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Venue: Virtual Room D clear filter
Thursday, April 9
 

9:28am GMT+07

Opening Remarks
Thursday April 9, 2026 9:28am - 9:30am GMT+07
Virtual Room D

Invited Guest & Session Chair
avatar for Usman Ali

Usman Ali

Lecturer, University of Education, Lahore, Vehari Campus, Pakistan

avatar for Dr. Bindiya Jain

Dr. Bindiya Jain

Associate Professor, Poornima University, India

Thursday April 9, 2026 9:28am - 9:30am GMT+07
Virtual Room D Bangkok, Thailand
  Virtual Room 1D, Virtual Room D

9:30am GMT+07

A Hybrid Deep Learning Approach for Network Intrusion Detection System in Software-Defined Networking
Thursday April 9, 2026 9:30am - 11:30am GMT+07
Virtual Room D
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.
Paper Presenter
avatar for Cristal James Tomy
Thursday April 9, 2026 9:30am - 11:30am GMT+07
Virtual Room D Bangkok, Thailand
  Virtual Room 1D, Virtual Room D

9:30am GMT+07

Artificial Intelligence-Enhanced Zero Trust Security Framework for Hybrid Cloud Enterprise Networks
Thursday April 9, 2026 9:30am - 11:30am GMT+07
Virtual Room D
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.
Paper Presenter
avatar for Emerson Joey Caro
Thursday April 9, 2026 9:30am - 11:30am GMT+07
Virtual Room D Bangkok, Thailand
  Virtual Room 1D, Virtual Room D

9:30am GMT+07

AutoNoteX: AI-Powered Multimodal Note Generation and Interactive Learning Assistant
Thursday April 9, 2026 9:30am - 11:30am GMT+07
Virtual Room D
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.
Paper Presenter
avatar for Indupriya Vempati
Thursday April 9, 2026 9:30am - 11:30am GMT+07
Virtual Room D Bangkok, Thailand
  Virtual Room 1D, Virtual Room D

9:30am GMT+07

Circular Shaped Wearable Patch Antennas for 6G Application
Thursday April 9, 2026 9:30am - 11:30am GMT+07
Virtual Room D
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.
Paper Presenter
avatar for Siddalingappagouda Biradar
Thursday April 9, 2026 9:30am - 11:30am GMT+07
Virtual Room D Bangkok, Thailand
  Virtual Room 1D, Virtual Room D

9:30am GMT+07

Extending Latency Models for Long-Sequence Inference: Nonlinear, Adaptive, and Empirical Enhancements
Thursday April 9, 2026 9:30am - 11:30am GMT+07
Virtual Room D
Authors - Koutaro HACHIYA, Ioannis PATIAS
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.
Paper Presenter
avatar for Ioannis PATIAS
Thursday April 9, 2026 9:30am - 11:30am GMT+07
Virtual Room D Bangkok, Thailand
  Virtual Room 1D, Virtual Room D

9:30am GMT+07

Immersive Virtual Reality for Awareness and Development of Emotional Self-Regulation Skills: The Case of the Dear Alfred Project
Thursday April 9, 2026 9:30am - 11:30am GMT+07
Virtual Room D
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.
Paper Presenter
avatar for Joao Paulo Sousa
Thursday April 9, 2026 9:30am - 11:30am GMT+07
Virtual Room D Bangkok, Thailand
  Virtual Room 1D, Virtual Room D

9:30am GMT+07

Multi-Secret Sharing Scheme with CSS Codes
Thursday April 9, 2026 9:30am - 11:30am GMT+07
Virtual Room D
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.
Paper Presenter
avatar for Sushmita Sarkar
Thursday April 9, 2026 9:30am - 11:30am GMT+07
Virtual Room D Bangkok, Thailand
  Virtual Room 1D, Virtual Room D

9:30am GMT+07

Persistent Authority in Agent Systems: Memory Poisoning, Provenance Laundering, and Remediation-Complete Containment
Thursday April 9, 2026 9:30am - 11:30am GMT+07
Virtual Room D
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.
Paper Presenter
avatar for Carl Kugblenu
Thursday April 9, 2026 9:30am - 11:30am GMT+07
Virtual Room D Bangkok, Thailand
  Virtual Room 1D, Virtual Room D

9:30am GMT+07

Profanity Analysis in Hollywood Movies
Thursday April 9, 2026 9:30am - 11:30am GMT+07
Virtual Room D
Authors - Nitika Gawande, Pradnya Bapat, Sanyukta Sasane, Trupti Bankar, Rakhi Dongaonkar, Rashmi Apte, Mangesh Bedekar
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.
Paper Presenter
avatar for Nitika Gawande
Thursday April 9, 2026 9:30am - 11:30am GMT+07
Virtual Room D Bangkok, Thailand
  Virtual Room 1D, Virtual Room D

9:30am GMT+07

Structured State Representation for Action-Masked Reinforcement Learning in Flexible Job Shop Scheduling
Thursday April 9, 2026 9:30am - 11:30am GMT+07
Virtual Room D
Authors - Kostiantyn Hrishchenko, Oleksii Pysarchuk
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.
Paper Presenter
avatar for Kostiantyn Hrishchenko
Thursday April 9, 2026 9:30am - 11:30am GMT+07
Virtual Room D Bangkok, Thailand
  Virtual Room 1D, Virtual Room D

11:30am GMT+07

Session Chair Concluding Remarks
Thursday April 9, 2026 11:30am - 11:32am GMT+07
Virtual Room D

Invited Guest & Session Chair
avatar for Usman Ali

Usman Ali

Lecturer, University of Education, Lahore, Vehari Campus, Pakistan

avatar for Dr. Bindiya Jain

Dr. Bindiya Jain

Associate Professor, Poornima University, India

Thursday April 9, 2026 11:30am - 11:32am GMT+07
Virtual Room D Bangkok, Thailand
  Virtual Room 1D, Virtual Room D

11:32am GMT+07

Session Closing and Information To Authors
Thursday April 9, 2026 11:32am - 11:35am GMT+07
Virtual Room D

Moderator
avatar for Gayatri Bhurguda
Thursday April 9, 2026 11:32am - 11:35am GMT+07
Virtual Room D Bangkok, Thailand
  Virtual Room 1D, Virtual Room D

12:13pm GMT+07

Opening Remarks
Thursday April 9, 2026 12:13pm - 12:15pm GMT+07
Virtual Room D

Invited Guest & Session Chair
avatar for Dr. Tarun Kumar

Dr. Tarun Kumar

Associate Professor, School of Computer Science, University of Petroleum and Energy Studies (UPES), Uttarakhand, India
Thursday April 9, 2026 12:13pm - 12:15pm GMT+07
Virtual Room D Bangkok, Thailand
  Virtual Room 2D, Virtual Room D

12:15pm GMT+07

An Accessible and Safety-Aware AI-Driven Digital Mental Health Platform for Visually Impaired and Multilingual Users
Thursday April 9, 2026 12:15pm - 2:15pm GMT+07
Virtual Room D
Authors - Abhishek Chaudhari, Mahalakshmi Bodireddy, Aditya Bhor, Onkar Dadas, Prajakta Shinkar, Chinmay Chougule
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.
Paper Presenter
avatar for Abhishek Chaudhari
Thursday April 9, 2026 12:15pm - 2:15pm GMT+07
Virtual Room D Bangkok, Thailand
  Virtual Room 2D, Virtual Room D

