Washing machines have a lot more buttons than they used to. They also operate very differently, using minimal amounts of water and soap to clean your clothes. Sometimes it seems like the laundry is ...

This repository provides code and workflows to test several state-of-the-art vehicle detection deep learning algorithms —including YOLOX, SalsaNext, and RandLA-Net— on a Flash Lidar dataset. The ...

Abstract: Accurately modeling the behavior of price-responsive flexible loads (PRFLs) is increasingly critical for enhancing power system flexibility. While existing inverse optimization methods can ...

As a driving force of the Fourth Industrial Revolution, deep learning methods have achieved significant success across various fields, including genetic and genomic studies. While individual-level ...

Abstract: The current industrial load demand is huge in scale and complex in power consumption characteristics. The deep learning prediction model built has difficulty in feature extraction leading to ...

Predicting the effects of multiple mutations on protein function is challenging due to the intricate interplay between residues. Machine learning has advanced these efforts, but traditional neural ...

We aimed to refine and validate a deep neural network model from the ECG to predict atrial fibrillation (AF) risk, using samples from diverse backgrounds: the Framingham Heart Study (FHS), UK Biobank, ...

Accurate disaster prediction combined with reliable uncertainty quantification is crucial for timely and effective decision-making in emergency management. However, traditional deep learning methods ...