Publications

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Conference Paper
Soliman, M. M., A. E. Hassanien, and H. M. Onsi, "A Robust 3D Mesh Watermarking Approach Using Genetic Algorithms", IEEE Intelligent Systems'2014, Poland - Warsaw , 24 -26 Sept. , 2014. Abstract

This paper proposes a new approach of 3D watermarking by ensuring the optimal preservation of mesh surfaces. The minimal surface distortion is enforced during watermark embedding stage using Genetic Algorithm (GA) optimization. The watermark embedding is performed only on set of selected vertices come out from k-means clustering technique. These vertices are used as candidates for watermark carriers that will hold watermark bits stream. A 3D surface preservation function is defined according to the distance of a vertex displaced by watermarking to the original surface. A study of the proposed methodology has high robustness against the common mesh attacks while preserving the original object surface during watermarking.

Awad, A. I., H. M. Zawbaa, H. A. Mahmoud, E. H. H. A. Nabi, R. H. Fayed, and A. E. Hassanien, "A robust cattle identification scheme using muzzle print images", Computer Science and Information Systems (FedCSIS), 2013 Federated Conference on: IEEE, pp. 529–534, 2013. Abstract
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Awad, A. I., H. M. Zawbaa, H. A. Mahmoud, E. H. H. A. Nabi, R. H. Fayed, and A. E. Hassanien, "A robust cattle identification scheme using muzzle print images", Computer Science and Information Systems (FedCSIS), 2013 Federated Conference on: IEEE, pp. 529–534, 2013. Abstract
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Soliman, M. M., A. E. Hassanien, and H. M. Onsi, "Robust watermarking approach for 3D triangular mesh using self organization map", Computer Engineering & Systems (ICCES), 2013 8th International Conference on: IEEE, pp. 99–104, 2013. Abstract
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Soliman, O. S., A. E. Hassanien, and N. El-Bendary, "A rough clustering algorithm based on entropy information", Soft Computing Models in Industrial and Environmental Applications, 6th International Conference SOCO 2011: Springer Berlin Heidelberg, pp. 213–222, 2011. Abstract
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Soliman, O. S., A. E. Hassanien, and N. El-Bendary, "A rough clustering algorithm based on entropy information", Soft Computing Models in Industrial and Environmental Applications, 6th International Conference SOCO 2011: Springer Berlin Heidelberg, pp. 213–222, 2011. Abstract
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Schaefer, G., H. Zhou, Qinghua Hu, and A. E. Hassanien, "Rough image colour quantisation", International Workshop on Rough Sets, Fuzzy Sets, Data Mining, and Granular-Soft Computing: Springer Berlin Heidelberg, pp. 217–222, 2009. Abstract
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Schaefer, G., H. Zhou, Qinghua Hu, and A. E. Hassanien, "Rough image colour quantisation", International Workshop on Rough Sets, Fuzzy Sets, Data Mining, and Granular-Soft Computing: Springer Berlin Heidelberg, pp. 217–222, 2009. Abstract
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El Bakrawy, L. M., N. I. Ghali, A. E. Hassanien, and T. - H. Kim, "A rough k-means fragile watermarking approach for image authentication", Computer Science and Information Systems (FedCSIS), 2011 Federated Conference on: IEEE, pp. 19–23, 2011. Abstract
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El Bakrawy, L. M., N. I. Ghali, A. E. Hassanien, and T. - H. Kim, "A rough k-means fragile watermarking approach for image authentication", Computer Science and Information Systems (FedCSIS), 2011 Federated Conference on: IEEE, pp. 19–23, 2011. Abstract
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Waleed Yamany, N. El-Bendary, Hossam M. Zawbaa, A. E. Hassanien, and Václav Snášel, "Rough Power Set Tree for Feature Selection and Classification: Case Study on MRI Brain Tumor", Innovations in Bio-inspired Computing and Applications. Advances in Intelligent Systems and Computing(Springer) , Czech republic , 2013.
Hassanien, A. E., and J. M. Ali, "Rough set approach for classification of breast cancer mammogram images", International Workshop on Fuzzy Logic and Applications: Springer Berlin Heidelberg, pp. 224–231, 2003. Abstract

