Authors - Reepu Abstract - Wearable devices are gaining more importance in the present day as they offer various advantages to the users and thereby enabling in generating better outcomes. Wearable devices gained higher focus mainly in the healthcare sector as they support in tracing the critical signs of the patients on a real time basis, provide better support to them as and when needed. The application of wearable devices enables in addressing different health care concerns in an effective manner, the current context witnessed many advancements in the wearable device’s domain. With the integration with other techniques like AI, Internet of Things and other sophisticated tools it can undergo major shift in the health care domain in protecting the lives of the individuals, move from being reactive mode of providing treatment to the patients to more predictive method, enhance clinical skill and outcomes. The overall value of implementing these tools and technologies support in enhancing the life of the patients and supports the practitioners in making better progress in clinical advancements and other areas.
Authors - Shreya Shukla, Mishti Kukreja, Ruchika Katariya Abstract - Attention deficit hyperactivity disorder (ADHD) is a common neurodevelopmental disorder with a prevalence rate ranging between 5 to 7.2% among children and between 2.5 to 6.7% among adults worldwide. In spite of its high prevalence rate, its diagnosis still relies mostly on clinical ratings that have the tendency to show inter-rater differences and confusions with symptoms of other conditions associated with ADHD. Neuroimaging methods, particularly rs-fMRI and sMRI, offer an innovative approach towards providing objective measures for understanding the neurobiological underpinnings of ADHD. This paper offers a systematic narrative review of deep learning methods for ADHD classification using fMRI/sMRI data from 2012 to 2025, with a specific focus on the recent period from 2021 to 2025 characterized by architectural diversity. We classify the literature into three main streams according to the neural networks adopted: (1) Convolutional Neural Networks (CNNs), which involve 2D CNNs, 3D CNNs, residual CNNs, dense CNNs, attention-based CNNs, and graph-based CNNs; (2) Vision Transformers (ViTs), which encompass conventional ViTs, Swin transformers, self-supervised ViTs, multi-modal ViTs, and brain foundation model ViTs; and (3) hybrid CNN-ViT models, which combine both local and global context representations. This work highlights the problems of heterogeneity among multiple sites, inconsistent evaluations, fairness, efficient inference, and clinical deployment. Note: This review does not follow the guidelines for systematic reviews (PRISMA 2020). It is an organized narrative review. Numerical comparisons between different works should be considered approximate due to variations in training/testing sets and data preprocessing.
Authors - Madhumati Pol, Rutuja Chaudhari, Sai Jadhav, Sri Sai Preethi Munnaluri, Rudrani Sarangdhar Abstract - The necessity of developing adaptive, autonomous, and intelligent security systems has developed significantly over time because of the increased volume and complexity of cyber attacks. Therefore, this research project will present an AI-powered multi agent self-evolution cybersecurity intelligence system. The purpose of the system will be for the real-time identification, classification, and prediction of cyber threats. The system will consist of three working agents: Network Monitoring Agent, System-Metrics Surveillance Agent and Threat Intelligence Agent. These agents will be supported by interpretable machine learning classifiers and light-weight Python-based data collection tools. A universal dataset converter will enable it to operate on all types of cybersecurity datasets, and a self-evolving element will allow it to continually update itself with additional information regarding current threats. Dashboards will be provided through the use of Streamlit in order to provide real-time timelines of attacks, CVE intelligence, anomaly detection, and real-time visualization of threats. Results from experimental testing show that the system can improve the accuracy of its threat detection as time progresses and perform well across various datasets. Overall, this work provides a self-learning, scalable, modular, and dataset-agnostic architecture for use within modern enterprise-level cybersecurity environments.
Authors - Elmar B. Noche, Randy Joy M. Ventayen Abstract - Breast cancer remains one of the leading causes of mortality among women, highlighting the need for reliable survival prediction tools. This study applied big data analytics and Cox Proportional Hazards regression to the METABRIC dataset, which contains clinical, pathological, and genomic records from over 2,000 breast cancer patients. Hadoop HDFS was used for distributed storage, while PySpark supported preprocessing and data transformation. After feature selection, six significant predictors were identified: inferred menopausal state, Nottingham Prognostic Index, oncotree code, type of breast surgery, cohort classification, and tumor size. The findings show that combining Hadoop-based infrastructure with interpretable survival modeling can support patient risk stratification, treatment planning, and precision oncology.
