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

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

Overview: Master deep learning with these 10 essential books blending math, code, and real-world AI applications for lasting ...

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

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 ...

Vital for Cloud Security? Where businesses increasingly shift operations to the cloud, how can they ensure robust security while managing machine identities? Non-Human Identities (NHIs) offer a ...

Are Organizations Prepared for the Challenges of Non-Human Identities? Understanding Non-Human Identities in Cybersecurity How do organizations safeguard their machine identities? One emerging focus ...

Review re-maps multi-view learning into four supervised scenarios and three granular sub-tiers, delivering the first unified blueprint for researchers to navigate classification, clustering, ...

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

Seoul National University Hospital researchers have developed an AI model that predicts the response to an anticonvulsant drug.

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