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Xiao, K., S. H. Ho, and A. E. Hassanien, "Brain magnetic resonance image lateral ventricles deformation analysis and tumor prediction", Malaysian Journal of Computer Science, vol. 20, no. 2: Faculty of Computer Science and Information Technology, pp. 115, 2007. Abstract
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Xiao, K., A. E. Hassanien, Y. Sun, and E. K. K. Ng, "Brain mr image tumor segmentation with ventricular deformation", Image and Graphics (ICIG), 2011 Sixth International Conference on: IEEE, pp. 297–302, 2011. Abstract
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Xiao, K., S. H. Ho, and A. E. Hassanien, "Brain magnetic resonance image lateral ventricles deformation analysis and tumor prediction", Malaysian Journal of Computer Science, vol. 20, no. 2: Faculty of Computer Science and Information Technology, pp. 115, 2007. Abstract
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Xiao, K., A. E. Hassanien, Y. Sun, and E. K. K. Ng, "Brain mr image tumor segmentation with ventricular deformation", Image and Graphics (ICIG), 2011 Sixth International Conference on: IEEE, pp. 297–302, 2011. Abstract
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Tharwat;, A., A. E. Hassanien;, and B. E. Elnaghi, "A BA-based algorithm for parameter optimization of support vector machine", Pattern recognition letter, 2017. AbstractWebsite

Support Vector Machine (SVM) parameters such as kernel parameter and penalty parameter (C) have a great impact on the complexity and accuracy of predicting model. In this paper, Bat algorithm (BA) has been proposed to optimize the parameters of SVM, so that the classification error can be reduced. To evaluate the proposed model (BA-SVM), the experiment adopted nine standard datasets which are obtained from UCI machine learning data repository. For verification, the results of the BA-SVM algorithm are compared with grid search, which is a conventional method of searching parameter values, and two well-known optimization algorithms: Genetic Algorithm (GA) and Particle Swarm Optimization (PSO). The experimental results proved that the proposed model is capable to find the optimal values of the SVM parameters and avoids the local optima problem. The results also demonstrated lower classification error rates compared with PSO and GA algorithms.

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Soliman, M. M., A. E. Hassanien, and H. M. Onsi, "A Blind Roubst Watermarking Scheme based on Progresive Mesh and Self Organization Maps", International conference on Advances in Security of Information and Communication Networks, (SecNet 2013) , Egypt, Springer , pp. 3-5 Sept, 2013, 2013. blind_compressed_wm.pdf
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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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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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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Soliman, M. M., A. E. Hassanien, and H. M. Onsi, "A Blind Robust 3D-Watermarking Scheme Based on Progressive Mesh and Self Organization Maps", Advances in Security of Information and Communication Networks: Springer Berlin Heidelberg, pp. 131–142, 2013. Abstract
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Shang-Ling, S. Z. Jui, W. Xiong, F. Yu, M. Fu, D. Wang, A. E. Hassanien, and K. Xiao, "Brain MR Image Tumor Segmentation with 3-Dimensional Intracranial Structure Deformation Features", IEEE Intelligent Systems, vol. 31, pp. 66-76, 2016. AbstractWebsite

Extraction of relevant features is of significant importance for brain tumor segmentation systems. To improve brain tumor segmentation accuracy, the authors present an improved feature extraction component that takes advantage of the correlation between intracranial structure deformation and the compression resulting from brain tumor growth. Using 3D nonrigid registration and deformation modeling techniques, the component measures lateral ventricular (LaV) deformation in volumetric magnetic resonance images. By verifying the location of the extracted LaV deformation feature data and applying the features on brain tumor segmentation with widely used classification algorithms, the authors evaluate the proposed component qualitatively and quantitatively with promising results on 11 datasets comprising real and simulated patient images.

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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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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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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Salama, M. A., and A. E. Hassanien, "Binarization and Validation in Formal Concept Analysis", International Journal of Systems Biology and Biomedical Technologies, vol. 1, issue 4, pp. 17-28, 2012. AbstractWebsite

Representation and visualization of continuous data using the Formal Concept Analysis (FCA) became an
important requirement in real-life fields. Application of formal concept analysis (FCA) model on numerical
data, a scaling or Discretization / binarization procedures should be applied as preprocessing stage. The
Scaling procedure increases the complexity of computation of the FCA, while the binarization process leads to a distortion in the internal structure of the input data set. The proposed approach uses a binarization procedure prior to applying FCA model, and then applies a validation process to the generated lattice to measure or ensure its degree of accuracy. The introduced approach is based on the evaluation of each attribute according to the objects of its extent set. To prove the validity of the introduced approach, the technique is applied on two data sets in the medical field which are the Indian Diabetes and the Breast Cancer data sets. Both data sets show the generation of a valid lattice.

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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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: IGI Global, pp. 897–914, 2017. 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.

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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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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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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.

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Mouhamed, M. R., H. M. Zawbaa, E. Al-Shammari, A. E. Hassanien, and V. Snasel, "Blind Watermark Approach for Map Authentication using Support Vector Machine", International conference on Advances in Security of Information and Communication Networks, (SecNet 2013) , Springer pp. 84–97, Cairo - Egypt, 3-5 Sept, 2013, . blind_watermark_approach_for_map_authentication_svm.pdf
Mouhamed, M. R., H. M. Zawbaa, E. T. Al-Shammari, A. E. Hassanien, and V. Snasel, "Blind watermark approach for map authentication using support vector machine", Advances in security of information and communication networks: Springer Berlin Heidelberg, pp. 84–97, 2013. 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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