Int. J. Computer Applications in Technology, Vol. 63, Nos. 1/2, 2020 83 Copyright © 2020 Inderscience Enterprises Ltd. Arabian horse identification based on whale optimised multi-class support vector machine Ayat Taha* Faculty of Science, Al-Azhar University, Cairo, Egypt Email: ayat_taha@ymail.com *Corresponding author Ashraf Darwish Faculty of Science, Helwan University, Cairo, Egypt Email: ashraf.darwish.eg@ieee.org Aboul Ella Hassanien Faculty of Computers and Information, Cairo University, Cairo, Egypt Email: aboitcairo@gmail.com Ahmed ElKholy Faculty of Science, Al-Azhar University, Cairo, Egypt Email: AhmedElkholySc@yahoo.com Abstract: In this study, a biometric identification approach for Arabian horse identification is proposed based on the optimised Multi-Class Support Vector Machine (MCSVM). The identification approach is performed in three phases: feature extraction, classification, and optimisation. The feature extraction phase uses Histogram of Oriented Gradient (HOG) to extract features vectors from muzzle print images of the Arabian horses and then stored in the database with its labels. The second phase is the classification phase which uses MCSVM for training and testing classification. Finally in the optimised MCSVM phase, three different swarms, Particle Swarm Optimisation (PSO), Grey Wolf Algorithm (GWA) and Whale Optimisation (WO), are used to optimise MCSVM parameters to enhance the identification accuracy of the Arabian horse. The results obtained show that the MCSVM achieves accuracy of 93.2% and increases to 97.4% with WO algorithm which achieves the best accuracy compared to PSO and GWA. Keywords: Arabian horse identification; histogram of oriented gradient; multi-class support vector machine; whale optimisation algorithm. Reference to this paper should be made as follows: Taha, A., Darwish, A., Hassanien, A.E. and ElKholy, A. (2020) ‘Arabian horse identification based on whale optimised multi-class support vector machine’, Int. J. Computer Applications in Technology, Vol. 63, Nos. 1/2, pp.83–92. Biographical notes: Ayat Taha received her BSc in 2010 and MSc degree in 2015, both from Al-Azhar University, Faculty of Science, Pure Mathematics and Computer Science Department, Cairo, Egypt. On March 2017, she pursued her PhD program from the Department of Computer Science at Science faculty, Al-Azhar University. She is Assistant Lecture of a computer science at the Cairo Institute for Computer Science and Commercial Sciences and a member of the Egyptian Scientific Research Group in Egypt (SRGE). Her research areas include biometrics, computer vision, image processing, pattern recognition and artificial intelligence, metaheuristic, machine learning, and deep learning. Ashraf Darwish is Associate Professor of Computer Science and Acting the Head of Mathematics and Computer Science Department at Faculty of Science, Helwan University, Egypt. He received the PhD degree in computer science from Saint Petersburg State University, Russian Federation in 2006. He received a BSc and MSc in Mathematics from Faculty of Science, Tanta University,