Training a large artificial intelligence model is expensive, not just in dollars, but in time, energy, and computational ...

David J. Silvester, a mathematics professor at the University of Manchester, has developed a novel machine-learning method to ...

Explore the Types of Machine Learning and their impact on AI. Learn how these core frameworks drive digital innovation and ...

A new study published in Genome Research presents an interpretable artificial intelligence framework that improves both the accuracy and transparency of genomic prediction, a key challenge in fields ...

A new study published in Genome Research presents an interpretable artificial intelligence framework that improves both the ...

An international research team developed a multi-stage intrusion detection system that uses supervised and unsupervised AI techniques to detect and mitigate cyber threats in smart renewable energy ...

Machine learning identifies HLA structural features linked to graft failure, improving prediction and donor selection in transplantation.

The Kolmogorov-Arnold Network (abbr. KAN) is a novel neural network architecture inspired by the Kolmogorov-Arnold ...

A new soil-moisture retrieval strategy has improved the accuracy of satellite-based moisture mapping by combining microwave reflection signals with vegetation-structure information that conventional ...

Abstract: Accurate battery lifetime estimation is crucial for health management and system safety. Data-driven research yields extensive feature sets, yet optimal feature selection is often impeded by ...

Factoring out nucleotide-level mutation biases from antibody language models dramatically improves prediction of functional mutation effects while reducing computational cost by orders of magnitude.

A total of 8,598 children were enrolled and classified into three groups: ADHD (n=3,678), subthreshold ADHD (s-ADHD) (n=1,495), and healthy controls (HC) (n=3,425). Data collection covered 40 ...