Driver Drowsiness Detection Using Gray Wolf Optimizer Based on Voice Recognition

Jasim, Sarah S. and Abdul Hassan, Alia K. and Turner, Scott (2022) Driver Drowsiness Detection Using Gray Wolf Optimizer Based on Voice Recognition. ARO-THE SCIENTIFIC JOURNAL OF KOYA UNIVERSITY, 10 (2). pp. 142-151. ISSN 2410-9355

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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.

Item Type: Article
Uncontrolled Keywords: Drowsiness, Artificial neural network, Feature extraction, Gray Wolf Optimizer, Normalization, Mel-frequency cepstral coefficients, Linear prediction coefficients
Subjects: Q Science > QA Mathematics > QA75 Electronic computers. Computer science
Divisions: ARO-The Scientific Journal of Koya University > VOL 10, NO 2 (2022)
Depositing User: Dr Salah Ismaeel Yahya
Date Deposited: 15 Dec 2022 08:35
Last Modified: 15 Dec 2022 08:35

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