Publications

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2014
Hassanien, A. E., M. Salama, J. Platos, and V. Snasel, "Rough local transfer function for cardiac disorders detection using heart sounds ", Logic Journal of the IGPL, Oxford, vol. (in press), 2014. Website
Azar, A. T., H. I. Elshazly, A. E. Hassanien, and A. M. Elkorany, "A random forest classifier for lymph diseases", Computer methods and programs in biomedicine, vol. 113, no. 2: Elsevier, pp. 465–473, 2014. Abstract
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Esraa Elhariri, N. El-Bendary, A. E. Hassanien, A. Badr, A. M. M. Hussein, and Václav Snášel, "Random forests based classification for crops ripeness stages", Proceedings of the Fifth International Conference on Innovations in Bio-Inspired Computing and Applications IBICA 2014: Springer International Publishing, pp. 205–215, 2014. Abstract
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Reham Gharbia, Ali Hassan El Baz, A. E. Hassanien, and M. F. Tolba, "Remote sensing image fusion approach based on Brovey and wavelets transforms", Proceedings of the Fifth International Conference on Innovations in Bio-Inspired Computing and Applications IBICA 2014: Springer International Publishing, pp. 311–321, 2014. Abstract
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Eid Emary, H. M. Zawbaa, A. E. Hassanien, G. Schaefer, and A. T. Azar, "Retinal blood vessel segmentation using bee colony optimisation and pattern search", Neural Networks (IJCNN), 2014 International Joint Conference on: IEEE, pp. 1001–1006, 2014. Abstract
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Eid Emary, H. M. Zawbaa, A. E. Hassanien, M. F. Tolba, and Václav Snášel, "Retinal vessel segmentation based on flower pollination search algorithm", Proceedings of the Fifth International Conference on Innovations in Bio-Inspired Computing and Applications IBICA 2014: Springer International Publishing, pp. 93–100, 2014. Abstract
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Eid Emary, H. M. Zawbaa, A. E. Hassanien, G. Schaefer, and A. T. Azar, "Retinal vessel segmentation based on possibilistic fuzzy c-means clustering optimised with cuckoo search", Neural Networks (IJCNN), 2014 International Joint Conference on: IEEE, pp. 1792–1796, 2014. Abstract
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Waleed Yamany, N. El-Bendary, H. 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: Springer International Publishing, pp. 259–270, 2014. Abstract
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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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2013
Asad, A. H., Eid Elamry, and A. E. Hassanien, "Retinal vessels segmentation based on water flooding model", The 9th IEEE International Computer Engineering Conference (ICENCO 2013) pp. 43 - 48 , Cairo, EGYPT -, December 29-30, , 2013.
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.
Mahmood, M. A., E. T. Al-Shammari, N. El-Bendary, A. E. Hassanien, and H. A. Hefny, "Recommender system for ground-level Ozone predictions in Kuwait", Computer Science and Information Systems (FedCSIS), 2013 Federated Conference on: IEEE, pp. 107–110, 2013. Abstract
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Gaber, T., A. E. Hassanien, and M. F. Tolba, "Repeated reselling permission multi-reselling approach for a license in DRM environment", Hybrid Intelligent Systems (HIS), 2013 13th International Conference on: IEEE, pp. 197–202, 2013. Abstract
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Asad, A. H., Eid Elamry, and A. E. Hassanien, "Retinal vessels segmentation based on water flooding model", Computer Engineering Conference (ICENCO), 2013 9th International: IEEE, pp. 43–48, 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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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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Salama, M. A., O. S. Soliman, I. Maglogiannis, A. E. Hassanien, and A. A. Fahmy, "Rough set-based identification of heart valve diseases using heart sounds", Rough Sets and Intelligent Systems-Professor Zdzisław Pawlak in Memoriam: Springer Berlin Heidelberg, pp. 475–491, 2013. Abstract
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Salama, M. A., O. S. Soliman, I. Maglogiannis, A. E. Hassanien, and A. A. Fahmy, "Rough set-based identification of heart valve diseases using heart sounds", Rough Sets and Intelligent Systems-Professor Zdzisław Pawlak in Memoriam: Springer Berlin Heidelberg, pp. 475–491, 2013. Abstract
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Mahmood, M. A., N. El-Bendary, A. E. Hassanien, and H. A. Hefny, "Rule Generation Approach for Granular Computing Using Rough Mereology", International Conference on Computer Research and Development, 5th (ICCRD 2013): ASME Press, 2013. Abstract
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2012
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.

Ahmed, Z., M. A. Salama, H. Hefny, and A. E. Hassanien, "Rough Sets-Based Rules Generation Approach: A Hepatitis C Virus Data Sets.", Advanced Machine Learning Technologies and Applications (AMLTA), Cairo Egypt, 8-10 Dec. , 2012. Abstract3220052.pdf

The risk of hepatitis-C virus is considered as a challenge in
the field of medicine. Applying feature reduction technique and generating
rules based on the selected features were considered as an important
step in data mining. It is needed by medical experts to analyze the generated
rules to find out if these rules are important in real life cases.
This paper presents an application of a rough set analysis to discover
the dependency between the attributes, and to generate a set of reducts
consisting of a minimal number of attributes. The experimental results
obtained, show that the overall accuracy offered by the rough sets is high.

Schaefer, G., Qinghua Hu, H. Zhou, J. F. Peters, and A. E. Hassanien, "Rough c-means and fuzzy rough c-means for colour quantisation", Fundamenta Informaticae, vol. 119, no. 1: IOS Press, pp. 113–120, 2012. Abstract
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Schaefer, G., Qinghua Hu, H. Zhou, J. F. Peters, and A. E. Hassanien, "Rough c-means and fuzzy rough c-means for colour quantisation", Fundamenta Informaticae, vol. 119, no. 1: IOS Press, pp. 113–120, 2012. Abstract
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Schaefer, G., Qinghua Hu, H. Zhou, J. F. Peters, and A. E. Hassanien, "Rough c-means and fuzzy rough c-means for colour quantisation", Fundamenta Informaticae, vol. 119, no. 1: IOS Press, pp. 113–120, 2012. Abstract
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