首页|Study Results from Khwaja Fareed University of Engineering and Information Techn ology Broaden Understanding of Artificial Intelligence (Ultra-Wide Band Radar Em powered Driver Drowsiness Detection with Convolutional Spatial Feature Engineeri ng ...)
Study Results from Khwaja Fareed University of Engineering and Information Techn ology Broaden Understanding of Artificial Intelligence (Ultra-Wide Band Radar Em powered Driver Drowsiness Detection with Convolutional Spatial Feature Engineeri ng ...)
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By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily News Daily News-Fresh data on artificial intelligence are presented in a new report. According to news reporting out of Punjab, Pakist an, by NewsRx editors, research stated, "Driving while drowsy poses significant risks, including reduced cognitive function and the potential for accidents, whi ch can lead to severe consequences such as trauma, economic losses, injuries, or death." Our news journalists obtained a quote from the research from Khwaja Fareed Unive rsity of Engineering and Information Technology: "The use of artificial intellig ence can enable effective detection of driver drowsiness, helping to prevent acc idents and enhance driver performance. This research aims to address the crucial need for real-time and accurate drowsiness detection to mitigate the impact of fatigue-related accidents. Leveraging ultra-wideband radar data collected over f ive minutes, the dataset was segmented into one-minute chunks and transformed in to grayscale images. Spatial features are retrieved from the images using a two- dimensional Convolutional Neural Network. Following that, these features were us ed to train and test multiple machine learning classifiers. The ensemble classif ier RF-XGB-SVM, which combines Random Forest, XGBoost, and Support Vector Machin e using a hard voting criterion, performed admirably with an accuracy of 96.6% ."
Khwaja Fareed University of Engineering and Information TechnologyPunjabPakistanArtificial IntelligenceEmergin g TechnologiesEngineeringMachine Learning
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