GlobalSpec Insights

Machine learning helping locate what cannot be seen

The key idea behind our framework is that life produces molecules with purpose, while nonliving chemistry does not. Cells ...
pv magazine Australia

Machine-learning models identify hidden physical defects in solar arrays

The software tool uses self-supervised learning to detect long-term defects in solar assets weeks or years before ...
pv magazine International

US researchers use machine learning to detect hidden solar array defects

US researchers say a self-supervised machine-learning tool can identify long-term physical defects in solar assets weeks or years before conventional inspections, potentially reducing operations and ...
pv-magazine-usa

Machine learning models identify hidden physical defects in solar arrays

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 ...
IEEE

Depression Analysis and Detection Using Machine Learning: Incorporating Gender Differences in a Comparative Study

Abstract: Depression is a significant mental health problem and presents a challenge for the machine learning field in the detection of this illness. This study explores automated depression ...
IEEE

Analyzing Machine Learning Techniques for Software Defect Prediction: A Comprehensive Performance Comparison

Abstract: This study used the JM1 dataset of software module metrics and tested various machine learning classifiers to detect defective modules. Classifiers used: Gradient Boosting, AdaBoost XGBoost ...