Authors - Jeba Priya J, N. Priya Abstract - Mental health challenges among young adults require innovative psychoeducational interventions. This study presents the development and preliminary evaluation of Dear Alfred, a serious virtual reality (VR) game designed to enhance emotional self-regulation and intergenerational empathy. Grounded in the Process Model of Emotion Regulation, the game immerses players in a narrative- driven experience addressing elderly isolation. The development followed an iterative methodology, resulting in a playable vertical slice tested on Meta Quest 2 and 3 platforms. This work contributes to the field by proposing a scalable, multidimensional approach at the intersection of psychology, technology, and education, highlighting the specific need for hardware-specific optimization in digital mental health solutions.
Authors - Ashwini V. Zadgaonkar, Sonali Potdar, Archana Bopche, Pranali Pawar, Rupali Vairagade, Yogita Hande Abstract - Time series prediction plays a critical role in monitoring and control of electrical power systems, particularly for detecting frequency fluctuations caused by imbalances between generation and demand. This study proposes an early warning framework for frequency fluctuation events using a hybrid k-Nearest Neighbour (KNN) and Dynamic Time Warping (DTW) approach combined with a global confidence interval based decision mechanism. Electricity frequency data collected from the New Zealand power grid over a six-month period were segmented into training, validation, and testing sequences. Alignment distances between historical and incoming sequences were used to identify precursor patterns indicative of impending frequency disturbances. Experimental results show that the proposed method achieves high warning accuracy with a very low false negative rate, outperforming baseline models such as ARIMA and LSTM. The findings demonstrate that KNN–DTW provides an effective and practical solution for early warning of frequency fluctuations, supporting improved operational reliability in modern power systems.
Authors - Arianna Cobb, Vishnu Kumar Abstract - Teen suicide remains a significant public health concern in the Unit ed States, with substantial geographic variation across counties. Understanding how socio-environmental and healthcare access factors relate to suicide risk can help identify communities that may benefit from targeted interventions. This study aims to support this effort by analyzing county-level teen suicide patterns using K-means clustering, an unsupervised machine learning technique. A da taset of 248 U.S. counties with reported teen suicide data was constructed using five-year aggregated suicide crude rates (2019-2023) alongside multiple socio environmental and healthcare indicators, including hospitalization rates, mental health provider availability, primary care provider rates, social association rates, uninsured population percentages, poverty levels, food insecurity, and rural population share. K-means clustering was then applied to identify county-level risk profiles. The results reveal two distinct county groups: one characterized by lower suicide rates, greater healthcare provider availability, stronger social as sociations, and lower socioeconomic disadvantage; and another characterized by higher suicide rates, reduced healthcare access, higher poverty and food in security, and greater rural residency. These findings highlight meaningful coun ty-level disparities and demonstrate the utility of machine learning approaches to identify regional risk profiles associated with teen suicide. The results may help inform public health strategies and policy efforts aimed at prioritizing re sources and expanding mental health services in high-risk communities.
Authors - Adhi Sree Praveen Pai, Alaganandha Pradeep, Jeremy Simon Moncey, Josin Kurian Athikalam, Lakshmi K.S. Abstract - This research investigates the digital footprint of mental health infor mation as it circulates on YouTube. Using a qualitative content analysis ap proach, the study examines 100 selected videos in conjunction with social media analytics to identify recurring patterns in the dissemination of mental health dis course. The findings reveal a mix of misleading or incomplete claims, educa tional resources, personal narratives, and recovery-oriented content, illustrating how mental health discussions shape and amplify user perspectives at both broad (macro) and specific (micro) levels within the evolving field of e-health. To in terpret these dynamics, the analysis applies Gibson’s theory of transactional af fordances, which illuminates key themes of risk, relevance, lived experience, credibility, and social support. By situating these themes within the broader con text of video-sharing platforms, the study underscores the importance of YouTube as a platform for mental health communication. It underscores its role in broader public conversations about health in the digital age. The future re search should investigate mental health discourse from other social media users.
Authors - Rahul Singh, Sachin B. Jadhav Abstract - Cloud cover generally limits the applicability of optical remote sensing images for tasks such as agriculture monitoring and disaster relief. Cloud removal is an inherently difficult problem because of the lack of spatial structures and spectral information. To effectively remove cloud contamination from SAR and optical images, we propose a speckle-aware global cross-attention network. The proposed SAR-optical cloud removal network architecture consists of a dual encoder with a global cross-attention mechanism that allows for effective cross-modal interactions. Additionally, a refining module and symmetric decoders improve the accuracy of the reconstructed image. Furthermore, we propose a speckle-aware gating mechanism to perform speckle filter adaptation. The experimental results affirm that our proposed network outperformed the baseline by increasing Peak Signal-to-Noise Ratio(PSNR) by +0.86 dB, Structural Similarity Index Measure(SSIM) by +0.142, and reducing the spectral distortion of the image. Additionally, we noticed a decrease in the Root Mean Square Error(RMSE) and Spectral Angle Mapper(SAM) values. This infers that selective SAR-Optical fusion with an adaptive noise-aware gating mechanism improves the accuracy of cloud-free optical images and optical remote sensing images.
