A systematic review of 78 papers examines how machine learning methods support demand forecasting and inventory ...

Researchers developed NiCLIP, an AI model that decodes cognitive tasks, concepts, and domains from brain activation patterns. Trained on over 23,000 papers, NiCLIP excels at group-level decoding, ...

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 ...

Successful test results of a new machine learning (ML) technique developed at Georgia Tech could help communities prepare for extreme weather and coastal flooding. The approach could also be applied ...

CBK is implementing AI use cases across 14 departments, with applications covering inflation forecasts, GDP nowcasting, ...

A random forest model trained on acoustic emission signals from granite hydraulic fracturing experiments forecast specimen ...

A team of researchers at the Universities of Lincoln, Sheffield, and Reading have developed a new method to improve the prediction of seasonal weather conditions in the U.K. and Northwest Europe. The ...

Researchers in China have applied a machine learning technology based on temporal convolutional networks in PV power forecasting for the first time. The new model reportedly outperforms similar models ...

Weather forecasts have historically had a time frame hurdle that most weather models can't accurately predict beyond, but a group of researchers at the Massachusetts Institute of Technology (MIT) say ...

Data mining is a technology for discovering useful patterns and knowledge from large amounts of data. This article explains ...