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 ...

Scientific knowledge advances through the interplay of empiricism and theory. Empirical observations of environmental ...

Nonalcoholic fatty liver disease (NAFLD) is the most common cause of chronic liver disease, and if it is accurately predicted ...

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 ...

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 ...

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

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 ...

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 ...

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, ...

Overview: Clear problem definitions prevent wasted effort and keep machine learning work focused.Clean, well-understood data ...