Authors - Yohan Ranasinghe, Janice Abeykoon, Samantha Kumara Senavirathna Abstract - Efficient blood supply chain management is a critical global impera tive in healthcare, yet it is consistently hampered by significant post-expiry blood wastage. This issue, prevalent across diverse healthcare systems, represents a considerable loss of a vital and non-substitutable resource, primarily stemming from challenges in accurate demand forecasting and dynamic inventory coordi nation. To address this pervasive problem, this research proposes and validates a novel data-driven framework. The approach leverages a multivariate deep learn ing forecasting model, specifically a Multivariate Long Short-Term Memory (LSTM) network, integrated into a comprehensive platform designed for proac tive inventory management. The model's development and empirical validation utilize historical blood collection and transfusion data (January 2020 – December 2024) from a cluster center of the National Blood Transfusion Service (NBTS) in Sri Lanka, serving as a representative case study to demonstrate real-world applicability. The framework incorporates multivariate factors such as historical transfusion patterns, seasonal variations, and interdependencies between blood groups to generate more accurate demand predictions. The integrated system, de signed to support real-time inventory monitoring, automated near-expiry track ing, and digital blood request and redistribution mechanisms, aims to align blood supply with anticipated demand. The findings of this research demonstrate that this integrated deep learning and inventory optimization framework significantly improves blood stock utilization, minimizes wastage, and enhances the overall efficiency of blood supply systems. It offers a scalable and ethically governed solution, contributing broadly to efforts in sustainable healthcare delivery world wide.
Authors - Rashmi Y Matt, Shreya Srinivasan, Venkata Sravani Revuri, Vismaya Murali, Chandravva Hebbi, Natarajan Abstract - Preparing for technical interviews has become very challenging for computer science students due to highly competitive hiring environments and the lack of company-specific practice resources. Existing resources and Generative platforms provide generic questions that do not reflect the specific patterns, technical focus areas, or expectations of different requirements.To address this gap, we present a system that combines a structured knowledge-graph-based retrieval module with a fine-tuned LLamA-2-7B model to generate company-specific technical interview questions. The data set contains 28,854 curated questions from 470 companies, which were cleaned and used for finetuning. The proposed framework also integrates an evaluation pipeline using both LLM-as-a-Judge and manual scoring to check validity, clarity, and technical correctness.The fine-tuned LLamA-2-7B model integrated with the knowledge graph retrieval achieved the best performance, which significantly outperformed other generative models in producing contextually appropriate and technically relevant questions. This approach aims to provide students with more targeted preparation resources aligned with real-world hiring expectations.
Authors - Halima Tuj Saydia, Partha Chakraborty Abstract - The mental health issues, such as stress and suicidal threats, have become a major public health concern for students and young adults. Early identification of such conditions is important for timely interventions and prevention. The study aims to develop a two-stage hierarchical framework to predict stress and suicide risk early. It is based on the questionnaire survey dataset of 1436 responses. The hierarchical method utilizes psychological and lifestyle characteristics gathered through surveys, thereby eliminating the need for physiological sensors. The first stage develops machine learning (ML) models, namely XGBoost, Random Forest (RF), and Support Vector Machine (SVM), to detect stress. These models have achieved an accuracy of 93%, 88%, and 83%, respectively. If the individual is detected as stressed, it moves to the second stage for suicide risk detection. Deep learning (DL) models, mainly Artificial Neural Network (ANN), Deep Neural Network (DNN), and Recurrent Neural Network (RNN), are developed in the second stage. They have achieved accuracy of 94%, 90%, and 89%, respectively. The study presents a scalable, data-driven framework that supports early mental health screening in resource-limited communities.
Authors - Satrasala Hari priya, Sabhya Kulkarni, Sindhu Baddela, Spoorthi Krishna Devadiga, Suja CM Abstract - This paper evaluates the quantum entanglement techniques for the detection of Parkinson’s disease using multimodal clinical data from the PPMI database. Four encoding techniques are evaluated: Amplitude Encoding, Dense Angle, IQP-based Pauli, and Hierarchical. The results of the analysis indicate that accuracy and the efficiency of the circuit are greatly impacted by the entanglement technique. Amplitude Encoding is the most efficient for NISQ computers (92.00% accuracy, 6-depth circuits), while Dense Angle provides the highest accuracy (92.59%). Hierarchical entanglement is the least efficient (80.86%), showing that too much depth causes optimization difficulties. These results provide practical recommendations for the design of quantum circuits for medical diagnosis.
