A large study applies advanced machine learning to identify shared risk factors and predictors of disease onset in patients with epilepsy and depression.

A deep learning-based real-time driver drowsiness detection and alert system using CNN-LSTM architecture. The model analyzes eye movements, mouth openness (yawning), and head pose to accurately ...

Apps that record visits are becoming popular, but they come with privacy and accuracy concerns. By Simar Bajaj At your next appointment, your doctor may have a new kind of assistant listening in: ...

This study intends to bring onboard and execute a real-time drowsiness alert system using machine learning that will monitor the drivers' eye movement behaviours, thus, reducing the risk of road ...

This research addresses the challenge of monitoring railway driver drowsiness using a real-time, vision-based system powered by convolutional neural networks, specifically the YOLOv8 architecture ...

ABSTRACT: This research presents a Driver Drowsiness Detection System (DDDS) that uses a Convolutional Neural Network (CNN) to improve road safety. The system uses a vast dataset of 97,860 images from ...