By applying new methods of machine learning to quantum chemistry research, Heidelberg University scientists have made significant strides in computational chemistry. They have achieved a major ...

Accurately tracking atmospheric greenhouse gases requires not only fast predictions but also reliable estimates of ...

Korea University researchers have developed a machine-learning framework that predicts solar cell efficiency from wafer quality, enabling early wafer screening and optimized production paths. Using ...

Machine learning for health data science, fuelled by proliferation of data and reduced computational costs, has garnered ...

Microelectromechanical systems (MEMS) electrothermal actuators are widely used in applications ranging from micro-optics and microfluidics to nanomaterial testing, thanks to their compact size and ...

Objective This study reviewed the current state of machine learning (ML) research for the prediction of sports-related injuries. It aimed to chart the various approaches used and assess their efficacy ...

Researchers have developed a new machine-learning-assisted approach to optimize micro-electro-discharge machining (µ-EDM) of a next-generation biocompatible titanium alloy, potentially improving the ...

Researchers sought to determine an effective approach to predict postembolization fever in patients undergoing TACE.

A recent study, “Picking Winners in Factorland: A Machine Learning Approach to Predicting Factor Returns,” set out to answer a critical question: Can machine learning techniques improve the prediction ...

Zero knowledge proofs enhance transparency while maintaining confidentiality in decision-making processes. Lagrange is ...