Authors - Chandan Kumar, Supriya Narad Abstract - In the contemporary digital landscape, the proliferation of cyber threats has become a pervasive and escalating concern, posing imminent dangers to individuals, businesses, and entire nations. Cyber intelligence emerges as a critical component in the ongoing battle against these threats, involving the systematic gathering, analysis, and dissemination of information pertaining to cyber threats, actors, and vulnerabilities. This research paper aims to provide an insightful examination of the existing landscape of cyber intelligence, delineating its fundamental sub-domains and highlighting areas ripe for future research. The paper begins by delving into the current state of cyber intelligence, emphasizing the dynamic nature of the digital threat landscape. It elucidates the multifaceted challenges posed by cyber threats, underscoring the need for a proactive and adaptive approach to intelligence gathering and analysis. This section also explores contemporary technologies and methodologies employed in cyber intelligence, ranging from advanced analytics and machine learning to threat intelligence platforms.
Authors - Shubham Kadam, Chhitij Raj, Pankajkumar Anawade, Deepak Sharma Abstract - Higher education in India is poised at a junction and change seems to be driven by the issues of quality, access and sustainable development. In this framework, the HR sustain ability is essential for recruiting, hiring and retaining competent employees. This paper discusses ICT enabled practices of Indian deemed universities in the direction of promoting HR sustaina bility. Drawing on Review of literature and theme analysis, it explores e-based practices such as e-recruitment, digital training, online performance management, wellness technologies digital knowledge collaborations platforms. The study reveals that adoption of ICTs promotes effective ness, transparency and inclusivity of HR functions through the maintenance continuous staff de velopment. Nonetheless, other contributors such as leadership support, digital literacy and policy environment were found to significantly influence implementation outcomes. Digital divides, lack of training, data privacy and cost are some of the other concerns highlighted by the review. An overview of future themes in which AI, personalized HR services, and eco- sustainable ICT platforms will play a significant role into developing Future-proofed University.
Authors - Mr. Shubham Kishor Kadam, chhitij Raj Abstract - Increasing demands of universities to become sustainable in their practice and the necessity to compete in the global arena have compelled higher education to the implementation of green communication infrastructure and smart ICT solutions in every facet of the university practice. As an ingredient of this change, there is the HR sustainability: that we will go digital faculty and staff, and at the same time retain them in friendly and efficient and inclusive systems that are environmentally friendly. The emergence of the green communication systems, intelli gent ICT infrastructures, and green HR practices is helping the higher education sector to fund their future in this paper. The article is narrowed down to new practices, such as the hiring without paper, the use of mobile based performance management and virtual training, that is generated under the secondary research and conceptual framework. It also talks about the benefits, chal lenges and opportunities of such system in higher learning institutions. The findings suggest that the effective adoption of the sustainable ICT will help improve the performance of the organiza tions, reducing the impact on the environment to a minimum and being part of the creation of the digitally resilient human resources.
Authors - Lakshmi BV, Anupriya S, Ningappa B, Diganth SD, RoopaRavish, Prasad B Honnavalli Abstract - Modern car infotainment head unit has become a highly connected cyber-physical system, incorporating Wi-Fi, Bluetooth, USB ports, and the Controller Area Network (CAN) bus. While such capabilities enhance the user experience, they also raise the susceptibility of the vehicle to attacks, and hence there is a need to assess the security of the vehicle. This paper performs a comprehensive penetration test on an infotainment system, examining wireless, wired, and in-car communication channels. For the Wi-Fi component, we performed a series of attacks such as Distributed Denial-of-Service (DDoS), deauthentication, MAC and IP spoofing attacks, creating fake access points, and WPA-based attacks to determine the robustness of the system against network-level threats. Bluetooth attacks included device snarfing, replay attacks, manual packet injection attacks, and unauthorized access to data. USB attacks were employed to analyze the dangers posed by connected devices, including the extraction of GPS information, log files, SMS messages, and access to the microphone and camera. For the CAN bus, we performed replay attacks, flooding attacks, manual frame injection attacks, and manipulation of sensor information such as humidity and temperature readings. The outcome of each of these attacks indicates that the infotainment system can serve as a means through which attackers gain access to the vehicle's network, and hence the need for enhanced authentication, improved security for the interfaces, and real-time monitoring for security breaches. This paper provides valuable information for enhancing the security of modern car infotainment systems and contributes to the efforts being made in the field of automotive cybersecurity.
