Keeping high-power particle accelerators at peak performance requires advanced and precise control systems. For example, the primary research machine at the U.S. Department of Energy's Thomas ...

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, ...

Abstract: This paper analyzes the performance of different LDA combinations with machine learning algorithms in predicting diabetes based on clinical data. The analysis involves patient records with ...

Dinosaur footprints are iconic fossils, but it is challenging to identify their makers. This is illustrated by a long-standing debate about whether some footprints from the Late Triassic-Early ...

According to Sawyer Merritt, Tesla's Full Self-Driving (FSD) V14 system, which relies solely on camera-based AI perception, demonstrated strong performance in severe fog conditions. In a recent ...

The workflow encompasses patient datacollection and screening, univariate regression analysis for initial variable selection, systematic comparison of 91 machine learning models,selection and ...

The semiconductor industry is increasingly turning to artificial intelligence as the solution for increasing complexity in test analytics, hoping algorithms can tame the growing flood of production ...

A recent study, “Picking Winners in Factorland: A Machine Learning Approach to Predicting Factor Returns,” set out to answer a critical question: Can machine learning techniques improve the prediction ...

Sri Ramachandra Faculty of Engineering and Technology, Sri Ramachandra Institute of Higher Education and Research, Chennai 600116, India ...

Development and Validation of an Ipsilateral Breast Tumor Recurrence Risk Estimation Tool Incorporating Real-World Data and Evidence From Meta-Analyses: A Retrospective Multicenter Cohort Study Data ...