Abstract: This work deals with the fabrication and validation of an innovative wearable single-channel electroencephalogram (EEG) system, designed for real-time monitoring of specific brain activity.
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 ...
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 the ...
Design a lightweight machine-learning pipeline that analyzes single-channel frontal EEG data (Fp1/Fp2) and accurately detects driver drowsiness in real-time. 50 Hz IIR notch filter + 0.5–30 Hz ...
A real-time driver drowsiness detection system using deep learning and computer vision techniques, developed to enhance road safety by identifying signs of driver fatigue through eye state ...
MCED tests utilize liquid biopsies to detect multiple cancer types early, using ctDNA and other biomarkers analyzed by machine learning. Machine learning models, including deep learning, enhance MCED ...
Abstract: The ”Driver Drowsiness Detector” system aims to improve road safety by creating a real time system that can detect and alert drivers when they become drowsy or fatigued. Drowsy driving is a ...
Some results have been hidden because they may be inaccessible to you
Show inaccessible results
Feedback