Hundreds of thousands of children in China have been separated from their parents. A Yale SOM study finds that a machine-learning approach could cut years off family reunification efforts by matching ...

Machine learning redesigns microscopic web sensors to be five times more flexible than nature-inspired versions, enabling detection of masses as small as trillionths of a gram.

For years, anti-virus tools have used behavioural analysis and machine learning to identify rogue programs and unusual user activity.

Artificial intelligence and machine learning have transformed how we process information, make decisions, and solve complex problems. Behind every ...

PG&E plans to remove century-old California dams. But there's a new obstacle: Trump Trump 'inclined' to keep ExxonMobil out of Venezuela after CEO response at White House meeting Princess Kate marks ...

Abstract: Detecting advanced persistent threats (APTs) at a host via data provenance has emerged as a valuable yet challenging task. Compared with attack rule matching, machine learning approaches ...

In some ways, Java was the key language for machine learning and AI before Python stole its crown. Important pieces of the data science ecosystem, like Apache Spark, started out in the Java universe.

PITTSBURGH--(BUSINESS WIRE)--Carnegie Learning, a global leader in AI-driven technology, curriculum, and professional learning solutions for K-12 education, announced today that MATHia and Patterns ...

Dwayne “The Rock” Johnson may be a king of the box office, but even he cannot compete with the awesome and terrifying commercial power of Taylor Swift. The Smashing Machine, the Johnson-starring, ...

ABSTRACT: Accurately predicting individual responses to antidepressant treatment is a critical step toward achieving personalized psychiatry and minimizing the ...

Learn how to use pattern-matching features in your Java programs, including pattern matching with switch statements, when clauses, sealed classes, and a preview of primitive type pattern matching in ...

Abstract: Out-of-distribution (OOD) detection presents a significant challenge in deploying pattern recognition and machine learning models, as they frequently fail to generalize to data from unseen ...