12:15pm GMT+07

ARTIFICIAL INTELLIGENCE AS A TOOL FOR IMPROVING PARTICIPATORY BUDGETING IN THE CONTEXT OF THE DIGITAL TRANSFORMATION OF UZBEKISTAN ECONOMY
Thursday April 9, 2026 12:15pm - 2:15pm GMT+07
Virtual Room D
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.
Paper Presenter
avatar for Shakhlo Rustamovna Abdullaeva
Thursday April 9, 2026 12:15pm - 2:15pm GMT+07
Virtual Room D Bangkok, Thailand
  Virtual Room 2D, Virtual Room D

12:15pm GMT+07

Computational Reconstruction of Missing Notes in Polyphonic Music Using Bigram-Based Entropy Models
Thursday April 9, 2026 12:15pm - 2:15pm GMT+07
Virtual Room D
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.
Paper Presenter
avatar for Michele Della Ventura
Thursday April 9, 2026 12:15pm - 2:15pm GMT+07
Virtual Room D Bangkok, Thailand
  Virtual Room 2D, Virtual Room D

12:15pm GMT+07

CYBERSECURITY CHALLENGES IN INTELLIGENT RAILWAY SYSTEMS: A Review of ICT Architectures, Threat Models, and AI-Based Defense Approaches
Thursday April 9, 2026 12:15pm - 2:15pm GMT+07
Virtual Room D
Authors - Roshna Dhakal, Khanista Namee
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.
Paper Presenter
avatar for Roshna Dhakal
Thursday April 9, 2026 12:15pm - 2:15pm GMT+07
Virtual Room D Bangkok, Thailand
  Virtual Room 2D, Virtual Room D

12:15pm GMT+07

Digital System Integration as a Procurement Strategy for Sustainable Urban Metro Systems: Evidence from Indian Case Studies
Thursday April 9, 2026 12:15pm - 2:15pm GMT+07
Virtual Room D
Authors - Bhonsle Rashmi Ravindra, Shankar Chaudhary, Shivoham Singh, Hemant Kothari, Raj Kothari
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.
Paper Presenter
avatar for Bhonsle Rashmi Ravindra
Thursday April 9, 2026 12:15pm - 2:15pm GMT+07
Virtual Room D Bangkok, Thailand
  Virtual Room 2D, Virtual Room D

12:15pm GMT+07

E-Health and Mental Well-Being: User Engagement with Mental Health Content on YouTube
Thursday April 9, 2026 12:15pm - 2:15pm GMT+07
Virtual Room D
Authors - Irmawan Rahyadi
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.
Paper Presenter
avatar for Irmawan Rahyadi
Thursday April 9, 2026 12:15pm - 2:15pm GMT+07
Virtual Room D Bangkok, Thailand
  Virtual Room 2D, Virtual Room D

12:15pm GMT+07

Early Mental Health Detection Using AI From Typing And Voice Patterns
Thursday April 9, 2026 12:15pm - 2:15pm GMT+07
Virtual Room D
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.
Paper Presenter
avatar for Ahir Jaimi
Thursday April 9, 2026 12:15pm - 2:15pm GMT+07
Virtual Room D Bangkok, Thailand
  Virtual Room 2D, Virtual Room D

12:15pm GMT+07

How Omnichannel ICT-Enabled Marketing Shapes Customer Engagement and Loyalty in Culinary Hospitality Services
Thursday April 9, 2026 12:15pm - 2:15pm GMT+07
Virtual Room D
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.
Paper Presenter
avatar for Aditya Nova Putra
Thursday April 9, 2026 12:15pm - 2:15pm GMT+07
Virtual Room D Bangkok, Thailand
  Virtual Room 2D, Virtual Room D

12:15pm GMT+07

Prediction of Academic Performance of Postgraduate Students at the Technical University of Manabí using Big Data and Machine Learning Techniques
Thursday April 9, 2026 12:15pm - 2:15pm GMT+07
Virtual Room D
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.
Paper Presenter
avatar for Steveen Eduardo Pinzon Morales
Thursday April 9, 2026 12:15pm - 2:15pm GMT+07
Virtual Room D Bangkok, Thailand
  Virtual Room 2D, Virtual Room D

12:15pm GMT+07

StewardFM: An Optimized Association Management Solution Utilizing the Deflate Compression Algorithm for Efficient Cloud Storage
Thursday April 9, 2026 12:15pm - 2:15pm GMT+07
Virtual Room D
Authors - Ain Geuel E. Escober, Rosicar E. Escober, Demelyn E. Monzon
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.
Paper Presenter
avatar for Ain Geuel E. Escober
Thursday April 9, 2026 12:15pm - 2:15pm GMT+07
Virtual Room D Bangkok, Thailand
  Virtual Room 2D, Virtual Room D

2:15pm GMT+07

Session Chair Concluding Remarks
Thursday April 9, 2026 2:15pm - 2:17pm GMT+07
Virtual Room D

Invited Guest & Session Chair
avatar for Dr. Tarun Kumar

Dr. Tarun Kumar

Associate Professor, School of Computer Science, University of Petroleum and Energy Studies (UPES), Uttarakhand, India
Thursday April 9, 2026 2:15pm - 2:17pm GMT+07
Virtual Room D Bangkok, Thailand
  Virtual Room 2D, Virtual Room D

2:17pm GMT+07

Session Closing and Information To Authors
Thursday April 9, 2026 2:17pm - 2:20pm GMT+07
Virtual Room D

Moderator
avatar for Gayatri Bhurguda
Thursday April 9, 2026 2:17pm - 2:20pm GMT+07
Virtual Room D Bangkok, Thailand
  Virtual Room 2D, Virtual Room D

2:58pm GMT+07

Opening Remarks
Thursday April 9, 2026 2:58pm - 3:00pm GMT+07
Virtual Room D

Invited Guest & Session Chair
avatar for Dr. Amar Buchade

Dr. Amar Buchade

Associate Professor, Vishwakarma Institute of Information Technology, India
Thursday April 9, 2026 2:58pm - 3:00pm GMT+07
Virtual Room D Bangkok, Thailand
  Virtual Room 3D, Virtual Room D

3:00pm GMT+07

A Hybrid AI–Terminology Microservice for Dual-Coded AYUSH EMRs
Thursday April 9, 2026 3:00pm - 5:00pm GMT+07
Virtual Room D
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.
Paper Presenter
avatar for Sarthak

Sarthak

India

Thursday April 9, 2026 3:00pm - 5:00pm GMT+07
Virtual Room D Bangkok, Thailand
  Virtual Room 3D, Virtual Room D

3:00pm GMT+07

Adaptive Hybrid PSO-GD with Stagnation Detection for Robust and Efficient Multimodal Optimization
Thursday April 9, 2026 3:00pm - 5:00pm GMT+07
Virtual Room D
Authors - Sowmini Devi Veeramachaneni, Yaswanth Gavini, Arun K Pujari
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.
Paper Presenter
avatar for Sowmini Devi Veeramachaneni
Thursday April 9, 2026 3:00pm - 5:00pm GMT+07
Virtual Room D Bangkok, Thailand
  Virtual Room 3D, Virtual Room D

