Integrating deep learning in optical microscopy enhances image analysis, overcoming traditional limitations and improving ...

Across the physical world, many intricate structures form via symmetry breaking. When a system with inherent symmetry ...

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 ...

CES 2026 showcases the latest AI-powered devices and systems, from vision chips for automotive to sensing solutions for AI ...

A deep learning framework combines convolutional and bidirectional recurrent networks to improve protein function prediction from genomic sequences. By automating feature extraction and capturing long ...

“We must strive for better,” said IBM Research chief scientist Ruchir Puri at a conference on AI acceleration organised by the computer company and the IEEE in November. He expects almost all language ...

The neural network approach uses multiple or “deep” layers that learn to identify increasingly complex features in data. The ...

WiMi Studies Quantum Hybrid Neural Network Model to Empower Intelligent Image Classification BEIJING, Jan. 15, 2026––WiMi Hologram Cloud Inc. (NASDAQ: WiMi) ("WiMi" or the "Company"), a leading global ...

One of the most difficult challenges in payment card fraud detection is extreme class imbalance. Fraudulent transactions ...

Monitoring forest health typically relies on remote sensing tools such as light detection and ranging (LiDAR), radar, and multispectral photography. While radar and LiDAR penetrate canopies to reveal ...

This project implements ResNet-50, a deep convolutional neural network with 50 layers that uses residual connections to enable training of very deep networks. The architecture includes identity ...