Authors - Nyuti Bhesania, Khushi Solanki, Bimal Patel, Purvi Prajapati, Priyanka Patel Abstract - The rapid advancement of information and communication technology (ICT) has accelerated the digital transformation of public sector governance, including tax administration. This study examines the impact of Indonesia’s Core Tax Administration System (Coretax) on micro, small, and medium enterprise (MSME) tax compliance within an ICT–behavioral framework. Using survey data from 300 MSME taxpayers and Structural Equation Modeling–Partial Least Squares (SEM-PLS), the study analyzes the direct and indirect effects of Coretax utilization on tax compliance through administrative efficiency and trust in the tax authority. The results indicate that Coretax utilization has a positive and significant effect on administrative efficiency, trust in the tax authority, and MSME tax compliance. Administrative efficiency and trust also significantly influence compliance, con-firming their mediating roles. These findings demonstrate that digital tax administration functions not only as a technological reform but also as an institutional and behavioral mechanism that reduces compliance burdens and strengthens vol-untary compliance. From a sustainable development perspective, improved MSME tax compliance supports Sustainable Development Goal (SDG) 8 by enhancing domestic revenue mobilization for inclusive economic growth, while the integrative and trust-building role of Coretax reflects SDG 17 through strengthened partnerships among government, technology providers, and taxpayers. This study contributes empirical evidence on digital tax systems in developing economies.
Authors - Ajidhashini Thulasidass, M. Suresh Abstract - Modern railway system increasingly rely on digital technologies such as Communication-Based Train Control (CBTC), European Train Control System (ETCS) and Supervisory Control and Data Acquisition (SCADA) systems, raising significant cyber-security challenges. We have seen 220% increase in attacks over five years from opportunistic ransomware to sophisticated targeted threats. This paper provides an overview of railway cybersecurity and surveys the coverage area considering ICT architectures, cyber threat models, and AI-based defense approaches. 75% of cases employed Distributed Denial of Service (DDoS) tactics while ransomware had affected 54% of the OT environments. We describe a comparative taxonomy of Artificial Intelligence and Ma-chine Learning approaches including the methods based on supervised learning, unsupervised learning, and advanced deep learning practices with detection accuracy as high as 97.46%. However, there exist several challenges: few available public data sets, lack of validation in real-world scenarios, demands for explain ability from that AI system and worries about adversarial robustness. We discuss eight potential research gaps, and future directions focusing on federated learning, digital twin development, multimodal AI fusion and safety-security co-engineering frameworks.
Authors - Vishnu Kumar, Natalia Miranda Abstract - Food insecurity remains a pressing public health and equity challenge in urban U.S. communities, with the Supplemental Nutrition Assistance Program (SNAP) serving as the primary federal mechanism for alleviating household food hardship. Despite its importance, SNAP participation varies substantially across neighborhoods, reflecting underlying socioeconomic disparities. This study leverages neighborhood-level data from Baltimore City to identify the key socioeconomic drivers of SNAP participation using explainable machine learning (ML) techniques. Three supervised ML models: Decision Tree, Random Forest, and XGBoost were developed and evaluated using standard regression metrics. The Random Forest model demonstrated the strongest predictive performance. Model interpretability was enhanced through Shapley Additive Explanations (SHAP), which quantified the contribution of each feature to predicted SNAP participation. Results indicate that lower income, shorter life expectancy, higher Temporary Assistance for Needy Families (TANF) participation, higher proportions of female-headed households, and lower educational attainment are associated with increased SNAP reliance. These findings highlight the complex interplay be-tween economic deprivation, social vulnerability, and neighborhood-level assistance utilization, offering actionable insights for policymakers and public health practitioners. By combining predictive accuracy with interpretability, explainable ML provides a robust framework for informing evidence-based interventions aimed at reducing food insecurity and promoting equity in urban communities.
