Optimizing artificial neural networks using LevyChaotic mapping on Wolf Pack optimization algorithm for detect driving sleepiness

Journal article

Turner, S., Jassin, S.S. and Hassan, A.K.A 2022. Optimizing artificial neural networks using LevyChaotic mapping on Wolf Pack optimization algorithm for detect driving sleepiness. Iraqi Journal of Computers, Communications, Control & Systems Engineering (IJCCCE). 22 (3), pp. 128-136. https://doi.org/10.33103/uot.ijccce.22.3.12

Publication dates
Authors	Turner, S., Jassin, S.S. and Hassan, A.K.A
Abstract	Artificial Neural Networks (ANNs) are utilized to solve a variety of problems in many domains. In this type of network, training and selecting parameters that define networks architecture play an important role in enhancing the accuracy of the network's output; Therefore, Prior to training, those parameters must be optimized. Grey Wolf Optimizer (GWO) has been considered one of the efficient developed approaches in the Swarm Intelligence area that is used to solve real-world optimization problems. However, GWO still faces a problem of the slump in local optimums in some places due to insufficient diversity. This paper proposes a novel algorithm Levy Flight- Chaotic Chen mapping on Wolf Pack Algorithm in Neural Network. It efficiently exploits the search regions to detect driving sleepiness and balance the exploration and exploitation operators, which are considered implied features of any stochastic search algorithm. Due to the lack of dataset availability, a dataset of 15 participants has been collected from scratch to evaluate the proposed algorithm's performance. The results show that the proposed algorithm achieves an accuracy of 99.3%.
Keywords	Electrooculography; Drowsiness; Neural network (NN); Grey Wolf Optimizer (GWO); Levy flight distribution
Year	2022
Journal	Iraqi Journal of Computers, Communications, Control & Systems Engineering (IJCCCE)
Journal citation	22 (3), pp. 128-136
Publisher	University of Technology, Iraq
ISSN	2617-3352
Digital Object Identifier (DOI)	https://doi.org/10.33103/uot.ijccce.22.3.12
Official URL	https://ijccce.uotechnology.edu.iq/article_176802.html
Online	Sep 2022
Publication process dates
Deposited	09 Feb 2023
Publisher's version	IJCCCE_Volume 22_Issue 3_Pages 128-136.pdf License All rights reserved File Access Level Open
Supplemental file	File Access Level Open
Output status	Published
Additional information	[1] J. P. T. Yusiong, "Optimizing artificial neural networks using cat swarm optimization algorithm," International Journal of Intelligent Systems and Applications, vol. 5, no. 1, p. 69, 2012. [2] H. Shi, "Evolving artificial neural networks using ga and momentum," in 2009 Second International Symposium on https://doi.org/10.1109/ISECS.2009.132 Electronic Commerce and Security, 2009, vol. 1: IEEE, pp. 475-478. [3] M. Paliwal and U. A. Kumar, "Neural networks and statistical techniques: A review of applications," Expert systems with applications, vol. 36, no. 1, pp. 2-17, 2009. https://doi.org/10.1016/j.eswa.2007.10.005 [4] A. K. A. Hassan and S. S. Jasim, "Integrating Neural Network With Genetic Algorithms For The Classification Plant Disease," Engineering and Technology Journal, vol. 28, no. 4, pp. 686-702, 2010. https://doi.org/10.1109/JLT.2010.2043777 [5] F. Valdez, "Swarm Intelligence: An Introduction, History and Applications," in HANDBOOK ON COMPUTATIONAL INTELLIGENCE: Volume 2: Evolutionary Computation, Hybrid Systems, and Applications: World Scientific, 2016, pp. 587-606. [6] X. Yu, S.-H. Wang, and Y.