Authors - Tejaswini Borkar, Kajal Salampuriya Abstract - This paper focuses on the product of state of the art artificial intelligence (AI) language models (that is, ChatGPT, Perplexity, and Grok) to generate and test algorithmic trading strategies in financial markets. With such AI tools in the field, the study examines the success of the tools in cases of generating trading signals, synthesizing market sentiment, and helping manage risks both through quantitative backtesting and through qualitative analysis. The conclusion is that though the procedures performed using AI-assisted tactics may be comparable to the findings of the use of conventional algorithmic processes and will outline beneficial information, the findings should undergo tangible verification and cautious human interventions to establish dependability and applicability. Our findings are indicators of the potential of the large language models as an addition to assist traders and researchers and indicate that caution is still necessary to integrate with the long-established quantitative methods and risk management functions.
Authors - Niraja Jain, Rajeev Kumar, Golnoosh Manteghi Abstract - Medical negligence litigation in India poses significant challenges to the justice delivery system due to the complexity of clinical evidence, fragmented legal documentation, and limited availability of structured decision-support mechanisms for legal practitioners. These challenges often result in delays, inconsistent legal reasoning, and increased cognitive burden on judges and lawyers handling medico-legal disputes. This paper presents the design and preliminary validation of a Judicial Decision Support System (JDSS) tailored specifically for medical negligence litigation in the Indian legal context. The proposed JDSS leverages advanced Natural Language Processing (NLP) techniques and supervised machine learning models to assist early-stage legal triage through automated case summarization, statutory section prediction, and precedent recommendation. Transformer-based language models are fine-tuned on publicly available Indian legal judgments and augmented with a domain-specific legal–medical ontology to bridge semantic gaps between clinical narratives and legal reasoning. Explainability is embedded at both the model and user-interface levels through attention visualization and feature attribution mechanisms, addressing transparency requirements critical for high-stakes judicial applications. The system has undergone formative evaluation through an exploratory stakeholder survey involving participants from legal, academic, and higher-education ecosystems in India. This evaluation focuses on perceived usefulness, trust, explainability expectations, and institutional readiness for AI-assisted judicial tools, rather than predictive performance. Findings from the survey informed key design choices, particularly the emphasis on explainable AI and modular deployment. While large-scale retrospective evaluation on real-world court data remains part of future work, the current study establishes a methodologically grounded and ethically aligned foundation for AI-assisted judicial decision support in resource-constrained legal environments, with scope for integration into India’s evolving digital judiciary infrastructure.
Authors - Thapanapong Sararat, Ratanachote Thienmongkol, Ruethai Nimnoi, Wongpanya S. Nuankaew, Pratya Nuankaew Abstract - Ensuring equitable access to library information systems is crucial in the digital era, particularly for visually impaired users who rely on assistive technologies. WebOPACs are key gateways to resources, but many remain difficult to use despite referencing accessibility standards. This study proposes a Disability-Centered Framework to improve accessibility and Universal Design in Thailand’s WebOPACs. Developed through design-based research, it integrates international accessibility literature, Universal Design principles, WCAG 2.1, and evaluation insights. The framework emphasizes three components: disability-focused design principles, classification of visually impaired users and needs, and task-specific accessibility requirements across perception, navigation, interaction, and assistive-technology compatibility. It also incorporates Thai linguistic, cultural, and technological conditions to bridge global standards and local implementation. Findings indicate that meaningful accessibility requires iterative testing and ongoing refinement rather than a one-time compliance check. This framework guides libraries, developers, and policymakers in enhancing WebOPAC accessibility and supporting inclusive access for visually impaired users in Thailand.
