Researchers have developed an artificial intelligence model capable of tracking a person’s sleep stages using only three ...

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

An offline, point-of-care algorithm on a smartphone fundus camera generated disease-specific outputs without cloud connectivity, addressing a major deployment barrier in low-resource screening ...

Machine learning models that use electronic health record data to predict obstructive sleep apnea had greater performance than two screening questionnaires, according to a poster presented at SLEEP ...

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 ...

Abstract: The face, an essential part of the body, communicates a vast array of information. When a driver is fatigued, their facial expressions, along with the frequency of blinking and yawning, ...

Claude Code generates computer code when people type prompts, so those with no coding experience can create their own programs and apps. By Natallie Rocha Reporting from San Francisco Claude Code, an ...

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 ...

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 ...