-D. Zhang, "CGNet: A graph-knowledge embedded convolutional neural network for detection of pneumonia," Information Processing & Management, vol. 58, no. 1, p. 102411, 2021. https://doi.org/10.1016/j.ipm.2020.102411 PMid:33100482 PMCid:PMC7569413 [7] N. Peifeng, N. Songpeng, and L. Nan, "The defect of the Grey Wolf optimization algorithm and its verification method [J]," Knowledge-Based Systems, vol. 171, pp. 37-43, 2019. https://doi.org/10.1016/j.knosys.2019.01.018 [8] M. Ramzan, H. U. Khan, S. M. Awan, A. Ismail, M. Ilyas, and A. Mahmood, "A survey on state-of-the-art drowsiness detection techniques," IEEE Access, vol. 7, pp. 61904-61919, 2019. https://doi.org/10.1109/ACCESS.2019.2914373 [9] A. Čolić, O. Marques, and B. Furht, Driver drowsiness detection: Systems and solutions. Springer, 2014. https://doi.org/10.1007/978-3-319-11535-1 [10] S. Charles Goldenbeld, " Fatigue 2018 " European Commission, Fatigue, European Commission, Directorate General for Transport February 2018. [11] "road casualties Great Britain, annual report: 2015," Department for Transport, Great Britain, 29 September 2016. [12] L. Wang and Y. Pei, "The impact of continuous driving time and rest time on commercial drivers' driving performance and recovery," Journal of safety research, vol. 50, pp. 11-15, 2014. https://doi.org/10.1016/j.jsr.2014.01.003 PMid:25142356 [13] I. Teyeb, O. Jemai, M. Zaied, and C. B. Amar, "A novel approach for drowsy driver detection using head posture estimation and eyes recognition system based on wavelet network," in IISA 2014, The 5th International Conference on Information, Intelligence, Systems and Applications, 2014: IEEE, pp. 379-384. [14] A. Bamidele et al., "Non-intrusive driver drowsiness detection based on face and eye tracking," Int J. Adv. Comput. Sci. Appl, vol. 10, pp. 549-569, 2019. [15] M. Conforth and Y. Meng, "Toward Evolving Neural Networks using Bio-Inspired Algorithms," in IC-AI, 2008, pp. 413-419. [16] Y. Da and G. Xiurun, "An improved PSO-based ANN with simulated annealing technique," Neurocomputing, vol. 63, pp. 527-533, 2005. [17] K. K. Kuok, S. Harun, and S. Shamsuddin, "Particle swarm optimization feedforward neural network for modeling runoff," International Journal of Environmental Science & Technology, vol. 7, no. 1, pp. 67-78, 2010. https://doi.org/10.1007/BF03326118 [18] B. A. Garro, H. Sossa, and R. A. Vázquez, "Back-propagation vs particle swarm optimization algorithm: which algorithm is better to adjust the synaptic weights of a feed-forward ANN?," International Journal of Artificial Intelligence, vol. 7, no. 11, pp. 208-218, 2011. https://doi.org/10.1002/elsc.201190015 [19] B. A. Garro, H. Sossa, and R. A. Vázquez, "Evolving neural networks: A comparison between differential evolution and particle swarm optimization," in International Conference in Swarm Intelligence, 2011: Springer, pp. 447-454. [20] M. Shariati et al., "Application of a hybrid artificial neural network-particle swarm optimization (ANN-PSO) model in behavior prediction of channel shear connectors embedded in normal and high-strength concrete," Applied Sciences, vol. 9, no. 24, p. 5534, 2019. https://doi.org/10.3390/app9245534 [21] R. Zhang and Z. Qiu, "Optimizing hyper-parameters of neural networks with swarm intelligence: A novel framework for credit scoring," Plos one, vol. 15, no. 6, p. e0234254, 2020. https://doi.org/10.1371/journal.pone.0234254 PMid:32502197 PMCid:PMC7274386 [22] A. Parsian, M. Ramezani, and N. Ghadimi, "A hybrid neural network-gray wolf optimization algorithm for melanoma detection," Biomedical Research (0970-938X), vol. 28, no. 8, 2017. [23] B. A. Garro, H. Sossa, and R. A. Vazquez, "Design of artificial neural networks using a modified particle swarm optimization algorithm," in 2009 International Joint Conference on Neural Networks, 2009: IEEE, pp. 938-945. [24] M. Costa, D. Oliveira, S. Pinto, and A. Tavares, "Detecting driver's fatigue, distraction and activity using a nonintrusive ai-based monitoring system," Journal of Artificial Intelligence and Soft Computing Research, vol. 9, no. 4, https://doi.org/10.2478/jaiscr-2019-0007 pp. 247-266, 2019. [25] M. Dreißig, M. H. Baccour, T. Schäck, and E. Kasneci, "Driver Drowsiness Classification Based on Eye Blink and Head Movement Features Using the k-NN Algorithm," in 2020 IEEE Symposium Series on Computational Intelligence (SSCI), 2020: IEEE, pp. 889-896. [26] C. J. de Naurois, C. Bourdin, A. Stratulat, E. Diaz, and J.