3:00pm GMT+07

Agentic AI in Supply Chain Management Systems: A Systematic Review
Thursday April 9, 2026 3:00pm - 5:00pm GMT+07
Virtual Room D
Authors - Amna Ali, Rida Hijab Basit
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.
Paper Presenter
avatar for Rida Hijab Basit
Thursday April 9, 2026 3:00pm - 5:00pm GMT+07
Virtual Room D Bangkok, Thailand
  Virtual Room 3D, Virtual Room D

3:00pm GMT+07

An Enhance Approach to Prevent Man-in-the-Middle Attack in Diffie-Hellman Key Exchange Protocol
Thursday April 9, 2026 3:00pm - 5:00pm GMT+07
Virtual Room D
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.
Paper Presenter
avatar for Bikram Bikash Das
Thursday April 9, 2026 3:00pm - 5:00pm GMT+07
Virtual Room D Bangkok, Thailand
  Virtual Room 3D, Virtual Room D

3:00pm GMT+07

Cross-Lingual Sentiment Analysis for Low-Resource Languages: A Case Study on Thai Railway Services
Thursday April 9, 2026 3:00pm - 5:00pm GMT+07
Virtual Room D
Authors - Dao Khanh Duy, Nguyen Hoang Hieu, Karn Nasritha, Khanista Namee
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.
Paper Presenter
avatar for Dao Khanh Duy
Thursday April 9, 2026 3:00pm - 5:00pm GMT+07
Virtual Room D Bangkok, Thailand
  Virtual Room 3D, Virtual Room D

3:00pm GMT+07

Ensemble Learning for High-Precision Prediction of Rare Critical Events
Thursday April 9, 2026 3:00pm - 5:00pm GMT+07
Virtual Room D
Authors - Nazar Melnyk, Oleksandr Korochkin
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.
Paper Presenter
avatar for Nazar Melnyk
Thursday April 9, 2026 3:00pm - 5:00pm GMT+07
Virtual Room D Bangkok, Thailand
  Virtual Room 3D, Virtual Room D

3:00pm GMT+07

IMPLEMENTING DIGITAL TWIN ARCHITECTURE THROUGH BIM AND IOT INTEGRATION FOR SUSTAINABLE MUSEUMS: MACA LIVING LAB APPLICATION
Thursday April 9, 2026 3:00pm - 5:00pm GMT+07
Virtual Room D
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.
Paper Presenter
avatar for Silvia Meschini
Thursday April 9, 2026 3:00pm - 5:00pm GMT+07
Virtual Room D Bangkok, Thailand
  Virtual Room 3D, Virtual Room D

3:00pm GMT+07

Information Delivery Specification (IDS) in the Construction Sector: A Systematic Literature Review
Thursday April 9, 2026 3:00pm - 5:00pm GMT+07
Virtual Room D
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.
Paper Presenter
avatar for Murat Aydın
Thursday April 9, 2026 3:00pm - 5:00pm GMT+07
Virtual Room D Bangkok, Thailand
  Virtual Room 3D, Virtual Room D

3:00pm GMT+07

Recent Advances in Nonconvex Optimization for Machine Learning
Thursday April 9, 2026 3:00pm - 5:00pm GMT+07
Virtual Room D
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.
Paper Presenter
avatar for Hiep. L. Thi
Thursday April 9, 2026 3:00pm - 5:00pm GMT+07
Virtual Room D Bangkok, Thailand
  Virtual Room 3D, Virtual Room D

3:00pm GMT+07

Reliability-Aware Late Fusion for Robust Multimodal Emotion Recognition under Modality Imbalance and Domain Shift
Thursday April 9, 2026 3:00pm - 5:00pm GMT+07
Virtual Room D
Authors - Karn Na Sritha, Khang Tran Chi Nguyen, Dao Khanh Duy, Khanista Namee
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.
Paper Presenter
avatar for Dao Khanh Duy
Thursday April 9, 2026 3:00pm - 5:00pm GMT+07
Virtual Room D Bangkok, Thailand
  Virtual Room 3D, Virtual Room D

5:00pm GMT+07

Session Chair Concluding Remarks
Thursday April 9, 2026 5:00pm - 5:02pm GMT+07
Virtual Room D

Invited Guest & Session Chair
avatar for Dr. Amar Buchade

Dr. Amar Buchade

Associate Professor, Vishwakarma Institute of Information Technology, India
Thursday April 9, 2026 5:00pm - 5:02pm GMT+07
Virtual Room D Bangkok, Thailand
  Virtual Room 3D, Virtual Room D

5:02pm GMT+07

Session Closing and Information To Authors
Thursday April 9, 2026 5:02pm - 5:05pm GMT+07
Virtual Room D

Moderator
avatar for Gayatri Bhurguda
Thursday April 9, 2026 5:02pm - 5:05pm GMT+07
Virtual Room D Bangkok, Thailand
  Virtual Room 3D, Virtual Room D
 
Friday, April 10
 

9:28am GMT+07

Opening Remarks
Friday April 10, 2026 9:28am - 9:30am GMT+07
Virtual Room D

Invited Guest & Session Chair
avatar for Prof. Vishnu Kumar

Prof. Vishnu Kumar

Assistant Professor, Morgan State University, United States
avatar for Dr. Dushyantsinh B. Rathod

Dr. Dushyantsinh B. Rathod

Professor & HOD, Gandhinagar Institute of Technology, India
Friday April 10, 2026 9:28am - 9:30am GMT+07
Virtual Room D Bangkok, Thailand
  Virtual Room 7D, Virtual Room D

9:30am GMT+07

A Governed Forecasting and Anomaly Detection Framework for Live Birth Planning in Provincial Health Systems
Friday April 10, 2026 9:30am - 11:30am GMT+07
Virtual Room D
Authors - Britt Kristoff B. Montalvo, Vicente Pitogo
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.
Paper Presenter
avatar for Britt Kristoff B. Montalvo
Friday April 10, 2026 9:30am - 11:30am GMT+07
Virtual Room D Bangkok, Thailand
  Virtual Room 7D, Virtual Room D

9:30am GMT+07

A Hybrid Wavelet CNN Vision Transformer Framework with Explainable AI for Medical Image Classification
Friday April 10, 2026 9:30am - 11:30am GMT+07
Virtual Room D
Authors - Shriram Dange, Namdeo. M. Sawant, Sumeet S Ingole, Somnath A. Zambare
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.
Paper Presenter
avatar for Shriram Dange
Friday April 10, 2026 9:30am - 11:30am GMT+07
Virtual Room D Bangkok, Thailand
  Virtual Room 7D, Virtual Room D

9:30am GMT+07

AI-Powered Investment Assistant
Friday April 10, 2026 9:30am - 11:30am GMT+07
Virtual Room D
Authors - Sherly K.K., Merin Jose, Aleena Gerard Nidhiry, Amit Shibu Kadambamoodan, Alfahad Shahi
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 .
Paper Presenter
avatar for Merin Jose
Friday April 10, 2026 9:30am - 11:30am GMT+07
Virtual Room D Bangkok, Thailand
  Virtual Room 7D, Virtual Room D

9:30am GMT+07

Beyond Continuity: Modeling Discontinuous Risk in Altcoin Portfolios via Merton Jump-Diffusion and EWMA Covariance
Friday April 10, 2026 9:30am - 11:30am GMT+07
Virtual Room D
Authors - Ekleen Kaur
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.
Paper Presenter
avatar for Ekleen Kaur

