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E. Emary, H. M. Zawbaa, boul Ella Hassanien, M. F. Tolba, and V. Snasel, ""Retinal vessel segmentation based on flower pollination search algorithm"", The 5th International Conference on Innovations in Bio-Inspired Computing and Applications (Springer), Ostrava, Czech Republic., 23-24 June, 2014. Abstractibica2014_p11.pdf

This paper presents an automated retinal blood vessels segmentation
approach based on flower pollination search algorithm (FPSA). The flower pollination
search is a new algorithm based on the flower pollination process of flowering
plants. The FPSA searches for the optimal clustering of the given retinal
image into compact clusters under some constrains. Shape features are used to
further enhance the clustering results using local search method. The proposed
retinal blood vessels approach is tested on a publicly available databases DRIVE
a of retinal images. The results demonstrate that the performance of the proposed
approach is comparable with state of the art techniques in terms of accuracy, sensitivity
and specificity.

E. Emary, H. M. Zawbaa, and A. E. Hassanien, "Binary ant lion approaches for feature selection", Neurocomputing, vol. 213, 2016. AbstractWebsite

In this paper, binary variants of the ant lion optimizer (ALO) are proposed and used to select the optimal feature subset for classification purposes in wrapper-mode. ALO is one of the recently bio-inspired optimization techniques that imitates the hunting process of ant lions. Moreover, ALO balances exploration and exploitation using a single operator that can adaptively searches the domain of solutions for the optimal solution. Binary variants introduced here are performed using two different approaches. The first approach takes only the inspiration of ALO operators and makes the corresponding binary operators. In the second approach, the native ALO is applied while its continuous steps are threshold using suitable threshold function after squashing them. The proposed approaches for binary ant lion optimizer (BALO) are utilized in the feature selection domain for finding feature subset that maximizing the classification performance while minimizing the number of selected features. The proposed binary algorithms were compared to three common optimization algorithms hired in this domain namely particle swarm optimizer (PSO), genetic algorithms (GAs), binary bat algorithm (BBA), as well as the native ALO. A set of assessment indicators is used to evaluate and compare the different methods over 21 data sets from the UCI repository. Results prove the capability of the proposed binary algorithms to search the feature space for optimal feature combinations regardless of the initialization and the used stochastic operators.

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.

E. Emary, H. M. Zawbaa, A. E. Hassanien, and B. PARV, " Multi-objective retinal vessel localization using flower pollination search algorithm with pattern search, , ", Advances in Data Analysis and Classification, , issue (27 May 2016 on line), , pp. pp 1-17, 2017. AbstractWebsite

This paper presents a multi-objective retinal blood vessels localization approach based on flower pollination search algorithm (FPSA) and pattern search (PS) algorithm. FPSA is a new evolutionary algorithm based on the flower pollination process of flowering plants. The proposed multi-objective fitness function uses the flower pollination search algorithm (FPSA) that searches for the optimal clustering of the given retinal image into compact clusters under some constraints. Pattern search (PS) as local search method is then applied to further enhance the segmentation results using another objective function based on shape features. The proposed approach for retinal blood vessels localization is applied on public database namely DRIVE data set. Results demonstrate that the performance of the proposed approach is comparable with state of the art techniques in terms of accuracy, sensitivity, and specificity with many extendable features.

Egiazarian, K., and A. E. Hassanien, "Special Issue: Soft Computing in Multimedia Processing", Informatica, vol. 29, issue 3, 2005. Abstractspecial_issue_soft_computing_in_multimedia_process.pdf

