Publications

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Hassanien, A. E., G. Schaefer, and A. Darwish, "Computational intelligence in speech and audio processing: recent advances", Soft Computing in Industrial Applications: Springer Berlin Heidelberg, pp. 303–311, 2010. Abstract
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Hassanien, A. - E., M. G. Milanova, T. G. Smolinski, and A. Abraham, "Computational intelligence in solving bioinformatics problems: Reviews, perspectives, and challenges", Computational Intelligence in Biomedicine and Bioinformatics: Springer Berlin Heidelberg, pp. 3–47, 2008. Abstract
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Hassanien, A. - E., A. Abraham, J. Kacprzyk, and J. F. Peters, "Computational intelligence in multimedia processing: foundation and trends", Computational Intelligence in Multimedia Processing: Recent Advances: Springer Berlin Heidelberg, pp. 3–49, 2008. Abstract
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Hassanien, A. E., "Classification and feature selection of breast cancer data based on decision tree algorithm", Studies in Informatics and Control, vol. 12, no. 1: INFORMATICS AND CONTROL PUBLICATIONS, pp. 33–40, 2003. Abstract
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Hassanien, A. E., "Classification and feature selection of breast cancer data based on decision tree algorithm", Studies in Informatics and Control, vol. 12, no. 1: INFORMATICS AND CONTROL PUBLICATIONS, pp. 33–40, 2003. Abstract
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Ibrahim, R. A., H. A. Hefny, and A. E. Hassanien, "Controlling Rumor Cascade over Social Networks", International Conference on Advanced Intelligent Systems and Informatics: Springer International Publishing, pp. 456–466, 2016. Abstract
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Mahmood, M. A., N. El-Bendary, A. E. Hassanien, and H. A. Hefny, "Classification Approach based on Rough Mereology", In Proceedings of the Second International Symposium on Intelligent Informatics (ISI'13), , Mysore, India, 23-24 August, 20, 2013. isi2013-india-classification_approach_based_on_rough_mereology.pdf
Mahmood, M. A., N. El-Bendary, A. E. Hassanien, and H. A. Hefny, "Classification Approach Based on Rough Mereology", Recent Advances in Intelligent Informatics: Springer International Publishing, pp. 175–184, 2014. Abstract
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Mahmoud, H. A., H. M. El Hadad, F. A. Mousa, and A. E. Hassanien, "Cattle classifications system using Fuzzy K-Nearest Neighbor Classifier", Informatics, Electronics & Vision (ICIEV), 2015 International Conference on: IEEE, pp. 1–5, 2015. Abstract
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Mohamed Tahoun, Abd El Rahman Shabayek, R. Reulke, and A. E. Hassanien, "Co-registration of Satellite Images Based on Invariant Local Features", IEEE Conf. on Intelligent Systems (2) 2014: 653-660, Poland - Warsaw , 24 -26 Sept. , 2014. Abstract

Detection and matching of features from satellite images taken from different sensors, viewpoints, or at different times are important tasks when manipulating and processing remote sensing data for many applications. This paper presents a scheme for satellite image co-registration using invariant local features. Different corner and scale based feature detectors have been tested during the keypoint extraction, descriptor construction and matching processes. The framework suggests a sub-sampling process which controls the number of extracted key points for a real time processing and for minimizing the hardware requirements. After getting the pairwise matches between the input images, a full registration process is followed by applying bundle adjustment and image warping then compositing the registered version. Harris and GFTT have recorded good results with ASTER images while both with SURF give the most stable performance on optical images in terms of better inliers ratios and running time compared to the other detectors. SIFT detector has recorded the best inliers ratios on TerraSAR-X data while it still has a weak performance with other optical images like Rapid-Eye and ASTER.

Mohamed Tahoun, Abd El Rahman Shabayek, R. Reulke, and A. E. Hassanien, "Co-registration of Satellite Images Based on Invariant Local Features", Intelligent Systems' 2014: Springer International Publishing, pp. 653–660, 2015. Abstract
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Mostafa, A., M. A. Fattah, A. E. Hassanien, H. Hefny, and G. S. Shao Ying Zhu, "CT Liver Segmentation Using Artificial Bee Colony Optimisation", 19th International Conference on Knowledge Based and Intelligent Information and Engineering Systems, Procedia Computer Science , Singapore, September, 2015. Abstract

The automated segmentation of the liver area is an essential phase in liver diagnosis from medical images. In this paper, we propose an artificial bee colony (ABC) optimisation algorithm that is used as a clustering technique to segment the liver in CT images. In our algorithm, ABC calculates the centroids of clusters in the image together with the region corresponding to each cluster. Using mathematical morphological operations, we then remove small and thin regions, which may represents flesh regions around the liver area, sharp edges of organs or small lesions inside the liver. The extracted regions are integrated to give an initial estimate of the liver area. In a final step, this is further enhanced using a region growing approach. In our experiments, we employed a set of 38 images, taken in pre-contrast phase, and the similarity index calculated to judge the performance of our proposed approach. This experimental evaluation confirmed our approach to afford a very good segmentation accuracy of 93.73% on the test dataset.