Ekleen Kaur

United States

Friday April 10, 2026 9:30am - 11:30am GMT+07
Virtual Room D Bangkok, Thailand
  Virtual Room 7D, Virtual Room D

9:30am GMT+07

Bridging Linguistic Diversity: Enhancing NER Performance through Large Language Models on Indian & Foreign Languages
Friday April 10, 2026 9:30am - 11:30am GMT+07
Virtual Room D
Authors - Makrand Dhanokar, Anirban Sarkar, Prajakta Dange Sant, Shivakarthik S, Krishnanjan Bhattacharjee, Swati Mehta
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.
Paper Presenter
avatar for Makrand Dhanokar
Friday April 10, 2026 9:30am - 11:30am GMT+07
Virtual Room D Bangkok, Thailand
  Virtual Room 7D, Virtual Room D

9:30am GMT+07

Explainable Deep Learning Driven Transaction-level Customer Spending Behavior Analysis for Fraud Detection in a Big Data Framework
Friday April 10, 2026 9:30am - 11:30am GMT+07
Virtual Room D
Authors - Asmaul Hosna Sadika, M. M. Musharaf Hussain, Mohammad Shamsul Arefin
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.
Paper Presenter
avatar for Asmaul Hosna Sadika
Friday April 10, 2026 9:30am - 11:30am GMT+07
Virtual Room D Bangkok, Thailand
  Virtual Room 7D, Virtual Room D

9:30am GMT+07

Eye Movements and Their Influence on Cognitive Processing
Friday April 10, 2026 9:30am - 11:30am GMT+07
Virtual Room D
Authors - Christian Vera, Christian Torres-Moran
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.
Paper Presenter
avatar for Christian Vera
Friday April 10, 2026 9:30am - 11:30am GMT+07
Virtual Room D Bangkok, Thailand
  Virtual Room 7D, Virtual Room D

9:30am GMT+07

Hybrid Deep Learning and Quantum Approach for Multimodal Deepfake Detection
Friday April 10, 2026 9:30am - 11:30am GMT+07
Virtual Room D
Authors - V. Abarna, R. Shyamala
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.
Paper Presenter
avatar for V. Abarna
Friday April 10, 2026 9:30am - 11:30am GMT+07
Virtual Room D Bangkok, Thailand
  Virtual Room 7D, Virtual Room D

9:30am GMT+07

Is Common Hardening Methods Really Sufficient? A Risk Analysis on Current ICS Vulnerabilities
Friday April 10, 2026 9:30am - 11:30am GMT+07
Virtual Room D
Authors - Emine YAZICI, Alper UGUR
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.  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.
Paper Presenter
avatar for Alper UGUR
Friday April 10, 2026 9:30am - 11:30am GMT+07
Virtual Room D Bangkok, Thailand
  Virtual Room 7D, Virtual Room D

9:30am GMT+07

Time-Synchronized Industrial Data Analytics for Current Unbalance Mitigation in HVJ Electric Boilers: An FMEA-Guided Approach
Friday April 10, 2026 9:30am - 11:30am GMT+07
Virtual Room D
Authors - Nurkholis, Katherin Indriawati
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).
Paper Presenter
avatar for Nurkholis

Nurkholis

Indonesia

Friday April 10, 2026 9:30am - 11:30am GMT+07
Virtual Room D Bangkok, Thailand
  Virtual Room 7D, Virtual Room D

11:30am GMT+07

Session Chair Concluding Remarks
Friday April 10, 2026 11:30am - 11:32am GMT+07
Virtual Room D

Invited Guest & Session Chair
avatar for Prof. Vishnu Kumar

Prof. Vishnu Kumar

Assistant Professor, Morgan State University, United States
avatar for Dr. Dushyantsinh B. Rathod

Dr. Dushyantsinh B. Rathod

Professor & HOD, Gandhinagar Institute of Technology, India
Friday April 10, 2026 11:30am - 11:32am GMT+07
Virtual Room D Bangkok, Thailand
  Virtual Room 7D, Virtual Room D

11:32am GMT+07

Session Closing and Information To Authors
Friday April 10, 2026 11:32am - 11:35am GMT+07
Virtual Room D

Moderator
avatar for Gayatri Bhurguda
Friday April 10, 2026 11:32am - 11:35am GMT+07
Virtual Room D Bangkok, Thailand
  Virtual Room 7D, Virtual Room D

12:13pm GMT+07

Opening Remarks
Friday April 10, 2026 12:13pm - 12:15pm GMT+07
Virtual Room D

Invited Guest & Session Chair
avatar for Dr. Bimal Patel

Dr. Bimal Patel

Associate Professor, KDPIT, CSPIT, CHARUSAT University, Gujarat, India
Friday April 10, 2026 12:13pm - 12:15pm GMT+07
Virtual Room D Bangkok, Thailand
  Virtual Room 8D, Virtual Room D

12:15pm GMT+07

AI-Driven Secure Intelligent Systems for Next-Generation Cyber-Physical Applications
Friday April 10, 2026 12:15pm - 2:15pm GMT+07
Virtual Room D
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.
Paper Presenter
avatar for Krutthika Hirebasur Krishnappa
Friday April 10, 2026 12:15pm - 2:15pm GMT+07
Virtual Room D Bangkok, Thailand
  Virtual Room 8D, Virtual Room D

12:15pm GMT+07

An Ensemble Learning Approach to Cardiac Catheterization
Friday April 10, 2026 12:15pm - 2:15pm GMT+07
Virtual Room D
Authors - Tahani Muftah Abdulsalam, Amina Abdo, Kaled milad, Nouri Bader Mahjoub, Suad Mohammed
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.
Paper Presenter
avatar for Amina Abdo
Friday April 10, 2026 12:15pm - 2:15pm GMT+07
Virtual Room D Bangkok, Thailand
  Virtual Room 8D, Virtual Room D

12:15pm GMT+07

An EOQ replenishment policy with varying deterioration, stock-sensitive demand and money inflation
Friday April 10, 2026 12:15pm - 2:15pm GMT+07
Virtual Room D
Authors - Sai Jagnyaseni Rana, Trailokyanath Singh, Pallavi Joshi, Sudhansu Sekhar Routray
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.
Paper Presenter
avatar for Sai Jagnyaseni Rana
Friday April 10, 2026 12:15pm - 2:15pm GMT+07
Virtual Room D Bangkok, Thailand
  Virtual Room 8D, Virtual Room D

12:15pm GMT+07

Analysis of Financial Market Dynamics using Neurocomputing for COVID 19 Regime Transitions
Friday April 10, 2026 12:15pm - 2:15pm GMT+07
Virtual Room D
Authors - N. Ahana Priyanka, R. Harishkanna, R. Sneka Nandhini
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 < 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.
Paper Presenter
avatar for HARISHKANNA RAMAMOORTHI
Friday April 10, 2026 12:15pm - 2:15pm GMT+07
Virtual Room D Bangkok, Thailand
  Virtual Room 8D, Virtual Room D

12:15pm GMT+07

Comparative Analysis of Machine Learning and Transformer-Based Models for News Topic Classification in Low-Resource Myanmar
Friday April 10, 2026 12:15pm - 2:15pm GMT+07
Virtual Room D
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.
Paper Presenter
avatar for Ei Sandar Myint
Friday April 10, 2026 12:15pm - 2:15pm GMT+07
Virtual Room D Bangkok, Thailand
  Virtual Room 8D, Virtual Room D

