Driver Drowsiness Detection Using Gray Wolf Optimizer Based on Voice Recognition
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.
Abdul-Hassan, A.K. and Hadi, I.H., 2020. A proposed authentication approach based on voice and fuzzy logic. In: Recent Trends in Intelligent Computing, Communication and Devices. Springer, Berlin, Heidelberg.
Abdulwahed, M.N., 2018. Analysis of image noise reduction using neural network. Engineering and Technology Journal, 36, pp.76-87.
Abed, I.S., 2019. Lung Cancer Detection from X-ray images by combined Backpropagation Neural Network and PCA. Engineering and Technology Journal, 37, pp.166-171.
Adwan, I., Milad, A., Abdullah, N.H., Widyatmoko, I., Mubaraki, M., Yazid, M.R. and Yusoff, N.I., 2022. Predicting asphalt pavement temperature by using neural network and multiple linear regression approach in the Eastern Mediterranean region. Journal of Engineering Science and Technology, 17, pp.0015-0032.
Alzu’bi, H.S., Al-Nuaimy, W. and Al-Zubi, N.S., 2013. EEG-based driver fatigue detection. In: 2013 Sixth International Conference on Developments in eSystems Engineering. IEEE, New Jersey, United States. pp.111-114.
Badr, A.A. and Abdul-Hassan, A.K., 2020. A review on voice-based interface for human-robot interaction. Iraqi Journal for Electrical and Electronic Engineering, 16, pp.91-102.
Bati, A.F. and Adam, N.E., 2006. Hybrid neuro-genetic based controller of power system. Iraqi Journal of Computers, Communication, Control and Systems Engineering, 6, pp.1-115.
Chen, J., Wang, H. and Hua, C., 2018. Assessment of driver drowsiness using electroencephalogram signals based on multiple functional brain networks. International Journal of Psychophysiology, 133, pp.120-130.
Dasgupta, A., Kabi, B., George, A., Happy, S. and Routray, A., 2015. A drowsiness detection scheme based on fusion of voice and vision cues. arXiv preprint arXiv:1509.04887.
Gamit, M.R. and Dhameliya, K., 2015. Isolated words recognition using MFCC, LPC and neural network. International Journal of Research in Engineering and Technology, 4, pp.146-149.
Greco, A., Marzi, C., Lanata, A., Scilingo, E.P. and Vanello, N., 2019. Combining electrodermal activity and speech analysis towards a more accurate emotion recognition system. In: 41st Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), IEEE, New Jersey, United States, pp.229-232.
Hassan, A. and Hadi, M., 2016. Sense-based information retrieval using artificial bee colony approach. International Journal of Applied Engineering Research, 11, pp.8708-8713.
Hassan, A.K. and Alawi, M., 2017. Proposed handwriting Arabic words classification based on discrete wavelet transform and support vector machine. Iraqi Journal of Science, 58, pp.1159-1168.
Hassan, A.K. and Jasim, S.S., 2010. Integrating neural network with genetic algorithms for the classification plant disease. Engineering and Technology Journal, 28, pp.686-702.
Hassan, A.K. and Mohammed, S.N., 2020. A novel facial emotion recognition scheme based on graph mining. Defence Technology, 16, pp.1062-1072.
Heidari, A.A. and Pahlavani, P., 2017. An efficient modified grey wolf optimizer with Lévy flight for optimization tasks. Applied Soft Computing, 60, pp.115-134.
Huang, X., Cheng, C. and Zhang, X.B., 2022. Machine learning and numerical investigation on drag reduction of underwater serial multi-projectiles. Defence Technology, 18, pp.229-237.
Huo, X.Q., Zheng, W.L. and Lu, B.L., 2016. Driving fatigue detection with a fusion of EEG and forehead EOG. In: 2016 International Joint Conference on Neural Networks (IJCNN), IEEE, New Jersey, United States. pp.897-904.
Jasim, S.S. and Hassan, A.K., 2022. Modern drowsiness detection techniques: A review. International Journal of Electrical and Computer Engineering, 12, pp.2986-2995.
Krajewski, J., Batliner, A. and Golz, M., 2009. Acoustic sleepiness detection: Framework and validation of a speech-adapted pattern recognition approach. Behavior Research Methods, 41, pp.795-804.
