Effective Automated Drowsiness Detection System Based on Machine Intelligence Techniques

This paper discusses the development of an effective automated drowsiness detection system using machine intelligence techniques. Drowsy driving is a significant safety concern that can result in accidents, injuries, and even fatalities. To address this issue, we have developed a model that uses machine intelligence techniques such as computer vision and machine learning. The proposed system combines several machine intelligence techniques to detect drowsiness accurately. Computer vision techniques analyze facial features and eye movements to detect signs of drowsiness. The Dataset used was collected by researchers at the University of Texas, Arlington. The system's performance is evaluated using various metrics, including accuracy, sensitivity, roc curve, f1 score, and specificity. The results show that the proposed system is highly effective in detecting drowsiness, with an accuracy of over 80%. Overall, the proposed automated drowsiness detection system is an effective and reliable tool to enhance driving safety. It demonstrates the potential of machine intelligence techniques in addressing real-world problems and improving human life quality.