12:15pm GMT+07

Integrating Storytelling and Game-Based Learning in Primary English Education: A Teacher-Based Needs Analysis
Friday April 10, 2026 12:15pm - 2:15pm GMT+07
Virtual Room D
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.
Paper Presenter
avatar for Min Wai Yan Oo
Friday April 10, 2026 12:15pm - 2:15pm GMT+07
Virtual Room D Bangkok, Thailand
  Virtual Room 8D, Virtual Room D

12:15pm GMT+07

Predictive Modelling approaches for Detection of Skin Disease - Future Research Directives
Friday April 10, 2026 12:15pm - 2:15pm GMT+07
Virtual Room D
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.
Paper Presenter
avatar for Sheela S Maharajpet
Friday April 10, 2026 12:15pm - 2:15pm GMT+07
Virtual Room D Bangkok, Thailand
  Virtual Room 8D, Virtual Room D

12:15pm GMT+07

Sentiment Analysis of Uzbek Social Media Posts: Methods and Research
Friday April 10, 2026 12:15pm - 2:15pm GMT+07
Virtual Room D
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.
Paper Presenter
avatar for Malika Suyunova Odil qizi
Friday April 10, 2026 12:15pm - 2:15pm GMT+07
Virtual Room D Bangkok, Thailand
  Virtual Room 8D, Virtual Room D

12:15pm GMT+07

Smart Campus Surveillance and Guidance System
Friday April 10, 2026 12:15pm - 2:15pm GMT+07
Virtual Room D
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% [email protected], 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.
Paper Presenter
avatar for Sriram V.A
Friday April 10, 2026 12:15pm - 2:15pm GMT+07
Virtual Room D Bangkok, Thailand
  Virtual Room 8D, Virtual Room D

12:15pm GMT+07

Unmasking Dark Patterns: A Data-Driven Framework for Detection and User Awareness
Friday April 10, 2026 12:15pm - 2:15pm GMT+07
Virtual Room D
Authors - Mugdha Kulkarni, Diya Oswal, Rudra Kadam, Sachin Pande, Gargi Meshram
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 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.
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.
Paper Presenter
avatar for Mugdha Atul Kulkarni
Friday April 10, 2026 12:15pm - 2:15pm GMT+07
Virtual Room D Bangkok, Thailand
  Virtual Room 8D, Virtual Room D

2:15pm GMT+07

Session Chair Concluding Remarks
Friday April 10, 2026 2:15pm - 2:17pm GMT+07
Virtual Room D

Invited Guest & Session Chair
avatar for Dr. Bimal Patel

Dr. Bimal Patel

Associate Professor, KDPIT, CSPIT, CHARUSAT University, Gujarat, India
Friday April 10, 2026 2:15pm - 2:17pm GMT+07
Virtual Room D Bangkok, Thailand
  Virtual Room 8D, Virtual Room D

2:17pm GMT+07

Session Closing and Information To Authors
Friday April 10, 2026 2:17pm - 2:20pm GMT+07
Virtual Room D

Moderator
avatar for Gayatri Bhurguda
Friday April 10, 2026 2:17pm - 2:20pm GMT+07
Virtual Room D Bangkok, Thailand
  Virtual Room 8D, Virtual Room D

2:58pm GMT+07

Opening Remarks
Friday April 10, 2026 2:58pm - 3:00pm GMT+07
Virtual Room D

Invited Guest & Session Chair
avatar for Dr. Seamus Lyons

Dr. Seamus Lyons

Assistant Professor, International College of Digital Innovation, Chiang Mai University, Thailand

avatar for Dr. Archana S. Banait

Dr. Archana S. Banait

Assistant Professor, Department of Computer Engineering, MET's Institute of Engineering, India
Friday April 10, 2026 2:58pm - 3:00pm GMT+07
Virtual Room D Bangkok, Thailand
  Virtual Room 9D, Virtual Room D

3:00pm GMT+07

A Hybrid RegNetX-Hierarchical Bidirectional Linear Cross Attention Framework for Medicinal Plant Leaf Identification
Friday April 10, 2026 3:00pm - 5:00pm GMT+07
Virtual Room D
Authors - Madhusmita Chakraborty, Vijay Kumar Nath, Deepika Hazarika
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.
Paper Presenter
avatar for Madhusmita Chakraborty
Friday April 10, 2026 3:00pm - 5:00pm GMT+07
Virtual Room D Bangkok, Thailand
  Virtual Room 9D, Virtual Room D

3:00pm GMT+07

Adult Learners’ Preferences for Pedagogical Interface Agents: An Analysis Based on Noticeable Features
Friday April 10, 2026 3:00pm - 5:00pm GMT+07
Virtual Room D
Authors - Ntima Mabanza
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.
Paper Presenter
avatar for Ntima Mabanza

Ntima Mabanza

South Africa

Friday April 10, 2026 3:00pm - 5:00pm GMT+07
Virtual Room D Bangkok, Thailand
  Virtual Room 9D, Virtual Room D

3:00pm GMT+07

Convolutional Neural Network Model Ablation for Accurate Single MRI Super-Resolution
Friday April 10, 2026 3:00pm - 5:00pm GMT+07
Virtual Room D
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.
Paper Presenter
avatar for Imene Kichah
Friday April 10, 2026 3:00pm - 5:00pm GMT+07
Virtual Room D Bangkok, Thailand
  Virtual Room 9D, Virtual Room D

3:00pm GMT+07

Drivers of Gen Z Impulsive Buying: Host, Emotion, and Quality in TikTok Live
Friday April 10, 2026 3:00pm - 5:00pm GMT+07
Virtual Room D
Authors - Aleah Prameswari Kalyana Merkadea Purnomo, Muhammad Aras
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.
Paper Presenter
avatar for Aleah Prameswari Kalyana Merkadea Purnomo
Friday April 10, 2026 3:00pm - 5:00pm GMT+07
Virtual Room D Bangkok, Thailand
  Virtual Room 9D, Virtual Room D

3:00pm GMT+07

How Word of Mouth, Branding, and Exclusivity Shape Consumer Visit Intention
Friday April 10, 2026 3:00pm - 5:00pm GMT+07
Virtual Room D
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.
Paper Presenter
avatar for Matthew Abrham Kristanto
Friday April 10, 2026 3:00pm - 5:00pm GMT+07
Virtual Room D Bangkok, Thailand
  Virtual Room 9D, Virtual Room D

3:00pm GMT+07

Hybrid AI-Enabled IoT Imaging Framework for Early-Stage Multi-Label Tomato Leaf Disease Detection
Friday April 10, 2026 3:00pm - 5:00pm GMT+07
Virtual Room D
Authors - Md. Abdul Malek Sobuj, Md. Faruk Abdullah Al Sohan, Afroza Nahar, Saeeda Sharmeen Rahman
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.
Paper Presenter
avatar for Md. Abdul Malek Sobuj
Friday April 10, 2026 3:00pm - 5:00pm GMT+07
Virtual Room D Bangkok, Thailand
  Virtual Room 9D, Virtual Room D

