The software tool developed by Stony Brook University uses self-supervised learning to detect long-term solar equipment damage weeks or years before manual inspections find it.

Structural defects that form during additive manufacturing, also known as 3D printing, are a barrier to some applications of this technology. Researchers used diagnostic tools and machine learning to ...

Researchers from Stony Brook University, in collaboration with Ecosuite and Ecogy Energy, have developed a self-supervised machine-learning algorithm designed to identify physical anomalies in solar ...

In food drying applications, machine learning has demonstrated strong capability in predicting drying rates, moisture ...

The severity of symptoms in posttraumatic stress disorder (PTSD) varies greatly across individuals in the first year after trauma and it remains difficult to predict whether someone might worsen, ...