The rapid evolution of the Internet of Medical Things (IoMT) has enabled pervasive patient monitoring through Wireless Body Area Networks (WBANs). However, energy depletion, high path-loss, link instability, and latency remain major barriers to achieving reliability in real-time healthcare applications. Existing schemes, such as Distance Aware Relaying Energy-efficient (DARE) and Link Aware and Energy Efficient Scheme for Body Area Networks (LAEEBA), mitigate individual constraints, distance and link quality respectively, but lack holistic optimization across energy, distance, and reliability dimensions. This paper proposes HEART (Hybrid Energy-Aware Routing Technique), a dual-sink, clusteri... More >

Breast cancer is one of the most prevalent and life-threatening diseases among women worldwide. Accurate diagnosis from histopathological biopsy samples is essential, yet manual examination is time-consuming and subject to inter-observer variability, particularly given the shortage of trained pathologists alongside the increasing number of cases. Deep learning, especially Convolutional Neural Networks (CNNs), has emerged as a powerful tool for classifying medical images by automatically extracting discriminative features from raw data. In this study, we investigate the use of the publicly available Breast Cancer Histopathological (BreakHis) image database, which contains benign and malignant... More >

The escalating global prevalence of diabetes renders effective screening for Diabetic Retinopathy (DR) indispensable to prevent irreversible vision loss. Although deep learning models, particularly Convolutional Neural Networks (CNNs), attain diagnostic accuracy comparable to that of human experts, their black-box nature erodes clinical trust. To harmonize accuracy with interpretability, this paper proposes a novel CNN architecture that reformulates DR grading as a regression task. By substituting traditional dense layers with a Global Average Pooling (GAP) layer, our approach substantially reduces model complexity and training time while enabling the generation of Regression Activation Maps... More >

Studies have established that an ideal environment is critical for maximizing a student's learning potential. For educators, monitoring student behavior, engagement, and psychological state is essential for ensuring effective instruction. This paper introduces an automated learning analytics system designed to assist teachers by analyzing these parameters and providing actionable feedback. The system utilizes multiple cameras in conjunction with deep learning and computer vision to record and analyze classroom sessions, assessing student movement, gestures, and posture to generate summary reports. The framework integrates several high-performance models to achieve this. For facial recognitio... More >

Real-time detection of violent behavior through surveillance technologies is increasingly important for public safety. This study tackles the challenge of automatically distinguishing violent from non-violent activities in continuous video streams. Traditional surveillance depends on human monitoring, which is time-consuming and error-prone, highlighting the need for intelligent systems that detect abnormal behaviors accurately with low computational cost. A key difficulty lies in the ambiguity of defining violent actions and the reliance on large annotated datasets, which are costly to produce. Many existing approaches also demand high computational resources, limiting real-time deployment... More >