New Electronics

Analogue’s AI generation

Analogue engineering still relies heavily on manual intervention, but that is changing with the growing use of AI/ML.
Hosted on MSN

Deep neural network from scratch in Python – fully connected feedforward tutorial

Learn how to build a fully connected, feedforward deep neural network from scratch in Python! This tutorial covers the theory, forward propagation, backpropagation, and coding step by step for a hands ...
Frontiers

Neural networks and foundation models: two strategies for EEG-to-fMRI prediction

Electroencephalography (EEG) and functional Magnetic Resonance Imaging (fMRI) are two widely used neuroimaging techniques, with complementary strengths and weaknesses. Predicting fMRI activity from ...
PNAS

Unified protein–small molecule graph neural networks for binding site prediction

Accurately identifying small molecule binding sites on proteins is fundamental to understanding protein function and enabling structure-based drug discovery, yet this critical step remains a major ...
GitHub

STM-Graph: A Python Framework for Spatio-Temporal Mapping and Graph Neural Network Predictions

STM-Graph is a Python framework for analyzing spatial-temporal urban data and doing predictions using Graph Neural Networks. It provides a complete end-to-end pipeline from raw event data to trained ...
Frontiers

Prediction of mechanical properties of steel fibre-reinforced concrete under elevated temperature using artificial neural network techniques (ann)

This study investigates the mechanical performance of Steel Fibre-Reinforced Concrete (SFRC) subjected to elevated temperatures using artificial neural network (ANN) modeling. While existing ...
Scientific Research Publishing

Occupational Diseases Risk Prediction by Neural Networks ()

This study explores the use of neural networks for occupational disease risk prediction based on worker and workplace characteristics. The goal is to develop a tool to assist occupational physicians ...