors - Priyanka Khalate\, Satish S. Banait\, Chandrakant Kokane\, Dnyanada Shinde\, Madhumati Pol\, Pravinkumar M. Sonsare Abstract - The emerging use of digital deepfake technology is creating a myriad of obstacles in verifying the authenticity of digital media. Most of today’s detection methods yield satisfactory results when applied to clean samples of content\, however\, they are still susceptible to adversarial perturbations specifically created to bypass these detection methods. The current research paper introduces DC-DAFDN\, a dual-stream architecture for detecting fraudulent digital content\, which fuses frequency-domain analysis using the Discrete Cosine Transform (DCT) with Space-Attention Mechanisms. The current architecture uses adversarial training to develop more robust features. The proposed model uses EfficientNet-B4 as a backbone\, augmented with Spatial Reduction Attention Blocks and Forged Fea tures Attention Modules to detect manipulation artifacts in the spatial domain\, while the parallel DCT stream analyzes inconsistencies in the frequency-domain. Through an adversarial training procedure using Fast Gradient Sign Method (FGSM)-induced adversarial perturbations\, the model learns robust feature sets that are resistant to evasion attacks. When evaluated on Face-Forensics++ dataset\, DC-DAFDN significantly improves upon the original Dual Attention for Deepfake Detection Network (DAFDN) in terms of adversarial robustness. When attacked with large adversarial perturbations (e.g.\, FGSM with ϵ ranging from 0.1 to 0.25)\, the DC-DAFDN architecture maintained greater than average accu racy enhancements from +2.74% up to +3.61%\, for an average accuracy increase of +3.36%\, for the tested att\, from all strengths. Our findings suggest that fusing frequency-domain analysis with adversarial training provides measurable improvement in the model’s robustness to adversarial attacks and simultaneously preserves the detection capabilities of the dual-attention method.
CATEGORIES:VIRTUAL ROOM 9F
LOCATION:Virtual Room F\, Bangkok\, Thailand
SEQUENCE:0
UID:689c78a5acb5aee903e706039264683d
URL:http://11thictisthailand.sched.com/event/689c78a5acb5aee903e706039264683d
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260417T125213Z
DTSTART:20260410T080000Z
DTEND:20260410T100000Z
SUMMARY:Machine learning modeling for the assessment of chest pain: a clinical study in the context of Baja California
DESCRIPTION:Authors - Cristian Castillo-Olea\, Clemente Zuniga Gil\, Angelica Huerta Abstract - Question paper preparation in educational institutions is conventionally manual and time-consuming\, often generating question papers of uneven difficulty and less diversity. This project solves the problem of automatic question paper generation from voluminous academic content available in multiple formats. The motivation for this work is reducing human effort and enhancing efficiency\, ensuring fair and balanced assessment generation\, while supporting modern digital learning environments. Input content\, in the form of text documents\, portable document files\, presentation slides\, images\, audio recordings\, and video lectures\, forms the bedrock of the proposed system\; first\, it gets preprocessed into a unified textual format through document parsing\, optical character recognition\, and speech-to-text techniques. Natural language processing approaches like sentence segmentation\, tokenization\, stop word removal\, and extraction of key concepts are subsequently applied on the meaningful and relevant identification of the contents. It follows a hybrid approach relying on the Transformer architecture: a classification model that assesses the importance of a sentence\, relevance of concepts\, and difficulty level\; and a generation model providing question types such as multiple choice\, short answer\, long answer\, case studies\, reasoning\, fill-in-blanks\, and programming. The proposed model goes through training and fine-tuning using publicly available datasets of question-answer pairs and pre- processed information in textbooks. In the experimental results\, the proof of efficiency by the proposed approach is shown in generating accurate and diverse question papers with high relevance. Such an approach would definitely ensure much better outcomes for the question papers and the assessment.
CATEGORIES:VIRTUAL ROOM 9F
LOCATION:Virtual Room F\, Bangkok\, Thailand
SEQUENCE:0
UID:fbe68b52da5288b2122681a7606f8cb6
URL:http://11thictisthailand.sched.com/event/fbe68b52da5288b2122681a7606f8cb6
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260417T125213Z
DTSTART:20260410T080000Z
DTEND:20260410T100000Z
SUMMARY:MF-HSINet: Adaptive Spectral–Spatial Fusion via Selective State-Space Modeling for Hyperspectral Image Analysis
DESCRIPTION:Authors - Y. C. A. Padmanabha Reddy\, Panigrahi Srikanth\, Kavita Goura Abstract - Advances in Artificial Intelligence\, Machine Learning and Internet of Things technologies have enabled wearable devices to sense as well as process and respond to human behaviour in real time. While most wearable devices today are used for health and fitness tracking. Many people face communication challenges such as language barriers\, difficulty understanding emotions or social cues\, social anxiety and accessibility issues for individuals with hearing or speech impairments. Existing systems often collect data but fail to provide meaningful\, real-time assistance during actual human interactions. This research paper presents a literature-based study on AI powered wearable devices designed to support and enhance human communication. The research papers are focusing on intelligent wearables that use multimodal sensors such as microphones\, cameras and sensors. These systems apply AI techniques to interpret speech\, gestures\, facial expressions and emotional signals in real time. The wearable devices considered include everyday consumer-oriented systems such as smart eyewear that provides audio visual assistance and wrist worn wearables that offer haptic feedback. The key focus of this study is to examine how such devices can deliver subtle\, real-time support through visual prompts\, audio cues or vibrations to improve conversational awareness and user confidence. The expected outcome is to identify current capabilities\, practical limitations and design considerations for developing human centric wearable technologies that move beyond passive tracking toward meaningful communication support.
CATEGORIES:VIRTUAL ROOM 9F
LOCATION:Virtual Room F\, Bangkok\, Thailand
SEQUENCE:0
UID:2d04f895e6457eb2f3756121ea535fc7
URL:http://11thictisthailand.sched.com/event/2d04f895e6457eb2f3756121ea535fc7
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260417T125213Z
DTSTART:20260410T080000Z
DTEND:20260410T100000Z
SUMMARY:Robust and Interpretable Credit Card Fraud Detection: A Systematic Evaluation of Machine Learning Models under Severe Class Imbalance
DESCRIPTION:Authors - Anisha Panja\, Ranjita Kumari Dash\, Biswajit Sahoo Abstract - Singer identification is a challenging task because of pitch and me lodic variations\, tempo\, vibrato\, and adaptive singing styles. This paper propos es a novel approach towards singer identification and classification by adapting a model originally meant for speaker recognition. Specifically\, this work utiliz es vector representations extracted from a pretrained Speech Brain Emphasized Channel Attention\, Propagation and Aggregation in Time Delay Neural Net work (ECAPA-TDNN) model. The research pipeline processes a custom curated dataset of four prominent Indian playback singers into fixed\, 8 second audio clips\, with mono channel sampled at 16 kHz and exported as wav files. The Speech Brain Emphasized Channel Attention\, Propagation and Aggrega tion (ECAPA) encoder transforms these labelled clips into fixed embeddings which are unique vector representations of voice characteristics of each audio clips. A suite of classical machine learning classifiers is trained on these em beddings. The study evaluates four of them namely\, Logistic Regression\, Sup port Vector Machines\, Random Forests\, and a Multi-Layer Perceptron (MLP). The MLP achieved the highest accuracy of 99.38% on held-out test data. Sup porting this result\, both confusion matrix analysis and t-SNE projection clearly demonstrate clear cluster separation based on individual singer identities. These findings thus collectively validate that ECAPA embeddings contain sufficient identity-bearing structure on a singing voice. This analysis thus concludes that adaptation of speaker recognition models with appropriate classifiers is a great ly effective and efficient approach for singer identification.
CATEGORIES:VIRTUAL ROOM 9F
LOCATION:Virtual Room F\, Bangkok\, Thailand
SEQUENCE:0
UID:f273f93d50ad18ce9fcdc0f6de97c5ed
URL:http://11thictisthailand.sched.com/event/f273f93d50ad18ce9fcdc0f6de97c5ed
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260417T125213Z
DTSTART:20260410T080000Z
DTEND:20260410T100000Z
SUMMARY:A Hierarchical Machine Learning Framework for Drug Supply Chain Management in Healthcare
DESCRIPTION:Authors - Abir Paul\, Priti Giri\, Rajdeep Ghatak\, Soumitra Sasmal\, Mauparna Nandan\, Partho Mallick\n Abstract -&nbsp\;Accurate forecasting of drug demand is one of the challenging areas in the healthcare service to reduce waste as well as shortages. Some recent studies focused only on predicting drug use demand for regions and hospitals\, missing an overall way to combine these forecasts.&nbsp\;In this study\, a multilevel machine learning framework is presented that merges regional tender demand predictions with monthly and seasonal order forecasting in hospitals and pharmacies. With historical drug usage\, the system captures time-based changes\, seasonal demands\, and also location specific behaviors . Models for regional tenders predict yearly procurement\, but models at hospitals and pharmacies try to tell the need of each month\, allowing better resource distribution.The rigorous experimental process showed better estimates and forecasting with less error than just making a single-level prediction. This framework helps to make better purchasing decisions and ensures a stable drug supply across healthcare systems. Health departments\, hospital chains\, and pharmacy groups can benefit from using a model .
CATEGORIES:VIRTUAL ROOM 9G
LOCATION:Virtual Room G\, Bangkok\, Thailand
SEQUENCE:0
UID:462e90b786cc4deceec8e2c6d2632b09
URL:http://11thictisthailand.sched.com/event/462e90b786cc4deceec8e2c6d2632b09
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260417T125213Z
DTSTART:20260410T080000Z
DTEND:20260410T100000Z
SUMMARY:Adaptive Per-Node Federated Deep Q-Learning for Anti-Jamming Spectrum Coordination in Tactical Electronic Warfare Networks
DESCRIPTION:Authors - Gagandeep Malhotra\, Dharm Singh Jat\n Abstract - Modern Electronic Warfare (EW) environments are very dynamic\, crowded\, and hostile\, which makes static or centralised spectrum-allocation strategies useless. To tackle these issues\, this paper introduces a completely adaptable Federated Deep Q-Network (A-FDQN) framework for each node\, which is built onto a high-fidelity ns-3.40 EW simulation environment. In this simulation each tactical radio has been configured to work as an independent federated client which trains a local DQN within itself based on metrics obtained from SINR\, congestion\, delay\, jitter\, and interference caused by jamming. A federated server then periodically collects client models using Federated Averaging (FedAvg) or Median method. This lets global learning happen without needing centralised state visibility or constant connectivity\, which is very important for networks on contested battlefields. Our framework is different from earlier RL and FL studies because it combines per-node reinforcement learning\, periodic federated aggregation\, partial client participation\, tuneable synchronisation frequency\, and realistic ns-3 modelling of mobility\, sweep jamming\, bursty traffic\, congestion hotspots\, and Wi-Fi PHY/MAC interactions. Our A-FDQN system dynamically changes the channel assignments at each node\, which greatly improves the packet delivery ratio\, minimum SINR\, fairness\, and delay when faced with challenging EW scenarios. This first of its kind end-to-end FRL architecture offers a decentralised\, resilient\, and operationally relevant way to manage the spectrum for next-generation military communications.
CATEGORIES:VIRTUAL ROOM 9G
LOCATION:Virtual Room G\, Bangkok\, Thailand
SEQUENCE:0
UID:d89b15975fb64e8d1de880c08209a8b7
URL:http://11thictisthailand.sched.com/event/d89b15975fb64e8d1de880c08209a8b7
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260417T125213Z
DTSTART:20260410T080000Z
DTEND:20260410T100000Z
SUMMARY:An IoT-Enabled System for Predictive Analysis of Cardiovascular Disease
DESCRIPTION:Authors - Harsh Vardhan\, Harsh Vikramaditya\, Doyelshree Bhui\, Shilpi Basak\, Soumitra Sasmal\, Subhajit Bhowmick\, Ishan Ghosh Abstract - Security audits present a unique and ever evolving challenge due to the dynamic nature of cyberthreats and complex regulations. Traditional compliance audits remain largely manual and labor inten sive\, resulting in vast inconsistencies. This paper introduces a solution to make compliance audits easier and faster by proposing a framework that leverages the use of Natural Language Processing and Large Lan guage Models to map organizational policies to frameworks and allows for real-time data from security controls to be validated against these complex security frameworks. Through a hybrid multi-model architec ture\, the solutions in this paper aim to enhance the accuracy and trans parency of compliance evaluations coupled with evidence-backed insights. The results demonstrate the potential of integrating intelligent auditing systems to deliver compliance assessments that are consistent\, accurate\, and rapid\; streamlining governance and improving cyber security posture management.
CATEGORIES:VIRTUAL ROOM 9G
LOCATION:Virtual Room G\, Bangkok\, Thailand
SEQUENCE:0
UID:7ae4f71b199008980e2b656db5630346
URL:http://11thictisthailand.sched.com/event/7ae4f71b199008980e2b656db5630346
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260417T125213Z
DTSTART:20260410T080000Z
DTEND:20260410T100000Z
SUMMARY:Digital Transformation in Healthcare Workforce Management: Implications for Retention in Allied Healthcare Services.
DESCRIPTION:Authors - Anjali Yawatkar\, Hemlata Gaikwad Abstract - Contemporary customer support systems require processing a massive number of user queries with low latency and high semantic relevance. Rule-based systems fail to capture context\, while fully LLM-based systems are computation ally expensive and suffer from high latency. This paper introduces an adaptive AI-assisted customer support automation system using an optimized Retrieval Augmented Generation (RAG) model. The proposed system combines Azure OpenAI embeddings\, FAISS-based vector search\, selective Cross-Encoder re ranking\, and a Learning-to-Rank (LambdaMART) model for adaptive score fu sion. Unlike vanilla RAG models\, the proposed system adaptively re-ranks only the top-k retrieved candidates\, trading off ranking precision and latency. Experi ments were carried out on a 1\,30\,000-sample e-commerce customer support da taset with query-response pairs annotated with intent labels. Compared to rule based retrieval\, embedding+FAISS\, and vanilla RAG models\, the proposed hy brid system showed improved top-1 retrieval precision with a concurrent reduc tion in end-to-end latency from 0.414s to 0.365s (≈11.8% relative improvement). The LambdaMART model adaptively learned weights from FAISS and Cross Encoder scores\, improving ranking robustness and eliminating misranked top re sponses. The system was implemented on Azure Machine Learning with a cloud scale pipeline and interactive Streamlit web interface\, showcasing the cost-effec tive inference capabilities of the proposed system via selective re-ranking.
CATEGORIES:VIRTUAL ROOM 9G
LOCATION:Virtual Room G\, Bangkok\, Thailand
SEQUENCE:0
UID:dfe6766cfc15daca329b2bc9625fcf34
URL:http://11thictisthailand.sched.com/event/dfe6766cfc15daca329b2bc9625fcf34
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260417T125213Z
DTSTART:20260410T080000Z
DTEND:20260410T100000Z
SUMMARY:Evaluating Prompt Design Strategies for Large Language Model Based Code Summarization
DESCRIPTION:Authors - Jaykumar Gandharva\, Hardika Menghani\, Tilak Brahmbhatt\, Nischay Agrawal Abstract - Modern Electronic Warfare (EW) environments are very dynamic\, crowded\, and hostile\, which makes static or centralised spectrum-allocation strategies useless. To tackle these issues\, this paper introduces a completely adaptable Federated Deep Q-Network (A-FDQN) framework for each node\, which is built onto a high-fidelity ns-3.40 EW simulation environment. In this simulation each tactical radio has been configured to work as an independent federated client which trains a local DQN within itself based on metrics obtained from SINR\, congestion\, delay\, jitter\, and interference caused by jamming. A federated server then periodically collects client models using Federated Averaging (FedAvg) or Median method. This lets global learning happen without needing centralised state visibility or constant connectivity\, which is very important for networks on contested battlefields. Our framework is different from earlier RL and FL studies because it combines per-node reinforcement learning\, periodic federated aggregation\, partial client participation\, tuneable synchronisation frequency\, and realistic ns-3 modelling of mobility\, sweep jamming\, bursty traffic\, congestion hotspots\, and Wi-Fi PHY/MAC interactions. Our A-FDQN system dynamically changes the channel assignments at each node\, which greatly improves the packet delivery ratio\, minimum SINR\, fairness\, and delay when faced with challenging EW scenarios. This first of its kind end-to-end FRL architecture offers a decentralised\, resilient\, and operationally relevant way to manage the spectrum for next-generation military communications.
CATEGORIES:VIRTUAL ROOM 9G
LOCATION:Virtual Room G\, Bangkok\, Thailand
SEQUENCE:0
UID:192d39b7edf778b180d655d7f8c0f1a0
URL:http://11thictisthailand.sched.com/event/192d39b7edf778b180d655d7f8c0f1a0
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260417T125213Z
DTSTART:20260410T080000Z
DTEND:20260410T100000Z
SUMMARY:GAHS for E-Commerce: A Generalized Authority-Hub Score for Evaluating Product Search Query Expansion on Unseen Queries
DESCRIPTION:Authors - Sachin Kumar\n Abstract - E-commerce search engines rely on Query Expansion (QE) to bridge the semantic gap between user queries and product catalogs\, but expansion can induce query drift\, where retrieved results diverge from the user’s original intent. Evaluating QE on novel or out-of-distribution queries is fundamentally intractable under the standard Cranfield paradigm\, which requires pre-compiled relevance judgments. This paper introduces the Generalized Authority-Hub Score (GAHS)\, an unsupervised evaluation metric that repurposes the product catalog’s relational structure— modeled as a product graph—as a dynamic proxy for retrieval quality. Drawing on the HITS algorithm\, GAHS quantifies the topical coherence of a retrieved product set without requiring explicit relevance judgments. Using the Amazon ESCI dataset\, we validate GAHS against MAP and nDCG@10 on a held-out seen query set\, demonstrating strong rank-order agreement (Kendall’s τ = 1.0 with MAP\, τ = 0.67 with nDCG@10). We further demonstrate its discriminative power on a disjoint unseen query set\, and discuss an observed performance reversal between the two query sets and its implications for QE evaluation methodology.
CATEGORIES:VIRTUAL ROOM 9G
LOCATION:Virtual Room G\, Bangkok\, Thailand
SEQUENCE:0
UID:400e4ba4a87d197bfe263dc4c20ab948
URL:http://11thictisthailand.sched.com/event/400e4ba4a87d197bfe263dc4c20ab948
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260417T125213Z
DTSTART:20260410T080000Z
DTEND:20260410T100000Z
SUMMARY:InnovateHub: A Secure and Scalable Portal for Monitoring Research and Innovation Excellence in Educational Institutions
DESCRIPTION:Authors - K Devi Priya\, P Saranya Durga\, Y Sony\, D Varun Sai Abstract - This paper presents a comprehensive implementation and evaluation of a secure electronic voting system built on the Ethereum blockchain platform. Proposing on Ethereum smart contracts\, Proof of Stake consensus\, and modern Web3 technologies and implemented the project. The implementation deals with key e-voting issues like voter authentication\, ballot privacy\, vote immutability and transparent auditability.We examine security threats\, offer Layer2 scaling design\, introduce concepts of zero-knowledge proofs in order to achieve higher privacy levels\, and measure the economic benefit of deployment on different scales. In our results\, we have shown that Ethereum has a significant basis to support decentralized voting systems\, but scalability and cost reduction remain an important challenge to large-scale elections. The paper ends with a set of practical recommendations on the deployment of production and the main directions on the further research in the field of blockchain-based democratic systems.
CATEGORIES:VIRTUAL ROOM 9G
LOCATION:Virtual Room G\, Bangkok\, Thailand
SEQUENCE:0
UID:3d1e4403693dc8ae790d83d6f8a3ffa6
URL:http://11thictisthailand.sched.com/event/3d1e4403693dc8ae790d83d6f8a3ffa6
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260417T125213Z
DTSTART:20260410T080000Z
DTEND:20260410T100000Z
SUMMARY:Machine Learning-Based Emotion Recognition Using Passive Smartphone Sensors for Music Recommendation
DESCRIPTION:Authors - Manav Thakar\, Nischay Agrawal\, Jaykumar Gandharva\, Manish Singh Abstract - Predicting and understanding the inhibitory activity associated with Breast Cancer resistance protein can assist in the drug discovery process by anticipating the potential drug resistance and drug-drug interactions. Prediction of BCRP inhibitors using machine learning can accelerate the identification of BCRP inhibitors by analyzing large datasets\, finding patterns in molecular structures\, and predicting interactions that would be time-consuming and expensive through traditional methods like high-throughput screening or trial-and-error experimentation. In the literature\, machine learning has been employed to develop techniques for predicting BCRP inhibition. However\, these methods often exhibit low prediction accuracy\, highlighting the need for improved prediction techniques with enhanced accuracy. In this research\, BCRP inhibition prediction has been carried out using features spaces fusion to enhance the features information with richer representation of data incorporating complementary aspects of molecule to get the increased accuracy for discovery of inhibitors for drugs of breast cancer. The experimental results show that the proposed technique has increased accuracy and precision for the discovery of BCRP inhibitors. The accuracy of the proposed technique is 97% which is higher than the techniques developed in literature. The study demonstrates that enhancing the features information by combining various compound properties creates a more richer and comprehensive feature space. This enhanced feature representation can significantly help in identifying BCRP inhibitors specifically and contribute to advancements in drug discovery overall.
CATEGORIES:VIRTUAL ROOM 9G
LOCATION:Virtual Room G\, Bangkok\, Thailand
SEQUENCE:0
UID:d86151b7931e68df21f550d6f2ae4c05
URL:http://11thictisthailand.sched.com/event/d86151b7931e68df21f550d6f2ae4c05
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260417T125213Z
DTSTART:20260410T080000Z
DTEND:20260410T100000Z
SUMMARY:Preventing Privilege Escalation in Linux Using a Kernel-Level Credential Monitoring Module
DESCRIPTION:Authors - Shaik Sohail Ahammed\, B. Rohan Teja\, R. Naga Sumithra\, D. Manasa\, T. N. V. D. Sai Krishna\n Abstract - Privilege Escalation is a major issue for securing Linux sys tems. When a user gains unauthorized root access he has the ability to access all system resources and manipulate them at will. In the past\, Linux has used Static Access Control Policies and User Space Monitoring Tools to secure system access. However\, these methods provide little in sight into how the kernel is modifying users credentials when permissions are changed. In this paper we propose a Kernel-Level solution to detect and prevent unauthorized privilege escalations. This detection/ preven tion occurs in real time via a Credential Transition Monitoring Mecha nism within the kernel layer\, which prevents the elevation of privileges by illegal means. To create the functionality necessary for the above\, a Linux Kernel Module (LKM) was created which utilizes kprobes to in tercept calls to the commit creds() function\, which is used to update a processes credentials in the kernel. To evaluate if the privilege escalation being requested is legitimate or malicious\, the LKM contains a Policy Based Evaluation Mechanism which evaluates each request to modify a process’s credentials. We tested our proposed solution using a con trolled test environment composed of a Virtual Machine (VM) running the Ubuntu Operating System. We ran two types of tests\, first were Le gitimate Administrative Operations utilizing the ”sudo” utility\, second were Simulated Privilege Escalation Attacks based upon SetUID Vul nerabilities. Our results show that the system effectively detected and blocked malicious privilege escalations\, while providing minimal over head to normal system operation.
CATEGORIES:VIRTUAL ROOM 9G
LOCATION:Virtual Room G\, Bangkok\, Thailand
SEQUENCE:0
UID:5b02cf6b4a5682cc8c5238dc563225bb
URL:http://11thictisthailand.sched.com/event/5b02cf6b4a5682cc8c5238dc563225bb
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260417T125213Z
DTSTART:20260410T080000Z
DTEND:20260410T100000Z
SUMMARY:TRAGEDY: TRAjectory-Guided Emotional Dialogue System
DESCRIPTION:Authors - Menna Elgabry\, Ali Hamdi Abstract - Mortality prediction for intensive care unit (ICU) patients with alcohol-related disorders remains insufficiently explored despite the distinct clinical characteristics and elevated risk profile of this population. Unlike general ICU cohorts\, these patients often present with impaired physiological function\, frequent complications\, and poorer overall outcomes. However\, few research works have taken this patient group into account for mortality prediction. This study addresses the gap by developing mortality prediction models specifically for ICU patients with alcohol-related disorders using multimodal electronic health record data. To capture the complex clinical status of patients\, we integrate six major data modalities in the first 24 hours after admission\, including demographics\, diagnoses\, medications\, procedures\, laboratory results/vital signs\, and patient outputs. A refined preprocessing pipeline was used to harmonize and process heterogeneous input data. In addition\, severe class imbalance is another challenging issue in resolving this mortality predict task. Therefore\, our work examines systematically several rebalancing strategies: no resampling\, oversampling\, undersampling\, and SMOTENC. Evaluated on both MIMIC-III and MIMIC-IV databases\, our proposed rebalanced multimodal data approach is effective for tackling the task. Indeed\, the experimental results show that CatBoost with random undersampling provides the most consistent and balanced effectiveness. Furthermore\, multimodal analysis demonstrates that combining diagnoses\, laboratory results/vital signs\, and medications substantially improves prediction\, while integrating all modalities achieves the best overall performance.
CATEGORIES:VIRTUAL ROOM 9G
LOCATION:Virtual Room G\, Bangkok\, Thailand
SEQUENCE:0
UID:fe7dcde6ff9b62f791ad0855e1caf82c
URL:http://11thictisthailand.sched.com/event/fe7dcde6ff9b62f791ad0855e1caf82c
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260417T125213Z
DTSTART:20260410T081500Z
DTEND:20260410T083000Z
SUMMARY:Environmental Cost of Intelligence - A Literature Survey for determining AI Eco Rating
DESCRIPTION:Authors - Sourabh Chordiya\, Subhrakanta Panda\, Akanksha Rathore Abstract - The rapid advancement of Artificial Intelligence (AI) and Large Language Models (LLMs) has unlocked powerful new capabilities for solving complex\, multi-step problems. However\, this progress has intensified concerns about the environmental sustainability of AI systems. While prior research has examined carbon emissions associated with training and inference in conventional LLM pipelines\, emerging paradigms such as Agentic AI\, where autonomous agents coordinate to execute multi-stage tasks\, and Retrieval-Augmented Generation (RAG) introduce additional layers of computation that remain insufficiently studied from an emissions perspective. In particular\, existing carbon measurement frameworks do not adequately capture the dynamic\, distributed\, and memory-intensive operations characteristic of these systems. This paper analyzes the limitations of current carbon accounting tools and available literature when applied to Agentic AI and RAG-based architectures. The widely used measurement frameworks capture only a fraction of the total computational footprint in such systems\, largely omitting emissions arising from memory access patterns\, retrieval processes\, and inter-agent communication. These overlooked components become increasingly significant as AI workflows shift from single-system inference toward multi-agent orchestration and knowledge retrieval pipelines. Based on this analysis\, the paper proposes directions for a comprehensive life-cycle carbon assessment framework and an Eco Rating tailored to next-generation AI systems. Such a framework must account for heterogeneous hardware usage\, dynamic inference paths\, retrieval infrastructure\, and communication overhead across distributed agents. The findings highlight a substantial blind spot in current sustainability evaluations and underscore the urgent need for standardized methodologies that reflect the true environmental impact of emerging AI paradigms.
CATEGORIES:PHYSICAL TECHNICAL SESSION 2A
LOCATION:Benchasiri 1\, Bangkok Marriott Hotel Sukhumvit\, Thailand
SEQUENCE:0
UID:b0810cf88b97609ac000350804ac37b7
URL:http://11thictisthailand.sched.com/event/b0810cf88b97609ac000350804ac37b7
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260417T125213Z
DTSTART:20260410T081500Z
DTEND:20260410T083000Z
SUMMARY:Empowering Policyholders: An AI-Driven Framework for Transparent and Efficient Healthcare Claims
DESCRIPTION:Authors - Jay Joshi\, Avneesh Jadhav\, Ishita Deshpande\, Ameya Dharap\, D. D. Sapkal\n Abstract - As medical insurance adoption continues to grow and its complexities continue to increase\, insured members require trustworthy and clear guidance\, transparency and timely progress updates throughout the insurance lifecycle. However\, users often run into fragmented information\, confusion in policy selection\, incomprehensible policy documents due to tremendous technical jargon and limited procedural guidance. This makes it difficult to understand coverage details and navigate claims smoothly\; particularly during medical emergencies. The absence of unified communication channels frequently leaves policyholders uncertain about eligibility\, documentation requirements and claim progress\, leading to stress and reduced trust in insurance services. This paper proposes a user-centric\, AI-enabled digital platform designed to improve transparency and communication between insured members and insurance service providers. The system focuses on simplifying policy discovery through personalized policy recommendations and interpretation through NLPbased clause summarization. These features enable users to gain a clear understanding of inclusions and exclusions\, which help them to make informed decisions. Additionally\, to support users during claims\, the RAG-based assistance module provides step-by-step guidance on eligibility\, document submission and claim procedures. By emphasizing clarity\, continuous guidance and transparency\, the proposed solution enhances user experience\, reduces claim-related anxiety and encourages trust and adoption of digital healthcare insurance services.
CATEGORIES:PHYSICAL TECHNICAL SESSION 2B
LOCATION:Benchasiri 2\, Bangkok Marriott Hotel Sukhumvit\, Thailand
SEQUENCE:0
UID:12dd8f3a00e6e6989f9557436535f0a1
URL:http://11thictisthailand.sched.com/event/12dd8f3a00e6e6989f9557436535f0a1
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260417T125213Z
DTSTART:20260410T081500Z
DTEND:20260410T083000Z
SUMMARY:Artificial Intelligence in the Automobile Industry: Autonomous and Assisted Driving Systems
DESCRIPTION:Authors - Atharva Patil\, Dibyanshu Singh\, Tanish Dadarkar\, Suman Madan\n Abstract - The use of artificial intelligence in the automotive system presents legal\, ethical\, and societal issues such as accountability\, safety\, human trust\, and data privacy. In the case of system failure\, explainable behaviour\, necessitating the complexity and opacity of AI-driven decision-making. Bias in the training dataset may cause unequal system performance in different traffic environments and road uses\, thus the need for representative data and validation. The vast amount of vehicle and data collected raises privacy issues\, thus the need for secure data handling and anonymization. Ethical system design should therefore consider fairness\, safety\, and accountability as primary engineering constraints for responsible AI-enabled vehicle deployment. They deliver safe\, more efficient and sustainable vehicles and services. Not only are the vehicles themselves being modernized through the technology\, but manufacturing processes and supply chain management on the backend are also changing.
CATEGORIES:PHYSICAL TECHNICAL SESSION 2C
LOCATION:Benchasiri 3\, Bangkok Marriott Hotel Sukhumvit\, Thailand
SEQUENCE:0
UID:b2e5097036e2c40a343e09eb1724293e
URL:http://11thictisthailand.sched.com/event/b2e5097036e2c40a343e09eb1724293e
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260417T125213Z
DTSTART:20260410T081500Z
DTEND:20260410T083000Z
SUMMARY:Multifunctional Superhydrophobic BNNS/PVA Nanocomposite Films on PMMA for UV Shielding\, Atmospheric Energy Harvesting\, and Self-Powered Smart City Surfaces
DESCRIPTION:Authors - Gunchita Kaur Wadhwa\, Rugved Dinesh Kshirsagar\n Abstract - Increasing infrastructure structures are being exposed to outdoor environmental factors such as UV\, water\, humidity\, temperature fluctuations and air pollutants. At the same time\, increasing trend of smart cities is highly dependent on successful implementation of wireless sensor networks to be able to measure e.g. the intensity of UV\, air quality\, temperature and humidity. Therefore\, this research focusses on developing multifunctional nanocomposite coating composed of BNNS dispersed in PVA deposited on PMMA transparent panels that provides an efficient solution to many challenges related to smart structure infrastructure. This research demonstrates a coating material that\, after optimizing its structural properties\, behaves as following in one step solution: (i) effective UV shield using boron nitride nanosheets as filler\, (ii) exhibiting superhydrophobic self-cleaning properties for water and chemicals after structure modification and chemical surface treatment\, (iii) acting as an atmospheric energy harvester by using the tribocatric effects between the coating and raindrops for charge extraction\, and (iv) behaving as micro-scale energy storage due to dielectric characteristics of BNNS within the coating\, which could be potential to power Internet of Things (IoT) low power consumption sensor nodes. The multifunctional coating therefore represents a new class of self-powered smart-city surfaces capable of protecting infrastructure materials while simultaneously harvesting and storing environmental energy. The proposed approach contributes to sustainable urban development and aligns with Sustainable Development Goals related to clean energy and resilient cities.
CATEGORIES:PHYSICAL TECHNICAL SESSION 2D
LOCATION:Benchasiri 4\, Bangkok Marriott Hotel Sukhumvit\, Thailand
SEQUENCE:0
UID:34bd1b8d66733692f7a1b8f3dc5b70b3
URL:http://11thictisthailand.sched.com/event/34bd1b8d66733692f7a1b8f3dc5b70b3
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260417T125213Z
DTSTART:20260410T083000Z
DTEND:20260410T084500Z
SUMMARY:Difference-Guided Bilateral U-Net for Breast Tumor Segmentation in Mammograms
DESCRIPTION:Authors - Kaja Mohideen A\, Senthil Prakash PN Abstract - Breast tumor segmentation using mammographic is a difficult task because mammographic images have low contrasts\, complex tissue structures\, and high inter patient variability. Radiologists commonly make left-right-breast comparisons to detect suspicious inconsistencies in the image of the left and right breast in the routine clinical practice. It is based on this bilateral diagnostic strategy that this paper suggests a difference-guided bilateral U-Net to inter pretable breast tumor segmentation. Paired left and right mammogram of the same patient are first adjusted by the horizontal flipping and intensity normali zation. A pixel-based difference image is then created to highlight disparities that are absolutely in nature to highlight areas that are asymmetric and which might reflect pathological alterations. To make the network learn both appear ance-based and asymmetry-driven representations\, the bilateral mammograms are proposed to be jointly processed with the respective difference map\, after which the network will be trained. This design enhances the performance of segmentation without compromising clinical interpretability because it explicit ly points out areas of interest. The suggested method is tested on publicly ac cessible data\, such as MIAS and CBIS-DDSM and real-time mammographic images obtained in a clinical setting. The experimental data indicate that differ ence-guided framework provides higher segmentation accuracy and lower false positive rates than single-breast U-Net models\, which implies that the frame work can be used to delineate breast tumors on automated mammography.
CATEGORIES:PHYSICAL TECHNICAL SESSION 2A
LOCATION:Benchasiri 1\, Bangkok Marriott Hotel Sukhumvit\, Thailand
SEQUENCE:0
UID:93bf9fdc043993d7272fcb68d897d900
URL:http://11thictisthailand.sched.com/event/93bf9fdc043993d7272fcb68d897d900
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260417T125213Z
DTSTART:20260410T083000Z
DTEND:20260410T084500Z
SUMMARY:Use of Artificial Intelligence in Cybersecurity Threat Detection: A Critical Review
DESCRIPTION:Authors - U.H.S. Rashmina Amarasinghe\, K.A Dilini T. Kulawansa Abstract - This literature review examines the expanding and critical role of Artificial Intelligence\, including Machine Learning and Deep Learning\, in countering increasingly complex cyber threats. The purpose of this review is to analyze the applications\, effectiveness\, challenges\, and future research directions of Artificial Intelligence driven technologies in threat detection. Artificial Intelligence driven systems significantly enhance the NIST Cybersecurity Framework functions (Identify\, Protect\, Detect\, Respond\, Recover). They excel at real time anomaly detection in massive datasets\, outperforming traditional signature-based methods against modern attacks like zero-day exploits and polymorphic mal-ware. Key techniques discussed include Support Vector Machines\, Decision Trees\, and various Neural Networks used in effective Intrusion Detection Systems and phishing classification. However\, the review highlights the dual nature of Artificial Intelligence\, noting the rise of Artificial Intelligence driven cyberattacks and the challenges posed by high resource demands and managing data quality. Ethical considerations\, specifically concerning privacy and transparency\, necessitate the development of Explainable Artificial Intelligence. Ultimately\, the future relies on Hybrid Augmented Intelligence\, a strong human\, Artificial Intelligence collaboration to maintain effective cyber defenses.
CATEGORIES:PHYSICAL TECHNICAL SESSION 2B
LOCATION:Benchasiri 2\, Bangkok Marriott Hotel Sukhumvit\, Thailand
SEQUENCE:0
UID:03bf7c251c8a9ed727da00ba904d946d
URL:http://11thictisthailand.sched.com/event/03bf7c251c8a9ed727da00ba904d946d
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260417T125213Z
DTSTART:20260410T083000Z
DTEND:20260410T084500Z
SUMMARY:Cryptanalysis of two Code-Based Blind Signatures
DESCRIPTION:Authors - Sapna Jyoti Patel\, Sumit Kumar Debnath\n Abstract - This paper analyses the blindness property of two code-based blind signature schemes: one by Chen et al. [17] and the other one by Ren et al. [19]. Both [17] and [19] claimed that their protocols provide blindness under brute force attacks. Through detailed analysis\, this paper demonstrates that the aforementioned code-based blind signature schemes (CBBSS)\, in practice\, do not satisfy the property of blindness. Moreover\, we use a zero-knowledge proof of knowledge (ZKPK) in [17] and [19] in order to achieve the blindness property.
CATEGORIES:PHYSICAL TECHNICAL SESSION 2C
LOCATION:Benchasiri 3\, Bangkok Marriott Hotel Sukhumvit\, Thailand
SEQUENCE:0
UID:1f073c810842a10a76a51ed388659123
URL:http://11thictisthailand.sched.com/event/1f073c810842a10a76a51ed388659123
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260417T125213Z
DTSTART:20260410T083000Z
DTEND:20260410T084500Z
SUMMARY:Adaptive Schrodinger Optimizer Enabled Deep Convolutional Generative Adversarial Network for Augmentation of Synthetic Kidney CT Image
DESCRIPTION:Authors - Arathi Kumaresan Chandirakala\, Sunantha Sodsee Abstract - Synthetic kidney image augmentation plays critical role in improvising quantity and diversity of health imaging data. But anatomic generation of visually realistic synthetic images remains as a major challenge\, often resulting in poorer texture quality\, mode collapse\, and loss of structural details. Existing approaches frequently struggle to preserve consistency in texture\, shape\, and intensity alterations\, limiting their effectiveness in clinical applications. To tackle these limitations\, the Adaptive Schrodinger Optimizer enabled Deep Convolutional Generative Adversarial Network (ASRA_DC-GAN) is proposed for augmenting synthetic kidney image. Initially\, input kidney Computed Tomography (CT) image is categorized as majority and minority class. Further\, image enhancing separation among elements is performed for both classes by Histogram Equalization. Further\, augmentation of synthetic kidney image is done through DC-GAN in case of minority classes. Herein\, DC-GAN is tuned by ASRA\, which is formed by combination of Adaptive concept and Schrodinger Optimizer (SRA). Finally\, the attained outputs are allowed for generation of augmented new balanced dataset. Performance of proposed ASRA_DC-GAN is assessed by Second-Derivative like entropy and Measure of Enhancement (SDME)\, which gained outstanding values of 0.839 and 46.90dB.
CATEGORIES:PHYSICAL TECHNICAL SESSION 2D
LOCATION:Benchasiri 4\, Bangkok Marriott Hotel Sukhumvit\, Thailand
SEQUENCE:0
UID:c61097c5138d8524077e2b4fcff6eda7
URL:http://11thictisthailand.sched.com/event/c61097c5138d8524077e2b4fcff6eda7
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260417T125213Z
DTSTART:20260410T084500Z
DTEND:20260410T090000Z
SUMMARY:Microwave Signals and Quantum Artificial Neural Network for Classification of Hand Activities
DESCRIPTION:Authors - Subham Ghosh\, Banani Basu\, Arnab Nandi Abstract - Radio-frequency based human activity recognition (HAR) using wearable antennas has recently gained interest due to its promise for comfortable and effective monitoring in applications such as smart healthcare and surveillance. However\, traditional deep learning (DL) models for HAR are often constrained due to their reliance on large datasets and poor generalization performance. This paper presents an innovative framework for capturing and recognizing two-hand movements by using the near-field of a wearable antenna. The proposed system innovatively integrates signal smoothing\, Morlet wavelet transform (MWT) time-frequency (TF) transformation\, feature extraction based on statistical significance using the Kruskal-Wallis test\, and a quantum artificial neural network (QANN) for robust feature learning and classification. The performance of the suggested technique is systematically compared against traditional machine learning models. Experimental results demonstrate that the proposed framework achieves superior classification performance for hand activity identification\, underscoring its efficacy and promise for wearable RF-based HAR systems.
CATEGORIES:PHYSICAL TECHNICAL SESSION 2A
LOCATION:Benchasiri 1\, Bangkok Marriott Hotel Sukhumvit\, Thailand
SEQUENCE:0
UID:e9783b90600839a0497fa8f23f90d193
URL:http://11thictisthailand.sched.com/event/e9783b90600839a0497fa8f23f90d193
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260417T125213Z
DTSTART:20260410T084500Z
DTEND:20260410T090000Z
SUMMARY:Use of Federated Learning for Privacy Preserving Healthcare Data Analytics: A Critical Review
DESCRIPTION:Authors - H.M.H.H. Gunarathne\, K.A. Dilini Kulawansa Abstract - Federated Learning enables the collaborative development of AI models in healthcare while preserving patient data confidentiality\, offering a promising solution to privacy\, regulatory\, and data transfer challenges. Unlike conventional centralized learning\, FL transmits only model updates\, including gradients or aggregated parameters\, rather than raw data\, thereby enabling multiple institutions to collaboratively train models while maintaining data confidentiality. This review outlines that FL ensures model accuracy and generalizability of the model in privacy-aware healthcare applications. It also discusses more privacy preservation methods that are implemented in combination with Federated Learning\, including Differential Privacy\, Homomorphic Encryption\, Secure Multi-Party Computation\, and blockchain-based systems\, which help to increase security\, trust\, and transparency. The paper has also reviewed the existing studies in the key areas of healthcare such as disease diagnosis\, medical im-aging\, remote patient monitoring\, predictive analytics and Electronic Health Record management. By demonstrating the potential of FL to enable scalable\, secure\, and privacy-preserving AI systems\, this review provides insights into its transformative role in advancing intelligent\, patient-centered healthcare solutions.
CATEGORIES:PHYSICAL TECHNICAL SESSION 2B
LOCATION:Benchasiri 2\, Bangkok Marriott Hotel Sukhumvit\, Thailand
SEQUENCE:0
UID:4688db897f9af13afcece0b56e880a2d
URL:http://11thictisthailand.sched.com/event/4688db897f9af13afcece0b56e880a2d
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260417T125213Z
DTSTART:20260410T084500Z
DTEND:20260410T090000Z
SUMMARY:Layered Authentication Weakness Analysis and Blockchain-Assisted Mitigation Framework for RFID-Based IoT Anti-Counterfeit Systems
DESCRIPTION:Authors - Haitham Al Habsi\, Norliza Mohamed\, Suriani Mohd Sam\, Hazilah Mad Kaidi\, Norulhusna Ahmad\n Abstract - RFID-enabled IoT systems have transformed supply chain traceability\, yet their authentication mechanisms remain critically exposed. Common threats include tag cloning\, replay attacks\, rogue reader exploitation\, and centralized database breaches. This paper examines authentication weaknesses through a five-layer IoT architectural model\, identifying four root causes: weak encryption\, static identifiers\, absent mutual authentication\, and over-reliance on centralized trust. These weaknesses are mapped across physical\, connectivity\, middleware\, analytics\, and application layers to illustrate how failures propagate systemically rather than in isolation. In response\, a blockchain assisted authentication framework is proposed\, combining lightweight cryptographic primitives\, immutable audit logging\, and smart contract-driven access control to eliminate single points of failure. Comparative analysis confirms that decentralized architectures substantially reduce replay and cloning risks while remaining compatible with existing RFID infrastructure. The findings offer a practical analytical foundation for building resilient\, adaptive authentication in next-generation IoT anti-counterfeit deployments.
CATEGORIES:PHYSICAL TECHNICAL SESSION 2C
LOCATION:Benchasiri 3\, Bangkok Marriott Hotel Sukhumvit\, Thailand
SEQUENCE:0
UID:ce3dc279513590fe0f88f33b30332c4f
URL:http://11thictisthailand.sched.com/event/ce3dc279513590fe0f88f33b30332c4f
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260417T125213Z
DTSTART:20260410T084500Z
DTEND:20260410T090000Z
SUMMARY:Shift-Aware Meta-Reinforcement Learning for Robust Auto-Scaling in Serverless Clouds
DESCRIPTION:Authors - Komendra Sahu\, Aayush Sahu\, Aparajita Vaish\, Kavita Jaiswal Abstract - The AWARE framework (USENIX ATC ’23) applied meta learning so reinforcement learning (RL) agents could adapt more quickly to different workload patterns. However\, this approach still assumes that workloads seen during deployment are similar to those used during train ing. When this assumption breaks\, system performance can decline. In the real world\, workload behavior often changes due to traffic spikes\, configuration updates\, or shifts in resource demand. Under these condi tions\, a fixed meta-policy may no longer reflect the current environment\, leading to unstable scaling decisions. To handle this \, we introduce a Shift-Aware Meta-PPO framework. The system tracks workload behav ior using the KL-divergence to detect changes in distribution. When a shift is detected\, the meta-buffers are cleared and exploration resumes\, allowing the RL agent to adjust its policy to the upcoming new work load. Tests show that this approach stays stable during workload changes and avoids the sharp performance drops seen in standard meta-learning methods under out-of-distribution (OOD) workloads.
CATEGORIES:PHYSICAL TECHNICAL SESSION 2D
LOCATION:Benchasiri 4\, Bangkok Marriott Hotel Sukhumvit\, Thailand
SEQUENCE:0
UID:d7a40a58996a14e713e6bcfb9e5e2103
URL:http://11thictisthailand.sched.com/event/d7a40a58996a14e713e6bcfb9e5e2103
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260417T125213Z
DTSTART:20260410T090000Z
DTEND:20260410T091500Z
SUMMARY:Deep Metric Learning for Morphometric and Meristic Identification of Megalaspis Cordyla Using Siamese Networks
DESCRIPTION:Authors - Mohd Hizami Ab Halim\, Suriani Mohd Sam\, Norliza Mohamed\, Hazilah Mad Kaidi\, Norulhusna Ahmad Abstract - Accurate identification of fish species based on morphometric and meristic characteristics is challenging\, particularly for commercially important species such as Megalaspis Cordyla\, due to subtle morphological differences and limited labelled data. This review examines recent advances in deep metric learning\, with a focus on Siamese network architectures\, for few-shot morphometric and meristic identification of M. Cordyla. We synthesize studies on metric-based similarity learning\, landmark-driven morphometric analysis\, and finegrained fish classification to show how Siamese networks effectively learn discriminative embedding spaces under low-data conditions. The review also analyzes reported performance comparisons across the literature\, including classification accuracy\, precision-recall behavior\, robustness to small training sets\, and generalization to unseen species or populations. Overall\, the findings indicate that Siamese and deep metric learning-based approaches consistently outperform conventional classification models in fine-grained fish identification tasks\, while highlighting open challenges such as the lack of standardized morphometric datasets for Megalaspis Cordyla\, limited meristic-aware benchmarking\, and the need for interpretable similarity measures to support fisheries science and biodiversity conservation.
CATEGORIES:PHYSICAL TECHNICAL SESSION 2A
LOCATION:Benchasiri 1\, Bangkok Marriott Hotel Sukhumvit\, Thailand
SEQUENCE:0
UID:1cc2c4e12147a55dec7fe226f69fc2c6
URL:http://11thictisthailand.sched.com/event/1cc2c4e12147a55dec7fe226f69fc2c6
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260417T125213Z
DTSTART:20260410T090000Z
DTEND:20260410T091500Z
SUMMARY:Use of Robust and Scalable Migration Strategies for Post-Quantum Cryptography in Enterprise Systems and Critical Infrastructure: A Critical Review
DESCRIPTION:Authors - D.M. Jarathne\, K. A. Dilini T. Kulawansa Abstract - The cryptographic systems underlying the digital infrastructure of the world present an existential risk to quantum computing. With wide deployment of cryptographically relevant quantum computers\, many commonly deployed asymmetric encryption algorithms including RSA and elliptic curve cryptography will be subject to attack through quantum algorithms such as the Shor algorithm. The present systematic literature review examines the feasible and scalable migration plans to deploy enterprise systems and critical infrastructure to post-quantum cryptography. The review explores migration frameworks\, implementation issues\, practical implementation\, and organization strategic recommendations based on the analysis of fifteen selected sources\, including research articles and technical standards. The review notes that there are four basic stages of migration which include diagnosis\, planning\, execution\, and maintenance. No-table obstacles are organizational issues\, technological constraints\, system over-load\, and industry-specific demands. Practical examples of successful migrations between web servers\, databases\, blockchain architectures\, and messaging systems have been reported\, and hybrid cryptographic solutions have become the most common transitional practice.
CATEGORIES:PHYSICAL TECHNICAL SESSION 2B
LOCATION:Benchasiri 2\, Bangkok Marriott Hotel Sukhumvit\, Thailand
SEQUENCE:0
UID:ce37f10f17102dcfccebfbda99b6ee57
URL:http://11thictisthailand.sched.com/event/ce37f10f17102dcfccebfbda99b6ee57
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260417T125213Z
DTSTART:20260410T090000Z
DTEND:20260410T091500Z
SUMMARY:Interpretable Machine Learning for Credit Card Churn Prediction: A Comparative Analysis and SHAP-Based Explanation Framework
DESCRIPTION:Authors - Timothy T Adeliyi\, Debajit Saikia\n Abstract - Banks rely heavily on long-term customer relationships to ensure sus-tainability\, profitability\, and competitive advantage. In an increasingly saturated financial services market\, customer churn poses a significant threat to revenue stability. Artificial intelligence (AI) and machine learning (ML) have enhanced predictive capabilities in churn modelling\; however\, the increasing complexity of high-performing models often limits human interpretability and trust. This study investigates how predictive accuracy can be balanced with interpretability in credit card churn modelling through an explainable machine learning frame-work. A quantitative mono-method design was adopted using a publicly available credit card churn dataset comprising approximately 10\,000 customer records. Following exploratory data analysis (EDA)\, multiple classification algorithms were implemented\, including logistic regression\, decision trees\, k-nearest neigh-bours\, support vector machines\, gradient boosting\, and random forests. The ran-dom forest model achieved the highest predictive performance (AUC = 0.940753) and was subsequently selected for interpretability analysis using Shap-ley Additive exPlanations (SHAP). The SHAP-based analysis enabled transpar-ent identification of feature importance and revealed the underlying drivers in-fluencing churn predictions. Graphical explanations were generated to enhance human understanding and support decision-making processes. The findings demonstrate that sustainable deployment of ML systems in banking requires a deliberate integration of predictive performance\, domain knowledge\, human-in-the-loop validation\, and continuous monitoring. This study contributes to the dis-course on trustworthy AI in financial analytics by illustrating how interpretability techniques can strengthen confidence in high-performing churn prediction mod-els without compromising accuracy.
CATEGORIES:PHYSICAL TECHNICAL SESSION 2C
LOCATION:Benchasiri 3\, Bangkok Marriott Hotel Sukhumvit\, Thailand
SEQUENCE:0
UID:d44a8ff1a0ee469b7f297dc2b3db4bb0
URL:http://11thictisthailand.sched.com/event/d44a8ff1a0ee469b7f297dc2b3db4bb0
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260417T125213Z
DTSTART:20260410T090000Z
DTEND:20260410T091500Z
SUMMARY:An Intelligent Mixed Reality Framework for Personalized Fashion Shopping using Avatar-Based Virtual Try-On and Hybrid Recommendation
DESCRIPTION:Authors - Atrey Kantharaj Urs\, Madhan Kumar Srinivasan Abstract - The proposed work presents a Mixed Reality (MR) shopping system designed to address persistent challenges in online fashion retail\, including fit uncertainty\, limited personalization\, and the lack of immersive experiences\, by integrating real-time virtual try-on\, avatar-based visualization\, and an AI-powered recom mendation engine. The system allows users to explore and evaluate garments as interactive three-dimensional models within their physical environment\, thereby improving confidence in style and fit decisions. A hybrid recommendation frame work combines body-feature matching\, content-based and collaborative filtering\, contextual interaction signals\, and foundational fashion design principles to gen erate personalized outfit suggestions\, while an AI assistant delivers explainable recommendations and interactive guidance throughout the shopping journey. By effectively bridging the gap between physical retail and digital platforms through adaptive AI models and MR visualization\, the system offers a practical alter native to conventional online shopping\, demonstrating the potential of Mixed Reality to create a more immersive\, intelligent\, and user-centric fashion shopping experience that enhances decision-making\, increases engagement\, and reduces product returns.
CATEGORIES:PHYSICAL TECHNICAL SESSION 2D
LOCATION:Benchasiri 4\, Bangkok Marriott Hotel Sukhumvit\, Thailand
SEQUENCE:0
UID:8449cd0835a9a744469e05603728d9da
URL:http://11thictisthailand.sched.com/event/8449cd0835a9a744469e05603728d9da
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260417T125213Z
DTSTART:20260410T091500Z
DTEND:20260410T093000Z
SUMMARY:UnderwaterGestureNet: Robust Hand Gesture Detection for Human-Underwater ROV Collaboration
DESCRIPTION:Authors - Aniket Chatterjee\, Anirban Dasgupta\, Parvez Aziz Boruah\, Raktim Acharjee Abstract - Underwater gesture detection is a well-known area of research in recent times that helps in communication between divers and Underwater Remotely Operated Vehicle (ROV). Hand gestures are commonly used in underwater environments as a straightforward and intuitive method for conveying commands or messages between divers and ROV. The ROV need to first detect and identify the human and then detect his/her hand and what type of gesture it is. However\, the underwater environment has many challenges: turbulent waters can disrupt the ROV navigation and obstruct the capture of clear video footage\, resulting in noisy images that complicates the accurate recognition of hand gestures. Besides that\, the ROV must process visual data and respond quickly\, especially in critical situations where quick decision making is required. This project work aims to optimize the ROV application program for improved real-time image processing and gesture recognition\, that helps in effective communication even under challenging underwater conditions. Six different models have been explored including techniques like Channel Attention Mechanism and Spatial Attention. Our developed model(UnderwaterGestureNet) have shown better result with less number of parameters. This lightweight model is more efficient to deploy in embedded system of an ROV.
CATEGORIES:PHYSICAL TECHNICAL SESSION 2A
LOCATION:Benchasiri 1\, Bangkok Marriott Hotel Sukhumvit\, Thailand
SEQUENCE:0
UID:480943553cbc4110e87b5599a040ee25
URL:http://11thictisthailand.sched.com/event/480943553cbc4110e87b5599a040ee25
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260417T125213Z
DTSTART:20260410T091500Z
DTEND:20260410T093000Z
SUMMARY:Systematic Evaluation of Conditional Random Field-as-RNN for Multi-Organ Chest X-Ray Segmentation
DESCRIPTION:Authors - Nailfaaz\, Wahyono Abstract - Accurate segmentation of anatomical structures in chest radiography (CXR) is critical for automated diagnosis. While CNNs achieve high regional overlap\, they struggle with precise organ boundaries due to X-ray projection artifacts. This study systematically evaluates 32 encoder–decoder configurations combining U-Net and DeepLabV3+ with ResNet\, MobileNet\, and EfficientNet families to isolate Conditional Random Field-as-RNN (CRF-as-RNN) refinement impact on boundary quality. Results show U-Net outperforms DeepLabV3+ in preserving anatomical details. Crucially\, a ”capacity threshold” is identified: CRF integration significantly reduces Hausdorff distances for lightweight models but yields diminishing returns for high-capacity backbones where baseline topology is already optimal.