Authors - Srishti Mathur, Hrishita Patra, Suhani Verma, Dhruva R Prasad, Shylaja S.S Abstract - The conventional way of preparing an advertisement is an elaborate process incorporating human subjectivity and human resources heavily dependent on creativity. Making advertisements by human effort can be regarded as an inefficient utilization of capital for small to medium-scale businesses due to increased cost of production. Even in current advancements in the development of generative techniques including LLM-based strategies for Advertisement Generation with Prompts, creating apt prompts for the depiction of products requires human expertise, making them less accessible. In order to overcome the challenges presented by the current models, we introduce a fast, affordable, and scalable platform for the automation of advertisement generation for products leveraging the capabilities of pre-trained diffusion models. The proposed system requires no training or fine-tuning since everything is performed at the inference level. The AI-aware system for designing assists in the identification of color schemes and attributes from the images of the products, whereas the descriptions and categories of the items help identify the theme and pattern recommendations for advertisements. These recommendations are channeled through a pre-trained Stable diffusion model guided by the LLaMA language model.
Authors - Sneha Visveswaran, Tanmay Praveen, Vidula Gurudutta, Yamini Sridhar, Chaithra T S5 Abstract - Arecanut crop management has traditionally depended on manual inspection for disease identification and harvest readiness assessment, a method that is both time-consuming and susceptible to human error. This study introduces an automated, image-based system designed to address two primary tasks: disease classification and ripeness assessment. The proposed pipeline initiates with data preparation, including resizing, normalization, and augmentation of arecanut images to enhance model robustness. A convolutional neural network architecture, incorporating additional feature extraction and optimization layers, is utilized to detect disease symptoms. A comparable deep-learning model is trained to classify ripeness stages based on visual characteristics. Model performance is evaluated using accuracy, precision, recall, and F1-score metrics to ensure reliability. The system is implemented via a user-friendly web interface, which allows real-time image uploads and immediate predictions, thereby facilitating practical application for farmers and agricultural stakeholders. This integrated solution provides a scalable and cost-effective approach to improving crop monitoring and supporting data-driven decision-making in arecanut cultivation.
Authors - Kate Lorreine M. Colot, Anjeneth G. Molina, Freely M. Wasawas, Ferlyn P. Calanda, Shem L. Gonzales, Richard B. Colasito Abstract - Despite the availability of digital voting systems, prior studies continue to identify gaps such as weak or voter authentication, security vulnerabilities and insufficient fraud prevention mechanisms. This paper presents BotoSafe, a secure and user-centered electronic voting (e-voting) platform developed for student government elections within educational institutions. The system implements multifactor authentication (MFA) using one-time password (OTP) verification and facial recognition with an anti-spoofing mechanism. To ensure the confidentiality and integrity of the voting process we employ the Advanced Encryption Standard in Galois/Counter Mode (AES-GCM). A developmental research design with a quantitative approach was used for the system development and evaluation. A mock election involving 84 students from Western Mindanao State University–Pagadian Campus was conducted, followed by a post-assessment survey. Results from the System Usability Scale (SUS) yielded a score of 72.08, indicating acceptable usability. User responses further showed that the system is easy to use, safe, and trustworthy for student elections. These findings indicate that BotoSafe is a viable e-voting solution for student government elections and may be further enhanced in future studies.
Authors - Eliza Borkute, Michael Savariapitchai, Vijeyandra Shahu, Deepak Sharma Chetan Parlikar Abstract - The current study aims to examine the significance of trust, perceived security, and awareness as factors that influence the adoption rate of UPI among private sector employees within the region of Chandrapur. The structured ques tionnaire has been designed to measure the following: a) trust factor regarding data protection and the correctness of the operations; b) perceived security level of UPI; c) awareness and knowledge about UPI functions; d) demographic characteristics related to education level, annual earning capacity, and age; and e) actual level of UPI adoption involving the use rate, continuous use of UPI, recommendations, and its integration with financial activities. Nonparametric statistical methods were used, including Spearman's rank correlation by investi gating the relationships of trust, security perception, awareness, and adoption. Kruskal-Wallis tests were conducted for finding group differences between ed ucation level and usage frequency. The results have accounted for strong, posi tive, and statistically significant associations between consumer trust, perceived security, awareness, and UPI adoption indicators. Education level revealed a partial moderating effect. Educated respondents tend to show higher trust and usage frequency in selected trust dimensions. However, this is not the case in all the aspects of this dimension. Additionally, the frequent users of UPI exhibit greater trust compared to the occasional users.
