Driver Drowsiness Detection Using Gray Wolf Optimizer Based on Voice Recognition

Keywords: Drowsiness, Artificial neural network, Feature extraction, Gray Wolf Optimizer, Normalization, Mel-frequency cepstral coefficients, Linear prediction coefficients


Globally, drowsiness detection prevents accidents. Blood biochemicals, brain impulses, etc., can measure tiredness. However, due to user discomfort, these approaches are challenging to implement. This article describes a voice-based drowsiness detection system and shows how to detect driver fatigue before it hampers driving. A neural network and Gray Wolf Optimizer are used to classify sleepiness automatically. The recommended approach is evaluated in alert and sleep-deprived states on the driver tiredness detection voice real dataset. The approach used in speech recognition is mel-frequency cepstral coefficients (MFCCs) and linear prediction coefficients (LPCs). The SVM algorithm has the lowest accuracy (71.8%) compared to the typical neural network. GWOANN employs 13-9-7-5 and 30-20-13-7 neurons in hidden layers, where the GWOANN technique had 86.96% and 90.05% accuracy, respectively, whereas the ANN model achieved 82.50% and 85.27% accuracy, respectively.


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Author Biographies

Sarah S. Jasim, Department of IT, Technical College of Management-Baghdad, Middle Technical University, Baghdad, Iraq

Sarah S. Jasim is a Lecturer at the Department of Computer Science, Department of IT, Technical college of Management-Baghdad, Middle Technical University. She got a B.Sc. degree in Computer Science, Al-Yarmouk University College, 2004, MSc. degree in Computer Sciences, University of Technology, 2008, and a Ph.D. degree in Computer Sciencel, University of Technology, 2022. Her research interests are in Data Mining, Document Recognition, Electronic Management and Computer security.

Alia K. Abdul Hassan , Department of Computer Science, University of Technology, Baghdad, Iraq

Alia K. Abdul Hassan is a Professor at the Department of Computer Science, University of Technology, Baghdad, Iraq. She got a B.Sc. degree in Computer Science, University of Technology in 1993, MSc. degree in Computer Science, University of Technology, 1999, and a Ph.D. degree in Computer Science, University of Technology in 2004. Her research interests are in soft computing, green computing, AI, data mining, software engineering, electronic management, and computer security.

Scott Turner , School of Engineering, Design, and Technology, Church Christ Church University, Kent, UK

Scott Turner is a Director of Computing at the Computing, School of Engineering, Design and Technology, Canterbury Christ Church University, Kent, UK. He got a B.Eng. degree in Electronics Engineering, University of Hull, UK, M.Sc. degree in Biomedical Instrumentation Engineering, University of Dundee, UK and the Ph.D. degree in applying evolutionary algorithms to evoked potentials, University of Leicester, UK. His research interests are in applied machine learning and pedagogy in computing. Dr. Turner is a member of IEEE, IET, British Computer Society. 


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How to Cite
Jasim, S. S., Abdul Hassan , A. K. and Turner , S. (2022) “Driver Drowsiness Detection Using Gray Wolf Optimizer Based on Voice Recognition”, ARO-THE SCIENTIFIC JOURNAL OF KOYA UNIVERSITY, 10(2), pp. 142-151. doi: 10.14500/aro.11000.