Human Action Recognition (HAR) in unconstrained video remains difficult due to cluttered backgrounds, camera motion, and long-range temporal dependencies. The recognition of human action is the most complex study in the area of Artificial Intelligence (AI) and Computer Vision (CV). Machine vision for online and offline video processing is typically employed in the development of human behavior recognition systems. In video broadcasting and analysis, identifying the type and content of human actions present in the footage is a fundamental requirement. In this article, we propose a compact and deployable Convolutional Neural Network-Long Short Term Memory (CNN-LSTM) framework that combines a 2... More >

Vietnam's Electric Vehicle (EV) market is expanding rapidly, yet public charging infrastructure development lags significantly, exhibiting pronounced spatial imbalance in dense urban cores. This study addresses this gap through an integrated demand forecasting and location optimization framework for District 1, Ho Chi Minh City. We develop a log-linear regression model using Vietnam's macroeconomic data (2003–2023), identifying GDP and CPI as dominant determinants of vehicle ownership (R$^2$ = 0.962). Forecasted vehicle stocks for 2026–2030 are translated into public charging demand through vehicle-type disaggregation and service-capacity modeling. Spatially, we propose a four-stage opti... More >

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 >