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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
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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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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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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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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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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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.

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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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.
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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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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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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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.
Waleed Yamany, N. El-Bendary, A. E. Hassanien, and Eid Emary, "Multi-Objective Cuckoo Search Optimization for Dimensionality Reduction", Procedia Computer Science, vol. 96: Elsevier, pp. 207–215, 2016. Abstract
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Moustafa Ahmed, A. Hafez, M. Elwak, A. E. Hassanien, and E. Hassanien, "A Multi-Objective Genetic Algorithm for Community Detection in Multidimensional Social Network", the 1st International Conference on Advanced Intelligent Systems and Informatics (AISI’15) Springer, Beni Suef University, Beni Suef, Egypt , Nov. 28-30,, 2015. Abstract

Multidimensionality in social networks is a great issue that
came out into view as a result of that most social media sites such as
Facebook, Twitter, and YouTube allow people to interact with each other
through di erent social activities. The community detection in such mul-
tidimensional social networks has attracted a lot of attention in the recent
years. When dealing with these networks the concept of community de-
tection changes to be, the discovery of the shared group structure across
all network dimensions such that members in the same group interact
with each other more frequently than those outside the group. Most of
the studies presented on the topic of community detection assume that
there is only one kind of relation in the network. In this paper, we propose
a multi-objective approach, named MOGA-MDNet, to discover commu-
nities in multidimensional networks, by applying genetic algorithms. The
method aims to nd community structure that simultaneously maximizes
modularity, as an objective function, in all network dimensions. This
method does not need any prior knowledge about number of communi-
ties. Experiments on synthetic and real life networks show the capability
of the proposed algorithm to successfully detect the structure hidden
within these networks.

Ahmed, M. M., A. I. Hafez, M. M. Elwakil, A. E. Hassanien, and E. Hassanien, "A multi-objective genetic algorithm for community detection in multidimensional social network", The 1st International Conference on Advanced Intelligent System and Informatics (AISI2015), November 28-30, 2015, Beni Suef, Egypt: Springer International Publishing, pp. 129–139, 2016. Abstract
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E. Emary, Waleed Yamany, A. E. Hassanien, and V. Snasel, "Multi-Objective Gray-Wolf Optimization for Attribute Reduction", International Conference on Communications, management, and Information technology (ICCMIT'2015), 2015. Abstract

Feature sets are always dependent, redundant and noisy in almost all application domains. These problems in The data always declined the performance of any given classifier as it make it difficult for the training phase to converge effectively and it affect also the running time for classification at operation and training time. In this work a system for feature selection based on multi-objective gray wolf optimization is proposed. The existing methods for feature selection either depend on the data description; filter-based methods, or depend on the classifier used; wrapper approaches. These two main approaches lakes of good performance and data description in the same system. In this work gray wolf optimization; a swarm-based optimization method, was employed to search the space of features to find optimal feature subset that both achieve data description with minor redundancy and keeps classification performance. At the early stages of optimization gray wolf uses filter-based principles to find a set of solutions with minor redundancy described by mutual information. At later stages of optimization wrapper approach is employed guided by classifier performance to further enhance the obtained solutions towards better classification performance. The proposed method is assessed against different common searching methods such as particle swarm optimization and genetic algorithm and also was assessed against different single objective systems. The proposed system achieves an advance over other searching methods and over the other single objective methods by testing over different UCI data sets and achieve much robustness and stability.

Emary, E., Waleed Yamany, A. E. Hassanien, and V. Snasel, "Multi-objective gray-wolf optimization for attribute reduction", Procedia Computer Science, vol. 65: Elsevier, pp. 623–632, 2015. Abstract
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