Abstract: Convolutional Neural Networks (CNNs) are extensively utilized for image classification due to their ability to exploit data correlations effectively. However, traditional CNNs encounter ...
Abstract: Aerial image classification plays a vital role in applications such as building footprint extraction, water/soil analysis, 3D reconstruction. Accurate classification enables timely ...
Abstract: One of the most critical neurological conditions is Brain tumors, timely and correct diagnosis is needed for effective treatment. Advances in neuroimaging technology such as MRI, limitations ...
Abstract: Current camera-based hand tracking is limited by occlusion and environmental factors, hindering practical applications in robotics and digital interaction. Although electromyography-based ...
Abstract: Eye diseases represent a critical global health concern, affecting approximately 2.2 billion individuals with visual impairments or blindness and underscoring the urgent need for accessible ...
Abstract: Chronic total occlusion (CTO) is a critical determinant of treatment efficacy in coronary artery disease, but its accurate diagnosis remains heavily reliant on the expertise of experienced ...
Abstract: The growing prevalence of internet usage has led to a substantial capacity in textual data. Text classification is an essential field in natural language processing (NLP). It differs in ...
Abstract: In this article, we propose a lightweight privacy-preserving convolutional neural network (LPP-CNN) framework for military vehicle image classification. Existing target classification ...
Abstract: Accurate real-time fault detection, localization, and classification techniques are necessary to maintain grid stability and prevent faults. Traditional techniques have low accuracy rates, ...
Abstract: The human face reveals significant information about an individual’s identity, age, gender, emotion, and ethnicity. In face-to-face communication, age plays a vital role, influencing ...
Abstract: Semi-supervised learning (SSL) has achieved remarkable progress in the field of medical image segmentation (MIS), but it still faces two main challenges. First, the consistency learning ...
Abstract: Skin diseases pose high challenges in the diagnostic process since they have multiple visual features and overlapping clinical presentation. In this paper, an efficient deep learning ...