3:00pm GMT+07

IoT-Based Smart Railway Crossing System Using Sensors for Real-Time Train Detection and Safety Enhancement
Friday April 10, 2026 3:00pm - 5:00pm GMT+07
Virtual Room D
Authors - Fahima Sultana Smrity, Md. Ibrahim Tanjim, Md. Faruk Abdullah Al Sohan, Afroza Nahar, Saeeda Sharmeen Rahman
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.
Paper Presenter
avatar for Fahima Sultana Smrity
Friday April 10, 2026 3:00pm - 5:00pm GMT+07
Virtual Room D Bangkok, Thailand
  Virtual Room 9D, Virtual Room D

3:00pm GMT+07

Performance Analysis of UAV Assisted Free Space Optical Communication Link
Friday April 10, 2026 3:00pm - 5:00pm GMT+07
Virtual Room D
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.
Paper Presenter
avatar for Subhrajyoti Sunani
Friday April 10, 2026 3:00pm - 5:00pm GMT+07
Virtual Room D Bangkok, Thailand
  Virtual Room 9D, Virtual Room D

3:00pm GMT+07

RadVision: Topological Data Analysis and Vision Transformers for Automated Radiology Report Generation
Friday April 10, 2026 3:00pm - 5:00pm GMT+07
Virtual Room D
Authors - Ayana Soman, Diya P. Varghese, Elizabeth Anna Liju, Ethel Jimmy, Liyan Grace Shaji, P R Neethu
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.
Paper Presenter
avatar for Ethel Jimmy
Friday April 10, 2026 3:00pm - 5:00pm GMT+07
Virtual Room D Bangkok, Thailand
  Virtual Room 9D, Virtual Room D

3:00pm GMT+07

When Credibility Goes Viral: Influencer Impact on TikTok Purchase Behavior
Friday April 10, 2026 3:00pm - 5:00pm GMT+07
Virtual Room D
Authors - Aura Meivia Safira Arsya, Ricardo Indra, Shafa Salsabila Risfi Febrian, Benedicta Kalyca Kyatimanyari
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.
Paper Presenter
avatar for Aura Meivia Safira Arsya
Friday April 10, 2026 3:00pm - 5:00pm GMT+07
Virtual Room D Bangkok, Thailand
  Virtual Room 9D, Virtual Room D

5:00pm GMT+07

Session Chair Concluding Remarks
Friday April 10, 2026 5:00pm - 5:02pm GMT+07
Virtual Room D

Invited Guest & Session Chair
avatar for Dr. Seamus Lyons

Dr. Seamus Lyons

Assistant Professor, International College of Digital Innovation, Chiang Mai University, Thailand

avatar for Dr. Archana S. Banait

Dr. Archana S. Banait

Assistant Professor, Department of Computer Engineering, MET's Institute of Engineering, India
Friday April 10, 2026 5:00pm - 5:02pm GMT+07
Virtual Room D Bangkok, Thailand
  Virtual Room 9D, Virtual Room D

5:02pm GMT+07

Session Closing and Information To Authors
Friday April 10, 2026 5:02pm - 5:05pm GMT+07
Virtual Room D

Moderator
avatar for Gayatri Bhurguda
Friday April 10, 2026 5:02pm - 5:05pm GMT+07
Virtual Room D Bangkok, Thailand
  Virtual Room 9D, Virtual Room D
 
Saturday, April 11
 

9:28am GMT+07

Opening Remarks
Saturday April 11, 2026 9:28am - 9:30am GMT+07
Virtual Room D

Invited Guest & Session Chair
avatar for Dr. Yogesh Mali

Dr. Yogesh Mali

Research Dean, Ajeenkya D Y Patil University, Lohegaon, India

Saturday April 11, 2026 9:28am - 9:30am GMT+07
Virtual Room D Bangkok, Thailand
  Virtual Room_10D, Virtual Room D

9:30am GMT+07

A STUDY IN UNDERSTANDING THE ROLE OF GREEN COMPUTING IN ACHIEVING SUSTAINABLE DEVELOPMENT
Saturday April 11, 2026 9:30am - 11:30am GMT+07
Virtual Room D
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.
Paper Presenter
avatar for Reepu

Reepu

India

Saturday April 11, 2026 9:30am - 11:30am GMT+07
Virtual Room D Bangkok, Thailand
  Virtual Room_10D, Virtual Room D

9:30am GMT+07

Cloud Computing Under Threat: Security Issues and Modern Solution
Saturday April 11, 2026 9:30am - 11:30am GMT+07
Virtual Room D
Authors - Zala Bhargavi Harshadbhai, Priyank D. Doshi
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.
Paper Presenter
avatar for Zala Bhargavi Harshadbhai
Saturday April 11, 2026 9:30am - 11:30am GMT+07
Virtual Room D Bangkok, Thailand
  Virtual Room_10D, Virtual Room D

9:30am GMT+07

Design of an intelligent warehouse management system at Dobladoras Cotopaxi
Saturday April 11, 2026 9:30am - 11:30am GMT+07
Virtual Room D
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.
Paper Presenter
avatar for Eduardo J. Lopez
Saturday April 11, 2026 9:30am - 11:30am GMT+07
Virtual Room D Bangkok, Thailand
  Virtual Room_10D, Virtual Room D

9:30am GMT+07

Digital Tax Administration and MSME Tax Compliance: Evidence from Indonesia’s Core Tax System in Supporting SDG 8 and SDG 17
Saturday April 11, 2026 9:30am - 11:30am GMT+07
Virtual Room D
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.
Paper Presenter
avatar for Surya Anugrah

Surya Anugrah

Indonesia

Saturday April 11, 2026 9:30am - 11:30am GMT+07
Virtual Room D Bangkok, Thailand
  Virtual Room_10D, Virtual Room D

9:30am GMT+07

Early Breast Cancer Detection Using Deep Learning Techniques
Saturday April 11, 2026 9:30am - 11:30am GMT+07
Virtual Room D
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.
Paper Presenter
avatar for Raina Thakkar

Raina Thakkar

Australia

Saturday April 11, 2026 9:30am - 11:30am GMT+07
Virtual Room D Bangkok, Thailand
  Virtual Room_10D, Virtual Room D

9:30am GMT+07

GraphAML-X: Knowledge-Graph AML with Entity Resolution and Audit-Ready Case Reasoning
Saturday April 11, 2026 9:30am - 11:30am GMT+07
Virtual Room D
Authors - Srikumar Nayak
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.
Paper Presenter
avatar for Srikumar Nayak

Srikumar Nayak

United States

Saturday April 11, 2026 9:30am - 11:30am GMT+07
Virtual Room D Bangkok, Thailand
  Virtual Room_10D, Virtual Room D

9:30am GMT+07

Modeling Annotation-Style Variability for Annotation- Free Skin Lesion Segmentation
Saturday April 11, 2026 9:30am - 11:30am GMT+07
Virtual Room D
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.
Paper Presenter
avatar for Nguyen Ngoc Dung
Saturday April 11, 2026 9:30am - 11:30am GMT+07
Virtual Room D Bangkok, Thailand
  Virtual Room_10D, Virtual Room D

9:30am GMT+07

Secure Medical Image Communication in the Post-Quantum Era: Development and Validation of a Comprehensive Dataset for Cryptographic Protocol Testing
Saturday April 11, 2026 9:30am - 11:30am GMT+07
Virtual Room D
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.
Paper Presenter
avatar for Chaitrasree S
Saturday April 11, 2026 9:30am - 11:30am GMT+07
Virtual Room D Bangkok, Thailand
  Virtual Room_10D, Virtual Room D

