Kernel methods represent a cornerstone in modern machine learning, enabling algorithms to efficiently derive non-linear patterns by implicitly mapping data into high‐dimensional feature spaces. At the ...

Jessica Lin and Zhenqi (Pete) Shi from Genentech describe a novel machine learning approach to predicting retention times for ...

As data privacy collides with AI’s rapid expansion, the Berkeley-trained technologist explains how a new generation of models is learning without crossing ethical lines.

In food drying applications, machine learning has demonstrated strong capability in predicting drying rates, moisture ...

Cancer is the second leading cause of death worldwide. Since age is one of the risk factors for the development of cancer, ...

Researchers from Peking University have conducted a comprehensive systematic review on the integration of machine learning into statistical methods for disease risk prediction models, shedding light ...

A scoping review shows machine learning models may help predict response to biologic and targeted synthetic DMARDs in ...

On October 3-4, 2025,The Federal Reserve Bank of Philadelphia and the Center for Applied AI at the University of Chicago Booth School of Business are co-hosting a conference on Frontiers in Machine ...

A scientist in Sweden has developed a new hybrid local features-based method using thermographs to identify faulty solar panels. A researcher from Sweden’s Jönköping University has proposed a machine ...

No audio available for this content. High-precision GNSS applications, such as real-time displacement monitoring and vehicle navigation, rely heavily on resolving carrier-phase ambiguities. However, ...

Jordan Awan receives funding from the National Science Foundation and the National Institute of Health. He also serves as a privacy consultant for the federal non-profit, MITRE. In statistics and ...