CATEGORIES:PHYSICAL TECHNICAL SESSION 2B
LOCATION:Benchasiri 2\, Bangkok Marriott Hotel Sukhumvit\, Thailand
SEQUENCE:0
UID:636e6d36a3a1b607ada5292d768d7f4f
URL:http://11thictisthailand.sched.com/event/636e6d36a3a1b607ada5292d768d7f4f
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260417T125213Z
DTSTART:20260410T091500Z
DTEND:20260410T093000Z
SUMMARY:Leveraging Large Language Models for Parallel Program Translation: A Comparative Study of FlanT5\, GPT-3.5\, and Gemini-1.0-Pro
DESCRIPTION:\n
CATEGORIES:PHYSICAL TECHNICAL SESSION 2C
LOCATION:Benchasiri 3\, Bangkok Marriott Hotel Sukhumvit\, Thailand
SEQUENCE:0
UID:e9dc718be7b6386c45e14cc6969fd17f
URL:http://11thictisthailand.sched.com/event/e9dc718be7b6386c45e14cc6969fd17f
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260417T125213Z
DTSTART:20260410T100000Z
DTEND:20260410T100200Z
SUMMARY:Session Chair Concluding Remarks
DESCRIPTION:\n
CATEGORIES:VIRTUAL ROOM 9A
LOCATION:Virtual Room A\, Bangkok\, Thailand
SEQUENCE:0
UID:c36bd20bcb244df860a39bd267425dc2
URL:http://11thictisthailand.sched.com/event/c36bd20bcb244df860a39bd267425dc2
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260417T125213Z
DTSTART:20260410T100000Z
DTEND:20260410T100200Z
SUMMARY:Session Chair Concluding Remarks
DESCRIPTION:\n
CATEGORIES:VIRTUAL ROOM 9B
LOCATION:Virtual Room B\, Bangkok\, Thailand
SEQUENCE:0
UID:63ce268b80ce3774f55b6b7c08965e82
URL:http://11thictisthailand.sched.com/event/63ce268b80ce3774f55b6b7c08965e82
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260417T125213Z
DTSTART:20260410T100000Z
DTEND:20260410T100200Z
SUMMARY:Session Chair Concluding Remarks
DESCRIPTION:\n
CATEGORIES:VIRTUAL ROOM 9C
LOCATION:Virtual Room C\, Bangkok\, Thailand
SEQUENCE:0
UID:9e9a4503df409d002409bccb786ce47c
URL:http://11thictisthailand.sched.com/event/9e9a4503df409d002409bccb786ce47c
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260417T125213Z
DTSTART:20260410T100000Z
DTEND:20260410T100200Z
SUMMARY:Session Chair Concluding Remarks
DESCRIPTION:\n
CATEGORIES:VIRTUAL ROOM 9D
LOCATION:Virtual Room D\, Bangkok\, Thailand
SEQUENCE:0
UID:5efc8f71c7d8bb3e592ff1ba6a8a176d
URL:http://11thictisthailand.sched.com/event/5efc8f71c7d8bb3e592ff1ba6a8a176d
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260417T125213Z
DTSTART:20260410T100000Z
DTEND:20260410T100200Z
SUMMARY:Session Chair Concluding Remarks
DESCRIPTION:\n
CATEGORIES:VIRTUAL ROOM 9E
LOCATION:Virtual Room E\, Bangkok\, Thailand
SEQUENCE:0
UID:059b070cba238eb2fa9f8a8ae3d7edb3
URL:http://11thictisthailand.sched.com/event/059b070cba238eb2fa9f8a8ae3d7edb3
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260417T125213Z
DTSTART:20260410T100000Z
DTEND:20260410T100200Z
SUMMARY:Session Chair Concluding Remarks
DESCRIPTION:\n
CATEGORIES:VIRTUAL ROOM 9F
LOCATION:Virtual Room F\, Bangkok\, Thailand
SEQUENCE:0
UID:ad18ccde58a5db3d920888b7517eedb4
URL:http://11thictisthailand.sched.com/event/ad18ccde58a5db3d920888b7517eedb4
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260417T125213Z
DTSTART:20260410T100000Z
DTEND:20260410T100200Z
SUMMARY:Session Chair Concluding Remarks
DESCRIPTION:\n
CATEGORIES:VIRTUAL ROOM 9G
LOCATION:Virtual Room G\, Bangkok\, Thailand
SEQUENCE:0
UID:23c46caa9c1109070422d36940b570fc
URL:http://11thictisthailand.sched.com/event/23c46caa9c1109070422d36940b570fc
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260417T125213Z
DTSTART:20260410T100200Z
DTEND:20260410T100500Z
SUMMARY:Session Closing and Information To Authors
DESCRIPTION:\n
CATEGORIES:VIRTUAL ROOM 9A
LOCATION:Virtual Room A\, Bangkok\, Thailand
SEQUENCE:0
UID:02a21adbc53e67ef3bea42ec61c67441
URL:http://11thictisthailand.sched.com/event/02a21adbc53e67ef3bea42ec61c67441
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260417T125213Z
DTSTART:20260410T100200Z
DTEND:20260410T100500Z
SUMMARY:Session Closing and Information To Authors
DESCRIPTION:\n
CATEGORIES:VIRTUAL ROOM 9B
LOCATION:Virtual Room B\, Bangkok\, Thailand
SEQUENCE:0
UID:0033bc445982a2850555eef416beaf06
URL:http://11thictisthailand.sched.com/event/0033bc445982a2850555eef416beaf06
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260417T125213Z
DTSTART:20260410T100200Z
DTEND:20260410T100500Z
SUMMARY:Session Closing and Information To Authors
DESCRIPTION:\n
CATEGORIES:VIRTUAL ROOM 9C
LOCATION:Virtual Room C\, Bangkok\, Thailand
SEQUENCE:0
UID:35141b929eb2f8eb6e937000f90a6889
URL:http://11thictisthailand.sched.com/event/35141b929eb2f8eb6e937000f90a6889
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260417T125213Z
DTSTART:20260410T100200Z
DTEND:20260410T100500Z
SUMMARY:Session Closing and Information To Authors
DESCRIPTION:\n
CATEGORIES:VIRTUAL ROOM 9D
LOCATION:Virtual Room D\, Bangkok\, Thailand
SEQUENCE:0
UID:e305215db65264ac043d3869375b230a
URL:http://11thictisthailand.sched.com/event/e305215db65264ac043d3869375b230a
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260417T125213Z
DTSTART:20260410T100200Z
DTEND:20260410T100500Z
SUMMARY:Session Closing and Information To Authors
DESCRIPTION:\n
CATEGORIES:VIRTUAL ROOM 9E
LOCATION:Virtual Room E\, Bangkok\, Thailand
SEQUENCE:0
UID:bedf9c156fdaabe7a4462b2a700e874b
URL:http://11thictisthailand.sched.com/event/bedf9c156fdaabe7a4462b2a700e874b
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260417T125213Z
DTSTART:20260410T100200Z
DTEND:20260410T100500Z
SUMMARY:Session Closing and Information To Authors
DESCRIPTION:\n
CATEGORIES:VIRTUAL ROOM 9F
LOCATION:Virtual Room F\, Bangkok\, Thailand
SEQUENCE:0
UID:45689712187933151d3c8215a33d76f6
URL:http://11thictisthailand.sched.com/event/45689712187933151d3c8215a33d76f6
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260417T125213Z
DTSTART:20260410T100200Z
DTEND:20260410T100500Z
SUMMARY:Session Closing and Information To Authors
DESCRIPTION:\n
CATEGORIES:VIRTUAL ROOM 9G
LOCATION:Virtual Room G\, Bangkok\, Thailand
SEQUENCE:0
UID:03b0d13331d570424ec8558ac8a719f0
URL:http://11thictisthailand.sched.com/event/03b0d13331d570424ec8558ac8a719f0
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260417T125213Z
DTSTART:20260411T022800Z
DTEND:20260411T023000Z
SUMMARY:Opening Remarks
DESCRIPTION:\n
CATEGORIES:VIRTUAL ROOM_10A
LOCATION:Virtual Room A\, Bangkok\, Thailand
SEQUENCE:0
UID:3e632c411c631018cef4bc4bc9dc533b
URL:http://11thictisthailand.sched.com/event/3e632c411c631018cef4bc4bc9dc533b
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260417T125213Z
DTSTART:20260411T022800Z
DTEND:20260411T023000Z
SUMMARY:Opening Remarks
DESCRIPTION:\n
CATEGORIES:VIRTUAL ROOM_10B
LOCATION:Virtual Room B\, Bangkok\, Thailand
SEQUENCE:0
UID:9653d280da635b9f5af42686aed311bc
URL:http://11thictisthailand.sched.com/event/9653d280da635b9f5af42686aed311bc
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260417T125213Z
DTSTART:20260411T022800Z
DTEND:20260411T023000Z
SUMMARY:Opening Remarks
DESCRIPTION:\n
CATEGORIES:VIRTUAL ROOM_10C
LOCATION:Virtual Room C\, Bangkok\, Thailand
SEQUENCE:0
UID:fc76a9b4b89abd4d9d2affd5f41fcdb7
URL:http://11thictisthailand.sched.com/event/fc76a9b4b89abd4d9d2affd5f41fcdb7
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260417T125213Z
DTSTART:20260411T022800Z
DTEND:20260411T023000Z
SUMMARY:Opening Remarks
DESCRIPTION:\n
CATEGORIES:VIRTUAL ROOM_10D
LOCATION:Virtual Room D\, Bangkok\, Thailand
SEQUENCE:0
UID:e417e0cb9a7fecfddab54462501c0c0a
URL:http://11thictisthailand.sched.com/event/e417e0cb9a7fecfddab54462501c0c0a
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260417T125213Z
DTSTART:20260411T022800Z
DTEND:20260411T023000Z
SUMMARY:Opening Remarks
DESCRIPTION:\n
CATEGORIES:VIRTUAL ROOM_10E
LOCATION:Virtual Room E\, Bangkok\, Thailand
SEQUENCE:0
UID:2f671e63435840f4b70c821d58e3dca1
URL:http://11thictisthailand.sched.com/event/2f671e63435840f4b70c821d58e3dca1
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260417T125213Z
DTSTART:20260411T022800Z
DTEND:20260411T023000Z
SUMMARY:Opening Remarks
DESCRIPTION:\n
CATEGORIES:VIRTUAL ROOM_10F
LOCATION:Virtual Room F\, Bangkok\, Thailand
SEQUENCE:0
UID:7f120021f0d395161f90cdb80b431760
URL:http://11thictisthailand.sched.com/event/7f120021f0d395161f90cdb80b431760
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260417T125213Z
DTSTART:20260411T022800Z
DTEND:20260411T023000Z
SUMMARY:Opening Remarks
DESCRIPTION:\n
CATEGORIES:VIRTUAL ROOM_10G
LOCATION:Virtual Room G\, Bangkok\, Thailand
SEQUENCE:0
UID:f51bd26c583d825611632b3c02d7e013
URL:http://11thictisthailand.sched.com/event/f51bd26c583d825611632b3c02d7e013
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260417T125213Z
DTSTART:20260411T023000Z
DTEND:20260411T043000Z
SUMMARY:A Deep Learning and Inventory Optimization Framework to Mitigate Post-Expiry Blood Wastage
DESCRIPTION:Authors - Yohan Ranasinghe\, Janice Abeykoon\, Samantha Kumara Senavirathna Abstract - Efficient blood supply chain management is a critical global impera tive in healthcare\, yet it is consistently hampered by significant post-expiry blood wastage. This issue\, prevalent across diverse healthcare systems\, represents a considerable loss of a vital and non-substitutable resource\, primarily stemming from challenges in accurate demand forecasting and dynamic inventory coordi nation. To address this pervasive problem\, this research proposes and validates a novel data-driven framework. The approach leverages a multivariate deep learn ing forecasting model\, specifically a Multivariate Long Short-Term Memory (LSTM) network\, integrated into a comprehensive platform designed for proac tive inventory management. The model's development and empirical validation utilize historical blood collection and transfusion data (January 2020 – December 2024) from a cluster center of the National Blood Transfusion Service (NBTS) in Sri Lanka\, serving as a representative case study to demonstrate real-world applicability. The framework incorporates multivariate factors such as historical transfusion patterns\, seasonal variations\, and interdependencies between blood groups to generate more accurate demand predictions. The integrated system\, de signed to support real-time inventory monitoring\, automated near-expiry track ing\, and digital blood request and redistribution mechanisms\, aims to align blood supply with anticipated demand. The findings of this research demonstrate that this integrated deep learning and inventory optimization framework significantly improves blood stock utilization\, minimizes wastage\, and enhances the overall efficiency of blood supply systems. It offers a scalable and ethically governed solution\, contributing broadly to efforts in sustainable healthcare delivery world wide.
CATEGORIES:VIRTUAL ROOM_10A
LOCATION:Virtual Room A\, Bangkok\, Thailand
SEQUENCE:0
UID:8a1a9f292ec7d7e71b407da09712c274
URL:http://11thictisthailand.sched.com/event/8a1a9f292ec7d7e71b407da09712c274
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260417T125213Z
DTSTART:20260411T023000Z
DTEND:20260411T043000Z
SUMMARY:A Hybrid Fine-Tuned LLM and RAG-Based Framework for Company-Specific Interview Question Generation
DESCRIPTION:Authors - Rashmi Y Matt\, Shreya Srinivasan\, Venkata Sravani Revuri\, Vismaya Murali\, Chandravva Hebbi\, Natarajan Abstract - Preparing for technical interviews has become very challenging for computer science students due to highly competitive hiring environments and the lack of company-specific practice resources. Existing resources and Generative platforms provide generic questions that do not reflect the specific patterns\, technical focus areas\, or expectations of different requirements.To address this gap\, we present a system that combines a structured knowledge-graph-based retrieval module with a fine-tuned LLamA-2-7B model to generate company-specific technical interview questions. The data set contains 28\,854 curated questions from 470 companies\, which were cleaned and used for finetuning. The proposed framework also integrates an evaluation pipeline using both LLM-as-a-Judge and manual scoring to check validity\, clarity\, and technical correctness.The fine-tuned LLamA-2-7B model integrated with the knowledge graph retrieval achieved the best performance\, which significantly outperformed other generative models in producing contextually appropriate and technically relevant questions. This approach aims to provide students with more targeted preparation resources aligned with real-world hiring expectations.
CATEGORIES:VIRTUAL ROOM_10A
LOCATION:Virtual Room A\, Bangkok\, Thailand
SEQUENCE:0
UID:c90b8c0970bb8059b09595dea928a341
URL:http://11thictisthailand.sched.com/event/c90b8c0970bb8059b09595dea928a341
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260417T125213Z
DTSTART:20260411T023000Z
DTEND:20260411T043000Z
SUMMARY:A Two-Stage Hierarchical Framework for Early Detection of Stress and Suicide Risk
DESCRIPTION:Authors - Halima Tuj Saydia\, Partha Chakraborty Abstract - The mental health issues\, such as stress and suicidal threats\, have become a major public health concern for students and young adults. Early identification of such conditions is important for timely interventions and prevention. The study aims to develop a two-stage hierarchical framework to predict stress and suicide risk early. It is based on the questionnaire survey dataset of 1436 responses. The hierarchical method utilizes psychological and lifestyle characteristics gathered through surveys\, thereby eliminating the need for physiological sensors. The first stage develops machine learning (ML) models\, namely XGBoost\, Random Forest (RF)\, and Support Vector Machine (SVM)\, to detect stress. These models have achieved an accuracy of 93%\, 88%\, and 83%\, respectively. If the individual is detected as stressed\, it moves to the second stage for suicide risk detection. Deep learning (DL) models\, mainly Artificial Neural Network (ANN)\, Deep Neural Network (DNN)\, and Recurrent Neural Network (RNN)\, are developed in the second stage. They have achieved accuracy of 94%\, 90%\, and 89%\, respectively. The study presents a scalable\, data-driven framework that supports early mental health screening in resource-limited communities.
CATEGORIES:VIRTUAL ROOM_10A
LOCATION:Virtual Room A\, Bangkok\, Thailand
SEQUENCE:0
UID:b5dbe4a5427b072d6b00bc8d6c88c45d
URL:http://11thictisthailand.sched.com/event/b5dbe4a5427b072d6b00bc8d6c88c45d
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260417T125213Z
DTSTART:20260411T023000Z
DTEND:20260411T043000Z
SUMMARY:Comparative Analysis of Quantum Entanglement Techniques for Parkinson’s Disease Detection: Evaluating Encoding Strategies in Quantum Machine Learning
DESCRIPTION:Authors - Satrasala Hari priya\, Sabhya Kulkarni\, Sindhu Baddela\, Spoorthi Krishna Devadiga\, Suja CM Abstract - This paper evaluates the quantum entanglement techniques for the detection of Parkinson’s disease using multimodal clinical data from the PPMI database. Four encoding techniques are evaluated: Amplitude Encoding\, Dense Angle\, IQP-based Pauli\, and Hierarchical. The results of the analysis indicate that accuracy and the efficiency of the circuit are greatly impacted by the entanglement technique. Amplitude Encoding is the most efficient for NISQ computers (92.00% accuracy\, 6-depth circuits)\, while Dense Angle provides the highest accuracy (92.59%). Hierarchical entanglement is the least efficient (80.86%)\, showing that too much depth causes optimization difficulties. These results provide practical recommendations for the design of quantum circuits for medical diagnosis.
CATEGORIES:VIRTUAL ROOM_10A
LOCATION:Virtual Room A\, Bangkok\, Thailand
SEQUENCE:0
UID:0edbd51278a4e9b342726c1ace972e52
URL:http://11thictisthailand.sched.com/event/0edbd51278a4e9b342726c1ace972e52
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260417T125213Z
DTSTART:20260411T023000Z
DTEND:20260411T043000Z
SUMMARY:Cyber Intelligence: A Promising Research Field
DESCRIPTION:Authors - Chandan Kumar\, Supriya Narad Abstract - In the contemporary digital landscape\, the proliferation of cyber threats has become a pervasive and escalating concern\, posing imminent dangers to individuals\, businesses\, and entire nations. Cyber intelligence emerges as a critical component in the ongoing battle against these threats\, involving the systematic gathering\, analysis\, and dissemination of information pertaining to cyber threats\, actors\, and vulnerabilities. This research paper aims to provide an insightful examination of the existing landscape of cyber intelligence\, delineating its fundamental sub-domains and highlighting areas ripe for future research. The paper begins by delving into the current state of cyber intelligence\, emphasizing the dynamic nature of the digital threat landscape. It elucidates the multifaceted challenges posed by cyber threats\, underscoring the need for a proactive and adaptive approach to intelligence gathering and analysis. This section also explores contemporary technologies and methodologies employed in cyber intelligence\, ranging from advanced analytics and machine learning to threat intelligence platforms.
CATEGORIES:VIRTUAL ROOM_10A
LOCATION:Virtual Room A\, Bangkok\, Thailand
SEQUENCE:0
UID:e5dc4e7b199ab0510bfcebbfc1b6c849
URL:http://11thictisthailand.sched.com/event/e5dc4e7b199ab0510bfcebbfc1b6c849
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260417T125213Z
DTSTART:20260411T023000Z
DTEND:20260411T043000Z
SUMMARY:ICT-ENABLED HUMAN RESOURCE SUSTAINABILITY IN HIGHER EDUCATION: A REVIEW OF PRACTICES AND CORRELATES IN INDIAN DEEMED UNIVERSITIES
DESCRIPTION:Authors - Shubham Kadam\, Chhitij Raj\, Pankajkumar Anawade\, Deepak Sharma Abstract - Higher education in India is poised at a junction and change seems to be driven by the issues of quality\, access and sustainable development. In this framework\, the HR sustain ability is essential for recruiting\, hiring and retaining competent employees. This paper discusses ICT enabled practices of Indian deemed universities in the direction of promoting HR sustaina bility. Drawing on Review of literature and theme analysis\, it explores e-based practices such as e-recruitment\, digital training\, online performance management\, wellness technologies digital knowledge collaborations platforms. The study reveals that adoption of ICTs promotes effective ness\, transparency and inclusivity of HR functions through the maintenance continuous staff de velopment. Nonetheless\, other contributors such as leadership support\, digital literacy and policy environment were found to significantly influence implementation outcomes. Digital divides\, lack of training\, data privacy and cost are some of the other concerns highlighted by the review. An overview of future themes in which AI\, personalized HR services\, and eco- sustainable ICT platforms will play a significant role into developing Future-proofed University.
CATEGORIES:VIRTUAL ROOM_10A
LOCATION:Virtual Room A\, Bangkok\, Thailand
SEQUENCE:0
UID:372de9954739bb60f178d2ffcff64a46
URL:http://11thictisthailand.sched.com/event/372de9954739bb60f178d2ffcff64a46
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260417T125213Z
DTSTART:20260411T023000Z
DTEND:20260411T043000Z
SUMMARY:INTEGRATING GREEN COMMUNICATION SYSTEMS\, SMART ICT\, AND HR SUSTAINABILITY INSIGHTS FOR FUTURE-READY UNIVERSITIES
DESCRIPTION:Authors - Mr. Shubham Kishor Kadam\, chhitij Raj Abstract - Increasing demands of universities to become sustainable in their practice and the necessity to compete in the global arena have compelled higher education to the implementation of green communication infrastructure and smart ICT solutions in every facet of the university practice. As an ingredient of this change\, there is the HR sustainability: that we will go digital faculty and staff\, and at the same time retain them in friendly and efficient and inclusive systems that are environmentally friendly. The emergence of the green communication systems\, intelli gent ICT infrastructures\, and green HR practices is helping the higher education sector to fund their future in this paper. The article is narrowed down to new practices\, such as the hiring without paper\, the use of mobile based performance management and virtual training\, that is generated under the secondary research and conceptual framework. It also talks about the benefits\, chal lenges and opportunities of such system in higher learning institutions. The findings suggest that the effective adoption of the sustainable ICT will help improve the performance of the organiza tions\, reducing the impact on the environment to a minimum and being part of the creation of the digitally resilient human resources.
CATEGORIES:VIRTUAL ROOM_10A
LOCATION:Virtual Room A\, Bangkok\, Thailand
SEQUENCE:0
UID:d52dc30212096e87fb5523a6f35dd5fa
URL:http://11thictisthailand.sched.com/event/d52dc30212096e87fb5523a6f35dd5fa
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260417T125213Z
DTSTART:20260411T023000Z
DTEND:20260411T043000Z
SUMMARY:Penetration Testing on Infotainment Head Unit
DESCRIPTION:Authors - Lakshmi BV\, Anupriya S\, Ningappa B\, Diganth SD\, RoopaRavish\, Prasad B Honnavalli Abstract - Modern car infotainment head unit has become a highly connected cyber-physical system\, incorporating Wi-Fi\, Bluetooth\, USB ports\, and the Controller Area Network (CAN) bus. While such capabilities enhance the user experience\, they also raise the susceptibility of the vehicle to attacks\, and hence there is a need to assess the security of the vehicle. This paper performs a comprehensive penetration test on an infotainment system\, examining wireless\, wired\, and in-car communication channels. For the Wi-Fi component\, we performed a series of attacks such as Distributed Denial-of-Service (DDoS)\, deauthentication\, MAC and IP spoofing attacks\, creating fake access points\, and WPA-based attacks to determine the robustness of the system against network-level threats. Bluetooth attacks included device snarfing\, replay attacks\, manual packet injection attacks\, and unauthorized access to data. USB attacks were employed to analyze the dangers posed by connected devices\, including the extraction of GPS information\, log files\, SMS messages\, and access to the microphone and camera. For the CAN bus\, we performed replay attacks\, flooding attacks\, manual frame injection attacks\, and manipulation of sensor information such as humidity and temperature readings. The outcome of each of these attacks indicates that the infotainment system can serve as a means through which attackers gain access to the vehicle's network\, and hence the need for enhanced authentication\, improved security for the interfaces\, and real-time monitoring for security breaches. This paper provides valuable information for enhancing the security of modern car infotainment systems and contributes to the efforts being made in the field of automotive cybersecurity.
CATEGORIES:VIRTUAL ROOM_10A
LOCATION:Virtual Room A\, Bangkok\, Thailand
SEQUENCE:0
UID:c1305b8bf3589e153a6c7de48baa7948
URL:http://11thictisthailand.sched.com/event/c1305b8bf3589e153a6c7de48baa7948
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260417T125213Z
DTSTART:20260411T023000Z
DTEND:20260411T043000Z
SUMMARY:The Role of Artificial Intelligence in Stock Market Prediction: Opportunities and Challenges
DESCRIPTION:Authors - Tejaswini Borkar\, Kajal Salampuriya Abstract - This paper focuses on the product of state of the art artificial intelligence (AI) language models (that is\, ChatGPT\, Perplexity\, and Grok) to generate and test algorithmic trading strategies in financial markets. With such AI tools in the field\, the study examines the success of the tools in cases of generating trading signals\, synthesizing market sentiment\, and helping manage risks both through quantitative backtesting and through qualitative analysis. The conclusion is that though the procedures performed using AI-assisted tactics may be comparable to the findings of the use of conventional algorithmic processes and will outline beneficial information\, the findings should undergo tangible verification and cautious human interventions to establish dependability and applicability. Our findings are indicators of the potential of the large language models as an addition to assist traders and researchers and indicate that caution is still necessary to integrate with the long-established quantitative methods and risk management functions.
CATEGORIES:VIRTUAL ROOM_10A
LOCATION:Virtual Room A\, Bangkok\, Thailand
SEQUENCE:0
UID:9f77d0f4628152c980b90a095df73e91
URL:http://11thictisthailand.sched.com/event/9f77d0f4628152c980b90a095df73e91
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260417T125213Z
DTSTART:20260411T023000Z
DTEND:20260411T043000Z
SUMMARY:Toward Explainable AI for Medical Negligence Adjudication in India
DESCRIPTION:Authors - Niraja Jain\, Rajeev Kumar\, Golnoosh Manteghi Abstract - Medical negligence litigation in India poses significant challenges to the justice delivery system due to the complexity of clinical evidence\, fragmented legal documentation\, and limited availability of structured decision-support mechanisms for legal practitioners. These challenges often result in delays\, inconsistent legal reasoning\, and increased cognitive burden on judges and lawyers handling medico-legal disputes. This paper presents the design and preliminary validation of a Judicial Decision Support System (JDSS) tailored specifically for medical negligence litigation in the Indian legal context. The proposed JDSS leverages advanced Natural Language Processing (NLP) techniques and supervised machine learning models to assist early-stage legal triage through automated case summarization\, statutory section prediction\, and precedent recommendation. Transformer-based language models are fine-tuned on publicly available Indian legal judgments and augmented with a domain-specific legal–medical ontology to bridge semantic gaps between clinical narratives and legal reasoning. Explainability is embedded at both the model and user-interface levels through attention visualization and feature attribution mechanisms\, addressing transparency requirements critical for high-stakes judicial applications. The system has undergone formative evaluation through an exploratory stakeholder survey involving participants from legal\, academic\, and higher-education ecosystems in India. This evaluation focuses on perceived usefulness\, trust\, explainability expectations\, and institutional readiness for AI-assisted judicial tools\, rather than predictive performance. Findings from the survey informed key design choices\, particularly the emphasis on explainable AI and modular deployment. While large-scale retrospective evaluation on real-world court data remains part of future work\, the current study establishes a methodologically grounded and ethically aligned foundation for AI-assisted judicial decision support in resource-constrained legal environments\, with scope for integration into India’s evolving digital judiciary infrastructure.
CATEGORIES:VIRTUAL ROOM_10A
LOCATION:Virtual Room A\, Bangkok\, Thailand
SEQUENCE:0
UID:61ba08c2165940048951fc482f58cb8f
URL:http://11thictisthailand.sched.com/event/61ba08c2165940048951fc482f58cb8f
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260417T125213Z
DTSTART:20260411T023000Z
DTEND:20260411T043000Z
SUMMARY:A Multimodal AI Framework for Automated Document Understanding and Structuring
DESCRIPTION:Authors - Sanket Shah\, Jenice Bhavsar\, Bhumi Shah\, Jishan Shaikh\, Khevana Raval\, Ekta Vyas Abstract - Dyslexia is a neurodevelopmental condition that impairs reading fluency and phonological processing across languages. Early identification in school settings remains difficult because the Dyslexia Assessment for Languages of India (DALI) assessment tool requires expert administration which makes it difficult to implement in practice. The latest developments in artificial intelligence allow researchers to evaluate reading patterns through inexpensive devices which people commonly use. The research presents a system framework that uses multiple methods to combine webcam-based eye-tracking with voice analysis and machine learning methods for early dyslexia detection. The system examines tabular gaze and speech features through gradient-boosted models while using convolutional neural networks to encode spatial gaze patterns which include a meta-learning layer for multimodal fusion. The proposed framework enables practical implementation through its web-based interface which connects to secure backend services\, thus providing schools with a privacy-protected and scalable method to conduct dyslexia assessments and provide personalized learning assistance in their resource-limited classrooms.
CATEGORIES:VIRTUAL ROOM_10B
LOCATION:Virtual Room B\, Bangkok\, Thailand
SEQUENCE:0
UID:bbd721a5e80af102d4c9c9808c372d09
URL:http://11thictisthailand.sched.com/event/bbd721a5e80af102d4c9c9808c372d09
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260417T125213Z
DTSTART:20260411T023000Z
DTEND:20260411T043000Z
SUMMARY:An Architectural Study and Implementation of RISC-V Vector Extension
DESCRIPTION:Authors - Geethashree A\, Surabhi M R\, Varshitha H N\, Vipul S\, Vivek M R\n Abstract - The RISC-V Vector Extension (RVV) enables scalable data-parallel processing through a flexible vector length architecture\, offers a standardized and scalable approach to vector computing. Derived from an analysis of existing RVV architectures\, this paper presents a focused architectural study and implementation of a basic RVV-based vector extension. Unlike complex\, high-performance designs\, the proposed architecture prioritizes simplicity and clarity\, implementing only essential vector arithmetic and memory instructions. The vector extension is integrated with a single-cycle scalar RISC-V core\, and instruction decoding is implemented and verified at RTL level. Functional simulation confirms correctness of RVV instruction decoding. This work bridges the gap between theoretical RVV studies and practical step-by-step hardware implementation.
CATEGORIES:VIRTUAL ROOM_10B
LOCATION:Virtual Room B\, Bangkok\, Thailand
SEQUENCE:0
UID:1d9bbd704c2dd8a38ad54bb94206ce25
URL:http://11thictisthailand.sched.com/event/1d9bbd704c2dd8a38ad54bb94206ce25
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260417T125213Z
DTSTART:20260411T023000Z
DTEND:20260411T043000Z
SUMMARY:An Explainable MSME schemes Question-Answering System using Large Language Models for Knowledge Graph Construction and Retrieval-Augmented Generation.
DESCRIPTION:Authors - Lalitha R\, Husna Sarirah Husin\, Suriana Ismail\, Nikitha S\, Kavya Darshini S\, Pooja M\n Abstract - The data from Tamil Nadu government MSME programs is a treasure trove\, but the information is fragmented and scattered in different kinds of documents. Consequently\, it becomes a task for both the public and the analysts to process the data and get important insights. The paper introduces LKD-RAG\, an explainable hybrid retrieval-augmented generation (RAG) system that relies on LLMs and KGs to make natural language queries possible on the data of these schemes collected from different sources. In the initial phase\, the LLM started autonomously to discover entities\, relations\, and attributes\, which eventually led to the creation of structured triples that signify factual statements (subject-predicate-object). The knowledge represented by these triples was loaded into Neo4j\, thereby producing a MSME Scheme KG that is specific to the domain. Also\, a document embedding layer was set up with SentenceTransformer ("all-MiniLM-L6-v2") that made it possible to do semantic retrieval of supporting textual evidence. When a query is made\, Gemini decodes the person’s inquiry\, finds relevant KG subgraphs and text embeddings\, and constructs a response that is grounded on the evidence. The subgraph that corresponds to the answer is shown to the user\, so the user can check what knowledge the model is relying on for its reasoning. Thus\, the process facilitates transparency and the use of explainable AI (XAI) in policy analytics. The results of the experiments indicate that the hybrid RAG model not only has the ability to generate factually accurate responses but also to provide interpretation through different Tamil Nadu MSME programs.
CATEGORIES:VIRTUAL ROOM_10B
LOCATION:Virtual Room B\, Bangkok\, Thailand
SEQUENCE:0
UID:82af8baa327fad3f28ca22afddc5141f
URL:http://11thictisthailand.sched.com/event/82af8baa327fad3f28ca22afddc5141f
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260417T125213Z
DTSTART:20260411T023000Z
DTEND:20260411T043000Z
SUMMARY:Attention Based Deep Convolutional Neural Net-work Model for Plant Disease Classification
DESCRIPTION:Authors - Krashn Kumar Tripathi\, Sachin B. Jadhav Abstract - In digital world\, cyber-attacks are becoming more sophisticated and popular. The conventional intrusion detection models are not adequate in challenging threat escapes. Importantly\, the major reason for increasing demand in the networks\, unauthorized access is increasing their interests in these areas. Various network environments and organizations are tackling numerous of attacks on their network at frequent times. Traditionally\, various manual methods are used for intrusion detection such as packet and flow analysis\, traffic log reviewers and monitoring the security. Nevertheless\, the manual techniques for such type of the detections takes too much time and also the result obtained is not up to the mark\, so due to this it is difficult to predict all types of attacks and intrusions for network security. To overcome these issues\, several conventional researches have concentrated on intrusion detection models to offer effective security to the networks. Conversely\, it results with accuracy and speed lacks. For enhancing the intrusion detection\, research make use of a Deep Learning (DL) Unravelled Spatial Features in Multilayer Perceptron with Gradient Jacobian Matrix. Gaussian Activation is used to enhance the Intrusion detection system for an effective classification. In the proposed research work we are using the RT-IoT dataset and the final efficiency has been analyzed by using various parameters like overall correctness\, actually correct\, correctly identified by the model\,and the balance between the both values of recall and precision (Harmonic Mean). Furthermore\, the current work and the proposed model is developed to contribute to avoid the different cyber threats by timely identifying such type of intrusion in the networks.
CATEGORIES:VIRTUAL ROOM_10B
LOCATION:Virtual Room B\, Bangkok\, Thailand
SEQUENCE:0
UID:f50ab15485253d71f2e5fa56c861e208
URL:http://11thictisthailand.sched.com/event/f50ab15485253d71f2e5fa56c861e208
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260417T125213Z
DTSTART:20260411T023000Z
DTEND:20260411T043000Z
SUMMARY:Balancing Learning and Digital Business: A Study of Technostress among Student Entrepreneurs
DESCRIPTION:Authors - Maulana Amirul Adha\, Maulana Paramaditya Ananta\, Bayu Suhendry\, Ria Rahma Nida\, Eka Dewi Utari\, Nur Athirah Sumardi Abstract - The challenge of generating accurate and contextually complete mod-els and prompts in Model-Driven Engineering (MDE) using Large Language Models (LLMs) is based on the current limitations in understanding the complex structured data. The significance of this issue lies at the heart of modern software development where MDE has taken the lead to advance development in the field moving towards with the aim of automating manual processes. To increase this automation\, the application of LLMs holds the potential to reduce the manual effort and reduce human error involved in the process. To address this\, we pro-pose a context-based prompt generation framework that integrates the techniques of Retrieval-Augmented Generation (RAG) with LLMs such as GPT-4 and CodeLlama to produce prompts that are contextually accurate and sound. Along with these LLMs\, tools like FAISS\, LangChain\, and PlantUML are also em-ployed to produce detailed and structurally accurate UML models and prompt to enhance MDE understandability. In summary\, the proposed framework aims to improve the accuracy and completeness of model generation by providing a con-textually correct prompt with a high level of accuracy and enhances the interpret-ability and ability of trust in AI-generated artifacts\, creating the way for more efficient\, automated\, and user-friendly MDE processes.
CATEGORIES:VIRTUAL ROOM_10B
LOCATION:Virtual Room B\, Bangkok\, Thailand
SEQUENCE:0
UID:1ed77cfd20377a09d30858403d464cb7
URL:http://11thictisthailand.sched.com/event/1ed77cfd20377a09d30858403d464cb7
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260417T125213Z
DTSTART:20260411T023000Z
DTEND:20260411T043000Z
SUMMARY:Chessboard state detection and game analysis using a two-stage R-CNN
DESCRIPTION:Authors - Pierre Buys\, Tevin Moodley\n Abstract - This paper presents a real-time chessboard state detection system that leverages computer vision and deep learning to automate a digital representation of a physical chess game. Traditional digitization systems either require manual input or specialized equipment. However\, the proposed system addresses this problem by capturing a chess game in real time through the use of a smartphone camera. Detected piece positions are mapped to standard board coordinates and translated into Forsyth-Edwards Notation (FEN)\, enabling seamless integration with existing chess engines for analysis and move suggestions. The system works by firstly localizing the chessboard via Canny edge detection as well as a Hough transform. Thereafter\, multi-class object detection is addressed by developing a two-stage R-CNN model alongside a single-stage YOLO model\, allowing for a comparative evaluation of their respective methodologies and performance. The described system achieves a localization precision of 98.77% per board coordinate\, whilst the two-stage R-CNN and single-stage YOLO models achieve a piece detection accuracy of 83.62% and 99.47%\, respectively.
CATEGORIES:VIRTUAL ROOM_10B
LOCATION:Virtual Room B\, Bangkok\, Thailand
SEQUENCE:0
UID:300bbf08070b54122db6d953699de642
URL:http://11thictisthailand.sched.com/event/300bbf08070b54122db6d953699de642
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260417T125213Z
DTSTART:20260411T023000Z
DTEND:20260411T043000Z
SUMMARY:Design and Development of a MATLAB-Based GUI for Automated Brain Tumor Detection Using MRI Image Processing Techniques
DESCRIPTION:Authors - Hardik Modi\, Mayur Makwana\, Sagarkumar Patel\, Dharmendra Chauhan\, Siddhi Patel\, Dhara Soni\, Malvi Patel\n Abstract - Early and accurate detection of brain tumors is a critical requirement in modern clinical diagnostics\, as it directly affects treatment planning\, disease prognosis\, and patient survival rates. The rapid increase in the availability and complexity of medical imaging data has intensified the need for reliable computer-aided diagnosis (CAD) systems to assist radiologists in consistent and precise tumor identification. Among various CAD techniques\, medical image segmentation plays a pivotal role in differentiating abnormal tumor tissue from healthy brain structures in diagnostic images. This paper presents an automated brain tumor detection framework based on medical image analysis\, implemented using a MATLAB-based graphical user interface. The proposed system processes Magnetic Resonance Imaging (MRI) and Computed Tomography (CT) scans through a structured processing pipeline that includes image acquisition\, noise reduction\, contrast enhancement\, feature-based segmentation\, and tumor region visualization. The segmentation methodology is designed to accurately localize tumor boundaries while minimizing false-negative detections\, which is a crucial requirement for clinical decision-making. The developed interface enables interactive visualization of segmented regions\, allowing efficient analysis without the need for extensive computational expertise. The proposed framework offers a user-friendly and computationally efficient platform that reduces reliance on manual interpretation and improves diagnostic repeatability across clinical environments. The novelty of this work lies in the seamless integration of automated tumor detection\, structured segmentation techniques\, and real-time visual interpretation within a unified MATLAB-based environment\, providing a practical and accessible CAD solution without dependence on complex hardware or deep learning infrastructures. Experimental observations indicate that the system enhances analysis efficiency and supports medical professionals in making faster\, more reliable\, and time-effective diagnostic decisions.
CATEGORIES:VIRTUAL ROOM_10B
LOCATION:Virtual Room B\, Bangkok\, Thailand
SEQUENCE:0
UID:8b2bb2f2c29b1b73350da34d09249fcf
URL:http://11thictisthailand.sched.com/event/8b2bb2f2c29b1b73350da34d09249fcf
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260417T125213Z
DTSTART:20260411T023000Z
DTEND:20260411T043000Z
SUMMARY:Managerial Perspectives on the Adoption of Artificial Intelligence in Small and Medium Enterprises (SMEs) in the Philippines: A Qualitative Study
DESCRIPTION:Authors - Najera R. Umpar\n Abstract - Artificial Intelligence (AI)\, as a technology\, has the potential to change the manner in which organizations are run in the world. However\, small and medium-sized enterprises (SMEs) in the Philippines have unique limitations in the use of AI in running the business. The study aims to explore the perceptions of SME managers in the Philippines on the use of AI\, with particular reference to the limitations and facilitators in the use of the technology in the business environment. In this study\, the researcher interviewed five SME managers from different sectors\, including retail\, manufacturing\, and service sectors. The researcher used thematic analysis to identify the commonalities in the decisions made by the SME managers on the use of AI in the business environment. The study revealed the perceptions of the SME managers on the use of AI in the business environment in the Philippines\, with the limitations and facilitators in the use of the technology in the business environment. The study provides practical insights that can guide strategies aimed at strengthening AI readiness and responsible adoption among SMEs in the Philippines.
CATEGORIES:VIRTUAL ROOM_10B
LOCATION:Virtual Room B\, Bangkok\, Thailand
SEQUENCE:0
UID:86f10e740eabf9712d34fc27510d3499
URL:http://11thictisthailand.sched.com/event/86f10e740eabf9712d34fc27510d3499
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260417T125213Z
DTSTART:20260411T023000Z
DTEND:20260411T043000Z
SUMMARY:P-L Binding Affinity Prediction Tool for Drug Discovery
DESCRIPTION:Authors - Nilay Shah\, Darsh Pandya\, Nisarg Patel\, Rudra Shah\, Umang Shah\, Dhaval Patel\, Priteshkumar Prajapati Abstract - Early and accurate detection of brain tumors is a critical requirement in modern clinical diagnostics\, as it directly affects treatment planning\, disease prognosis\, and patient survival rates. The rapid increase in the availability and complexity of medical imaging data has intensified the need for reliable computer-aided diagnosis (CAD) systems to assist radiologists in consistent and precise tumor identification. Among various CAD techniques\, medical image segmentation plays a pivotal role in differentiating abnormal tumor tissue from healthy brain structures in diagnostic images. This paper presents an automated brain tumor detection framework based on medical image analysis\, implemented using a MATLAB-based graphical user interface. The proposed system processes Magnetic Resonance Imaging (MRI) and Computed Tomography (CT) scans through a structured processing pipeline that includes image acquisition\, noise reduction\, contrast enhancement\, feature-based segmentation\, and tumor region visualization. The segmentation methodology is designed to accurately localize tumor boundaries while minimizing false-negative detections\, which is a crucial requirement for clinical decision-making. The developed interface enables interactive visualization of segmented regions\, allowing efficient analysis without the need for extensive computational expertise. The proposed framework offers a user-friendly and computationally efficient platform that reduces reliance on manual interpretation and improves diagnostic repeatability across clinical environments. The novelty of this work lies in the seamless integration of automated tumor detection\, structured segmentation techniques\, and real-time visual interpretation within a unified MATLAB-based environment\, providing a practical and accessible CAD solution without dependence on complex hardware or deep learning infrastructures. Experimental observations indicate that the system enhances analysis efficiency and supports medical professionals in making faster\, more reliable\, and time-effective diagnostic decisions.
CATEGORIES:VIRTUAL ROOM_10B
LOCATION:Virtual Room B\, Bangkok\, Thailand
SEQUENCE:0
UID:205d1f0e89ef3d9ae8829a8c085811a6
URL:http://11thictisthailand.sched.com/event/205d1f0e89ef3d9ae8829a8c085811a6
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260417T125213Z
DTSTART:20260411T023000Z
DTEND:20260411T043000Z
SUMMARY:Safe Human body Earthing Footwear in modern digital age
DESCRIPTION:Authors - D.K. Chaturvedi\, Tipu Sultan Abstract - A real-time operating system (RTOS) should be able to recover from interruptions. Since RTOS systems are used in safety-critical environments\, this function is essential for ensuring system availability and reliability. However\, while many of the current anomaly detection techniques can detect faults\, they do not provide any means for recovery. Therefore\, in this paper\, I propose a self-repairing RTOS framework that utilizes reinforcement learning (RL) to automatically select the best course of action to take when an anomalous event arises. I propose a Q-Learning agent that learns to recover from six types of common faults\, including: sensor degradation\, stuck sensor\, priority inversion\, memory leaks\, sporadic overloads\, and task starvation. The framework is built on FreeRTOS\, and the agent utilizes an 8-dimensional state space and the six different types of recovery options available for each fault. The overall success rate of the system was 99.2 % after 5\,000 training episodes\, with average success rates of 98.0 % and 99.9 % when handling individual faults. The RL agent completely prevented system crashes and returned the system to normal operation within an average of 0.06 ms after an interruption occurred. The training results provide strong evidence that the model learned to operate effectively and consistently\, with its success rate improving from 97.0 % during early training stages to 100 % after training was completed. Therefore\, this study demonstrates a practical\, production-ready method to implement autonomous fault recoveries in RTOSs in automotive applications. To our knowledge\, this is the first successful implementation of RL for autonomous\, self-repairing behaviors in this area.
CATEGORIES:VIRTUAL ROOM_10B
LOCATION:Virtual Room B\, Bangkok\, Thailand
SEQUENCE:0
UID:a0608754dc9597b9cdb1afd7876f8ed8
URL:http://11thictisthailand.sched.com/event/a0608754dc9597b9cdb1afd7876f8ed8
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260417T125213Z
DTSTART:20260411T023000Z
DTEND:20260411T043000Z
SUMMARY:A Comprehensive Review of Machine Learning–Based Crop Recommendation Systems Using Soil Quality Parameters
DESCRIPTION:Authors - Rashmi Vipat\, Priyank Doshi\n Abstract - Agriculture plays a vital role in ensuring food security\, yet traditional crop selection and yield estimation practices often fail to account for complex interactions among soil\, climatic\, and environmental factors. Recent advances in machine learning (ML) have shown significant potential in addressing these challenges by enabling data-driven decision support for farmers. This paper presents a comprehensive review of machine learning–based crop recommendation and yield prediction techniques\, focusing on their effectiveness in improving agricultural productivity and sustainability. The study analyzes various supervised and ensemble learning models applied to soil quality parameters such as nitrogen\, phosphorus\, potassium\, pH\, moisture\, and climatic variables. Emphasis is placed on multimodal data integration\, highlighting how the fusion of soil\, weather\, and remote sensing data enhances prediction accuracy. The review also discusses current limitations\, including data scarcity\, model generalization\, and real-time deployment challenges\, particularly in resource-con-strained farming environments. Finally\, the paper identifies key research gaps and future directions toward developing robust\, scalable\, and intelligent agricultural decision-support systems.
CATEGORIES:VIRTUAL ROOM_10C
LOCATION:Virtual Room C\, Bangkok\, Thailand
SEQUENCE:0
UID:a563ba75328ceb9a104f571ff5c95973
URL:http://11thictisthailand.sched.com/event/a563ba75328ceb9a104f571ff5c95973
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260417T125213Z
DTSTART:20260411T023000Z
DTEND:20260411T043000Z
SUMMARY:A Decision-Oriented Evaluation of Self-Supervised Learning for Chest X-ray Pneumonia Classification
DESCRIPTION:Authors - Amit Kalita\, Himashree Kalita\, Manjit Kalita\, Abhijit Chakraborty\, Kalpita Dey\, Prajukta Deb Abstract - The significance of M- Health platforms to promote health equity has reached critical levels as digitalization in the healthcare sector continues to grow post pandemic. M-Health platform utilization in developing countries like Bangladesh has unique challenges: inconsistent adoption of the digital healthcare system\, thus leading to a suboptimal delivery of healthcare services to customers. Using blended models i.e.\, Expectation-Confirmation Model (ECM)\, UTAUT2\, and the DeLone & McLean IS Success Model\, with Training on Virtual Consultation Skills as the moderating variable\, the study intends to examine the adoption intention of healthcare providers to continuously use M-Health Platforms for a myriad of services like virtual consultation\, remote patient monitoring\, electronic prescriptions\, and e-health record keeping. This study used Partial Least Squares Structural Equation Modeling (PLS-SEM) to evaluate 898 responses. Social influence\, relative advantage\, regulatory clarity\, digital literacy\, trust in technology\, and system quality\, which collectively improve doctors’ satisfaction with virtual consultation platforms\, were identified as important to the purpose of the study. The results offer concrete steps that healthcare providers\, platform creators\, and policymakers can take to build and improve a solid and dependable M-Health platform that encourages sustained partnership with physicians by alleviating resistance that physicians may have about M-Health platforms in comparable developing countries.
CATEGORIES:VIRTUAL ROOM_10C
LOCATION:Virtual Room C\, Bangkok\, Thailand
SEQUENCE:0
UID:15ac2df0b7c1c37f31ef6168e8545a55
URL:http://11thictisthailand.sched.com/event/15ac2df0b7c1c37f31ef6168e8545a55
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260417T125213Z
DTSTART:20260411T023000Z
DTEND:20260411T043000Z
SUMMARY:A Strategic Framework for Integrating ICT-Driven Intelligent Systems into Organizational Decision-Making
DESCRIPTION:Authors - Yaram Srinivasa Reddy\, Bairoju Sreelatha\, Shankar Lingam. M Abstract - Knowledge from a resource-rich source domain is leveraged in traditional transfer learning to enhance classification in a relatively data-scarce target domain. However\, the resulting target models often suffer from overfitting and limited generalization\, which restricts their utility in noisy and resource-constrained environments such as remote sensing. To mitigate these limitations\, this work introduces a nuclear norm–regularized teacher–student framework for hyperspectral scene classification. In particular\, the student model is regularized with the nuclear norm to encourage low-rank parameter representations\, improving robustness to ambient noise. Further\, we introduce a relative reconstruction loss (RRL) metric to measure the robustness of the student model to environment noise. Trained on several benchmark datasets\, the proposed student model attains up to 87.0% classification accuracy on the independent test sets of UC Merced and EuroSAT\, while remaining substantially lighter than the teacher network. Further\, relative reconstruction values are computed for different amounts of noise added in the input space\; RRL saturate to values less than 1.0 for all the datasets\, substantiating that the regularized student model is indeed robust. The competitive performance of the regularized student model compared to the teacher network\, its lightweight design\, together with RRL values less than one\, suggest that the proposed student model can effectively be deployed in noisy and resource-constrained environments such as edge and fog devices.
CATEGORIES:VIRTUAL ROOM_10C
LOCATION:Virtual Room C\, Bangkok\, Thailand
SEQUENCE:0
UID:c74e3d2ac6fe3642813b5fd8273226b9
URL:http://11thictisthailand.sched.com/event/c74e3d2ac6fe3642813b5fd8273226b9
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260417T125213Z
DTSTART:20260411T023000Z
DTEND:20260411T043000Z
SUMMARY:Advances in Clone Attack Detection on Social Media Platforms: A Comprehensive Review of Machine Learning and Deep Learning Approaches
DESCRIPTION:Authors - Nagesh Sharma\, Priyanka Yadav\, Kavita Singh Abstract - An accurate determination of childhood malnutrition is necessary for preventive measures. This paper proposes a modified scoring scheme comprising two new elements: the Integrated Anthropometric Score (IAS) and the Hybrid Integrated Score (HIS). IAS uses six primary anthropometric measurements\, such as BMI\, MUAC\, WHZ\, WAZ\, HAZ\, and skinfold thickness\, along with selected interaction terms that capture the non-linear connections between growth parameters. The weights are determined by regularized logistic regression\, allowing the score to be transparent while still adapting to the statistical structure of the data set. To further stabilize the predictions\, the HIS combines BAI\, IAS\, and a machine learning probability component to make the predictions robust in both synthetic and real-world samples. The models were developed using a synthetic dataset of 9\,456 children and tested with five-fold cross-validation and a separate real-world dataset of 38 children. Interaction selection and regularization were performed to control noise sensitivity and avoid overfitting. The findings indicate that the IAS model outperforms BAI with its higher cross-validated accuracy (0.93) and strong performance on real data (0.95). The HIS stays consistent in accuracy across areas and indicates better generalization. The results suggest that by combining multidimensional anthropometric characteristics\, interaction-aware modeling\, and hybrid learning\, a new\, more adaptable\, and clinically interpretable tool for predicting nutritional risk has been developed\, surpassing traditional composite indices.
CATEGORIES:VIRTUAL ROOM_10C
LOCATION:Virtual Room C\, Bangkok\, Thailand
SEQUENCE:0
UID:5148d3e68aa81b5dbe171c8e6743bdae
URL:http://11thictisthailand.sched.com/event/5148d3e68aa81b5dbe171c8e6743bdae
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260417T125213Z
DTSTART:20260411T023000Z
DTEND:20260411T043000Z
SUMMARY:ALOPA: An AI-Powered Lightweight On-Device Private Assistant
DESCRIPTION:Authors - Mehak Mukesh Agrawal\, Saumya Kumari\, Gaganam H V S M Soma Sai\, Ankit A. Bhurane\n Abstract - Most existing artificial intelligence (AI) based assistants are cloud-dependent and require constant internet connectivity. User data is sent to external servers for processing. While this data is often encrypted\, it is prone to risks such as cloud security threats. Additionally\, users need to be cautious not to share sensitive information. To overcome the aforementioned privacy and internet availability concerns\, this paper proposes a completely offline\, on-device\, cross-device\, and open-source system to ensure complete data privacy. The proposed system was tested with several datasets\, including AI2 Reasoning Challenge\, SQuAD 1.1\, CoNLL 2003\, GSM8K and StrategyQA to evaluate the closed-form question answering (QA)\, contextual understanding\, named entity recognition\, mathematical reasoning and truthfulness\, respectively\, and with five on-device large language models (LLMs)\, including Gemma3 1B\, SmolLM 1.7B\, Qwen2 1.5B\, TinyLlama-1.1B\, and Phi-2. The system achieved the highest score for closed-form accuracy of 1.0. Its performance on reasoning ranged from 0.01 to 0.23. Truthfulness scores ranged from 0.24 to 0.59. High F1 scores for named entity recognition ranged from 0.74 to 0.79\, and contextual understanding scores ranged from 0.02 to 0.17 across the different LLMs. The average response time of the system on mobile and desktop devices was evaluated and observed to vary according to system capability and model size. The system allows users to choose between multiple wake words specific to the Indian context. The proposed system functions on limited RAM and in constrained resource environments.
CATEGORIES:VIRTUAL ROOM_10C
LOCATION:Virtual Room C\, Bangkok\, Thailand
SEQUENCE:0
UID:9a68966813900efe83db7e0b85925235
URL:http://11thictisthailand.sched.com/event/9a68966813900efe83db7e0b85925235
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260417T125213Z
DTSTART:20260411T023000Z
DTEND:20260411T043000Z
SUMMARY:An Explainable AI-Driven Framework for Movie Recommendation System using Big Data Analytics
DESCRIPTION:Authors - Mahzuzah Afrin\, Rajasree Das Chaiti\, Gazi Tahsina Sharmin Jahin\, M. M. Musharaf Hussain\, Mohammad Shamsul Arefin Abstract - Reliable identification of pneumonia from chest radiographs plays a central role in supporting clinical decision-making and patient management. Although deep learning models have shown favourable results for automated diagnosis\, most existing studies rely on fully supervised training and mainly evaluate performance using accuracy or ROC-AUC metrics. Such evaluations may fail to capture clinical decision reliability\, particularly in imbalanced medical datasets. In this work\, we examine the effectiveness of self-supervised learning (SSL) for chest X-ray pneumonia classification through a controlled empirical study. A contrastive pretraining strategy is used to learn image representations from unlabeled chest X-rays\, followed by supervised linear evaluation. The SSL-pretrained model is compared with a fully supervised model trained from scratch under identical experimental conditions. Our experiments reveal that the supervised baseline attains a slightly higher ROC-AUC\; however\, this improvement comes at the cost of increased false positive predictions\, leading to lower overall accuracy. In contrast\, the SSL-pretrained model exhibits a distinct prediction pattern. It achieves higher accuracy and notably improved precision and F1-score\, indicating more balanced and reliable predictions. Precision– recall analysis further demonstrates the advantage of SSL in reducing false positive decisions. In addition\, Grad-CAM visualizations suggest that the SSL-pretrained model focuses on clinically relevant lung regions. From a clinical decision-making perspective\, these results suggest that self-supervised learning offers tangible advantages for chest X-ray analysis when prediction reliability is prioritized. This distinction is especially relevant in clinical settings.
