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.