Mostafa, A., A. Fouad, M. A. Fattah, A. E. Hassanien, H. Hefny, S. Y. Zhu, and G. Schaefer, "CT liver segmentation using artificial bee colony optimisation", Procedia Computer Science, vol. 60: Elsevier, pp. 1622–1630, 2015. Abstract
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Panda, M., N. El-Bendary, M. A. Salama, A. E. Hassanien, and A. Abraham, "Computational social networks: Tools, perspectives, and challenges", Computational Social Networks: Springer London, pp. 3–23, 2012. Abstract
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Panda, M., N. El-Bendary, M. A. Salama, A. E. Hassanien, and A. Abraham, "Computational social networks: Tools, perspectives, and challenges", Computational Social Networks: Springer London, pp. 3–23, 2012. Abstract
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Panda, M., N. El-Bendary, M. A. Salama, A. E. Hassanien, and A. Abraham, "Computational social networks: Tools, perspectives, and challenges", Computational Social Networks: Springer London, pp. 3–23, 2012. Abstract
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Peters, J. F., and S. K. Pal, "Cantor, fuzzy, near, and rough sets in image analysis", Rough fuzzy image analysis: Foundations and methodologies: CRC Press, pp. 1–1, 2010. Abstract
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Rizk Masoud, A. E. Hassanien, and Siddhartha Bhattacharyya, "Chaotic Crow Search Algorithm for Fractional Optimization Problems", Applied soft computing , 2018. Abstract

This paper presents a chaotic crow search algorithm (CCSA) for solving fractional optimization problems (FOPs). To refine the global convergence speed and enhance the exploration/exploitation tendencies, the proposed CCSA integrates chaos theory (CT) into the CSA. CT is introduced to tune the parameters of the standard CSA, yielding four variants, with the best chaotic variant being investigated. The performance of the proposed CCSA is validated on twenty well-known fractional benchmark problems. Moreover, it is validated on a fractional economic environmental power dispatch problem by attempting to minimize the ratio of total emissions to total fuel cost. Finally, the proposed CCSA is compared with the standard CSA, particle swarm optimization (PSO), firefly algorithm (FFA), dragonfly algorithm (DA) and grey wolf algorithm (GWA). Additionally, the efficiency of the proposed CCSA is justified using the non parametric Wilcoxon signed-rank test. The experimental results prove that the proposed CCSA outperforms other algorithms in terms of quality and reliability.

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Salama, M., M. Panda, Y. Elbarawy, A. E. Hassanien, and A. Abraham, "Computational Social Networks: Security and Privacy", Computational Social Networks: Springer London, pp. 3–21, 2012. Abstract
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Salama, M., M. Panda, Y. Elbarawy, A. E. Hassanien, and A. Abraham, "Computational Social Networks: Security and Privacy", Computational Social Networks: Springer London, pp. 3–21, 2012. Abstract
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Schaefer, G., A. Hassanien, and J. Jiang, Computational Intelligence in Medical Imaging: Techniques and Applications, : CRC press, 2009. Abstract
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Schaefer, G., Niraj P. Doshi, Qinghua Hu, and A. E. Hassanien, "Classification of HEp-2 Cell Images using Compact Multi-Scale Texture Information and Margin Distribution Based Bagging ", The 2nd International Conference on Advanced Machine Learning Technologies and Applications , Egypt, November 17-19, , 2014.
Schaefer, G., N. P. Doshi, Qinghua Hu, and A. E. Hassanien, "Classification of HEp-2 Cell Images Using Compact Multi-Scale Texture Information and Margin Distribution Based Bagging", International Conference on Advanced Machine Learning Technologies and Applications: Springer International Publishing, pp. 299–308, 2014. Abstract
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Smolinski, T. G., M. G. Milanova, and A. - E. Hassanien, Computational Intelligence in Biomedicine and Bioinformatics: Current trends and applications, : Springer, 2009. Abstract
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Smolinski, T. G., M. G. Milanova, and A. - E. Hassanien, Computational Intelligence in Biomedicine and Bioinformatics: Current trends and applications, : Springer, 2009. Abstract
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