The International Arab Journal of Information Technology (IAJIT)


Muzzle Classification Using Neural Networks

There are multiple techniques used in image classification such as Support Vector Machines (SVM), Artificial Neural Networks (ANN), Genetic Algorithms (GA), Fuzzy measures, and Fuzzy Support Vector Machines (FSVM). Classification of muzzle depending on one of this artificial technique has become widely known for guaranteeing the safety of cattle products and assisting in veterinary disease supervision and control. The aim of this paper is to focus on using neural network technique for image classification. First, the area of interest in the captured image of muzzle is detected then pre- processing operations such as histogram equalization and morphological filtering have been used for increasing the contrast and removing noise of the image. Then, using box-counting algorithm to extract the texture feature of each muzzle. This feature is used for learning and testing stage of the neural network for muzzle classification. The experimental result shows that after 15 input cases for each image in neural training step, the testing result is true and gives us the correct muzzle detection. Therefore, neural networks can be applied in classification of bovines for breeding and marketing systems registration.


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[31] Yeganeh H., Ziaei A., and Rezaie A., “A Novel Approach for Contrast Enhancement Based on Histogram Equalization,” in Proceeding of the International Conference on Computer and Communication Engineering, Coimbatore, pp. 256-260, 2008. Ibrahim El-henawy received the M.S. and Ph.D. degrees in computer science from State University of New York, USA in 1980 and 1983, respectively. Currently, he is a professor in computer science and mathematics department, Zagazig University. His current research interests are mathematics, operations research, statistics, networks, optimization, Intelligent Computing, Computer Theory, digital image processing, and pattern recognition. Hazem El-Bakry (Mansoura, EGYPT 20-9-1970) received B.Sc. degree in Electronics Engineering, and M.Sc. in Electrical Communication Engineering from the Faculty of Engineering, Mansoura University - Egypt, in 1992 and 1995 respectively. Dr. El-Bakry received Ph. D degree from University of Aizu-Japan in 2007. Currently, he is associate professor at the Faculty of Computer Science and Information Systems - Mansoura University-Egypt. His research interests include neural networks, pattern recognition, image processing, biometrics, cooperative intelligent systems and electronic circuits. In these areas, he has published many papers in major international journals and refereed international conferences. According to academic measurements, now the total number of citations for his publications is 2899. The H-index of his publications is 28. Dr. El-Bakry has the United States Patent No. 20060098887, 2006. Furthermore, he is associate editor and referee for some major journals. Moreover, he has been awarded the Japanese Computer and Communication prize in April 2006 and the best paper prize in two conferences cited by ACM. Dr. El-Bakry has been selected in who Asia 2006 and BIC 100 educators in Africa 2008. Hagar El-Hadad graduated from Faculty of Computers and Information, Minia University, Minia, Egypt in 2008. Hagar received her master degree in 2011 in Information Systems from the Faculty of Computers and Information, Mansoura University, Mansoura, Egypt. Hagar is teaching assistant in faculty of computer and information systems, Beni-Suef University Beni-Suef, Egypt. Hagar main research interests are in the areas of data mining such as (text-numbers-Images).