CATEGORIES:VIRTUAL ROOM_10C
LOCATION:Virtual Room C\, Bangkok\, Thailand
SEQUENCE:0
UID:42d6fc0cba851410c27bf961e1e88a1b
URL:http://11thictisthailand.sched.com/event/42d6fc0cba851410c27bf961e1e88a1b
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260417T125213Z
DTSTART:20260411T023000Z
DTEND:20260411T043000Z
SUMMARY:Data Availability and Uptime in Cloud Storage: Redundancy Models and Storage Techniques
DESCRIPTION:Authors - Prerna Agarwal\, Pranav Shrivastava\, Samya Ali\, Sachit Dadwal\, Shubh Om Yadav\, Saquib Hussain\, Kareena Tuli Abstract - Most existing artificial intelligence (AI) based assistants are cloud-dependent and require constant internet connectivity. User data is sent to external servers for processing. While this data is often encrypted\, it is prone to risks such as cloud security threats. Additionally\, users need to be cautious not to share sensitive information. To overcome the aforementioned privacy and internet availability concerns\, this paper proposes a completely offline\, on-device\, cross-device\, and opensource system to ensure complete data privacy. The proposed system was tested with several datasets\, including AI2 Reasoning Challenge\, SQuAD 1.1\, CoNLL 2003\, GSM8K and StrategyQA to evaluate the closed-form question answering (QA)\, contextual understanding\, named entity recognition\, mathematical reasoning and truthfulness\, respectively\, and with five on-device large language models (LLMs)\, including Gemma3 1B\, SmolLM 1.7B\, Qwen2 1.5B\, TinyLlama-1.1B\, and Phi-2. The system achieved the highest score for closed-form accuracy of 1.0. Its performance on reasoning ranged from 0.01 to 0.23. Truthfulness scores ranged from 0.24 to 0.59. High F1 scores for named entity recognition ranged from 0.74 to 0.79\, and contextual understanding scores ranged from 0.02 to 0.17 across the different LLMs. The average response time of the system on mobile and desktop devices was evaluated and observed to vary according to system capability and model size. The system allows users to choose between multiple wake words specific to the Indian context. The proposed system functions on limited RAM and in constrained resource environments.
CATEGORIES:VIRTUAL ROOM_10C
LOCATION:Virtual Room C\, Bangkok\, Thailand
SEQUENCE:0
UID:7a428952162efcc5e9c5d41cdd76d145
URL:http://11thictisthailand.sched.com/event/7a428952162efcc5e9c5d41cdd76d145
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260417T125213Z
DTSTART:20260411T023000Z
DTEND:20260411T043000Z
SUMMARY:Helmet and Number Plate detection System
DESCRIPTION:Authors - Roshani Tawale\, Jayshri Todase\, Manisha Bharati\n Abstract - Enforcement of helmet regulations and accurate vehicle identification remain essential components of intelligent traffic management systems. Conventional supervision approaches depend heavily on manual inspection\, which is labor-intensive and unsuitable for continuous large-scale monitoring. This study presents an automated framework for helmet violation detection and number plate lo-calization using the YOLOv8 deep learning architecture [3]. The proposed system supports static image analysis\, recorded video processing\, and live-stream detection within a unified pipeline. Performance is assessed using precision\, re-call\, and mean Average Precision (mAP@50). Experimental findings demonstrate consistent detection reliability and validate the framework’s applicability for real-time traffic surveillance systems.
CATEGORIES:VIRTUAL ROOM_10C
LOCATION:Virtual Room C\, Bangkok\, Thailand
SEQUENCE:0
UID:6fc038d12ffbea5df5bb0b7871efa187
URL:http://11thictisthailand.sched.com/event/6fc038d12ffbea5df5bb0b7871efa187
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260417T125213Z
DTSTART:20260411T023000Z
DTEND:20260411T043000Z
SUMMARY:Objective Evaluation of Transfer Learning Models for Multimodal Human Activity Recognition
DESCRIPTION:Authors - Gunjan Pareek\, Rajiv Singh\, Swati Nigam\n Abstract - This research examines the transfer learning deep learning models in multimodal human activity recognition based on wearable sensor data. Raw IMU signals are converted to Gramian Angular Field (GAF) images to improve the feature representation and tested on WISDM and PAMAP2 datasets of 18 activity classes. Five CNN models\, namely VGG16\, MobileNetV2\, ResNet50\, DenseNet121\, and EfficientNetB0\, are trained and evaluated in the same conditions and measured by classification accuracy\, statistical significance\, and computation efficiency. GAF representations are always better than raw signals. DenseNet121 and ResNet50 have 99% accuracy\, VGG16 and MobileNetV2 perform competitively and EfficientNetB0 performs worse. Most of the differences in performance are statistically significant (p &lt\; 0.05).
CATEGORIES:VIRTUAL ROOM_10C
LOCATION:Virtual Room C\, Bangkok\, Thailand
SEQUENCE:0
UID:5f3e6715c07b4c3c431500ab7596aa7d
URL:http://11thictisthailand.sched.com/event/5f3e6715c07b4c3c431500ab7596aa7d
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260417T125213Z
DTSTART:20260411T023000Z
DTEND:20260411T043000Z
SUMMARY:Prompt Engineering Beyond Techniques for Large Language Models: A Cross-Domain Review
DESCRIPTION:Authors - Devang Rupesh Dalvi\, Gaurav Suresh Malik\, Abhishek Jairaj Kunder\n Abstract - Prompt engineering has emerged as an essential paradigm in leveraging desired behaviors from large language models (LLMs) without altering their parameters. Although the majority of the current literature has revolved around the introduction of novel prompt engineering strategies\, there has been comparatively less emphasis on the contribution of the evaluation and optimization of prompts in concrete systems. In this paper\, we offer a specialized review of prompt engineering from an evaluation/optimization centric viewpoint with a larger nod to conceptual developments and illumination rather than detailing the comparisons of approaches. Furthermore\, we attempt to establish the concrete importance of prompt engineering via a real-life application\, which resulted in improved performances in tasks through the process of prompt refinement and informal evaluations without the need to change the architecture and weights of the models. The paper will also introduce the deficiencies in prompt engineering in the realms of re-producibility\, robustness\, and the unavailability of standardized approaches in the aspect of concrete evaluations.
CATEGORIES:VIRTUAL ROOM_10C
LOCATION:Virtual Room C\, Bangkok\, Thailand
SEQUENCE:0
UID:e356449418be09137b0c7dc04858e147
URL:http://11thictisthailand.sched.com/event/e356449418be09137b0c7dc04858e147
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260417T125213Z
DTSTART:20260411T023000Z
DTEND:20260411T043000Z
SUMMARY:A STUDY IN UNDERSTANDING THE ROLE OF GREEN COMPUTING IN ACHIEVING SUSTAINABLE DEVELOPMENT
DESCRIPTION:Authors - Reepu Abstract - This paper presents a hybrid diagnostic approach for an engine air-path benchmark characterised by environmental variability\, limited labelled faults\, and the need for reliable online decisions. The proposed method combines physics-guided residual features with datadriven temporal representation learning. Residuals derived from grey-box relations capture physically meaningful deviations\, while a lightweight encoder extracts temporal patterns across operating regimes. To enhance robustness under changing ambient conditions\, the model is explicitly conditioned on measured environmental variables and trained to favour stable representations across sessions. An open-set decision policy with calibrated rejection is incorporated to reduce misclassification when encountering unseen fault magnitudes or insufficient evidence. The method is evaluated under the official benchmark protocol using online processing constraints and standard metrics\, including false alarm rate\, detection rate\, isolation rate\, detection delay\, and computational cost. Results show improved reliability compared to competitive baselines\, with lower false alarms\, higher detection and isolation performance\, and stable behaviour across sessions. The approach remains computationally efficient and suitable for real-time deployment in practical diagnostic pipelines.
CATEGORIES:VIRTUAL ROOM_10D
LOCATION:Virtual Room D\, Bangkok\, Thailand
SEQUENCE:0
UID:5581dccdf2da210332f5981ac2c2eb7c
URL:http://11thictisthailand.sched.com/event/5581dccdf2da210332f5981ac2c2eb7c
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260417T125213Z
DTSTART:20260411T023000Z
DTEND:20260411T043000Z
SUMMARY:Cloud Computing Under Threat: Security Issues and Modern Solution
DESCRIPTION:Authors - Zala Bhargavi Harshadbhai\, Priyank D. Doshi\n Abstract - Brain tumor classification using MRI is very important for early diagnosis. While convolutional neural networks (CNNs) showed strong performance in medical image analysis\, but transformer-based architectures have recently gained popularity because of their ability to model long-range spatial dependencies through self-attention mechanisms. Our work lines up two such models - Vision Transformer and Swin Transformer to see how each handles tumor spot-ting in brain MRIs from the BRISC2025 collection. Same training setup applied to both keep things balanced and evaluated on the official test split for ensuring fairness. The official test set showed that both ViT (99.17 ± 0.26%) and Swin (99.27 ± 0.13%) have nearly identical predictive performance. Despite similar outcomes\, their inner workings differ sharply behind the scenes. Swin Trans-former have approximately 40% and inference cost by nearly 50% compared to ViT while maintaining similar accuracy. The study provides insights into the performance and efficiency of trade-offs between global and hierarchical trans-former architectures in medical imaging applications.
CATEGORIES:VIRTUAL ROOM_10D
LOCATION:Virtual Room D\, Bangkok\, Thailand
SEQUENCE:0
UID:3ee09fe542f2d4ce0e6607b8c6c8b58a
URL:http://11thictisthailand.sched.com/event/3ee09fe542f2d4ce0e6607b8c6c8b58a
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260417T125213Z
DTSTART:20260411T023000Z
DTEND:20260411T043000Z
SUMMARY:Design of an intelligent warehouse management system at Dobladoras Cotopaxi
DESCRIPTION:Authors - Eduardo J. Lopez\, Angelin Y. Alarcon\, Marco Riofrio-Morales\, Jose E. Naranjo Abstract - Higher education institutions often face challenges with fragmented student services and the reliance on manual workflows. Although Large Language Models (LLMs) present opportunities for service integration\, their application in administrative contexts introduces specific risks\, notably “transactional hallucinations” and the potential for unauthorized system actions. To explore potential mitigations for these challenges\, this paper presents SUEMas as a proposed alternative: a configuration-driven\, multi-agent ecosystem designed to help regulate LLM interactions within university domains. The proposed framework implements a Dynamic Tool Registry aimed at enforcing phase-aware tool exposure\, alongside a Closed-World Action Gating mechanism intended to restrict sensitive operations to verified session candidates. Initial evaluations of this proposal indicate that SUEMas can support consistent policy enforcement\, achieving high recall in RAG-based tasks under test conditions. Furthermore\, the system maintained strong multi-turn coherence while keeping latency low\, suggesting that structured security governance might practically coexist with conversational flexibility.
CATEGORIES:VIRTUAL ROOM_10D
LOCATION:Virtual Room D\, Bangkok\, Thailand
SEQUENCE:0
UID:6ecada6d541e00624e4f4ad824bc7227
URL:http://11thictisthailand.sched.com/event/6ecada6d541e00624e4f4ad824bc7227
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260417T125213Z
DTSTART:20260411T023000Z
DTEND:20260411T043000Z
SUMMARY:Digital Tax Administration and MSME Tax Compliance: Evidence from Indonesia’s Core Tax System in Supporting SDG 8 and SDG 17
DESCRIPTION:Authors - Surya Anugrah\, Dwi Handarini\, Eka Septariana Puspa\, Windy Permata Suyono\, Sabo Hermawan\, Irima Rahmadani\, Nazwa Febriyani Abstract - This paper presents the design\, modelling\, fabrication flow and analysis of multi-functional photonic crystal (PhC) nano-cavity sensors integrated with cantilever beams and diaphragms on a Silicon-On- Insulator (SOI) platform. The device architecture leverages defect-based two-dimensional PhC nano-cavities to obtain high quality (Q) factors and small mode volumes\, while mechanically compliant structures transduce force and pressure into measurable optical resonance shifts. Biochemical and chemical detection is achieved via refractive-index based transduction and temperature sensing via thermo-optic effects. A machinelearning (ML)-assisted calibration and sensitivity enhancement framework is proposed to improve resolution and compensate for fabrication tolerances. Fi-nite-difference time-domain (FDTD) optical simulations and finite-element method (FEM) mechanical simulations validate device performance. Noise analysis\, limit-of-detection (LOD) calculations\, and comparison against state-of-the-art devices are provided. The architecture is CMOS-compatible and suitable for lab-on-chip photonic sensing applications.
CATEGORIES:VIRTUAL ROOM_10D
LOCATION:Virtual Room D\, Bangkok\, Thailand
SEQUENCE:0
UID:9591fb64019fc9b616130e5e790dc90c
URL:http://11thictisthailand.sched.com/event/9591fb64019fc9b616130e5e790dc90c
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260417T125213Z
DTSTART:20260411T023000Z
DTEND:20260411T043000Z
SUMMARY:Early Breast Cancer Detection Using Deep Learning Techniques
DESCRIPTION:Authors - Raina Thakkar Abstract - This work investigates the Evolutionary Matrix Factorization (EMF) model proposed in Evolving Matrix-Factorization-Based Collaborative Filtering Using Genetic Programming. The EMF model employs genetic programming to optimize the matrix product function used in traditional Matrix Factorization recommender systems. The primary objective of this project is to develop a GP-based matrix factorization model that outperforms EMF in prediction accuracy. To facilitate comparison\, we first reproduce the EMF model’s results using standardized metrics. Subsequently\, we design and implement a custom data structure for GP\, along with the full pipeline for reproducible model execution. Finally\, we analyze the performance of our proposed model and compare it against EMF\, demonstrating its improvements in prediction precision.
CATEGORIES:VIRTUAL ROOM_10D
LOCATION:Virtual Room D\, Bangkok\, Thailand
SEQUENCE:0
UID:bf190e9e5d896c7371c2c0be2e73ed2f
URL:http://11thictisthailand.sched.com/event/bf190e9e5d896c7371c2c0be2e73ed2f
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260417T125213Z
DTSTART:20260411T023000Z
DTEND:20260411T043000Z
SUMMARY:GraphAML-X: Knowledge-Graph AML with Entity Resolution and Audit-Ready Case Reasoning
DESCRIPTION:Authors - Srikumar Nayak\n Abstract - Anti–money laundering (AML) monitoring is difficult because suspicious behavior is rarely a single abnormal transaction\; it is usually a short sequence of linked transfers across many entities. Standard tabular models miss these links and often produce alerts that are hard to justify during review. To address this\, we propose GraphAML-X\, a practical pipeline that turns raw transaction logs into a knowledge graph and produces case-level evidence for analysts. The main issue we target is fragmented identity (the same actor appearing under noisy identifiers) and weak case explanations (high scores without clear paths or rule triggers). GraphAML-X first performs entity resolution to merge duplicate accounts and identifiers using rules plus a learned match score\, so the graph represents real actors. It then learns temporal graph embeddings from the timeordered transaction network to capture multi-hop laundering patterns such as rapid circulation and hub–spoke behavior. Finally\, it combines graph risk with rule-hybrid case reasoning: regulatory red-flag rules propose candidate alerts\, and the graph model ranks them while emitting audit-ready evidence (top subgraph paths\, key neighbors\, and triggered rules) and alert-volume control via a calibrated threshold. Using the Micro-AmlSim dataset\, GraphAML-X achieves an AUC-ROC of 0.982 and an AUC-PR of 0.741\, improving the strongest baseline GNN by +0.034 AUC-PR. At a fixed alert rate of 1% of transactions\, it attains 0.686 recall while reducing false alerts by 18.9% compared to rule-only screening. These results show that GraphAML-X can improve detection while producing reviewable and policy-aligned AML cases.
CATEGORIES:VIRTUAL ROOM_10D
LOCATION:Virtual Room D\, Bangkok\, Thailand
SEQUENCE:0
UID:5bb8c490d6d6e22cbebfb942592f664c
URL:http://11thictisthailand.sched.com/event/5bb8c490d6d6e22cbebfb942592f664c
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260417T125213Z
DTSTART:20260411T023000Z
DTEND:20260411T043000Z
SUMMARY:Modeling Annotation-Style Variability for Annotation- Free Skin Lesion Segmentation
DESCRIPTION:Authors - Nguyen Ngoc Dung\, Doan Van Thang Abstract - Memory encryption is a key security requirement for modern computing systems\, addressing vulnerabilities between CPUs and main memory. Traditional storage encryption is insufficient for protecting volatile data in RAM\, which remains exposed to bus sniffing\, cold boot attacks\, and side-channel exploits. This paper therefore systematically reviews memory encryption techniques focused on hardware-based solutions like Intel Total Memory Encryption (TME)\, Multi-Key TME\, and AMD Secure Memory Encryption\, which provide robust protection while minimising performance overhead. The paper also explores integrity protection via Merkle trees and side-channel countermeasures against Differential Power Analysis and Simple Power Analysis attacks. Additionally\, granular memory encryption methods for multi-tenant environments are discussed\, highlighting their role in isolating sensitive data across security domains. By examining security guarantees and performance trade-offs\, we emphasise the necessity of efficient memory encryption to safeguard against evolving threats targeting the CPU-memory interface\, providing hardware engineers a foundation for ensuring data confidentiality and integrity.
CATEGORIES:VIRTUAL ROOM_10D
LOCATION:Virtual Room D\, Bangkok\, Thailand
SEQUENCE:0
UID:41fce6af42f13174e5d812725fb913a2
URL:http://11thictisthailand.sched.com/event/41fce6af42f13174e5d812725fb913a2
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260417T125213Z
DTSTART:20260411T023000Z
DTEND:20260411T043000Z
SUMMARY:Secure Medical Image Communication in the Post-Quantum Era: Development and Validation of a Comprehensive Dataset for Cryptographic Protocol Testing
DESCRIPTION:Authors - Chaitrasree S\, Srinidhi G A Abstract - The Research will shows how app-based omnichannel ICT-enabled marketing shapes customer engagement and service loyalty in the culinary hospitality industry within an urban emerging-market context. Drawing on an ICT-centered and service-systems perspective\, the research conceptualizes mobile applications as integration hubs that coordinate multiple service modes—delivery\, dine-in\, takeaway\, and drive-thru—into a unified customer experience. The study approach was using a quantitative design with a cross-sectional survey of 150 chain-restaurant mobile app users in Jakarta. Structural Equation Modeling (PLS-SEM) were used to analyze the data. The results shows that app-based omnichannel ICT-enabled marketing has a positive and significant effect on customer engagement and service loyalty. Customer engagement also demonstrates a positive effect on service loyalty and mediates the relationship between omnichannel ICT-enabled marketing and loyalty\, partially. These findings suggest that perceived ICT integration quality\, reflected through consistency\, seamlessness\, and coordination across service modes\, plays a pivotal role in translating technology-enabled service design into relational outcomes. This study contributes to the ICT literature specially in hospitality by extending omnichannel research beyond a marketing-centric perspective and highlighting the strategic role of integrated mobile app infrastructures in high-frequency culinary service environments. Based on a managerial standpoint\, the results emphasize the importance of treating mobile applications as core service platforms that support engagement-driven loyalty in chain-restaurant operations.
CATEGORIES:VIRTUAL ROOM_10D
LOCATION:Virtual Room D\, Bangkok\, Thailand
SEQUENCE:0
UID:b8709cd15d217b139130793b087678e1
URL:http://11thictisthailand.sched.com/event/b8709cd15d217b139130793b087678e1
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260417T125213Z
DTSTART:20260411T023000Z
DTEND:20260411T043000Z
SUMMARY:Support Vector Regression via Granular Ball Computing Approach
DESCRIPTION:Authors - Pei-Yi Hao Abstract - Digital transformation is reshaping education systems worldwide\, with significant implications for rural and underserved regions. In India\, initiatives aligned with the National Education Policy (2020) have promoted online learning platforms\, digital classrooms\, and technology-enabled teacher training to enhance access\, equity\, and quality in education. However\, rural schools continue to face structural challenges such as limited infrastructure\, digital divides\, and inadequate teacher preparedness\, which influence the effectiveness of digital integration.This conceptual paper examines the transformation of rural education in India from traditional teacher-centred classrooms to digitally enabled learning ecosystems. Grounded in Constructivist Learning Theory\, the Technology Acceptance Model (TAM)\, Diffusion of Innovation Theory\, and the TPACK framework\, the study proposes an integrated conceptual model linking digital infrastructure\, pedagogical innovation\, and teacher competence to improved access\, engagement\, and learning outcomes. The paper argues that digital transformation represents a systemic pedagogical and institutional reform rather than a mere technological shift. Its success depends on inclusive infrastructure development\, sustained teacher capacity building\, and context-sensitive implementation in rural settings.
CATEGORIES:VIRTUAL ROOM_10D
LOCATION:Virtual Room D\, Bangkok\, Thailand
SEQUENCE:0
UID:d86420fed28fcc74f471c1bbc08cd5f8
URL:http://11thictisthailand.sched.com/event/d86420fed28fcc74f471c1bbc08cd5f8
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260417T125213Z
DTSTART:20260411T023000Z
DTEND:20260411T043000Z
SUMMARY:Toward Sustainable Smart Contract Security: A Comprehensive Survey of Vulnerability Detection Methods and Approaches
DESCRIPTION:Authors - Aryan Dholi and Malathi P\n Abstract - Smart contract vulnerabilities have continuously been a major source of threat to blockchain security\, with billions of dollars being accounted for losses every year. This review paper delves into over 15 different detection methods utilizing static analysis\, dynamic monitoring\, machine learning\, and hybrid approaches. Sustainability metrics such as the Green Detection Score and the Energy Efficiency Index are first proposed by us to gauge the environmental cost in relation to the accuracy. From our review of 28 papers\, we conducted research studies to points out a significant discovery: transformer models reach 0.91 F1-score but use 1\,475× more energy than static analyzers. Hybrid approaches present a viable compromise with 0.89 F1-score and 62% energy savings. We thus offer deployment advice\, sustainable architecture templates\, and a 2030 roadmap for green blockchain security.
CATEGORIES:VIRTUAL ROOM_10D
LOCATION:Virtual Room D\, Bangkok\, Thailand
SEQUENCE:0
UID:58661d42a24e526d5b3cb3c6d3e25762
URL:http://11thictisthailand.sched.com/event/58661d42a24e526d5b3cb3c6d3e25762
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260417T125213Z
DTSTART:20260411T023000Z
DTEND:20260411T043000Z
SUMMARY:AI-Driven Automation in Software Engineering: A Survey of Fine-Tuning\, Agentic Workflows\, and Vector Intelligence
DESCRIPTION:Authors - Sohesh Gandhe\, Aditya Shirwalkar\, Prathmesh Jomde\, Shreyash Dhavale\, Anil M. Bhadgale\n Abstract - Automatically generating Unified Modeling Language (UML) diagrams from unstructured software requirements remains one of the persistent challenges in modern software engineering. This paper introduces an intelligent project management framework that transforms client-provided requirement documents into accurate UML diagrams with minimal human intervention. Our system leverages Optical Character Recognition (OCR) to extract text from various document formats\, employs a fine-tuned model for intelligent prompt synthesis\, and utilizes a fine-tuned CodeLLaMA 7B model trained on prompt-to-MermaidJS code mappings. The generated diagrams—including sequence diagrams\, flow charts\, and Gantt charts—are rendered in real time through an integrated Mermaid Live Editor\, providing immediate visual feedback within the project management interface. The experimental evaluation demonstrates substantial improvements in automation efficiency\, reduced manual modeling effort\, and improved consistency in UML generation. Our approach bridges the gap between natural language requirements and formal system design artifacts\, offering a practical solution for automated software documentation and project planning at scale.
CATEGORIES:VIRTUAL ROOM_10E
LOCATION:Virtual Room E\, Bangkok\, Thailand
SEQUENCE:0
UID:1aa1c5d3b78c497934a457877abf9518
URL:http://11thictisthailand.sched.com/event/1aa1c5d3b78c497934a457877abf9518
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260417T125213Z
DTSTART:20260411T023000Z
DTEND:20260411T043000Z
SUMMARY:AI-Enabled Data-Driven Modeling and Optimization of Aircraft Climb Performance for Real-Time Decision Support Systems
DESCRIPTION:Authors - Trupti Shripad Tagare\, K.L.Sudha\, Nagendra Kumble\, Sanketh T S\, Belliappa M\n Abstract - The current developments in the design of aircraft have remarkably improved their overall performance. The parameter Rate of Climb (RoC) plays a very vital role in planning the trajectory of the flight\, optimum fuel utilization and flight safety and is of significance for both technicians and pilots. The factors affecting RoC are weight of the aircraft\, its design\, and the atmospheric state. In this study\, the estimation of real time RoC using predictive AI and deep learning is presented. The model is trained on real time flight data collected from Radome Technologies\, Bengaluru. The parameters like drag\, thrust\, weight\, climb angle and airspeed are provided as inputs to the model after preprocessing. The results show that the system achieves an enhanced predication accuracy with R2 of 0.9396\, Root Mean Squared Error (RMSE) of 861.69 feet per minute and Mean Absolute Error (MAE) of 659 feet per minute. The efficiency and capability of several aircrafts can be measured and analysed using the rate of climb. The work greatly finds its important role in ground-based flight planning tools and in onboard decision-support systems. The fuel requirements for the aircraft can be reduced significantly by setting an optimum ROC. This will result in reduced costs and sustainable solutions. This work contributes to overall performance and safety\, as the aircraft will maintain the optimal ascent using AI driven climb profile optimization.
CATEGORIES:VIRTUAL ROOM_10E
LOCATION:Virtual Room E\, Bangkok\, Thailand
SEQUENCE:0
UID:16e6a9fc077292733e228ee0c0d3b760
URL:http://11thictisthailand.sched.com/event/16e6a9fc077292733e228ee0c0d3b760
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260417T125213Z
DTSTART:20260411T023000Z
DTEND:20260411T043000Z
SUMMARY:Application of Data Mining to Analyze the Emotional Impact of Virtual Reality on Users
DESCRIPTION:Authors - Glenn Erick Zambrano Estupinan\, Maria Genoveva Moreira Santos\n Abstract - Virtual Reality (VR) has gradually become an increasingly relevant technological tool in higher education\, not only because of its innovative nature\, but also due to its ability to create immersive experiences capable of capturing students’ attention and generating meaningful emotional responses. In this con-text\, the aim of this study was to analyze the immediate emotional impact produced by a virtual reality experience on university students\, using data mining techniques to identify patterns within the collected responses. The research followed a quantitative approach\, with a descriptive–correlational and cross-sectional design\, and included the participation of 305 students from the Faculty of Computer Sciences at the Technical University of Manabí. Each participant engaged in an immersive experience lasting approximately five minutes using the Meta Quest 2 device. After the activity\, a Likert-type questionnaire\, with a scale ranging from 1 to 5\, was applied in order to evaluate variables such as perceived immersion\, realism of the environment\, level of attention\, emotional interaction\, empathy\, and enthusiasm before and after the experience. The collected data were subsequently analyzed through exploratory and correlational analysis\, as well as through several data mining techniques\, including Principal Component Analysis (PCA)\, k-means clustering\, and Apriori association rules. Overall\, the results suggest that the virtual reality experience generated predominantly positive emotional responses among the students.
CATEGORIES:VIRTUAL ROOM_10E
LOCATION:Virtual Room E\, Bangkok\, Thailand
SEQUENCE:0
UID:a432d846ce877bf0b414b310170c5c54
URL:http://11thictisthailand.sched.com/event/a432d846ce877bf0b414b310170c5c54
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260417T125213Z
DTSTART:20260411T023000Z
DTEND:20260411T043000Z
SUMMARY:CyberSentinel: An Autonomous AI-Driven Framework for Real-Time Threat Hunting and Self-Healing in Windows Environments
DESCRIPTION:Authors - N. Revathy\, V. Latha Sivasankari\, Nikileshwar V\, Surendhiran G\, Abijith M\, Sheik Mohamed S\n Abstract - Enterprise networks face escalating cyber threats as cloud\, IoT\, and remote work adoption expand attack surfaces. Traditional signature-based detection and manual response suffer average breach detection intervals of 287 days\, failing to scale against rising alert volumes [9]. CyberSentinel addresses this through an autonomous pipeline processing Windows Security Event Logs: Isolation Forest anomaly detection on engineered behavioral features\, large language model (LLM) threat explanations via local Ollama inference\, and automated remediation including account deactivation\, process termination\, and firewall adjustment. A Flask web dashboard provides real-time threat visualization. Evaluation across 72 hours on a controlled Windows 10 Enterprise testbed with 28 injected anomalies confirms an F1-score of 0.78\, 84.2% remediation success\, and mean end-to-end latency of 24.7 seconds. The modular Python architecture enables fully autonomous operation on standard Windows hosts without dedicated SOC infrastructure.
CATEGORIES:VIRTUAL ROOM_10E
LOCATION:Virtual Room E\, Bangkok\, Thailand
SEQUENCE:0
UID:c8ec5f3aa08017e1fa0f6b10824b13b1
URL:http://11thictisthailand.sched.com/event/c8ec5f3aa08017e1fa0f6b10824b13b1
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260417T125213Z
DTSTART:20260411T023000Z
DTEND:20260411T043000Z
SUMMARY:Design and Implementation of a Covert Communication Channel Using TCP/IP Header Steganography
DESCRIPTION:Authors - Palgulla Rangaswami Reddy\, Palla Maheswara Rao\, Gogineni Hari Prasad\, Guthikonda Akhila\, T.V. Sai Krishna\n Abstract - The implementation and design of a covert communication channel that embeds hidden information within TCP/IP packet headers rather than within the actual payload of the packets is presented as a project. This is different than traditional embedding methods (steganography)\, which typically embed data into multime dia files\, in that steganography in this case utilizes header fields that are not cur rently in use or can be modified so that TCP/IP packets can transmit hidden data. The fields that are used to transmit hidden data are the IP Identification Field\, TCP Sequence Number\, TCP Acknowledgment Number\, and TCP Window Size. The sender module encodes and generates packets\, and the receiver retrieves packets\, extracts encoded bits\, and reassembles data from the encoded bits found in the packets. The integrity of the data is verified using a checksum (SHA-256) and packet loss is reported. The lack of a payload will further enhance the stealth various data transmission methods may enjoy as it will circumvent conventional intrusion detection techniques (which primarily examine the payload data within packets). This project will demonstrate the ability to use this or similar covert communication channels to implement covert communication systems. In addi tion\, covert communication channels can be used for different types of files and demonstrate the security and educational value of covert channel research in net work security.
CATEGORIES:VIRTUAL ROOM_10E
LOCATION:Virtual Room E\, Bangkok\, Thailand
SEQUENCE:0
UID:aaff138950b89628957d22553516d8b2
URL:http://11thictisthailand.sched.com/event/aaff138950b89628957d22553516d8b2
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260417T125213Z
DTSTART:20260411T023000Z
DTEND:20260411T043000Z
SUMMARY:Diabetes Prediction from Health Indicators: A Machine Learning Approach
DESCRIPTION:Authors - Busrat Jahan\, Kevin Osei-Onomah\, Mansi Bhavsar\, Hermela Dessie\, Apu Chandra Bhowmik\n Abstract - In the global health sector\, Diabetes is a major concern which needs accurate and effective models for early prediction. This work is quantitative re-search work. The dataset was collected from CDC Diabetes Health Indicators\, and we used Light Gradient Boosting Machine (LightGBM) model for predicting diabetes. Since this research work is binary classification-based work\, in our data preprocessing stage\, we used Synthetic Minority Oversampling Technique (SMOTE) for controlling class imbalance and for feature selection we used Chi-square test to improve the model performance. The proposed LightGBM model showed its ability to recognize complex correlation between diabetes-related health indicators with the training accuracy of 92% and a ROC-AUC score of 0.97 on the test dataset. Overall\, the findings highlight that predictive accuracy is significantly improved after applying both imbalance data controlling and most correlated feature selection techniques.
CATEGORIES:VIRTUAL ROOM_10E
LOCATION:Virtual Room E\, Bangkok\, Thailand
SEQUENCE:0
UID:0e764835a91f0eab1497e344aef597f2
URL:http://11thictisthailand.sched.com/event/0e764835a91f0eab1497e344aef597f2
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260417T125213Z
DTSTART:20260411T023000Z
DTEND:20260411T043000Z
SUMMARY:Digital Transformation of Rural Education in India: From Chalkboards to Connected Classrooms
DESCRIPTION:Authors - Ruby Bisht\, Amit Kumar Uniyal\n Abstract - Digital transformation is reshaping education systems worldwide\, with significant implications for rural and underserved regions. In India\, initiatives aligned with the National Education Policy (2020) have promoted online learning platforms\, digital classrooms\, and technology-enabled teacher training to enhance access\, equity\, and quality in education. However\, rural schools continue to face structural challenges such as limited infrastructure\, digital divides\, and inadequate teacher preparedness\, which influence the effectiveness of digital integration.This conceptual paper examines the transformation of rural education in India from traditional teacher-centred classrooms to digitally enabled learning ecosystems. Grounded in Constructivist Learning Theory\, the Technology Acceptance Model (TAM)\, Diffusion of Innovation Theory\, and the TPACK framework\, the study proposes an integrated conceptual model linking digital infrastructure\, pedagogical innovation\, and teacher competence to improved access\, engagement\, and learning outcomes. The paper argues that digital transformation represents a systemic pedagogical and institutional reform rather than a mere technological shift. Its success depends on inclusive infrastructure development\, sustained teacher capacity building\, and context-sensitive implementation in rural settings.
CATEGORIES:VIRTUAL ROOM_10E
LOCATION:Virtual Room E\, Bangkok\, Thailand
SEQUENCE:0
UID:27296d2bc56ff0a27656bd95411c7547
URL:http://11thictisthailand.sched.com/event/27296d2bc56ff0a27656bd95411c7547
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260417T125213Z
DTSTART:20260411T023000Z
DTEND:20260411T043000Z
SUMMARY:ICT for Sustainable Development: Enhancing Teachers’ Work–Life Balance and Job Satisfaction
DESCRIPTION:Authors - Ruby Bisht\, Amit Kumar Uniyal\n Abstract - The rapid growth of Information and Communication Technologies (ICT) has profoundly altered educational systems by redefining teaching practices\, institutional processes\, and professional expectations. Within the broader context of sustainable development and smart education\, ICT has emerged as an important facilitator of efficiency\, accessibility\, and innovation. This paper presents a conceptual analysis of how ICT can contribute to sustainable development through its influence on teachers’ work–life balance and job satisfaction in ICT-enabled learning environments. While ICT adoption has the potential to enhance instructional flexibility\, autonomy\, and efficiency\, excessive digital connectivity\, intensified workload\, and blurred work–life boundaries may adversely affect teachers’ well-being. The paper identifies work life balance as a key mediating factor linking ICT use to job satisfaction and long term professional sustainability. Furthermore\, the study situates teachers’ well being within the broader framework of sustainable development\, emphasizing its relevance to Sustainable Development Goals such as SDG 3 (Good Health and Well-Being)\, SDG 4 (Quality Education)\, and SDG 8 (Decent Work and Economic Growth). The analysis underscores the need for human-centred\, policy-driven\, and ethically oriented ICT integration strategies that prioritize teacher well-being alongside technological advancement. The paper contributes to the discourse on sustainable and intelligent education systems by highlighting that the long-term effectiveness of ICT-driven educational transformation depends on balanced digital practices that support teachers’ work–life balance and job satisfaction.
CATEGORIES:VIRTUAL ROOM_10E
LOCATION:Virtual Room E\, Bangkok\, Thailand
SEQUENCE:0
UID:a12e30d5c946467aa97f1a9f51421f10
URL:http://11thictisthailand.sched.com/event/a12e30d5c946467aa97f1a9f51421f10
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260417T125213Z
DTSTART:20260411T023000Z
DTEND:20260411T043000Z
SUMMARY:Predicting Future Communication Skill Demand in Higher Education Using LSTM: A Temporal Forecasting Framework for Employability Analytics
DESCRIPTION:Authors - Vasumathi R\, Kalpana Y\n Abstract - Graduate communication competency gaps represent a critical barrier to the workforce readiness in the Indian higher education\, yet existing assessment infrastructure measures a credential completion rather than the skill trajectories over time. This paper presents a LSTM-CDSF (Long Short-Term Memory Communication Demand and Skill Forecasting)\, a temporal deep learning based framework that predicts the future communication skill demand from the sequential monthly assessment records and also quantifies per skill gaps against the industry benchmarks. The framework operates on a synthetic dataset of 240 students observed over a period of 18 months calibrated to published NASSCOM and India Skills Report statistics. LSTM-CDSF achieves a Mean Absolute Error of 1.468\, RMSE of 1.837\, MAPE of 2.61%\, and R² of 0.9249 on a held-out test set of 480 sequences\, demonstrating consistent performance improvements over the Linear Regression\, ARIMA\, and a naïve baseline across all the evaluated metrics. Gap analysis reveals that the Digital Communication (gap: 25.4 points) and the Intercultural Communication (gap: 23.5 points) requires the most urgent curriculum interventions.
CATEGORIES:VIRTUAL ROOM_10E
LOCATION:Virtual Room E\, Bangkok\, Thailand
SEQUENCE:0
UID:fe5287a823e3816e14764483c7e39d2e
URL:http://11thictisthailand.sched.com/event/fe5287a823e3816e14764483c7e39d2e
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260417T125213Z
DTSTART:20260411T023000Z
DTEND:20260411T043000Z
SUMMARY:Specialized Low-Power ASIC Architecture for Computing Eigenvalues in Two-Dimensional Linear Transformations
DESCRIPTION:Authors - M. Kamaraju\, B. Rajasekhar\, V.N.V.R. Karthik\, V.N.L. Mahima\, Y.H.V. Satya Narayana\, R. Pujitha\n Abstract - This manuscript presents a dedicated Application-Specific Integrated Circuit (ASIC) architecture purpose-designed for computing eigenvalues of two-dimensional square matrices in resource-constrained embedded systems. The fundamental challenge motivating this work stems from the computational intensity of eigenvalue decomposition in digital signal processing\, robotics control systems\, and embedded analytics\, where conventional software implementations incur unacceptable latency and power overhead. The proposed solution lever-ages the closed-form algebraic solution inherent to 2×2 matrices\, eliminating iterative numerical methods and their associated performance penalties. Our design employs a direct characteristic-equation approach mapped onto dedicated arithmetic circuits including parallel multipliers\, adders\, and a specialized square-root computation unit implementing the non-restoring digit-re-currence algorithm. The Verilog RTL synthesized using Cadence Genus in a 180 nm CMOS standard cell library yields a compact silicon footprint of 1\,703 square micrometers utilizing 196 standard cells\, with measured power dissipation of 0.5738 milliwatts at 100-megahertz operation. Timing closure is achieved with positive slack under worst-case process-voltage-temperature conditions. The high dynamic-to-static power ratio of 98.66 percent to 1.34 percent indicates activity-dominated power behavior\, confirming successful implementation of low-leakage design principles. These metrics demonstrate that the proposed architecture constitutes an effective hardware acceleration solution for eigenvalue computation in battery-powered and always-on applications where conventional approaches prove infeasible.
CATEGORIES:VIRTUAL ROOM_10E
LOCATION:Virtual Room E\, Bangkok\, Thailand
SEQUENCE:0
UID:fb9cff25d5c761e3840d453bce53fb0a
URL:http://11thictisthailand.sched.com/event/fb9cff25d5c761e3840d453bce53fb0a
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260417T125213Z
DTSTART:20260411T023000Z
DTEND:20260411T043000Z
SUMMARY:A Hybrid CNN–RNN Framework for Audio-Based Bimodal Authentication
DESCRIPTION:Authors - Arpita Choudhury\, Pinki Roy\, Sivaji Bandyopadhyay Abstract - Modern agriculture faces several challenges such as uncertain crop selection\, inefficient fertilizer usage\, and changing soil conditions. To address these issues\, this research proposes an integrated AI/MLbased system that combines crop recommendation\, fertilizer recommendation\, and time-series prediction. The system utilizes IoT sensor data\, including soil nutrients (N\, P\, K) and environmental parameters such as temperature and humidity\, to support data-driven decision-making. Random Forest models are used for crop and fertilizer recommendation\, while an LSTM-based model is applied for predicting future soil conditions using time-series data. Basic preprocessing techniques are used to ensure data quality\, and results are presented through a simple and user-friendly dashboard. Experimental results demonstrate strong performance\, with 96% accuracy for crop recommendation and reliable prediction trends for time-series forecasting. Designed for offline use with minimal computational requirements\, the system is suitable for deployment in rural and resource-constrained environments\, highlighting the practical role of AI/ML in modern precision agriculture.
CATEGORIES:VIRTUAL ROOM_10F
LOCATION:Virtual Room F\, Bangkok\, Thailand
SEQUENCE:0
UID:1e9137e131a6daffa89528d301bd4a58
URL:http://11thictisthailand.sched.com/event/1e9137e131a6daffa89528d301bd4a58
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260417T125213Z
DTSTART:20260411T023000Z
DTEND:20260411T043000Z
SUMMARY:AyurKOSH dataset: Machine-Readable Ayurvedic Knowledge Graph for Computational Intelligence
DESCRIPTION:Authors - Sharayu Mirasdar\, Mangesh Bedekar\n Abstract - Ayurveda\, India's ancient system of medicine\, is full of inter-connected knowledge about diseases\, their symptoms\, herb and formulation (compounds). However\, texts such as Charaka Samhita are mostly unstructured and cannot be readily analysed computationally. This work presents AyurKOSH which is a machine-readable\, high-quality Ayurvedic dataset that is designed as a Knowledge Graph (KG) in order to support Artificial Intelligence driven research. The dataset is represented as subject–predicate–object triplets\, which enables semantic interoperability\, graph traversal\, and multi-hop inferencing across entities. The dataset is designed by following schema-driven ontology which standardizes relationships between various nodes such as diseases\, symptoms\, pharmacological attributes\, and compound formulations. DB Schema ensures consistency and computational tractability. AyurKOSH has the structured data of diseases and related symptoms\, drug preparations\, herbs and the detailed pharmacological properties are Rasa\, Guna\, Virya\, Vipaka\, Karma. The graph structure shows real-world biomedical network characteristics such as high sparsity and low average degree\, which makes it suitable for embedding-based learning\, graph neural networks\, and explainable AI frameworks. Moreover\, there is botanical metadata and herb-substitution relationships added for the prediction of synergy and repurposing of drugs. The dataset facilitates applications in biomedical NLP\, and automated reasoning systems and clinical decision assistance\, and pedagogy in integrative medicine. AyurKOSH became available for academic and non-commercial research under CC BY-NC-SA 4.0 license.
CATEGORIES:VIRTUAL ROOM_10F
LOCATION:Virtual Room F\, Bangkok\, Thailand
SEQUENCE:0
UID:3eb64ca664ae28b8cf7b2d31a84d6cec
URL:http://11thictisthailand.sched.com/event/3eb64ca664ae28b8cf7b2d31a84d6cec
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260417T125213Z
DTSTART:20260411T023000Z
DTEND:20260411T043000Z
SUMMARY:Dimensionally Reduced CNN Embeddings for Soundscape Data Classification with Active Learning
DESCRIPTION:Authors - Liz Huancapaza Hilasaca\, Maria Cristina Ferreira de Oliveira\, Rosane Minghim Abstract - The abstract of the study emphasizes the thorough discussion of cussword usage in Hollywood films over a period of thirty five years\, from 1990 to 2025\, particularly in genres such as Action\, Comedies\, and Romances. On the basis of a carefully selected dataset of cusswords from Kaggle along with a considerable subtitle file dataset (.srt)\, the results have been obtained to determine whether profanity has been used over the years with an appropriate level of intensity in the respective genres of films.
CATEGORIES:VIRTUAL ROOM_10F
LOCATION:Virtual Room F\, Bangkok\, Thailand
SEQUENCE:0
UID:b95b9a871fbca77df6fa0579d81af8ef
URL:http://11thictisthailand.sched.com/event/b95b9a871fbca77df6fa0579d81af8ef
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260417T125213Z
DTSTART:20260411T023000Z
DTEND:20260411T043000Z
SUMMARY:Optimizing Smart Home Scheduling Using Enhanced Metaheuristic Algorithms Under Electricity Constraints
DESCRIPTION:Authors - Lanja Azeez Abdalqadir\, Aram Mahmood Ahmed\, Rozha Kamal Ahmed\, Dirk Draheim\n Abstract - This study explores advanced metaheuristic optimization algorithms to improve smart home energy management under constrained electricity supply\, aiming to reduce costs and enhance energy efficiency. It addresses challenges such as dynamic pricing and unstable supply\, particularly common in developing regions. Five algorithms—Particle Swarm Optimization (PSO)\, Bat Algorithm (BAT)\, Fitness Dependent Optimization (FDO)\, Marine Predators Algorithm (MPA)\, and Single Candidate Optimization (SCO)—are evaluated\, along with enhanced versions of MPA\, FDO\, and SCO incorporating Lévy flight and Oppo-sition-Based Learning (OBL). OBL improves exploration and exploitation in FDO and MPA\, while Lévy flight enhances SCO’s ability to escape local optima. A novel cyclic rebounding technique is introduced to manage appliance sched-ules exceeding 24-hour limits. Tested across three scheduling scenarios\, results show that MPA-OBL consistently achieves the lowest energy costs. Overall\, the proposed enhancements significantly improve energy optimization in supply-constrained environments.
CATEGORIES:VIRTUAL ROOM_10F
LOCATION:Virtual Room F\, Bangkok\, Thailand
SEQUENCE:0
UID:867f3a75e3242fceb5f77915848cbfc0
URL:http://11thictisthailand.sched.com/event/867f3a75e3242fceb5f77915848cbfc0
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260417T125213Z
DTSTART:20260411T023000Z
DTEND:20260411T043000Z
SUMMARY:Review of Modern Energy Harvesting Strategies: Comparative Insights and Performance Evaluation
DESCRIPTION:Authors - Purva Trivedi\, Arun Parakh\, Shurbhit Surage Abstract - Awareness regarding consumer sentiments will benefit a business en tity and/or a company in making their marketing strategies more effective and engaging in the current digital marketing context. In traditional marketing sce narios\, since there is a lack of actual emotional aspect in expressing views in real time contexts\, it has always been challenging for a business to perform a signifi cant adjustment in their marketing campaigns and achieve a greater success rate. The proposed idea focuses on AI and ML-based approaches for sentiment analy sis in digital marketing. The framework is made up of seven core steps: data collection\, preprocessing and data cleaning\, sentiment analysis models\, feature extraction and model train ing\, sentiment classification and analysis\, insights and decision-making\, and ap plication in digital marketing. From social media to e-commerce reviews to online discussions\, consumer sentiment data comes from many digital sources. The text for analysis is standardized\, and noise is cleaned in data prepara tion. Then\, apart from other artificial intelligence-based sentiment classification models\, sentiments are classified as positive\, negative\, or neutral using lexicon based\, machine learning\, and deep learning approaches. The learned knowledge enables businesses to react dynamically to consumer sentiment\, target advertise ments\, and adjust marketing strategies. Businesses will be able to conduct more profitable promotions\, communicate with customers better\, and monitor real-time sentiment through this AI-driven sentiment analysis platform. The paper emphasizes the benefit of incorporating artificial intelligence in decision-making within digital marketing\, even in ad dressing issues like ambiguous sentiment expression management and multi-lan guage data. This paper provides a strategic way towards maximum customer in teraction and brand loyalty and also emphasizes the need for sentiment analysis that is sustained by available data in modern digital marketing.
CATEGORIES:VIRTUAL ROOM_10F
LOCATION:Virtual Room F\, Bangkok\, Thailand
SEQUENCE:0
UID:ec2de1e583a1b3bff1e158ffdd50423a
URL:http://11thictisthailand.sched.com/event/ec2de1e583a1b3bff1e158ffdd50423a
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260417T125213Z
DTSTART:20260411T023000Z
DTEND:20260411T043000Z
SUMMARY:Session-Level Impostor Detection Using Mouse-Based Behavioral Biometrics
DESCRIPTION:Authors - Soumyadeep Basak\, Shubham Sahu\, Sankur Kundu\, Ankita Ray Chowdhury Abstract - Hyperspectral image (HSI) classification requires effective modeling of high-dimensional spectral signatures and fine-grained spa tial structures while maintaining computational efficiency for real-world deployment. Although recent Transformer- and state-space-based ap proaches enhance long-range dependency modeling\, they often introduce substantial architectural complexity and computational overhead. To ad dress these challenges\, we propose MF-HSINet\, a lightweight dual branch framework that enables adaptive spectral–spatial fusion via se lective state-space modeling. The spectral branch captures inter-band de pendencies\, the spatial branch extracts local structural patterns\, and the proposed Mamba-Enhanced Attention Fusion (MAF) module integrates these complementary representations through selective state updates\, cross-attention\, and adaptive gating to achieve pixel-wise feature balanc ing. This design preserves discriminative local details while strengthen ing global contextual modeling with reduced parameter complexity. Ex tensive experiments on nine benchmark hyperspectral datasets demon strate that MF-HSINet achieves competitive and consistent performance in terms of Overall Accuracy\, Average Accuracy\, and Kappa coefficient\, while offering improved efficiency and inference speed\, making it suitable for practical and resource-constrained HSI applications.
CATEGORIES:VIRTUAL ROOM_10F
LOCATION:Virtual Room F\, Bangkok\, Thailand
SEQUENCE:0
UID:6fcc8f66178f9eb864f46cf31e292c48
URL:http://11thictisthailand.sched.com/event/6fcc8f66178f9eb864f46cf31e292c48
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260417T125213Z
DTSTART:20260411T023000Z
DTEND:20260411T043000Z
SUMMARY:SMART SYSTEM FOR IDENTIFYING LEAF DISEASE DETECTION USING AI AND COMPUTER VISION
DESCRIPTION:Authors - N. Revathy\, Tamilmani M\, Naveena P\, Mariya Nisha S\, Mega varshini V\, Karthik B Abstract - Virtual Learning Environments (VLEs) are commonly evaluated through expert-driven frameworks that lack reproducibility and objective prioritization of defining features. This study proposes a data-driven framework integrating a Systematic Literature Review (SLR) and the iKeyCriteria method to identify and logically classify core VLE characteristics. A corpus of peer-re-viewed studies was analyzed and divided into VLE-focused (P) and contrastive non-VLE (Q) contexts. Criteria extraction and validation were conducted using tfidf (Term Frequency-Inverse Document Frequency) weighting and Boolean logical matrices to determine necessary and sufficient conditions. Results indicate that structured delivery of learning materials (91.5% in P vs. 12.7% in Q) and shared collaborative workspaces (82.1% vs. 18.2%) function as sufficient but not necessary discriminators of VLEs. In contrast\, self-assessment and summative assessment appear frequently across both contexts and are therefore non-distinctive. The proposed framework provides a reproducible and bias-reduced mechanism for distinguishing defining VLE features\, bridging systematic review methodologies with logical condition analysis. These findings support evidence-based prioritization in the design and evaluation of digital learning systems and contribute to advancing objective classification approaches in educational technology research.
CATEGORIES:VIRTUAL ROOM_10F
LOCATION:Virtual Room F\, Bangkok\, Thailand
SEQUENCE:0
UID:7afe768f51db0741caa288bd8d164719
URL:http://11thictisthailand.sched.com/event/7afe768f51db0741caa288bd8d164719
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260417T125213Z
DTSTART:20260411T023000Z
DTEND:20260411T043000Z
SUMMARY:Toward an interactive data warehouse design based on a federeted ontology
DESCRIPTION:Authors - Tegawende Brigitte KIENTEGA\, Sadouanouan MALO Abstract - Navigation of mobile robots using GPS is widely available but use of GPS is sometimes either costly\, not suitable for security reason\, not available in indoor environments\, or underground operational fields. This work provides a greedy method of path planning for a mobile robot from a starting point to the given destination point in a GPS-denied field where a set of access points (AP) are deployed randomly. Using these APs\, the robot is able to calculate its current position at any moment as well as it chooses the next position to move further towards the destination. An efficient algorithm is designed to guide the robot to reach to its destination successfully taking into account that all the holes are convex\, if exists within the field of interest. An analysis of the deployment strategy of the APs is provided in order to guarantee the successful path planning by the robot without backtracking any sub path.
CATEGORIES:VIRTUAL ROOM_10F
LOCATION:Virtual Room F\, Bangkok\, Thailand
SEQUENCE:0
UID:e35079e5e043fe55bac3af475fa64bcf
URL:http://11thictisthailand.sched.com/event/e35079e5e043fe55bac3af475fa64bcf
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260417T125213Z
DTSTART:20260411T023000Z
DTEND:20260411T043000Z
SUMMARY:Wavelet-Driven Intelligent Model for Multi-Class Breast Cancer Diagnosis
DESCRIPTION:Authors - Ambati Abhinavya\, Jarupula Sunitha\, Raparthy Navya\, Rama Valupadasu Abstract - Internet of Things (IoT) devices are growing in domains because of their reliability and efficiency in monitoring\, real-time detection and automated support. However\, these IoT systems have also introduced security challenges. These devices are vulnerable to cyber threats\, where attackers exploit weak points in the system to steal sensitive information. One of the attacks is the Distributed Denial of Service (DDoS) attack\, which disrupts services by overwhelming systems and making them inaccessible to legitimate users. IoT devices are resource-constrained\, so reducing feature dimensionality is essential to lower computational overhead and complexity. IoT devices generate data for detecting cyber-attacks\, but sharing such data across organizations raises privacy concerns. To address these challenges\, the proposed approach is designed in two phases. In the first phase\, a hybrid feature selection technique using mutual information\, permutation feature importance\, and Greedy wrapper-based feature selection with cross-validation is applied to extract relevant features. In the second phase\, Federated Learning (FL) is applied to train the model without sharing raw data among clients. Within the FL framework\, Random Forest (RF) algorithm is utilized for training due to its robustness and classification capability. The proposed model is evaluated under two data distribution scenarios: mildly non-IID and strongly non-IID conditions. Experimental results demonstrate that the model achieved an accuracy of 99.69% in a mildly non-IID scenario and 98.36% under strongly non-IID conditions\, highlighting the effectiveness and reliability of the proposed framework for secure IoT-based DDoS attack detection.
CATEGORIES:VIRTUAL ROOM_10F
LOCATION:Virtual Room F\, Bangkok\, Thailand
SEQUENCE:0
UID:d4f92e49248f2d5d3b33aaa222e22bb5
URL:http://11thictisthailand.sched.com/event/d4f92e49248f2d5d3b33aaa222e22bb5
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260417T125213Z
DTSTART:20260411T023000Z
DTEND:20260411T043000Z
SUMMARY:Word-Level Plagiarism Detection using Cosine Similarity\, Euclidean Distance and Manhattan Distance Metrics
DESCRIPTION:Authors - Kalidasu Lochani Krishna Priya\, Nupur Ajit Kale\, Apeksha Pandurang Mujumale\, Anagha Vijaysinha Rajput\n Abstract - The &nbsp\;large &nbsp\;online &nbsp\;data &nbsp\;consist &nbsp\;of &nbsp\;duplication &nbsp\;and &nbsp\;plagiarized &nbsp\;contents. Due &nbsp\;to &nbsp\;Artificial &nbsp\;Intelligence\, &nbsp\;data &nbsp\;generation &nbsp\;has &nbsp\;become &nbsp\;very &nbsp\;easy. &nbsp\;But\, &nbsp\;it may &nbsp\;also &nbsp\;lack &nbsp\;an &nbsp\;ethical &nbsp\;data &nbsp\;generation &nbsp\;process. &nbsp\;Hence\, &nbsp\;there &nbsp\;is &nbsp\;a &nbsp\;need &nbsp\;of validating &nbsp\;plagiarism &nbsp\;free &nbsp\;data &nbsp\;for &nbsp\;authentic &nbsp\;usage. &nbsp\;In &nbsp\;this &nbsp\;research &nbsp\;work\, authors &nbsp\;focus &nbsp\;on &nbsp\;word-level &nbsp\;plagiarism &nbsp\;detection &nbsp\;methods &nbsp\;in &nbsp\;Natural &nbsp\;Language Processing. &nbsp\;The &nbsp\;proposed &nbsp\;method &nbsp\;uses &nbsp\;a &nbsp\;comparative &nbsp\;analysis &nbsp\;of &nbsp\;cosine similarity\, &nbsp\;Euclidean &nbsp\;distance &nbsp\;and &nbsp\;Manhattan &nbsp\;distance &nbsp\;methods &nbsp\;for &nbsp\;word-level plagiarism &nbsp\;detection &nbsp\;for &nbsp\;different &nbsp\;n-gram &nbsp\;sizes. &nbsp\;The &nbsp\;inculcation &nbsp\;of &nbsp\;n-gram &nbsp\;size improved &nbsp\;the &nbsp\;accuracy &nbsp\;compared &nbsp\;to &nbsp\;unigram &nbsp\;based &nbsp\;methods. &nbsp\;The &nbsp\;experimental results &nbsp\;of &nbsp\;the &nbsp\;cosine &nbsp\;similarity &nbsp\;method &nbsp\;outperform &nbsp\;Euclidean &nbsp\;and &nbsp\;Manhattan distance &nbsp\;methods &nbsp\;by &nbsp\;achieving &nbsp\;an &nbsp\;average &nbsp\;accuracy &nbsp\;range &nbsp\;of &nbsp\;88 &nbsp\;% &nbsp\;to &nbsp\;92 &nbsp\;% &nbsp\;and 75 &nbsp\;% &nbsp\;to &nbsp\;80 &nbsp\;% &nbsp\;for &nbsp\;direct &nbsp\;plagiarism &nbsp\;and &nbsp\;lightly &nbsp\;paraphrased &nbsp\;text &nbsp\;respectively. The future work is to identify reused images and visual contents.
