To address these challenges, Associate Professor Takuya Taniguchi from the Center for Data Science and Ryo Fukasawa from Graduate School of Advanced Science and Engineering at Waseda University, Japan ...

The ability to anticipate what comes next has long been a competitive advantage -- one that's increasingly within reach for developers and organizations alike, thanks to modern cloud-based machine ...

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 ...

Electrical power systems engineers need practical methods for predicting solar output power under varying environmental conditions of a single panel. By integrating an Arduino-based real-time data ...

Filling gaps in data sets or identifying outliers—that's the domain of the machine learning algorithm TabPFN, developed by a team led by Prof. Dr. Frank Hutter from the University of Freiburg. This ...