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 ...
Some results have been hidden because they may be inaccessible to you
Show inaccessible results