CATEGORIES:VIRTUAL ROOM_10F
LOCATION:Virtual Room F\, Bangkok\, Thailand
SEQUENCE:0
UID:25b5c3e61872133f46e5addcf416554a
URL:http://11thictisthailand.sched.com/event/25b5c3e61872133f46e5addcf416554a
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260417T125213Z
DTSTART:20260411T023000Z
DTEND:20260411T043000Z
SUMMARY:A Novel Approach to Intrusion Detection in IoT Networks using Machine Learning Techniques
DESCRIPTION:Authors - Chinmayee Padhy\, Himansu Mohan Padhy\, Pranati Mishra\, Nabin Kumar Nag Abstract - Establishing an institution's excellence requires measuring their innovation and research accomplishments. Tracking\, verifying\, and evaluating innovation and research output in an efficient manner is currently constrained by a lack of efficient reporting systems and disorganized methods of obtaining the necessary data. The creation of InnovateHub\, a web-based\, secure\, scalable\, and cloud-based platform that provides a centralized system for analysing\, managing\, and visualizing research and innovation throughout the world's education sector. The InnovateHub provides a central location where a single point of access can be used to collect and process all types of innovation and research information via an effective system\; an interactive dashboard and analytical visualisation allows users easy access to relevant information. InnovateHub provides a role and permissions-based access control mechanism to preserve the data privacy and accountability of Administrators\, Faculty\, and Students. InnovateHub also supports Multi Factor Authentication (MFA) using JSON Web Tokens (JWT) for multiple layers of security and verification of user identity as well as One Time Passcode (OTP) confirmed through email\, and uses cryptographic hashing to provide a form of security for storing documents and provides a biometric face-based verification system (i.e.\, facial recognition) to authenticate a user during critical submission phases. Automated certificate generation and contribution recognition mechanisms at InnovateHub provide additional visibility into\, and motivation for\, users' contributions to the platform. Utilizing the MERN Stack and AWS for Hosting of MERN Stack: Utilizing the MERN Stack (MongoDB\, Express\, React\, Node.js) & AWS to Host a MERN Stack Application Innovative Hosting Solutions by AWS Include Amazon EC2 Instances to Host Both the Application Back End as Well as Application Front End Services and Amazon S3 for Secure and Scalable Storage of Research Document & Certificate Generation. Experimental Deployment Indicates Reliable Operation\, High Availability and Secure Handling of Data During Real Time Utilization within the Loss Prevention Environment. Innovate Hub Provides Real Time Analytics\, Secure Verification & Cloud Scaleability for Institutional Research Governance and the Development of a Data Driven Platform of Continuous Innovation and Growth through the Development of a Data Driven Innovation Platform.
CATEGORIES:VIRTUAL ROOM_10G
LOCATION:Virtual Room G\, Bangkok\, Thailand
SEQUENCE:0
UID:d95628dce157f5fc3c24cbc507773aa2
URL:http://11thictisthailand.sched.com/event/d95628dce157f5fc3c24cbc507773aa2
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260417T125213Z
DTSTART:20260411T023000Z
DTEND:20260411T043000Z
SUMMARY:A Semantic Video Search System Using Vision–Language Models and Vector Similarity Retrieval
DESCRIPTION:Authors - Pranav Rao\, Pranav S Acharya\, Rishika Nayana Naarayan\, Shreya M Hegde\, Pavan A C Abstract - The rapid expansion of cloud computing\, Internet of Things (IoT)\, 5G networks\, and distributed enterprise infrastructures has significantly in creased the complexity and attack surface of modern networks. Traditional net work security mechanisms—primarily based on static rules and signature-based detection—are increasingly ineffective against advanced persistent threats (APTs)\, zero-day exploits\, polymorphic malware\, and encrypted attack chan nels. Artificial Intelligence (AI) and Machine Learning (ML) have emerged as transformative technologies capable of enabling adaptive\, predictive\, and au tonomous cybersecurity systems. This paper presents a comprehensive technical framework for AI-driven network security. We propose a hybrid architecture in tegrating supervised classification\, unsupervised anomaly detection\, and deep learning-based behavioral modeling. Mathematical formulations for intrusion detection\, anomaly detection\, and adversarial robustness are provided. The framework is evaluated using benchmark intrusion detection datasets\, and per formance is analyzed using standard metrics including accuracy\, precision\, re call\, F1-score\, and ROC-AUC. Results demonstrate that AI-driven models sig nificantly outperform traditional signature-based approaches in detecting zero day and evasive attacks. The paper concludes by discussing adversarial machine learning risks and future directions toward autonomous and self-healing net work security ecosystems.
CATEGORIES:VIRTUAL ROOM_10G
LOCATION:Virtual Room G\, Bangkok\, Thailand
SEQUENCE:0
UID:2a11c3efa00463ff6cf8f03fa96340c7
URL:http://11thictisthailand.sched.com/event/2a11c3efa00463ff6cf8f03fa96340c7
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260417T125213Z
DTSTART:20260411T023000Z
DTEND:20260411T043000Z
SUMMARY:CAMSA (Context-Aware Mobile Systems with Agents): An Agentic\, Model-Driven Architecture
DESCRIPTION:Authors - Rosa Cristina Pesantez\, Estevan Gomez-Torres\, Cesar Adrian Guayasamin Abstract - The vast implementation of cloud computing has uplifted the modern IT practices by improving scalability\, flexibility\, and budget efficiency. In contrast\, there has been an increase in energy consumption\, which results in carbon emissions. This happens because of overusage\, overconsumption\, overprovisioning\, unused capacity\, and inefficient data center management. These days\, data centers act as the sole contributor to global greenhouse gas (GHG) emissions\; therefore\, sustainable cloud operations are essential in addressing this challenge. GreenOps\, or green operations\, defines the cloud deployment and operational practices that take place but also considers the environmental impact\; it depicts energy-efficient infrastructure design\, optimized resource usage\, virtualization\, and the integration of renewable energy resources. This survey presents a summary of green cloud computing\, including the current trends\, challenges\, energy-aware scheduling algorithms\, and optimization techniques for obtaining energy-efficient cloud deployment.
CATEGORIES:VIRTUAL ROOM_10G
LOCATION:Virtual Room G\, Bangkok\, Thailand
SEQUENCE:0
UID:ce6c63c7cb6be1d8e2f8705a07aa569e
URL:http://11thictisthailand.sched.com/event/ce6c63c7cb6be1d8e2f8705a07aa569e
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260417T125213Z
DTSTART:20260411T023000Z
DTEND:20260411T043000Z
SUMMARY:DC-DAFDN: Adversarially Robust Deepfake Detection via Dual-Stream Architecture with Frequency-Domain Analysis
DESCRIPTION:Authors - Govind Sambare\, Sarika Deokate\, Saurabh Dhakite\, Sahil Ambokar\, Gargi Barve Abstract - Static perimeter-based security architectures are now inef fective in the current threat scenario. The ability of attackers to obtain legitimate credentials and the presence of zero-day exploits often cause real-time breaches of the network perimeter. An area of concern is the real-time monitoring of these systems. In the current scenario\, security monitoring is performed in a segregated manner\, where network analysts analyze time-stamped network logs and identity analysts analyze time stamped login attempts\, without cross-referencing in real time between these two domains. The proposed solution is a fusion platform capable of ingestion of raw network transport data and real-time human element monitoring data. This is achieved through the integration of two dif ferent threat detection mechanisms using a FastAPI backend. The first threat detection system will be the Network Threat Detector (NTD)\, im plemented in Python and using the Scapy library to parse deep packet data in real time for flow analysis. The second threat detection system will be a JavaScript tracker designed for monitoring digital behavioral indicators and calculating real-time metrics such as mouse velocities\, ac celerations\, kinematic jerk\, and typing speeds. Real-time monitoring will be achieved through a machine learning framework with three different modules for inferring user intent using the Random Forest algorithm\, detecting anomalous statistical patterns using the Isolation Forest algo rithm\, and detecting malicious plaintext syntax using Logistic Regres sion. The system has been tested in a lab scenario and has been able to classify user session states into four different states: Engaged\, Con fused\, Frustrated and Suspicious with accuracy exceeding 95%. These digital behavioral indicators will be fed into the Network Transport Data (NTD)\, allowing the computation of a real-time risk score.
CATEGORIES:VIRTUAL ROOM_10G
LOCATION:Virtual Room G\, Bangkok\, Thailand
SEQUENCE:0
UID:763f2dd832a0429b2bac63066719a7fb
URL:http://11thictisthailand.sched.com/event/763f2dd832a0429b2bac63066719a7fb
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260417T125213Z
DTSTART:20260411T023000Z
DTEND:20260411T043000Z
SUMMARY:Developing a Touchless Control System using Computer Vision-Based Hand Gestures
DESCRIPTION:Authors - Duc Thinh Nguyen\, Diem Huyen Nguyen Ngoc\, Khoa Tran Thi-Minh\n Abstract - In the present-day context\, presentations and computer-based interac tion play a crucial role in various domains\, particularly in education and business. Traditionally\, users have to rely on physical devices such as mouses\, keyboards\, or laser. Although these devices meet the basic requirements\, they still reveal many limitations regarding mobility\, continuity\, and dependence on battery life. To address these limitations\, hand gesture-based presentation control systems have emerged as a promising solution due to their intuitive\, natural\, and engaging interaction style. This paper proposes a touchless system that enables users to control common desktop operations as well as presentations in a natural manner using hand gestures captured via a standard webcam. The proposed system lev erages OpenCV for real-time video acquisition and preprocessing\, while Medi aPipe framework is employed for hand tracking and landmark extraction. From the experiments\, our system can process in real-time with the accuracy of approx imately 92%. As a result\, users can seamlessly control slides\, use virtual mouse operations\, annotate presentation content\, and engage with the audience in a more interactive and natural way without physical contact.
CATEGORIES:VIRTUAL ROOM_10G
LOCATION:Virtual Room G\, Bangkok\, Thailand
SEQUENCE:0
UID:d0071f040cf0efc3a3cc6a5c580713f1
URL:http://11thictisthailand.sched.com/event/d0071f040cf0efc3a3cc6a5c580713f1
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260417T125213Z
DTSTART:20260411T023000Z
DTEND:20260411T043000Z
SUMMARY:Future Research Directions in AI-Driven Privacy Policy Compliance Analysis and Privacy Protection Frameworks
DESCRIPTION:Authors - Deepali Lokare\, Pankaj Chandre\, Prashant Dhotre\n Abstract - The rapid expansion of digital services has significantly increased the collection and processing of personal data through online platforms such as e-commerce systems\, social media applications\, and digital payment services. To regulate the use of personal information\, governments worldwide have introduced data protection regulations such as the General Data Protection Regulation (GDPR)\, the Digital Personal Data Protection Act (DPDPA)\, and the California Consumer Privacy Act (CCPA). Organizations publish privacy policies to inform users about their data practices\; however\, these policies are often lengthy\, complex\, and difficult for users to understand. Consequently\, users frequently accept privacy policies without fully reviewing how their personal data is collected\, processed\, and shared. Recent research has explored automated approaches for privacy policy analysis using artificial intelligence techniques\, including machine learning\, natural language processing\, and large language models. Retrieval-Augmented Generation (RAG) has further enhanced compliance evaluation by linking policy statements with relevant regulatory clauses. Despite these advancements\, challenges remain\, such as the lack of standardised datasets\, limited explainability of AI decisions\, dependence on prompt design\, and insufficient validation with regulatory experts. This paper discusses future research directions in AI-driven privacy policy compliance analysis and highlights emerging opportunities for improving regulatory compliance assessment\, user privacy protection\, and transparent privacy governance in digital ecosystems.
CATEGORIES:VIRTUAL ROOM_10G
LOCATION:Virtual Room G\, Bangkok\, Thailand
SEQUENCE:0
UID:881e9d68bd79eed3034159a423d4d3d9
URL:http://11thictisthailand.sched.com/event/881e9d68bd79eed3034159a423d4d3d9
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260417T125213Z
DTSTART:20260411T023000Z
DTEND:20260411T043000Z
SUMMARY:Multimodal Sketch-Conditioned Photorealistic Multiview Image Generation for 3D Modeling
DESCRIPTION:Authors - Samiksha M\, Sharanya G S\, Shrina Anahosur\, Surabhi K C\, Surabhi Narayan\n Abstract - Multi-angle image synthesis is highly important when it comes to the generation of 3D scenes. But the current methods are either ex pensive in terms of computational costs or lack photorealism in their outputs. We propose a novel sketch and text based multiview image generation approach that solves the above-mentioned problems by mak ing use of multimodal diffusion models efficiently. Our pipeline utilises DreamShaper v8 for converting the input sketch and text into a pho torealistic 2D image and then passes this 2D image into a fine-tuned Zero123plus model for the final generation of consistent multiview im ages\, showing a 43.69% improvement in the overall perceptual quality compared to baseline sketch-to-multiview models. Moreover\, our pipeline shows flexibility in scalability by generating anywhere from 6 to 64 consis tent multiview images according to the requirements of the downstream tasks. We demonstrate the success of our pipeline through extensive ex periments conducted using voxel-based grid approaches and Neural Ra diance Fields (NeRF). Our pipeline greatly reduces computational costs\, all while maintaining photorealism in the outputs\, confirming the poten tial of sketch and text based multimodal conditioning as an intuitive and efficient paradigm for controlled 3D content generation.
CATEGORIES:VIRTUAL ROOM_10G
LOCATION:Virtual Room G\, Bangkok\, Thailand
SEQUENCE:0
UID:b28aae1e3188f5cd1f46ee1b8c81cd09
URL:http://11thictisthailand.sched.com/event/b28aae1e3188f5cd1f46ee1b8c81cd09
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260417T125213Z
DTSTART:20260411T023000Z
DTEND:20260411T043000Z
SUMMARY:NETSENTINELS : NETWORK SECURITY PRACTICES FOR SURVIVING
DESCRIPTION:Authors - Balasubramanian M\, Arasu Prabhu V S\, Nalini Subramanian Abstract - Privilege Escalation is a major issue for securing Linux sys tems. When a user gains unauthorized root access he has the ability to access all system resources and manipulate them at will. In the past\, Linux has used Static Access Control Policies and User Space Monitoring Tools to secure system access. However\, these methods provide little in sight into how the kernel is modifying users credentials when permissions are changed. In this paper we propose a Kernel-Level solution to detect and prevent unauthorized privilege escalations. This detection/ preven tion occurs in real time via a Credential Transition Monitoring Mecha nism within the kernel layer\, which prevents the elevation of privileges by illegal means. To create the functionality necessary for the above\, a Linux Kernel Module (LKM) was created which utilizes kprobes to in tercept calls to the commit creds() function\, which is used to update a processes credentials in the kernel. To evaluate if the privilege escalation being requested is legitimate or malicious\, the LKM contains a Policy Based Evaluation Mechanism which evaluates each request to modify a process’s credentials. We tested our proposed solution using a con trolled test environment composed of a Virtual Machine (VM) running the Ubuntu Operating System. We ran two types of tests\, first were Le gitimate Administrative Operations utilizing the ”sudo” utility\, second were Simulated Privilege Escalation Attacks based upon SetUID Vul nerabilities. Our results show that the system effectively detected and blocked malicious privilege escalations\, while providing minimal over head to normal system operation.
CATEGORIES:VIRTUAL ROOM_10G
LOCATION:Virtual Room G\, Bangkok\, Thailand
SEQUENCE:0
UID:b7acfff52b38682e7958ad36dcb79882
URL:http://11thictisthailand.sched.com/event/b7acfff52b38682e7958ad36dcb79882
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260417T125213Z
DTSTART:20260411T023000Z
DTEND:20260411T043000Z
SUMMARY:Spatio-Temporal Crime Pattern Analysis and Forecasting Using Data Mining and Predictive Modeling
DESCRIPTION:Authors - Noel Milliones\, Vicente Pitogo\, Mark Phil Pacot Abstract - The sensitive information in the healthcare industry along with the increasing phe nomenon of the use of intelligent health-related devices makes it a very difficult task to ensure the privacy of patients as well as carry out precise analysis. The centralized methodology in cur-rent machine learning models requires the exchange of raw information of patients from different healthcare institutions and health related devices to the centralized computer system through the network. However\, due to the privacy issues and network traffic issues in this methodology\, the proposal proposes the development of a privacy-preserving health analytics platform. Here in this proposed methodology\, every healthcare center as well as health-related device has its own local machine learning model without transferring even a single piece of information outside. However\, the models also employ disease-specific models including CNN heart diseases models of 95 percent accuracy\, Gradient Boosting Classifier Diabetes models of 93 percent accuracy models\, along with SVM models of liver diseases along with 96 percent GridSearch models. Each edge device carries out the data preprocessing for the local environment\, as well as the processes of model training and the transmission of secure updates\, in such a way that the sensitive patient data has never left the environment. The platform presented proves the idea that edge computing and collaborative learning can lead to scalable and secure healthcare analytics with high predictive performance.
CATEGORIES:VIRTUAL ROOM_10G
LOCATION:Virtual Room G\, Bangkok\, Thailand
SEQUENCE:0
UID:b0204222037af222afcad17b8cc0e2c4
URL:http://11thictisthailand.sched.com/event/b0204222037af222afcad17b8cc0e2c4
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260417T125213Z
DTSTART:20260411T023000Z
DTEND:20260411T043000Z
SUMMARY:The Clinical Nursing Mobile Application Tool (CNMAT) for Follow-up Care in Lusaka\, Zambia: Design\, Development\, and Expert Usability Evaluation
DESCRIPTION:Authors - Etambuyu Akufuna\, Mayumbo Nyirenda\, Ruth Wahila\, Marjorie kabinga Makukula Abstract - As the primary cause of death worldwide\, cardiovascular disease (CVD) necessitates accurate early detection methods. We provide a machine learning approach for predicting heart illness using clinical health data that is enabled by the Internet of Things. An SVM classifier that was trained using 14 Cleveland Heart The disease dataset separates patients at high risk from those in good health. Preprocessing\, feature standardisation\, and GridSearch Cross-Validation hyperparameter optimisation are all included in the workflow. The model outperforms a number of benchmark techniques in the literature with an accuracy of 93.33% and an AUC of 0.97. A scalable and comprehensible basis for IoT-based clinical decision assistance is confirmed by comparative outcomes.
CATEGORIES:VIRTUAL ROOM_10G
LOCATION:Virtual Room G\, Bangkok\, Thailand
SEQUENCE:0
UID:192663db8c1403a33f19e91e353bab59
URL:http://11thictisthailand.sched.com/event/192663db8c1403a33f19e91e353bab59
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260417T125213Z
DTSTART:20260411T043000Z
DTEND:20260411T043200Z
SUMMARY:Session Chair Concluding Remarks
DESCRIPTION:\n
CATEGORIES:VIRTUAL ROOM_10A
LOCATION:Virtual Room A\, Bangkok\, Thailand
SEQUENCE:0
UID:8edd4348fb1d09fab65e20a718fe00f7
URL:http://11thictisthailand.sched.com/event/8edd4348fb1d09fab65e20a718fe00f7
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260417T125213Z
DTSTART:20260411T043000Z
DTEND:20260411T043200Z
SUMMARY:Session Chair Concluding Remarks
DESCRIPTION:\n
CATEGORIES:VIRTUAL ROOM_10B
LOCATION:Virtual Room B\, Bangkok\, Thailand
SEQUENCE:0
UID:4e3a2457db47b3dc8f3c4ce25f3a7520
URL:http://11thictisthailand.sched.com/event/4e3a2457db47b3dc8f3c4ce25f3a7520
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260417T125213Z
DTSTART:20260411T043000Z
DTEND:20260411T043200Z
SUMMARY:Session Chair Concluding Remarks
DESCRIPTION:\n
CATEGORIES:VIRTUAL ROOM_10C
LOCATION:Virtual Room C\, Bangkok\, Thailand
SEQUENCE:0
UID:ed12569141ac5bbb2306f51d4d292cff
URL:http://11thictisthailand.sched.com/event/ed12569141ac5bbb2306f51d4d292cff
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260417T125213Z
DTSTART:20260411T043000Z
DTEND:20260411T043200Z
SUMMARY:Session Chair Concluding Remarks
DESCRIPTION:\n
CATEGORIES:VIRTUAL ROOM_10D
LOCATION:Virtual Room D\, Bangkok\, Thailand
SEQUENCE:0
UID:b38e5caffe1e1ac61b24cb297303976e
URL:http://11thictisthailand.sched.com/event/b38e5caffe1e1ac61b24cb297303976e
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260417T125213Z
DTSTART:20260411T043000Z
DTEND:20260411T043200Z
SUMMARY:Session Chair Concluding Remarks
DESCRIPTION:\n
CATEGORIES:VIRTUAL ROOM_10E
LOCATION:Virtual Room E\, Bangkok\, Thailand
SEQUENCE:0
UID:9490d13e19ca756efb245b8a34210e1e
URL:http://11thictisthailand.sched.com/event/9490d13e19ca756efb245b8a34210e1e
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260417T125213Z
DTSTART:20260411T043000Z
DTEND:20260411T043200Z
SUMMARY:Session Chair Concluding Remarks
DESCRIPTION:\n
CATEGORIES:VIRTUAL ROOM_10F
LOCATION:Virtual Room F\, Bangkok\, Thailand
SEQUENCE:0
UID:06636b25d44a229f0d385c6607364f31
URL:http://11thictisthailand.sched.com/event/06636b25d44a229f0d385c6607364f31
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260417T125213Z
DTSTART:20260411T043000Z
DTEND:20260411T043200Z
SUMMARY:Session Chair Concluding Remarks
DESCRIPTION:\n
CATEGORIES:VIRTUAL ROOM_10G
LOCATION:Virtual Room G\, Bangkok\, Thailand
SEQUENCE:0
UID:c1a76fb6b2ae82eecfeba83080903ba3
URL:http://11thictisthailand.sched.com/event/c1a76fb6b2ae82eecfeba83080903ba3
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260417T125213Z
DTSTART:20260411T043200Z
DTEND:20260411T043500Z
SUMMARY:Session Closing and Information To Authors
DESCRIPTION:\n
CATEGORIES:VIRTUAL ROOM_10A
LOCATION:Virtual Room A\, Bangkok\, Thailand
SEQUENCE:0
UID:304939c976f987e8bbb3008d1ce93c99
URL:http://11thictisthailand.sched.com/event/304939c976f987e8bbb3008d1ce93c99
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260417T125213Z
DTSTART:20260411T043200Z
DTEND:20260411T043500Z
SUMMARY:Session Closing and Information To Authors
DESCRIPTION:\n
CATEGORIES:VIRTUAL ROOM_10B
LOCATION:Virtual Room B\, Bangkok\, Thailand
SEQUENCE:0
UID:8b33ee5e3130ea522781027e0e968032
URL:http://11thictisthailand.sched.com/event/8b33ee5e3130ea522781027e0e968032
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260417T125213Z
DTSTART:20260411T043200Z
DTEND:20260411T043500Z
SUMMARY:Session Closing and Information To Authors
DESCRIPTION:\n
CATEGORIES:VIRTUAL ROOM_10C
LOCATION:Virtual Room C\, Bangkok\, Thailand
SEQUENCE:0
UID:5d56f945cdd640a7a3738dfd0c3482a4
URL:http://11thictisthailand.sched.com/event/5d56f945cdd640a7a3738dfd0c3482a4
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260417T125213Z
DTSTART:20260411T043200Z
DTEND:20260411T043500Z
SUMMARY:Session Closing and Information To Authors
DESCRIPTION:\n
CATEGORIES:VIRTUAL ROOM_10D
LOCATION:Virtual Room D\, Bangkok\, Thailand
SEQUENCE:0
UID:2337998e97f80739a741802c17bcdb92
URL:http://11thictisthailand.sched.com/event/2337998e97f80739a741802c17bcdb92
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260417T125213Z
DTSTART:20260411T043200Z
DTEND:20260411T043500Z
SUMMARY:Session Closing and Information To Authors
DESCRIPTION:\n
CATEGORIES:VIRTUAL ROOM_10E
LOCATION:Virtual Room E\, Bangkok\, Thailand
SEQUENCE:0
UID:1ac7fcda4ea8448013dfc65971f51b5e
URL:http://11thictisthailand.sched.com/event/1ac7fcda4ea8448013dfc65971f51b5e
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260417T125213Z
DTSTART:20260411T043200Z
DTEND:20260411T043500Z
SUMMARY:Session Closing and Information To Authors
DESCRIPTION:\n
CATEGORIES:VIRTUAL ROOM_10F
LOCATION:Virtual Room F\, Bangkok\, Thailand
SEQUENCE:0
UID:32561988e0d889c05f5229afc196311b
URL:http://11thictisthailand.sched.com/event/32561988e0d889c05f5229afc196311b
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260417T125213Z
DTSTART:20260411T043200Z
DTEND:20260411T043500Z
SUMMARY:Session Closing and Information To Authors
DESCRIPTION:\n
CATEGORIES:VIRTUAL ROOM_10G
LOCATION:Virtual Room G\, Bangkok\, Thailand
SEQUENCE:0
UID:07eb72257986bf069c1626535afd3992
URL:http://11thictisthailand.sched.com/event/07eb72257986bf069c1626535afd3992
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260417T125213Z
DTSTART:20260411T051300Z
DTEND:20260411T051500Z
SUMMARY:Opening Remarks
DESCRIPTION:\n
CATEGORIES:VIRTUAL ROOM_11A
LOCATION:Virtual Room A\, Bangkok\, Thailand
SEQUENCE:0
UID:a34186975f989350531cb68bd07b3df7
URL:http://11thictisthailand.sched.com/event/a34186975f989350531cb68bd07b3df7
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260417T125213Z
DTSTART:20260411T051300Z
DTEND:20260411T051500Z
SUMMARY:Opening Remarks
DESCRIPTION:\n
CATEGORIES:VIRTUAL ROOM_11B
LOCATION:Virtual Room B\, Bangkok\, Thailand
SEQUENCE:0
UID:23e08fdae1d7e3edd9848c6eb3620c60
URL:http://11thictisthailand.sched.com/event/23e08fdae1d7e3edd9848c6eb3620c60
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260417T125213Z
DTSTART:20260411T051300Z
DTEND:20260411T051500Z
SUMMARY:Opening Remarks
DESCRIPTION:\n
CATEGORIES:VIRTUAL ROOM_11C
LOCATION:Virtual Room C\, Bangkok\, Thailand
SEQUENCE:0
UID:89bfe3c4eeff515d5877faf6d634a476
URL:http://11thictisthailand.sched.com/event/89bfe3c4eeff515d5877faf6d634a476
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260417T125213Z
DTSTART:20260411T051300Z
DTEND:20260411T051500Z
SUMMARY:Opening Remarks
DESCRIPTION:\n
CATEGORIES:VIRTUAL ROOM_11D
LOCATION:Virtual Room D\, Bangkok\, Thailand
SEQUENCE:0
UID:b7d29a8cb8a2b3d3ca52ed3c6d4fea11
URL:http://11thictisthailand.sched.com/event/b7d29a8cb8a2b3d3ca52ed3c6d4fea11
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260417T125213Z
DTSTART:20260411T051300Z
DTEND:20260411T051500Z
SUMMARY:Opening Remarks
DESCRIPTION:\n
CATEGORIES:VIRTUAL ROOM_11E
LOCATION:Virtual Room E\, Bangkok\, Thailand
SEQUENCE:0
UID:6ebc25e5d13afdf90176eb69d7005b66
URL:http://11thictisthailand.sched.com/event/6ebc25e5d13afdf90176eb69d7005b66
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260417T125213Z
DTSTART:20260411T051300Z
DTEND:20260411T051500Z
SUMMARY:Opening Remarks
DESCRIPTION:\n
CATEGORIES:VIRTUAL ROOM_11F
LOCATION:Virtual Room F\, Bangkok\, Thailand
SEQUENCE:0
UID:6b70845f82da7f2627189d27680666ff
URL:http://11thictisthailand.sched.com/event/6b70845f82da7f2627189d27680666ff
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260417T125213Z
DTSTART:20260411T051300Z
DTEND:20260411T051500Z
SUMMARY:Opening Remarks
DESCRIPTION:\n
CATEGORIES:VIRTUAL ROOM_11G
LOCATION:Virtual Room G\, Bangkok\, Thailand
SEQUENCE:0
UID:ff973fcd368f2150e06e77e896fcb7d1
URL:http://11thictisthailand.sched.com/event/ff973fcd368f2150e06e77e896fcb7d1
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260417T125213Z
DTSTART:20260411T051500Z
DTEND:20260411T071500Z
SUMMARY:A Disability-Centered Framework for Enhancing Accessibility and Universal Design in WebOPAC Systems: Emphasizing Visually Impaired Users in Thailand
DESCRIPTION:Authors - Thapanapong Sararat\, Ratanachote Thienmongkol\, Ruethai Nimnoi\, Wongpanya S. Nuankaew\, Pratya Nuankaew Abstract - Ensuring equitable access to library information systems is crucial in the digital era\, particularly for visually impaired users who rely on assistive technologies. WebOPACs are key gateways to resources\, but many remain difficult to use despite referencing accessibility standards. This study proposes a Disability-Centered Framework to improve accessibility and Universal Design in Thailand’s WebOPACs. Developed through design-based research\, it integrates international accessibility literature\, Universal Design principles\, WCAG 2.1\, and evaluation insights. The framework emphasizes three components: disability-focused design principles\, classification of visually impaired users and needs\, and task-specific accessibility requirements across perception\, navigation\, interaction\, and assistive-technology compatibility. It also incorporates Thai linguistic\, cultural\, and technological conditions to bridge global standards and local implementation. Findings indicate that meaningful accessibility requires iterative testing and ongoing refinement rather than a one-time compliance check. This framework guides libraries\, developers\, and policymakers in enhancing WebOPAC accessibility and supporting inclusive access for visually impaired users in Thailand.
CATEGORIES:VIRTUAL ROOM_11A
LOCATION:Virtual Room A\, Bangkok\, Thailand
SEQUENCE:0
UID:f881ad3e59a0dbb1c87e1f5541f076e9
URL:http://11thictisthailand.sched.com/event/f881ad3e59a0dbb1c87e1f5541f076e9
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260417T125213Z
DTSTART:20260411T051500Z
DTEND:20260411T071500Z
SUMMARY:AD-GENIUS:Adaptive Diffusion-based GENerative Framework with Intelligent User-guided Styling and LLM-driven prompt reasoning for automated advertisement generation
DESCRIPTION:Authors - Srishti Mathur\, Hrishita Patra\, Suhani Verma\, Dhruva R Prasad\, Shylaja S.S\n Abstract - The conventional way of preparing an advertisement is an elaborate process incorporating human subjectivity and human resources heavily dependent on creativity. Making advertisements by human effort can be regarded as an inefficient utilization of capital for small to medium-scale businesses due to increased cost of production. Even in current advancements in the development of generative techniques including LLM-based strategies for Advertisement Generation with Prompts\, creating apt prompts for the depiction of products requires human expertise\, making them less accessible. In order to overcome the challenges presented by the current models\, we introduce a fast\, affordable\, and scalable platform for the automation of advertisement generation for products leveraging the capabilities of pre-trained diffusion models. The proposed system requires no training or fine-tuning since everything is performed at the inference level. The AI-aware system for designing assists in the identification of color schemes and attributes from the images of the products\, whereas the descriptions and categories of the items help identify the theme and pattern recommendations for advertisements. These recommendations are channeled through a pre-trained Stable diffusion model guided by the LLaMA language model.
CATEGORIES:VIRTUAL ROOM_11A
LOCATION:Virtual Room A\, Bangkok\, Thailand
SEQUENCE:0
UID:8c6868aebd21fd214ab1d71aa20ae707
URL:http://11thictisthailand.sched.com/event/8c6868aebd21fd214ab1d71aa20ae707
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260417T125213Z
DTSTART:20260411T051500Z
DTEND:20260411T071500Z
SUMMARY:Areca Nut Disease and Ripeness Detection Model
DESCRIPTION:Authors - Sneha Visveswaran\, Tanmay Praveen\, Vidula Gurudutta\, Yamini Sridhar\, Chaithra T S5\n Abstract - Arecanut crop management has traditionally depended on manual inspection for disease identification and harvest readiness assessment\, a method that is both time-consuming and susceptible to human error. This study introduces an automated\, image-based system designed to address two primary tasks: disease classification and ripeness assessment. The proposed pipeline initiates with data preparation\, including resizing\, normalization\, and augmentation of arecanut images to enhance model robustness. A convolutional neural network architecture\, incorporating additional feature extraction and optimization layers\, is utilized to detect disease symptoms. A comparable deep-learning model is trained to classify ripeness stages based on visual characteristics. Model performance is evaluated using accuracy\, precision\, recall\, and F1-score metrics to ensure reliability. The system is implemented via a user-friendly web interface\, which allows real-time image uploads and immediate predictions\, thereby facilitating practical application for farmers and agricultural stakeholders. This integrated solution provides a scalable and cost-effective approach to improving crop monitoring and supporting data-driven decision-making in arecanut cultivation.
CATEGORIES:VIRTUAL ROOM_11A
LOCATION:Virtual Room A\, Bangkok\, Thailand
SEQUENCE:0
UID:fa0cdcfb4d09548e2bfd6fa8e4ecffe4
URL:http://11thictisthailand.sched.com/event/fa0cdcfb4d09548e2bfd6fa8e4ecffe4
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260417T125213Z
DTSTART:20260411T051500Z
DTEND:20260411T071500Z
SUMMARY:BotoSafe: A Web-App Voting Platform with Multifactor Authentication and Data Analytics
DESCRIPTION:Authors - Kate Lorreine M. Colot\, Anjeneth G. Molina\, Freely M. Wasawas\, Ferlyn P. Calanda\, Shem L. Gonzales\, Richard B. Colasito\n Abstract - Despite the availability of digital voting systems\, prior studies continue to identify gaps such as weak or voter authentication\, security vulnerabilities and insufficient fraud prevention mechanisms. This paper presents BotoSafe\, a secure and user-centered electronic voting (e-voting) platform developed for student government elections within educational institutions. The system implements multifactor authentication (MFA) using one-time password (OTP) verification and facial recognition with an anti-spoofing mechanism. To ensure the confidentiality and integrity of the voting process we employ the Advanced Encryption Standard in Galois/Counter Mode (AES-GCM). A developmental research design with a quantitative approach was used for the system development and evaluation. A mock election involving 84 students from Western Mindanao State University–Pagadian Campus was conducted\, followed by a post-assessment survey. Results from the System Usability Scale (SUS) yielded a score of 72.08\, indicating acceptable usability. User responses further showed that the system is easy to use\, safe\, and trustworthy for student elections. These findings indicate that BotoSafe is a viable e-voting solution for student government elections and may be further enhanced in future studies.
CATEGORIES:VIRTUAL ROOM_11A
LOCATION:Virtual Room A\, Bangkok\, Thailand
SEQUENCE:0
UID:172c8095fec3dd4b6b9ea11012d69752
URL:http://11thictisthailand.sched.com/event/172c8095fec3dd4b6b9ea11012d69752
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260417T125213Z
DTSTART:20260411T051500Z
DTEND:20260411T071500Z
SUMMARY:Consumer Trust\, Security\, and Awareness as Determinants of UPI Adoption among Private Sector Employees in Chandrapur
DESCRIPTION:Authors - Eliza Borkute\, Michael Savariapitchai\, Vijeyandra Shahu\, Deepak Sharma Chetan Parlikar Abstract - The current study aims to examine the significance of trust\, perceived security\, and awareness as factors that influence the adoption rate of UPI among private sector employees within the region of Chandrapur. The structured ques tionnaire has been designed to measure the following: a) trust factor regarding data protection and the correctness of the operations\; b) perceived security level of UPI\; c) awareness and knowledge about UPI functions\; d) demographic characteristics related to education level\, annual earning capacity\, and age\; and e) actual level of UPI adoption involving the use rate\, continuous use of UPI\, recommendations\, and its integration with financial activities. Nonparametric statistical methods were used\, including Spearman's rank correlation by investi gating the relationships of trust\, security perception\, awareness\, and adoption. Kruskal-Wallis tests were conducted for finding group differences between ed ucation level and usage frequency. The results have accounted for strong\, posi tive\, and statistically significant associations between consumer trust\, perceived security\, awareness\, and UPI adoption indicators. Education level revealed a partial moderating effect. Educated respondents tend to show higher trust and usage frequency in selected trust dimensions. However\, this is not the case in all the aspects of this dimension. Additionally\, the frequent users of UPI exhibit greater trust compared to the occasional users.
CATEGORIES:VIRTUAL ROOM_11A
LOCATION:Virtual Room A\, Bangkok\, Thailand
SEQUENCE:0
UID:4dbca6a0bddce27442d05942fb9a8049
URL:http://11thictisthailand.sched.com/event/4dbca6a0bddce27442d05942fb9a8049
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260417T125213Z
DTSTART:20260411T051500Z
DTEND:20260411T071500Z
SUMMARY:Enhanced Hybrid Fact-Checking for Believable Fake News Detection
DESCRIPTION:Authors - Tanay Balakrishna\, Vishal Kumar Rahul\, Yugabharathi E\, Samanvi P\, Vinay Joshi Abstract - The rapid spread of online news has made it more difficult to distinguish factually based reporting from misleading content. Many factchecking systems fail to detect false articles that appear professional and realistic\, which leads to widespread disinformation. Most models rely on surface characteristics and neglect semantic coherence and factual consistency. An Improved Hybrid Fact-Checking System that combines language understanding\, adversarial training\, rule-based plausibility checks\, and claim level web verification. These components run together in an ensemble model using BERT\, BiLSTM\, and an XGBoost meta-classifier to merge multiple evidence sources. Experiments on benchmark and curated datasets show an accuracy of 96.84% and a recall of 98%\, outperforming existing deep learning methods. The results show that blending linguistic analysis with external verification leads to a robust and interpretable approach for automated fact-checking
CATEGORIES:VIRTUAL ROOM_11A
LOCATION:Virtual Room A\, Bangkok\, Thailand
SEQUENCE:0
UID:f0f55039c5bb221d8e8d44fc15026482
URL:http://11thictisthailand.sched.com/event/f0f55039c5bb221d8e8d44fc15026482
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260417T125213Z
DTSTART:20260411T051500Z
DTEND:20260411T071500Z
SUMMARY:Halo CME Detection Using Aditya-L1 SWIS-ASPEX Data with Optimized LSTM Networks
DESCRIPTION:Authors - Shraddha Mankar\, Tanishq Thuse\, Prasanna Khebade\, Ritik Kumar Singh\, Shravani Shirpurkar Abstract - Coronal Mass Ejections (CMEs) occurring in halo configuration are considered one of the most serious threats coming from space weather that can cause disruptions to most of the Earth’s geomagnetic facilities. The present study is about a hybrid machine learning system that detects the halo CMEs and predicts their Earth impact in real-time using the particle data coming from the in-situ India’s Aditya-L1 mission placed at L1 Lagrange point. We apply physics-informed feature extraction from SWIS-ASPEX payload measurements\, obtaining alpha-to-proton density ratios\, bulk velocity gradients\, thermal parameters\, and velocity anisotropy indices as CME markings. A Long Short-Term Memory (LSTM) neural network tuned through the Spider Cuckoo Optimization Algorithm processes 24-hour sequential windows of these features to distinguish between CME and non-CME events. The system also includes the modeling of Parker spiral propagation for Earth arrival time estimation and it is made available through a React-based dashboard with explainable AI components. The performance of the system reveals that it achieves a 98% detection rate along with a mean absolute error of 0.001 in the prediction of the normalized impact index. A comparison with historic halo CME catalogs indicates that our method has reduced false alarms by 85% when compared with threshold-based techniques while keeping the recall rate at 90%. The operational version of the system grants a 45-60 minute notification for the arrival of the CME\, thus enabling the sensitive infrastructure to take preventive measures.
CATEGORIES:VIRTUAL ROOM_11A
LOCATION:Virtual Room A\, Bangkok\, Thailand
SEQUENCE:0
UID:0a01650d4f9320f5a4cdf165c4989200
URL:http://11thictisthailand.sched.com/event/0a01650d4f9320f5a4cdf165c4989200
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260417T125213Z
DTSTART:20260411T051500Z
DTEND:20260411T071500Z
SUMMARY:Project Based Learning and Digitalization Quality (SDG 4 & SDG 8) Evaluating Mobile First Web Design For SMEs
DESCRIPTION:Authors - Sabo Hermawan\, Ryna Parlyna\, Surya Anugrah\, Inkreswari Retno Hardini\, Bayu Suhendry\, Ria Rahma Nida\, Windy Permata Suyono\, Nur Lisa Rahmaningtyas\, Eka Septariana Puspa\, Cornellius Seno Adriano\, Alifah Nur Rahmawati Abstract - Smart parking systems have developed as a critical solution to urban challenges such as traffic congestion\, disorganized space utilization\, and delays in manual parking searches. This study presents a smart parking framework that employs a Raspberry Pi 4GB\, a camera module\, and a servo motor for automated parking management. The system integrates a Haar Cascade classifier and YOLOv11 for accurate vehicle detection\, while utilizing IR and ultrasonic sensors for obstacle identification. Real-time slot availability is displayed through an LCD interface. To ensure uninterrupted functionality\, the system is powered by a solar panel with a rechargeable battery\, enabling autonomous operation during power outages. Experimental results validate the reliability of vehicle recognition under varying illumination conditions\, efficient gate control\, and improved accuracy compared to conventional sensor-based approaches. This design offers a scalable\, cost-effective\, and energy-sustainable framework for urban parking solutions. Future work includes integration with cloud-based IoT platforms for centralized monitoring\, optimization of YOLOv11 through lightweight variants for edge deployment\, and extension to multi-level parking facilities with dynamic slot availability updates.
CATEGORIES:VIRTUAL ROOM_11A
LOCATION:Virtual Room A\, Bangkok\, Thailand
SEQUENCE:0
UID:094d1b120236a30ede4275224a20aaea
URL:http://11thictisthailand.sched.com/event/094d1b120236a30ede4275224a20aaea
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260417T125213Z
DTSTART:20260411T051500Z
DTEND:20260411T071500Z
SUMMARY:Student Driven Development of SQL Based Inventory Systems for MSMEs\, Integrating ChatGPT and SDG 12
DESCRIPTION:Authors - Sabo Hermawan\, Ryna Parlyna\, Surya Anugrah\, Inkreswari Retno Hardini\, Bayu Suhendry\, Ria Rahma Nida\, Eka Dewi Utari\, Nur Lisa Rahmaningtyas\, Cornellius Seno Adriano\, Alifah Nur Rahmawati Abstract - This research investigates the performance of transformer-based models\, BERT\, ALBERT\, and RoBERTa\, fine-tuned for sentiment classification on the Women’s Clothing E-Commerce Reviews dataset. The overall task was executed under both 3-class and 5-class sentiment classification schemes. Each model was trained under the same conditions and evaluated comprehensively. In the 3-class task\, RoBERTa achieved an F1-score of 91.7% and an AUC of 0.967\, surpassing previous best-reported results. BERT also showed competitive performance with an F1-score of 90.2% and an AUC of 0.951. These results establish the superior generalisation ability and discriminative power of transformer models\, particularly RoBERTa\, in classifying sentiment from unstructured review text. ALBERT\, while computationally efficient\, showed reduced accuracy and AUC\, indicating that extensive parameter sharing can hinder fine-grained sentiment resolution. The models exhibit broadly consistent behaviour in the 5-class setting\, with RoBERTa maintaining a lead. A modest decline in F1 and AUC is evident\, reflecting the greater difficulty introduced by finer class granularity. This research validates transformer architectures in a commercial Natural Language Processing scenario\, demonstrating the superiority of transformer-based models over traditional baselines in both accuracy and robustness.
CATEGORIES:VIRTUAL ROOM_11A
LOCATION:Virtual Room A\, Bangkok\, Thailand
SEQUENCE:0
UID:c03d3c3e15ac3b4efbd8dc15129b31a9
URL:http://11thictisthailand.sched.com/event/c03d3c3e15ac3b4efbd8dc15129b31a9
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260417T125213Z
DTSTART:20260411T051500Z
DTEND:20260411T071500Z
SUMMARY:The Impact of Social Media Influencers on Consumer Preferences and Purchase Intentions: An Empirical Study
DESCRIPTION:Authors - Jitesh Kriplani\, Michael Savariapitchai\, Vijeyandra Shahu\, Deepak Sharma\, Chetan Parlikar Abstract - The present investigation discusses the influence of social media in fluencers on the choices made by consumers and their buying behavior\, espe cially in connection with important personality traits of the influencer\, such as emotional engagement\, authenticity\, and reliability. The scientists conducted a well-organized survey questionnaire that collected primary information from 360 respondents in the Wardha District. Using Spearman's rank correlations re sults indicated strong\, positive and statistically significant relationships between influencer behaviors and consumer purchase behaviors indicating that influenc ers have a significant impact on consuming behaviors of consumers. The results of a one-way ANOVA found that perceptions of influencer credibility (includ ing honesty and sponsorship disclosure)\, as well as perceptions of emotional engagement and authenticity\, were significantly different depending on the fre quency of social media use by the participant. The demographic analysis also examined differences in consumer reactions depending on age\, gender\, and in come\, finding no significant difference across age groups\, but significant differ ences related to income and gender. The study concludes that consumer en gagement increases with more frequent social media use and influencer effec tiveness is significantly related to the authenticity\, transparency\, and credibility of the communication. Findings highlighted the need for focused influencer marketing content based on demographics providing empirical evidence of in fluencer marketing on consumer behavior.
CATEGORIES:VIRTUAL ROOM_11A
LOCATION:Virtual Room A\, Bangkok\, Thailand
SEQUENCE:0
UID:0cc68bdff1098f72d82096679da1ccbc
URL:http://11thictisthailand.sched.com/event/0cc68bdff1098f72d82096679da1ccbc
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260417T125213Z
DTSTART:20260411T051500Z
DTEND:20260411T071500Z
SUMMARY:A Comparative Analysis Framework for Automated Hu-man Detection
DESCRIPTION:Authors - Jason Elroy Martis\, Ronith\, Anvitha Rao\, Vignesh Salian\, Apoorva Shetty\, Philomina Princiya Mascarenhas Abstract - The task of recovering high-level architectures from embedded software systems is error-prone and difficult\, and state-of-the-art methods still rely on static analysis or heuristics and lack explainability. To address these challenges\, an explainable and automated method for recovering high-level architectural diagrams directly from source code is suggested. Specifically\, this method begins with the generation of function call graphs at the function level via static analysis and functions grouping into domain-agnostic component classes\, generating a component graph. Components are then augmented with semantic attributes learned via CodeBERT embeddings\, facilitating a light graph convolutional network (light GCN) model for learning-component interactions reflecting structure and semantics. Methods for explainability via gradients are incorporated for emphasizing prominent components and edges\, helping in developer understanding\, validation\, and tuning of predicted architectures. The performance of this method on several embedded projects showed accuracy as high as 91.87%\, precision of 96.48%\, recall of 86.90%\, and an F1-score of 91.44%. Use cases have shown successful extraction and interpretation of critical paths\, bottlenecks\, and unusual architectures and highlight explainable insights that enable efficient analysis and thus make it a highly significant progress in explainable AI for embedded software.
CATEGORIES:VIRTUAL ROOM_11B
LOCATION:Virtual Room B\, Bangkok\, Thailand
SEQUENCE:0
UID:4641f0ee7d62fa4265509ad8c86594c9
URL:http://11thictisthailand.sched.com/event/4641f0ee7d62fa4265509ad8c86594c9
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260417T125213Z
DTSTART:20260411T051500Z
DTEND:20260411T071500Z
SUMMARY:A NEXT-GENERATION EDGE–CLOUD ICT ARCHITECTURE FOR SELF-LEARNING AND AUTONOMOUS INTELLIGENT SYSTEMS
DESCRIPTION:Authors - Nazia Sultana\, Kumar P K Abstract - This research details the design and implementation of the AI-Driven Penalty Performance Analysis System\, a desktop application aimed at bridging the technological divide in football analytics. The system focuses particularly on environmental and situational influences\, such as crowd size\, match context\, and time of day\, on penalty outcomes. The system employs a robust data pipeline and a comparative evaluation of multiple machine learning classifiers to predict the likelihood of penalty kick success. Using a dataset of professional penalties\, we engineered novel features such as a ‘PressureIndex‘ to quantify situational fac tors. A suite of models\, including Logistic Regression\, K-Nearest Neighbours\, Decision Tree\, Random Forest\, and Gradient Boosting\, was trained and evalu ated. The optimal Gradient Boosting model achieved an accuracy of 79.1% and an AUC-ROC score of 0.87. A critical contribution is the integration of Explain able AI (XAI) using SHapley Additive exPlanations (SHAP)\, which transforms the system from a predictive ’black box’ into a transparent\, diagnostic tool. This provides coaches and players with actionable\, data-driven insights\, validating the system’s potential to democratize advanced sports analytics.
CATEGORIES:VIRTUAL ROOM_11B
LOCATION:Virtual Room B\, Bangkok\, Thailand
SEQUENCE:0
UID:5214179b010c93f226f6d8c9d3d2e25f
URL:http://11thictisthailand.sched.com/event/5214179b010c93f226f6d8c9d3d2e25f
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260417T125213Z
DTSTART:20260411T051500Z
DTEND:20260411T071500Z
SUMMARY:An AI-Enabled Fuzzy Intelligent Decision System for ESG Performance Measurement in Legal Infrastructure: Evidence from Indian Law Firms
DESCRIPTION:Authors - Ankita Manohar Walawalkar\, Chun-Wei Remen Lin\, Suman Kumar\, Ming-Yen Wang Abstract - The growing dependence on digital platforms for service discovery has revealed a substantial visibility gap for local businesses and independent service providers. Skilled professionals\, in-cluding electricians\, beauticians\, bakers\, tutors\, mechanics\, tailors\, and photographers\, frequently encounter challenges in reaching potential customers due to limited marketing expertise\, financial barriers\, and the lack of an integrated digital marketplace. This study introduces SkillBizz\, a mo-bile platform intended to connect local service providers and businesses with nearby users through a community-driven\, location-aware interface. The application features a scrollable home feed that prioritizes services and businesses based on geographical proximity\, allowing users to refine their results using filters such as service category\, budget range\, distance\, and popularity. Service providers can promote their offerings through multimedia posts that highlight services\, offers\, and announcements\, while users engage through familiar social media features\, including likes\, comments\, saves\, and shares. By facilitating free and organic visibility without reliance on paid advertising\, SkillBizz aims to support local entrepreneurship and foster trust-based service discovery. The proposed platform aims to create a digital marketplace that seeks to enhance com-munity engagement\, improve service accessibility\, and promote sustainable economic growth. In a short survey\, students rated the app’s ease of navigation and overall usefulness highly\, with an average satisfaction score of 4.5/5\, indicating strong acceptance and positive user experience. Shop owners noted that the app provides an easy way to share product updates\, promotions\, and service news directly with local customers\, with 80% expressing interest in continued usage due to time-saving benefits and improved customer reach.
CATEGORIES:VIRTUAL ROOM_11B
LOCATION:Virtual Room B\, Bangkok\, Thailand
SEQUENCE:0
UID:58064d183445f5421ab4edc4dcf06b54
URL:http://11thictisthailand.sched.com/event/58064d183445f5421ab4edc4dcf06b54
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260417T125213Z
DTSTART:20260411T051500Z
DTEND:20260411T071500Z
SUMMARY:An Integrated Anthropometric Index Based Framework for Child Malnutrition Prediction
DESCRIPTION:Authors - Karuna A. Katakadhond\, Manohar Madgi Abstract - Groundnut being a major oilseed crops\, contributes to nearly 10% of the total value of produce from agricultural crops in India. Several researches indicate that disease infestations at different stages of crop growth can lead to 30-70% of yield reduction and significant economic losses. This challenge can be addressed by using Artificial Intelligence (AI) based smart monitoring and recommendation systems through early detection\, identification\, and prediction of crop diseases. The primary objective of the study is to develop an AI driven smart monitoring framework capable of detecting\, identifying\, and predicting biotic and abiotic factors responsible for major disease occurrences in groundnut plants. Additionally\, the systems goal is to provide an effective and efficient recommendation system for sustainable agriculture from an integrated and practical perspective with its technical and economic performance to the farmers for managing the field level infestations. This includes prediction of diseases and timely recommendation of plant protection chemicals which may reduce the yield loss and enhance the productivity of the crop.
CATEGORIES:VIRTUAL ROOM_11B
LOCATION:Virtual Room B\, Bangkok\, Thailand
SEQUENCE:0
UID:d0cf462f146d117835d76a410e101b98
URL:http://11thictisthailand.sched.com/event/d0cf462f146d117835d76a410e101b98
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260417T125213Z
DTSTART:20260411T051500Z
DTEND:20260411T071500Z
SUMMARY:Eco-Friendly Low-Cost Smart Parking System Using Raspberry Pi and Vision-Based Approach
DESCRIPTION:Authors - Usman Ali\, Ghulam Mohayud Din\, Sajid\, Ayesha Ali\, Munawar Hussain\, Muhammad Mujeeb Akbar Abstract - The proliferation of misinformation on social media poses significant social\, political and economic risks. This research proposes an AI-based fake news detection system that leverages deep learning (BERT and LSTM) and Explainable Artificial Intelligence (XAI) frameworks to classify online fake news as Fake or True. The proposed architecture processes textual data through Natural Language Processing (NLP) techniques for semantic and contextual analysis. To ensure Interpretability\, SHAP and LIME is Integrated to visualize the rationale behind classification results. The system was trained using balanced datasets augmented through SMOTE\, achieving over 95% accuracy. A web-based interface was developed to facilitate real-time text and URL verification\, providing confidence scores and explanations. This approach minimizes human intervention\, enhances transparency and explainable frameworks yields an accurate and trust-worthy tool for combating misinformation.
CATEGORIES:VIRTUAL ROOM_11B
LOCATION:Virtual Room B\, Bangkok\, Thailand
SEQUENCE:0
UID:135952513cf097fd89bd660b77675691
URL:http://11thictisthailand.sched.com/event/135952513cf097fd89bd660b77675691
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260417T125213Z
DTSTART:20260411T051500Z
DTEND:20260411T071500Z
SUMMARY:From Black Box to Evidence: A Techno-Legal Framework for Liability Attribution in Autonomous Vehicles using XAI and Forensic Logging
DESCRIPTION:Authors - Suphawatchara Malanond\, Pongsarun Boonyopakorn Abstract - In the food supply industry\, differentiating between cultivated and weedy rice is crucial since the latter interferes with production and competes for essential resources. This research utilizes the YOLOv8 object detection model to automate the classification of rice grains to improve the separation process. The dataset was gathered during the harvesting phase and annotated utilizing a typical bounding-box methodology. Multiple configurations were evaluated with different model sizes (nano\, small\, medium) and training epochs. The optimal results attained a precision of 0.845\, a recall of 0.779\, and a mAP@50 of 0.822. These findings indicate that YOLOv8 enables near real-time identification at the grain level\, diminishing dependence on manual verification. The study yielded a lightweight prototype developed to demonstrate and reflect the application of the trained model for rapid\, image-based screening by non-technical users. The significance of the study lies in its support for more effective rice quality management and its contribution to strengthening food security and sustainable agriculture.
