If data used to train artificial intelligence models for medical applications, such as hospitals across the Greater Toronto ...

Guangzhou Key Laboratory for New Energy and Green Catalysis, School of Chemistry and Chemical Engineering, Guangzhou University, Guangzhou 510006, China Joint Institute of Guangzhou University & ...

The integration of Machine Learning stands as a prominent solution to unravel the intricacies inherent to scientific data. While diverse machine learning algorithms find utility ... being led by ...

Three typical machine learning models, including random forest forest by penalizing attributes (FPA) and rotation forest were merged by random subspace algorithm. Twelve evaluation factors, including ...

Artificial intelligence and robotics are revolutionizing drug discovery by automating laboratory processes and enabling rapid ...

This paper proposes an unorthodox method for malaria prognosis based on an extreme learning machine (ELM) algorithm. In this regard, Convolutional Neural Networks (CNN), ELM, and double hidden layer ...

“It can discover algorithms ... worked in machine learning research, it wasn’t my experience that you could build a scientific tool and immediately see real-world impact at this scale.

Machine learning algorithms can be applied to recorded S-parameters in order to classify the stage of Alzheimer's disease in the brain. •The research presented in this work targets research in ...

"Each project addresses a different challenge associated with operating a large-scale ... of machine learning surrogate models for each radiation detector and used an offline optimization ...

The ability to anticipate what comes next has long been a competitive advantage -- one that's increasingly within reach for developers and organizations alike, thanks to modern cloud-based machine ...