Authors - Hector Rafael Morano Okuno Abstract - This work proposes an intelligent system for automatic food-image-based recognition and calorie estimation to meet the emerging demand for accurate dietary monitoring and personalized nutrition recommendations. Conventional food-logging methods are cumbersome, prone to errors, and mostly fail to capture portion sizes, hence motivating an end-to-end computer vision and depth-based approach. The proposed system utilizes a custom-curated Indian food image dataset of eighty classes, collected, labeled, and preprocessed to make it robust enough to present various variations in lighting, background, etc. A deep learning model was then trained for detecting and classifying food with high precision. The overall classification accuracy achieved by the proposed system is ninety-seven percent. The depth understanding of the detected food regions will provide an approximation of volume and weight, leading to relatively better calorie calculations. Nutritional analysis gets integrated into the system by relating the type and estimated weight of food to the standard nutritional information for detailed insights in terms of calories, proteins, fats, car-bohydrates, fiber, and micronutrient content. The results for evaluation reveal strong detection, minimum estimation error, and efficient real-time processing, which clearly show its applications. In this paper, an approach that combines recognition by image, depth estimation by portion, and nutrition logic capable of leading to a strong solution for diet determination has been introduced.
Authors - D.Swetha, Senthilkumar Selvaraj, K.M.Madhan Prasanth, D.Nihal Abstract - The rapid expansion of digital commerce platforms has significantly transformed on- line transactional systems; however, conventional centralized architectures continue to face critical challenges related to security, transparency, data integrity, and trust management. Traditional e-commerce systems rely heavily on centralized databases, making them vulnerable to data tam- pering, unauthorized access, fraudulent transactions, and single points of failure. To address these limitations, this paper proposes a secure, scalable, and modular web-based e-commerce system that is architecturally designed for integration with blockchain technology and smart contracts. The proposed system is implemented using widely adopted web technologies, with a responsive frontend and a robust backend to support essential functionalities such as user authentication, product catalog management, shopping cart operations, order processing, inventory management, and administrative control. The architecture emphasizes separation of concerns, enabling flexibility, maintainability, and future extensibility. A key contribution of this work lies in the incorporation of a blockchain-ready framework that enables immutable transaction recording and enhanced trace- ability across the entire transaction lifecycle. Smart contracts automate transaction validation and order execution. The system also introduces an AI-based anomaly detection mechanism using a Deep Q-Network to detect fraudulent behavior. Experimental validation demonstrates reliable per- formance and scalability.
Authors - Areej Almazroa, Sara Albahlal, Dalia Alswailem, Dhay Altamimi, Aljoharah Aldaej, Heba Kurdi Abstract - Monitoring marine litter is essential for planetary and human survival. This study proposes a novel framework integrating satellite data and big data analytics to assess marine litter distribution in coastal and oceanic environments. Leveraging open-source imagery from COPERNICUS Sentinel-2 and LANDSAT, the framework utilizes reflectance methodologies and image processing to identify and classify marine debris, focusing on spectral bands from visible blue (490 nm) to short-wave infrared (1610 nm). A pilot case study in San Diego, California, demonstrates the approach’s feasibility. The study explores the potential of microwave radiometry and machine learning for material detection and contour analysis, showing how satellite data can support dynamic and cross-platform monitoring systems. Results validate the use of remote sensing technologies to map plastic debris, providing a replicable methodology that combines emergent (e.g., satellites, drones) and traditional (e.g., sampling) techniques. This approach contributes to a deeper understanding of plastic pollution pathways, sources, and impacts across economic sectors. By generating harmonized data on mismanaged plastic waste, the study informs sustainability strategies and circular economy practices, helping redesign systemic plastic management and supporting local and global environmental governance.
Authors - Sonali S. Gaikwad, Jyotsna S. Gaikwad Abstract - In this semi-systematic literature review, a detailed study of the role of Human-Computer Interaction (HCI) in creating game-based solutions for Attention-Deficit/Hyperactivity Disorder (ADHD) among children is conducted. Six peer-reviewed research studies were selected. The study demonstrates that HCI can serve as a major therapeutic mechanism by transforming digital platform-based cognitive training into engaging, interactive experiences. These approaches not only improve focus but also enhance the overall effectiveness of interventions. Key findings from the analyzed studies are discussed, and future research directions are proposed, including multimodal hybrid systems with adaptive personalization and accessibility features to further improve outcomes for children with ADHD.
