Authors - Abhishek Chaudhari, Mahalakshmi Bodireddy, Aditya Bhor, Onkar Dadas, Prajakta Shinkar, Chinmay Chougule Abstract - The growing mental health challenges around the globe need access to scalable, available, and safety conscious digital interventions. The paper describes a mental health support platform, based on AI, which combines conversational intelligence, multi-therapeutic persona modeling, structured mood analytics, proactive crisis identification, multi-lingual interaction, and voice-based access in a secure full stack design. The system, which runs on the Google Gemini AI, provides context-sensitive therapeutic dialogue and performs four-dimensional mood analysis of anxiety, stress, depression, and wellbeing, allowing longitudinal assessment by providing interactive dashboards and automated reporting. A safety-first crisis override system offers validated emergency capacity in the high-risk situations. The platform also includes multilingual voice feedback to facilitate inclusion of the visually impaired users and non-English speaking communities in providing inclusive digital mental health care. The proposed system is capable of changing the prevalent perception that AI and its applications may never be responsible and scalable because it integrates therapeutic diversity, structured analytics, accessibility features, and proactive safety controls into a single framework.
Authors - Anvar Saidmakhmudovich Usmanov, Mikhail Borisovich Khamidulin, Shakhlo Rustamovna Abdullaeva, Fazilat Dzhamoliddinovna Akhmedova, Shoh-Jakhon Khamdаmov Abstract - This paper presents a data-driven forecasting and anomaly detection dashboard for live births in Surigao del Norte, utilizing the Family Health Service Information System (FHSIS) data from 2021 and onwards. The research methodology is based on the CRISP-DM framework, with business under-standing for the needs of maternal services planning in the provinces and municipalities, data preparation for municipalities by quarters, time aware modeling, evaluation, and deployment through the API and visualization layer. The research employs several machine learning techniques for forecasting, such as ARIMA/SARIMA, Exponential Smoothing (ETS and Holt-Winters), and the Prophet method, along with a naïve method. The performance of the models is evaluated through the symmetric Mean Absolute Percentage Error (sMAPE), Root Mean Squared Error (RMSE), Mean Absolute Error (MAE), and Mean Absolute Scaled Error (MASE). A strict evaluation criterion for the deployment of the model is also implemented, such as the availability of sufficient data points in the past for the model to be deployed (i.e., 12 data points in the past), the accuracy of the model (sMAPE < 20%), and the performance of the model in comparison with the naïve method (MASE < 1). A low confidence filter is also implemented for the series with intermittent data to prevent incorrect results. The results show high reliability of the forecasting model for the entire province and better interpretability for strategic planning. However, the results also show that some of the municipalities with low population volumes and intermittent data points pose a challenge in the operation of the model.
Authors - Michele Della Ventura Abstract - Feature representations that are both high-dimensional and reduce redundancy often prove to be significant constraints on the performance of object detection. In this study, we present the first hybrid metaheuristic feature selection framework that combines the enhanced grey wolf optimizer (EGWO) and firefly algorithm (FA) with a deep learning-based detection pipeline. The proposed EGWO-EFA method for identifying useful and compact feature subsets has been shown to reduce dimensionality by over 99.99% on the Pascal VOC and Brain Tumor M2PBP datasets. The experiments conducted demonstrate that, compared to classical feature selection, this method has an improved F1-score and precision, by an average of 2%. In addition, the overall pipeline execution time is considerably shorter. These results show that hybrid metaheuristic optimization is an effective approach to scalable and efficient object detection for high-dimensional feature representations.
Authors - Roshna Dhakal, Khanista Namee Abstract - Modern railway system increasingly rely on digital technologies such as Communication-Based Train Control (CBTC), European Train Control System (ETCS) and Supervisory Control and Data Acquisition (SCADA) systems, raising significant cyber-security challenges. We have seen 220% increase in attacks over five years from opportunistic ransomware to sophisticated targeted threats. This paper provides an overview of railway cybersecurity and surveys the coverage area considering ICT architectures, cyber threat models, and AI-based defense approaches. 75% of cases employed Distributed Denial of Service (DDoS) tactics while ransomware had affected 54% of the OT environments. We describe a comparative taxonomy of Artificial Intelligence and Ma-chine Learning approaches including the methods based on supervised learning, unsupervised learning, and advanced deep learning practices with detection accuracy as high as 97.46%. However, there exist several challenges: few available public data sets, lack of validation in real-world scenarios, demands for explain ability from that AI system and worries about adversarial robustness. We discuss eight potential research gaps, and future directions focusing on federated learning, digital twin development, multimodal AI fusion and safety-security co-engineering frameworks.
Authors - Bhonsle Rashmi Ravindra, Shankar Chaudhary, Shivoham Singh, Hemant Kothari, Raj Kothari Abstract - Urban metro rail systems are the key to urban sustainable mobility; however, in spite of the developed technologies, projects regularly experience delays and contractual disputes. These perceived challenges are highly attributed by prior scholarship to matters of the execution phase and restricted illumination is given on the institutional circumstances that form system performance in ICT intensive infrastructure. This paper examines procurement strategy as a govern ance tool that affects the results of digital system integration and sustainability in Indian metro rail projects. Based on statutory performance audit reports and com parative case studies, the analysis indicates that fragmented procurement arrange ments fragment the integration functions to several contracts, leading to coordi nation failure, delayed commissioning, and high claims. Instead, the more coor dinated procurement models with consolidated interdependent systems and de fined integration roles have a better coordination structure and predictable deliv ery. The results indicate that the problem of metro project integration is more of an institutional than a technological problem. This research study adds to the body of knowledge on infrastructure governance by noting the design of procure ment to be one of the determinatives in the realization of effective and sustainable urban transit outcomes.
