River flow: New machine learning methods could improve environmental predictions Algorithm is 'taught' rules of the physical world to help researchers make better predictions ...

Machine-learning algorithms are responsible for the vast majority of the artificial intelligence advancements and applications you hear about. (For more background, check out our first flowchart ...

The study reported that Predict+ was deemed fair for 98.6% of regression predictions, 99.4% of substantial clinical benefit (SCB) classification predictions, and 100% of minimal clinically ...

Credit: Journal of Geophysical Research: Machine Learning and Computation (2025). DOI: 10.1029/2024JH000507 Comparison of FVCOM, ML and FVCOM-ML results for storm surge prediction.

In 2017, a study out of Johnson’s lab showed that machine learning could help predict with remarkable accuracy how long it would take for the fault the researchers created to start quaking.

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 ...

But machine learning—training computer algorithms to analyze large amounts of data to look for patterns or signals—suggests that some of the small seismic signals might matter after all.

Particularly, GNNs exhibit promising performance with minimal prediction errors in voltage drop estimation. The incorporation of GNNs marks a groundbreaking advancement in accurate IR drop prediction.

Optimizing Outcome Prediction in Diffuse Large B-Cell Lymphoma by Use of Machine Learning and Nationwide Lymphoma Registries: A Nordic Lymphoma Group Study. JCO Clin Cancer Inform 2 , 1-13 (2018). DOI ...

Machine learning can be valuable in supporting sustainable development of biomass if it is applied across the entire lifecyle of biomass and biomass-derived products, according to a new study.