9:30am GMT+07

Support Vector Regression via Granular Ball Computing Approach
Saturday April 11, 2026 9:30am - 11:30am GMT+07
Virtual Room D
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.
Paper Presenter
avatar for Pei-Yi Hao
Saturday April 11, 2026 9:30am - 11:30am GMT+07
Virtual Room D Bangkok, Thailand
  Virtual Room_10D, Virtual Room D

9:30am GMT+07

Toward Sustainable Smart Contract Security: A Comprehensive Survey of Vulnerability Detection Methods and Approaches
Saturday April 11, 2026 9:30am - 11:30am GMT+07
Virtual Room D
Authors - Aryan Dholi and Malathi P
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.
Paper Presenter
avatar for Aryan Dholi
Saturday April 11, 2026 9:30am - 11:30am GMT+07
Virtual Room D Bangkok, Thailand
  Virtual Room_10D, Virtual Room D

11:30am GMT+07

Session Chair Concluding Remarks
Saturday April 11, 2026 11:30am - 11:32am GMT+07
Virtual Room D

Invited Guest & Session Chair
avatar for Dr. Yogesh Mali

Dr. Yogesh Mali

Research Dean, Ajeenkya D Y Patil University, Lohegaon, India

Saturday April 11, 2026 11:30am - 11:32am GMT+07
Virtual Room D Bangkok, Thailand
  Virtual Room_10D, Virtual Room D

11:32am GMT+07

Session Closing and Information To Authors
Saturday April 11, 2026 11:32am - 11:35am GMT+07
Virtual Room D

Moderator
avatar for Gayatri Bhurguda
Saturday April 11, 2026 11:32am - 11:35am GMT+07
Virtual Room D Bangkok, Thailand
  Virtual Room_10D, Virtual Room D

12:13pm GMT+07

Opening Remarks
Saturday April 11, 2026 12:13pm - 12:15pm GMT+07
Virtual Room D

Invited Guest & Session Chair
avatar for Prof. ESTEVAN GOMEZ

Prof. ESTEVAN GOMEZ

Professor, University of the Armed Forces ESPE, Ecuador
avatar for Dr. Nitin Sakhare

Dr. Nitin Sakhare

Assistant Professor, Department of Computer Engineering, Vishwakarma Institute of Information Technology, India
Saturday April 11, 2026 12:13pm - 12:15pm GMT+07
Virtual Room D Bangkok, Thailand
  Virtual Room_11D, Virtual Room D

12:15pm GMT+07

A GPU-to-NPU Transition Framework for Energy-Efficient Deployment of Medical Vision Models
Saturday April 11, 2026 12:15pm - 2:15pm GMT+07
Virtual Room D
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.
Paper Presenter
avatar for Sunkyo Jeong

Sunkyo Jeong

Republic of Korea

Saturday April 11, 2026 12:15pm - 2:15pm GMT+07
Virtual Room D Bangkok, Thailand
  Virtual Room_11D, Virtual Room D

12:15pm GMT+07

AI Applications in Environmental Management: A Scoping Review
Saturday April 11, 2026 12:15pm - 2:15pm GMT+07
Virtual Room D
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.
Paper Presenter
avatar for Francklin Rivas
Saturday April 11, 2026 12:15pm - 2:15pm GMT+07
Virtual Room D Bangkok, Thailand
  Virtual Room_11D, Virtual Room D

12:15pm GMT+07

Cross-Domain Point-of-Interest Recommendation for Tourism- A Reinforcement Learning Approach
Saturday April 11, 2026 12:15pm - 2:15pm GMT+07
Virtual Room D
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.
Paper Presenter
avatar for Malika Acharya
Saturday April 11, 2026 12:15pm - 2:15pm GMT+07
Virtual Room D Bangkok, Thailand
  Virtual Room_11D, Virtual Room D

12:15pm GMT+07

DEFCM: Effective Customer Segmentation via Deep Embedded Fuzzy C-Means
Saturday April 11, 2026 12:15pm - 2:15pm GMT+07
Virtual Room D
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.
Paper Presenter
avatar for Than Ngoc Thien
Saturday April 11, 2026 12:15pm - 2:15pm GMT+07
Virtual Room D Bangkok, Thailand
  Virtual Room_11D, Virtual Room D

12:15pm GMT+07

Enhanced Object Detection Using Metaheuristic Feature Selection in Deep Learning
Saturday April 11, 2026 12:15pm - 2:15pm GMT+07
Virtual Room D
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.
Paper Presenter
avatar for Neha Aggarwal
Saturday April 11, 2026 12:15pm - 2:15pm GMT+07
Virtual Room D Bangkok, Thailand
  Virtual Room_11D, Virtual Room D

12:15pm GMT+07

Interpreting Digital Brand Narratives: A Roland Barthes’ Semiotic Approach to the HMNS ‘Untitled Humans’ Instagram Reels Campaign
Saturday April 11, 2026 12:15pm - 2:15pm GMT+07
Virtual Room D
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.
Paper Presenter
avatar for Muhammad Elfata Rasyid Hammuda
Saturday April 11, 2026 12:15pm - 2:15pm GMT+07
Virtual Room D Bangkok, Thailand
  Virtual Room_11D, Virtual Room D

12:15pm GMT+07

Phishing in the Era of Generative Artificial Intelligence: A Systematic Survey of Attack Models, Detection Strategies, and Explainability
Saturday April 11, 2026 12:15pm - 2:15pm GMT+07
Virtual Room D
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.
Paper Presenter
avatar for Pankaj Chandre
Saturday April 11, 2026 12:15pm - 2:15pm GMT+07
Virtual Room D Bangkok, Thailand
  Virtual Room_11D, Virtual Room D

12:15pm GMT+07

Potholes, Cracks, and Open Manholes Detection using CBAM-YOLOv8 and GPS Coordinate Extraction via OCR for Location Mapping and Automated Alerts
Saturday April 11, 2026 12:15pm - 2:15pm GMT+07
Virtual Room D
Authors - Sabid Rahman, Sadah Anjum Shanto, Segufta Nasrin Tamanna, Zurin Alam Aongon, Md. Soadul Islam, Nasirul Islam
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.
Paper Presenter
avatar for Sabid Rahman

Sabid Rahman

Bangladesh

Saturday April 11, 2026 12:15pm - 2:15pm GMT+07
Virtual Room D Bangkok, Thailand
  Virtual Room_11D, Virtual Room D

12:15pm GMT+07

Towards Interpretable Edge Intelligence: Explainable AI in Resource-Constrained IoT Devices
Saturday April 11, 2026 12:15pm - 2:15pm GMT+07
Virtual Room D
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.
Paper Presenter
avatar for Pankaj Chandre
Saturday April 11, 2026 12:15pm - 2:15pm GMT+07
Virtual Room D Bangkok, Thailand
  Virtual Room_11D, Virtual Room D

12:15pm GMT+07

User Classification in E-Commerce Platforms: A Machine Learning Based Approach
Saturday April 11, 2026 12:15pm - 2:15pm GMT+07
Virtual Room D
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.
Paper Presenter
avatar for Dao Thi Huong
Saturday April 11, 2026 12:15pm - 2:15pm GMT+07
Virtual Room D Bangkok, Thailand
  Virtual Room_11D, Virtual Room D