CATEGORIES:VIRTUAL ROOM_11B
LOCATION:Virtual Room B\, Bangkok\, Thailand
SEQUENCE:0
UID:6493bc83d3c97fb2d4716be85c3a754f
URL:http://11thictisthailand.sched.com/event/6493bc83d3c97fb2d4716be85c3a754f
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260417T125213Z
DTSTART:20260411T051500Z
DTEND:20260411T071500Z
SUMMARY:Integrating OCR and AI for Automated Medication Label and Medical Appointment Management in Digital Health Systems
DESCRIPTION:Authors - Wongpanya S. Nuankaew\, Parichat Janjom\, Khwanchiwa Khumdaeng\, Rattiyaporn Laemchat\, Thapanapong Sararat\, Pratya Nuankaew Abstract - Communication has been a topic as ancient as man and at the same time so important that\, over time\, various forms have been cre- ated to facilitate it\, among which stand out: mail\, telephony\, telegrams\, and fax\, to name a few. Nowadays many people use instant messaging applications to communicate with each other by feeling that their con- versations are protected. However\, that feeling could not be further from reality and should not be taken lightly\, since there are always groups focused on taking advantage of the vulnerability of this kind of applica- tions\, resulting in users’ privacy being compromised. In this paper\, we present the development of an instant messaging application that inte- grates a novel key establishment protocol based on a quantum-resistant algorithm. Our application employs cutting-edge lattice-based crypto- graphic techniques\, ensuring robust security against quantum attacks while maintaining operational efficiency. Obtained results show the ap- plication’s viability by offering a practical solution to safeguard mobile communication in the impending quantum era.
CATEGORIES:VIRTUAL ROOM_11B
LOCATION:Virtual Room B\, Bangkok\, Thailand
SEQUENCE:0
UID:20dfe31a0f6a38de5949988b0ed8aa87
URL:http://11thictisthailand.sched.com/event/20dfe31a0f6a38de5949988b0ed8aa87
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260417T125213Z
DTSTART:20260411T051500Z
DTEND:20260411T071500Z
SUMMARY:Leveraging MRI-Based Knowledge Vectors for Accurate Classification of Neurodegenerative Diseases
DESCRIPTION:Authors - Rashmi Shivanadhuni\, Martha Sheshikala Abstract - The rapid expansion of QR-code payment systems has positioned QRIS as a key component of Indonesia’s national digital payment infrastructure. While prior studies have largely focused on initial adoption\, limited empirical evidence explains the factors that sustain long-term usage of QR-code payments in mobile banking. This study investigates the determinants of sustained QRIS adoption by examining the roles of perceived usefulness\, perceived ease of use\, trust\, and perceived security\, with user satisfaction as a mediating variable. Using a quantitative approach\, survey data were collected from QRIS users of mobile banking applications and analyzed using Structural Equation Modeling (SEM). The results indicate that perceived usefulness\, trust\, and perceived security significantly enhance user satisfaction\, which in turn strongly predicts sustained adoption of QRIS in mobile banking. Perceived ease of use shows a weaker direct effect\, suggesting that post-adoption behavior is driven more by value realization and trust than by usability alone. These findings contribute to ICT and fintech literature by highlighting user satisfaction as a critical post-adoption mechanism for sustaining engagement with national digital payment systems. Practically\, the study offers insights for policymakers\, banks\, and system designers to strengthen the long-term viability of QR-based payment infrastructures through trust-building and value-enhancing strategies.
CATEGORIES:VIRTUAL ROOM_11B
LOCATION:Virtual Room B\, Bangkok\, Thailand
SEQUENCE:0
UID:e6e464b0dadad881ad67609d43dd790d
URL:http://11thictisthailand.sched.com/event/e6e464b0dadad881ad67609d43dd790d
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260417T125213Z
DTSTART:20260411T051500Z
DTEND:20260411T071500Z
SUMMARY:Smart Fuzzy AI modeling for optimizing low-carbon practices in the construction industry
DESCRIPTION:Authors - Suman Kumar\, Yeneneh Tamirat Negash\, Ankita Manohar Walawalkar\, Ming-Yen Wang Abstract - The backbone of modern data infrastructure which demands strategies to ensure data availability and uptime is Cloud Storage. This paper provides a complete overview of redundancy models and storage techniques that are used to maintain data availability and uptime in cloud storage systems. It covers core redundancy methods like data replication\, erasure coding\, Raid and disk-level redundancy\, multi-cloud redundancy and hybrid models. This paper also provides storage techniques that support data availability like distributed file systems and object storage platforms for scalability and flexible access. Additionally\, the paper also presents a literature review of key research findings and compares models that demonstrates substantial improvements in reliability and storage efficiency. It also covers the challenges related to computational complexity and monitoring precision. By synthesizing theoretical and practical perspectives\, this research guides the design of cloud storage solution which balance availability\, cost and recovery objectives and also help stakeholders to meet stringent service level agreements in increasingly heterogeneous and large-scale cloud infrastructure.
CATEGORIES:VIRTUAL ROOM_11B
LOCATION:Virtual Room B\, Bangkok\, Thailand
SEQUENCE:0
UID:42fd8d47a9ea7ab97d5a850bbea9a05a
URL:http://11thictisthailand.sched.com/event/42fd8d47a9ea7ab97d5a850bbea9a05a
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260417T125213Z
DTSTART:20260411T051500Z
DTEND:20260411T071500Z
SUMMARY:The Emergence\, Adoption\, and Challenges of AI-Driven Human Resources Management: A Systematic Review
DESCRIPTION:Authors - Massoud Moslehpour\, Suman Kumar\, Hanif Rizaldy\, Ankita Manohar Walawalkar\, Thanaporn Phattanaviroj Abstract - Accurate identification of paddy crop growth stages plays a crucial role in effective agricultural planning\, crop management\, and yield estimation. Paddy cultivation is highly sensitive to environmental conditions\, disease progression\, and growth variability\, making continuous and automated monitoring essential. This paper presents an AI-driven framework for automated paddy growth stage identification and yield readiness estimation using deep convolutional neural networks. The proposed system employs the EfficientNetV2-S architecture trained on heterogeneous paddy plant image datasets collected from multiple public sources. To address inconsistencies in labeling across datasets\, a semantic stage-mapping mechanism is introduced to map dataset-specific visual classes into standardized paddy growth stages. Furthermore\, a confidence-weighted yield readiness index is formulated to provide an interpretable estimate of crop maturity and harvest readiness based on predicted growth stages. The trained model is deployed using a Flask-based web application that supports real-time inference\, result visualization\, and storage of historical predictions. Experimental results demonstrate stable convergence\, high classification accuracy\, and reliable generalization across different growth stages. The proposed framework effectively bridges visual growth stage classification and yield estimation\, offering a practical and scalable solution for precision agriculture and decision support systems.
CATEGORIES:VIRTUAL ROOM_11B
LOCATION:Virtual Room B\, Bangkok\, Thailand
SEQUENCE:0
UID:4b75a9a6a5be85cf615362adc161a2b3
URL:http://11thictisthailand.sched.com/event/4b75a9a6a5be85cf615362adc161a2b3
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260417T125213Z
DTSTART:20260411T051500Z
DTEND:20260411T071500Z
SUMMARY:A Lightweight Trust-Based Blockchain Framework for Secure IoT Communication Under Network Attacks
DESCRIPTION:Authors - Shruti Thakur\, Shilpa Nikhil Bhosale\, Priti Prakash Jorvekar\, Sandeep Muktinath Chitalkar\, Harshala Shingne\, Rupali Vairagade Abstract - This study examines the effectiveness of ensemble learning models for detecting fraud in e-wallet transactions under extreme class imbalance and temporal dependence. Using the PaySim bench-mark dataset\, a time-aware experimental framework is developed that incorporates forward-chaining evaluation\, imbalance-aware resampling\, hyperparameter optimisation\, probability calibration\, and cost-sensitive threshold tuning to reflect real-world deployment conditions. RF and XGBoost are systematically compared across multiple dataset scales and train–test splitting strategies. Empirical findings show that XGBoost consistently outperforms RF\, achieving the highest F1-score\, maintaining PR-AUC above 0.88\, and demonstrating near-perfect ROC-AUC\, indicating strong discriminative capability. Following isotonic calibration\, XGBoost also produces the lowest Brier score\, highlighting superior probability reliability for risk-based decisions. Performance gains plateau beyond a 75% training share\, while XGBoost preserves stable performance as the test window expands\, unlike RF. Overall\, the results support prioritising gradient boosting models\, adopting time-aware validation\, and integrating calibrated risk scoring in operational e-wallet fraud detection systems.
CATEGORIES:VIRTUAL ROOM_11C
LOCATION:Virtual Room C\, Bangkok\, Thailand
SEQUENCE:0
UID:4dde805aa108d6470e9d90d7e55c6789
URL:http://11thictisthailand.sched.com/event/4dde805aa108d6470e9d90d7e55c6789
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260417T125213Z
DTSTART:20260411T051500Z
DTEND:20260411T071500Z
SUMMARY:Artificial Intelligence in Migration Management: Opportunities\, Challenges\, and Policy Implications
DESCRIPTION:Authors - Shoh-Jakhon Khamdаmov\, Muazzam Akramova\, Rano Abdullaevna Sadikova\, Azamat Kasimov\, Jasurbek Pozilovich Kurbonov\, Alisher Bakberganovich Sherov\, Dilshoda Akramova Abstract - Attention Deficit Hyperactivity Disorder (ADHD) is one of the most common neurodevelopmental disorders in children\, characterized by inattention\, hyperactivity\, and impulsivity that impair academic and social functioning. Due to its heterogeneous presentation and symptom overlap with other cognitive disorders\, early and accurate diagnosis remains challenging. This study proposes a multimodal machine learning framework integrating behavioral\, neuroimaging\, and physiological data to predict ADHD in children. Convolutional Neural Networks (CNNs) are used to extract features from brain MRI scans\, Long Short-Term Memory (LSTM) networks model temporal patterns in physiological signals such as EEG and heart rate variability\, and ensemble learning methods incorporate behavioral and clinical attributes. Both feature-level and decision-level fusion strategies are evaluated. Results on benchmark datasets show that the multimodal model consistently outperforms unimodal approaches in accuracy\, sensitivity\, and F1- score\, demonstrating the potential of AI-driven multimodal systems for early\, objective\, and interpretable ADHD diagnosis.
CATEGORIES:VIRTUAL ROOM_11C
LOCATION:Virtual Room C\, Bangkok\, Thailand
SEQUENCE:0
UID:5a67ec5ee3e3aa71430ecb5c41187b14
URL:http://11thictisthailand.sched.com/event/5a67ec5ee3e3aa71430ecb5c41187b14
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260417T125213Z
DTSTART:20260411T051500Z
DTEND:20260411T071500Z
SUMMARY:Explainable AI for Automated Risk Assessment in Phishing Email Detection
DESCRIPTION:Authors - Matjere Matsebe\, Nobubele Angel Shozi Abstract - It is possible to increase the acceptability of small wind turbines for wind regions with low wind velocities for rural as well as urban sectors by placing them inside diffusers. The research on development of various diffusers is a major re-search area nowadays. Curved flanged diffusers can deliver better performance by adding a cylindrical throat section between converging and diverging sections. This research paper presents a systematic study on short curved flanged diffusers with converging-diverging sections and extended uniform throat between them. Twenty-five diffuser models are studied using Computational Fluid Dynamics using ANSYS Fluent. These models are finalized using the design of experiments for six variables at five levels. The throat diameter for all diffuser models is fixed. The investigation is performed by considering radial average velocity and percentage velocity variation along the radial planes. The global velocities are observed as 1.18 to 1.47 times that of the radial average velocities. The diffuser dimensions are optimized to maximize radial average velocity and to minimize the velocity variation along the radial planes. The diffuser with optimized dimensions is manufactured and tested experimentally in a wind tunnel. Good matching is seen between the predicted results and experimental results. The optimized diffuser has the ability to produce more than two times the power that of the turbine without a diffuser.
CATEGORIES:VIRTUAL ROOM_11C
LOCATION:Virtual Room C\, Bangkok\, Thailand
SEQUENCE:0
UID:d52801cd5af639de53f619f9908f9cbd
URL:http://11thictisthailand.sched.com/event/d52801cd5af639de53f619f9908f9cbd
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260417T125213Z
DTSTART:20260411T051500Z
DTEND:20260411T071500Z
SUMMARY:Grammatical POS tagging of lexical units of the Uzbek language (adjective and numeral word classes)
DESCRIPTION:Authors - Murodov Gayrat Nekovich\, Kholmuhamedov Bakhtiyor Farkhodovich\, Avezov Sukhrob Sobirovich\, Khudayberganov Nizomaddin Uktambay ogli\, Yunusova Maftuna Shokirovna\, Mansurova Shahinabonu Najmiddin qizi Abstract - The classification of ECG signals continues to be a major focus in intelligent healthcare systems\, especially for the early identification of cardiac arrhythmias. In this work\, we propose a hybrid probabilistic neural strategy that integrates Bayesian Networks with Artificial Neural Networks (ANNs) to enhance the reliability of ECG classification. The approach begins by extracting informative ECG features\, such as crosscorrelation and phase-based characteristics. A Bayesian Network is then applied to model the probabilistic dependencies among these features and identify those most relevant to classification. At the same time\, an ANN is trained on the refined feature set to learn complex non-linear patterns present in the signals. The two models are subsequently combined through a weighted voting mechanism to form an ensemble classifier. Experimental evaluation using an ECG dataset indicates that the proposed ensemble achieves higher accuracy and stability compared to its individual components. Notably\, the method demonstrates strong capability in distinguishing multiple arrhythmia categories\, which are typically difficult to classify. Overall\, the results highlight the promise of hybrid probabilistic–neural models for improving automated ECG interpretation and supporting more accurate diagnosis of cardiac abnormalities.
CATEGORIES:VIRTUAL ROOM_11C
LOCATION:Virtual Room C\, Bangkok\, Thailand
SEQUENCE:0
UID:a27616aa5ce1386c7e7417f186fd3232
URL:http://11thictisthailand.sched.com/event/a27616aa5ce1386c7e7417f186fd3232
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260417T125213Z
DTSTART:20260411T051500Z
DTEND:20260411T071500Z
SUMMARY:Haze to Vision: Pipeline for Underwater Image Restoration\, Enhancement and Object Detection
DESCRIPTION:Authors - K S Shubham\, Uma Mudengudi\, Ujwala Patil Abstract - Secure\, compliant\, and interoperable data sharing remains a core bottleneck for cross-organizational analytics and AI\, particularly under evolving privacy regulations\, contractual obligations\, and adversarial threats. This paper introduces HARMONIA\, a pluggable\, risk-aware data sharing framework that integrates policy-as-code enforcement\, continuous compliance monitoring\, provenance-grade evidence\, and revocation with machine unlearning. HARMONIA is inspired by the iterative Analyzer–Mechanic and Conductor–Observer operational pattern described in the HARMONIA strategic perspective\, generalizing its quality-gate-and-repair loop to a policyand- risk-gated release lifecycle. We formalize an architecture that separates governance\, control\, and data planes\; define a release-mode lattice that enables explainable fallbacks among raw export\, masking\, kanonymity\, differential privacy\, synthetic data\, query-only access\, and federated compute\; and propose an evidence model aligned with W3C PROV. We provide a proof-of-concept (POC) blueprint implemented with commodity components (OPA\, OAuth2/OIDC\, PostgreSQL\, and object storage) and specify interfaces that support end-to-end request-to-release-to-revocation workflows\, including batch-scoped unlearning for model derivatives. The paper concludes with an evaluation methodology and a standards-aligned roadmap for deployment in sovereign data spaces.
CATEGORIES:VIRTUAL ROOM_11C
LOCATION:Virtual Room C\, Bangkok\, Thailand
SEQUENCE:0
UID:b2cfee13d12d29560366ade299b1a95a
URL:http://11thictisthailand.sched.com/event/b2cfee13d12d29560366ade299b1a95a
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260417T125213Z
DTSTART:20260411T051500Z
DTEND:20260411T071500Z
SUMMARY:Moderating Effects of E-Consultation Training on Sustainable M-Health Adoption Among Physicians
DESCRIPTION:Authors - Mehzabul Hoque Nahid\, Fatema Tuz Zahra\, Mubashshir Bin Mahbub\, Saleh Ahmed Jalal Siam Abstract - Personalizing learning in higher education presents a significant challenge due to the difficulty of providing individual feedback to large student cohorts. This study proposes an intelligent tutoring system based on a multi-agent architecture utilizing Large Language Models (LLMs) to address scalability and adaptability issues. The proposed architecture integrates two complementary subsystems: a reactive module that answers student queries using Retrieval-Augmented Generation (RAG) to ensure accuracy based on course materials\, and a proactive module that autonomously analyzes student profiles to generate personalized study plans without direct instructor intervention. The system was implemented using Lang- Graph for agent orchestration and MongoDB for state persistence. Experimental validation was conducted using a curated golden dataset from a university course. Results demonstrate a retrieval precision of 94.2% and a faithfulness score of 87.8%\, significantly mitigating hallucinations common in monolithic models. Furthermore\, the operational cost analysis indicates high financial viability for mass implementation. This dual approach offers a robust solution for automated\, highquality educational support\, effectively bridging the gap between standardized teaching and personalized learning needs.
CATEGORIES:VIRTUAL ROOM_11C
LOCATION:Virtual Room C\, Bangkok\, Thailand
SEQUENCE:0
UID:472d934ad1508b8e94f200261f5dee37
URL:http://11thictisthailand.sched.com/event/472d934ad1508b8e94f200261f5dee37
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260417T125213Z
DTSTART:20260411T051500Z
DTEND:20260411T071500Z
SUMMARY:Quantum-Enhanced Healthcare Data Augmentation: A Three-Pillar Framework Integrating QRNG\, Statistical AI\, and Generative AI for Clinical Data Synthesis
DESCRIPTION:Authors - Vemuri Bharath Kumar\, Anjan Babu G\n Abstract - Healthcare data scarcity poses significant challenges for machine learning applications in clinical settings\, particularly for conditions with limited patient populations. This paper presents a novel quantumenhanced data augmentation framework that addresses this challenge through a three-pillar architecture: Quantum Random Number Generation (QRNG) for true randomness\, Statistical AI for intelligent parameter optimization\, and Generative AI for clinical interpretability. Our implementation utilizes Bell state quantum circuits to generate genuinely random perturbations\, ensuring higher entropy than classical pseudorandom methods. The framework incorporates medical domain knowledge through constraint-aware augmentation\, maintaining clinical validity while generating synthetic patient records. Experimental evaluation on the Pima Indians Diabetes dataset (768 samples\, 8 features) demonstrates that our quantum-enhanced approach achieves 100% medical constraint compliance while generating high-quality synthetic data. The system provides both command-line and web interfaces\, with automatic fallback to classical methods when quantum resources are unavailable. Our contributions include: the first practical application of quantum computing to healthcare data augmentation\, an AI-driven optimization system that automatically determines augmentation parameters\, integration with large language models for non-technical summarization of validation reports\, and a production-ready implementation with comprehensive validation mechanisms. The framework represents a significant advancement in synthetic medical data generation\, offering a scalable solution for addressing data scarcity in healthcare AI applications.
CATEGORIES:VIRTUAL ROOM_11C
LOCATION:Virtual Room C\, Bangkok\, Thailand
SEQUENCE:0
UID:bc0621f5af121e239c809b0079ad3a89
URL:http://11thictisthailand.sched.com/event/bc0621f5af121e239c809b0079ad3a89
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260417T125213Z
DTSTART:20260411T051500Z
DTEND:20260411T071500Z
SUMMARY:Review Paper on Early Detection of Keratoconus
DESCRIPTION:Authors - Sandhya Awate\, Vipin Kumar Gupta Abstract - Rural communities face significant challenges in accessing essential healthcare services due to language barriers\, limited health literacy\, and insufficient medical support. Difficulties in understanding medical information\, communicating symptoms\, and interpreting diagnostic reports further hinder effective healthcare delivery. Additionally\, unreliable internet connectivity restricts the reach of conventional digital health platforms. To address these challenges\, this paper presents a Multilingual AI Health Assistant designed to operate on low-cost edge devices\, enabling offline functionality to ensure continuous access and data privacy in low-connectivity areas. The proposed system integrates Artificial Intelligence (AI)\, Machine Learning (ML)\, Natural Language Processing (NLP)\, Optical Character Recognition (OCR)\, and speech recognition\, allowing users to interact in their native languages via text or voice. It analyzes user-reported symptoms to predict probable health conditions\, translates complex medical reports and prescriptions into simplified\, localized explanations\, and provides recommendations for nearby healthcare facilities. Unlike internetdependent telemedicine systems\, this edge-based solution processes data directly on the device\, safeguarding sensitive health information while maintaining reliability. By bridging linguistic and literacy gaps\, the proposed assistant empowers rural populations with accessible and actionable healthcare insights\, ultimately improving health outcomes in underserved regions.
CATEGORIES:VIRTUAL ROOM_11C
LOCATION:Virtual Room C\, Bangkok\, Thailand
SEQUENCE:0
UID:a3affa4e8510f6b43fb5dd020b0a3d2f
URL:http://11thictisthailand.sched.com/event/a3affa4e8510f6b43fb5dd020b0a3d2f
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260417T125213Z
DTSTART:20260411T051500Z
DTEND:20260411T071500Z
SUMMARY:Solar-Powered Drones with an Innovative Range Scoring System
DESCRIPTION:Authors - P. Sivaperumal\, R. Naresh\, S. Prawin\, B. E. Viruthatchanan Abstract - The food portion estimation is a critical component of automated dietary assessment systems\, enabling better monitoring of nutritional intake and supporting healthcare\, weight management\, and public health applications. Traditional self-reporting methods are often inaccurate and time-consuming\, motivating the need for computer vision–based approaches that can reliably estimate food portions from images captured in real-world conditions. This paper presents deep learning pipeline for food portion estimation that integrates image preprocessing\, deep learning–based segmentation\, and geometric volume computation. The data preprocessing with Mask R-CNN used for precise food seg-mentation\, providing pixel-level masks and bounding boxes that isolate individual food items from complex backgrounds. The segmented mask is used to estimate the pixel area of the food region. Experimental evaluation demonstrates that the proposed method achieves high segmentation accuracy\, with a segmentation IoU of 87.6%\, precision of 90.3%\, recall of 88.9%\, and an F1-score of 89.6%. The pixel area estimation error is limited to 6.8%\, resulting in an overall portion estimation accuracy of 89.1%\, indicating reliable and consistent performance across different food images. The proposed framework highlights the effectiveness of combining deep instance segmentation with geometric volume estimation for accurate food portion assessment. Future work will focus on multi-view image integration and real-time deployment in mobile dietary monitoring systems to enhance robustness and scalability.
CATEGORIES:VIRTUAL ROOM_11C
LOCATION:Virtual Room C\, Bangkok\, Thailand
SEQUENCE:0
UID:6584c3d0407b9c9fb3a8da9383018e8b
URL:http://11thictisthailand.sched.com/event/6584c3d0407b9c9fb3a8da9383018e8b
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260417T125213Z
DTSTART:20260411T051500Z
DTEND:20260411T071500Z
SUMMARY:Survey of Hallucination in Large Language Models: Detection\, Mitigation\, and Future Directions
DESCRIPTION:Authors - Chalani Dinitha\, Saadh Jawwadh\n Abstract - Automated Image Enhancement from CCTV surveillance relies heavily on accurate image segmentation\; however\, real-world footage is often degraded by low illumination\, motion blur\, occlusion\, and background clutter\, causing conventional segmentation models to lose boundary precision and small object details. This paper proposes EdgeLite-CrimSegNet\, a novel lightweight boundary-aware segmentation network designed specifically for crime scene analysis. Unlike existing fast segmentation models that prioritize global context\, the proposed architecture adopts a boundary-first learning strategy\, where crime-relevant contours are explicitly extracted and refined before region-level segmentation. A compact edge-aware encoder\, boundary-guided feature refinement module\, and progressive region filling strategy are introduced to improve segmentation accuracy while maintaining real-time performance. Experiments on CCTV frames derived from the UCF-Crime dataset demonstrate improved boundary preservation\, higher IOU\, and better segmentation of overlapping and small objects compared to conventional lightweight segmentation networks\, confirming the suitability of EdgeLite-CrimSegNet for real-time surveillance applications.
CATEGORIES:VIRTUAL ROOM_11C
LOCATION:Virtual Room C\, Bangkok\, Thailand
SEQUENCE:0
UID:6b011968b3c9bde3e69b2b7b77e40950
URL:http://11thictisthailand.sched.com/event/6b011968b3c9bde3e69b2b7b77e40950
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260417T125213Z
DTSTART:20260411T051500Z
DTEND:20260411T071500Z
SUMMARY:A GPU-to-NPU Transition Framework for Energy-Efficient Deployment of Medical Vision Models
DESCRIPTION:Authors - Sunkyo Jeong\, Yongbeom Park Abstract - The brisk developments of advanced deep learning techniques have led to diverse applications of it in different sectors\, including healthcare sec tor. Breast cancer is one of the most common and deadly cancer amongst women and the success percentage of the treatment depends heavily on the stage at which the detection happens. This field opens gateway of deep learn ing application in detecting of breast cancer tumour type at an early stage. In this research paper\, model and the application of a CNN based early breast cancer detection algorithm is proposed. In this approach\, the Wisconsin Hos pital Breast Cancer Database is considered to train the model and test the accu racy of the model. This study shows promising results by concluding Convolu tional neural network-based model is 98.24 % accurate which this better than previous models. Moreover\, this paper proves that such application of deep learning techniques holds huge promise for bettering healthcare sector.
CATEGORIES:VIRTUAL ROOM_11D
LOCATION:Virtual Room D\, Bangkok\, Thailand
SEQUENCE:0
UID:0f64ada0b2c2794ee8a693d9624ee3ec
URL:http://11thictisthailand.sched.com/event/0f64ada0b2c2794ee8a693d9624ee3ec
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260417T125213Z
DTSTART:20260411T051500Z
DTEND:20260411T071500Z
SUMMARY:AI Applications in Environmental Management: A Scoping Review
DESCRIPTION:Authors - Francklin Rivas\, Thanh Tran\, Jorge J Roman\, Aysha Al Ketbi Abstract - The rapid proliferation of GenAI has transformed the phishing threat landscape into one characterized by realistic\, tailored\, and scalable attacks on text-based\, web-based\, and multimodal platforms. The success rate of social engineering attacks has increased significantly due to advances in large language models\, deep-fake technology\, and automated phishing-as-a-service offerings. Despite notable advances in current phishing detection technologies\, many oper ate as black-box systems and struggle to detect AI-generated\, context-specific\, zero-day phishing attempts. The resulting lack of transparency\, combined with poor realistic dataset quality and inadequate resilience against adaptive threats\, has further amplified trust concerns. This survey presents a comprehensive over view of the detection strategies based on semantic\, structural\, and multi-quality feature representations\, with a concise review of the models of GenAI-enabled phishing attacks. Various detection methodologies\, including machine learning\, deep learning\, and fusion-based techniques\, are reviewed\, with an emphasis on explainable AI methods like SHAP\, LIME\, attention visualization\, and Grad CAM\, which provide more understandable interpretations of AI-driven deci sions. To facilitate transparent\, reliable\, and trustworthy phishing defenses that make use of GenAI\, the survey concludes with discussions of response mecha nisms\, privacy-preserving learning strategies\, and governance issues\, with open questions and potential directions for future research.
CATEGORIES:VIRTUAL ROOM_11D
LOCATION:Virtual Room D\, Bangkok\, Thailand
SEQUENCE:0
UID:3a7f06b27afd336f41a62d91c1d5a8e2
URL:http://11thictisthailand.sched.com/event/3a7f06b27afd336f41a62d91c1d5a8e2
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260417T125213Z
DTSTART:20260411T051500Z
DTEND:20260411T071500Z
SUMMARY:Cross-Domain Point-of-Interest Recommendation for Tourism- A Reinforcement Learning Approach
DESCRIPTION:Authors - Malika Acharya\, Ankit Jain Abstract - Since Lin and Zadeh proposed granular computing in 1996\, an increasing number of researchers have begun to study information granularity\, which simulates human cognition to handle complex problems. Granular computing advocates observing and analyzing the same problem at different levels of granularity. Coarser granularity leads to more efficient learning processes and stronger robustness to noise\, whereas finer granularity is able to capture more detailed characteristics of objects. Selecting appropriate granularity according to different application scenarios can therefore solve practical problems more effectively. This paper proposes a novel support vector regression algorithm via granular computing approach\, which constructs regression models using granular balls generated from the dataset as inputs rather than individual data points. First\, we analyze the geometric relationship between classification tasks and regression tasks. Then\, based on this geometric relationship\, we employ twin support vector classification algorithm via granular computing approach to address regression problems.
CATEGORIES:VIRTUAL ROOM_11D
LOCATION:Virtual Room D\, Bangkok\, Thailand
SEQUENCE:0
UID:5cbf27d7c94fb31a0a9012ccb947dffb
URL:http://11thictisthailand.sched.com/event/5cbf27d7c94fb31a0a9012ccb947dffb
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260417T125213Z
DTSTART:20260411T051500Z
DTEND:20260411T071500Z
SUMMARY:DEFCM: Effective Customer Segmentation via Deep Embedded Fuzzy C-Means
DESCRIPTION:Authors - Dang Trong Hop\, Than Ngoc Thien Abstract - Medical image classification is of immense importance in the context of early-stage diagnosis of various neurological diseases\, including Alzheimer’s disease and brain tumours. However\, it remains infeasible for conventional deep learning architectures to efficiently encode frequency domain information and long-range spatial dependencies found in medical images. In this paper\, a novel Hybrid Wavelet CNN Vision Trans-former\, coupled with Explainable Artificial Intelligence\, has been proposed for efficient and accurate medical image classification. In the proposed architecture\, the application of discrete wavelet transform\, convolutional neural networks\, and Vision transformers for medical image classification has been presented. Additionally\, explainability aspects have been addressed using the Grad-CAM technique. The proposed model was experimented with using two datasets: one for Alzheimer’s disease MRI and another for brain tumours. The experimental results reveal that the proposed deep learning architecture achieves an accuracy of 96.8%\, precision of 0.96\, and recall of 0.97\, F1score of 0.97 for the brain tumours dataset\, which beats conventional CNN\, vision Transformer\, and Wavelet CNN architectures. The integration of explainable AI further enhances model transparency and clinical reliability\, making the proposed framework suitable for real-world medical diagnostic applications.
CATEGORIES:VIRTUAL ROOM_11D
LOCATION:Virtual Room D\, Bangkok\, Thailand
SEQUENCE:0
UID:dc0c63247c19ac20025e64fdab82fbc9
URL:http://11thictisthailand.sched.com/event/dc0c63247c19ac20025e64fdab82fbc9
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260417T125213Z
DTSTART:20260411T051500Z
DTEND:20260411T071500Z
SUMMARY:Enhanced Object Detection Using Metaheuristic Feature Selection in Deep Learning
DESCRIPTION:Authors - Neha Aggarwal\, Rajiv Singh\, Swati Nigam Abstract - One advantage of using Large Language Models (LLMs) is the automation of tasks and the analysis of information. Engineering drawings\, on the other hand\, are standardized representations of products\; they document their dimensions and geometries. Users can utilize them for manufacturing parts\, assembly guides\, and engineering analysis\, among other uses. This article aims to 1) evaluate whether an LLM is capable of interpreting engineering drawings\, 2) identify how it interprets them\, as it may use a standard on which the generation of these drawings or the interpretation of images is based\, and 3) determine if users as students can employ LLMs as a guide to interpret drawings. The results showed that the user requesting an interpretation of an engineering drawing must be familiar with the field\, as the LLM sometimes fails to extract the correct in-formation from a drawing\; furthermore\, any detail in the drawing can confuse the LLM. Once the LLM extracts the correct information from the drawing\, it can use it to generate CNC code to machine a part\, predict its behavior using a neural network\, or perform engineering analysis\, to name just a few examples.
CATEGORIES:VIRTUAL ROOM_11D
LOCATION:Virtual Room D\, Bangkok\, Thailand
SEQUENCE:0
UID:3ae019ec7a310f6bbcb3cdd8dd515ab6
URL:http://11thictisthailand.sched.com/event/3ae019ec7a310f6bbcb3cdd8dd515ab6
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260417T125213Z
DTSTART:20260411T051500Z
DTEND:20260411T071500Z
SUMMARY:Interpreting Digital Brand Narratives: A Roland Barthes’ Semiotic Approach to the HMNS ‘Untitled Humans’ Instagram Reels Campaign
DESCRIPTION:Authors - Muhammad Elfata Rasyid Hammuda\, Irmawan Rahyadi Abstract - Inventory management in warehouse environments frequently faces recurring limitations related to material searching\, manual record updating\, and control inconsistencies\, which increase delays and disrupt operational continuity. This study develops an intelligent stock-tracking system based on weight sensing using load cells\, signal conditioning through the HX711 module\, and processing via an ESP32 microcontroller\, with real-time data transmission using MQTT and visualization through a Unity-based mobile application with augmented reality (AR) support. The study included the diagnosis of the current process through process mapping and ABC analysis to prioritize critical consumables\, the design of the system architecture\, the implementation of the IoT prototype and its integration with the AR interface\, and performance evaluation through time comparisons\, before-and-after record analysis\, and administration of the System Usability Scale (SUS) questionnaire. Findings indicate operational improvements in efficiency and record consistency\, along with a favorable perceived usability among the evaluators.
CATEGORIES:VIRTUAL ROOM_11D
LOCATION:Virtual Room D\, Bangkok\, Thailand
SEQUENCE:0
UID:41b54e975583701ad8f596f7e32f6580
URL:http://11thictisthailand.sched.com/event/41b54e975583701ad8f596f7e32f6580
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260417T125213Z
DTSTART:20260411T051500Z
DTEND:20260411T071500Z
SUMMARY:Phishing in the Era of Generative Artificial Intelligence: A Systematic Survey of Attack Models\, Detection Strategies\, and Explainability
DESCRIPTION:Authors - Ikram Ahamed Mohamed\, Hafiz Abdulla\, Mohaideen Mohamed Mohabilasha\, Fiyaz Ahmed\, Pankaj Chandre\, Rohini Bhosale Abstract - The Electric vehicles (EVs) are one way to help the environment by reducing carbon emissions and aiming for the net zero supply chain in logistics. This paper is a complete readiness assessment frame work for the green logistics practices on using electrics vehicles. The method categorizes preparation factors in five key categories\, i.e.\, strategic and governance commitment\, technological and infrastructure capability\, financial and Investment Capacity\, operational and human resource readiness\, and environmental and policy alignment. It is pro-posed to use a multi-criteria decision-making framework to analyze the relation-ship between these variables and quantify the level of organizational readiness by using language evaluation scales converted to fuzzy numbers. The study con-tributes to the theoretical knowledge of creating a unified property of the various readiness criteria in a unitary evaluation framework and synthesises empirical methods with a measurable metric of the uptake of the electric vehicle in the logistics networks. Practically\, the framework assists logistics managers\, legislators\, and sustainability planners in identifying issues\, establishing priorities on investments\, and accelerating the transition to the low-carbon transportation systems. The findings support the concept of fact-based decision-making that can lead to a green logistics revolution which can expand and remain sustainable.
CATEGORIES:VIRTUAL ROOM_11D
LOCATION:Virtual Room D\, Bangkok\, Thailand
SEQUENCE:0
UID:e660beb7ced656a74240ffbd7fcc1708
URL:http://11thictisthailand.sched.com/event/e660beb7ced656a74240ffbd7fcc1708
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260417T125213Z
DTSTART:20260411T051500Z
DTEND:20260411T071500Z
SUMMARY:Potholes\, Cracks\, and Open Manholes Detection using CBAM-YOLOv8 and GPS Coordinate Extraction via OCR for Location Mapping and Automated Alerts
DESCRIPTION:Authors - Sabid Rahman\, Sadah Anjum Shanto\, Segufta Nasrin Tamanna\, Zurin Alam Aongon\, Md. Soadul Islam\, Nasirul Islam\n Abstract - This research suggests a system for the real-time detection of road hazards\, specifically potholes\, cracks\, and open manholes\, using deep learning and image processing\, and pinpointing the exact geographical location of the defects. These defects can cause road accidents\, vehicle damage\, traffic congestion\, and other inconveniences. To solve these\, a YOLOv8m model integrated with the CBAM module was developed for enhanced feature attention and trained on a custom dataset of 2\,400 road images containing the three hazard classes. The model achieved a mAP@50 of 82.2%\, and the individual class performance scores are 72.2% for potholes\, 81.0% for cracks\, and 93.3% for open manholes\, and a recall of 76.4%\, demonstrating reliable performance under varied conditions. An OCR module was integrated with the CBAM-YOLOv8 model to extract GPS coordinates from user-captured photos and videos\, and an interactive mapping interface was designed to show and report the exact locations of detected hazards for timely action by authorities.
CATEGORIES:VIRTUAL ROOM_11D
LOCATION:Virtual Room D\, Bangkok\, Thailand
SEQUENCE:0
UID:95ff8f633015069f89439510ac7f59c9
URL:http://11thictisthailand.sched.com/event/95ff8f633015069f89439510ac7f59c9
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260417T125213Z
DTSTART:20260411T051500Z
DTEND:20260411T071500Z
SUMMARY:Towards Interpretable Edge Intelligence: Explainable AI in Resource-Constrained IoT Devices
DESCRIPTION:Authors - Mandar K Mokashi\, Sonali P Bhoite\, Vishal Nayakwadi\, Atul P Kulkarni\, Parikshit Mahalle\, Pankaj Chandre Abstract - The purpose of this study is to examine the impact of DAT\, AIR and ICM to-ward DAM in SMEs and at the same time determine the moderating effect of in-ternal control maturity. Drawing on the technology–organization–environment (TOE) framework and Resource-Based View (RBV)\, this study utilises a quantitative approach by employing Partial Least Squares Structural Equation Model-ling (PLS-SEM). Data was collected through structured questionnaires sent out to SMEs that have begun using digital audit tools. The relationships with DAM of DAT\, AIR and ICM presented evidence on the individual impact on DAM indicating that technological readiness\, organizational willingness to accept AI solutions successfully and mature internal controls are vital. Nevertheless\, internal control maturity is not conducive to stronger.
CATEGORIES:VIRTUAL ROOM_11D
LOCATION:Virtual Room D\, Bangkok\, Thailand
SEQUENCE:0
UID:d87831108c86f839d20e40e6db1530f7
URL:http://11thictisthailand.sched.com/event/d87831108c86f839d20e40e6db1530f7
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260417T125213Z
DTSTART:20260411T051500Z
DTEND:20260411T071500Z
SUMMARY:User Classification in E-Commerce Platforms: A Machine Learning Based Approach
DESCRIPTION:Authors - Nguyen Thi Hoi\, Dao Thi Huong Abstract - This аrticle exаmines the impаct of аccelerаted digitаlizаtion of the Uzbek econo-my on improving the effectiveness of pаrticipаtory budgeting. Reforms аimed аt creаting а "New Uzbekistаn" hаve elevаted pаrticipаtory budgeting to а key tool for citizen engаgement аnd increаsing the trаnspаrency of budget аllocаtion. However\, the complexity аnd multifаceted nаture of this work аnd the further de-velopment of pаrticipаtory budgeting require the constаnt аdаptаtion of proce-dures\, tools\, аnd mаnаgement аpproаches to the emerging digitаl reаlities. The purpose of this study is to substаntiаte the need to trаnsform the pаrticipаtory budgeting mechаnism using аrtificiаl intelligence technologies аnd propose prаcticаl solutions to improve the efficiency\, fаirness\, аnd sustаinаbility of this process. Bаsed on аn аnаlysis of the regulаtory frаmework аnd current prаctices in implementing pаrticipаtory budgeting projects in the Republic of Uzbekistаn\, key chаllenges limiting the potentiаl of pаrticipаtory budgeting hаve been identi-fied\, including: low digitаl literаcy аmong some of the populаtion\, limited func-tionаlity of digitаl plаtforms\, insufficient аutomаtion of project evаluаtion аnd se-lection processes\, weаk integrаtion with government informаtion systems\, аnd а lаck of аnаlyticаl tools for forecаsting sociаl performаnce. The study proposes аreаs for improving the mechаnism\, including expаnding the functionаlity of the Open Budget plаtform\, implementing аrtificiаl intelligence\, big dаtа\, аnd digitаl plаtforms to increаse the openness аnd effectiveness of аnаlyticаl dаtа\, аs well аs using elements of finаnciаl modeling to forecаst future stаte budget expenditures аnd develop multifаctor criteriа for аssessing the effec-tiveness of pаrticipаtory budgeting projects. The prаcticаl significаnce of the аrti-cle lies in the development of а comprehensive аpproаch to modernizing pаr-ticipаtory budgeting\, which contributes to increаsing citizen trust in government institutions\, optimizing the use of budgetаry resources\, аnd аchieving the goаls of the Digitаl Uzbekistаn 2030 strаtegy. The results obtаined cаn be used by gov-ernment аgencies\, locаl governments\, аnd developers of digitаl solutions in public finаnce.
CATEGORIES:VIRTUAL ROOM_11D
LOCATION:Virtual Room D\, Bangkok\, Thailand
SEQUENCE:0
UID:c9de7a51f3267b347c0101269d1233c2
URL:http://11thictisthailand.sched.com/event/c9de7a51f3267b347c0101269d1233c2
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260417T125213Z
DTSTART:20260411T051500Z
DTEND:20260411T071500Z
SUMMARY:AI for Crop Health: Evaluating the Performance of Deep Learning Models for Leaf Disease Classification
DESCRIPTION:Authors - Humma Ghaffar\, Usman Ali\, Muhammad Arfan\, Sajid\, Muhmmad Mujeeb Akbar Abstract - The growing mental health challenges around the globe need access to scalable\, available\, and safety conscious digital interventions. The paper describes a mental health support platform\, based on AI\, which combines conversational intelligence\, multi-therapeutic persona modeling\, structured mood analytics\, proactive crisis identification\, multi-lingual interaction\, and voice-based access in a secure full stack design. The system\, which runs on the Google Gemini AI\, provides context-sensitive therapeutic dialogue and performs four-dimensional mood analysis of anxiety\, stress\, depression\, and wellbeing\, allowing longitudinal assessment by providing interactive dashboards and automated reporting. A safety-first crisis override system offers validated emergency capacity in the high-risk situations. The platform also includes multilingual voice feedback to facilitate inclusion of the visually impaired users and non-English speaking communities in providing inclusive digital mental health care. The proposed system is capable of changing the prevalent perception that AI and its applications may never be responsible and scalable because it integrates therapeutic diversity\, structured analytics\, accessibility features\, and proactive safety controls into a single framework.
CATEGORIES:VIRTUAL ROOM_11E
LOCATION:Virtual Room E\, Bangkok\, Thailand
SEQUENCE:0
UID:506b3dc54ef737ab53d0cd469856f21c
URL:http://11thictisthailand.sched.com/event/506b3dc54ef737ab53d0cd469856f21c
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260417T125213Z
DTSTART:20260411T051500Z
DTEND:20260411T071500Z
SUMMARY:An Intent-Aware UEBA Framework for Insider Threat Detection Using DBLOF and UTCG
DESCRIPTION:Authors - Pranay Kavthankar\, Rutuj Koli\, Ronit Ghadi\, Yug Mora\, Abhijit Joshi Abstract - Speech-to-Speech Translation (S2ST) has evolved from cas caded pipelines into end-to-end neural architectures. However\, preserv ing emotion\, prosody\, and speaker identity across languages remains challenging. This survey examines state-of-the-art emotion and identity preserving S2ST and neural TTS systems\, covering discrete-representation models\, end-to-end systems\, and cascaded pipelines. We analyze architec tures including Translatotron\, VQ-Translatotron\, SeamlessM4T\, VALL E\, VALL-E X\, VITS\, YourTTS\, StyleTTS2\, and XTTSv2. The survey discusses speaker identity preservation (x-vectors\, d-vectors\, codec repre sentations)\, prosody modeling (pitch\, duration\, energy)\, emotion reten tion (categorical\, dimensional\, embeddings)\, datasets\, evaluation met rics\, and challenges including data scarcity\, cross-lingual emotion trans fer\, and computational costs. We propose future directions toward large scale expressive datasets\, improved cross-lingual modeling\, and respon sible AI practices.
CATEGORIES:VIRTUAL ROOM_11E
LOCATION:Virtual Room E\, Bangkok\, Thailand
SEQUENCE:0
UID:fc62c639df49b1fc376c779697585c5c
URL:http://11thictisthailand.sched.com/event/fc62c639df49b1fc376c779697585c5c
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260417T125213Z
DTSTART:20260411T051500Z
DTEND:20260411T071500Z
SUMMARY:Beyond Awareness: Fostering Long-term Behavioral Resilience via Gamified Mesh Communities of Practice
DESCRIPTION:Authors - Maykin Warasart\, Pallop Piriyasurawong\, Panita Wannapiroon\, Prachyanun Nilsook Abstract - This paper introduces an AI-based investment assistant that helps users to understand the fundamental principles of the financial markets. This work is mainly focused on stock market data to provide accurate insights and helps in various decision-making purposes. The rising volatility in the financial markets\, massive data set\, and the complexity of financial instruments\, makes decision-making in financial sectors more difficult to individual investors.In order to cope with this problem\, our model integrates time series forecasts\, large language model intelligence with real-time financial information with interactive visualizations and personalized insights. The suggested system will interpret user queries in natural language with the help of a Large Language Model (Gemini 2.5 Flash) and extracts the corresponding stock tickers and financial objects and transforms them into structured inputs to be used in predictive analysis. Past and current stock market data are retrieved with the help of yfinance API and fed into an LSTM-based time-series predictive model that predicts future price fluctuations.The results predicted are presented in interactive charts created with Plotly\, which users can analyze trends easily and compare several stocks. The system can also give personalized recommendations\, textual summaries of stock movements (moving up or down)\, multi-turn chatbot conversations\, portfolio\, wishlist and real time price moves besides forecasting. The proposed investment assistant improves the gap between complicated financial information and practical results by incorporating natural language comprehension\, deep learningbased prediction\, and intuitive visualization etc. The system promotes user knowledge and helps them in effective decision making .
CATEGORIES:VIRTUAL ROOM_11E
LOCATION:Virtual Room E\, Bangkok\, Thailand
SEQUENCE:0
UID:6f88ee875ef5140f6cd340aa1c2c5223
URL:http://11thictisthailand.sched.com/event/6f88ee875ef5140f6cd340aa1c2c5223
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260417T125213Z
DTSTART:20260411T051500Z
DTEND:20260411T071500Z
SUMMARY:Comparative Evaluation of the Energy Efficiency of Spiking Neural Networks on Conventional Platforms and Implications for Neuromorphic Hardware
DESCRIPTION:Authors - Gabriel M. da Silva\, Nicolas O. da Rocha\, Heloise V. C. Brito\, Joao V. N. M. da Silva\, Sergio A. S. da Silva\, Anderson R. de Souza\, Carlos A. O. de Freitas\, Vandermi Joao da Silva\n Abstract - Spiking Neural Networks (SNNs) have been investigated as a biologically inspired alternative for efficient information processing\, particularly in energy-sensitive applications. This work presents a comparative evaluation of the energy efficiency of different SNN techniques\, including Liquid State Machines (LSM)\, Recurrent Spiking Neural Networks (RSNN)\, Spiking Convolutional Neural Networks (SCNN)\, and learning based on Spike-Timing Dependent Plasticity (STDP). The experiments were conducted on conventional hardware plat-forms\, namely an Android smartphone and a notebook\, using simulated implementations of SNNs without dedicated neuromorphic acceleration. The analysis considered different network scales by varying the number of neurons and was based on neural activity metrics\, particularly the total number of generated spikes\, employed as a proxy for the indirect estimation of energy consumption during audio signal processing. The results demonstrate a consistent relationship between neural activity and estimated energy consumption\, as well as an energy saturation behavior as network complexity increases. Differences among the an-alyzed techniques are more pronounced in small-scale configurations\, whereas larger networks exhibit convergent patterns of neural activity and energy consumption. Although conducted in a digital simulation environment\, this study highlights the limitations of conventional platforms for the efficient execution of SNNs and reinforces the potential of dedicated neuromorphic hardware for embedded and low-power applications.
CATEGORIES:VIRTUAL ROOM_11E
LOCATION:Virtual Room E\, Bangkok\, Thailand
SEQUENCE:0
UID:82d0690f7c20ddd4179d0aca2341b508
URL:http://11thictisthailand.sched.com/event/82d0690f7c20ddd4179d0aca2341b508
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260417T125213Z
DTSTART:20260411T051500Z
DTEND:20260411T071500Z
SUMMARY:From Placards to Platforms: Digitizing Airport Meet-and-Greet Operations
DESCRIPTION:Authors - Maykin Warasart\, Veerasith Wongkarn\, Phonesavanh Nammakone\, Duangtavanh Thatsaphone\n Abstract - Manual correction of written examination scripts is still the default practice in many institutions\, but it is slow\, tiring for evaluators\, and not always consistent\, especially when large numbers of papers must be graded in a short time. In this work we look at how recent advances in optical character recognition (OCR)\, machine learning (ML)\, and natural language processing (NLP) can be used together to support automatic evaluation of both objective and descriptive answers. In this paper We study a two–stage system: first\, a handwriting recognizer based on convolutional and recurrent neural networks (CRNN) is used to read handwritten responses from scanned answer sheets\; next\, the recognized text is scored using semantic and syntactic similarity measures driven by transformer-based language models. By training the recognizer on a mixture of public handwriting corpora and locally collected scripts\, and by combining keyword features with sentence-level embeddings\, the system is able to approximate faculty grading patterns with good accuracy. This study examines the way that real tests are administered\, including variations in writing styles\, background noise in scans\, the arrangement of answers on paper\, and terms related to specific subjects. We clearly address each of those factors in our approach. Teachers won’t vanish because of this setup\; instead\, it aims to ease their ongoing tasks while offering fairness and consistency across student results.
CATEGORIES:VIRTUAL ROOM_11E
LOCATION:Virtual Room E\, Bangkok\, Thailand
SEQUENCE:0
UID:2f23c37b93a1f5268ad1008a04e98da4
URL:http://11thictisthailand.sched.com/event/2f23c37b93a1f5268ad1008a04e98da4
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260417T125213Z
DTSTART:20260411T051500Z
DTEND:20260411T071500Z
SUMMARY:Generation Of A New Dataset For An Attack Detection System Towards 5G Network Infrastructure Security
DESCRIPTION:Authors - Hai D. Nguyen\, Nguyen Ngoc Quan\, Viet H. Le\, Mai T. Nguyen\, Nguyen Huy Trung\, Le Duc Huy\, Nhu Son Nguyen Abstract - Military forces launch offensive operations to defeat and destroy enemy. Battlefield surveillance enables provisioning of timely and correct battle space information to commanders\, both prior and during the launch of offensive operations. Static battlefield surveillance devices have certain limitations which restrict their usage during offensive operations. In the current paper\, we review the requirement of surveillance devices during various periods of offensive operations\, the limitations of static surveillance devices and efficacy of Unmanned Aerial Vehicles (UAVs) as prime battlefield surveillance device for offensive operations. We then explore the possibility of connecting UAVs with existing cellular base stations and with vehicle mounted cellular base stations which can be moved into enemy territory with the progress of offensive operations. Furthermore\, a UAV communication model for enhanced battlefield surveillance during offensive operations is presented after analyzing various antenna techniques utilized to achieve desired data rates for UAV operations.
CATEGORIES:VIRTUAL ROOM_11E
LOCATION:Virtual Room E\, Bangkok\, Thailand
SEQUENCE:0
UID:8ad3cd7729706b95527792387360e5ef
URL:http://11thictisthailand.sched.com/event/8ad3cd7729706b95527792387360e5ef
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260417T125213Z
DTSTART:20260411T051500Z
DTEND:20260411T071500Z
SUMMARY:Large Language Model-based Development of a School Medical Consultation System
DESCRIPTION:Authors - Quan Nguyen\, Chau Vo\, Phung Nguyen Abstract - In order to create reliable connectivity where there is no direct line-of-sight (LOS) path between ground terminals\, this study provides the design and performance evaluation of a dual-hop Unmanned Aerial Vehicle (UAV) assisted free space optical communication system. The proposed ground–UAV–UAV–ground architecture enables non-LOS communication by employing aerial relays to bypass physical obstructions and extend transmission coverage. Three modulation formats—Non-Return to Zero (NRZ)\, Return to Zero (RZ)\, and Carrier-Suppressed Return to Zero (CSRZ)—under various weather conditions and turbulence regimes are used to assess the system performance. While all modulation schemes perform closely for different attenuation level\, differences in performance is prominent under turbulence\, CSRZ demonstrates superior robustness\, followed by NRZ and RZ.
CATEGORIES:VIRTUAL ROOM_11E
LOCATION:Virtual Room E\, Bangkok\, Thailand
SEQUENCE:0
UID:d0310dbd343780e92b7aaad7d9192f25
URL:http://11thictisthailand.sched.com/event/d0310dbd343780e92b7aaad7d9192f25
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260417T125213Z
DTSTART:20260411T051500Z
DTEND:20260411T071500Z
SUMMARY:Massive MIMO Enabled UAV Communication Model for Enhanced Battlefield Surveillance during Offensive Military Operations
DESCRIPTION:Authors - Rajesh Kapoor\, Vishal Goyal\, Aasheesh Shukla Abstract - This paper presents a systematic review of visual sarcasm detection research with a focus on learning-based approaches. The review examines input representations\, feature extraction methods\, model architectures\, datasets\, and evaluation practices reported in the literature. Studies are analyzed with respect to the use of visual information\, including images and image–text pairs\, along with associated deep learning frameworks such as convolutional\, transformer-based\, and hybrid models. A structured search strategy\, defined inclusion criteria\, and an analytical framework are employed to ensure consistency and reproducibility of the review process. The findings are synthesized to identify prevailing research patterns\, methodological limitations\, and gaps related to visual feature representation\, model design\, and experimental consistency. By organizing and comparing existing approaches\, this systematic review provides a consolidated reference and supports future research in visual sarcasm detection.
CATEGORIES:VIRTUAL ROOM_11E
LOCATION:Virtual Room E\, Bangkok\, Thailand
SEQUENCE:0
UID:c49a3831e7ba392dae6e5b37a3c479e1
URL:http://11thictisthailand.sched.com/event/c49a3831e7ba392dae6e5b37a3c479e1
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260417T125213Z
DTSTART:20260411T051500Z
DTEND:20260411T071500Z
SUMMARY:MULTIMODAL DEEP SPATIO-TEMPORAL FRAMEWORK FOR AUTOMATED CROP MAPPING AND YIELD PREDICTION USING SENSOR IMAGES
DESCRIPTION:Authors - G. Sabera\, Kanajam Murali Krishna\, N. Sabitha\, Tummala Purnima\, A. Naresh\, Shaik Janbhasha Abstract - Complementing the continuous deep integration of culture and tour-ism\, the tourism market environment and visitor consumption demand are constantly evolving\, with cultural theme attractions playing an increasingly prominent role in tourism industry development. Tourism resources constitute the basic foundation of scenic destination development\, while scientific and effective tour-ism marketing provide a key factor in enhancing market competitiveness and achieving sustainable development. Relying on the cultural resources of the Song Dynasty and martial arts culture\, The Song Dynasty of Kungfu City has formed a distinctive thematic identity against the background of cultural–tourism integration and has gained a particular level of market attention. However\, its tourism marketing practices still face practical challenges such as brand strengthening\, intensified market competition\, and changing visitor expectations. This study takes The Song Dynasty of Kungfu City as the research object and analyzes the current status of its tourism marketing\, exploring the developmental foundation and practical challenges faced by the scenic area under the contemporary tourism market environment. A qualitative research approach is adopted. Relevant data were collected through field observation and in-depth interviews to review the scenic area’s tourism marketing activities. Based on this\, the SWOT analytical framework was applied to systematically examine the strengths\, weaknesses\, opportunities\, and threats associated with the tourism marketing status of The Song Dynasty of Kungfu City.