Authors - Tanay Balakrishna, Vishal Kumar Rahul, Yugabharathi E, Samanvi P, Vinay Joshi Abstract - The rapid spread of online news has made it more difficult to distinguish factually based reporting from misleading content. Many factchecking systems fail to detect false articles that appear professional and realistic, which leads to widespread disinformation. Most models rely on surface characteristics and neglect semantic coherence and factual consistency. An Improved Hybrid Fact-Checking System that combines language understanding, adversarial training, rule-based plausibility checks, and claim level web verification. These components run together in an ensemble model using BERT, BiLSTM, and an XGBoost meta-classifier to merge multiple evidence sources. Experiments on benchmark and curated datasets show an accuracy of 96.84% and a recall of 98%, outperforming existing deep learning methods. The results show that blending linguistic analysis with external verification leads to a robust and interpretable approach for automated fact-checking
Authors - Shraddha Mankar, Tanishq Thuse, Prasanna Khebade, Ritik Kumar Singh, Shravani Shirpurkar Abstract - Coronal Mass Ejections (CMEs) occurring in halo configuration are considered one of the most serious threats coming from space weather that can cause disruptions to most of the Earth’s geomagnetic facilities. The present study is about a hybrid machine learning system that detects the halo CMEs and predicts their Earth impact in real-time using the particle data coming from the in-situ India’s Aditya-L1 mission placed at L1 Lagrange point. We apply physics-informed feature extraction from SWIS-ASPEX payload measurements, obtaining alpha-to-proton density ratios, bulk velocity gradients, thermal parameters, and velocity anisotropy indices as CME markings. A Long Short-Term Memory (LSTM) neural network tuned through the Spider Cuckoo Optimization Algorithm processes 24-hour sequential windows of these features to distinguish between CME and non-CME events. The system also includes the modeling of Parker spiral propagation for Earth arrival time estimation and it is made available through a React-based dashboard with explainable AI components. The performance of the system reveals that it achieves a 98% detection rate along with a mean absolute error of 0.001 in the prediction of the normalized impact index. A comparison with historic halo CME catalogs indicates that our method has reduced false alarms by 85% when compared with threshold-based techniques while keeping the recall rate at 90%. The operational version of the system grants a 45-60 minute notification for the arrival of the CME, thus enabling the sensitive infrastructure to take preventive measures.
Authors - Sabo Hermawan, Ryna Parlyna, Surya Anugrah, Inkreswari Retno Hardini, Bayu Suhendry, Ria Rahma Nida, Windy Permata Suyono, Nur Lisa Rahmaningtyas, Eka Septariana Puspa, Cornellius Seno Adriano, Alifah Nur Rahmawati Abstract - Smart parking systems have developed as a critical solution to urban challenges such as traffic congestion, disorganized space utilization, and delays in manual parking searches. This study presents a smart parking framework that employs a Raspberry Pi 4GB, a camera module, and a servo motor for automated parking management. The system integrates a Haar Cascade classifier and YOLOv11 for accurate vehicle detection, while utilizing IR and ultrasonic sensors for obstacle identification. Real-time slot availability is displayed through an LCD interface. To ensure uninterrupted functionality, the system is powered by a solar panel with a rechargeable battery, enabling autonomous operation during power outages. Experimental results validate the reliability of vehicle recognition under varying illumination conditions, efficient gate control, and improved accuracy compared to conventional sensor-based approaches. This design offers a scalable, cost-effective, and energy-sustainable framework for urban parking solutions. Future work includes integration with cloud-based IoT platforms for centralized monitoring, optimization of YOLOv11 through lightweight variants for edge deployment, and extension to multi-level parking facilities with dynamic slot availability updates.