2:15pm GMT+07

Session Chair Concluding Remarks
Saturday April 11, 2026 2:15pm - 2:17pm GMT+07
Virtual Room D

Invited Guest & Session Chair
avatar for Prof. ESTEVAN GOMEZ

Prof. ESTEVAN GOMEZ

Professor, University of the Armed Forces ESPE, Ecuador
avatar for Dr. Nitin Sakhare

Dr. Nitin Sakhare

Assistant Professor, Department of Computer Engineering, Vishwakarma Institute of Information Technology, India
Saturday April 11, 2026 2:15pm - 2:17pm GMT+07
Virtual Room D Bangkok, Thailand
  Virtual Room_11D, Virtual Room D

2:17pm GMT+07

Session Closing and Information To Authors
Saturday April 11, 2026 2:17pm - 2:20pm GMT+07
Virtual Room D

Moderator
avatar for Gayatri Bhurguda
Saturday April 11, 2026 2:17pm - 2:20pm GMT+07
Virtual Room D Bangkok, Thailand
  Virtual Room_11D, Virtual Room D

2:58pm GMT+07

Opening Remarks
Saturday April 11, 2026 2:58pm - 3:00pm GMT+07
Virtual Room D

Invited Guest & Session Chair
avatar for Kazi Asif Ahmed

Kazi Asif Ahmed

Senior Lecturer, Department of Computer Science, American Internation University, Bangladesh

avatar for Dr. Firdos Sheikh

Dr. Firdos Sheikh

Associate Professor & Deputy HoD, CSE, Poornima University, India
Saturday April 11, 2026 2:58pm - 3:00pm GMT+07
Virtual Room D Bangkok, Thailand
  Virtual Room_12D, Virtual Room D

3:00pm GMT+07

A Robust Multi-Class Age Detection Framework Using EfficientNetB3 Feature Transfer and Optimized Classification Head
Saturday April 11, 2026 3:00pm - 5:00pm GMT+07
Virtual Room D
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.
Paper Presenter
avatar for Gargi P. Lad
Saturday April 11, 2026 3:00pm - 5:00pm GMT+07
Virtual Room D Bangkok, Thailand
  Virtual Room_12D, Virtual Room D

3:00pm GMT+07

Emotion- and Speaker-Preserving Speech-to-Speech Translation: A Survey
Saturday April 11, 2026 3:00pm - 5:00pm GMT+07
Virtual Room D
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.
Paper Presenter
avatar for Navaneeth Reddy Nalla
Saturday April 11, 2026 3:00pm - 5:00pm GMT+07
Virtual Room D Bangkok, Thailand
  Virtual Room_12D, Virtual Room D

3:00pm GMT+07

Generative AI in Software Development: The Role of AI Coding Assistants and the Future of Low-Code/No-Code Systems
Saturday April 11, 2026 3:00pm - 5:00pm GMT+07
Virtual Room D
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.
Paper Presenter
avatar for Ch Sandeep Kumar
Saturday April 11, 2026 3:00pm - 5:00pm GMT+07
Virtual Room D Bangkok, Thailand
  Virtual Room_12D, Virtual Room D

3:00pm GMT+07

Hybrid Optimization based Closed-Loop Identification and Data-Driven Controller Reconstruction of Interacting Multivariable System
Saturday April 11, 2026 3:00pm - 5:00pm GMT+07
Virtual Room D
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.
Paper Presenter
avatar for Arathi B K
Saturday April 11, 2026 3:00pm - 5:00pm GMT+07
Virtual Room D Bangkok, Thailand
  Virtual Room_12D, Virtual Room D

3:00pm GMT+07

IOT Enabled Monitoring and Forecasting of Emerging Air and Water Pollutants for Early Detection
Saturday April 11, 2026 3:00pm - 5:00pm GMT+07
Virtual Room D
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.
Paper Presenter
avatar for Susmita Adhikary
Saturday April 11, 2026 3:00pm - 5:00pm GMT+07
Virtual Room D Bangkok, Thailand
  Virtual Room_12D, Virtual Room D

3:00pm GMT+07

Radiographic Analysis System for Early Detection of Osteoporosis
Saturday April 11, 2026 3:00pm - 5:00pm GMT+07
Virtual Room D
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.
Paper Presenter
avatar for Tina Chugera
Saturday April 11, 2026 3:00pm - 5:00pm GMT+07
Virtual Room D Bangkok, Thailand
  Virtual Room_12D, Virtual Room D

3:00pm GMT+07

Real-Time Non-Autoregressive Neural Text-to-Speech Using FastSpeech 2 and HiFi-GAN
Saturday April 11, 2026 3:00pm - 5:00pm GMT+07
Virtual Room D
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.
Paper Presenter
avatar for Harshwardhan Sawant
Saturday April 11, 2026 3:00pm - 5:00pm GMT+07
Virtual Room D Bangkok, Thailand
  Virtual Room_12D, Virtual Room D

3:00pm GMT+07

Smart Career Assist: An AI-Powered Personalized Career Guidance and Recruitment Automation System
Saturday April 11, 2026 3:00pm - 5:00pm GMT+07
Virtual Room D
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.
Paper Presenter
avatar for G VAMSI KRISHNA
Saturday April 11, 2026 3:00pm - 5:00pm GMT+07
Virtual Room D Bangkok, Thailand
  Virtual Room_12D, Virtual Room D

3:00pm GMT+07

Smart Glasses Development Challenges: Technical, Human, and Market Perspectives
Saturday April 11, 2026 3:00pm - 5:00pm GMT+07
Virtual Room D
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.
Paper Presenter
avatar for Abhishek Sawant
Saturday April 11, 2026 3:00pm - 5:00pm GMT+07
Virtual Room D Bangkok, Thailand
  Virtual Room_12D, Virtual Room D

3:00pm GMT+07

Solcanvas: Simplifying Solana Project Discovery
Saturday April 11, 2026 3:00pm - 5:00pm GMT+07
Virtual Room D
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.
Paper Presenter
avatar for Atharva Sachan
Saturday April 11, 2026 3:00pm - 5:00pm GMT+07
Virtual Room D Bangkok, Thailand
  Virtual Room_12D, Virtual Room D

5:00pm GMT+07

Session Chair Concluding Remarks
Saturday April 11, 2026 5:00pm - 5:02pm GMT+07
Virtual Room D

Invited Guest & Session Chair
avatar for Kazi Asif Ahmed

Kazi Asif Ahmed

Senior Lecturer, Department of Computer Science, American Internation University, Bangladesh

avatar for Dr. Firdos Sheikh

Dr. Firdos Sheikh

Associate Professor & Deputy HoD, CSE, Poornima University, India
Saturday April 11, 2026 5:00pm - 5:02pm GMT+07
Virtual Room D Bangkok, Thailand
  Virtual Room_12D, Virtual Room D

5:02pm GMT+07

Session Closing and Information To Authors
Saturday April 11, 2026 5:02pm - 5:05pm GMT+07
Virtual Room D

Moderator
avatar for Gayatri Bhurguda
Saturday April 11, 2026 5:02pm - 5:05pm GMT+07
Virtual Room D Bangkok, Thailand
  Virtual Room_12D, Virtual Room D
 

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