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Amira El sayed, A. E. Hassanien, S. E. - O. Hanafy, and M. Tolba, "Multi-layer hybrid machine learning techniques for anomalies detection and classification approach. ", 13th IEEE International Conference on Hybrid Intelligent Systems |(HIS13) Tunisia, 4-6 Dec. pp. 216-221, 2013, Tunisia, , 4-6 Dec, 2013.
Aziz, A. S. A., A. E. Hassanien, S. E. - O. Hanaf, and M. F. Tolba, "Multi-layer hybrid machine learning techniques for anomalies detection and classification approach", Hybrid Intelligent Systems (HIS), 2013 13th International Conference on: IEEE, pp. 215–220, 2013. Abstract
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Zhu, Z., Z. Wang;, T. Li;, X. Wang, H. Liu, and A. E. Hassanien, "Multi-knowledge extraction algorithm using Group Search Optimization for brain dataset analysis", 2nd International Conference on Computing for Sustainable Global Development (INDIACom) 11-13 March, pp. 1891 – 1896, , India, 11 March, 2015.
Zhu, Z., Z. Wang, T. Li, X. Wang, H. Liu, A. E. Hassanien, and W. Yang, "Multi-knowledge extraction algorithm using Group Search Optimization for brain dataset analysis", Computing for Sustainable Global Development (INDIACom), 2015 2nd International Conference on: IEEE, pp. 1891–1896, 2015. Abstract
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Guangyao Dai, H. L.  Zongmei Wang, Chao Yang, Aboul Ella Hassanieny, and W. Yang, "A Multi-granularity Rough Set Algorithm for Attribute Reduction through Particles Particle Swarm Optimization", IEEE iInternational Computer Engineering Conference - ICENCO , Cairo, 30 Dec, 2015.
Dai, G., Z. Wang, C. Yang, H. Liu, A. E. Hassanien, and W. Yang, "A multi-granularity rough set algorithm for attribute reduction through particles particle swarm optimization", Computer Engineering Conference (ICENCO), 2015 11th International: IEEE, pp. 303–307, 2015. Abstract
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Esraa Elhariri, N. El-Bendary, Mohamed Mostafa M. Fouad, Jan Platoš, A. E. Hassanien, and A. M. M. Hussein., "Multi-class SVM Based Classification Approach for Tomato Ripeness, ", Innovations in Bio-inspired Computing and Applications. Advances in Intelligent Systems and Computing(Springer) , Czech republic , 2013.
Esraa Elhariri, N. El-Bendary, M. M. M. Fouad, Jan Platoš, A. E. Hassanien, and A. M. M. Hussein, "Multi-class SVM based classification approach for tomato ripeness", Innovations in Bio-inspired Computing and Applications: Springer International Publishing, pp. 175–186, 2014. Abstract
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Sami, M., N. El-Bendary, and A. E. Hassanien, "Multi-Class Image Annotation Approach using Particle Swarm Optimization.", The IEEE International Conference on Hybrid Intelligent Systems (HIS2012).. , Pune. India, 4-7 Dec. 2012,, pp. 103 - 108., 2012. Abstract

This paper presents an automatic image annotation approach for region labeling. The proposed approach is based on multi-class k-nearest neighbor, K-means, and particle swarm optimization algorithms for feature weighting, in conjunction with normalized cuts based image segmentation technique. This hybrid approach refines the output of multi-class classification that is based on the usage of k-nearest neighbor classifier for automatically labeling image regions from different classes. Each input image is segmented using the normalized cuts segmentation algorithm in order to subsequently create a descriptor for each segment. Particle swarm optimization algorithm is employed as a search strategy to identify an optimal feature subset. Experimental results and comparative performance evaluation, for results obtained from the proposed particle swarm optimization based approach and another support vector machine based approach presented in previous work, demonstrate that the proposed particle swarm optimization based approach outperforms the support vector machine based one, regarding annotation accuracy, for the used dataset.