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Egiazarian, K., and A. E. Hassanien, "Editorial: special issue on soft computing in multimedia processing", Informatica, vol. 29, no. 3: Slovenian Society Informatika, pp. 251–253, 2005. Abstract
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Egiazarian, K., and A. E. Hassanien, "Editorial: special issue on soft computing in multimedia processing", Informatica, vol. 29, no. 3: Slovenian Society Informatika, pp. 251–253, 2005. Abstract
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Eid, H. F., A. E. Hassanien, and T. - H. Kim, "Leaf plant identification system based on Hidden Na{\"ıve bays classifier", Advanced Information Technology and Sensor Application (AITS), 2015 4th International Conference on: IEEE, pp. 76–79, 2015. Abstract
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Eid, H. F., A. T. Azar, and A. E. Hassanien, "Improved real-time discretize network intrusion detection system", Proceedings of seventh international conference on bio-inspired computing: theories and applications (BIC-TA 2012): Springer India, pp. 99–109, 2013. Abstract
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Eid, H. F., M. A. Salama, and A. E. Hassanien, "A Feature Selection Approach for Network Intrusion Classification: The Bi-Layer Behavioral-Based", International Journal of Computer Vision and Image Processing (IJCVIP), vol. 3, no. 4: IGI Global, pp. 51–59, 2013. Abstract
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Eid, H. F., A. E. Hassanien, T. - H. Kim, and S. Banerjee, "Linear correlation-based feature selection for network intrusion detection model", Advances in Security of Information and Communication Networks: Springer Berlin Heidelberg, pp. 240–248, 2013. Abstract
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Eid, H. F., A. Darwish, A. E. Hassanien, and T. - H. Kim, "Intelligent hybrid anomaly network intrusion detection system", Communication and networking: Springer Berlin Heidelberg, pp. 209–218, 2012. Abstract
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Eid, H. F., M. A. Salama, A. E. Hassanien, and T. - H. Kim, "Bi-layer behavioral-based feature selection approach for network intrusion classification", International Conference on Security Technology: Springer Berlin Heidelberg, pp. 195–203, 2011. Abstract
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Eid, H. F., M. A. Salama, A. E. Hassanien, and T. - H. Kim, "Bi-layer behavioral-based feature selection approach for network intrusion classification", International Conference on Security Technology: Springer Berlin Heidelberg, pp. 195–203, 2011. Abstract
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Eid, H. F., A. T. Azar, and A. E. Hassanien, "Improved real-time discretize network intrusion detection system", Proceedings of seventh international conference on bio-inspired computing: theories and applications (BIC-TA 2012): Springer India, pp. 99–109, 2013. Abstract
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Eid, H. F., A. Darwish, A. E. Hassanien, and T. - H. Kim, "Intelligent hybrid anomaly network intrusion detection system", Communication and networking: Springer Berlin Heidelberg, pp. 209–218, 2012. Abstract
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Eid Emary, H. zawbaa, A. E. Hassanien, G. Schaefer, and A. T. Azar, " Retinal Vessel Segmentation based on Possibilistic Fuzzy c-means Clustering Optimised with Cuckoo Search", The annual IEEE International Joint Conference on Neural Networks (IJCNN) – July 6-, Beijing, China, 6 July, 2014.
Eid Emary, H. M. Zawbaa, A. E. Hassanien, G. Schaefer, and A. T. Azar, "Retinal blood vessel segmentation using bee colony optimisation and pattern search", Neural Networks (IJCNN), 2014 International Joint Conference on: IEEE, pp. 1001–1006, 2014. Abstract
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Eid Emary, H. M. Zawbaa, and A. E. Hassanien, "Binary grey wolf optimization approaches for feature selection", Neurocomputing, vol. 172: Elsevier, pp. 371–381, 2016. Abstract
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Eid Emary, H. M. Zawbaa, A. E. Hassanien, M. F. Tolba, and Václav Snášel, "Retinal vessel segmentation based on flower pollination search algorithm", Proceedings of the Fifth International Conference on Innovations in Bio-Inspired Computing and Applications IBICA 2014: Springer International Publishing, pp. 93–100, 2014. Abstract
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Eid Emary, H. M. Zawbaa, A. E. Hassanien, G. Schaefer, and A. T. Azar, "Retinal vessel segmentation based on possibilistic fuzzy c-means clustering optimised with cuckoo search", Neural Networks (IJCNN), 2014 International Joint Conference on: IEEE, pp. 1792–1796, 2014. Abstract
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Eid Emary, H. M. Zawbaa, K. K. A. Ghany, A. E. Hassanien, and B. Parv, "Firefly optimization algorithm for feature selection", Proceedings of the 7th Balkan Conference on Informatics Conference: ACM, pp. 26, 2015. Abstract
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Eid Emary, H. zawbaa, A. E. Hassanien, G. Schaefer, and A. T. Azar, " Retinal Blood Vessel Segmentation using Bee Colony Optimisation and Pattern Search ", The annual IEEE International Joint Conference on Neural Networks (IJCNN) – July 6-, Beijing, China, 6 July, 2014.
El Bakrawy, L. M., N. I. Ghali, A. E. Hassanien, and A. Abraham, "An associative watermarking based image authentication scheme", Intelligent Systems Design and Applications (ISDA), 2010 10th International Conference on: IEEE, pp. 823–828, 2010. Abstract
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