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Salama, M. A., and A. E. Hassanien, "Binarization and validation in formal concept analysis", International Journal of Systems Biology and Biomedical Technologies (IJSBBT), vol. 1, no. 4: IGI Global, pp. 16–27, 2012. Abstract
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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.

Emary, E., H. M. Zawbaa, and A. E. Hassanien, "Binary ant lion approaches for feature selection", Neurocomputing, vol. 213: Elsevier, pp. 54–65, 2016. Abstract
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Emarya, E., H. M. Zawbaab, and A. E. Hassanien, "Binary Gray Wolf Optimization Approaches for Feature Selection", Neurocomputing, 2015. AbstractWebsite

In this work, a novel binary version of the gray wolf optimization (GWO) is proposed and used to select optimal feature subset for classification purposes. Gray wolf optimizer (GWO) is one of the latest bio-inspired optimization techniques, which simulate the hunting process of gray wolves in nature. The binary version introduced here is performed using two different approaches. In the first approach, individual steps toward the first three best solutions are binarized and then stochastic crossover is performed among the three basic moves to find the updated binary gray wolf position. In the second approach, sigmoidal function is used to squash the continuous updated position, then stochastically threshold these values to find the updated binary gray wolf position. The two approach for binary gray wolf optimization (bGWO) are hired in the feature selection domain for finding feature subset maximizing the classification accuracy while minimizing the number of selected features. The proposed binary versions were compared to two of the common optimizers used in this domain namely particle swarm optimizer and genetic algorithms. A set of assessment indicators are used to evaluate and compared the different methods over 18 different datasets from the UCI repository. Results prove the capability of the proposed binary version of gray wolf optimization (bGWO) to search the feature space for optimal feature combinations regardless of the initialization and the used stochastic operators.

Emary, E., H. M. Zawbaa, and A. E. Hassanien, "Binary grey wolf optimization approaches for feature selection", Neurocomputing, vol. 172, issue 8, pp. 371–381, 2016. Website
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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Sahlol, A. T., C. Y. Suen, H. M. Zawbaa, A. E. Hassanien, and M. A. Fattah, "Bio-inspired BAT optimization algorithm for handwritten Arabic characters recognition", Evolutionary Computation (CEC), 2016 IEEE Congress on: IEEE, pp. 1749–1756, 2016. Abstract
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Sahlol, A. T., and A. E. Hassanien, "Bio-Inspired Optimization Algorithms for Arabic Handwritten Characters", Handbook of Research on Machine Learning Innovations and Trends, USA, IGI, 2017. Abstract

There are still many obstacles for achieving high recognition accuracy for Arabic handwritten optical character recognition system, each character has a different shape, as well as the similarities between characters. In this chapter, several feature selection-based bio-inspired optimization algorithms including Bat Algorithm, Grey Wolf Optimization, Whale optimization Algorithm, Particle Swarm Optimization and Genetic Algorithm have been presented and an application of Arabic handwritten characters recognition has been chosen to see their ability and accuracy to recognize Arabic characters. The experiments have been performed using a benchmark dataset, CENPARMI by k-Nearest neighbors, Linear Discriminant Analysis, and random forests. The achieved results show superior results for the selected features when comparing the classification accuracy for the selected features by the optimization algorithms with the whole feature set in terms of the classification accuracy and the processing time. The experiments have been performed using a benchmark dataset, CENPARMI by k-Nearest neighbors, Linear Discriminant Analysis, and random forests. The achieved results show superior results for the selected features when comparing the classification accuracy for the selected features by the optimization algorithms with the whole feature set in terms of the classification accuracy and the processing time.

Sahlol, A. T., and A. E. Hassanien, "Bio-Inspired Optimization Algorithms for Arabic Handwritten Characters", Handbook of Research on Machine Learning Innovations and Trends: IGI Global, pp. 897–914, 2017. Abstract
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Elharir, E., N. El-Bendary, and A. E. Hassanien, "Bio-inspired optimization for feature set dimensionality reduction", 3rd International Conference on Advances in Computational Tools for Engineering Applications (ACTEA),, Beirut, Lebanon, 13-15 July , 2016. Abstract

In this paper, two novel bio-inspired optimization algorithms; namely Dragonfly Algorithm (DA) and Grey Wolf Optimizer (GWO), have been applied for fulfilling the goal of feature set dimensional reduction. The proposed classification system has been tested via solving the problem of Electromyography (EMG) signal classification with optimal features subset selection. The obtained experimental results showed that the GWO based Support Vector Machines (SVM) classification algorithm has achieved an accuracy of 93.22% using 31% of the total extracted features. It also outperformed both the typical SVM algorithm, with no feature set optimization, and the DA based optimized feature set SVM classification, for the tested EMG dataset.

