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Hassanien, A. E., N. El-Bendary, M. Kudělka, and Václav Snášel, "Breast cancer detection and classification using support vector machines and pulse coupled neural network", Proceedings of the Third International Conference on Intelligent Human Computer Interaction (IHCI 2011), Prague, Czech Republic, August, 2011: Springer Berlin Heidelberg, pp. 269–279, 2013. Abstract
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Hassanien, A. E., N. El-Bendary, M. Kudělka, and Václav Snášel, "Breast cancer detection and classification using support vector machines and pulse coupled neural network", Proceedings of the Third International Conference on Intelligent Human Computer Interaction (IHCI 2011), Prague, Czech Republic, August, 2011: Springer Berlin Heidelberg, pp. 269–279, 2013. Abstract
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Sayed, G. I., A. Darwish, A. E. Hassanien, and J. - S. Pan, "Breast Cancer Diagnosis Approach Based on Meta-Heuristic Optimization Algorithm Inspired by the Bubble-Net Hunting Strategy of Whales", International Conference on Genetic and Evolutionary Computing: Springer International Publishing, pp. 306–313, 2016. Abstract
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Hassanien, A. E., and T. - H. Kim, "Breast cancer diagnosis system based on machine learning techniques", Applied Logic journal, vol. 10, issue 4, pp. 277–284, 2012. AbstractWebsite

This article introduces a hybrid approach that combines the advantages of fuzzy sets, pulse coupled neural networks (PCNNs), and support vector machine, in conjunction with wavelet-based feature extraction. An application of breast cancer MRI imaging has been chosen and hybridization approach has been applied to see their ability and accuracy to classify the breast cancer images into two outcomes: normal or non-normal. The introduced approach starts with an algorithm based on type-II fuzzy sets to enhance the contrast of the input images. This is followed by performing PCNN-based segmentation algorithm in order to identify the region of interest and to detect the boundary of the breast pattern. Then, wavelet-based features are extracted and normalized. Finally, a support vector machine classifier was employed to evaluate the ability of the lesion descriptors for discrimination of different regions of interest to determine whether they represent cancer or not. To evaluate the performance of presented approach, we present tests on different breast MRI images. The experimental results obtained, show that the overall accuracy offered by the employed machine learning techniques is high compared with other machine learning techniques including decision trees, rough sets, neural networks, and fuzzy artmap.

Hassanien, A. E., and T. - H. Kim, "Breast cancer MRI diagnosis approach using support vector machine and pulse coupled neural networks", Journal of Applied Logic, vol. 10, no. 4: Elsevier, pp. 277–284, 2012. Abstract
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Peters, J. F., and S. K. Pal, "Cantor, fuzzy, near, and rough sets in image analysis", Rough fuzzy image analysis: Foundations and methodologies: CRC Press, pp. 1–1, 2010. Abstract
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Hamdy, A., N. El-Bendary, A. Khodeir, M. M. M. Fouad, A. E. Hassanien, and H. Hefny, "Cardiac disorders detection approach based on local transfer function classifier", Computer Science and Information Systems (FedCSIS), 2013 Federated Conference on: IEEE, pp. 55–61, 2013. Abstract
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Ayeldeen, H., O. Hegazy, and A. E. Hassanien, "Case selection strategy based on K-means clustering", Information Systems Design and Intelligent Applications: Springer India, pp. 385–394, 2015. Abstract
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Ayeldeen, H., O. Hegazy, and A. E. Hassanien, "Case selection strategy based on K-means clustering,", The Second International Conference on INformation systems Design and Intelligent Applications ((INDIA 15), Kalyani, India, January 8-9 , 2015.
Ayeldeen, H., O. Shaker, O. Hegazy, and A. E. Hassanien, "Case-based reasoning: A knowledge extraction tool to use", The Second International Conference on INformation systems Design and Intelligent Applications ((INDIA 15), Kalyani, India, January 8-9 , 2015.
Ayeldeen, H., O. Shaker, O. Hegazy, and A. E. Hassanien, "Case-Based Reasoning: A Knowledge Extraction Tool to Use", Information systems design and intelligent applications: Springer India, pp. 369–378, 2015. Abstract
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Ayeldeen, H., M. A. Fattah, O. Shaker, A. E. Hassanien, and T. - H. Kim, "Case-Based Retrieval Approach of Clinical Breast Cancer Patients", Computer, Information and Application (CIA), 2015 3rd International Conference on: IEEE, pp. 38–41, 2015. Abstract
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Mahmoud, H. A., H. M. El Hadad, F. A. Mousa, and A. E. Hassanien, "Cattle classifications system using Fuzzy K-Nearest Neighbor Classifier", Informatics, Electronics & Vision (ICIEV), 2015 International Conference on: IEEE, pp. 1–5, 2015. Abstract
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Alaa Tharwat, T. Gaber, A. E. Hassanien, H. A. Hassanien, and M. F. Tolba, "Cattle Identi cation using Muzzle Print Images based on Texture Features Approach", The 5th International Conference on Innovations in Bio-Inspired Computing and Applications (Springer) IBICA2014, Ostrava, Czech Republic., 22-24 June, 2014. Abstractibica2014_p26.pdf

The increasing growth of the world trade and growing con-
cerns of food safety by consumers need a cutting-edge animal identi-
cation and traceability systems as the simple recording and reading
of tags-based systems are only eective in eradication programs of na-
tional disease. Animal biometric-based solutions, e.g. muzzle imaging
system, oer an eective and secure, and rapid method of addressing
the requirements of animal identication and traceability systems. In
this paper, we propose a robust and fast cattle identication approach.
This approach makes use of Local Binary Pattern (LBP) to extract local
invariant features from muzzle print images. We also applied dierent
classiers including Nearest Neighbor, Naive Bayes, SVM and KNN for
cattle identication. The experimental results showed that our approach
is superior than existed works as ours achieves 99,5% identication accu-
racy. In addition, the results proved that our proposed method achieved
this high accuracy even if the testing images are rotated in various angels
or occluded with dierent parts of their sizes.

