Discover the power of predictive modeling to forecast future outcomes using regression, neural networks, and more for improved business strategies and risk management.

US researchers say a self-supervised machine-learning tool can identify long-term physical defects in solar assets weeks or years before conventional inspections, potentially reducing operations and ...

The fund seeks to enable researchers to make leaps rather than incremental advances in the natural sciences and engineering.

The software tool developed by Stony Brook University uses self-supervised learning to detect long-term solar equipment damage weeks or years before manual inspections find it.

“Our goal was to create a system that turns complex market data into clear, actionable insights,” said Alexander Henry , VP Trade at Exluno. “This AI-powered tool empowers traders to make informed ...

With such increased predictive knowledge of solar systems, these anomaly detectors can significantly reduce costs of O&M, a major component of project economics in solar development. There is great ...

CIGO Tracker's AI system continuously refines its predictions by analyzing new delivery data and identifying emerging patterns. The machine learning algorithms adapt to seasonal variations, changing ...

According to the MarkTechPost report, CatBoost’s high accuracy and processing speed, along with its broad versatility, allowed it to be included among six non-US technologies that have succeeded in be ...

Despite advanced algorithms and automation, one truth remains: Effective cybersecurity requires a careful balance between ...

1 Department of Nursing, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China 2 Department of Critical Care Medicine, First Affiliated Hospital of ...

Researchers at the Indian Institute of Science (IISc) and the Qatar Science and Technology Research Center (QSRTC) have developed a new automated method to assess corrosion in industrial equipment ...