-L. Vercher, "Detection and prediction of driver drowsiness using artificial neural network models," Accident Analysis & Prevention, vol. 126, pp. 95-104, 2019. https://doi.org/10.1016/j.aap.2017.11.038 PMid:29203032 [27] T. Vesselenyi, S. Moca, A. Rus, T. Mitran, and B. Tătaru, "Driver drowsiness detection using ANN image processing," in IOP conference series: materials science and engineering, 2017, vol. 252, no. 1: IOP Publishing, p. 012097. [28] Z. Li, Q. Zhang, and X. Zhao, "Performance analysis of K-nearest neighbor, support vector machine, and artificial neural network classifiers for driver drowsiness detection with different road geometries," International Journal of Distributed Sensor Networks, vol. 13, no. 9, p. 1550147717733391, 2017. [29] C. J. de Naurois, C. Bourdin, C. Bougard, and J.-L. Vercher, "Adapting artificial neural networks to a specific driver enhances detection and prediction of drowsiness," Accident Analysis & Prevention, vol. 121, pp. 118-128, 2018. https://doi.org/10.1016/j.aap.2018.08.017 PMid:30243040 [30] B. C. R. M. Araújo, "Drowsiness Detection Using a Headband and Artificial Neural Networks," Universidade de Coimbra, 2019. [31] Y. Wang, P. Picton, S. Turner, and G. Attenburrow, "Predicting leather handle like an expert by artificial neural networks," Applied Artificial Intelligence, vol. 25, no. 2, pp. 180-192, 2011. https://doi.org/10.1080/08839514.2011.545218 [32] H. Moayedi, H. Nguyen, and L. K. Foong, "Nonlinear evolutionary swarm intelligence of grasshopper optimization algorithm and gray wolf optimization for weight adjustment of neural network," Engineering with Computers, pp.1-11, 2019. [33] S. Mirjalili, S. M. Mirjalili, and A. Lewis, "Grey wolf optimizer," Advances in engineering software, vol. 69, pp. https://doi.org/10.1016/j.advengsoft.2013.12.007 46-61, 2014. [34] G. Negi, A. Kumar, S. Pant, and M. Ram, "GWO: a review and applications," International Journal of System Assurance Engineering and Management, pp. 1-8. [35] L. Xu, H. Wang, W. Lin, T. A. Gulliver, and K. N. Le, "GWO-BP neural network based OP performance prediction for mobile multiuser communication networks," IEEE Access, vol. 7, pp. 152690-152700, 2019. https://doi.org/10.1109/ACCESS.2019.2948475 [36] J. He and F. Xu, "Chaotic-search-based cultural algorithm for solving unconstrained optimization problem," Modelling and Simulation in Engineering, vol. 2011, 2011. [37] A. A. Heidari and P. Pahlavani, "An efficient modified grey wolf optimizer with Lévy flight for optimization tasks," Applied Soft Computing, vol. 60, pp. 115-134, 2017. https://doi.org/10.1016/j.asoc.2017.06.044 [38] Z. Xiu and W. Zhen-Hua, "Improved wolf pack algorithm based on tent chaotic mapping and Levy flight," in 2017 https://doi.org/10.1109/ICRIS.2017.48 International Conference on Robots & Intelligent System (ICRIS), 2017: IEEE, pp. 165-169. [39] A. A. Dubkov, B. Spagnolo, and V. V. Uchaikin, "Lévy flight superdiffusion: an introduction," International Journal of Bifurcation and Chaos, vol. 18, no. 09, pp. 2649-2672, 2008. https://doi.org/10.1142/S0218127408021877 [40] G. Iacca, V. C. dos Santos Junior, and V. V. de Melo, "An improved Jaya optimization algorithm with Levy flight," Expert Systems with Applications, vol. 165, p. 113902, 2021. https://doi.org/10.1016/j.eswa.2020.113902 [41] J.-C. Nuñez-Perez, V.-A. Adeyemi, Y. Sandoval-Ibarra, F.-J. Perez-Pinal, and E. Tlelo-Cuautle, "Maximizing the chaotic behavior of fractional order chen system by evolutionary algorithms," Mathematics, vol. 9, no. 11, p. 1194, https://doi.org/10.3390/math9111194 2021. [42] E. Tlelo-Cuautle, A. D. Pano-Azucena, O. Guillén-Fernández, and A. Silva-Juárez, "Synchronization and applications of fractional-order chaotic systems," in Analog/Digital Implementation of Fractional Order Chaotic Circuits and Applications: Springer, 2020, pp. 175-201. [43] M. Saikia and B. Baruah, "Chaotic map based image encryption in Spatial domain: a brief survey," in Proceedings of the First International Conference on Intelligent Computi g and Communication, 2017: Springer, pp. 569-579.

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