International Journal of Neutrosophic Science IJNS 2690-6805 2692-6148 10.54216/IJNS https://www.americaspg.com/journals/show/2165 2020 2020 Department of Physics, College of Sciences, University of Bisha, PO Box 344, Bisha 61922, Saudi Arabia Abdel-Haleem Abdel Abdel-Aty Department of Pharmaceutics, College of Pharmacy, Qassim University, Al Qassim 51452, Saudi Arabia Ahmed A. H. Abdellatif Department of Mathematics, College of Science and Humanities in Alkharj, Prince Sattam Bin Abdulaziz University, Alkharj 11942, Saudi Arabiaك School of Technology, Woxsen University- Hyderabad-502345, Telangana State, India. Kottakkaran Sooppy Nisar School of Technology, Woxsen University- Hyderabad-502345, Telangana State, India Shankar Rao Munjam Department of Computer Science, College of Science and Arts in Gurayat, Jouf University, Saudi Arabiaك Computer Science Department, Faculty of Computers and Information Assiut University, Egypt. Rasha M. Abd El El-Aziz Department of Computer Science, College of Science and Arts in Gurayat, Jouf University, Saudi Arabia; Information System Department, Faculty of Computers and Information, Assiut University, Assiut, Egypt. Ahmed I. Taloba Driver drowsiness has been identified as a major cause of roadway accidents globally. Efficiently determining the extent of drowsiness can greatly enhance preventive measures. This study proposes a novel approach, combining convolutional neural networks (CNN) and Gated Recurrent Units (GRU) to dynamically evaluate both the presence of drowsiness and its severity based on the Eye Aspect Ratio (EAR). By bridging spatial features extracted by CNNs with temporal sequences through GRU, our model offers a robust and real-time assessment of drowsiness levels, paving the way for enhanced safety measures in vehicular systems. Incorporating Neutrosophic Logic enables a more robust representation of uncertainty and ambiguity in the data and enhances the accuracy of driver drowsiness level detection within the Hybrid CNN-GRU framework. The model’s hybrid CNN-GRU structure combines CNN layers to extract spatial information from Human eye Images and GRU units to represent temporal correlations between frames. In-car cameras and sensors must be integrated to implement the suggested system in real-time and enable continuous driver behavior monitoring. The system alerts early warnings and takes action when drowsiness is detected, lowering the likelihood of accidents caused by weary drivers. The CNN-GRU hybrid architecture accurately detects fatigue during real-time driving. Performance metrics, including accuracy, recall, and F1-score, are provided for comparative research utilizing baseline models. Model behavior may be understood by visualizing tiredness detection and carefully examining false positives and negatives. The proposed CNN-GRU framework outperforms traditional methods such as SVM, KNN, and BPNN by achieving a significantly higher accuracy of 99.5%. It increases the recognition of driver tiredness by proposing a trustworthy and adaptable hybrid CNN-GRU deep learning system. This project is implemented in Python; it offers a practical and versatile solution for real-time driver drowsiness level detection. The proposed technology has the potential to dramatically increase traffic safety by sending out early warnings and taking steps to lessen the risks related to driver fatigue. 2023 2023 144 161 10.54216/IJNS.220212 https://www.americaspg.com/articleinfo/21/show/2165