Authors - Bharathi A, Mohan Kumar P, Subha B Abstract - Rupture of an intracranial aneurysm results in catastrophic subarachnoid hemorrhage with a 30–40% fatality rate. Although treatment decisions are guided by clinical risk scores (PHASES, ELAPSS), recent research suggests that morphological analysis and computational fluid dynamics (CFD) may offer better rupture prediction. This study looked at 92 middle cerebral artery aneurysms from the CMHA dataset, which included 71 that had ruptured and 21 that had not. We evaluated four feature sets: Clinical-Basic (13 variables), Clinical-Scores (adding PHASES and ELAPSS; 15 variables), Scores and Morphology (24 variables), and Full (28 variables). We trained logistic regression models using 5- fold cross-validation with a 20% test set. We used bootstrap validation (1000 iterations) and Bonferroni-corrected feature importance analysis to reduce overfitting. The AUC for the Clinical-Basic set was 0.891±0.063. Performance was enhanced to a maximum AUC of 0.976±0.034 by adding PHASES and ELAPSS. The Full model achieved an AUC of 0.981±0.029, with neither morphological nor hemodynamic variables giving much further improvement. Significant variance was revealed by bootstrap analysis (95% CI: 0.764-0.998). At 90% specificity, the test set's AUC was 0.933, but its sensitivity was only 14.3%. The primary contributors were ELAPSS (F=143.2, p<10⁻¹) and PHASES (F=38.4, p<10⁻¹), whereas morphological and hemodynamic characteristics did not exhibit any significant correlations. Clinical scores demonstrated strong discrimination, but CFD-derived parameters offered minimal additional value in this small, imbalanced, single-center group. The wide confidence intervals and class imbalance limit clinical recommendations. Further validation in larger, multicenter studies is necessary.
Authors - Tirupathi Rao Dockara, Manisha Malhotra Abstract - AI and data platforms are increasingly expected to deliver end-to-end business automation under rapid market and regulatory change. However, prevailing platform construction strategies remain predominantly top-down: teams standardize a generic capability stack and subsequently customize it for heterogeneous domains through code, integration glue, and service forks. This approach amplifies technical debt, fragments governance, and makes continuous adaptation expensive. This paper introduces the Inverse Vertex Pyramid (IVP), a design pattern that reverses the direction of platform derivation. IVP begins at the use-case vertex by conducting rigorous analysis of high-value specialized automation scenarios and generalizes them into explicit, machine-actionable platform descriptors (metadata models, domain ontologies, policy/workflow specifications, and capability contracts) that form a stable, reusable core. Specialization is realized primarily via declarative configuration and policy changes, rather than code rewrites. We formalize IVP as a pattern, propose a reference architecture separating control and execution planes, and provide a comparative analysis against layered architectures, domain-driven design, and microservice platforms. A proof-of-concept walkthrough in regulated claims automation illustrates the generalization mechanism and highlights how IVP can reduce re-engineering, improve governance consistency, and accelerate time-to-market. The paper concludes with limitations, threats to validity, and a research agenda for automated use-case mining, formal verification of policies, and quantitative evaluation of platform agility.
Authors - Nishant Shah, Ansh Bajpai, Shrivaths S. Nair, Manas Verma K, Sabitha S Abstract - Digital accessibility in higher education is a key requirement to ensure the inclusion of students with hearing disabilities. However, institutional plat-forms often present barriers that limit autonomy, understanding of information, and full participation. The objective of this study was to evaluate the user experience of students with hearing disabilities on the EVIRTUAL, SGA, and SIS platforms of the Technical University of Manabí, identifying perceptions, accessibility barriers, and improvement proposals. A descriptive, exploratory study with a mixed-methods approach was conducted. The population consisted of seventy-eight students with hearing disabilities registered in the Inclusion Unit, from which an intentional subsample of ten participants was selected. A structured sur-vey with Likert-type scales and a participatory observation form were applied in real interaction situations with the platforms. Quantitative analysis was carried out using descriptive statistics, while qualitative information was organized into thematic categories. The results show that half of the participants achieve full autonomy in the use of the platforms, forty percent require intermittent support, and the rest need constant assistance. Regarding clarity of information and con-tent comprehension, intermediate responses predominate, which reveals recur-rent difficulties. The main barriers identified were a confusing interface, non-intuitive navigation, insufficient visual supports, and the need for external assistance. The study proposes improvements such as customizable subtitles, step-by-step visual guides, an accessibility button, a sign language interpreter avatar, and optimization for mobile devices, aimed at strengthening autonomy and user experience.