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Hassanien, A. E., and J. M. Ali, "Rough set approach for classification of breast cancer mammogram images", International Workshop on Fuzzy Logic and Applications: Springer Berlin Heidelberg, pp. 224–231, 2003. Abstract
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P. K. Nizar Banu, H. H. Inbarani, A. T. Azar, H. S. Own, and A. E. Hassanien, "Rough Set Based Feature Selection for Egyptian Neonatal Jaundice ", The 2nd International Conference on Advanced Machine Learning Technologies and Applications , Egypt, November 17-19, , 2014.
Banu, P. K. N., H. H. Inbarani, A. T. Azar, H. S. Own, and A. E. Hassanien, "Rough set based feature selection for egyptian neonatal jaundice", International Conference on Advanced Machine Learning Technologies and Applications: Springer International Publishing, pp. 367–378, 2014. Abstract
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Al-Qaheri, H., S. Zamoon, A. E. Hassanien, and A. Abraham, "Rough set generating prediction rules for stock price movement", Computer Modeling and Simulation, 2008. EMS'08. Second UKSIM European Symposium on: IEEE, pp. 111–116, 2008. Abstract
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Al-Qaheri, H., S. Zamoon, A. E. Hassanien, and A. Abraham, "Rough set generating prediction rules for stock price movement", Computer Modeling and Simulation, 2008. EMS'08. Second UKSIM European Symposium on: IEEE, pp. 111–116, 2008. Abstract
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Elshazly, Hanaa, N.; Ghali, A. Korany, and A. E. Hassanien, "Rough sets and genetic algorithms: A hybrid approach to breast cancer classification", World Congress on Information and Communication Technologies (WICT), pp. 260 - 265 , India, Oct. 30 2012-Nov. Abstract

The use of computational intelligence systems such as rough sets, neural networks, fuzzy set, genetic algorithms, etc., for predictions and classification has been widely established. This paper presents a generic classification model based on a rough set approach and decision rules. To increase the efficiency of the classification process, boolean reasoning discretization algorithm is used to discretize the data sets. The approach is tested by a comparatif study of three different classifiers (decision rules, naive bayes and k-nearest neighbor) over three distinct discretization techniques (equal bigning, entropy and boolean reasoning). The rough set reduction technique is applied to find all the reducts of the data which contains the minimal subset of attributes that are associated with a class label for prediction. In this paper we adopt the genetic algorithms approach to reach reducts. Finally, decision rules were used as a classifier to evaluate the performance of the predicted reducts and classes. To evaluate the performance of our approach, we present tests on breast cancer data set. The experimental results obtained, show that the overall classification accuracy offered by the employed rough set approach and decision rules is high compared with other classification techniques including Bayes and k-nearest neighbor.

Elshazly, H. I., N. I. Ghali, A. M. E. Korany, and A. E. Hassanien, "Rough sets and genetic algorithms: A hybrid approach to breast cancer classification", Information and Communication Technologies (WICT), 2012 World Congress on: IEEE, pp. 260–265, 2012. Abstract
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Elshazly, H. I., N. I. Ghali, A. M. E. Korany, and A. E. Hassanien, "Rough sets and genetic algorithms: A hybrid approach to breast cancer classification", Information and Communication Technologies (WICT), 2012 World Congress on: IEEE, pp. 260–265, 2012. Abstract
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Elshazly, H. I., N. I. Ghali, A. M. E. Korany, and A. E. Hassanien, "Rough sets and genetic algorithms: A hybrid approach to breast cancer classification", Information and Communication Technologies (WICT), 2012 World Congress on: IEEE, pp. 260–265, 2012. Abstract
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Hassanien, A. E., and H. Own, "Rough sets for prostate patient analysis", Proceedings of International Conference on Modeling and Simulation (MS2006), 2006. Abstract
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Hassanien, A. E., and H. Own, "Rough sets for prostate patient analysis", Proceedings of International Conference on Modeling and Simulation (MS2006), 2006. Abstract
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zaki, A., M. A. Salama, H. Hefny, and A. E. Hassanien, "Rough sets-based rules generation approach: A hepatitis c virus data sets", International Conference on Advanced Machine Learning Technologies and Applications: Springer Berlin Heidelberg, pp. 52–59, 2012. Abstract
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zaki, A., M. A. Salama, H. Hefny, and A. E. Hassanien, "Rough sets-based rules generation approach: A hepatitis c virus data sets", International Conference on Advanced Machine Learning Technologies and Applications: Springer Berlin Heidelberg, pp. 52–59, 2012. Abstract
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