CATEGORIES:VIRTUAL ROOM_11E
LOCATION:Virtual Room E\, Bangkok\, Thailand
SEQUENCE:0
UID:a65a8d5c2e6dedfd3a035fae68cbdb88
URL:http://11thictisthailand.sched.com/event/a65a8d5c2e6dedfd3a035fae68cbdb88
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260417T125213Z
DTSTART:20260411T051500Z
DTEND:20260411T071500Z
SUMMARY:The Integration of VFX and Animation in Traditional Film Production: Enhancing Visual Storytelling
DESCRIPTION:Authors - Sambhram Pattanayak\, Akankasha Kathuria\, Shreesha Mairaru Abstract - Reliable prediction of rare critical events is a key enabler for modern risk management\, civil protection\, and decision support sys tems\, yet it remains challenging due to extreme class imbalance and strict requirements on false alarm rates. We present an ensemble learn ing framework that combines a deep feed-forward neural network with a Random Forest classifier\, complemented by temporal feature engineering and precision-oriented optimization. The approach addresses three ob jectives: extracting informative temporal and regional patterns from raw event logs\, learning calibrated probabilistic scores under severe imbalance using focal loss\, and tuning per-region decision thresholds to achieve high precision while preserving acceptable recall. As a case study we apply the framework to air alert prediction over 25 administrative regions across 38 months\, totalling 774\,125 hourly observations. The system attains 96.13% accuracy\, 75.1% precision\, and 77.9% recall\, demonstrating that high-precision early warning is feasible in strongly imbalanced settings. The framework is applicable to a wide range of safety-critical rare event prediction tasks.
CATEGORIES:VIRTUAL ROOM_11E
LOCATION:Virtual Room E\, Bangkok\, Thailand
SEQUENCE:0
UID:b1ae8ef2ce10c68bd24d7cadee11403d
URL:http://11thictisthailand.sched.com/event/b1ae8ef2ce10c68bd24d7cadee11403d
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260417T125213Z
DTSTART:20260411T051500Z
DTEND:20260411T071500Z
SUMMARY:A Review on Visual Sarcasm Detection
DESCRIPTION:Authors - Neeraj Mathur\, Jiby Mariya Jose Abstract - Material Control Systems (MCS) serve as a critical software layer that coordinates material flow by issuing transport commands\, tracking material lo-cations\, and interfacing with factory equipment and automated handling systems. Although the term may appear to focus primarily on inventory management\, it is most commonly used in high-tech environments such as semiconductor manufacturing to describe the software layer that manages\, directs\, and optimizes the movement\, storage\, and routing of materials (e.g.\, wafers and carriers) within a production or logistics environment. This paper presents the development and implementation of a novel Physical AI–based Material Control System. Unlike traditional MCS architectures that rely on rigid rule-based dispatching\, the proposed approach leverages a Physical AI plat-form to enable unified and adaptive control across heterogeneous hardware\, including stockers\, Autonomous Mobile Robots (AMRs)\, and Overhead Hoist Transport (OHT) systems. By integrating real-time sensor fusion and adaptive motion planning\, the proposed system enhances process logistics in semiconductor backend facilities\, where high-mix production requires highly dynamic coordination between storage and transport resources.
CATEGORIES:VIRTUAL ROOM_11F
LOCATION:Virtual Room F\, Bangkok\, Thailand
SEQUENCE:0
UID:b652a2be4d18ae31cc058922e9dbedaf
URL:http://11thictisthailand.sched.com/event/b652a2be4d18ae31cc058922e9dbedaf
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260417T125213Z
DTSTART:20260411T051500Z
DTEND:20260411T071500Z
SUMMARY:Advanced Intelligent Intrusion Detection Systems for IoT: A Review
DESCRIPTION:Authors - Maryam Ghazi Ali\, Bindu V. R\n Abstract - The Internet of Things (IoT) has spread rapidly\, significantly increasing several secu-rity vulnerabilities\, as traditional detection systems are becoming insufficient to manage the vol-ume and diversity of traffic that characterizes modern networks. The review provides a compre-hensive analysis of recent advances in learning-based intrusion detection systems (IDS)\, focusing primarily on deep learning\, traditional learning\, machine learning\, and hybrid frameworks. Through critically evaluating a diverse range of state-of-the-art studies\, the review explores dif-ferent methodological solutions\, data\, and performance measurement in the field. The available empirical results show that\, although deep learning models are better at identifying complex pat-terns in the data\, traditional machine learning algorithms require less computational power. In addition\, hybrid and ensemble models often outperform single-method options\, but often with high computational cost. The review outlines a number of important challenges\, including the issue of class imbalance and the fact that models are not very interpretable. It argues that light-weight and interpretable AI systems should be a priority in future studies\, and the gap between theoretical academic frameworks and practical IoT applications would be minimized.
CATEGORIES:VIRTUAL ROOM_11F
LOCATION:Virtual Room F\, Bangkok\, Thailand
SEQUENCE:0
UID:ee4ca5c62bcf8cc06ac87f79ac5147f0
URL:http://11thictisthailand.sched.com/event/ee4ca5c62bcf8cc06ac87f79ac5147f0
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260417T125213Z
DTSTART:20260411T051500Z
DTEND:20260411T071500Z
SUMMARY:An Adaptive and QoS-Aware Trust Framework for Secure V2X Communication with Machine Learning–Based Anomaly Detection
DESCRIPTION:Authors - Aditi Jha\, Ravi Shankar Pandey Abstract - Indoor air quality (IAQ) is a frequently overlooked determinant of health in rural villages\, where the extensive use of solid fuels for cooking and space-heating generates elevated concentrations of airborne pollutants. This study presents an integrated\, low-cost protocol for improving IAQ in rural dwellings\, combining real-time environmental monitoring\, simplified digital modelling and passive strategies of ventilation and biophilic design. The methodology can be structured into three steps: Conceptual digital twin\, feedback interface\, ventilation strategies\, biophilic integration. Conceptual digital twin is based on the mapping of each dwelling linked to Arduino low-cost\, stand-alone sensors (CO₂\, PM₂.₅\, temperature and relative humidity) that collect data at temporal resolution of one minute. An immediate feedback interface based on visual and/or acoustic indicators that prompt residents to take corrective actions (selective opening of windows\, activation of cross-breezes)\, when exposure thresholds - derived from WHO Air Quality Guidelines - are exceeded. Data-driven natural-ventilation strategies – optimal ventilation windows identified through time-series analysis of sensor data\, calibrated to local weather conditions and occupancy profiles to maximise air exchange while minimising heat losses. Biophilic integration implies the introduction of resilient plant species with proven phytoremediation capacity\, as Epipremnum aureum) which could reduce CO₂ level\, with quantitative guidance on density (two to three plants per main room) and optimal placement. Using low-cost IoT sensors\, the protocol monitors environmental parameters and pollutant concentrations in real time. The system targets specific safety and comfort thresholds\, aiming to maintain CO₂ levels below 700 ppm and PM₂.₅ below 50 μg/m³ to optimize occupant health (Wu et al\, 2021). These thresholds\, derived from World Health Organization (WHO) guidelines\, are essential to ensure occupant satisfaction and well-being. The ultimate objective is to define a scalable and replicable intervention model capable of combining digital technologies and natural solutions for the sustainable regeneration of fragile territories.
CATEGORIES:VIRTUAL ROOM_11F
LOCATION:Virtual Room F\, Bangkok\, Thailand
SEQUENCE:0
UID:628dc21e8ba15c361e6eb10ee5498bea
URL:http://11thictisthailand.sched.com/event/628dc21e8ba15c361e6eb10ee5498bea
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260417T125213Z
DTSTART:20260411T051500Z
DTEND:20260411T071500Z
SUMMARY:An Ethereum Framework for Secure Electronic Voting Systems
DESCRIPTION:Authors - Atul Pawar\, Ganesh Deshmukh\, Rajesh Lomte\, Sahil Ambokar\, Vedant Bankewar\, Sanket Ahirrao Abstract - This study explored teachers’ perspectives on the need for an interac tive digital storytelling application to support English language learning at the primary level. Using a teacher-based needs analysis\, data were collected through expert review of research instruments and in-depth interviews with English teachers working in international school contexts. The findings reveal that teach ers perceive digital storytelling as an effective approach for enhancing student engagement\, motivation\, and contextualized language learning. Teachers high lighted the importance of integrating interactive elements such as narrative audio\, visuals\, game-based tasks\, immediate feedback\, and reward systems to support vocabulary development\, comprehension\, and learner autonomy. The results also indicate a need for applications that are curriculum-aligned\, age-appropriate\, and easy to use in classroom settings. Based on the identified needs\, the study pro vides design implications for the development of an interactive digital storytell ing application that combines storytelling and game-based learning principles. This research contributes to the growing body of literature on digital storytelling and offers practical guidance for educators and developers seeking to design ef fective language learning applications.
CATEGORIES:VIRTUAL ROOM_11F
LOCATION:Virtual Room F\, Bangkok\, Thailand
SEQUENCE:0
UID:c4ecb2d34574c4eadd57906dafd420cb
URL:http://11thictisthailand.sched.com/event/c4ecb2d34574c4eadd57906dafd420cb
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260417T125213Z
DTSTART:20260411T051500Z
DTEND:20260411T071500Z
SUMMARY:An Optimized Cryptographic Algorithm for Privacy-Preserving Big Data Processing in the Cloud
DESCRIPTION:Authors - Veenu Singh\, Saurabh Singhal Abstract - Many AI agents store observations\, summaries\, and retrieved content in persistent memory\, then reuse that material in later planning and action. This creates a failure mode that standard incident response does not fully address. If malicious content is written into durable memory\, patching the vulnerable component\, rotating credentials\, and restarting the agent do not remove the poisoned state. The agent can restart clean\, retrieve the same memory\, and act on it again. We call this provenance laundering: external-origin content is later consumed with authority it should not have. We formalize this mechanism\, show that remediation without memory purge leaves residual impact over time\, and examine seven production memory architectures against this threat model. We then define a containment primitive based on provenance metadata\, namespace separation\, and an inference-time non-escalation gate\, and evaluate it with ablation across two frameworks. In our experiments\, unauthorized behavior persisted after standard remediation and stopped only after memory purge. These results suggest that incident response for persistent-memory agents should treat purge as a required step rather than an optional cleanup action.
CATEGORIES:VIRTUAL ROOM_11F
LOCATION:Virtual Room F\, Bangkok\, Thailand
SEQUENCE:0
UID:158a81064eed533835ed71902204c1b6
URL:http://11thictisthailand.sched.com/event/158a81064eed533835ed71902204c1b6
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260417T125213Z
DTSTART:20260411T051500Z
DTEND:20260411T071500Z
SUMMARY:Bias Aware Legal Case Classification And Judgement Interpretation
DESCRIPTION:Authors - Nitesh Varman V R\, Sanjith Ganesa P\, Rahul Veeramachaneni\, Korapati Mohan Aditya\, Bagavathi Sivakumar Abstract - With the development of cloud computing and big data technology\, data handling particularly in handling big data\, while also mentioning the dangers of privacy and security violations in delegating the processing of sensitive data to cloud computing has increased. The conventional encryption method that demands the decryption of data for processing\, which could result in the leakage of sensitive data and performance inefficiencies are no longer valid. The paper introduces the Optimized Privacy-Preserving Cryptographic Processing Algorithm (OPCPA)\, which reduces computational complexity through the use of light-weight encryption\, adaptive data partitioning\, hierarchical key management\, and parallel processing of encrypted data. The proposed algorithm is compared to conventional methods using the KDD Cup 1999 dataset and outperforms them in terms of processing speed\, throughput\, and resource utilization.
CATEGORIES:VIRTUAL ROOM_11F
LOCATION:Virtual Room F\, Bangkok\, Thailand
SEQUENCE:0
UID:a404bbd980bc3cbcc0aa80b6bb07db69
URL:http://11thictisthailand.sched.com/event/a404bbd980bc3cbcc0aa80b6bb07db69
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260417T125213Z
DTSTART:20260411T051500Z
DTEND:20260411T071500Z
SUMMARY:Causal Characterization of Adulterant- Specific Sensor Responses in Multi-Sensor Milk Detection
DESCRIPTION:Authors - Kashish Goyal\, Parteek Kumar\, Karun Verma Abstract - The clinical deployment of continuous epileptic seizure forecasting systems is severely hindered by the cold-start problem. Current state-of-the-art deep learning models require patient-specific fine-tuning\, necessitating the recording of multiple seizures from a newly admitted patient before the system becomes operational. To achieve immediate clinical utility\, forecasting models must operate in a zero-shot capacity. This paper presents a Zero-Shot Cross-Patient Transfer Framework\, leveraging the Horizon-Aware Graph Transformer as a universal feature extractor\, coupled with the Strict Discipline Protocol as a rigid domain adaptation layer. By anchoring the batch normalization layers to a global source distribution and utilizing a brief interictal calibration phase\, the framework mitigates the severe covariate shift inherent in cross-patient electroencephalogram signals. Experimental validation on the CHB-MIT dataset demonstrates a sensitivity of 87.3% with a false alarm rate of 0.28 per hour\, achieving a Time-to-Utility of exactly 10 minutes\, a 99.9% reduction compared to conventional patient-specific approaches requiring 5-14 days of monitoring. The framework successfully bypasses patientspecific training\, offering immediate clinical interoperability while minimizing alarm fatigue through disciplined feature scaling.
CATEGORIES:VIRTUAL ROOM_11F
LOCATION:Virtual Room F\, Bangkok\, Thailand
SEQUENCE:0
UID:a3f516ed8e543adeb5dbd74cb5d9d15b
URL:http://11thictisthailand.sched.com/event/a3f516ed8e543adeb5dbd74cb5d9d15b
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260417T125213Z
DTSTART:20260411T051500Z
DTEND:20260411T071500Z
SUMMARY:Classification Performance of Linear Frequency-Modulated Signals in an Autocorrelation Processing Device
DESCRIPTION:Authors - The Quan Trong\, Nguyen Trong Nhan Abstract - The integration of large language models (LLMs) into primary educa tion remains limited in low resource\, diglossic languages like Sinhala. General purpose models often produce grammatically inconsistent or cognitively over whelming output for young learners. This paper introduces a grade-adaptive\, con straint-driven framework for automated Sinhala story and quiz generation target ing Grades 1-5. Building upon an 8-billion-parameter Sinhala-adapted LLaMA 3 model\, we apply Quantized Low-Rank Adaptation (QLoRA) using a curated multi-task educational dataset. The system enforces tier-specific linguistic con straints separating conversational Sinhala for lower grades from formal written Sinhala for upper grades while embedding strict structural rules such as con trolled sentence counts (5-6 vs. 7-8) and validated multiple-choice formats (3 vs. 4 options). Evaluation on 100 structured prompts demonstrated substantial im provements over a zero-shot baseline: structural compliance increased from 64% to 93%\, and hallucination-related failures decreased from 31% to 8%. Further more\, evaluation against 50 unseen real-world classroom prompts yielded a 0.0% crash rate and 95% register adherence\, confirming robust qualitative perfor mance. Results demonstrate that diglossia-aware dataset engineering and con straint-aware fine-tuning enable reliable\, pedagogically aligned deployment of LLMs in low-resource primary learning environments.
CATEGORIES:VIRTUAL ROOM_11F
LOCATION:Virtual Room F\, Bangkok\, Thailand
SEQUENCE:0
UID:c084d0f41359299a88c35cf8e2b27760
URL:http://11thictisthailand.sched.com/event/c084d0f41359299a88c35cf8e2b27760
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260417T125213Z
DTSTART:20260411T051500Z
DTEND:20260411T071500Z
SUMMARY:Territorial conditioning of Intention to use AI in Latin America: mind the digital divide
DESCRIPTION:Authors - Maria Veronica Alderete\n Abstract -&nbsp\;This study extends the empirical literature on the relationship between intention to use Artificial Intelligence (AI)\, the digital divide\, and regional ine-qualities in Latin America. To the best of our knowledge\, no prior research has examined the AI gap by combining data at the subnational (regional) level across countries. The analysis relies on a sample of 208 regions from 10 Latin American countries. A structural equation model is estimated to assess the relationships among digital infrastructure\, socioeconomic factors\, and intention to use ChatGPT. The results show that household internet access has a positive and statistically significant effect on intention to use ChatGPT. Data center presence indirectly re-inforces AI intention use through its positive association with internet access\, while rurality exerts a negative effect. Education levels and platform-based em-ployment (e.g.\, Uber) are also positively associated with intention to use AI. The findings suggest that AI adoption is structurally conditioned by foundational digi-tal infrastructure\, regional human capital\, and exposure to platform-based labor markets. Although the expansion of the gig economy fosters intention to use AI\, AI diffusion simultaneously increases the importance of formal education.
CATEGORIES:VIRTUAL ROOM_11F
LOCATION:Virtual Room F\, Bangkok\, Thailand
SEQUENCE:0
UID:602027d41dec205af5fa831576819b7d
URL:http://11thictisthailand.sched.com/event/602027d41dec205af5fa831576819b7d
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260417T125213Z
DTSTART:20260411T051500Z
DTEND:20260411T071500Z
SUMMARY:The Impact of Post-Adoption Expectations on Continuance Intentions of Community Health Workers to Use mHealth in Malawi
DESCRIPTION:Authors - Donald Flywell Malanga\, Wallace Chigona\n Abstract - Mobile Health (mHealth) has been regarded as a potentially transform-ative element for enhancing health service delivery in low-income nations. The effective integration of technology relies on ongoing usage rather than just initial acceptance. While the body of literature on factors influencing continued mHealth use is expanding\, post-adoption expectations are proposed as indicators of the success or failure of mHealth implementation. There is limited research on how community health workers' post-adoption expectations influence their inten-tions to persist in using mHealth in developing regions. Consequently\, this study explores the effect of post-adoption expectations on satisfaction and ongoing us-age behaviour regarding mHealth among community health workers in Malawi\, which represents a developing country context. The research introduces a frame-work that builds upon the expectation confirmation model and incorporates ele-ments from the updated information success model. A mixed-methods conver-gent design was utilised for the study. Data were collected through surveys and semi-structured interviews with community health workers who utilise Cstock. Cstock is an mHealth application that facilitates the ordering of medical supplies via text message. The findings generally support the notion that post-usage use-fulness\, along with information quality\, system quality\, and service quality\, pos-itively influences community health workers’ satisfaction and their intention to continue using the Cstock application. The results indicate that the ongoing usage behaviour of mHealth among community health workers is shaped not solely by behavioural expectation beliefs (i.e.\, post-usage usefulness) but also by objective expectation beliefs\, including system quality\, service quality\, and information quality. Therefore\, these findings provide valuable insights to policymakers\, practitioners\, mHealth developers\, and other relevant parties regarding the post-user expectations essential for maintaining future mHealth solutions in develop-ing countries\, particularly in Malawi.
CATEGORIES:VIRTUAL ROOM_11F
LOCATION:Virtual Room F\, Bangkok\, Thailand
SEQUENCE:0
UID:19b936a00f8ae058c379ce5f0bf01bf2
URL:http://11thictisthailand.sched.com/event/19b936a00f8ae058c379ce5f0bf01bf2
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260417T125213Z
DTSTART:20260411T051500Z
DTEND:20260411T071500Z
SUMMARY:A Multimodal Knowledge-Driven Framework for Urban Scene Summarization
DESCRIPTION:Authors - Hemamalini Siranjeevi\, Swaminathan Venkatraman\, Dharshini V\, Gayathri A\, Sushma Sri R\n Abstract - Urban environments generate massive video data from surveillance and mobile sensors\, necessitating efficient and intelligent summarization for smart city and transportation systems. This paper proposes a multimodal video summarization framework that moves beyond object-centric analysis toward high-level urban scene understanding. Unlike traditional methods that rely on low-level visual features or isolated object detection\, the proposed approach captures contextual relationships and temporal continuity through a multi-stage pipeline. The system integrates multimodal perception\, combining deep learning-based object detection\, multi-object tracking\, and acoustic analysis to preserve entity identities and environmental context. We employ relational inference and motion heuristics to model spatial and semantic interactions\, which are then structured into a Dynamic Knowledge Graph (DKG) representing entities\, interactions\, and temporal events. A semantic synthesis module\, powered by a transformer-based language model\, generates concise\, coherent\, and semantically meaningful summaries. This architecture enables scalable\, context-aware video summarization adaptable to real-world urban applications.
CATEGORIES:VIRTUAL ROOM_11G
LOCATION:Virtual Room G\, Bangkok\, Thailand
SEQUENCE:0
UID:04a92a476c96036a3625e28ef7853d64
URL:http://11thictisthailand.sched.com/event/04a92a476c96036a3625e28ef7853d64
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260417T125213Z
DTSTART:20260411T051500Z
DTEND:20260411T071500Z
SUMMARY:A Review on Large Language Models in Engineering
DESCRIPTION:Authors - Nithin Gattappagari\, Lakshmi Sagar S\, Reddy Lokesh K\, Banu Prakash N\, Asritha A\, Varalakshmi U\, Karthik P\, Praveen Kumar Rayani Abstract - Conventional one-time authentication cannot prevent session hijacking after login. This paper proposes a session-level impostor de tection framework based on Siamese learning over mouse dynamics for continuous authentication. The model combines statistical behavioral de scriptors with lightweight temporal modeling (Conv1D+GRU) to learn compact embeddings for open-set verification. It supports one-shot en rollment by comparing a query session against a single verified reference session and stores non-reversible embeddings instead of raw trajectories to improve privacy. We evaluate on Balabit and SAPiMouse under se vere class imbalance using balanced batching\, semi-hard negative mining\, and focal contrastive loss. The framework achieves AUROC 0.95/0.96\, F1 0.80/0.85\, and accuracy 0.92/0.93\, with 46K trainable parameters and approximately 15ms inference time\, indicating practical deployment potential.
CATEGORIES:VIRTUAL ROOM_11G
LOCATION:Virtual Room G\, Bangkok\, Thailand
SEQUENCE:0
UID:72e87835ff8487e544ead6009f662d58
URL:http://11thictisthailand.sched.com/event/72e87835ff8487e544ead6009f662d58
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260417T125213Z
DTSTART:20260411T051500Z
DTEND:20260411T071500Z
SUMMARY:Integrated AI System for Suspicious Activity Detection in Public Surveillance
DESCRIPTION:Authors - Rishav Kumar Agrawal\, Maharshi Bhowmick\, Mir Abbas Hussain\, Sachin\, Vaishali Shinde Abstract - This paper presents a platform for scalable validation\, visu alization\, and explanation of synthetic tabular data in a rigorous and operationally practical workflow. The system integrates statistical test ing\, dimensionality reduction\, anomaly detection\, and AI-assisted in terpretation into a single analysis pipeline. Through an insurance-data case study\, we show that the platform can detect subtle distributional artifacts\, support utility–privacy trade-off assessment\, and provide in terpretable evidence that is difficult to obtain from isolated univariate checks. We conclude by discussing practical value\, current limitations\, and directions for future development.
CATEGORIES:VIRTUAL ROOM_11G
LOCATION:Virtual Room G\, Bangkok\, Thailand
SEQUENCE:0
UID:7c278b07b5036844ff38dbb0f500c6cd
URL:http://11thictisthailand.sched.com/event/7c278b07b5036844ff38dbb0f500c6cd
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260417T125213Z
DTSTART:20260411T051500Z
DTEND:20260411T071500Z
SUMMARY:Leveraging AI for Customer Insights in Oman Digital Marketing
DESCRIPTION:Authors - Rowena Ocier Sibayan\, Hazel C. Tagalog\, Ronald S. Cordova\n Abstract - As digital marketing expands in Oman\, many organizations struggle to transform large volumes of customer data into actionable insights. This study presents an AI-driven marketing intelligence framework designed for non-technical users\, combining automated customer segmentation\, sentiment analysis\, and personalized recommendations. The framework employs an autoencoder-based feature extraction approach to capture key behavioral patterns\, followed by K-Means clustering to define meaningful customer segments (Berahmand et al.\, 2024). A fine-tuned BERT model analyzes multilingual feedback in Arabic and English to assess customer sentiment (Manias et al.\, 2023). The framework was evaluated using 12 months of campaign data from 450 customers across multiple Omani businesses. Analysis revealed four distinct customer groups and an overall positive sentiment of +0.55. Controlled A/B experiments demonstrated that AI-guided campaigns outperformed traditional methods\, increasing conversion rates by 27%\, improving retention by 15%\, and generating a threefold return on marketing spend. These results indicate that accessible AI tools can deliver measurable marketing benefits in emerging markets and provide a scalable solution for Gulf-region businesses.
CATEGORIES:VIRTUAL ROOM_11G
LOCATION:Virtual Room G\, Bangkok\, Thailand
SEQUENCE:0
UID:c42b95b12730277e287c6c2c57b92a4d
URL:http://11thictisthailand.sched.com/event/c42b95b12730277e287c6c2c57b92a4d
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260417T125213Z
DTSTART:20260411T051500Z
DTEND:20260411T071500Z
SUMMARY:MalViT: Malware Classification Using Vision Transformers With Robustness Analysis Via Obfuscation
DESCRIPTION:Authors - Maria George Anthraper\, Kusuma Sanjaykumar\, Sinchana K C\, V R\, Badri Prasad Abstract - Post-quantum migration is increasingly constrained by time: deployed cryptographic mechanisms may need to be retired\, hybridized\, or re-keyed before effective security margins fall below asset-specific pol icy thresholds. This timing problem is complicated by uncertainty in clas sical hardware acceleration\, algorithmic progress\, implementation ero sion\, and the arrival of cryptographically relevant quantum comput ers. This paper presents a compact probabilistic pipeline that translates evolving assumptions and evidence into decision-facing migration guid ance. The approach couples three layers: (i) a security-trajectory model that encodes expected margin erosion under scenario parameters\, (ii) a latent-regime model that represents partially observed risk states and updates them as evidence changes\, and (iii) an option-style timing layer that quantifies the diminishing value of delaying migration as thresholds approach. Outputs are conditional on stated assumptions and are in tended to be reported with sensitivity bands and lead-time constraints. In practice\, the pipeline is intended to be re-run as assumptions and evidence evolve\, preserving an auditable trail from scenario inputs to in termediate states and final decision artifacts. The primary deliverables are comparative rankings and conservative “start-by” windows under stated assumptions\, rather than single predicted break dates.
CATEGORIES:VIRTUAL ROOM_11G
LOCATION:Virtual Room G\, Bangkok\, Thailand
SEQUENCE:0
UID:30c80d10fc0b7c4f572e3a90a807388f
URL:http://11thictisthailand.sched.com/event/30c80d10fc0b7c4f572e3a90a807388f
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260417T125213Z
DTSTART:20260411T051500Z
DTEND:20260411T071500Z
SUMMARY:Online Product Fake Review Identification Using Deep Learning Algorithm
DESCRIPTION:Authors - Jayalakshmi D\, N. Priya\n Abstract - Online product reviews play a key role in the success or failure of an e-commerce business. Often\, online reviews from previous customers provide buyers with detailed advice about the product and help them decide before purchasing a product or service. However\, some e-commerce products can be promoted or damaged by fraudsters who post fake reviews. Synthetic Reviews (SRs) have the capacity to deceive consumers\, influence purchasing decisions\, and lead to losses. Thus\, SRs pose a significant risk to e-commerce companies and content creators\, undermining consumer loyalty and brand reputation. Specifically\, the development of AI-generated fake reviews has made them harder to detect\, as they are very similar to human-written texts. This review paper presents a Deep Learning (DL)-based framework that offers comprehensive insight into fraud and synthetic review detection in an evolving e-commerce environment. This review paper discusses the importance of DL for detecting online product fake reviews in sentiment analysis using various approaches based on Graph Convolutional Network (GCN)\, Hierarchical Graph Attention Network (HGAN) Sentiment Majority Voting Classifiers (SMVC)\, Convolutional Neural Networks with Bidirectional Long Short-Term Memory Networks (CNN-Bi-LSTMs)\, and a proposed Optimized Bidirectional Encoder Representation Transformers (OBERT) model. This review paper focused on the importance of DL models\, particularly the GCN\, for effective identification of fake online reviews. This review paper proposed a DL algorithm for fake review detection in online products and demonstrated its practical application in a real-world scenario.
CATEGORIES:VIRTUAL ROOM_11G
LOCATION:Virtual Room G\, Bangkok\, Thailand
SEQUENCE:0
UID:66df194f716f9f08b2fe6ae85bc5e6f9
URL:http://11thictisthailand.sched.com/event/66df194f716f9f08b2fe6ae85bc5e6f9
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260417T125213Z
DTSTART:20260411T051500Z
DTEND:20260411T071500Z
SUMMARY:Security and Reliability Aspects of FSO Integration into Private 5G Networks
DESCRIPTION:Authors - Miroslav Cech\, Rastislav Roka\n Abstract - Private 5G networks require a reliable\, high-capacity\, and secure transport infrastructure\, especially in industrial and critical applications. Free Space Optics is a promising solution enabling multi-gigabit transmissions with low latency and increased physical security. The article analyses the possibili ties of integrating FSO technology into Standalone Non-Public Network and Public Network Integrated Non-Public Network architectures and evaluates the role of FSO links as a transport or interconnection layer and their impact on la tency\, reliability\, and security for 5G services such as eMBB\, URLLC\, and mMTC. The article then summarizes current research trends\, including the use of artificial intelligence and machine learning to optimize FSO-based transmission.
CATEGORIES:VIRTUAL ROOM_11G
LOCATION:Virtual Room G\, Bangkok\, Thailand
SEQUENCE:0
UID:35be1ddc31d31e5410bb0b9172a0de59
URL:http://11thictisthailand.sched.com/event/35be1ddc31d31e5410bb0b9172a0de59
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260417T125213Z
DTSTART:20260411T051500Z
DTEND:20260411T071500Z
SUMMARY:Sentiment Analysis of Press Releases in the Automobile Sector Using Python
DESCRIPTION:Authors - Tanmoy De\, Vimal Kumar\, Pratima Verma\n Abstract - The process of operating modern engineering companies is often compartmentalized due to the straightforward nature of the operations requirements that mani-fest themselves within the realm of the software creation and hardware manufacturing. The absence of integration between Agile practices and Waterfall lifecycles is a waste of administrative resources and delays time-to-market. A hybrid project management SaaS is offered in this project called Converge\, which will target the integration of these areas without sacrificing the integrity of the data stored in digital code repositories and physical Bill of Materials (BoM). The adoption of Multi-Modal Documentation\, Real-time State Synchronization and IoT-oriented Task Automation have their measures of efficiency of workflow\, responsiveness of interface\, and cross-domain data consistency. The most recent breakthroughs in Natural Language Processing (NLP) and Computer Vision are used to make the experience more practical\; a custom AI pipeline based on the ResNet50 and LSTM networks are able to extract visual storyboards of technical video reports with an impressive F Score of 83.00% (with 79.20% Precision and 86.50% Recall)\, and Transformer based models (including BART) are able to generate structured textual summaries with the leading ROUGE-L score of 0.42. The system is anchored on a dynamic split-brain architecture to display coherent information in either Kanban boards or Gantt charts as the case arises. Status updates increase exponentially with integrated IoT triggers to computerize the execution of tasks via a direct hardware to software communication. The survey is based on the trade offs between the flexibility of UI\, the complexity of the database schema\, and the latency of the API to compare the old siloed tools to this new hybrid framework. The future of engineering management relies on new tendencies\, such as Hybrid Machine Learning\, to predictively allocate resources\, cutting the error rates in estimating the effort by three times (MMRE to 0.32) with the help of such dominant historical measures of resources as Lines of Code (feature importance score of 0.73) and automated reporting of resource depend-ency. Finally\, it is demonstrated that the suggested architecture with the support of a CNN optimized backend video storage\, which will save 61.80% of the time at a small cost of 2.30% BDBR\, will save about 60% of time on manual docu-mentation and synchronize assets in real-time with a latency less than 200ms (2 seconds).
CATEGORIES:VIRTUAL ROOM_11G
LOCATION:Virtual Room G\, Bangkok\, Thailand
SEQUENCE:0
UID:8cb978aa022bbdf6e16ce840c7367ede
URL:http://11thictisthailand.sched.com/event/8cb978aa022bbdf6e16ce840c7367ede
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260417T125213Z
DTSTART:20260411T051500Z
DTEND:20260411T071500Z
SUMMARY:Software Quality Assessment of a Lending Automation System Using ISO/IEC 25010
DESCRIPTION:Authors - Dennis A. Dizon\, Gleen A. Dalaorao\n Abstract - Access to formal financial services remains limited in many develop ing regions\, largely due to economic and infrastructural constraints. This study uses the ISO/IEC 25010 as the evaluation framework to present a software quality assessment of a lending automation system installed in a financial insti tution in Butuan City\, Philippines. The evaluation focuses on five essential as pects of software quality: usability\, reliability\, functional suitability\, perfor mance efficiency\, and security. Usability surveys using SUS and UMUX-Lite\, operational and performance testing\, and an evaluation of security and data privacy compliance were used to gather empirical data. According to the results\, the system achieved high performance with an average inference latency of 0.208 ms per record\, uptime reliability of ≥99.5%\, excellent usability with a mean SUS score of 82.5\, and full compliance with data privacy regulations. Predictive analytics\, specifically the Random Forest model with isotonic calibration\, further enhanced the automated loan assessment’s interpretability and reliability. The system proved that it is appropriate for real-world applications and can encourage financial inclusion in resource-constrained environments\, as it exceeded the intended benchmarks for each quality model. To guarantee the long-term adoption of lending automation technologies\, the study emphasizes the significance of thorough software quality evaluation in addition to predictive accuracy.
CATEGORIES:VIRTUAL ROOM_11G
LOCATION:Virtual Room G\, Bangkok\, Thailand
SEQUENCE:0
UID:272dd892b049d82c36d3d438fa449802
URL:http://11thictisthailand.sched.com/event/272dd892b049d82c36d3d438fa449802
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260417T125213Z
DTSTART:20260411T051500Z
DTEND:20260411T071500Z
SUMMARY:Towards Transparent Cybersecurity: A Survey of Explainable AI Techniques in Intrusion Detection Systems
DESCRIPTION:Authors - Nita Dimble\, Satish Narayanrav Gujar Abstract - The fabrication of components across various industries is accom plished through welding. Although welding has been practiced for more than a hundred years\, defects may still occur during the welding process. Thus\, indus trial standards require welded joints to be inspected and evaluated to ensure their quality and reliability. Conventional ultrasonic testing (UT) has long been widely used in industry for detecting and evaluating defects in weld specimens. Over the last few decades\, advances in sensor technology and signal analysis techniques have significantly advanced ultrasonic testing methods. Advanced methods\, such as Time Of Flight Diffraction (TOFD)\, are more likely to detect linear defects. However\, one of the major challenges in applying TOFD to the inspection of austenitic stainless steel (ASS) weldments is noise in the signals. Various signal processing approaches have been developed to suppress such noise\, each with its own advantages and limitations. In this work\, the focus is placed on the applica tion of multi-level discrete wavelet transform (DWT) decompositions with ‘n’- order wavelet filters for de-noising ultrasonic TOFD A-scan signals. The results show that this approach achieves greater improvement in signal-to-noise ratio (SNR) while requiring less computational time.
CATEGORIES:VIRTUAL ROOM_11G
LOCATION:Virtual Room G\, Bangkok\, Thailand
SEQUENCE:0
UID:22687f81f4c0e6c67359b1e2df902f22
URL:http://11thictisthailand.sched.com/event/22687f81f4c0e6c67359b1e2df902f22
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260417T125213Z
DTSTART:20260411T071500Z
DTEND:20260411T071700Z
SUMMARY:Session Chair Concluding Remarks
DESCRIPTION:\n
CATEGORIES:VIRTUAL ROOM_11A
LOCATION:Virtual Room A\, Bangkok\, Thailand
SEQUENCE:0
UID:357c24fbeea9c67278ed3bf3513fb5bf
URL:http://11thictisthailand.sched.com/event/357c24fbeea9c67278ed3bf3513fb5bf
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260417T125213Z
DTSTART:20260411T071500Z
DTEND:20260411T071700Z
SUMMARY:Session Chair Concluding Remarks
DESCRIPTION:\n
CATEGORIES:VIRTUAL ROOM_11B
LOCATION:Virtual Room B\, Bangkok\, Thailand
SEQUENCE:0
UID:5317ebd11b79ffe1707220533b5988d9
URL:http://11thictisthailand.sched.com/event/5317ebd11b79ffe1707220533b5988d9
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260417T125213Z
DTSTART:20260411T071500Z
DTEND:20260411T071700Z
SUMMARY:Session Chair Concluding Remarks
DESCRIPTION:\n
CATEGORIES:VIRTUAL ROOM_11C
LOCATION:Virtual Room C\, Bangkok\, Thailand
SEQUENCE:0
UID:7092ecda40c36998ed223e10f187c724
URL:http://11thictisthailand.sched.com/event/7092ecda40c36998ed223e10f187c724
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260417T125213Z
DTSTART:20260411T071500Z
DTEND:20260411T071700Z
SUMMARY:Session Chair Concluding Remarks
DESCRIPTION:\n
CATEGORIES:VIRTUAL ROOM_11D
LOCATION:Virtual Room D\, Bangkok\, Thailand
SEQUENCE:0
UID:eb3e3788bcc2a9f63585e538eaa85fce
URL:http://11thictisthailand.sched.com/event/eb3e3788bcc2a9f63585e538eaa85fce
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260417T125213Z
DTSTART:20260411T071500Z
DTEND:20260411T071700Z
SUMMARY:Session Chair Concluding Remarks
DESCRIPTION:\n
CATEGORIES:VIRTUAL ROOM_11E
LOCATION:Virtual Room E\, Bangkok\, Thailand
SEQUENCE:0
UID:4d64c94ca5bbfc578db5ea70fbc812d1
URL:http://11thictisthailand.sched.com/event/4d64c94ca5bbfc578db5ea70fbc812d1
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260417T125213Z
DTSTART:20260411T071500Z
DTEND:20260411T071700Z
SUMMARY:Session Chair Concluding Remarks
DESCRIPTION:\n
CATEGORIES:VIRTUAL ROOM_11F
LOCATION:Virtual Room F\, Bangkok\, Thailand
SEQUENCE:0
UID:6a34da0af0285a610906bf50c32a7479
URL:http://11thictisthailand.sched.com/event/6a34da0af0285a610906bf50c32a7479
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260417T125213Z
DTSTART:20260411T071500Z
DTEND:20260411T071700Z
SUMMARY:Session Chair Concluding Remarks
DESCRIPTION:\n
CATEGORIES:VIRTUAL ROOM_11G
LOCATION:Virtual Room G\, Bangkok\, Thailand
SEQUENCE:0
UID:7d925700ac46577022b08b8fdf7fafe4
URL:http://11thictisthailand.sched.com/event/7d925700ac46577022b08b8fdf7fafe4
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260417T125213Z
DTSTART:20260411T071700Z
DTEND:20260411T072000Z
SUMMARY:Session Closing and Information To Authors
DESCRIPTION:\n
CATEGORIES:VIRTUAL ROOM_11A
LOCATION:Virtual Room A\, Bangkok\, Thailand
SEQUENCE:0
UID:ed3749b368f72315a4427021e7b3da6a
URL:http://11thictisthailand.sched.com/event/ed3749b368f72315a4427021e7b3da6a
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260417T125213Z
DTSTART:20260411T071700Z
DTEND:20260411T072000Z
SUMMARY:Session Closing and Information To Authors
DESCRIPTION:\n
CATEGORIES:VIRTUAL ROOM_11B
LOCATION:Virtual Room B\, Bangkok\, Thailand
SEQUENCE:0
UID:36ea3b2a73993d5ffbd31792e08c05e7
URL:http://11thictisthailand.sched.com/event/36ea3b2a73993d5ffbd31792e08c05e7
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260417T125213Z
DTSTART:20260411T071700Z
DTEND:20260411T072000Z
SUMMARY:Session Closing and Information To Authors
DESCRIPTION:\n
CATEGORIES:VIRTUAL ROOM_11C
LOCATION:Virtual Room C\, Bangkok\, Thailand
SEQUENCE:0
UID:1bc856623bcfeebbc3e326c9530960d0
URL:http://11thictisthailand.sched.com/event/1bc856623bcfeebbc3e326c9530960d0
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260417T125213Z
DTSTART:20260411T071700Z
DTEND:20260411T072000Z
SUMMARY:Session Closing and Information To Authors
DESCRIPTION:\n
CATEGORIES:VIRTUAL ROOM_11D
LOCATION:Virtual Room D\, Bangkok\, Thailand
SEQUENCE:0
UID:8b413ddb92214cfe2422ebe1b2c495cb
URL:http://11thictisthailand.sched.com/event/8b413ddb92214cfe2422ebe1b2c495cb
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260417T125213Z
DTSTART:20260411T071700Z
DTEND:20260411T072000Z
SUMMARY:Session Closing and Information To Authors
DESCRIPTION:\n
CATEGORIES:VIRTUAL ROOM_11E
LOCATION:Virtual Room E\, Bangkok\, Thailand
SEQUENCE:0
UID:a90ae22f5786b4c0f81a40812045f685
URL:http://11thictisthailand.sched.com/event/a90ae22f5786b4c0f81a40812045f685
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260417T125213Z
DTSTART:20260411T071700Z
DTEND:20260411T072000Z
SUMMARY:Session Closing and Information To Authors
DESCRIPTION:\n
CATEGORIES:VIRTUAL ROOM_11F
LOCATION:Virtual Room F\, Bangkok\, Thailand
SEQUENCE:0
UID:1d0e9200c3925b9cee236d01e6e9a56a
URL:http://11thictisthailand.sched.com/event/1d0e9200c3925b9cee236d01e6e9a56a
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260417T125213Z
DTSTART:20260411T071700Z
DTEND:20260411T072000Z
SUMMARY:Session Closing and Information To Authors
DESCRIPTION:\n
CATEGORIES:VIRTUAL ROOM_11G
LOCATION:Virtual Room G\, Bangkok\, Thailand
SEQUENCE:0
UID:6626f60314d2d1acee3721c84a074a27
URL:http://11thictisthailand.sched.com/event/6626f60314d2d1acee3721c84a074a27
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260417T125213Z
DTSTART:20260411T075800Z
DTEND:20260411T080000Z
SUMMARY:Opening Remarks
DESCRIPTION:\n
CATEGORIES:VIRTUAL ROOM_12A
LOCATION:Virtual Room A\, Bangkok\, Thailand
SEQUENCE:0
UID:2a10ebeff63cb8426220482a7e4e6a66
URL:http://11thictisthailand.sched.com/event/2a10ebeff63cb8426220482a7e4e6a66
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260417T125213Z
DTSTART:20260411T075800Z
DTEND:20260411T080000Z
SUMMARY:Opening Remarks
DESCRIPTION:\n
CATEGORIES:VIRTUAL ROOM_12B
LOCATION:Virtual Room B\, Bangkok\, Thailand
SEQUENCE:0
UID:bbc9074afccac8739c8fad691f2b20e7
URL:http://11thictisthailand.sched.com/event/bbc9074afccac8739c8fad691f2b20e7
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260417T125213Z
DTSTART:20260411T075800Z
DTEND:20260411T080000Z
SUMMARY:Opening Remarks
DESCRIPTION:\n
CATEGORIES:VIRTUAL ROOM_12C
LOCATION:Virtual Room C\, Bangkok\, Thailand
SEQUENCE:0
UID:b9b8aeb365f8102829ce34a525b60288
URL:http://11thictisthailand.sched.com/event/b9b8aeb365f8102829ce34a525b60288
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260417T125213Z
DTSTART:20260411T075800Z
DTEND:20260411T080000Z
SUMMARY:Opening Remarks
DESCRIPTION:\n
CATEGORIES:VIRTUAL ROOM_12D
LOCATION:Virtual Room D\, Bangkok\, Thailand
SEQUENCE:0
UID:9d5f35f3a3118cbd14a80def31196f7a
URL:http://11thictisthailand.sched.com/event/9d5f35f3a3118cbd14a80def31196f7a
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260417T125213Z
DTSTART:20260411T075800Z
DTEND:20260411T080000Z
SUMMARY:Opening Remarks
DESCRIPTION:\n
CATEGORIES:VIRTUAL ROOM_12E
LOCATION:Virtual Room E\, Bangkok\, Thailand
SEQUENCE:0
UID:2dde1ae7b3d310215d8d2b4583042fa5
URL:http://11thictisthailand.sched.com/event/2dde1ae7b3d310215d8d2b4583042fa5
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260417T125213Z
DTSTART:20260411T075800Z
DTEND:20260411T080000Z
SUMMARY:Opening Remarks
DESCRIPTION:\n
CATEGORIES:VIRTUAL ROOM_12F
LOCATION:Virtual Room F\, Bangkok\, Thailand
SEQUENCE:0
UID:83eaa43d4c3009c6121f98923d154216
URL:http://11thictisthailand.sched.com/event/83eaa43d4c3009c6121f98923d154216
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260417T125213Z
DTSTART:20260411T075800Z
DTEND:20260411T080000Z
SUMMARY:Opening Remarks
DESCRIPTION:\n
CATEGORIES:VIRTUAL ROOM_12G
LOCATION:Virtual Room G\, Bangkok\, Thailand
SEQUENCE:0
UID:2c3e2b38c0bbadcf36cfaf5326b7e311
URL:http://11thictisthailand.sched.com/event/2c3e2b38c0bbadcf36cfaf5326b7e311
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260417T125213Z
DTSTART:20260411T080000Z
DTEND:20260411T100000Z
SUMMARY:A Chaos-Based Permutation–Diffusion Framework for Secure and Efficient Digital Image Encryption
DESCRIPTION:Authors - Selvamani K\, Saranraj S\, Muthusundar SK\, Kanimozhi S\, Mohana Suganthi N Abstract - The phishing attack through email remains a significant threat to cybersecurity because the attack has become highly advanced\, flexible\, and widely spread among individuals and organizations. The phishing tricks\, such as personalized social engineering\, impersonated identities\, and malicious links\, have evolved fast and made the traditional email security measures less useful. As such\, numerous schemes of email phishing attack detection and prevention have been suggested\, combining rule-based approaches with machine learning\, deep learning\, natural language processing\, and sophisticated artificial intelligence systems. This review paper provides a detailed discussion of the currently existing email phishing detection and prevention frameworks\, their architectural elements\, detection schemes\, and preventive schemes. The paper systematically evaluates the conventional\, machine learning\, and more advanced AI-driven methods with their advantages\, weaknesses\, and flexibility to the changing phishing threats. The synthesis of existing research trends and unaddressed issues makes the review valuable to researchers and cybersecurity practitioners and will allow building solid\, scalable\, and intelligent email phishing defense systems.
CATEGORIES:VIRTUAL ROOM_12A
LOCATION:Virtual Room A\, Bangkok\, Thailand
SEQUENCE:0
UID:8a6f7ad85c7e3eb96684d0c56e7e6e24
URL:http://11thictisthailand.sched.com/event/8a6f7ad85c7e3eb96684d0c56e7e6e24
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260417T125213Z
DTSTART:20260411T080000Z
DTEND:20260411T100000Z
SUMMARY:A Hybrid Semantic–Linguistic Framework for Clinical Detection of Drug–Drug Interactions and Contraindications
DESCRIPTION:Authors - Mohanad A. Deif\, Mohamed A. Hafez\, Samar Mouakket\, Mohamed Abstract - Polypharmacy and multiple chronic conditions in older adults increase the likelihood of adverse drug events caused by drug–drug interactions (DDIs) and contraindications. Many clinical decision support systems still have limited ability to use patient context and to exchange knowledge in a consistent semantic form. This study presents a hybrid semantic–linguistic framework for automated DDI detection by combining biomedical natural language processing\, ontology-based reasoning\, and risk scoring. The framework uses BioBERT to extract relevant information and represents it using RDF knowledge graphs\, OWL 2 DL ontologies\, and SWRL rules. In an evaluation with 1\,000 synthetic patient profiles containing RxNorm-coded medications and SNOMED CTencoded diagnoses\, the system identified a wide range of clinically important interaction patterns. Statistical testing showed that age and the number of medications were strongly associated with alert frequency (p < 0.001). These findings suggest that the proposed approach can improve medication safety by providing explainable clinical decision support.
CATEGORIES:VIRTUAL ROOM_12A
LOCATION:Virtual Room A\, Bangkok\, Thailand
SEQUENCE:0
UID:38284ba51ed4a1c55224fc2584be0803
URL:http://11thictisthailand.sched.com/event/38284ba51ed4a1c55224fc2584be0803
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260417T125213Z
DTSTART:20260411T080000Z
DTEND:20260411T100000Z
SUMMARY:A Quantum-Resistant Security Framework for Real-Time Financial Transaction Systems
DESCRIPTION:Authors - Selvamani K\, Kanimozhi S\, Muthusundar S K\, Saranraj S\, Jagadeesh K Abstract - Multi-object tracking (MOT) is a pillar of many computer vision applications such as video surveillance\, self-driving and crowd analysis [1]. The main difficulty does not only exist in correct identification of objects but also in consistent identities of objects in different frames when there is occlusion\, camera motion and changes in scene density [14]. The paper introduces a highly advanced MOT system\, combining the latest YOLOv8x detector with a modified and improved version of the original ByteTrack association system\, which is called RobustBoTSORTTracker [14]. With the new detection quality of YOLOv8x and the robustness of low-confidence detections in ByteTrack\, augmented with selective improvements of BoT-SORT including camera motion compensation and exponential moving average smoothing\, the proposed system demonstrates significant gains on the MOT15 benchmark [7]. Experimental findings indicate a MOTA of 55.6\, IDF1 of 72.2\, precision of 74.3 and a recall of 95.7\, which is significantly higher than the previous baselines under similar conditions.
CATEGORIES:VIRTUAL ROOM_12A
LOCATION:Virtual Room A\, Bangkok\, Thailand
SEQUENCE:0
UID:49a0e5e749a74a7d47dcb1dceb92bb40
URL:http://11thictisthailand.sched.com/event/49a0e5e749a74a7d47dcb1dceb92bb40
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260417T125213Z
DTSTART:20260411T080000Z
DTEND:20260411T100000Z
SUMMARY:Adverse Rainy Condition Classification Using Customize Lightweight CNN Models for UAVs
DESCRIPTION:Authors - Abhay Saxena\, Ankit Kumar\, Prasant Kumar Sahu\n Abstract - In this paper\, we address the problem of rainy condition classification in order to allow autonomous systems to ensure safe operation in different weather conditions of rain\, especially for drones. The earlier weather condition classification methods are inclined towards using big and computationally costly models and cannot thus be employed in real-time on resource-constrained platforms such as drones and edge devices. The motivation behind this work is to introduce a light-weight\, efficient deep model which would be able to classify various rain conditions with low computational cost so that it may be deployed efficiently on low-resource devices. We present a novel CNN architecture and evaluate its performance on a collection of seven distinct rain conditions. The models are bench marked against some of the state-of-the-art pretrained models to demonstrate the compromise between efficiency and accuracy. Performance is evaluated using accuracy\, inference time\, and model size. The model has accuracy 95.93% with least model size 89.09 KB with inference time of 32.664 ms bridging the gap in lightweight and real-time classification.
CATEGORIES:VIRTUAL ROOM_12A
LOCATION:Virtual Room A\, Bangkok\, Thailand
SEQUENCE:0
UID:1ae218329df9f3ba57ccd9e717ac5880
URL:http://11thictisthailand.sched.com/event/1ae218329df9f3ba57ccd9e717ac5880
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260417T125213Z
DTSTART:20260411T080000Z
DTEND:20260411T100000Z
SUMMARY:Bonding of material and Electrical Properties of EVA shoes under various Physical and Manufacturing conditions
DESCRIPTION:Authors - Arjun Verma\, D.K. Chaturvedi\n Abstract - Ethylene and vinyl acetate or EVA is a co-polymer used as a substitute for a lot of materials. EVA is a versatile material and it has a lot of applications ranging from electronics\, healthcare\, footwear\, building applications etc. It is mainly used in sport shoes due to its property to absorb shock impact and insulation properties. In addition\, EVA is very cost-friendly\, produces no odor\, and light in weight material. But with overuse of it\, the cellular structure chang-es and can affect the shoes' quality and insulation properties. In addition to the cellular structure\, the air molecules present in it also collapse. This paper focus-es on the bonding properties of EVA at different temperatures and its dielectric properties under different operating and manufacturing conditions. The upper\, bottom\, and sides of EVA shoes are exposed to high voltage till the breakdown. The experimentation was done at Electrical HV laboratory on the university campus where a 100kV HVAC testing system is available. This paper presents the tabulated results on the dielectric strength of EVA shoes under varying operating conditions. Additionally\, it examines the bonding properties of EVA shoes at different manufacturing temperatures\, aiming to predict their lifespan\, quality\, and finish. The results of these studies are thoroughly discussed within the document.
CATEGORIES:VIRTUAL ROOM_12A
LOCATION:Virtual Room A\, Bangkok\, Thailand
SEQUENCE:0
UID:3c0232677dbcf972d5be80e171a65baa
URL:http://11thictisthailand.sched.com/event/3c0232677dbcf972d5be80e171a65baa
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260417T125213Z
DTSTART:20260411T080000Z
DTEND:20260411T100000Z
SUMMARY:Development of a Context-based Prompt Generation Framework to Enhance Model-Driven Engineering using Retrieval-Augmented Generation with Large Language Models
DESCRIPTION:Authors - Nasika Ijaz\, Farooque Azam\, Saliha Ejaz\, Muhammad Waseem Anwar Abstract - Anomaly detection in dynamic cybersecurity networks has been a promising problem that has been addressed using Graph Neural Networks (GNNs). Today’s network topologies are too difficult to handle for traditional methods\; the topologies are too dynamic and complex. The main contribution of this study is the evaluation of three GNN models\, Graph Convolutional Networks (GCNs)\, Graph Attention Networks (GATs)\, and RepographGAN\, in terms of effectiveness to detect anomalies in dynamic network environments. Conventional anomaly detection techniques such as logistic regression\, support vectors machines (SVM) and decision trees are compared against the models. The results demonstrate that RegraphGAN is superior to the other models in terms of accuracy\, precision\, recall\, F1 score\, and AUC-ROC\, and is thus very effective at identifying anomalies. However\, as computing resources are required for it\, a compromise between performance and computing resources is found. Despite the lower accuracy of GCN and GAT\, these provide more computationally efficient solutions that are appropriate for real time deployment constraints in such resource constrained environments. The findings provide a basis for future research that can optimize scalability and computational efficiency for large scale applications and in the context suggest the use of GNNs for improving cybersecurity systems.
CATEGORIES:VIRTUAL ROOM_12A
LOCATION:Virtual Room A\, Bangkok\, Thailand
SEQUENCE:0
UID:8eee5bce87d2a4c0ef1dc9f89b8a968e
URL:http://11thictisthailand.sched.com/event/8eee5bce87d2a4c0ef1dc9f89b8a968e
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260417T125213Z
DTSTART:20260411T080000Z
DTEND:20260411T100000Z
SUMMARY:Enhancing Password Guessing Efficiency: A Partition-Aware TCN Approach Beyond PGTCN
DESCRIPTION:Authors - Aaqib Hakeem\, Akshay V\, Parthav Mathu\, Kotnada Yogesh\, Gokul Kannan Sadasivam Abstract - Passwords remain one of the most widely deployed authentication mechanisms despite well-documented vulnerabilities to guessing attacks. Recent deep learning approaches\, including Password Guessing using Temporal Convolutional Networks (PGTCN)\, have demonstrated that sequence modeling can effectively capture structural regularities in leaked password corpora. However\, practical performance often depends not only on model architecture but also on training stability\, batching strategy\, and decoding configuration. In this work\, we investigate a partition-aware training and generation pipeline built around a single Temporal Convolutional Network (TCN). Rather than introducing additional architectural complexity\, the proposed framework emphasizes standardized preprocessing\, balanced data partitioning for stable batching\, optimized training procedures\, and large-batch probabilistic decoding. A lightweight buffering layer is incorporated to decouple generation from evaluation and improve throughput without requiring distributed training infrastructure. Experiments on multiple real-world leaked password datasets show consistent\, though modest\, improvements in match rate compared to the PGTCN baseline under same-site evaluation. The results suggest that careful optimization and pipeline-level design can yield measurable gains in candidate ordering while maintaining reproducibility and implementation simplicity.