Awad, A. I., A. E. Hassanien, and H. M. Zawbaa, "A cattle identification approach using live captured muzzle print images", Advances in Security of Information and Communication Networks: Springer Berlin Heidelberg, pp. 143–152, 2013. Abstract
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Alaa Tharwat, T. Gaber, and A. E. Hassanien, "Cattle Identification based on Muzzle Images using Gabor Features and SVM Classifier ", The 2nd International Conference on Advanced Machine Learning Technologies and Applications , Egypt, November 17-19, , 2014.
Alaa Tharwat, T. Gaber, and A. E. Hassanien, "Cattle identification based on muzzle images using gabor features and SVM classifier", International Conference on Advanced Machine Learning Technologies and Applications: Springer International Publishing, pp. 236–247, 2014. Abstract
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Awad, A. I., H. zawbaa, and A. E. Hassanien, "A Cattle Identification of Approach Using Live Captured Muzzle Print Images", International conference on Advances in Security of Information and Communication Networks, (SecNet 2013) , Springer , Egypt, 3-5 Sept, , 2013. a_cattle_identification.pdf
Alaa Tharwat, T. Gaber, A. E. Hassanien, H. A. Hassanien, and M. F. Tolba, "Cattle identification using muzzle print images based on texture features approach", Proceedings of the Fifth International Conference on Innovations in Bio-Inspired Computing and Applications IBICA 2014: Springer International Publishing, pp. 217–227, 2014. Abstract
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Alaa Tharwat, and A. E. Hassanien, "Chaotic antlion algorithm for parameter optimization of support vector machine", Applied Intelligence, vol. 48, issue 3, pp. 670–686, 2018. AbstractWebsite

Support Vector Machine (SVM) is one of the well-known classifiers. SVM parameters such as kernel parameters and penalty parameter (C) significantly influence the classification accuracy. In this paper, a novel Chaotic Antlion Optimization (CALO) algorithm has been proposed to optimize the parameters of SVM classifier, so that the classification error can be reduced. To evaluate the proposed algorithm (CALO-SVM), the experiment adopted six standard datasets which are obtained from UCI machine learning data repository. For verification, the results of the CALO-SVM algorithm are compared with grid search, which is a conventional method of searching parameter values, standard Ant Lion Optimization (ALO) SVM, and three well-known optimization algorithms: Genetic Algorithm (GA), Particle Swarm Optimization (PSO), and Social Emotional Optimization Algorithm (SEOA). The experimental results proved that the proposed algorithm is capable of finding the optimal values of the SVM parameters and avoids the local optima problem. The results also demonstrated lower classification error rates compared with GA, PSO, and SEOA algorithms.

Rizk Masoud, A. E. Hassanien, and Siddhartha Bhattacharyya, "Chaotic Crow Search Algorithm for Fractional Optimization Problems", Applied soft computing , 2018. Abstract

This paper presents a chaotic crow search algorithm (CCSA) for solving fractional optimization problems (FOPs). To refine the global convergence speed and enhance the exploration/exploitation tendencies, the proposed CCSA integrates chaos theory (CT) into the CSA. CT is introduced to tune the parameters of the standard CSA, yielding four variants, with the best chaotic variant being investigated. The performance of the proposed CCSA is validated on twenty well-known fractional benchmark problems. Moreover, it is validated on a fractional economic environmental power dispatch problem by attempting to minimize the ratio of total emissions to total fuel cost. Finally, the proposed CCSA is compared with the standard CSA, particle swarm optimization (PSO), firefly algorithm (FFA), dragonfly algorithm (DA) and grey wolf algorithm (GWA). Additionally, the efficiency of the proposed CCSA is justified using the non parametric Wilcoxon signed-rank test. The experimental results prove that the proposed CCSA outperforms other algorithms in terms of quality and reliability.

Elshazly, H. I., M. Waly, A. M. Elkorany, and A. E. Hassanien, "Chronic eye disease diagnosis using ensemble-based classifier", Engineering and Technology (ICET), 2014 International Conference on: IEEE, pp. 1–6, 2014. Abstract
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Hassanien, A. E., "Classification and feature selection of breast cancer data based on decision tree algorithm", Studies in Informatics and Control, vol. 12, no. 1: INFORMATICS AND CONTROL PUBLICATIONS, pp. 33–40, 2003. Abstract
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Hassanien, A. E., "Classification and feature selection of breast cancer data based on decision tree algorithm", Studies in Informatics and Control, vol. 12, no. 1: INFORMATICS AND CONTROL PUBLICATIONS, pp. 33–40, 2003. Abstract
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Hassanien, A. E., and J. M. Ali, "Classification and Retrieval of Images from Databases Using Rough Set Theory", Distributed Artificial Intelligence, Agent Technology, and Collaborative Applications: IGI Global, pp. 179–198, 2009. Abstract
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