Authors - Sabo Hermawan, Ryna Parlyna, Surya Anugrah, Inkreswari Retno Hardini, Bayu Suhendry, Ria Rahma Nida, Eka Dewi Utari, Nur Lisa Rahmaningtyas, Cornellius Seno Adriano, Alifah Nur Rahmawati Abstract - This research investigates the performance of transformer-based models, BERT, ALBERT, and RoBERTa, fine-tuned for sentiment classification on the Women’s Clothing E-Commerce Reviews dataset. The overall task was executed under both 3-class and 5-class sentiment classification schemes. Each model was trained under the same conditions and evaluated comprehensively. In the 3-class task, RoBERTa achieved an F1-score of 91.7% and an AUC of 0.967, surpassing previous best-reported results. BERT also showed competitive performance with an F1-score of 90.2% and an AUC of 0.951. These results establish the superior generalisation ability and discriminative power of transformer models, particularly RoBERTa, in classifying sentiment from unstructured review text. ALBERT, while computationally efficient, showed reduced accuracy and AUC, indicating that extensive parameter sharing can hinder fine-grained sentiment resolution. The models exhibit broadly consistent behaviour in the 5-class setting, with RoBERTa maintaining a lead. A modest decline in F1 and AUC is evident, reflecting the greater difficulty introduced by finer class granularity. This research validates transformer architectures in a commercial Natural Language Processing scenario, demonstrating the superiority of transformer-based models over traditional baselines in both accuracy and robustness.
Authors - Jitesh Kriplani, Michael Savariapitchai, Vijeyandra Shahu, Deepak Sharma, Chetan Parlikar Abstract - The present investigation discusses the influence of social media in fluencers on the choices made by consumers and their buying behavior, espe cially in connection with important personality traits of the influencer, such as emotional engagement, authenticity, and reliability. The scientists conducted a well-organized survey questionnaire that collected primary information from 360 respondents in the Wardha District. Using Spearman's rank correlations re sults indicated strong, positive and statistically significant relationships between influencer behaviors and consumer purchase behaviors indicating that influenc ers have a significant impact on consuming behaviors of consumers. The results of a one-way ANOVA found that perceptions of influencer credibility (includ ing honesty and sponsorship disclosure), as well as perceptions of emotional engagement and authenticity, were significantly different depending on the fre quency of social media use by the participant. The demographic analysis also examined differences in consumer reactions depending on age, gender, and in come, finding no significant difference across age groups, but significant differ ences related to income and gender. The study concludes that consumer en gagement increases with more frequent social media use and influencer effec tiveness is significantly related to the authenticity, transparency, and credibility of the communication. Findings highlighted the need for focused influencer marketing content based on demographics providing empirical evidence of in fluencer marketing on consumer behavior.
Authors - Selvamani K, Saranraj S, Muthusundar SK, Kanimozhi S, Mohana Suganthi N Abstract - The phishing attack through email remains a significant threat to cybersecurity because the attack has become highly advanced, flexible, and widely spread among individuals and organizations. The phishing tricks, such as personalized social engineering, impersonated identities, and malicious links, have evolved fast and made the traditional email security measures less useful. As such, numerous schemes of email phishing attack detection and prevention have been suggested, combining rule-based approaches with machine learning, deep learning, natural language processing, and sophisticated artificial intelligence systems. This review paper provides a detailed discussion of the currently existing email phishing detection and prevention frameworks, their architectural elements, detection schemes, and preventive schemes. The paper systematically evaluates the conventional, machine learning, and more advanced AI-driven methods with their advantages, weaknesses, and flexibility to the changing phishing threats. The synthesis of existing research trends and unaddressed issues makes the review valuable to researchers and cybersecurity practitioners and will allow building solid, scalable, and intelligent email phishing defense systems.
Authors - Mohanad A. Deif, Mohamed A. Hafez, Samar Mouakket, Mohamed Abstract - Polypharmacy and multiple chronic conditions in older adults increase the likelihood of adverse drug events caused by drug–drug interactions (DDIs) and contraindications. Many clinical decision support systems still have limited ability to use patient context and to exchange knowledge in a consistent semantic form. This study presents a hybrid semantic–linguistic framework for automated DDI detection by combining biomedical natural language processing, ontology-based reasoning, and risk scoring. The framework uses BioBERT to extract relevant information and represents it using RDF knowledge graphs, OWL 2 DL ontologies, and SWRL rules. In an evaluation with 1,000 synthetic patient profiles containing RxNorm-coded medications and SNOMED CTencoded diagnoses, the system identified a wide range of clinically important interaction patterns. Statistical testing showed that age and the number of medications were strongly associated with alert frequency (p < 0.001). These findings suggest that the proposed approach can improve medication safety by providing explainable clinical decision support.
