Chronic illness, particularly, Type 2 Diabetes Mellitus (T2DM), is a critical health issue in Sri Lanka with the prevalence being attributed to lifestyle and dietary choices and socioeconomic aspects. Although the global research has already applied Artificial Intelligence in predicting diabetes, no research has been conducted in Sri Lanka since no country-relevant datasets or culturally …

Diabetes Risk Prediction Models A Comparative Review of Traditional Machine Learning, and Agentic AI ApproachesRead More

The rapid expansion of the Internet of Things (IoT) has created a strong demand for sustainable, reliable, and maintenance-free power sources for distributed sensor nodes. Traditional battery-powered IoT systems suffer from limited lifetime, frequent replacement requirements, high maintenance costs, scalability limitations, and environmental concerns. Energy harvesting provides an attractive alternative by converting ambient sources such …

Energy Harvesting-Enabled Self‑Powered Internet of Things Systems: A Comprehensive Review of Multi‑Source Architectures and Ultra‑Low‑Power IntegrationRead More

Automated biometric attendance monitoring has emerged as a critical research domain within educational technology, driven by the persistent limitations of conventional attendance methods such as manual roll calls, sign-in sheets, and card-based systems. Recent advances in deep learning and computer vision have substantially transformed this field, enabling the development of robust, contactless, and scalable face …

A Review of Deep Learning-Based Automated Biometric Attendance Monitoring Systems for Educational Institutions: Algorithms, Architectures, and Deployment ChallengesRead More

Medical imaging has become a cornerstone of modern healthcare, enabling early diagnosis, treatment planning, and disease monitoring across a wide spectrum of conditions. From the first X-ray images in the late 19th century to contemporary applications of artificial intelligence (AI) in radiology, imaging technologies have transformed clinical practice. However, these advances raise profound ethical concerns. …

Ethical Considerations in Medical Imaging Navigating Patient Rights, Data Integrity and Technological InnovationRead More

Sonar systems are widely used for underwater sensing applications such as navigation, target detection, and environmental monitoring. However, conventional sonar signal processing techniques often rely on handcrafted features and model-based assumptions, which can limit performance in complex and noisy underwater environments. In recent years, learning-based approaches, particularly those using neural networks, have gained increasing attention …

A Review of Learning-Based Sonar Signal Processing Using Neural NetworksRead More