Abstract: In smart industries, cyber-attacks are the most consequential threats that can harm networks. Preventing cyberattacks from interrupting services is vital and challenging. Recently, ...

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

We retrospectively enrolled 115 primary RPS patients (2013–2024), splitting into training (N = 86) and validation (N = 29) sets. An end-to-end DL model was designed to forecast LRFS using ...

Deep learning uses multi-layered neural networks that learn from data through predictions, error correction and parameter adjustments. It started with the ...

A total of 17.9 million people were estimated to be living with rheumatoid arthritis globally in 2021, representing a 13.2% increase in incidence since 1990. The global burden of rheumatoid arthritis ...

A deep learning model accurately identified mitral valve prolapse (MVP) from transthoracic echocardiograms (TTE), and its predictions were associated with clinical endpoints such as mitral ...

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 ...

The acquisition sites include: CALTECH, California Institute of Technology; CMU, Carnegie Mellon University; KKI, Kennedy Krieger Institute; LEUVEN, University of Leuven; MAX, Ludwig Maximilians ...

This repository contains the implementation, benchmarks, and supporting tools for my MSc dissertation project: Self-learning Variational Autoencoder for EEG Artifact Removal (Key code only). Benchmark ...