Authors - Sabarishwaran V, Gomathi K, Andey Phani Vinay, Jagadeeswaran V, Ranjith Kumar M Abstract - The rapid expansion of digital commerce platforms has significantly transformed on- line transactional systems; however, conventional centralized architectures continue to face critical challenges related to security, transparency, data integrity, and trust management. Traditional e-commerce systems rely heavily on centralized databases, making them vulnerable to data tam- pering, unauthorized access, fraudulent transactions, and single points of failure. To address these limitations, this paper proposes a secure, scalable, and modular web-based e-commerce system that is architecturally designed for integration with blockchain technology and smart contracts. The proposed system is implemented using widely adopted web technologies, with a responsive frontend and a robust backend to support essential functionalities such as user authentication, product catalog management, shopping cart operations, order processing, inventory management, and administrative control. The architecture emphasizes separation of concerns, enabling flexibility, maintainability, and future extensibility. A key contribution of this work lies in the incorporation of a blockchain-ready framework that enables immutable transaction recording and enhanced trace- ability across the entire transaction lifecycle. Smart contracts automate transaction validation and order execution. The system also introduces an AI-based anomaly detection mechanism using a Deep Q-Network to detect fraudulent behavior. Experimental validation demonstrates reliable per- formance and scalability.
Authors - Sowmyashree N, Madhu Sunkanur, Impana M, Suchithra B S, Hemalatha P G Abstract - The existence of a growing social media has created complex cyber systems in which vast quantities of interactions constitute substantial issues regarding misinformation, privacy invasion, deception of identities, and destructive behavioural tendencies. The regularity of involvement in this type of big systems requires sophisticated systems that are able to judge the motive of the user, content validity and suspicious activities within real time. Overall interest will be to develop a universal trust calculation system that will be more secure and effective in ensuring privacy and increasing the accuracy of suspicious or malicious users in social sites. The proposed Multi-Layer Federated Trust Framework algorithm is a combination of peer-based user reputation scoring, feature-based content authenticity detection, federated trust indicators aggregation, and anomaly detection with the help of behavioural anomalies. These approaches cooperate with secure aggregation and decentralized learning in removing the uncoded information exposure and enable the computation of trust at scale. The proposed algorithm is experimentally confirmed, and the obtained results are 95.2, 94.1, 93.5, and 93.8, corresponding to a minimum latency of 65 ms and a privacy preservation score of 0.98. The general results indicate a viable and holistic response that adds to secure interactions, blocks malicious acts and encourages trust in the actual social media settings.
Authors - Md. Shahidul Islam, Hasina Islam Abstract - Cross-domain recommendations are imperative in the growing tourism industry and with the increasing means of communication. Preference drift, preference transfer, and unfamiliarity with places have an overbearing impact on recommender systems. Most approaches do not address geometric misalignment across domains, which is essential for cross-domain preference shift analysis in recommendation tasks. We propose Procrustes-Based Contextual Thompson Sampling (P-CTS) for Cross-Domain POI Recommendation, integrating adversarial domain-invariant learning, optimal geometric alignment via Procrustes transformation, and adaptive Thompson Sampling with sleeping bandit management. First, the embeddings are constructed to model the preference drift across the domains. Next, the Procrustes transformation aligns source and target embedding spaces via optimal rotation, scaling, and translation. In the last phase, we initialize Beta priors with similarity-weighted pseudo-counts derived from the aligned embeddings. The experiments on Gowalla and Foursquare across domains demonstrate 5.1% improvements in Precision@5 and 9.75% improvements in cold-start accuracy, suggesting an adaptive exploration-exploitation trade-off.
Authors - Binh Pham Nguyen Thanh, Long Duong Phi, Phung Thi-Kim Nguyen, Nhan Thi Cao Abstract - The rapid proliferation of Internet of Things (IoT) devices has significantly increased the digital attack surface, which, in turn, has raised network vulnerability to sophisticated Distributed Denial of Service (DDoS) campaigns that could reduce the effectiveness of traditional signature-based Intrusion Detection System (IDS). Furthermore, conventional Machine Learning (ML) approaches are often subject to manual feature engineering and lack the capture of complex spatial and temporal dependencies, which are essential to detect subtle, polymorphic threats. In this regard, the present work proposes a lightweight hybrid Deep Learning (DL) architecture for reliable (DDoS) detection. The proposed approach integrates spatial feature extraction using a Convolutional Neural Network (CNN) with a Bidirectional Long Short-Term Memory (BiLSTM) network to capture temporal correlations, further enhanced by an additive attention mechanism that underlines the importance of flow segments relevant to recognition. To mitigate issues with computational complexity, a two-phase hybrid feature selection approach, a combination of Information Gain (IG) and Dynamic Particle Swarm Optimization (PSO) would be utilized to select an optimal subset of features. The performance of the model was evaluated using the CICDDoS2019 benchmark dataset. The feature selection process was able to reduce the input space from 80 to 17 relevant features. The combined CNN-BiLSTM model, along with threshold optimization, was able to achieve an accuracy of 94.1%, which indicates a significant improvement in the reduction of false negatives and validates the effectiveness of the proposed method in a secure IoT environment.