CATEGORIES:VIRTUAL ROOM_12A
LOCATION:Virtual Room A\, Bangkok\, Thailand
SEQUENCE:0
UID:bbfe67b17f1d50cdcd8252fddb38370a
URL:http://11thictisthailand.sched.com/event/bbfe67b17f1d50cdcd8252fddb38370a
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260417T125213Z
DTSTART:20260411T080000Z
DTEND:20260411T100000Z
SUMMARY:Image Forgery Detection Using Convolutional Autoencoder
DESCRIPTION:Authors - Sushant Maji\, Sachin B. Jadhav Abstract - The offline signature validation by means of hand written signature is also a significant consideration in the financial\, legal and ad- ministrative authentication systems. However\, this is particularly challenging because of the inaccessibility of dynamic data of handwriting such as pen-pressure and stroke-velocity\, and small training samples. The paper describes a modified version of Siamese-Transformer model called SigNeura\, which is also improved with Synthetic Pen Pressure Map Generation to refine the accuracy of the verification in the few-shot learning. The adaptive thresholding\, and utilization of the stroke-width estimation is applied to obtain synthetic pressure maps and fill in the dynamic information of the synthetic grayscale signatures with the static grayscale signatures. The Siamese network is optimized on discriminative embeddings and Transformer encoders are optimized on triplet long range contextual dependencies. The analysis conducted on benchmarking data using experiments demonstrates that SigNeura is a significantly superior approach than conventional CNN and Siamese-based approaches with a high level of accuracy and resistance to skilled forgeries.
CATEGORIES:VIRTUAL ROOM_12A
LOCATION:Virtual Room A\, Bangkok\, Thailand
SEQUENCE:0
UID:7560f5b89f149e72a894f24b923829ce
URL:http://11thictisthailand.sched.com/event/7560f5b89f149e72a894f24b923829ce
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260417T125213Z
DTSTART:20260411T080000Z
DTEND:20260411T100000Z
SUMMARY:Personality Rights–Based Financial Inclusion through ICT: Reconceptualizing Digital Finance as a Rights-Dependent Process
DESCRIPTION:Authors - Siddharth Joshi\, Deepti Kiran\, Dev Kumar Yadav\, Harshit Sinha\, Abhishek Kukreti Abstract - Artificial Intelligence (AI)\, as a technology\, has the potential to change the manner in which organizations are run in the world. However\, small and medium-sized enterprises (SMEs) in the Philippines have unique limitations in the use of AI in running the business. The study aims to explore the perceptions of SME managers in the Philippines on the use of AI\, with particular reference to the limitations and facilitators in the use of the technology in the business environment. In this study\, the researcher interviewed five SME managers from different sectors\, including retail\, manufacturing\, and service sectors. The researcher used thematic analysis to identify the commonalities in the decisions made by the SME managers on the use of AI in the business environment. The study revealed the perceptions of the SME managers on the use of AI in the business environment in the Philippines\, with the limitations and facilitators in the use of the technology in the business environment. The study provides practical insights that can guide strategies aimed at strengthening AI readiness and responsible adoption among SMEs in the Philippines.
CATEGORIES:VIRTUAL ROOM_12A
LOCATION:Virtual Room A\, Bangkok\, Thailand
SEQUENCE:0
UID:55602dcd25816b84814e542a9c66fa80
URL:http://11thictisthailand.sched.com/event/55602dcd25816b84814e542a9c66fa80
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260417T125213Z
DTSTART:20260411T080000Z
DTEND:20260411T100000Z
SUMMARY:Privacy Preservation Techniques in Big Datasets: A Comprehensive Review
DESCRIPTION:Authors - Ambrish Kumar Sharma\, Swati Namdev Abstract - The volume of data is growing gradually in all around by various sec-tors like e-commerce\, stock market\, medical\, banking\, education\, social networks (Facebook\, Twitter\, WhatsApp) and also because of the utilization of the internet and mobile apps. Privacy and security have always been important issues with big datasets. Big datasets may be a collection of facts that has huge and multiplex structure like sensors\, emails\, weblogs and images. Sensitive information about individuals\, which is usually evident or hidden in data\, is susceptible to various privacy attacks and high risks of privacy disclosure. Constructing a secure and reliable environment for big dataset requires a distinction between existing approaches so that we can develop a unique solution in future for this that maximizes data privacy. This paper offers insights into the overview of big datasets\, big dataset privacy problems and various privacy preservation techniques with comparative study used in big datasets.
CATEGORIES:VIRTUAL ROOM_12A
LOCATION:Virtual Room A\, Bangkok\, Thailand
SEQUENCE:0
UID:62b8032f4d7d7e2c2901e7254dc7242d
URL:http://11thictisthailand.sched.com/event/62b8032f4d7d7e2c2901e7254dc7242d
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260417T125213Z
DTSTART:20260411T080000Z
DTEND:20260411T100000Z
SUMMARY:A Hybrid Deep Learning–Based Intrusion Detection System with Enhanced Feature Optimization for DDoS Attack Detection
DESCRIPTION:Authors - Chaitra Sai Chakravarthi Ganapaneni\, Rishik Reddy Cheruku\, Venkata Karthik Chamarthi\, Venkata Sasidhar Kommu\, Malathi P Abstract - Academic websites function as institutional interfaces connecting universi-ties with multiple stakeholder groups. Many institutions face challenges in developing web presences that address usability\, accessibility\, and stakeholder needs simultaneously. Existing frameworks address isolated dimensions without providing integrated guidance. This research proposes a conceptual design framework for academic websites that integrates Web Con-tent Accessibility Guidelines (WCAG) 2.1 Level AA standards with Nor-man's design principles. The framework consists of four core segments (In-terface Design\, Content Accessibility\, Technical Performance\, User Experience) and four modular add-ons categories (Career and Job Opportunities\, Student Projects Showcase\, Alumni Community\, Industry Collaboration). Framework validation employed dual evaluation methods to ensure both conceptual soundness and stakeholder relevance. Expert judgment assessment (n=5) achieved complete agreement on conceptual soundness. Quantitative user assessment (n=450) across six stakeholder groups showed that framework components achieved good performance levels (mean scores 3.58 to 3.70) and add-ons features received high priority classifications (mean scores 3.62 to 3.80). The framework contributes systematic integration of accessibility standards with design principles and provides guidance for institutions developing academic websites.
CATEGORIES:VIRTUAL ROOM_12B
LOCATION:Virtual Room B\, Bangkok\, Thailand
SEQUENCE:0
UID:495b65537197b3d4c6aef513ff6a9463
URL:http://11thictisthailand.sched.com/event/495b65537197b3d4c6aef513ff6a9463
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260417T125213Z
DTSTART:20260411T080000Z
DTEND:20260411T100000Z
SUMMARY:A Topological Framework for Human-Bot Classification in Social Networks using AutoML Optimization
DESCRIPTION:Authors - Amulya Saxena\, Pratibha Joshi\, Adwitiya Sinha Abstract - Global food security and hunger mitigation is one of the major challenges ahead of us. The global population specifically from underdeveloped countries are quite vulnerable to climate change and its impact in abnormal weather conditions and related bad crop leading to food shortages. In today’s globalised world\, where a disruption in food supply chain has its own impact on potentially everyone in the planet is a mounting challenge to surpass. The advent of Artificial Intelligence\, specifically Computer Vision techniques prove to be extremely helpful in identifying the data pattern of the images of the cultivated land\, its anomalies and is insightful in giving the challenges of farming such as affect of bad weather\, bad crop prediction\, crop distribution etc. The availability of high-quality geospatial data from the satellites such as Sentinel 1/2\, Landsat is extremely helpful for advanced ML techniques to provide timely predictions so that a corrective action can be taken in time. This study focuses on an AI-driven approach that predicts land where Rice will be produced vs. no crop land using satellite optical data and its variates\, radar logs\, weather data and location information.
CATEGORIES:VIRTUAL ROOM_12B
LOCATION:Virtual Room B\, Bangkok\, Thailand
SEQUENCE:0
UID:92089c91198b4bb4803b45b259a9d8ad
URL:http://11thictisthailand.sched.com/event/92089c91198b4bb4803b45b259a9d8ad
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260417T125213Z
DTSTART:20260411T080000Z
DTEND:20260411T100000Z
SUMMARY:A Transparent and Resource-Efficient AutoML Framework with Agentic Guidance and Meta-Heuristic Feature Selection
DESCRIPTION:Authors - Arin Bansal\, Pranshu CBS Negi Abstract - The research provides a description of WaveTrust\, which is a trust-conscious and energy-efficient routing protocol that is applied to Underwater Wireless Sensor Networks (UWSNs) based on reinforced Q-learning and trust assessment. Neutral trust and network deployment initiate the protocol. During the process of routing data in real time\, monitoring of the behavior by the nodes is required with respect to four metrics namely Packet Forwarding Ratio\, Energy Behavior Consistency\, Latency Observance and Link Quality Indicator. The calculation of the trust is performed according to the direct and indirect observations and makes it possible to determine malicious nodes. Q-learning routing strategy The routing strategy uses weighted rewards according to energy\, trust and latency in updating paths such that it favors nodes with high-trust and high-Q-value. The nodes dynamically revise the trust and Q-values about the received feedback during transmission of data. The sink node keeps on broadcasting the global updates of the updated trust thresholds and routing updates. The simulation outcomes have indicated that WaveTrust is better than T-AODV\, FuzzyTrust on the basis of packet delivery ratio\, detection accuracy\, energy consumption\, routing overhead and an apparent strength on the capability to work in dynamic and resource limited underwater setting. This creates the impression that WaveTrust is quite flexible protocol and has the capability of providing secure and energy efficient routing in UWSNs.
CATEGORIES:VIRTUAL ROOM_12B
LOCATION:Virtual Room B\, Bangkok\, Thailand
SEQUENCE:0
UID:4050f1b8a0937185a42ba74e9827459e
URL:http://11thictisthailand.sched.com/event/4050f1b8a0937185a42ba74e9827459e
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260417T125213Z
DTSTART:20260411T080000Z
DTEND:20260411T100000Z
SUMMARY:An Intelligent Strategy Simulation Framework Using Telemetry Data for Time-Dependent Decision Optimization
DESCRIPTION:Authors - Harita Venkatesan Abstract - Fusion-based multimodal models typically assume full modality availability at inference\, an assumption that often fails in real-world settings. When a modality is missing\, common strategies such as zerovector masking or unimodal fallback can lead to unstable predictions. We propose CORE\, an embedding-level framework that completes multimodal representations by integrating original and cross-modally reconstructed embeddings in a fusion-consistent manner prior to fusion. CORE employs lightweight bidirectional cross-modal imagination networks with a cycle-consistency constraint to preserve shared semantic structure across modalities. The model is trained with stochastic modality dropout\, enabling unified inference under complete and incomplete modality configurations. Experiments on a multimodal MRI–text classification task for lumbar spine analysis demonstrate that CORE yields more stable predictions than zero-vector masking under severe modality absence\, while maintaining comparable performance when all modalities are present.
CATEGORIES:VIRTUAL ROOM_12B
LOCATION:Virtual Room B\, Bangkok\, Thailand
SEQUENCE:0
UID:c8cba36e78d9c5eafe62d8d092c0543f
URL:http://11thictisthailand.sched.com/event/c8cba36e78d9c5eafe62d8d092c0543f
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260417T125213Z
DTSTART:20260411T080000Z
DTEND:20260411T100000Z
SUMMARY:Automated Answer Sheet Evaluation System
DESCRIPTION:Authors - Latha N. R.\, Pallavi G B\, Shyamala G.\, Abubakar Mohammedshafee Matte\, Aditya Dinesh Netrakar\, Akshara Singa\, Akshata Hosmani Abstract - Tourism has become a strategic pillar in China’s transition toward a service-oriented economy\, the world cultural heritage sites play an important role in promoting cultural–tourism integration in both China and global. The Dazu Rock Carvings is located in Chongqing\, well known by their unique synthesis of Buddhist\, and Taoist ideas and their wonderful stone-carving artistry. Recently\, the Dazu site received growing number in tourist arrivals and tourism-related revenue due to the regional rapid development as well as the strategic support\; however\, compared with other outstanding heritage destinations such as the Mogao Grottoes\, the reception capacity\, product diversity\, brand influence\, and market performance of Dazu still remain relatively weak. This study adopts a mixed qualitative–quantitative case study design. Data are collected from official tourism statistics and cultural heritage management reports published by national and local authorities in between 2018-2024. Descriptive analysis is used to explore the trends in tourist arrivals\, tourism revenue\, and related industrial effects. Based on the findings\, the study identifies key dimensisons on sustainable development and proposes a marketing path centered on cultural IP empowerment\, industrial ecosystem construction\, and digital technology-driven innovation\, offering practical guidance for similar heritage destinations.
CATEGORIES:VIRTUAL ROOM_12B
LOCATION:Virtual Room B\, Bangkok\, Thailand
SEQUENCE:0
UID:960594fc17b426883cc81d3c98f46208
URL:http://11thictisthailand.sched.com/event/960594fc17b426883cc81d3c98f46208
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260417T125213Z
DTSTART:20260411T080000Z
DTEND:20260411T100000Z
SUMMARY:Automating Diabetic Retinopathy Grading Through a Hybrid Deep Learning Model
DESCRIPTION:Authors - Deepa V\, Atul Anilkumar\, Sheena Susan Andrews Abstract - Organizations are rapidly embedding artificial intelligence (AI)\, including generative AI\, into core business functions\, but making AI sustainable across environmental\, social\, and economic dimensions is still challenging\, especially when data governance is weak. Public estimates suggest data centres consumed roughly 415 TWh of electricity in 2024 and may rise toward ~945 TWh by 2030 under a base-case trajectory\, while reported AI-related incidents reached a new high in 2024. In parallel\, industry signals point to fast enterprise adoption of GenAI and ongoing leakage of sensitive information through tools that are not properly governed. Taken together\, these patterns increase sustainability risks that are often data-mediated in practiceshaped by data quality and representativeness\, provenance and documentation\, access control\, privacy protections\, and end-to-end lifecycle management. Although data governance is widely seen as “foundational” to responsible AI\, the concrete mechanisms linking governance capabilities to sustainable AI outcomes\, and the ways to measure them\, remain dispersed across data management\, AI governance\, and sustainability research. This paper consolidates peer-reviewed research\, public standards\, and open industry evidence to position data governance as an operational\, measurable capability for Sustainable AI\, one that converts sustainability goals into decision rights\, lifecycle controls\, and auditable outcomes. It contributes: (i) a capability-based taxonomy of data governance tailored to AI lifecycles\; (ii) six evidence-grounded impact pathways showing how governance mechanisms influence outcomes (quality and fairness\; documentation and auditability\; privacy and security\; interoperability and reuse\; lifecycle stewardship\; and sustainability instrumentation)\; and (iii) the Sustainable AI Data Governance Impact Model (SAI-DGIM)\, accompanied by testable hypotheses (H1–H8) and a KPI-oriented measurement framework that can be validated using survey constructs\, system telemetry\, and governance artifacts. For practitioners\, the model offers a practical roadmap to embed governance controls directly into AI delivery workflows and treat sustainability metrics as release criteria\, not just retrospective reporting. For researchers\, it provides aligned constructs\, hypotheses\, and measurement guidance to rigorously assess how organizational data governance shapes Sustainable AI outcomes at scale.
CATEGORIES:VIRTUAL ROOM_12B
LOCATION:Virtual Room B\, Bangkok\, Thailand
SEQUENCE:0
UID:76f4e0225f2750c78b6aa532146d81d3
URL:http://11thictisthailand.sched.com/event/76f4e0225f2750c78b6aa532146d81d3
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260417T125213Z
DTSTART:20260411T080000Z
DTEND:20260411T100000Z
SUMMARY:CORE: Cross-Modal Embedding Reconstruction for Robust Multimodal Learning under Missing Modalities with a Lumbar Spine Case Study
DESCRIPTION:Authors - Nhat Ho Minh\, Long Le Pham Tien\, Kien Nguyen Trung\, An Pham Nam\, Trong Nhan Phan Abstract - The fast increase in the number of unstructured digital documents in academic\, industrial\, and personal fields has generated an urgent requirement to have intelligent systems to read\, arrange and structure document automatically. Traditional document organization methods have traditionally been heavily based on either manual intervention or rule-based methods\, neither scalable nor efficient nor error free. The current paper is a multimodal AI architecture to assist document under-understanding and structuring that uses large language models (LLMs) and vision language models to handle heterogeneous document types. The suggested framework does semantic metadata extraction\, classification of documents as well as structural organization of textual and visual documents. It uses a modular three-layer design\, including an AI processing layer\, service oriented backend\, and cross platform user interfaces. The system is also developed to support secure functioning in the offline mode\, which guarantees the privacy of data and the low-latency processing. The effectiveness of the pro-posed frame-work has been proved through experimental assessment\, as it will be seen that classifying documents and categorizing images are very precise. The findings show that multimodal AI is remarkably better in document understanding and automation than traditional systems.
CATEGORIES:VIRTUAL ROOM_12B
LOCATION:Virtual Room B\, Bangkok\, Thailand
SEQUENCE:0
UID:6e663d9856c94f867f8324db9b79c69c
URL:http://11thictisthailand.sched.com/event/6e663d9856c94f867f8324db9b79c69c
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260417T125213Z
DTSTART:20260411T080000Z
DTEND:20260411T100000Z
SUMMARY:Gamified ADHD Interventions through Human Computer Interaction: A Thorough Study of BCI\, AR and Cognitive Training Games for Children with ADHD
DESCRIPTION:Authors - S M Mazharul Hoque Chowdhury\, Ruth West\, Stephanie Ludi Abstract - The prediction of liver disease through clinical data analysis faces difficulties because current machine learning methods fail to handle class imbalance and produce incorrect probability assessments. The existing supervised and ensemble methods use fixed decision thresholds together with heuristic weighting methods which results in biased predictions that compromise their ability to achieve balanced performance. The research introduces CAL-WE++ which serves as a Calibration- Weighted Ensemble system that uses an MCC-Optimized Threshold to forecast liver disease. The system employs five-fold stratified cross-validation without data leakage to produce out-of-fold probability results. The model weights are determined by evaluating both the model's ability to distinguish between outcomes (measured through ROC-AUC) and its accuracy in predicting probabilities (assessed through Expected Calibration Error ECE). The Matthews Correlation Coefficient (MCC) serves as the optimization method to determine the final classification threshold which helps to solve class imbalance problems. The Indian Liver Patient Dataset (583 records\; 416 diseased\, 167 non-diseased) experiments show that CAL-WE++ achieves a mean cross-validation MCC of 0.3474 and a test MCC of 0.4487 which exceeds the performance of baseline classifiers. The model achieves a ROC-AUC score of 0.8140 and a PR-AUC score of 0.9272 while maintaining a low ECE value of 0.0774 which demonstrates strong ability to distinguish between different outcomes and accurate probability assessments. The CAL-WE++ framework offers medical professionals a decision-making system that maintains balance between multiple criteria while delivering dependable outcomes for medical datasets with unequal class distributions.
CATEGORIES:VIRTUAL ROOM_12B
LOCATION:Virtual Room B\, Bangkok\, Thailand
SEQUENCE:0
UID:fbf85f0e2b8b7f6e1eaa3e498ac3e6e4
URL:http://11thictisthailand.sched.com/event/fbf85f0e2b8b7f6e1eaa3e498ac3e6e4
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260417T125213Z
DTSTART:20260411T080000Z
DTEND:20260411T100000Z
SUMMARY:Objective Evaluation of YOLO Architectures for Crowd Detection
DESCRIPTION:Authors - Nidhi Pruthi\, Rajiv Singh\, Swati Nigam Abstract - Automatic Speech Recognition (ASR) systems have achieved remarkable progress through deep learning and Transformer-based architectures\, demonstrating near-human accuracy on clean audio. However\, their performance degrades significantly under challenging conditions and specialized domains. This comprehensive study evaluates leading commercial ASR APIs—Google Cloud Speech-to-Text\, Microsoft Azure Speech Service\, AssemblyAI\, Deepgram\, OpenAI Whisper\, Speechmatics\, and others—across multiple dimensions: general speech recognition\, low-quality forensic-like audio\, domain-specific mathematical notation\, and personalized speaker adaptation. Results demonstrate 100% accuracy on clean audio for leading systems (Deepgram\, Speechmatics\, Webkit SpeechRecognition)\, but dramatic performance degradation to 10− 81% word error rates on forensic-like audio. Analysis of domain-specific challenges reveals that none of the tested commercial ASR systems natively support direct transcription of mathematical symbols and Greek letters into structured symbolic output (e.g.\, LaTeX). The study identifies critical limitations in robustness\, modularity\, and domain adaptation\, while highlighting promising customization mechanisms including custom vocabularies\, language models\, and post-processing integration. Performance improvements through speaker personalization ranged from 3% for natural voices to 10% for synthetic voices. Despite notable advances in end-to-end and Transformer-based approaches\, ASR systems remain unsuitable for forensic applications and specialized domains without substantial customization and post-processing. Future research must address low-resource performance\, linguistic diversity\, robustness in extreme noise\, and the integration of Large Language Models for semantic understanding. This paper synthesizes recent advances and critical gaps\, providing a roadmap for advancing ASR technology in specialized and challenging acoustic environments.
CATEGORIES:VIRTUAL ROOM_12B
LOCATION:Virtual Room B\, Bangkok\, Thailand
SEQUENCE:0
UID:1946b1ecb8693f4383b64923d62bff9b
URL:http://11thictisthailand.sched.com/event/1946b1ecb8693f4383b64923d62bff9b
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260417T125213Z
DTSTART:20260411T080000Z
DTEND:20260411T100000Z
SUMMARY:Wireless Implanted Devices for Arrhythmia Detection and Management: A Technological Overview
DESCRIPTION:Authors - G Naga sree suma\, A. Kamala kumari Abstract - The existence of a growing social media has created complex cyber systems in which vast quantities of interactions constitute substantial issues regarding misinformation\, privacy invasion\, deception of identities\, and destructive behavioural tendencies. The regularity of involvement in this type of big systems requires sophisticated systems that are able to judge the motive of the user\, content validity and suspicious activities within real time. Overall interest will be to develop a universal trust calculation system that will be more secure and effective in ensuring privacy and increasing the accuracy of suspicious or malicious users in social sites. The proposed Multi-Layer Federated Trust Framework algorithm is a combination of peer-based user reputation scoring\, feature-based content authenticity detection\, federated trust indicators aggregation\, and anomaly detection with the help of behavioural anomalies. These approaches cooperate with secure aggregation and decentralized learning in removing the uncoded information exposure and enable the computation of trust at scale. The proposed algorithm is experimentally confirmed\, and the obtained results are 95.2\, 94.1\, 93.5\, and 93.8\, corresponding to a minimum latency of 65 ms and a privacy preservation score of 0.98. The general results indicate a viable and holistic response that adds to secure interactions\, blocks malicious acts and encourages trust in the actual social media settings.
CATEGORIES:VIRTUAL ROOM_12B
LOCATION:Virtual Room B\, Bangkok\, Thailand
SEQUENCE:0
UID:7c709fe83819bfb1da6a0c357587bb1e
URL:http://11thictisthailand.sched.com/event/7c709fe83819bfb1da6a0c357587bb1e
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260417T125213Z
DTSTART:20260411T080000Z
DTEND:20260411T100000Z
SUMMARY:A Deep Reinforcement Learning-Based Adaptive Large Neighborhood Search for Commercial Territory Design Problem
DESCRIPTION:Authors - Viet Anh DUONG\, Hai Phong BUI\, Van Son NGUYEN Abstract - This article presents a neuro-symbolic modelling approach grounded in qualitative data collected from 25 sports clubs located in R´eunion. The study develops a methodological chain linking structured semantic extraction\, ontological formalisation in OWL\, and agent-based simulation implemented in NetLogo. Rather than modifying structural scenarios across experiments\, the design introduces two contrasting organisational sensitivity profiles derived from field observations: a damped profile and a high-gain profile. The structural configurations remain identical between profiles\; only the coefficients of the commitment update function vary\, ensuring strict experimental comparability. Results indicate that identical structural conditions produce differentiated collective trajectories depending on internal sensitivity parameters. In highgain configurations\, dominance-weighted interactions increase variance and generate polarised engagement distributions\, whereas damped configurations maintain relative stability across scenarios. These findings suggest that modelling organisational sensitivity parameters is critical for understanding the robustness of digitally mediated collaboration in volunteer-based organisations.
CATEGORIES:VIRTUAL ROOM_12C
LOCATION:Virtual Room C\, Bangkok\, Thailand
SEQUENCE:0
UID:9765c6570e0a23a81881cb21409f9845
URL:http://11thictisthailand.sched.com/event/9765c6570e0a23a81881cb21409f9845
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260417T125213Z
DTSTART:20260411T080000Z
DTEND:20260411T100000Z
SUMMARY:Agro-climate Machine Learning Model for Rice Yield Prediction in the Ilocos Region
DESCRIPTION:Authors - Allezandra A. Adriano\, Joshua Basile Mhar L. Austria\, Benjamin L. Carnate\, Xamantha Angelique E. Ruiz\, Wilben Christie R. Pagtaconan Abstract - Plant diseases due to various pathogens can cause significant loss in yield and productivity. The classification of these diseases is necessary to prevent damage to crops. For classification\, a large number of Machine learning and deep learning algorithms have been developed. In this research\, five classes of plant leaves and a further fifteen different diseases of these plants (three subcategories for each class) are used for classification. In the proposed methodology\, we have used three pre-trained models\, namely\, ResNet 152v2\, InceptionResNetV2\, and mGoogleNet\, and a custom-built model. This research has used three basic steps to classify the disease categories\, namely image preprocessing\, image segmentation\, and feature extraction. Fifteen thousand plant leaf images have been collect-ed from the online available Kaggle PlantVillage dataset. This data is present in a JPG file format. After the class label distribution of the dataset\, the dataset is first trained and then tested on these deep learning models. The label distribution is done in such a way that each of these fifteen categories has 80% training images and 20% validation images. We have used different performance measures\, namely\, precision\, recall\, F1-score\, and support\, to calculate the accuracy. The obtained validation accuracy of ResNet152V2 is 97%\, GoogleNet is 96%\, Incep-tionResNetV2 is 93%\, and a custom-built model is 99%. These results show that the custom-built model has attained the highest accuracy. These models can also be used to build a recommender system framework for the recommendation of fertilizers in the future.
CATEGORIES:VIRTUAL ROOM_12C
LOCATION:Virtual Room C\, Bangkok\, Thailand
SEQUENCE:0
UID:6307f9b8ca31f6843e80d51b48720acc
URL:http://11thictisthailand.sched.com/event/6307f9b8ca31f6843e80d51b48720acc
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260417T125213Z
DTSTART:20260411T080000Z
DTEND:20260411T100000Z
SUMMARY:Harnessing Artificial Intelligence in Social Media Marketing for Promoting Sustainable Consumer Behavior in the FMCG Sector: A Study in Telangana State\, India
DESCRIPTION:Authors - E. Praveen Kumar\, Shankar Lingam. M Abstract - Quantum computers are a major threat to the existing encryption mechanisms. In terms of security\, the traditional encryption algorithm depends on complex problems like discrete logarithm as well as factorization of integer. Shor’s algorithm is believed to break the current Public Key Encryption algorithms such as Advanced Encryption Standard (AES). Therefore\, several research are carried out in the area of PQC (Post Quantum Cryptography). PQC are based on very complex mathematical problems like Learning with error (LWE) which are robust against quantum computers. The National Institute of Standard and Technology (NIST) has initiated several rounds of standardization process for PQC algorithms\, among which NTRU\, SABER\, CRYSTAL-KYBER are the leading candidates. CRYSTALS-KYBER (Kyber) is the first chosen PQC for standardization. This works explores the recent development in Crystals Kyber implementation and its optimization. Researchers can approach for new research challenges and target for improvement thereby increasing efficiency.
CATEGORIES:VIRTUAL ROOM_12C
LOCATION:Virtual Room C\, Bangkok\, Thailand
SEQUENCE:0
UID:be36913bc8fb2f38746d004e634e6cc4
URL:http://11thictisthailand.sched.com/event/be36913bc8fb2f38746d004e634e6cc4
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260417T125213Z
DTSTART:20260411T080000Z
DTEND:20260411T100000Z
SUMMARY:Implementation of RAG Techniques for Vietnamese Public Investment Law
DESCRIPTION:Authors - An Doan Van\, Dong Nguyen Doan\, Quynh Tran Duc\, Thuan Nguyen Quang\, Bao Phan Gia\, Hieu Doan Minh\, Van Khanh Doan Abstract - Performance bottlenecks in Python programs arise from a wide variety of sources\, and no single technique reliably catches them all. This paper proposes CodeForge\, a sequential three-stage optimization system that unites deterministic Abstract Syntax Tree (AST) inspection\, CodeBERT embedding-based retrieval\, and Gemini LLM-driven rewriting into one end-to-end pipeline. A rule engine in the first stage pinpoints well-known structural problems\; a neural similarity search in the second stage captures harder-to-spot variants\; and a Gemini LLM in the third stage performs the actual rewrite\, guided by a structured hint block assembled from both preceding stages. Before any result is returned\, a configurable validator rejects changes that fail minimum speedup\, memory\, or complexity criteria. Alongside each accepted optimization\, a composite confidence score and a plain-language rationale are produced. Tests on six representative Python patterns show that hint-guided LLM prompting raises successful detection from four to six out of six cases compared with unguided prompting\, while the validation layer blocks every harmful transformation in the test suite. The system is available as a FastAPI REST service accepting both raw source text and uploaded .py files.
CATEGORIES:VIRTUAL ROOM_12C
LOCATION:Virtual Room C\, Bangkok\, Thailand
SEQUENCE:0
UID:718eeed86d304777c28eb11411dd6839
URL:http://11thictisthailand.sched.com/event/718eeed86d304777c28eb11411dd6839
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260417T125213Z
DTSTART:20260411T080000Z
DTEND:20260411T100000Z
SUMMARY:Predictive Techniques for Traffic Congestion Management in Intelligent Transportation Systems
DESCRIPTION:Authors - Jyotika R. Yadav\, Arpit A. Jain Abstract - Internet of Things (IoT) with AI techniques help healthcare industry for patient monitoring and diagnosis. Wearable devices integrated with the Internet of Medical Things (IoMT) have transformed modern healthcare by enabling continuous\, real-time monitoring of physiological parameters. The rapid evolution of Artificial Intelligence (AI)\, Machine Learning (ML)\, Deep Learning (DL)\, edge computing\, and federated learning has further enhanced the reliability\, privacy\, and intelligence of such systems. Wearable devices like smart watch or smart sensors help doctors to monitor patient’s daily activities. However\, these devices generate huge amount of data on day-to-day basis which makes analysis\, monitoring\, and diagnosis challenging. Machine Learning or Deep Learning models used for handling such large healthcare data. This survey consolidates and critically reviews recent research works to provide a holistic understanding of the current state-of-the-art in wearable AI-enabled healthcare. A detailed comparative analysis is provided to highlight similarities\, differences\, strengths\, and limitations of existing approaches. Finally\, key challenges and future research directions are discussed to guide the development of secure\, scalable\, and intelligent wearable healthcare solutions.
CATEGORIES:VIRTUAL ROOM_12C
LOCATION:Virtual Room C\, Bangkok\, Thailand
SEQUENCE:0
UID:1c87ce7aa530f430cb4156c6fa1147a0
URL:http://11thictisthailand.sched.com/event/1c87ce7aa530f430cb4156c6fa1147a0
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260417T125213Z
DTSTART:20260411T080000Z
DTEND:20260411T100000Z
SUMMARY:Real-time Health Monitoring using AI and Wearable Devices: A Survey
DESCRIPTION:Authors - Shweta H. Jambukia\, Pooja R. Makawana\, Prapti G. Trivedi Abstract - This paper presents a case study on a High Voltage Jet (HVJ) electric boiler\, focusing on current unbalance (CU) risk identification and mitigation us ing a combined data-analytics and Failure Mode and Effects Analysis (FMEA) framework. Power-quality assessment follows IEC 61000-4-30 for voltage un balance (VU)\, while CU interpretation refers to NEMA MG-1 and IEEE recom mendations. The proposed workflow integrates (i) instrument classification (Class A for voltage)\, (ii) time synchronization across logger/PLC/power-quality analyzer to avoid timestamp drift\, and (iii) historian-based data pre-processing (outlier cleaning\, scaling\, and missing-data handling) prior to statistical analysis. Results show an average CU of 6.85% with a standard deviation of 0.48% and a maximum of 15.92%\, indicating operational periods exceeding common industry limits. FMEA highlights electrode aging/damage\, loose/corroded cable connec tions\, and supply power-quality issues as the dominant contributors. Recom mended actions include online phase-current monitoring\, improved water-chem istry and blowdown management\, and control optimization of the VFD-driven boiler circulation pump (BCP).
CATEGORIES:VIRTUAL ROOM_12C
LOCATION:Virtual Room C\, Bangkok\, Thailand
SEQUENCE:0
UID:c983a373c8c6aab6a259b81bf8749341
URL:http://11thictisthailand.sched.com/event/c983a373c8c6aab6a259b81bf8749341
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260417T125213Z
DTSTART:20260411T080000Z
DTEND:20260411T100000Z
SUMMARY:Real-Time Multi-Object Detection and Tracking for Autonomous Systems Using Ultralytics YOLO
DESCRIPTION:Authors - Priyanka K\, Vinay R K\, Vansh Jain\, Vinit Kulkarni Abstract - This study examines the influence of both demographic and natural factors on climate change risk perception in New Zealand. Using data from a nationally representative survey\, the analysis applies exploratory factor analysis to construct a composite measure of risk perception\, followed by correlation and regression modeling to evaluate the relative contribution of environmental exposure and human characteristics. The findings indicate that while natural factors such as temperature anomalies and extreme weather exposure significantly shape perceived risk\, demographic variables including prior disaster experience\, trust in scientific institutions\, and media exposure exert a stronger overall influence. These results underscore the importance of incorporating social and behavioral dimensions into climate risk assessments and policy development to enhance public engagement and adaptive capacity.
CATEGORIES:VIRTUAL ROOM_12C
LOCATION:Virtual Room C\, Bangkok\, Thailand
SEQUENCE:0
UID:70acb79804a600d1b712b0b898c0cb27
URL:http://11thictisthailand.sched.com/event/70acb79804a600d1b712b0b898c0cb27
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260417T125213Z
DTSTART:20260411T080000Z
DTEND:20260411T100000Z
SUMMARY:Real-Time Sign Language Recognition: A Lightweight Adaptive Framework
DESCRIPTION:Authors - Piyush Tewari\, Rohit\, Rujal Agarwal\, Yanshi Sharma Abstract - Current Network Intrusion Detection Systems (NIDS) typically analyze traffic as independent tabular records\, largely ignoring the relational and temporal dependencies inherent in real-world communications. This limitation is particularly critical for detecting botnets\, which rely on coordinated\, evolving interactions rather than isolated malicious packets. To address this\, we propose a topology-aware framework that models network traffic as a sequence of dynamic communication graphs. Using the CICIDS2017 dataset\, we construct sliding-window snapshots where IP addresses form nodes and flows form edges. A spatiotemporal graph neural network is employed to learn evolving structural representations\, integrated with a novel learnable gated fusion mechanism that adaptively balances graph-based context with conventional flowlevel statistics. The model is optimized using a hybrid objective combining class-weighted cross-entropy and center loss to mitigate data imbalance. Experimental results demonstrate that the framework achieves improved performance on structural attacks\, with botnet detection reaching an AUC of 0.999. Furthermore\, the learned gating values reveal a strong model preference for topological features over static statistics\, empirically validating that structural context is superior for identifying coordinated threats. These findings underscore the effectiveness of spatiotemporal modeling in enhancing the robustness and interpretability of next-generation NIDS.
CATEGORIES:VIRTUAL ROOM_12C
LOCATION:Virtual Room C\, Bangkok\, Thailand
SEQUENCE:0
UID:a53d73fc279603701d70061e50a1ca21
URL:http://11thictisthailand.sched.com/event/a53d73fc279603701d70061e50a1ca21
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260417T125213Z
DTSTART:20260411T080000Z
DTEND:20260411T100000Z
SUMMARY:Speckle-Aware Gated Cross-Attention Fusion Network for SAR-Optical Cloud Removal
DESCRIPTION:Authors - Bikkam Hemanth Reddy\, Allu Eswar Kaushik\, Tiyyagura Mohit Reddy\, Kuruboor Venkatesha Deepak\, Bharathi D\n Abstract - Cloud cover generally limits the applicability of optical remote sensing images for tasks such as agriculture monitoring and disaster relief. Cloud removal is an inherently difficult problem because of the lack of spatial structures and spectral information. To effectively remove cloud contamination from SAR and optical images\, we propose a speckle-aware global cross-attention network. The proposed SAR-optical cloud removal network architecture consists of a dual encoder with a global cross-attention mechanism that allows for effective cross-modal interactions. Additionally\, a refining module and symmetric decoders improve the accuracy of the reconstructed image. Furthermore\, we propose a speckle-aware gating mechanism to perform speckle filter adaptation. The experimental results affirm that our proposed network outperformed the baseline by increasing Peak Signal-to-Noise Ratio(PSNR) by +0.86 dB\, Structural Similarity Index Measure(SSIM) by +0.142\, and reducing the spectral distortion of the image. Additionally\, we noticed a decrease in the Root Mean Square Error(RMSE) and Spectral Angle Mapper(SAM) values. This infers that selective SAR-Optical fusion with an adaptive noise-aware gating mechanism improves the accuracy of cloud-free optical images and optical remote sensing images.
CATEGORIES:VIRTUAL ROOM_12C
LOCATION:Virtual Room C\, Bangkok\, Thailand
SEQUENCE:0
UID:a86e189f024e2bd8114ad47a91718baa
URL:http://11thictisthailand.sched.com/event/a86e189f024e2bd8114ad47a91718baa
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260417T125213Z
DTSTART:20260411T080000Z
DTEND:20260411T100000Z
SUMMARY:THE EXPANDING ROLE OF ARTIFICIAL INTELLIGENCE IN THE MODERN WORLD
DESCRIPTION:Authors - Azamat Kasimov\, Kholida Bekpolatovna Saidrasulova\, Zebo Abduxalilovna Shomirova\, Shoh-Jakhon Khamdаmov\, Safiya Karimova\, Dilshoda Akramova\, Doniyor Niyozmetov Abstract - Inconsistent medication intake is a major issue\, especially for elderly individuals and patients with memory problems [1]. The MediMitra: Voice Enabled Medicine Alert System seeks to tackle this problem by offering an automated\, low-cost and user-friendly medication reminder solution. The system combines Raspberry Pi with Optical Character Recognition (OCR) technology to pull medicine names\, dosage details and intake times directly from scanned prescriptions. This reduces manual input and user reliance. The information is stored in a central database and connected to a scheduler that sends timely voice alerts through smart speakers or Bluetooth devices. This ensures users receive reliable and easy-to-access reminders. The OCR module is designed for high accuracy in processing printed prescription images by using image preprocessing techniques like noise reduction and thresholding\, which helps in effectively extracting key medication details [2]. The system focuses on accessibility\, affordability and ease of use in home or clinical settings. Overall\, MediMitra provides a useful technological solution to improve medication adherence and supports independent living. It also has potential for future integration with health-monitoring systems.
CATEGORIES:VIRTUAL ROOM_12C
LOCATION:Virtual Room C\, Bangkok\, Thailand
SEQUENCE:0
UID:d15be1527fe2e6a7648a0e003c81bb49
URL:http://11thictisthailand.sched.com/event/d15be1527fe2e6a7648a0e003c81bb49
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260417T125213Z
DTSTART:20260411T080000Z
DTEND:20260411T100000Z
SUMMARY:A Robust Multi-Class Age Detection Framework Using EfficientNetB3 Feature Transfer and Optimized Classification Head
DESCRIPTION:Authors - Gargi P. Lad\, Abhijeet R. Raipurkar Abstract - Remote sensing imagery plays an important role in applications such as environmental monitoring\, disaster management\, urban planning and agricultural analysis. However\, the spatial resolution of such imagery is often limited by sensor constraints\, revisit frequency and acquisition cost. To address this challenge\, this paper presents RCAN-RS\, an enhanced Residual Channel Attention Network for remote sensing image super-resolution. The proposed model extends the RCAN framework through three targeted modifications: a dual-pooling channel attention mechanism\, a spectral attention module and an edge enhancement module. These components are designed to improve detail reconstruction while preserving inter-channel consistency and sharp structural boundaries in remote sensing imagery. The model was trained and evaluated on the DOTA dataset un-der a 2× super-resolution setting from 256 × 256 to 512 × 512 pixels. Quantitative evaluation using both conventional image-quality metrics and remote-sensing-oriented measures shows that RCAN-RS achieves a mean PSNR of 34.42 dB\, SSIM of 0.9398\, Edge Preservation Index of 0.9524\, ERGAS of 6.68 and UQI of 0.9846 on the test set. These results demonstrate the effectiveness of integrating attention-guided and edge-aware mechanisms for remote sensing image super-resolution.
CATEGORIES:VIRTUAL ROOM_12D
LOCATION:Virtual Room D\, Bangkok\, Thailand
SEQUENCE:0
UID:4408aa22a94e9bad1ae059a3e01beead
URL:http://11thictisthailand.sched.com/event/4408aa22a94e9bad1ae059a3e01beead
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260417T125213Z
DTSTART:20260411T080000Z
DTEND:20260411T100000Z
SUMMARY:Emotion- and Speaker-Preserving Speech-to-Speech Translation: A Survey
DESCRIPTION:Authors - A. Harshavardhan\, Krishna Anirudh Gunturi\, Rikhil Rao Janagama\, Navaneeth Reddy Nalla\, N V Abhijeet Mukund\, Avire Kaushik Abstract - The Age classification is a critical task in computer vision with widespread applications in fields such as healthcare\, security\, and autonomous systems. This project presents a deep learning approach for multi-class image classification using feature extraction with the EfficientNetB3 architecture. The model was trained on a dataset that has images labeled according to different age groups\, where the images were preprocessed\, normalized\, and sized to a steady resolution appropriate for EfficientNetB3 input. Data handling was simplified using pandas and ImageDataGenerator\, ensuring proper splitting into training\, validation\, and test sets\, with suitable shuffling and augmentation strategies applied to improve generalization. This model influences EfficientNetB3 as a feature extractor\, combined with a custom classification head containing Batch Normalization\, L1/L2 regularization with Dense layers\, Dropout\, and a SoftMax output layer. This model was trained using the Adamax optimizer and categorical cross-entropy loss\, with performance monitored through accuracy and loss metrics over multiple epochs. Training history was seen to identify the epochs corresponding to the best validation performance. Assessment of the model on the test data-set includes loss\, accuracy\, confusion matrix\, and a comprehensive classification report with precision\, recall\, and F1-score for each set. The results demonstrate that transfer learning\, combined with careful preprocessing and regularization\, can achieve robust performance in image classification tasks. This pipeline provides a producible and scalable framework for multi-class image classification and can be extended to other datasets and real-world applications requiring automatic image recognition.
CATEGORIES:VIRTUAL ROOM_12D
LOCATION:Virtual Room D\, Bangkok\, Thailand
SEQUENCE:0
UID:694351c32949a42ddc3186822bdf036f
URL:http://11thictisthailand.sched.com/event/694351c32949a42ddc3186822bdf036f
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260417T125213Z
DTSTART:20260411T080000Z
DTEND:20260411T100000Z
SUMMARY:Generative AI in Software Development: The Role of AI Coding Assistants and the Future of Low-Code/No-Code Systems
DESCRIPTION:Authors - M Purushotham\, Ch Sandeep Kumar\, G Jayendra Kumar\, Tummalapalli Venkata Jayanth\, Akula Manoj Kumar\, Purna Saradhi Chinthapalli. Abstract - Wireless Body Area Network (WBAN) is an innovative network system\, which consists of numerous wearable or implantable devices that monitors and transmits the physio-logical data. Designing a wearable patch antenna for WBAN is a challenging\, because human body is a lossy medium which can absorb and scatter electromagnetic waves\, thus leads to degrade of antenna performance. In this paper\, the proposed antenna is a wearable 6G microstrip patch antenna\, which is very flexible and light with a flat surface\, unlike traditional counterparts and these can be placed directly on a human body and are comfortable to wear for long periods. The antenna is designed\, simulated\, and analyzed using Computer Simulated Technology (CST) studio suite and the design consists of microstrip patch\, substrate\, feedline\, and ground plane. The simulation parameters such as S-Parameter\, Voltage Standing Wave Ration (VSWR) and far field radiation are calculated. The results of proposed wearable 6G patch antennas with varying THz frequencies shows\, it is very appropriate for WBAN at 2.56THz and 4THz.
CATEGORIES:VIRTUAL ROOM_12D
LOCATION:Virtual Room D\, Bangkok\, Thailand
SEQUENCE:0
UID:6a2c4ced7ed48a6e69749d5c953167f9
URL:http://11thictisthailand.sched.com/event/6a2c4ced7ed48a6e69749d5c953167f9
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260417T125213Z
DTSTART:20260411T080000Z
DTEND:20260411T100000Z
SUMMARY:Hybrid Optimization based Closed-Loop Identification and Data-Driven Controller Reconstruction of Interacting Multivariable System
DESCRIPTION:Authors - Arathi B K\, Rishikeshwar Kumaresan\, S Kanagalakshmi\, Sathish Kumar S Abstract - Single magnetic resonance imaging (MRI) super‑resolution remains challenging due to the substantial heterogeneity between low‑ and high‑resolution (LR-SR) inputs. This paper presents an ablation analysis of three convolutional neural‑network architectures\, namely Conv2D\, fully convolutional network (FCN)\, and U‑Net\, combined with four activation functions (Linear\, Tanh\, ReLU\, Leaky ReLU). LR inputs are generated through mean- and max‑pooling with a 6×6 scale factor\, enabling evaluation under both smooth and heterogeneous degradation conditions. The results show that U‑Net achieves the highest reconstruction accuracy\, reducing MAE by 8% relative to FCN and 10% relative to Conv2D. ReLU-based activations provide stable convergence for shallow models\, while the U-Net remains robust across all activation functions. These findings emphasise the importance of selecting appropriate architectures and activation functions to achieve robust and high‑quality MRI super‑resolution in real‑world applications.
CATEGORIES:VIRTUAL ROOM_12D
LOCATION:Virtual Room D\, Bangkok\, Thailand
SEQUENCE:0
UID:1a64764ac5f07a294574e6378c572121
URL:http://11thictisthailand.sched.com/event/1a64764ac5f07a294574e6378c572121
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260417T125213Z
DTSTART:20260411T080000Z
DTEND:20260411T100000Z
SUMMARY:IOT Enabled Monitoring and Forecasting of Emerging Air and Water Pollutants for Early Detection
DESCRIPTION:Authors - Susmita Adhikary\, Aswin Babu VP\, Dinesh U\, Harish M\, Karthik M\, Gokul A Abstract - Urban metro rail systems are the key to urban sustainable mobility\; however\, in spite of the developed technologies\, projects regularly experience delays and contractual disputes. These perceived challenges are highly attributed by prior scholarship to matters of the execution phase and restricted illumination is given on the institutional circumstances that form system performance in ICT intensive infrastructure. This paper examines procurement strategy as a govern ance tool that affects the results of digital system integration and sustainability in Indian metro rail projects. Based on statutory performance audit reports and com parative case studies\, the analysis indicates that fragmented procurement arrange ments fragment the integration functions to several contracts\, leading to coordi nation failure\, delayed commissioning\, and high claims. Instead\, the more coor dinated procurement models with consolidated interdependent systems and de fined integration roles have a better coordination structure and predictable deliv ery. The results indicate that the problem of metro project integration is more of an institutional than a technological problem. This research study adds to the body of knowledge on infrastructure governance by noting the design of procure ment to be one of the determinatives in the realization of effective and sustainable urban transit outcomes.
CATEGORIES:VIRTUAL ROOM_12D
LOCATION:Virtual Room D\, Bangkok\, Thailand
SEQUENCE:0
UID:02f8240296b0737591382d8af9646b68
URL:http://11thictisthailand.sched.com/event/02f8240296b0737591382d8af9646b68
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260417T125213Z
DTSTART:20260411T080000Z
DTEND:20260411T100000Z
SUMMARY:Radiographic Analysis System for Early Detection of Osteoporosis
DESCRIPTION:Authors - Mousami Turuk\, Anirudha Page\, Tina Chugera\, Gauri Desale\, Mrunmayee Kulkarni Abstract - Unstructured vehicle traffic (i.e. those containing multiple users such as automobile drivers\, pedestrians\, cyclists\, and even animals) creates a significant challenge for road safety. This work presents the development of a real-time road risk assessment (RRA) system for analyzing dashcam video that combines several computer vision techniques: object detection\, semantic segmentation\, multi-object tracking\, and alert classification\, into a unified\, integrated processing pipeline. Object detection and multi-object tracking are accomplished using the YOLOv8m and ByteTrack with Kalman Filter algorithms. Additionally\, semantic segmentation of the road scene is achieved using a SegFormer-B2. Finally\, a segmentation-assisted fusion filter and perspective-aware danger zone are applied (to define each point in the field of view as belonging to a zone with certain levels of risk). The Road Intrusion Risk Score (RIRS) is a composite score that quantifies the severity of intrusion accumulated over time\, and provides graduated alert levels. Testing of the system on COCO val2017 and four dashcam videos produced reliable object detections with significantly fewer false positives and very close to real-time performance\, demonstrating the potential of the system to improve driver assistance systems in unstructured road environments.
CATEGORIES:VIRTUAL ROOM_12D
LOCATION:Virtual Room D\, Bangkok\, Thailand
SEQUENCE:0
UID:6a59467bd51708efbffc316270630e08
URL:http://11thictisthailand.sched.com/event/6a59467bd51708efbffc316270630e08
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260417T125213Z
DTSTART:20260411T080000Z
DTEND:20260411T100000Z
SUMMARY:Real-Time Non-Autoregressive Neural Text-to-Speech Using FastSpeech 2 and HiFi-GAN
DESCRIPTION:Authors - Mousami Turuk\, Harshwardhan Sawant\, Jatin Bhate\, Yash Gosavi\, Sakshi Hosamani Abstract - The global tourism industry has strongly recovered in the post-pandemic era\, with border tourism becoming an important platform for regional economic cooperation and cultural exchange. Nong Khai\, Thailand\, with its geographic advantages and its role as a cross-border hub\, has the potential to transform from a transit point into a cultural hub. However\, its tourism destination image has been constrained by its perception as a transit point. This study\, based on tourism destination image theory and the cognitive-affective frame-work\, integrates online review text analysis and semi-structured interviews to analyze the cognitive\, emotional\, and overall dimensions of Nong Khai's tour-ism image. The results show that Nong Khai’s tourism image reflects a triad of culture\, ecology\, and cross-border relations. Buddhist culture and the Mekong River are key attractions\, but visitors generally have short stays and low spending. 52% of cross-border tourists view it as a transit point to Vientiane. Positive feedback accounts for 65.17%\, largely driven by cultural experiences and local service friendliness\; negative feedback accounts for 8.86%\, focusing on inefficient transportation\, poor facility maintenance\, and weak cultural symbolism. Based on these findings\, this paper suggests four optimization strategies: enhancing the Buddhist cultural experience\, improving service systems\, strengthening digital marketing\, and promoting cross-border collaboration. This study provides empirical evidence for Nong Khai’s efforts to overcome the transit point challenge and offers a model for ASEAN border cities to build differentiated tourism images and sustainable development paths.
CATEGORIES:VIRTUAL ROOM_12D
LOCATION:Virtual Room D\, Bangkok\, Thailand
SEQUENCE:0
UID:31b7f64be0148d85f8699efdf06b6d1e
URL:http://11thictisthailand.sched.com/event/31b7f64be0148d85f8699efdf06b6d1e
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260417T125213Z
DTSTART:20260411T080000Z
DTEND:20260411T100000Z
SUMMARY:Smart Career Assist: An AI-Powered Personalized Career Guidance and Recruitment Automation System
DESCRIPTION:Authors - CH VENKATA NARAYANA\, G VAMSI KRISHNA\, K SIDDARTHA\, G MADHU Abstract - Software-Defined Networking (SDN) offers central control and management of traffic flow\, which is currently facing increasing security threats from ever-changing and voluminous attacks. The traditional signature-based intrusion detection system is not capable of identifying unknown attacks in real time. The proposed paper suggests a hybrid model for intrusion detection based on CNN and Transformer architectures for Software-Defined Networking. The proposed model will be tested and validated on a real-time testbed based on the Mininet network simulator\, Open vSwitch\, and Ryu Controller. The proposed model will be trained on the InSDN dataset and will utilize the SHAP technique for model interpretation and will be capable of automatic mitigation of attacks by blocking malicious traffic.
CATEGORIES:VIRTUAL ROOM_12D
LOCATION:Virtual Room D\, Bangkok\, Thailand
SEQUENCE:0
UID:7e9c7c4afa81c943ce417ff8939f2a0c
URL:http://11thictisthailand.sched.com/event/7e9c7c4afa81c943ce417ff8939f2a0c
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260417T125213Z
DTSTART:20260411T080000Z
DTEND:20260411T100000Z
SUMMARY:Smart Glasses Development Challenges: Technical\, Human\, and Market Perspectives
DESCRIPTION:Authors - Abhishek Sawant\, Manas Bhansali\, Naman Shah\, Mandar Kakade Abstract - The integration of Traditional Medicine (TM) into global healthcare standards faces challenges due to the gap between clinician-entered free text and standardized terminologies like ICD-11. In India\, AYUSH providers must document diagnoses using local terms while also supporting dual coding across NAMASTE\, ICD-11 Traditional Medicine Module 2 (TM2)\, and ICD-11 Biomedicine. However\, most EMRs do not provide unified support for these coding systems. This paper proposes a human-centric\, AI-Assisted Terminology Microservice that standardizes diagnosis entry and automates the mapping between these terminologies. The system has a hybrid architecture. A Spring Boot orchestration layer manages the terminology graph and the EMR-facing APIs. Meanwhile\, a Python-based machine learning service handles semantic matching from free-text descriptions to concept codes. It uses TF-IDF features and a Linear Support Vector Machine(SVM) classifier that is trained on a Silver Standard Dataset of approximately 3\,250 synthetic clinical descriptions covering 75 common health issues\,morbidities\, with conservative lexical augmentation applied during training to improve robustness. A safety-critical fallback mechanism was designed\, which detects predictions with confidence below θ = 0.45 and directs out-ofdistribution inputs to manual search workflows. This ensures a human-in-the-loop model and makes it safe to use in clinical environments. The microservice provides APIs that are EMR-friendly and produce dual-coded FHIR format diagnosis resources. This setup ensures safety along with scalability and interoperability so that it can be deployed in diverse healthcare environment.
CATEGORIES:VIRTUAL ROOM_12D
LOCATION:Virtual Room D\, Bangkok\, Thailand
SEQUENCE:0
UID:103e7a9d3d3b829340337258cd8088a2
URL:http://11thictisthailand.sched.com/event/103e7a9d3d3b829340337258cd8088a2
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260417T125213Z
DTSTART:20260411T080000Z
DTEND:20260411T100000Z
SUMMARY:Solcanvas: Simplifying Solana Project Discovery
DESCRIPTION:Authors - Atharva Sachan\, Aryan Gupta\, Aditya Varshney\, Abhishek Sharma\, Surendra Kumar Keshari\, Veepin Kumar Abstract - Mobile Health (mHealth) has been regarded as a potentially transform-ative element for enhancing health service delivery in low-income nations. The effective integration of technology relies on ongoing usage rather than just initial acceptance. While the body of literature on factors influencing continued mHealth use is expanding\, post-adoption expectations are proposed as indicators of the success or failure of mHealth implementation. There is limited research on how community health workers' post-adoption expectations influence their inten-tions to persist in using mHealth in developing regions. Consequently\, this study explores the effect of post-adoption expectations on satisfaction and ongoing us-age behaviour regarding mHealth among community health workers in Malawi\, which represents a developing country context. The research introduces a frame-work that builds upon the expectation confirmation model and incorporates ele-ments from the updated information success model. A mixed-methods conver-gent design was utilised for the study. Data were collected through surveys and semi-structured interviews with community health workers who utilise Cstock. Cstock is an mHealth application that facilitates the ordering of medical supplies via text message. The findings generally support the notion that post-usage use-fulness\, along with information quality\, system quality\, and service quality\, pos-itively influences community health workers’ satisfaction and their intention to continue using the Cstock application. The results indicate that the ongoing usage behaviour of mHealth among community health workers is shaped not solely by behavioural expectation beliefs (i.e.\, post-usage usefulness) but also by objective expectation beliefs\, including system quality\, service quality\, and information quality. Therefore\, these findings provide valuable insights to policymakers\, practitioners\, mHealth developers\, and other relevant parties regarding the post-user expectations essential for maintaining future mHealth solutions in develop-ing countries\, particularly in Malawi.
