Integrating deep learning in optical microscopy enhances image analysis, overcoming traditional limitations and improving ...
Detecting concealed explosives and chemical threats constitutes a critical challenge in global security, yet current ...
A deep learning framework combines convolutional and bidirectional recurrent networks to improve protein function prediction from genomic ...
Cervical cancer detection and diagnosis are undergoing a transformation with the integration of advanced deep learning (DL) technologies. Despite ...
Mount Sinai analysis looks at the effectiveness of electrocardiograms analyzed via deep learning as a tool for early COPD detection ...
ABSTRACT: This study proposes a multimodal AI model for classifying Vietnamese digital learning materials by integrating three key information sources: text content, image and graphic features, and ...
📋 Project Overview This project presents a novel CBAM-guided channel pruning framework for efficient osteoporosis classification using knee X-ray images. The methodology achieves 55.9% parameter ...
Abstract: The classification of cognitive load is crucial to evaluate mental effort in various tasks. Compared to physiological measures such as electroencephalography (EEG), electrocardiography (ECG) ...
Article subjects are automatically applied from the ACS Subject Taxonomy and describe the scientific concepts and themes of the article. Figure 1 illustrates the overall workflow of the hyperspectral ...
Abstract: In recent years, deep learning methods have been playing an important role in extracting features from ground penetrating radar (GPR) images to detect underground targets rapidly. However, ...
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