Authors - Wani Zahidah Mohd Subari, Shuzlina Abdul-Rahman, Mohamad Faizal Ab Jabal, Sharifalillah Nordin Abstract - Role-playing games (RPGs) allow the player to take on a specific role and complete different missions during gameplay. Their diversity enables a range of ap-plications beyond entertainment, as they are often used in educational contexts. Learning content can be embedded in common components, such as game fields, tasks, objects, or non-playing characters (NPCs). The paper presents several educational RPGs with their features and characteristics, and existing models of didactic video games. It proposes a two-level metamodel for describing an educational RPG. The metamodel is divided into five main components (world, educational aspects, quest, playing character, and NPCs), and their taxonomies are presented briefly. The authors propose a conceptual model that includes the interrelationships among the components mentioned. In addition, their interpretations and significance for the development of RPG educational games are explained. An example of the metamodel is represented through a quest from a real educational RPG in the field of Chemistry. The presented RPG metamodel improves under-standing and helps to better design, develop, and integrate such games into various learning environments. The presented taxonomy can serve as a useful template for structuring design details.
Authors - Prabhat Kumar Gupta, Perumal T, Karthick Pannerselvam Abstract - Generation Large language models, as well as retrieval-augmented generation (RAG), are highly performing on semantic queries, but with considerable latency as they require embedding computation, a vector similarity search, and generation at inference time. Such delays make them inappropriate in time-sensitive and domain-specific retrieval activities. In this paper, the Hierarchy Latent Retrieval Model (HLRM) which is a deterministic architecture will be introduced and able to answer semantic queries in O(1) constant time. HLRM unites hierarchical semantic routing and semantic hashing so that pre-validated units of knowledge can be directly illuminated without the need to search methods or language model informing of their existence at run time. All computationally expensive processes are done offline, which means that embedding processes or vector databases are not needed to run a query. Milliseconds-response time with very high exact-match accuracy is proved under experimental assessment on an orderly institutional knowledge environment. The findings suggest that HLRM offers an alternative of fast, interpretable, and reliable systems to the generative retrieval systems in non-random settings where precision and response latency is paramount.
Authors - Khedkar Aboli Audumbar, Uday Pandit Khot, Balaji G. Hogade Abstract - Malicious or compromised internal users can act like normal users with valid login credentials and thus become difficult to detect. As a result of their similarity to normal users, traditional methods of detecting intrusions, have difficulty identifying the subtle and changing behaviors of malicious insiders. This paper introduces a comprehensive User and Entity Behavior Analytics (UEBA) framework to help detect malicious insiders. It works by analyzing activity logs generated by the enterprise. Further it performs data cleaning and feature engineering; creating behavioral profiles for each user based upon the attributes of time, environment, and behavior. These profiles are used to model normal interaction patterns and with the DBLOF algorithm, an outlier score for each profile is created. The outlier score indicates whether or not a given user’s behavior has deviated from normal. In order to make the proposed system adaptable to changing environments over time, it utilizes deep learning algorithms to detect changes in behavior and to increase the accuracy of anomalous behavior detection. The proposed system also enables the ingestion of real-time data, the evaluation of risk, and the display of alerts in a visual format. Thus, providing the scalability and operational performance required to support large-scale organizations. Overall, the proposed system represents a reliable, modular, and understandable UEBA framework. It is capable of accurately detecting malicious insider threats and representing an efficient method for proactively mitigating risks through security operations within enterprises.
Authors - Poonam Chaudhary, Rita Chhikara, Nupur Prakash Abstract - This work addresses the challenge of Isolated Sign Language Recognition (ISLR) on mobile and edge devices, where computational resources, memory, and energy budgets are severely constrained. Existing approaches based on pixel-level three-dimensional convolutional neural networks are computationally expensive and sensitive to background variations, while recurrent models such as Long Short-Term Memory networks suffer from a sequential processing bottleneck that limits parallel execution on modern hardware accelerators. To overcome these limitations, this paper proposes a hybrid Adaptive Graph Convolutional Network (A-GCN) and Transformer architecture that decouples spatial and temporal modeling of skeletal sign representations. The A-GCN employs a learnable adjacency matrix to capture dynamic and semantically meaningful spatial relationships between skeletal landmarks, while the Transformer encoder leverages parallel self-attention to model long-range temporal dependencies without recurrence. Experimental evaluation on the 250-class Google Isolated Sign Language Recognition dataset demonstrates a Top-1 accuracy of 78.90%, outperforming a Bi-LSTM baseline by 6.96%. In addition, the proposed model achieves a throughput of 400.55 frames per second with a latency of 2.50 ms on accelerator hardware and maintained real-time performance on consumer-grade devices. These results demonstrate that landmark-based, parallel architectures enable accurate, real-time, and privacy-preserving sign language recognition suitable for deployment on standard mobile devices.