Sami, M., N. El-Bendary, and A. E. Hassanien, "Multi-class image annotation approach using particle swarm optimization", Hybrid Intelligent Systems (HIS), 2012 12th International Conference on: IEEE, pp. 103–108, 2012. Abstract
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Aziz, A. S. A., S. E. - O. Hanafy, and A. E. Hassanien, "Multi-agent artificial immune system for network intrusion detection and classification", 9th International Conference on Soft Computing Models in Industrial and Environmental Applications, Bilbao, Spain, 25th - 27th Jun, 2014.
Aziz, A. S. A., S. E. - O. Hanafi, and A. E. Hassanien, "Multi-agent artificial immune system for network intrusion detection and classification", International Joint Conference SOCO’14-CISIS’14-ICEUTE’14: Springer International Publishing, pp. 145–154, 2014. Abstract
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Abraham, A., H. Liu, and A. E. Hassanien, "Multi swarms for neighbor selection in peer-to-peer overlay networks", Telecommunication Systems, vol. 46, no. 3: Springer Netherlands, pp. 195–208, 2011. Abstract
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Abraham, A., H. Liu, and A. E. Hassanien, "Multi swarms for neighbor selection in peer-to-peer overlay networks", Telecommunication Systems, vol. 46, no. 3: Springer Netherlands, pp. 195–208, 2011. Abstract
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Hassanien, A. E., H. M. Moftah, A. T. Azar, and M. Shoman, "MRI breast cancer diagnosis hybrid approach using adaptive ant-based segmentation and multilayer perceptron neural networks classifier", Applied Soft Computing, vol. 14: Elsevier, pp. 62–71, 2014. Abstract
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Hassanien, A. E., and T. - H. Kim, "MRI Breast cancer diagnosis approach using support vector machine and pulse coupled neural networks", Journal of Applied Logic - Elsevier, 2012. Abstract

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 MRI breast cancer imaging has been chosen and hybridization approach have 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 were 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, "MRI Breast cancer diagnosis approach using support vector machine and pulse coupled neural networks", Journal of Applied Logic-Elsevier, 2012. Abstract
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Hassanien, A. E., and T. - H. Kim, "MRI Breast cancer diagnosis approach using support vector machine and pulse coupled neural networks", Journal of Applied Logic-Elsevier, 2012. Abstract
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Hassanien, A. E., and T. - H. Kim, "MRI Breast cancer diagnosis approach using support vector machine and pulse coupled neural networks", Journal of Applied Logic-Elsevier, 2012. Abstract
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M.Moftah, A. E. Hassanien, A. Taher, and M. Shoman, "MRI Breast cancer diagnosis approach using adaptive Ant-based segmentation and Multilayer Perceptron NN classifier", Applied Soft Computing, Elsiever, vol. 14, issue Part A, pp. 62-71, 2014. Website
Sayed, G. I., and A. E. Hassanien, "Moth-flame swarm optimization with neutrosophic sets for automatic mitosis detection in breast cancer histology images", Applied Intelligence: Springer US, pp. 1–12, 2017. Abstract
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Waleed Yamany, M. O. H. A. M. M. E. D. FAWZY, Alaa Tharwat, and A. E. Hassanien, "Moth-flame optimization for training multi-layer perceptrons", Computer Engineering Conference (ICENCO), 2015 11th International: IEEE, pp. 267–272, 2015. Abstract
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Yamanya, W., A. T. Mohammed Fawzy, and A. E. Hassanien, "Moth-Flame Optimization for Training Multi-layer Perceptrons", IEEE iInternational Computer Engineering Conference - ICENCO , Cairo, 30 Dec, 2015.
Staelens, S., and I. Lemahieu, "Monte Carlo Based Image Reconstruction in Emission Tom ography", Computational Intelligence in Medical Imaging: Techniques and Applications: CRC Press, pp. 407, 2009. Abstract
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Staelens, S., and I. Lemahieu, "Monte Carlo Based Image Reconstruction in Emission Tom ography", Computational Intelligence in Medical Imaging: Techniques and Applications: CRC Press, pp. 407, 2009. Abstract
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