Martin, V.P., Rouas, J.L., Boyer, F. and Philip, P., 2021. Automatic Speech Recognition systems errors for accident-prone sleepiness detection through voice. In: 2021 29th European Signal Processing Conference (EUSIPCO), IEEE, New Jersey, United States. pp.541-545.
Nwobi-Okoye, C.C. and Ochieze, B.Q., 2018. Age hardening process modelling and optimization of aluminium alloy A356/Cow horn particulate composite for brake drum application using RSM, ANN and simulated annealing. Defence Technology, 14, pp.336-345.
Okfalisa, Handayani, L., Juwita, P.D., Affandes, M., Fauzi, S.S. and Saktioto., 2021. Coronary heart disease using support vector machine. Journal of Engineering Science and Technology, 16, p.16.
Ooi, J.S., Ahmad, S.A., Chong, Y.Z., Ali, S.H., Ai, G. and Wagatsuma, H., 2016. Driver emotion recognition framework based on electrodermal activity measurements during simulated driving conditions. In: 2016 IEEE EMBS Conference on Biomedical Engineering and Sciences (IECBES), IEEE, New Jersey, United States. pp.365-369.
Pane, E.S., Hendrawan, M.A., Wibawa, A.D. and Purnomo, M.H., 2017. Identifying rules for electroencephalograph (EEG) emotion recognition and classification. In: 2017 5th International Conference on Instrumentation, Communications, Information Technology, and Biomedical Engineering (ICICI-BME), IEEE, New Jersey, United States. pp.167-172.
Rashid, T.A. and Abdullah, S.M., 2018. A hybrid of an artificial bee colony, genetic algorithm, and neural network for diabetic Mellitus diagnosing. ARO-The Scientific Journal of Koya University, 6, pp.55-64.
Salam, M. and Hassan, A.A., 2019. Offline isolated Arabic handwriting character recognition system based on SVM. International Arab Journal of Information Technology, 16, pp.467-472.
Tao, P., Sun, Z. and Sun, Z., 2018. An improved intrusion detection algorithm based on GA and SVM. IEEE Access, 6, pp.13624-13631.
Wankhade, S.B. and Kharat, P.A., 2017. A novel two-tier classifier based on K-nearest neighbour and neural network classifier for emotion recognition using EEG signals. International Journal of Latest Technology in Engineering, Management and Applied Science (IJLTEMAS), 6, p.7.
Xu, L., Wang, H., Lin, W., Gulliver, T.A. and Le, K.N., 2019. GWO-BP neural network-based OP performance prediction for mobile multiuser communication networks. IEEE Access, 7, pp.152690-152700.
Yoshida, R., Nakayama, T., Ogitsu, T., Takemura, H., Mizoguchi, H., Yamaguchi, E., Inagaki, S., Takeda, Y., Namatame, M., Sugimoto, M. and Kusunoki, F., 2014. Feasibility study on estimating visual attention using electrodermal activity. Proceedings of the International Conference on Sensing Technology, 2014, pp.589-595.
Yu, X., Wang, S.H. and Zhang, Y.D., 2021. CGNet: A graph-knowledge embedded convolutional neural network for detection of pneumonia. Information Processing and Management, 58, p.102411.
Yusiong, J.P., 2012. Optimizing artificial neural networks using cat swarm optimization algorithm. International Journal of Intelligent Systems and Applications, 5, p.69.
Zeng, N., Qiu, H., Wang, Z., Liu, W., Zhang, H. and Li, Y., 2018. A new switching-delayed-PSO-based optimized SVM algorithm for diagnosis of Alzheimer’s disease. Neurocomputing, 320, pp.195-202.
Zhang, F., Su, J., Geng, L. and Xiao, Z., 2017, Driver fatigue detection based on eye state recognition. In: 2017 International Conference on Machine Vision and Information Technology (CMVIT), IEEE, New Jersey, United States. pp.105-110.
Zhang, L., 2019. Analysis of Machine Learning Algorithms for the Recognition of Basic Emotions: Data Mining of Psychophysiological Sensor Information. Ulm Universität, Germany.
Copyright (c) 2022 Sarah S. Jasim, Alia Ka. Abdul Hassan , Scott Turner
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