Chongwei Shi Advances Biostatistical Methods and Computational Biology Through Deep Learning and Statistical Shape Analysis

A deep learning framework combines convolutional and bidirectional recurrent networks to improve protein function prediction from genomic ...
EurekAlert!

Chongqing Medical University team: dual-branch graph attention network enables personalized prediction of ECT efficacy in adolescent depression

Major Depressive Disorder (MDD) is a leading cause of disability among adolescents, yet the efficacy of Electroconvulsive ...
Opinion
SpaceNewsOpinion

We need a ‘Planetary Neural Network’ for AI-enabled space infrastructure protection

For example, a Convolutional Neural Network (CNN) trained on thousands of radar echoes can recognize the unique spatial signature of a small metallic fragment, even when its signal is partially masked ...
InfoWorld

The most popular neural network styles and how they work

Learn about the most prominent types of modern neural networks such as feedforward, recurrent, convolutional, and transformer networks, and their use cases in modern AI. Neural networks are the ...
EurekAlert!

Transfer learning-enhanced physics-informed neural network (TLE-PINN): A breakthrough in melt pool prediction for laser melting

The TLE-PINN method integrates EPINN and deep learning models through a transfer learning framework, combining strong physical constraints and efficient computational capabilities to accurately ...
pv magazine International

PV module fault diagnosis uses convolutional neural network

Researchers in China have created a dataset of various PV faults and normalized it to accommodate different array sizes and typologies. After testing the new approach in combination with the 1D-CNN ...