Machine learning for health data science, fuelled by proliferation of data and reduced computational costs, has garnered considerable interest among researchers. The debate around the use of machine ...

The agency’s 31% year-over-year surge in AI use cases includes work with predictive models and surveillance technologies that ...

Researchers have significantly enhanced an artificial intelligence tool used to rapidly detect bacterial contamination in ...

Researchers have demonstrated, for the first time, that transfer learning can significantly enhance material Z-class identification in muon tomography, even in scenarios with limited or completely ...

QA teams now use machine learning to analyze past test data and code changes to predict which tests will fail before they run. The technology examines patterns from previous test runs, code commits, ...

New forms of fentanyl are created every day. For law enforcement, that poses a challenge: How do you identify a chemical you've never seen before? Researchers at Lawrence Livermore National Laboratory ...

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

TinyML sensors detect chainsaws, gunshots, and animal calls offline, offering a new way to protect wildlife in remote ...

Abstract: This study presents a comprehensive benchmarking of 33 machine learning (ML) algorithms for bearing fault classification using vibration data, with a focus on real-world deployment in ...

A new low-power sensor node framework combines sensing and machine learning, with the potential to enhance real-time ...

Medical researchers at Mass General Brigham say the self-supervised foundational model can identify inherent features from ...

Quiq reports on the role of automation in customer service, highlighting tools like AI for questions, ticket classification, ...