Authors - Subin Simon, Prathilothamai M Abstract - Deep learning has shown significant potential in medical image classification; however, a systematic comparison of deep feature extraction strategies for multi class diabetic eye disease assessment remains limited. This study presents a comprehensive comparative analysis of seven deep learning architectures, including conventional CNN, pretrained VGG16, Vision Transformer (ViT), Conformer, hybrid CNN ViT, and attention-augmented variants incorporating Squeeze-and-Excitation (SE) and Convolutional Block Attention Module (CBAM). All models are evaluated under a unified preprocessing and training framework to ensure fair performance comparison.The investigation focuses on analyzing how different architectural paradigms capture discriminative local and global retinal features relevant to disease classification. Extensive experiments are conducted on public fundus image datasets using standard evaluation metrics, including accuracy, precision, recall, and F1-score. Experimental results demonstrate that hybrid and attention-integrated architectures outperform standalone CNN and transformer models. In particular, the Conformer architecture achieves the best overall performance, reaching approximately 91% classification accuracy in the four class setting (Diabetic Retinopathy, Glaucoma, Cataract, and Normal), while the CNN ViT model attains approximately 89% accuracy.These findings highlight the effectiveness of combining convolutional operations with global self-attention mechanisms for robust and discriminative feature extraction in automated diabetic eye disease classification.
Authors - M.Murugesen, Priyanka P Abstract - Deep learning–based medical image models have achieved expert level performance in GPU-based research environments [1–3]. However, relia ble deployment in real clinical systems remains challenging due to constraints related to power consumption, hardware stability, and long-term operation. While prior studies have focused on improving model architectures or hardware accelerators [4,5], relatively limited attention has been devoted to systematical ly managing the transition from GPU-based development to NPU-based de ployment environments. This study formulates the GPU-to-NPU transition as an independent deployment research problem. Rather than proposing a new model architecture, we focus on preserving functional equivalence when trans ferring a validated GPU-trained medical vision model to an NPU-based infer ence environment. The proposed framework consists of reference model fixa tion, intermediate representation (IR)-based conversion [13–15], operator com patibility management, inference pipeline alignment, and output-level function al equivalence validation. The framework is evaluated through deployment of a ResNet-50–based pa thology classification model on a commercial ATOM NPU platform. Experi mental results demonstrate a 99.1% agreement rate (991/1,000 samples) be tween GPU-based and NPU-based inference outputs, confirming consistent de cision behavior despite architectural differences. These findings indicate that deployment reliability depends more on execution environment control and preprocessing alignment than on model architecture modification. By redefining deployment as a structured research problem, this work pro vides a reproducible methodology for translating research-grade medical AI models into energy-efficient NPU inference systems under practical clinical constraints.
Authors - Jayanthi J, P.Uma Maheswari, S.Uma Maheswari, Arun Kumar, Karishma V R Abstract - The rapid migration of artificial intelligence from cloud platforms to edge-based Internet of Things environments has intensified the demand for transparent and trustworthy decision-making under severe resource constraints. While edge intelligence enables low-latency and privacy-preserving analytics, the opacity of deployed models limits user trust, accountability, and regulatory acceptance. Existing explainability techniques largely assume cloud-level resources, making them unsuitable for real-time and energy-limited edge deployments. In order to close this gap, this work develops an interpretable intelligence framework that is resource-aware and adaptable, specifically designed for limited IoT systems. The suggested approach integrates interpretability directly into the decision-making process, allowing for the generation of faithful, lightweight explanations in addition to predictions while dynamically adjusting to operational context and runtime restrictions. Further balancing local responsiveness with system- level insight aggregation and secure governance is achieved through hierarchical explanation control. Transparency, efficiency, and scalability are all in line with the framework's treatment of explainability as a fundamental system capacity. The study shows that adaptive, deployment-aware explainability can greatly improve edge intelligence's operational reliability and trustworthiness. These insights establish a foundation for building accountable and interpretable AI systems in real-world IoT environments.
