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 ...
Across the physical world, many intricate structures form via symmetry breaking. When a system with inherent symmetry ...
In a study titled Recent Applications of Machine Learning Algorithms for Pesticide Analysis in Food Samples, published in the ...
Tech Xplore on MSN
Detection of concealed explosives using terahertz spectral imaging and deep learning
Detecting concealed explosives and chemical threats constitutes a critical challenge in global security, yet current ...
Edge AI addresses high-performance, low-latency requirements by embedding intelligence directly into industrial devices.
Recent research (2024-2025) consistently demonstrates the advantages of integrated AI-VR training: Knowledge Acquisition: ...
A deep learning framework combines convolutional and bidirectional recurrent networks to improve protein function prediction from genomic ...
Researchers at Chungnam National University have developed a deep learning method that predicts stable defect configurations in nematic liquid crystals in milliseconds rather than hours. This rapid ...
A new IAEA research project will investigate how artificial intelligence can be used to strengthen non-destructive testing techniques used in disaster response. The project aims to enable faster, ...
IAEA experts assess damages to a building after a devastating 7.7 magnitude earthquake in Myanmar in March 2025 (Photos: G. M-Seminario/IAEA).
Across the physical world, many intricate structures form via symmetry breaking. When a system with inherent symmetry transitions into an ordered ...
CES 2026 showcases the latest AI-powered devices and systems, from vision chips for automotive to sensing solutions for AI ...
Some results have been hidden because they may be inaccessible to you
Show inaccessible results