As an emerging technology in the field of artificial intelligence (AI), graph neural networks (GNNs) are deep learning models ...

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 ...

This workshop will consider several applications based on machine learning classification and the training of artificial neural networks and deep learning.

Overview: Master deep learning with these 10 essential books blending math, code, and real-world AI applications for lasting expertise.From neural networks to N ...

Background Suicide rates have increased over the last couple of decades globally, particularly in the United States and among populations with lower economic status who present at safety-net ...

CNN in deep learning is a special type of neural network that can understand images and visual information. It works just like human vision: first it detects edges, lines and then recognizes faces and ...

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 ...