Esraa Elhariri, N. El-Bendary, and A. E. Hassanien, "Bio-inspired optimization for feature set dimensionality reduction", Advances in Computational Tools for Engineering Applications (ACTEA), 2016 3rd International Conference on: IEEE, pp. 184–189, 2016. Abstract
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Sayed, G. I., M. Soliman, and A. E. Hassanien, "Bio-inspired Swarm Techniques for Thermogram Breast Cancer Detection", Medical Imaging in Clinical Applications: Springer International Publishing, pp. 487–506, 2016. Abstract
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Hassanien, A. E., T. - H. Kim, J. Kacprzyk, and A. I. Awad, Bio-inspiring Cyber Security and Cloud Services: Trends and Innovations, : Springer, 2014. Abstract
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Soliman, M. M., A. E. Hassanien, and H. M. Onsi, "Bio-inspiring Techniques in Watermarking Medical Images: A Review", Bio-inspiring Cyber Security and Cloud Services: Trends and Innovations: Springer Berlin Heidelberg, pp. 93–114, 2014. Abstract
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Asmaa Hashem Sweidan, Nashwa El-Bendary, O. M. H. A. E. H.:, "Biomarker-Based Water Pollution Assessment System Using Case-Based Reasoning", Proceedings of the Second Euro-China Conference on Intelligent Data Analysis and Applications, ECC 2015, pp. 547-557,, Ostrava, Czech Republic, June 29 - July , 2015. Abstract

This paper presents Case-Based Reasoning (CBR) system to asses water pollution based on fish liver histopathology as biomarker. The proposed approach utilizes fish liver microscopic images in order to asses water pollution based on knowledge stored in the case-based database and stores likelihood description of the previous solutions in order to make the knowledge stored more flexible. The proposed case-based reasoning system consists of 5 phases; namely case representation (pre-processing and feature extraction), retrieve, reuse/adapt, revise, and retain phases. After applying pre-processing and feature extraction algorithms on the input images, similarity between the input and case base database is being calculated in order to retrieve similarity. Experimental results show that the performance of CBR systems increases according to the number of retrieved cases in each scenario against each strategy. The proposed system achieved 95.9

Asmaa Hashem Sweidan, N. El-Bendary, O. M. Hegazy, and A. E. Hassanien, "Biomarker-Based Water Pollution Assessment System Using Case-Based Reasoning", Intelligent Data Analysis and Applications: Springer International Publishing, pp. 547–557, 2015. Abstract
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Amin, R., T. Gaber, G. ElTaweel, and A. E. Hassanien, "Biometric and traditional mobile authentication techniques: Overviews and open issues", Bio-inspiring cyber security and cloud services: trends and innovations: Springer Berlin Heidelberg, pp. 423–446, 2014. Abstract
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Gaber, T., Alaa Tharwat, A. E. Hassanien, and V. Snasel, "Biometric cattle identification approach based on Weber’s Local Descriptor and AdaBoost classifier", Computers and Electronics in Agriculture, vol. 122 , issue March 2016 , pp. 55–66, 2016. Website
Gaber, T., Alaa Tharwat, A. E. Hassanien, and V. Snasel, "Biometric cattle identification approach based on weber’s local descriptor and adaboost classifier", Computers and Electronics in Agriculture, vol. 122: Elsevier, pp. 55–66, 2016. Abstract
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Raffat, M. M., R. Ahmed, and A. E. Hassanien, "Blind 2D vector data watermarking approach using random table and polar coordinates", 2nd IEEE International Conference on Uncertainty Reasoning and Knowledge Engineering (URKE) , Jalarta Turky , 14-15 Aug., pp. 67 – 70, 2012. Abstract

In this paper, a blind robust watermark approach for authentication 2D Map based on random table and polar coordinates mapping is presented. Firstly, All vertices will mapped into polar coordinate system. Then, the watermark is embedded using the random table of the decimal valued of the polar coordinates through the digit substitution of the decimal part. Theoretical analysis and excremental results shows that the presented approach is robust against a various attacks such as rotation, scaling and translation and also good imperceptibility.

Raffat, M. M., R. Ahmed, and A. E. Hassanien, "Blind 2D vector data watermarking approach using random table and polar coordinates", 2nd IEEE International Conference on Uncertainty Reasoning and Knowledge Engineering (URKE): 14-15 Aug., 2012. Abstract
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Mouhamed, M. R., A. M. Rashad, and A. E. Hassanien, "Blind 2D vector data watermarking approach using random table and polar coordinates", Uncertainty Reasoning and Knowledge Engineering (URKE), 2012 2nd International Conference on: IEEE, pp. 67–70, 2012. Abstract
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Raffat, M. M., R. Ahmed, and A. E. Hassanien, "Blind 2D vector data watermarking approach using random table and polar coordinates", 2nd IEEE International Conference on Uncertainty Reasoning and Knowledge Engineering (URKE): 14-15 Aug., 2012. Abstract
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Mona M. Soliman, A. E. Hassanien, and H. M. Ons, "A Blind 3D Watermarking Approach for 3D Mesh Using Clustering Based Methods", IJCVIP - International Journal of Computer Vision and Image Processing, vol. 3, issue 2, pp. 43-53, 2013. Website
Soliman, M. M., A. E. Hassanien, and H. M. Onsi, "A Blind 3D Watermarking Approach for 3D Mesh Using Clustering Based Methods", International Journal of Computer Vision and Image Processing (IJCVIP), vol. 3, no. 2: IGI Global, pp. 43–53, 2013. Abstract
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