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 >

Breast cancer remains one of the most significant health challenges, being the second leading cause of death among women worldwide. Early and accurate diagnosis is critical to improving treatment outcomes and increasing survival rates. In this study, we present an innovative application of the WRN-28-2 model, a deep convolutional neural network pre-trained on ImageNet, for the classification of histopathological breast cancer images from the BreakHis dataset. By leveraging transfer learning, the model was fine-tuned to differentiate between benign and malignant cases, achieving a remarkable classification accuracy of 99.16% on the test set. Moreover, the model outperformed existing state-of-... More >

Breast Cancer (BC) is still one of the most significant, life-threatening, and prevalent diseases that affects women all around the globe. The early recognition and strategies of effective treatment measures improve the rate of survival among patients significantly, contributing to a critical research area in medical science. This review presents a comprehensive review of recent trends and advancements in the recognition of BC recognition, diagnosis, and treatment. It covers multiple imaging modalities, including Magnetic Resonance Imaging (MRI), ultrasound, mammography, and histopathology, along with various approaches of Machine Learning (ML) and Deep Learning (DL) that enhance the efficie... 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 >

The Generalized Intersection over Union (GIoU) and the Manhattan distance between axis-aligned boxes represented either as corner coordinates or their center and size, are extended to accept a range of bounding boxes as ground truth, producing the metrics RIoU, $R_1$ and $R^t_1$, respectively. In the context of Table Detection it is shown that this box relaxation procedure allows training object detection models with partial or inexact annotations. For the Table Structure Recognition task, several code improvements to Microsoft's open-source Table Transformer increase all $\mathrm{GriTS}$ metrics on PubTables-1M, with the overall accuracy increasing from 0.8326 to 0.8433. Then box relaxation... More >

Lung diseases such as COVID-19, pneumonia, and tuberculosis remain major public health challenges worldwide, emphasizing the urgent demand for accurate and efficient diagnostic methods. This research explores the use of a Convolutional Neural Network (CNN)-based framework for binary classification of chest X-ray images to detect abnormalities. The methodology incorporates preprocessing techniques such as image resizing, normalization, data augmentation, and grayscale transformation to improve input data quality. CNN architecture comprising convolutional, pooling, fully connected, and dropout layers were trained and evaluated on publicly available datasets. The model attained a test accuracy... More >

The rapid advancement in machine learning and artificial intelligence has significantly enhanced capabilities in multimedia content creation, particularly in the domain of deepfake generation. Deepfakes leverage complex neural networks to create hyper-realistic manipulated audio-visual content, raising profound ethical, societal, and security concerns. This paper presents a comprehensive survey of contemporary trends in deepfake video research, focusing on both generation and detection methodologies. The study categorizes deepfakes into three primary types: facial manipulation, lip-synchronization, and audio deepfakes, further subdividing them into face swapping, face generation, attribute m... More >

The challenge with multifocus images lies in different regions being in focus across various shots, resulting in some areas appearing blurry while others are sharp. This issue is prevalent in fields such as medical imaging, remote sensing, and photography, where clear and detailed images are essential. This project introduces a novel approach to multifocus image fusion by integrating the Marr--Hildreth edge detection technique with Discrete Cosine Transform (DCT), Stationary Wavelet Transform (SWT), and Discrete Wavelet Transform (DWT). The Marr--Hildreth algorithm detects edges by identifying zero-crossings in the Laplacian of a Gaussian-blurred image, effectively highlighting areas with si... More >