Publications

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2012
Hamdy, A., H. Hefny, M. A. Salama, A. E. Hassanien, and T. - H. Kim, "The importance of handling multivariate attributes in the identification of heart valve diseases using heart signals", Computer Science and Information Systems (FedCSIS), 2012 Federated Conference on: IEEE, pp. 75–79, 2012. Abstract
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Sami, M., A. E. Hassanien, N. El-Bendary, and R. C. Berwick, "Incorporating random forest trees with particle swarm optimization for automatic image annotation", Computer Science and Information Systems (FedCSIS), 2012 Federated Conference on: IEEE, pp. 763–769, 2012. Abstract
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Sami, M., A. E. Hassanien, N. El-Bendary, and R. C. Berwick, "Incorporating random forest trees with particle swarm optimization for automatic image annotation", Computer Science and Information Systems (FedCSIS), 2012 Federated Conference on: IEEE, pp. 763–769, 2012. Abstract
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Asad, A. H., A. T. Azar, and A. E. Hassanien, "Integrated features based on gray-level and hu moment-invariants with ant colony system for retinal blood vessels segmentation", International Journal of Systems Biology and Biomedical Technologies (IJSBBT), vol. 1, no. 4: IGI Global, pp. 60–73, 2012. Abstract
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Asad, A. H., A. T. Azar, and A. E. Hassanien, "Integrated features based on gray-level and hu moment-invariants with ant colony system for retinal blood vessels segmentation", International Journal of Systems Biology and Biomedical Technologies (IJSBBT), vol. 1, no. 4: IGI Global, pp. 60–73, 2012. 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., 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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Kacprzyk, J., and L. C. Jain, Intelligent Systems Reference Library, Volume 26, , 2012. Abstract
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Kacprzyk, J., and L. C. Jain, Intelligent Systems Reference Library, Volume 26, , 2012. Abstract
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2011
Hassanien, A. E., "Intelligent Hybrid Anomaly Network Intrusion Detection System.", Communication and Networking - International Conference, FGCN 2011, Jeju Island, Korea, 8-10 December, 2011. Abstract

Intrusion detection systems (IDSs) is an essential key for network defense. The hybrid intrusion detection system combines the individual base classifiers and feature selection algorithm to maximize detection accuracy and minimize computational complexity. We investigated the performance of Genetic algorithm-based feature selection system to reduce the data features space and then the hidden naïve bays (HNB) system were adapted to classify the network intrusion into five outcomes: normal, and four anomaly types including denial of service, user-to-root, remote-to-local, and probing. In order to evaluate the performance of introduced hybrid intrusion system, several groups of experiments are conducted and demonstrated on NSL-KDD dataset. Moreover, the performances of intelligent hybrid intrusion system have been compared with the results of well-known feature selection algorithms. It is found that, hybrid intrusion system produces consistently better performances on selecting the subsets of features which resulting better classification accuracies (98.63%).

Hassanien, A. E., "Intelligent Hybrid Anomaly Network Intrusion Detection System.", Communication and Networking-International Conference, FGCN 2011, 2011. Abstract

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El Emary, I. M. M., and A. E. Hassanien, "Intelligent agent in telecommunication systems", Telecommunication Systems, vol. 46, no. 3: Springer, pp. 191–193, 2011. Abstract
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El Emary, I. M. M., and A. E. Hassanien, "Intelligent agent in telecommunication systems", Telecommunication Systems, vol. 46, no. 3: Springer, pp. 191–193, 2011. Abstract
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Salama, M. A., N. El-Bendary, A. E. Hassanien, K. Revett, and A. A. Fahmy, "Interval-based attribute evaluation algorithm", Computer Science and Information Systems (FedCSIS), 2011 Federated Conference on: IEEE, pp. 153–156, 2011. Abstract
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Salama, M. A., N. El-Bendary, A. E. Hassanien, K. Revett, and A. A. Fahmy, "Interval-based attribute evaluation algorithm", Computer Science and Information Systems (FedCSIS), 2011 Federated Conference on: IEEE, pp. 153–156, 2011. Abstract
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2010
Hassanien, A. E., "International Conference on Intelligent Systems Design and Applications (ISDA)", International Conference on Intelligent Systems Design and Applications (ISDA), Egypt, IEEE, 2010.
2009
Tobin, K. W., E. Chaum, J. Gregor, T. P. Karnowski, J. R. Price, and J. Wall, "Image Informatics for Clinical and Preclinical Biomedical Analysis", Computational Intelligence in Medical Imaging: Techniques and Applications: CRC Press, pp. 239, 2009. Abstract
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Hassanien, A. E., "Intelligence techniques for prostate ultrasound image analysis", International Journal of Hybrid Intelligent Systems, vol. 6, no. 3: IOS Press, pp. 155–167, 2009. Abstract
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Hassanien, A. E., "Intelligence techniques for prostate ultrasound image analysis", International Journal of Hybrid Intelligent Systems, vol. 6, no. 3: IOS Press, pp. 155–167, 2009. Abstract
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Hassanien, A. E., H. Al-Qaheri, G. Schaefer, and S. Banerjee, "Intelligent analysis of prostate ultrasound images", Nature & Biologically Inspired Computing, 2009. NaBIC 2009. World Congress on: IEEE, pp. 263–268, 2009. Abstract
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Hassanien, A. E., H. Al-Qaheri, G. Schaefer, and S. Banerjee, "Intelligent analysis of prostate ultrasound images", Nature & Biologically Inspired Computing, 2009. NaBIC 2009. World Congress on: IEEE, pp. 263–268, 2009. Abstract
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2003
Ali, J. M., and A. E. Hassanien, "An Iris Recognition System to Enhance E-Security, Advanced Modeling and Optimization", vol, vol. 5, pp. 93–104, 2003. Abstract

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Hassanien, A. E., and J. M. H. Ali, "Image classification and retrieval algorithm based on rough set theory", South African Computer Journal, vol. 2003, no. 30: South African Computer Society (SAICSIT), pp. 9–16, 2003. Abstract
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Hassanien, A. E., "Intelligent data analysis of breast cancer based on rough set theory", International Journal on Artificial Intelligence Tools, vol. 12, no. 04: World Scientific Publishing Company, pp. 465–479, 2003. Abstract
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