CATEGORIES:VIRTUAL ROOM_12D
LOCATION:Virtual Room D\, Bangkok\, Thailand
SEQUENCE:0
UID:e575890089d3532f7c6c826dacc358bc
URL:http://11thictisthailand.sched.com/event/e575890089d3532f7c6c826dacc358bc
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260417T125213Z
DTSTART:20260411T080000Z
DTEND:20260411T100000Z
SUMMARY:A Multimodal Framework for Integrated Software and Hardware Project Orchestration
DESCRIPTION:Authors - Sanchit Prashant Joshi\, Vedant Vipin Joshi\, Aditya Arun Mangalekar\, G.S.Mundada Abstract - Malware classification is essential in cyber-security. It en ables prevention of threats by identifying and accurately classifying ma licious software. It also helps in understanding attacker behavior\, enhanc ing threat intelligence\, and improving the overall effectiveness of security systems. It is increasingly critical as adversaries now employ obfuscation techniques to avoid detection. Traditional models such as Convolutional Neural Networks (CNN) often struggle with such obfuscated malware samples. In this paper\, we propose MalViT\, a Vision Transformer (ViT) based framework for robust malware classification using grayscale image representations of malware binaries. The ViT is fine-tuned on a prepro cessed Malimg dataset. To evaluate the robustness of the model\, real world obfuscation techniques such as Encryption\, Dead code insertion\, Random masking and Junk Padding are simulated. ViT model is initially f ine-tuned on the clean samples and later on a combination of the clean and obfuscated samples. Both models are evaluated on the clean and obfuscated test sets to highlight the robustness of the model. The final model achieved a combined accuracy of 94.52 % on both the clean and obfuscated samples. The results demonstrate that MalViT maintains a competitive performance under obfuscation. This project highlights the potential of ViTs in building resilient malware classification systems and provides a foundation for future work in transformer based architecture for malware analysis.
CATEGORIES:VIRTUAL ROOM_12E
LOCATION:Virtual Room E\, Bangkok\, Thailand
SEQUENCE:0
UID:a1c62db7e4636022cddba9927d19c97c
URL:http://11thictisthailand.sched.com/event/a1c62db7e4636022cddba9927d19c97c
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260417T125213Z
DTSTART:20260411T080000Z
DTEND:20260411T100000Z
SUMMARY:AI-Powered Wearable Devices Enhancing Human Communication and Interaction Using Intelligent Systems
DESCRIPTION:Authors - Samiksha Ganesh Zagade\, Arya Mahesh Parkar\, Suman Madan\n Abstract - Advances in Artificial Intelligence\, Machine Learning and Internet of Things technologies have enabled wearable devices to sense as well as process and respond to human behaviour in real time. While most wearable devices today are used for health and fitness tracking. Many people face communication challenges such as language barriers\, difficulty understanding emotions or social cues\, social anxiety and accessibility issues for individuals with hearing or speech impairments. Existing systems often collect data but fail to provide meaningful\, real-time assistance during actual human interactions. This research paper presents a literature-based study on AI powered wearable devices designed to support and enhance human communication. The research papers are focusing on intelligent wearables that use multimodal sensors such as microphones\, cameras and sensors. These systems apply AI techniques to interpret speech\, gestures\, facial expressions and emotional signals in real time. The wearable devices considered include everyday consumer-oriented systems such as smart eyewear that provides audio visual assistance and wrist worn wearables that offer haptic feedback. The key focus of this study is to examine how such devices can deliver subtle\, real-time support through visual prompts\, audio cues or vibrations to improve conversational awareness and user confidence. The expected outcome is to identify current capabilities\, practical limitations and design considerations for developing human centric wearable technologies that move beyond passive tracking toward meaningful communication support.
CATEGORIES:VIRTUAL ROOM_12E
LOCATION:Virtual Room E\, Bangkok\, Thailand
SEQUENCE:0
UID:bad5637fc2a9be6f0cf53582374759ea
URL:http://11thictisthailand.sched.com/event/bad5637fc2a9be6f0cf53582374759ea
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260417T125213Z
DTSTART:20260411T080000Z
DTEND:20260411T100000Z
SUMMARY:Comparative Study of Emerging DL Models in BTD
DESCRIPTION:Authors - Adnan Hasan\, Ishaan Mishra\, Jyotiska Bose\, Jada Viswa Chaitanya Sai\, Jai Kumar\, Kaif Akhter\, Ranjita Kumari Dash Abstract - In the present-day context\, presentations and computer-based interac tion play a crucial role in various domains\, particularly in education and business. Traditionally\, users have to rely on physical devices such as mouses\, keyboards\, or laser. Although these devices meet the basic requirements\, they still reveal many limitations regarding mobility\, continuity\, and dependence on battery life. To address these limitations\, hand gesture-based presentation control systems have emerged as a promising solution due to their intuitive\, natural\, and engaging interaction style. This paper proposes a touchless system that enables users to control common desktop operations as well as presentations in a natural manner using hand gestures captured via a standard webcam. The proposed system lev erages OpenCV for real-time video acquisition and preprocessing\, while Medi aPipe framework is employed for hand tracking and landmark extraction. From the experiments\, our system can process in real-time with the accuracy of approx imately 92%. As a result\, users can seamlessly control slides\, use virtual mouse operations\, annotate presentation content\, and engage with the audience in a more interactive and natural way without physical contact.
CATEGORIES:VIRTUAL ROOM_12E
LOCATION:Virtual Room E\, Bangkok\, Thailand
SEQUENCE:0
UID:dcddeba1f34a2797af6e4fa0a892f461
URL:http://11thictisthailand.sched.com/event/dcddeba1f34a2797af6e4fa0a892f461
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260417T125213Z
DTSTART:20260411T080000Z
DTEND:20260411T100000Z
SUMMARY:DDoS attacks Detection Using Hybrid Feature Selection methods and Federated Learning
DESCRIPTION:Authors - Jyoti Chandel\, Meenakshi Mittal\n Abstract - Internet of Things (IoT) devices are growing in domains because of their reliability and efficiency in monitoring\, real-time detection and automated support. However\, these IoT systems have also introduced security challenges. These devices are vulnerable to cyber threats\, where attackers exploit weak points in the system to steal sensitive information. One of the attacks is the Distributed Denial of Service (DDoS) attack\, which disrupts services by overwhelming systems and making them inaccessible to legitimate users. IoT devices are resource-constrained\, so reducing feature dimensionality is essential to lower computational overhead and complexity. IoT devices generate data for detecting cyber-attacks\, but sharing such data across organizations raises privacy concerns. To address these challenges\, the proposed approach is designed in two phases. In the first phase\, a hybrid feature selection technique using mutual information\, permutation feature importance\, and Greedy wrapper-based feature selection with cross-validation is applied to extract relevant features. In the second phase\, Federated Learning (FL) is applied to train the model without sharing raw data among clients. Within the FL framework\, Random Forest (RF) algorithm is utilized for training due to its robustness and classification capability. The proposed model is evaluated under two data distribution scenarios: mildly non-IID and strongly non-IID conditions. Experimental results demonstrate that the model achieved an accuracy of 99.69% in a mildly non-IID scenario and 98.36% under strongly non-IID conditions\, highlighting the effectiveness and reliability of the proposed framework for secure IoT-based DDoS attack detection.
CATEGORIES:VIRTUAL ROOM_12E
LOCATION:Virtual Room E\, Bangkok\, Thailand
SEQUENCE:0
UID:42d3a0a2f4eb278766d010964dcc329e
URL:http://11thictisthailand.sched.com/event/42d3a0a2f4eb278766d010964dcc329e
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260417T125213Z
DTSTART:20260411T080000Z
DTEND:20260411T100000Z
SUMMARY:Design and Development of Artificial Stuttered Speech Corpora
DESCRIPTION:Authors - P.N. Deorukhakar\, V.B. Waghmare\, I.K. Mujawar\, R.Y. Patil Abstract - Convolutional Neural Networks (CNNs) have been widely and successfully applied to bioacoustic and passive acoustic monitoring tasks\, including soundscape classification. However\, the high dimension ality of CNN-derived embeddings often results in increased computa tional cost and reduced efficiency\, particularly in iterative learning frame works such as Active Learning (AL) and in scenarios with limited labeled data. This work addresses these limitations by proposing a method for adapting CNN architectures to generate compact and discriminative em beddings tailored to soundscape data classification. The proposed ap proach leverages transfer learning and incorporates three progressively reduced dense layers (512\, 256\, and 128 neurons)\, enabling dimensional ity reduction to be learned intrinsically during network training rather than applied as a post-processing step. Experimental evaluations con ducted across multiple soundscapes datasets under the Active Learning paradigm demonstrate that the proposed embeddings consistently out perform conventional CNN embeddings (CNNE) in terms of classification performance and the efficient use of labeled data. These results indicate that integrating dimensionality reduction directly into CNN training en hances representation quality and robustness\, offering an effective solu tion for soundscape data classification in labeling-constrained environ ments.
CATEGORIES:VIRTUAL ROOM_12E
LOCATION:Virtual Room E\, Bangkok\, Thailand
SEQUENCE:0
UID:73f46bb2fe6afeafddc9a1ffa5afe304
URL:http://11thictisthailand.sched.com/event/73f46bb2fe6afeafddc9a1ffa5afe304
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260417T125213Z
DTSTART:20260411T080000Z
DTEND:20260411T100000Z
SUMMARY:Frugal Digital Twins: A Holistic Framework for Rural IAQ Management through IoT and Biophilic Integration
DESCRIPTION:Authors - Domenico D’Uva\n Abstract - Indoor air quality (IAQ) is a frequently overlooked determinant of health in rural villages\, where the extensive use of solid fuels for cooking and space-heating generates elevated concentrations of airborne pollutants. This study presents an integrated\, low-cost protocol for improving IAQ in rural dwellings\, combining real-time environmental monitoring\, simplified digital modelling and passive strategies of ventilation and biophilic design. The methodology can be structured into three steps: Conceptual digital twin\, feedback interface\, ventilation strategies\, biophilic integration. Conceptual digital twin is based on the mapping of each dwelling linked to Arduino low-cost\, stand-alone sensors (CO₂\, PM₂.₅\, temperature and relative humidity) that collect data at temporal resolution of one minute. An immediate feedback interface based on visual and/or acoustic indicators that prompt residents to take corrective actions (selective opening of windows\, activation of cross-breezes)\, when exposure thresholds - derived from WHO Air Quality Guidelines - are exceeded. Data-driven natural-ventilation strategies – optimal ventilation windows identified through time-series analysis of sensor data\, calibrated to local weather conditions and occupancy profiles to maximise air exchange while minimising heat losses. Biophilic integration implies the introduction of resilient plant species with proven phytoremediation capacity\, as Epipremnum aureum) which could reduce CO₂ level\, with quantitative guidance on density (two to three plants per main room) and optimal placement. Using low-cost IoT sensors\, the protocol monitors environmental parameters and pollutant concentrations in real time. The system targets specific safety and comfort thresholds\, aiming to maintain CO₂ levels below 700 ppm and PM₂.₅ below 50 μg/m³ to optimize occupant health (Wu et al\, 2021). These thresholds\, derived from World Health Organization (WHO) guidelines\, are essential to ensure occupant satisfaction and well-being. The ultimate objective is to define a scalable and replicable intervention model capable of combining digital technologies and natural solutions for the sustainable regeneration of fragile territories.
CATEGORIES:VIRTUAL ROOM_12E
LOCATION:Virtual Room E\, Bangkok\, Thailand
SEQUENCE:0
UID:28f0fbd10822fdfd93779b5c967516bd
URL:http://11thictisthailand.sched.com/event/28f0fbd10822fdfd93779b5c967516bd
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260417T125213Z
DTSTART:20260411T080000Z
DTEND:20260411T100000Z
SUMMARY:IntelliTask: An AI‑Driven Enterprise Task Management System
DESCRIPTION:Authors - Kritika Singhal\, Khushi Madeshiya\, Utkarsh Upadhyay\, Siser Pratap Singh\, Surendra Kr. Keshari\, Veepin Kumar Abstract - The integration of artificial intelligence in the academic en vironment has been rapidly growing since late 2022. One of the most widely adopted artificial intelligence tools in engineering is the large lan guage model. By using large language models\, the engineering students can generate assignment answers\, solve problems through code\, and ex plain engineering concepts. Unlike traditional approaches\, the large lan guage models can reduce time and simplify the students’ work. Many researchers have worked on artificial intelligence tools\, most specifically large language models for engineers. This paper reviews the literature on the application of artificial intelligence tools in the following five areas of engineering education\, which include programming\, problem-solving in the core subjects\, intelligent tutoring\, technical writing\, and simula tion support. Further\, this paper discusses the main challenges of large language models in engineering education. Finally\, this article concludes by outlining the future scope of large language models in engineering.
CATEGORIES:VIRTUAL ROOM_12E
LOCATION:Virtual Room E\, Bangkok\, Thailand
SEQUENCE:0
UID:1cc8644a22703971181722ff7b2b1681
URL:http://11thictisthailand.sched.com/event/1cc8644a22703971181722ff7b2b1681
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260417T125213Z
DTSTART:20260411T080000Z
DTEND:20260411T100000Z
SUMMARY:Ransomware Detection Using Hardware Performance Counter and Machine Learning
DESCRIPTION:Authors - S.Venkata Rakesh\, K.Tarun Kumar\, A.Lohith\, M.Nirupama Bhatt\n Abstract - One of the world's most destructive types of malware is ransomware\, which results in huge financial and data loss around the globe. Current signature-based detection methodologies do not work for the detection of these types of ransomware because they have no way to identify them prior to their creation (zero-day) or when a variant of the ransomware is created (polymorphic). A behaviour-based ransomware detection methodology that involves the use of CPU Hardware Performance Counters (HPC) in combination with machine learning models for the purpose of detecting ransomware activity is the focus of this project. The following HPC metrics will be used to monitor the execution of a program or application while it is executing: instruction count\; cache references\; cache hits\; branch instructions\; and CPU cycles. These low-level architectural events will provide information on the unique behaviour characteristics of a ransomware program or application based on the types of behaviours exhibited by the encryption pro-cesses of a ransomware program or application. A labelled dataset of HPC traces of typical programs/applications will be developed by running both standard pro-grams/applications and ransomware in a controlled testing environment. Several supervised learning models such as Random Forest\, Support Vector Machines\, and Logistic Regression will be trained and validated on the labelled dataset. The experimental results show that ransomware activity causes significantly different HPC metrics\, thereby allowing the correct identification of ransomware. The pro-posed methodology will offer a real-time\, graphical user interface for real-time monitoring and graphical representation of the detected ransomware program or application.
CATEGORIES:VIRTUAL ROOM_12E
LOCATION:Virtual Room E\, Bangkok\, Thailand
SEQUENCE:0
UID:8dbae33c95ef10154ee8a8d2319e3a7c
URL:http://11thictisthailand.sched.com/event/8dbae33c95ef10154ee8a8d2319e3a7c
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260417T125213Z
DTSTART:20260411T080000Z
DTEND:20260411T100000Z
SUMMARY:Real-Time Road Risk Assessment Using Segmentation-Guided Multi-Object Tracking and Alert Classification
DESCRIPTION:Authors - Vasavi Ravuri\, S. Lalitha Geetanjali\, T. Bhavana Sri\, V. Praveen\, M. Mokshgna Teja\n Abstract - Unstructured vehicle traffic (i.e. those containing multiple users such as automobile drivers\, pedestrians\, cyclists\, and even animals) creates a significant challenge for road safety. This work presents the development of a real-time road risk assessment (RRA) system for analyzing dashcam video that combines several computer vision techniques: object detection\, semantic segmentation\, multi-object tracking\, and alert classification\, into a unified\, integrated processing pipeline. Object detection and multi-object tracking are accomplished using the YOLOv8m and ByteTrack with Kalman Filter algorithms. Additionally\, semantic segmentation of the road scene is achieved using a SegFormer-B2. Finally\, a segmentation-assisted fusion filter and perspective-aware danger zone are applied (to define each point in the field of view as belonging to a zone with certain levels of risk). The Road Intrusion Risk Score (RIRS) is a composite score that quantifies the severity of intrusion accumulated over time\, and provides graduated alert levels. Testing of the system on COCO val2017 and four dashcam videos produced reliable object detections with significantly fewer false positives and very close to real-time performance\, demonstrating the potential of the system to improve driver assistance systems in unstructured road environments.
CATEGORIES:VIRTUAL ROOM_12E
LOCATION:Virtual Room E\, Bangkok\, Thailand
SEQUENCE:0
UID:e468dba895cfbf15ee998ede69c5bf26
URL:http://11thictisthailand.sched.com/event/e468dba895cfbf15ee998ede69c5bf26
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260417T125213Z
DTSTART:20260411T080000Z
DTEND:20260411T100000Z
SUMMARY:TAbXplain: Generative Conditional Sampling for Explainable AI in Tabular Data
DESCRIPTION:Authors - Nathula Dayarathne\, Guhanathan Poravi Abstract - This paper presents a novel methodology for predicting bug severity and priority in software development using machine learning models. The approach involves leveraging a manually curated dataset labelled with the support of industry experts\, enabling the incorporation of domainspecific knowledge into feature selection and classification. A K-Means clustering method is initially employed to label the collected data\, ensuring accurate grouping and feature extraction. The study identifies and utilizes 16 key features for classification and develops separate models for severity and priority prediction. These models\, trained on the expertly labelled dataset\, achieve high performance with accuracy metrics above 90%. This study uniquely combines K-Means pre-labelling with expert validation to reduce manual annotation while maintaining model accuracy. The proposed method demonstrates the effectiveness of combining clustering techniques with expert-driven labelling for improving bug management processes. By automating severity and priority classification\, this research contributes to enhancing the efficiency and reliability of software development workflows.
CATEGORIES:VIRTUAL ROOM_12E
LOCATION:Virtual Room E\, Bangkok\, Thailand
SEQUENCE:0
UID:62512b795023e45da246b13eddf39fe9
URL:http://11thictisthailand.sched.com/event/62512b795023e45da246b13eddf39fe9
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260417T125213Z
DTSTART:20260411T080000Z
DTEND:20260411T100000Z
SUMMARY:A Comprehensive Survey of Phishing Detection Techniques: Machine Learning\, Deep Learning\, and Explainable AI Perspectives
DESCRIPTION:Authors - Dhanashri Amol Gore\, Satish Narayanrav Gujar Abstract - The wide use of machine learning in the field of medical imaging has caused concern with regard to patient information security\, especially when mod els are being trained over multiple health care systems in a distributed manner. Centralized learning requires transferring raw patient data to a central server where there is an extreme risk of data breach and unauthorized access to patients' personal information. Violations of health care regulations (HIPAA and GDPR) can occur in a centralized system because of the transfer of patients' data. Feder ated Learning (FL) addresses these issues by allowing collaborative model de velopment on individual client devices. Therefore\, the sensitive patient data will remain at its source institution. This paper provides a thorough comparative study of centralized learning and federated learning methods for detecting pneumonia utilizing chest X-rays from the publicly available Kaggle Chest X-Ray Pneumo nia dataset. Three architecture types (Support Vector Machine (SVM)\, Convolu tional Neural Network (CNN) and Long Short-Term Memory (LSTM)) were tested in both centralized and federated environments utilizing the FedAvg ag gregation method. Only the model weights were shared between the clients and the central server\; therefore\, patient data was maintained private through the en tire model training process. Experimental results demonstrated that federated learning produced superior performance than centralized learning for all three architectures (81.1%\, 84.6%\, and 82.7% for SVM\, CNN and LSTM respec tively). The performance metrics for centralized learning were 76.6%\, 76.3%\, and 81.6%. This superior performance of FL is attributed to the inherent regular ization effect of local class-balancing within the federated clients that reduces the inherent class imbalance in the dataset. Overall\, our research demonstrates that FL is not only a viable privacy-preserving solution to centralized training but offers improved generalization in the medical imaging domain with imbalanced classes and is a suitable solution for application in distributed health care envi ronments.
CATEGORIES:VIRTUAL ROOM_12F
LOCATION:Virtual Room F\, Bangkok\, Thailand
SEQUENCE:0
UID:d53aa0aee37ebcc0e5a8f95d675490fa
URL:http://11thictisthailand.sched.com/event/d53aa0aee37ebcc0e5a8f95d675490fa
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260417T125213Z
DTSTART:20260411T080000Z
DTEND:20260411T100000Z
SUMMARY:A Rebalanced Multimodal Data Approach to Mortality Prediction for ICU Patients with Alcohol-Related Disorders
DESCRIPTION:Authors - Vu Nguyen\, Chau Vo Abstract - Artificial intelligence (AI) offers powerful capabilities for understanding stakeholder perceptions of corporate sustainability initiatives. This study investigates how AI‑driven sentiment analysis can support sustainable business decision‑making by analyzing secondary data from social media platforms\, online re-views\, and ESG reports. Using advanced text mining and trans-former‑based sentiment classification techniques\, the research identifies patterns in public opinion regarding environmental\, social\, and governance practices across industries. Topic modeling is applied to detect emerging sustainability themes\, while sentiment trend analysis provides actionable insights for improving stakeholder engagement and brand reputation. The findings reveal how organizations can leverage real‑time sentiment data to guide strategic investments\, enhance communication strategies\, and strengthen commitment to green practices. By integrating AI‑based natural language processing with sustainability management\, this research contributes to evidence‑based decision‑making frameworks that enable businesses to respond effectively to societal expectations and achieve long‑term competitive and environ-mental advantages.
CATEGORIES:VIRTUAL ROOM_12F
LOCATION:Virtual Room F\, Bangkok\, Thailand
SEQUENCE:0
UID:dad0b85acddbbe31630c746368fa6c52
URL:http://11thictisthailand.sched.com/event/dad0b85acddbbe31630c746368fa6c52
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260417T125213Z
DTSTART:20260411T080000Z
DTEND:20260411T100000Z
SUMMARY:AI Based Framework for Post-Earthquake Damage Severity Prediction
DESCRIPTION:Authors - A. Viji Amutha Mary\, Ram Swagath B\, Ruthresh E\, S Jancy\, B. Shamreen Ahamed\n Abstract - As one of the most damaging natural risks\, earthquakes require quick situational consciousness for emergency response as well as control. Usual impact assessment methods use larger on field surveys conducted after a disaster\, which delays decision making and results in a poor comprehension of damaged zones. An automated analysis pipeline processes high resolution imagery from satellites and land based seismic data to extract land use change patterns\, information on terrain change in shape and signs of structural damage. An XGBoost model is then used to classify the extracted spatial features\, estimate severe levels and produce dynamic earthquake risk maps. During seismic emergencies\, the system supports resource distribution and rescue planning by enabling quicker and more accurate estimation of open areas. The suggested hybrid model greatly outperforms traditional disaster assessment techniques in terms of accuracy\, processing speed or scalability\, according to experimental evaluation\, underscoring its potential to transform preventive earthquake disaster management as well as prepare strategies.
CATEGORIES:VIRTUAL ROOM_12F
LOCATION:Virtual Room F\, Bangkok\, Thailand
SEQUENCE:0
UID:7eeb41f3c40047d93e87f721ed5b9217
URL:http://11thictisthailand.sched.com/event/7eeb41f3c40047d93e87f721ed5b9217
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260417T125213Z
DTSTART:20260411T080000Z
DTEND:20260411T100000Z
SUMMARY:An Empirical Analysis of Various Techniques for Object Detection and Video Summarization from CCTV Surveillance
DESCRIPTION:Authors - Shital Waghamare\, Swati Shekapure\, Girija Chiddarwar\, Shital Waghamare Abstract - Public administrations generate extensive administrative data through routine governance processes yet it is weakly based on verifiable evidence. This paper introduces a human-centric policy intelligence system based on execution-level administrative data for provision of accountable and evidence-based policy-making. The framework brings together governance-conscious data ingestion\, cryptographic hash-based verification including permissioned blockchain systems to control the integrity of data\, cross-domain data harmonisation to overcome administrative silos\, and explainable machine learning models to create interpretable supporting insights. The framework is specifically meant as a human-in-the-loop system\, maximizing policy foresight\, administrative discretion\, and accountability to the law. The validation with actual Mahatma Gandhi National Rural Employment Guarantee Act administrative data of the year 2022–2023 proves that the framework can be used to stress the implementation issues and regional inequalities without computerising policy-related decisions. The suggested solution is lightweight\, scaled down to fit in the existing open-sector digital infrastructure.
CATEGORIES:VIRTUAL ROOM_12F
LOCATION:Virtual Room F\, Bangkok\, Thailand
SEQUENCE:0
UID:69fcae93f698759f5dcfe5f88f308db0
URL:http://11thictisthailand.sched.com/event/69fcae93f698759f5dcfe5f88f308db0
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260417T125213Z
DTSTART:20260411T080000Z
DTEND:20260411T100000Z
SUMMARY:Beyond Perception: Advancements\, Challenges & Ethical Dimensions of AI & Computer Vision
DESCRIPTION:Authors - Zarif Bin Akhtar\, Ifat Al Baqee Abstract - Recent advancements in Artificial Intelligence (AI)\, Machine Learning (ML)\, and Deep Learning (DL) have accelerated the capabilities of Computer Vision (CV) across domains such as healthcare\, autonomous systems\, manufacturing\, and intelligent surveillance. This research exploration presents a comprehensive investigation into the technological evolution\, practical applications\, and ethical implications of modern CV systems. Through a mixed-methods approach combining available knowledge analysis\, empirical model evaluation\, and expert interviews\, the study assesses the performance of state-of-the-art architectures including Convolutional Neural Networks (CNN)\, Vision Transformers\, YOLO-based detectors\, and diffusion models—across diverse real-world deployment scenarios. Experimental findings highlight significant improvements in image classification\, object detection\, semantic segmentation\, autonomous navigation\, driven by techniques such as transfer learning\, ensemble modeling\, and model optimization for edge devices. Despite these advancements\, challenges persist regarding data quality\, interpret-ability\, bias\, and privacy\, particularly in high-stakes environments. The study emphasizes the need for responsible AI governance\, human-centric design\, and standardized regulatory frameworks to ensure safe and equitable adoption of visual AI. Furthermore\, emerging trends such as multi-modal learning\, edge-based inference\, and foundation models are discussed as catalysts for the next generation of contextaware and resource-efficient CV systems. This work provides a holistic perspective on current CV capabilities\, identifies key limitations\, and outlines strategic future directions for developing robust\, sustainable\, and ethically aligned AI-driven vision technologies.
CATEGORIES:VIRTUAL ROOM_12F
LOCATION:Virtual Room F\, Bangkok\, Thailand
SEQUENCE:0
UID:792947ef24b0def96da704d0fef81726
URL:http://11thictisthailand.sched.com/event/792947ef24b0def96da704d0fef81726
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260417T125213Z
DTSTART:20260411T080000Z
DTEND:20260411T100000Z
SUMMARY:Federated Digital Twin Catalogs: A Split-Trust Architecture for Secure Data Publication
DESCRIPTION:Authors - Fernando Latorre\, Ivan Becerro\, Nuria Sala Abstract - The rapid expansion of interconnected networks\, cloud infrastruc tures\, and IoT environments has significantly increased the complexity of mod ern cyber threats\, necessitating intelligent and adaptive Intrusion Detection Sys tems (IDS). While machine learning and deep learning techniques have im proved detection accuracy\, their black-box nature limits transparency\, interpret ability\, and analyst trust in high-stakes cybersecurity environments. This lack of explainability hinders forensic validation\, regulatory compliance\, and resilience against adversarial manipulation. To address these challenges\, this paper pre sents a comprehensive survey of Explainable Artificial Intelligence (XAI) tech niques applied to IDS and proposes a reference hybrid architecture that inte grates deep packet inspection\, dual-model detection\, multi-level explanation mechanisms\, adversarial robustness monitoring\, and governance-aware logging. The architecture combines high-performance deep learning models with inter pretable components and an explanation fusion engine to balance detection ac curacy with transparency. Furthermore\, security implications such as explana tion leakage and adversarial manipulation are analyzed. The study highlights evaluation metrics\, open challenges\, and future research directions toward trustworthy and transparent cybersecurity systems. The findings emphasize that secure explainability is essential for next-generation IDS deployment in distrib uted and resource-constrained environments.
CATEGORIES:VIRTUAL ROOM_12F
LOCATION:Virtual Room F\, Bangkok\, Thailand
SEQUENCE:0
UID:aa87612f9d6429d4695b33ffbcd33716
URL:http://11thictisthailand.sched.com/event/aa87612f9d6429d4695b33ffbcd33716
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260417T125213Z
DTSTART:20260411T080000Z
DTEND:20260411T100000Z
SUMMARY:Harnessing AI-Driven Sentiment Analysis for Sustainable Business Decision‑Making
DESCRIPTION:Authors - Sanjay Kumar\, Vimal Kumar\, Sahilali Saiyed\, Pratima Verma\, J.R. Ashlin Nimo Abstract - As online shopping has become increasingly popular\, companies must utilize social media to develop and improve customer experience. This study examined customer interaction sentiment regarding online shopping through automated systems to classify comments on social media sites like Twitter\, Facebook\, and Instagram. This research study compared three machine learning and natural language processing (NLP) techniques: Bidirectional Gated Recurrent Units (GRUs)\, Random Forests\, and Naïve Bayes. Customer reviews were classified as positive\, negative\, and neutral\, as well as analyzed for time-related patterns. The classification framework was constructed by using sentiment analysis\, feature extraction\, and data preprocessing techniques. Furthermore\, model training and performance assessment were executed through Naïve Bayes and Support Vector Machines. Of all the models studied\, the Bidirectional GRU had the best performance with an accuracy of 88.08 %. The results of this study help companies understand customer preferences better\, and thereby refine their products\, services\, and marketing techniques.
CATEGORIES:VIRTUAL ROOM_12F
LOCATION:Virtual Room F\, Bangkok\, Thailand
SEQUENCE:0
UID:8556069b17a03da434286bf3eb30d0c1
URL:http://11thictisthailand.sched.com/event/8556069b17a03da434286bf3eb30d0c1
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260417T125213Z
DTSTART:20260411T080000Z
DTEND:20260411T100000Z
SUMMARY:Sentiment Analysis of Press Releases in the Automobile Sector Using Python
DESCRIPTION:Authors - Tanmoy De\, Vimal Kumar\, Pratima Verma Abstract - The traditional centralized insurance operation has contributed to insurance fraud due to poor identity verification systems\, fragmented data sharing\, and slow manual validation\, all leading to substantial financial loss and loss of faith in the integrity of the operation. This research aims to develop a framework for an insurance operation that provides security\, transparency\, intelligence\, and improved fraud detection accu- racy while meeting the privacy and interoperability needs of insurers and their related stakeholders. The proposed framework is a decentralized solution that employs blockchain\, self-sovereign identity (SSI)\, artificial intel- ligence (AI)\, and federated learning to create secure identity cre- ation processes\, transparent policy management\, and intelligent verification of claims. The results of experimental evaluations of the proposed framework show that it provides increased fraud detection accuracy\, reduced duration of processes\, and improvements in transparency over current processes. Thus the suggested method improves efficiency and trust in insurance ecosystems and can be applied to real-world implementations with sophisticated identity integration and extensive blockchain networks.
CATEGORIES:VIRTUAL ROOM_12F
LOCATION:Virtual Room F\, Bangkok\, Thailand
SEQUENCE:0
UID:7f0a470189a2e25f723188c22244e66d
URL:http://11thictisthailand.sched.com/event/7f0a470189a2e25f723188c22244e66d
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260417T125213Z
DTSTART:20260411T080000Z
DTEND:20260411T100000Z
SUMMARY:SMART GARAGE SECURITY AND MONITORING USING IOT
DESCRIPTION:Authors - A. Viji Amutha Mary\, S. Chanikya\, S Gayathri Sarayu\, S Jancy\, B. Shamreen Ahamed\n Abstract - This work presents an intelligent solution to render residential garages more secure and safer. We developed an IoT platform to address frequent. homeowner issues\, including leaving the accidentally. garage door open\, looking to know whether it is your car\, or noticing anything unusual. At its core\, the system uses an internet connected ESP 32 microcontroller through Wi-Fi. In order to identify a vehicle inside\, we added an ultrasonic sensor which calculates the proximity to the closest object. A simple magnetic switch\, mounted on the garage door indicates when the door is ajar or closed. Our software processes these readings\, and puts logic to alert you whether the door has been long or long been opened when your car is not home\, which poses a possible security threat. An extra optional motion sensor may also be added. guards in case of any unforeseen motion in the garage.
CATEGORIES:VIRTUAL ROOM_12F
LOCATION:Virtual Room F\, Bangkok\, Thailand
SEQUENCE:0
UID:004632e330ddab22794863eebf51767e
URL:http://11thictisthailand.sched.com/event/004632e330ddab22794863eebf51767e
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260417T125213Z
DTSTART:20260411T080000Z
DTEND:20260411T100000Z
SUMMARY:The Impact of Short-Form Video Platforms on Social Media Marketing Strategies
DESCRIPTION:Authors - Ashavaree Das\, Dimo Valev\, Sambhram Pattanayak\, Prashant Kamal Abstract - The rise of short-form video (SFV) platforms like TikTok\, Instagram Reels\, and YouTube Shorts has caused a fundamental shift in digital marketing\, moving from static images to engaging\, multimodal strategies. These platforms utilize advanced "interest-graph" algorithms and unique user interfaces that significantly alter consumer attention spans and engagement patterns. Traditional marketing metrics often fall short in these environments\, requiring new approaches that emphasize immediacy and authenticity. This paper explores the key intersection of algorithmic recommendation biases\, content memorability\, and technical video quality. To address these challenges\, we propose an integrated framework that combines advanced blind video quality assessment (BVQA) with generative enhancements to optimize content for short-form formats. By incorporating technical insights from affective computing and recommender systems alongside strategic marketing goals\, this study explores how "lo-fi" aesthetics and influencer-led credibility influence consumer attitudes. Our findings offer a roadmap for managing user-generated content (UGC) and algorithmic biases to enhance brand resonance and purchase intent in today's digital economy.
CATEGORIES:VIRTUAL ROOM_12F
LOCATION:Virtual Room F\, Bangkok\, Thailand
SEQUENCE:0
UID:1b6937bae0b86e2f1ef6bf9650448d57
URL:http://11thictisthailand.sched.com/event/1b6937bae0b86e2f1ef6bf9650448d57
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260417T125213Z
DTSTART:20260411T080000Z
DTEND:20260411T100000Z
SUMMARY:A Usability Evaluation of a Mixed Reality Cooking Assistant
DESCRIPTION:Authors - Sreenath M. V.\, Abhigna Suresh Babu\, Addanki Naga Sai Greeshmitha\, C. R. Ananya\, Lakshmi M.\, Mohan S. G.\n Abstract - Conventional recipe formats interrupt cooking workflows by requiring repeated attention shifts to external devices. This paper presents Beyond the Cookbook\, a Mixed Reality (MR) cooking assistant developed for Meta Quest headsets. The system delivers spatially anchored\, context-aware instructions using persistent holographic overlays\, synchronized narration\, and multimodal interaction including voice commands\, controller input\, and hand-tracking gestures. By integrating passthrough MR and spatial mapping\, the assistant enables hands-free and hygienic guidance directly within the user’s kitchen environment. A usability study with twenty-one participants demonstrates high interaction reliability\, instructional clarity\, and user confidence. The results validate the feasibility of MR-based procedural learning support in domestic settings.
CATEGORIES:VIRTUAL ROOM_12G
LOCATION:Virtual Room G\, Bangkok\, Thailand
SEQUENCE:0
UID:82287fefa5c3de77ef6bbcb20a267e1d
URL:http://11thictisthailand.sched.com/event/82287fefa5c3de77ef6bbcb20a267e1d
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260417T125213Z
DTSTART:20260411T080000Z
DTEND:20260411T100000Z
SUMMARY:Adaptive Sparse Reconstruction of ECG Signals Using Fast Independent Component Analysis
DESCRIPTION:Authors - Dinesh O. Shirsath\, Swati V.Sankpal\n Abstract - This paper presents a hybrid denoising pipeline for multi-channel electrocardiogram (ECG) recordings. First\, blind source separation (BSS) isolates putative sources (cardiac\, motion\, muscle\, baseline drift). Second\, each separated component is represented sparsely in a suitable transform or learned dictionary\; small / noise-dominated coefficients are attenuated and the component reconstructed. Finally\, recombination yields a denoised ECG that preserves waveform morphology while suppressing compound\, nonstationary noise. The paper describes the mathematical model\, algorithmic steps\, implementation tips\, evaluation metrics\, and practical considerations for deployment.
CATEGORIES:VIRTUAL ROOM_12G
LOCATION:Virtual Room G\, Bangkok\, Thailand
SEQUENCE:0
UID:2adf4f5aefdbf58a76702deb38f35b13
URL:http://11thictisthailand.sched.com/event/2adf4f5aefdbf58a76702deb38f35b13
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260417T125213Z
DTSTART:20260411T080000Z
DTEND:20260411T100000Z
SUMMARY:AI-ML Powered Crop Yield Prediction System
DESCRIPTION:Authors - Aarya Sagar Sonawane\, Rutuja Rajendra Thorwat\, Shravani Rajeev Deshpande\, A. R. Bankar Abstract - A significant security issue facing organizations is insider threats since one has access to privileged information and the behavior of users keeps evolving. Current solutions can be un-explainable\, unable to manage new behavior patterns\, generate high false positives\, and un privacy friendly because of centralized data analysis. To solve these problems\, this paper presents EXPLAIN-ITD\, an explainable\, adaptive and privacy-aware artificial intelligence system to detect insider threats. The framework is an integration of multi-modal data fusion\, dual memory continuous learning\, explainable risk scoring\, human feedback in the loop and federated learning and differential privacy. As the exper imental findings have demonstrated\, EXPLAIN-ITD has a better level of accuracy in detection\, a lower level of false alarms and better interpreta bility than the current approaches.
CATEGORIES:VIRTUAL ROOM_12G
LOCATION:Virtual Room G\, Bangkok\, Thailand
SEQUENCE:0
UID:d17f98dd6f6b167d5797f2a74b6341bf
URL:http://11thictisthailand.sched.com/event/d17f98dd6f6b167d5797f2a74b6341bf
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260417T125213Z
DTSTART:20260411T080000Z
DTEND:20260411T100000Z
SUMMARY:An Agentic AI-Based Framework for Crop Production Analysis Using Agro-Climatic and Soil Parameters
DESCRIPTION:Authors - Kamalakar S\, Anjan Babu G\, Ravi Kumar G Abstract - Artificial intelligence has become an important tool for addressing environmental challenges because it can analyze large datasets\, detect patterns\, and support accurate predictions. As climate change increases pressure on natural and built environments\, organizations adopt AI to improve monitoring\, optimize resource use\, and inform sustainability decisions\, though research remains fragmented. This review examines studies from 2020 to 2025 and assesses how AI is applied in renewable energy\, water management\, agriculture\, waste management and the circular economy\, and environmental health and public safety. A major objective of this synthesis is to highlight commonly employed functions by researchers and practitioners such as forecasting\, anomaly detection\, and operational optimization\, alongside emerging model frameworks that strengthen environmental management. While AI offers meaningful benefits\, it also presents challenges related to governance\, transparency\, and the energy demands of large scale models. This review consolidates developments and identifies priorities for future research.
CATEGORIES:VIRTUAL ROOM_12G
LOCATION:Virtual Room G\, Bangkok\, Thailand
SEQUENCE:0
UID:096c2285e04ae2ee07c7a3e10de081b8
URL:http://11thictisthailand.sched.com/event/096c2285e04ae2ee07c7a3e10de081b8
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260417T125213Z
DTSTART:20260411T080000Z
DTEND:20260411T100000Z
SUMMARY:An Intelligent Optimal Routing Framework for MANETs Based on AODV and Dolphin Partner Optimization Technique
DESCRIPTION:Authors - Anil Kumar Bandani\, Anupama Bollampally\, Ramesh Deshpande B Saritha\, P Rajesh Abstract - Transformer-based models in modern applications struggle with continual learning due to catastrophic forgetting. This paper presents Lapis Whale\, a framework that incorporates a Selective Replay Utilization Mechanism (SERUM) to help a model retain previously learned knowledge while adapting to new tasks. The approach leverages a memory buffer to replay representative samples from earlier tasks during training. Experiments on the CIFAR-100 dataset show improved accuracy retention and reduced forgetting compared to standard fine-tuning methods. The framework is computationally efficient and well-suited for real-world adaptive AI systems.
CATEGORIES:VIRTUAL ROOM_12G
LOCATION:Virtual Room G\, Bangkok\, Thailand
SEQUENCE:0
UID:b731aef0543fa2f3bbbb484424de1690
URL:http://11thictisthailand.sched.com/event/b731aef0543fa2f3bbbb484424de1690
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260417T125213Z
DTSTART:20260411T080000Z
DTEND:20260411T100000Z
SUMMARY:Early Blindness Stage Diagnosis Using a Fully Connected Neural Network Architecture
DESCRIPTION:Authors - Suman Kumar Mandal\, Wendrila Biswas\, Jaydev Mishra Abstract - Glaucoma is an optic neuropathy that is progressive and one of the most common causes of permanent blindness in the world. The retinal fundus images used to diagnose the condition are still time-consuming and highly reliant on the clinical expertise to detect the condition early\, before the loss of vision becomes severe. In this experiment\, we suggest a deep learning model that will use the ResNet50 architecture to identify retinal fundus images as belonging to one of two categories: Referable Glaucoma (RG) and Non-Referable Glaucoma (NRG). ResNet50 has been selected because it has good feature ex-traction (residual learning and deep convolutional learning). The standard performance measures were used to assess the trained model\, such as accuracy\, precision\, recall\, F1-score\, and area under the ROC curve. The experimental findings indicate that the suggested approach yields consistent and accurate classification of RG and NRG cases\, and it can be used to assist the ophthalmologist in clinical decision-making. The paper demonstrates how deep learning models could assist in further development of early glaucoma detection and mass screening\, which\, in their turn\, can contribute to better patient outcomes and prevention of blindness before its onset.
CATEGORIES:VIRTUAL ROOM_12G
LOCATION:Virtual Room G\, Bangkok\, Thailand
SEQUENCE:0
UID:0ded5cc1fa58ef9038fc077aefe7bbbf
URL:http://11thictisthailand.sched.com/event/0ded5cc1fa58ef9038fc077aefe7bbbf
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260417T125213Z
DTSTART:20260411T080000Z
DTEND:20260411T100000Z
SUMMARY:Ensemble Deep Learning for Rapid and Accurate Diagnosis of Neurodegenerative Diseases
DESCRIPTION:Authors - S. Jayaraj\, G. Anjan Babu\, Krishnamurthy Kavitha\n Abstract - As neurodegenerative diseases like Huntington’s become a global health priority\, the difficulty of early and accurate radiological diagnosis remains a significant hurdle. While Deep Learning\, predominantly CNNs (Convolutional Neural Networks)\, offers a clarification for medical image classification\, performance is often hindered by the inadequacy of high-grade datasets. This research addresses these limitations by proposing an ensemble deep learning model that integrates ResNet\, MobileNet\, and VGG16 architectures. By combining these networks\, the study achieves enhanced robustness and superior classification accuracy compared to standalone models. This automated framework serves as a vital clinical support tool\, enabling faster interventions\, improved treatment planning\, and a reduction in the global burden of neurodegenerative disorders [10\,12].
CATEGORIES:VIRTUAL ROOM_12G
LOCATION:Virtual Room G\, Bangkok\, Thailand
SEQUENCE:0
UID:80d0f9e4e8249e7d57d98b6856ffefb6
URL:http://11thictisthailand.sched.com/event/80d0f9e4e8249e7d57d98b6856ffefb6
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260417T125213Z
DTSTART:20260411T080000Z
DTEND:20260411T100000Z
SUMMARY:EXPLAIN-ITD: An Explainable\, Adaptive\, and Privacy-Preserving AI Framework for Insider Threat Detection
DESCRIPTION:Authors - Abhijit Dnyaneshwar Jadhav\, Prashant G. Ahire\, Madhuri Hiwale\n Abstract - A significant security issue facing organizations is insider threats since one has access to privileged information and the behavior of users keeps evolving. Current solutions can be un-explainable\, unable to manage new behavior patterns\, generate high false positives\, and un privacy friendly because of centralized data analysis. To solve these problems\, this paper presents EXPLAIN-ITD\, an explainable\, adaptive and privacy-aware artificial intelligence system to detect insider threats. The framework is an integration of multi-modal data fusion\, dual memory continuous learning\, explainable risk scoring\, human feedback in the loop and federated learning and differential privacy. As the exper imental findings have demonstrated\, EXPLAIN-ITD has a better level of accuracy in detection\, a lower level of false alarms and better interpreta bility than the current approaches.
CATEGORIES:VIRTUAL ROOM_12G
LOCATION:Virtual Room G\, Bangkok\, Thailand
SEQUENCE:0
UID:4258636b9d042b695c87ef5863fa2dfc
URL:http://11thictisthailand.sched.com/event/4258636b9d042b695c87ef5863fa2dfc
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260417T125213Z
DTSTART:20260411T080000Z
DTEND:20260411T100000Z
SUMMARY:Inverse Vertex Pyramid (IVP) A Bottom-Up\, Metadata- and Policy-Driven Design Pattern for Scalable AI and Data Automation Platforms
DESCRIPTION:Authors - Tirupathi Rao Dockara\, Pradeep Rajagopal Kirthivasan Abstract - Healthcare data scarcity poses significant challenges for machine learning applications in clinical settings\, particularly for conditions with limited patient populations. This paper presents a novel quantumenhanced data augmentation framework that addresses this challenge through a three-pillar architecture: Quantum Random Number Generation (QRNG) for true randomness\, Statistical AI for intelligent parameter optimization\, and Generative AI for clinical interpretability. Our implementation utilizes Bell state quantum circuits to generate genuinely random perturbations\, ensuring higher entropy than classical pseudorandom methods. The framework incorporates medical domain knowledge through constraint-aware augmentation\, maintaining clinical validity while generating synthetic patient records. Experimental evaluation on the Pima Indians Diabetes dataset (768 samples\, 8 features) demonstrates that our quantum-enhanced approach achieves 100% medical constraint compliance while generating high-quality synthetic data. The system provides both command-line and web interfaces\, with automatic fallback to classical methods when quantum resources are unavailable. Our contributions include: the first practical application of quantum computing to healthcare data augmentation\, an AI-driven optimization system that automatically determines augmentation parameters\, integration with large language models for non-technical summarization of validation reports\, and a production-ready implementation with comprehensive validation mechanisms. The framework represents a significant advancement in synthetic medical data generation\, offering a scalable solution for addressing data scarcity in healthcare AI applications.
CATEGORIES:VIRTUAL ROOM_12G
LOCATION:Virtual Room G\, Bangkok\, Thailand
SEQUENCE:0
UID:bb246b9a8b3d22bab1478534d8e93347
URL:http://11thictisthailand.sched.com/event/bb246b9a8b3d22bab1478534d8e93347
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260417T125213Z
DTSTART:20260411T080000Z
DTEND:20260411T100000Z
SUMMARY:Memory Security: From Cold Boot Attacks to Side-Channel Defenses
DESCRIPTION:Authors - Jyotiprakash Mishra\, Sanjay K. Sahay\, Swati Mishra\, Aman Pathak\n Abstract - Memory encryption is a key security requirement for modern computing systems\, addressing vulnerabilities between CPUs and main memory. Traditional storage encryption is insufficient for protecting volatile data in RAM\, which remains exposed to bus sniffing\, cold boot attacks\, and side-channel exploits. This paper therefore systematically reviews memory encryption techniques focused on hardware-based solutions like Intel Total Memory Encryption (TME)\, Multi-Key TME\, and AMD Secure Memory Encryption\, which provide robust protection while minimising performance overhead. The paper also explores integrity protection via Merkle trees and side-channel countermeasures against Differential Power Analysis and Simple Power Analysis attacks. Additionally\, granular memory encryption methods for multi-tenant environments are discussed\, highlighting their role in isolating sensitive data across security domains. By examining security guarantees and performance trade-offs\, we emphasise the necessity of efficient memory encryption to safeguard against evolving threats targeting the CPU-memory interface\, providing hardware engineers a foundation for ensuring data confidentiality and integrity.
CATEGORIES:VIRTUAL ROOM_12G
LOCATION:Virtual Room G\, Bangkok\, Thailand
SEQUENCE:0
UID:bf8b886399aba2e3fb79737798429ed4
URL:http://11thictisthailand.sched.com/event/bf8b886399aba2e3fb79737798429ed4
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260417T125213Z
DTSTART:20260411T100000Z
DTEND:20260411T100200Z
SUMMARY:Session Chair Concluding Remarks
DESCRIPTION:\n
CATEGORIES:VIRTUAL ROOM_12A
LOCATION:Virtual Room A\, Bangkok\, Thailand
SEQUENCE:0
UID:68eb37f0c283c1ba9f200b02cf289e77
URL:http://11thictisthailand.sched.com/event/68eb37f0c283c1ba9f200b02cf289e77
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260417T125213Z
DTSTART:20260411T100000Z
DTEND:20260411T100200Z
SUMMARY:Session Chair Concluding Remarks
DESCRIPTION:\n
CATEGORIES:VIRTUAL ROOM_12B
LOCATION:Virtual Room B\, Bangkok\, Thailand
SEQUENCE:0
UID:2477f651cae0ca51a42b0fb288061ad2
URL:http://11thictisthailand.sched.com/event/2477f651cae0ca51a42b0fb288061ad2
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260417T125213Z
DTSTART:20260411T100000Z
DTEND:20260411T100200Z
SUMMARY:Session Chair Concluding Remarks
DESCRIPTION:\n
CATEGORIES:VIRTUAL ROOM_12C
LOCATION:Virtual Room C\, Bangkok\, Thailand
SEQUENCE:0
UID:e94b29c8d19cea815e3b36733bc3edb4
URL:http://11thictisthailand.sched.com/event/e94b29c8d19cea815e3b36733bc3edb4
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260417T125213Z
DTSTART:20260411T100000Z
DTEND:20260411T100200Z
SUMMARY:Session Chair Concluding Remarks
DESCRIPTION:\n
CATEGORIES:VIRTUAL ROOM_12D
LOCATION:Virtual Room D\, Bangkok\, Thailand
SEQUENCE:0
UID:6997c582e83cefd9faea43d037171b49
URL:http://11thictisthailand.sched.com/event/6997c582e83cefd9faea43d037171b49
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260417T125213Z
DTSTART:20260411T100000Z
DTEND:20260411T100200Z
SUMMARY:Session Chair Concluding Remarks
DESCRIPTION:\n
CATEGORIES:VIRTUAL ROOM_12E
LOCATION:Virtual Room E\, Bangkok\, Thailand
SEQUENCE:0
UID:2f108d2dfeb0d406a3a6ff7c1272c41c
URL:http://11thictisthailand.sched.com/event/2f108d2dfeb0d406a3a6ff7c1272c41c
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260417T125213Z
DTSTART:20260411T100000Z
DTEND:20260411T100200Z
SUMMARY:Session Chair Concluding Remarks
DESCRIPTION:\n
CATEGORIES:VIRTUAL ROOM_12F
LOCATION:Virtual Room F\, Bangkok\, Thailand
SEQUENCE:0
UID:95e62fa90b43ab35cc2f0cb340ab5378
URL:http://11thictisthailand.sched.com/event/95e62fa90b43ab35cc2f0cb340ab5378
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260417T125213Z
DTSTART:20260411T100000Z
DTEND:20260411T100200Z
SUMMARY:Session Chair Concluding Remarks
DESCRIPTION:\n
CATEGORIES:VIRTUAL ROOM_12G
LOCATION:Virtual Room G\, Bangkok\, Thailand
SEQUENCE:0
UID:7d408accb47f9136a795b8d6b9858dbd
URL:http://11thictisthailand.sched.com/event/7d408accb47f9136a795b8d6b9858dbd
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260417T125213Z
DTSTART:20260411T100200Z
DTEND:20260411T100500Z
SUMMARY:Session Closing and Information To Authors
DESCRIPTION:\n
CATEGORIES:VIRTUAL ROOM_12A
LOCATION:Virtual Room A\, Bangkok\, Thailand
SEQUENCE:0
UID:093eabb183cc1ed88a87a42d51ce07f9
URL:http://11thictisthailand.sched.com/event/093eabb183cc1ed88a87a42d51ce07f9
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260417T125213Z
DTSTART:20260411T100200Z
DTEND:20260411T100500Z
SUMMARY:Session Closing and Information To Authors
DESCRIPTION:\n
CATEGORIES:VIRTUAL ROOM_12B
LOCATION:Virtual Room B\, Bangkok\, Thailand
SEQUENCE:0
UID:84607745a188905e27f32910b0510e22
URL:http://11thictisthailand.sched.com/event/84607745a188905e27f32910b0510e22
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260417T125213Z
DTSTART:20260411T100200Z
DTEND:20260411T100500Z
SUMMARY:Session Closing and Information To Authors
DESCRIPTION:\n
CATEGORIES:VIRTUAL ROOM_12C
LOCATION:Virtual Room C\, Bangkok\, Thailand
SEQUENCE:0
UID:a2357fe9bad1baf2654c44420cc74f0c
URL:http://11thictisthailand.sched.com/event/a2357fe9bad1baf2654c44420cc74f0c
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260417T125213Z
DTSTART:20260411T100200Z
DTEND:20260411T100500Z
SUMMARY:Session Closing and Information To Authors
DESCRIPTION:\n
CATEGORIES:VIRTUAL ROOM_12D
LOCATION:Virtual Room D\, Bangkok\, Thailand
SEQUENCE:0
UID:84ca45b7d461fbf02fc2fe43f0cbba92
URL:http://11thictisthailand.sched.com/event/84ca45b7d461fbf02fc2fe43f0cbba92
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260417T125213Z
DTSTART:20260411T100200Z
DTEND:20260411T100500Z
SUMMARY:Session Closing and Information To Authors
DESCRIPTION:\n
CATEGORIES:VIRTUAL ROOM_12E
LOCATION:Virtual Room E\, Bangkok\, Thailand
SEQUENCE:0
UID:87ed52b460e71b1704c9d889eb979319
URL:http://11thictisthailand.sched.com/event/87ed52b460e71b1704c9d889eb979319
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260417T125213Z
DTSTART:20260411T100200Z
DTEND:20260411T100500Z
SUMMARY:Session Closing and Information To Authors
DESCRIPTION:\n
CATEGORIES:VIRTUAL ROOM_12F
LOCATION:Virtual Room F\, Bangkok\, Thailand
SEQUENCE:0
UID:f831e6095ee78a79a3cd53aa031d439d
URL:http://11thictisthailand.sched.com/event/f831e6095ee78a79a3cd53aa031d439d
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260417T125213Z
DTSTART:20260411T100200Z
DTEND:20260411T100500Z
SUMMARY:Session Closing and Information To Authors
DESCRIPTION:\n
CATEGORIES:VIRTUAL ROOM_12G
LOCATION:Virtual Room G\, Bangkok\, Thailand
SEQUENCE:0
UID:d8ffcd5a482c6df8b7b0b45975bf56d9
URL:http://11thictisthailand.sched.com/event/d8ffcd5a482c6df8b7b0b45975bf56d9
END:VEVENT
END:VCALENDAR