Authors - Phat Ly Tan, My Nguyen Kieu, Phung Nguyen Thi Kim Abstract - This paper presents an automated approach for cardiac arrhythmia detection using ECG signals from the CPSC2018 database. The proposed pipeline includes band-pass filtering, normalization, and segmentation of raw ECG recordings, conversion of ECG segments into 2D grayscale images, and multi-label arrhythmia classification using CNN based on a DenseNet architecture. According to the official CPSC2018 labeling scheme, ECG segments are categorized into multiple clinically relevant rhythm types, including normal sinus rhythm and major arrhythmias such as first-degree atrioventricular block, atrial fibrillation, right bundle branch block, left bundle branch block, ventricular ectopic beat, premature atrial contraction, ST-segment elevation, and ST-segment depression. The DenseNet-based architecture combines an oversampling training strategy to alleviate class imbalance. Experimental results on the CPSC2018 database demonstrate the effectiveness of the proposed image-based ECG classification approach, highlighting its potential to assist clinicians in ECG interpretation and early diagnosis of cardiac disorders.
Authors - Renato E. Salcedo, Elmar B. Noche Abstract - The rapid proliferation of Artificial Intelligence (AI) in education has prompted growing scholarly and policy interest in how higher education institutions across the Asia-Pacific region are systematically incorporating AI into teaching, learning, research, and governance. This paper presents a systematic review of 68 peer-reviewed studies, institutional policy documents, and government reports published between 2018 and 2024, examining the extent to which AI is being institutionalized within Asia-Pacific higher education systems in alignment with Education for Sustainable Development (ESD) principles. Using the PRISMA framework and a content analysis methodology, this review identifies four dominant institutionalization pathways: curriculum integration, research infrastructure development, policy formalization, and faculty capacity-building. Findings indicate significant heterogeneity across countries, with East Asian economies particularly China, Japan, and South Korea exhibiting more advanced levels of AI policy coherence, while Southeast Asian and Pacific Island nations remain in nascent stages of formal AI institutionalization. Critical barriers include data governance deficits, algorithmic inequity risks, underfunded professional development pipelines, and insufficient alignment between national AI strategies and ESD frameworks. The review concludes with a set of evidence-based recommendations for regional policymakers, university administrators, and international development organizations to accelerate equitable, sustainable AI institutionalization across the Asia-Pacific higher education landscape.
Authors - Abstract - This research investigated the potential for improving the reliability of the Central Pangasinan Electric Cooperative (CENPELCO) San Carlos 20MVA Substation distribution system through optimized placement of Automatic Circuit Reclosers (ACRs). The study employed a Steady-State Genetic Algorithm (SSGA) implemented in MATLAB to identify optimal ACR locations that minimized SAIDI, SAIFI, and maximized EENS. The algorithm was validated using the IEEE 34-node test feeder, demonstrating its effectiveness in balancing competing objectives. The validated SSGA was then applied to the CENPELCO system, resulting in significant improvements in SAIDI values across all feeders. The research introduced a novel approach to using EENS to represent the number of connected customers at each node, refining the SAIDI calculation and providing a more accurate measure of the impact of outages on consumers. The optimized ACR placement strategy consistently brought SAIDI values below the NEA standard for on-grid electric cooperatives, indicating a substantial enhancement in the reliability of the CENPELCO distribution system. The study provides valuable insights for CENPELCO and other power system operators seeking to improve system reliability and minimize the impact of power outages.
Authors - Phway Phway Aung, Tin Zar Thaw Abstract - Multi-object tracking (MOT) is widely applied in surveillance, traffic monitoring, and autonomous systems. Most MOT systems are created by combining DeepSORT and YOLO series. The original DeepSORT relies on IoU as-sociation-based matching and a fixed age threshold deletion algorithm which of-ten leads to incorrect associations, premature track removal, and frequent ID switches under occlusion or fast motion. To address these limitations, YOLOv9-Based Multi-Object Tracking System is proposed by using GIoU for more reliable geometric association and the enhanced filtering algorithm that are integrated class validation, motion uncertainty estimation with consign similarity, and a Re-Identification (ReID) memory buffer for reducing ID switching. To analyze the performance of the proposed MOT system we compare two cases: IoU and GIoU on Original DeepSORT and the improved DeepSORT and original DeepSORT based on MOT16 videos’ sequences. Experimental evaluation demonstrates that the proposed YOLOv9-Based Multi-Object Tracking System achieves more sta-ble and accurate tracking performance compared to the original DeepSORT system in two cases.
