Machine learning holds great promise for classifying and identifying fossils, and has recently been marshaled to identify trackmakers of dinosaur ...

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

Market growth is driven by industrial automation, predictive maintenance demand, AI/ML analytics adoption, IoT integration, and the need to reduce downtime and operational costs.Austin, Jan. 27, 2026 ...

In a study titled Recent Applications of Machine Learning Algorithms for Pesticide Analysis in Food Samples, published in the ...

SCAN project aims to build European GNSS-based and AI-driven technologies to detect and assess roadway pavement problems.

Models using established cardiovascular disease risk factors had satisfactory predictive performance for 5-year CVD risk in ...

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 ...

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

New TMS biomarkers combined with machine learning accurately classified major depressive disorder. Learn more about this ...

Carnegie Mellon University’s leadership in AI and learning science is reflected in a wide range of initiatives.

Dr Michele Orini shares how machine learning can help identify critical VT ablation targets for a safer, data-driven ...