Lawrence Berkeley National Laboratory (Berkeley Lab) is working to transform petabytes of imaging data from advanced light ...

The team utilized machine learning to analyze public data from the National Health and Nutrition Examination Survey.

In an interview with Technology Networks, Dr. Daniel Reker discusses how machine learning is improving data-scarce areas of drug discovery.

Unlock the full InfoQ experience by logging in! Stay updated with your favorite authors and topics, engage with content, and download exclusive resources. Sub‑100-ms APIs emerge from disciplined ...

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, ...

At-a-Glance: Machine learning streamlines pipeline inspections by automating signal interpretation, predicting defect growth, and prioritizing digs—cutting cycle time, false alarms, and OPEX while ...

Background Early graft failure within 90 postoperative days is the leading cause of mortality after heart transplantation. Existing risk scores, based on linear regression, often struggle to capture ...

Machine learning, a key enabler of artificial intelligence, is increasingly used for applications like self-driving cars, medical devices, and advanced robots that work near humans — all contexts ...