Authors - Irmawan Rahyadi Abstract -This research investigates the digital footprint of mental health infor mation as it circulates on YouTube. Using a qualitative content analysis ap proach, the study examines 100 selected videos in conjunction with social media analytics to identify recurring patterns in the dissemination of mental health dis course. The findings reveal a mix of misleading or incomplete claims, educa tional resources, personal narratives, and recovery-oriented content, illustrating how mental health discussions shape and amplify user perspectives at both broad (macro) and specific (micro) levels within the evolving field of e-health. To in terpret these dynamics, the analysis applies Gibson’s theory of transactional af fordances, which illuminates key themes of risk, relevance, lived experience, credibility, and social support. By situating these themes within the broader con text of video-sharing platforms, the study underscores the importance of YouTube as a platform for mental health communication. It underscores its role in broader public conversations about health in the digital age. The future re search should investigate mental health discourse from other social media users.
Authors - Ahir Jaimi, Niyati Patel, Nirav Bhatt Abstract - This research studied the economic impact and perceptions of air pollution, particularly PM2.5, in Chiang Mai Province, Thailand, using the Multiple Indicators Multiple Causes model (MIMIC model) and Mixed Data Sampling Regression (MIDAS model). The MIMIC model analyzed data from questionnaires administered to 5 0 7 respondents and examined factors influencing public perception of hotspots and PM2.5. The MIDAS model analyzed the impact of monthly PM2.5 levels and monthly hotspot counts on quarterly Gross Provincial Product (GPP), using data from 2019 to 2023.The MIMIC model analysis revealed that perception of burning or activities causing hotspots was the most influential factor in determining public perception of the impact of PM2.5. The effectiveness of government efforts to address the pollution problem had a negative correlation, while demographic and socioeconomic characteristics showed no statistically significant impact. This indicates that public perception is more influenced by received information or education than by personal characteristics. The MIDAS model highlighted the economic impact of hotspots and air pollution. The analysis results indicate that When hotspots or burning occur, these activities have a statistically significant positive impact on the province's GPP. A 1% increase in hotspots is correlated with an approximately 0 .14% increase in quarterly GPP, suggesting that economic activity or agricultural burning may lead to increased economic activity and consequently a short-term increase in GPP. Conversely, a decrease in PM2.5 concentration in the previous month resulted in an approximately 0.47% decrease in quarterly GPP, demonstrating that the economic costs of air pollution occur with a delayed effect rather than simultaneously. Therefore, this research highlights the importance of the correlation between short-term economic benefits and polluting activities, as well as the delayed economic losses resulting from poor and toxic air quality. This research emphasizes the importance of air quality management, risk communication and support, and economic and environmental policies to address the long-term economic and social impacts of PM2.5 pollution.
Authors - Aditya Nova Putra, Budi Riyanto, Alda Chairani, Sandy Dwiputra Yubianto Abstract - This study examines the determinants of continuance intention in YouTube live streaming consumption among Indonesian Generation Z, focusing on social interaction, entertainment, passing time, and enjoyment. Drawing upon Uses and Gratifications Theory and Computer-Mediated Communication, this research situates live streaming as an interactive digital environment where audiences actively negotiate social and emotional experiences. A quantitative explanatory survey was conducted among 108 Generation Z subscribers of the Windah Basudara YouTube channel, and the data were analyzed using Partial Least Squares Structural Equation Modeling (PLS-SEM). The findings reveal that social interaction and passing time significantly influence continuance intention, whereas entertainment and enjoyment do not demonstrate significant effects. These results suggest that sustained engagement in live streaming environments is driven more by interactive and habitual gratifications than by purely hedonic motivations. By highlighting the contextual dynamics of Indonesian gaming live streaming, this study extends the application of Uses and Gratifications Theory in synchronous digital media settings and offers practical implications for content creators seeking to strengthen audience retention strategies.
Authors - Steveen Eduardo Pinzon Morales, Yandry Jose Olarte Sancan, Marely del Rosario Cruz Felipe, Maricela Pinargote-Ortega Abstract - The recent decade has witnessed a more increase on the impact of applying and implementing green computing which mainly focuses in protecting the overall nature of the environment. Within the scope of this comprehensive assessment of the relevant literature, the most recent advancements in energyefficient software design, sustainable hardware design, and improved algorithms are examined and compiled. A wide range of enterprises use cloud computing for its adaptability, reliability, speed, and cost-effectiveness. The proliferation of cloud computing is affecting a shift in the manner in which we network. The application of these new technologies are mainly focused on the overall protection of the environmental aspects, they are more targeting in reducing the emission of dangerous type of gases and substances, use renewable mode of energy and thereby focusing in protecting the world for the future generations. The article is mainly involved in understanding the overall nature of implementing the green computing in realizing the overall development aspect.
Authors - Ain Geuel E. Escober, Rosicar E. Escober, Demelyn E. Monzon Abstract - This study presents the development of StewardFM, an information management system designed to evaluate the effectiveness of the Deflate compression algorithm in optimizing storage for associations and small organizations with limited cloud VPS resources. By integrating membership, event, collection, and budget management into one platform, StewardFM reduces storage overhead while maintaining essential functionality, offering a cost-efficient and scalable solution for resource-constrained organizational environments.
