Publications

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Journal Article
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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Zawbaa, H. M., N. El-Bendary, A. E. Hassanien, and T. - H. Kim, "Machine learning-based soccer video summarization system", Multimedia, Computer Graphics and Broadcasting: Springer Berlin Heidelberg, pp. 19–28, 2011. Abstract
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Zawbaa, H. M., N. El-Bendary, A. E. Hassanien, and T. - H. Kim, "Machine learning-based soccer video summarization system", Multimedia, Computer Graphics and Broadcasting: Springer Berlin Heidelberg, pp. 19–28, 2011. Abstract
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Kompatsiaris, Y., S. Nikolopoulos, T. Lidy, and A. Rauber, "Media Search Cluster White Paper on" Search Computing".", ERCIM News, vol. 2012, no. 88, 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

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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Kudelka, M., V. Snasel, Z. Horak, A. E. Hassanien, A. Abraham, and J. D. Velásquez, "A novel approach for comparing web sites by using MicroGenres", Engineering Applications of Artificial Intelligence,, vol. 35, pp. 178-198, 2014. AbstractWebsite

In this paper, a novel approach is introduced to compare web sites by analysing their web page content. Each web page can be expressed as a set of entities called MicroGenres, which in turn are abstractions about design patterns and genres for representing the page content. This description is useful for web page and web site classification and for a deeper insight into the web site׳s social context.

The web site comparison is useful for extracting patterns which can be used for improving Web search engine effectiveness, the identification of best practices in web site design and of course in the organization of web page content to personalize the web user experience on a web site.

The effectiveness of the proposed approach was tested in a real world case, with e-shop web sites showing that a web site can be represented in a high level of abstraction by using MicroGenres, the contents of which can then be compared and given a measure corresponding to web site similarity. This measure is very useful for detecting web communities on the Web, i.e., a group of web sites sharing similar contents, and the result is essential in performing a focused and effective information search as well as minimizing web page retrieval.

Kudelka, M., V. Snasel, Z. Horak, A. E. Hassanien, A. Abraham, and J. D. Velásquez, "A novel approach for comparing web sites by using MicroGenres", Engineering Applications of Artificial Intelligence, vol. 35: Pergamon, pp. 187–198, 2014. Abstract
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Azar, A. T., S. S. Kumar, H. H. Inbarani, and A. E. Hassanien, "Pessimistic multi-granulation rough set-based classification for heart valve disease diagnosis", International Journal of Modelling, Identification and Control, vol. 26, no. 1: Inderscience Publishers (IEL), pp. 42–51, 2016. Abstract
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Karam, H., A. E. Hassanien, and M. Nakajima, "Petri Net Modeling Methods For Generating Self-Similar Fractal Images", 映像情報メディア学会技術報告, vol. 22, no. 45: 一般社団法人映像情報メディア学会, pp. 13–18, 1998. Abstract
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Karam, H., A. E. Hassanien, and M. Nakajima, "Petri Net Modeling Methods for Generating Self-Similar Fractal Images (マルチメディア情報処理研究会)", 映像情報メディア学会誌: 映像情報メディア, vol. 52, no. 12: 一般社団法人映像情報メディア学会, pp. 1807, 1998. Abstract

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Hassanien, A. E., A. Abraham, J. F. Peters, and J. Kacprzyk, "Rough sets in medical imaging: foundations and trends", Computational Intelligence in Medical Imaging: Techniques and Applications, pp. 47–87, 2008. Abstract
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Hassanien, A. E., A. Abraham, J. F. Peters, and J. Kacprzyk, "Rough sets in medical imaging: foundations and trends", Computational Intelligence in Medical Imaging: Techniques and Applications, pp. 47–87, 2008. Abstract
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Hassanien, A. E., A. Abraham, J. F. Peters, and J. Kacprzyk, "Rough sets in medical imaging: foundations and trends", Computational Intelligence in Medical Imaging: Techniques and Applications, pp. 47–87, 2008. Abstract
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Hassanien, A., M. Nachtegael, D. VAN DER WEKEN, H. NOBUHARA, and E. Kerre, Soft Computing in Image Processing, : Springer, 2006. Abstract
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Hassanien, A., M. Nachtegael, D. VAN DER WEKEN, H. NOBUHARA, and E. Kerre, Soft Computing in Image Processing, : Springer, 2006. Abstract
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KAMAL, K. A. R. E. E. M., A. GHANY, M. A. Moneim, N. I. Ghali, A. E. Hassanien, and H. A. Hefny, A symmetric bio-hash function based on fingerprint minutiae and principal curves approach, , 2011. Abstract
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KAMAL, K. A. R. E. E. M., A. GHANY, M. A. Moneim, N. I. Ghali, A. E. Hassanien, and H. A. Hefny, A symmetric bio-hash function based on fingerprint minutiae and principal curves approach, , 2011. Abstract
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Karam, H., A. E. Hassanien, and M. Nakajima, "Three-Dimentional Image Information Media. Animation of Linear Fractal Shapes using Polar Decomposition Interpolation.", 映像情報メディア学会誌, vol. 53, no. 3: The Institute of Image Information and Television Engineers, pp. 411–416, 1999. Abstract
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Khairy, M., Alaa Tharwat, T. Gaber, and A. E. Hassanien, "A wheelchair control system using the human machine interaction: Single-modal and Multi-modal approaches", ournal of Intelligent Systems (JISYS), vol. In press, 2017.