Authors - Selvamani K, Kanimozhi S, Muthusundar S K, Saranraj S, Jagadeesh K Abstract - Multi-object tracking (MOT) is a pillar of many computer vision applications such as video surveillance, self-driving and crowd analysis [1]. The main difficulty does not only exist in correct identification of objects but also in consistent identities of objects in different frames when there is occlusion, camera motion and changes in scene density [14]. The paper introduces a highly advanced MOT system, combining the latest YOLOv8x detector with a modified and improved version of the original ByteTrack association system, which is called RobustBoTSORTTracker [14]. With the new detection quality of YOLOv8x and the robustness of low-confidence detections in ByteTrack, augmented with selective improvements of BoT-SORT including camera motion compensation and exponential moving average smoothing, the proposed system demonstrates significant gains on the MOT15 benchmark [7]. Experimental findings indicate a MOTA of 55.6, IDF1 of 72.2, precision of 74.3 and a recall of 95.7, which is significantly higher than the previous baselines under similar conditions.
Authors - Abhay Saxena, Ankit Kumar, Prasant Kumar Sahu Abstract - In this paper, we address the problem of rainy condition classification in order to allow autonomous systems to ensure safe operation in different weather conditions of rain, especially for drones. The earlier weather condition classification methods are inclined towards using big and computationally costly models and cannot thus be employed in real-time on resource-constrained platforms such as drones and edge devices. The motivation behind this work is to introduce a light-weight, efficient deep model which would be able to classify various rain conditions with low computational cost so that it may be deployed efficiently on low-resource devices. We present a novel CNN architecture and evaluate its performance on a collection of seven distinct rain conditions. The models are bench marked against some of the state-of-the-art pretrained models to demonstrate the compromise between efficiency and accuracy. Performance is evaluated using accuracy, inference time, and model size. The model has accuracy 95.93% with least model size 89.09 KB with inference time of 32.664 ms bridging the gap in lightweight and real-time classification.
Authors - Arjun Verma, D.K. Chaturvedi Abstract - Ethylene and vinyl acetate or EVA is a co-polymer used as a substitute for a lot of materials. EVA is a versatile material and it has a lot of applications ranging from electronics, healthcare, footwear, building applications etc. It is mainly used in sport shoes due to its property to absorb shock impact and insulation properties. In addition, EVA is very cost-friendly, produces no odor, and light in weight material. But with overuse of it, the cellular structure chang-es and can affect the shoes' quality and insulation properties. In addition to the cellular structure, the air molecules present in it also collapse. This paper focus-es on the bonding properties of EVA at different temperatures and its dielectric properties under different operating and manufacturing conditions. The upper, bottom, and sides of EVA shoes are exposed to high voltage till the breakdown. The experimentation was done at Electrical HV laboratory on the university campus where a 100kV HVAC testing system is available. This paper presents the tabulated results on the dielectric strength of EVA shoes under varying operating conditions. Additionally, it examines the bonding properties of EVA shoes at different manufacturing temperatures, aiming to predict their lifespan, quality, and finish. The results of these studies are thoroughly discussed within the document.
Authors - Nasika Ijaz, Farooque Azam, Saliha Ejaz, Muhammad Waseem Anwar Abstract - Anomaly detection in dynamic cybersecurity networks has been a promising problem that has been addressed using Graph Neural Networks (GNNs). Today’s network topologies are too difficult to handle for traditional methods; the topologies are too dynamic and complex. The main contribution of this study is the evaluation of three GNN models, Graph Convolutional Networks (GCNs), Graph Attention Networks (GATs), and RepographGAN, in terms of effectiveness to detect anomalies in dynamic network environments. Conventional anomaly detection techniques such as logistic regression, support vectors machines (SVM) and decision trees are compared against the models. The results demonstrate that RegraphGAN is superior to the other models in terms of accuracy, precision, recall, F1 score, and AUC-ROC, and is thus very effective at identifying anomalies. However, as computing resources are required for it, a compromise between performance and computing resources is found. Despite the lower accuracy of GCN and GAT, these provide more computationally efficient solutions that are appropriate for real time deployment constraints in such resource constrained environments. The findings provide a basis for future research that can optimize scalability and computational efficiency for large scale applications and in the context suggest the use of GNNs for improving cybersecurity systems.
