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 >

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 >