Authors - Rimon Kumer Roy, Jannatul Ferdous, Kazi Lutfur Nahar Mithila, Sabbir Islam, Mohammad Zahid Hassan, Sadah Anjum Shanto Abstract - Early identification of ophthalmic disease is critical to pre serve eyesight. We present DeepEye, a stacking-ensemble framework for multi-disease classification on the Eye Disease Image Dataset (EDID, Mendeley Data). After standardized preprocessing and augmentation, f ive architectures ResNet50, VGG16, DenseNet121, EfficientNet-B4, and Vision Transformer were trained and evaluated. The final ensemble in tegrates the top base models with a logistic regression meta-learner op timized via hyperparameter tuning. On a held-out test set, DeepEye achieves 91.34% accuracy and AUC of 0.9965, outperforming all con stituent models and exhibiting stable gains across cross validation folds. Model transparency is supported with Grad-CAM visualizations that lo calize disease-relevant regions, enhancing clinical interpretability. These results indicate that combining convolutional and transformer backbones within a tuned stacking framework yields a high-accuracy, explainable approach for automated eye disease detection in healthcare settings.
Authors - Nguyen Thi Hoi, Vu Thi Anh Hong, Dang Thi Anh Tho, Dang Thuy Linh, Nguyen Khanh Linh Abstract - The increasing use of renewable energy sources has made the integra tion of Flexible AC transmission system (FACTS) devices into contemporary power systems, an important area of research. The function and effectiveness of FACTS devices in enhancing power quality and preserving stability in traditional power systems and those that significantly count on renewable energy source are comprehensively examined in this study. Variability and unpredictability brought about by renewable energy sources can negatively impact the voltage profile, particularly at high penetration levels. Devices from the Flexible AC Transmis sion System, like the Thyristor-Controlled Series Capacitor (TCSC) & Static Var Compensator (SVC), provide efficient ways to improve system stability and dy namically regulate voltage. This paper investigates a coordinated control strategy of SVC and TCSC for improving voltage profiles in a transmission network with high renewable energy integration. Using an IEEE-14 bus test system, various scenarios of renewable penetration are simulated to analyze the performance of coordinated FACTS operation. The findings show that the suggested coordinated control improves overall system dependability and power transfer capabilities in addition to reducing voltage variations and reactive power imbalances. The study highlights the importance of optimal placement and coordinated tuning of FACTS devices as a cost effective solution for enabling secure and stable opera tion of renewable-rich power grids.
Authors - Josue Piedra, Nelson Piedra Abstract - Accurate crop production forecasting is essential for sustainable agricultural planning, effective resource management, and long-term food security. Conventional statistical and regression-based models often fail to capture the complex, nonlinear relationships that exist among agro-climatic variables, soil characteristics, and crop yield [1]. To address these limitations, this paper proposes an agentic artificial intelligence (AI)–based framework for crop production analysis that integrates autonomous decision-making with machine learning and deep learning techniques. The proposed framework utilizes agro-climatic and soil parameters such as temperature, humidity, soil moisture, cultivated area, and seasonal information to model crop production behaviour. Three predictive approaches— Linear Regression, Random Forest, and CNN–LSTM—are implemented and evaluated within the agentic framework using Root Mean Square Error (RMSE), Mean Absolute Error (MAE), and the Coefficient of Determination (R2) as performance metrics. Experimental results demonstrate that the Random Forest model significantly outperforms the other models, achieving an RMSE of 0.56, MAE of 0.31, and R2 value of 0.96. These findings highlight the effectiveness of agent-driven machine learning systems in accurately modelling agricultural data and supporting intelligent decision-making for crop yield optimization.
Authors - Priyanka Halder, Anupam Sinha Abstract - This study analyzes the extent to which credibility from influencers impacts consumers' buying behavior. The focus will be on how the intention to buy impacts this relationship as the problem is being analyzed in the context of social commerce on TikTok. The study is developed within the framework of Source Credibility Theory which suggests that consumers’ perception and consequent behavior are influenced by the perceived degree of the spokesperson’s Attractiveness, Trustworthiness, and Expertise. The study employs a quantitative explanatory methodology. A purposive sampling technique was used to collect data from a sample of 100 active TikTok users who follow the provided influencer. The analyzed relationships will be quantified using Structural Equation Modelling with Partial Least Squares (SEM-PLS). The research results concluded that influencer credibility increases the intention to buy, but does not increase the purchasing decision. The intention to buy completely mediates the relationship between influencer credibility and purchasing decision. This demonstrates that influencer credibility is a significant factor in the intention to buy behavior, but it is the intention that is essential in order to convert the persuasive influence into actual buying behavior. The study contributes to digital marketing communication research by extending Source Credibility Theory to the context of short-video social commerce platforms.