Authors - Aaqib Hakeem, Akshay V, Parthav Mathu, Kotnada Yogesh, Gokul Kannan Sadasivam Abstract - Passwords remain one of the most widely deployed authentication mechanisms despite well-documented vulnerabilities to guessing attacks. Recent deep learning approaches, including Password Guessing using Temporal Convolutional Networks (PGTCN), have demonstrated that sequence modeling can effectively capture structural regularities in leaked password corpora. However, practical performance often depends not only on model architecture but also on training stability, batching strategy, and decoding configuration. In this work, we investigate a partition-aware training and generation pipeline built around a single Temporal Convolutional Network (TCN). Rather than introducing additional architectural complexity, the proposed framework emphasizes standardized preprocessing, balanced data partitioning for stable batching, optimized training procedures, and large-batch probabilistic decoding. A lightweight buffering layer is incorporated to decouple generation from evaluation and improve throughput without requiring distributed training infrastructure. Experiments on multiple real-world leaked password datasets show consistent, though modest, improvements in match rate compared to the PGTCN baseline under same-site evaluation. The results suggest that careful optimization and pipeline-level design can yield measurable gains in candidate ordering while maintaining reproducibility and implementation simplicity.
Authors - Sushant Maji, Sachin B. Jadhav Abstract - The offline signature validation by means of hand written signature is also a significant consideration in the financial, legal and ad- ministrative authentication systems. However, this is particularly challenging because of the inaccessibility of dynamic data of handwriting such as pen-pressure and stroke-velocity, and small training samples. The paper describes a modified version of Siamese-Transformer model called SigNeura, which is also improved with Synthetic Pen Pressure Map Generation to refine the accuracy of the verification in the few-shot learning. The adaptive thresholding, and utilization of the stroke-width estimation is applied to obtain synthetic pressure maps and fill in the dynamic information of the synthetic grayscale signatures with the static grayscale signatures. The Siamese network is optimized on discriminative embeddings and Transformer encoders are optimized on triplet long range contextual dependencies. The analysis conducted on benchmarking data using experiments demonstrates that SigNeura is a significantly superior approach than conventional CNN and Siamese-based approaches with a high level of accuracy and resistance to skilled forgeries.
Authors - Siddharth Joshi, Deepti Kiran, Dev Kumar Yadav, Harshit Sinha, Abhishek Kukreti Abstract - Artificial Intelligence (AI), as a technology, has the potential to change the manner in which organizations are run in the world. However, small and medium-sized enterprises (SMEs) in the Philippines have unique limitations in the use of AI in running the business. The study aims to explore the perceptions of SME managers in the Philippines on the use of AI, with particular reference to the limitations and facilitators in the use of the technology in the business environment. In this study, the researcher interviewed five SME managers from different sectors, including retail, manufacturing, and service sectors. The researcher used thematic analysis to identify the commonalities in the decisions made by the SME managers on the use of AI in the business environment. The study revealed the perceptions of the SME managers on the use of AI in the business environment in the Philippines, with the limitations and facilitators in the use of the technology in the business environment. The study provides practical insights that can guide strategies aimed at strengthening AI readiness and responsible adoption among SMEs in the Philippines.
Authors - Ambrish Kumar Sharma, Swati Namdev Abstract - The volume of data is growing gradually in all around by various sec-tors like e-commerce, stock market, medical, banking, education, social networks (Facebook, Twitter, WhatsApp) and also because of the utilization of the internet and mobile apps. Privacy and security have always been important issues with big datasets. Big datasets may be a collection of facts that has huge and multiplex structure like sensors, emails, weblogs and images. Sensitive information about individuals, which is usually evident or hidden in data, is susceptible to various privacy attacks and high risks of privacy disclosure. Constructing a secure and reliable environment for big dataset requires a distinction between existing approaches so that we can develop a unique solution in future for this that maximizes data privacy. This paper offers insights into the overview of big datasets, big dataset privacy problems and various privacy preservation techniques with comparative study used in big datasets.
