CNN and random forest model to detect multiple faults in bifacial PV systems, including dust, shading, aging, and cracks. Using simulated I-V curves and a 180-day synthetic dataset, the model achieved ...

Researchers have tested eight stand-alone deep learning methods for PV cell fault detection and have found that their accuracy was as high as 73%. All methods were trained and tested on the ELPV ...

A forecasting-driven framework integrates ARIMA, LSTM, and ensemble learning to optimize cloud resource scheduling. By predicting CPU, memory, and network demands in real time, it enhances utilization ...

Advanced UAV sensor integration and machine learning may improve corn AGB predictions, providing scalable solutions for ...

Demand forecasting remains one of the most complex challenges in retail management. As consumer behavior evolves rapidly, ...

Physics-based ML framework designs IDPs—biomolecules without fixed structures that underlie key functions and diseases such as Parkinson’s.