Publications

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2012
Moftah, H. M., A. E. Hassanien, N. Ghali, and M. Shoman, Multi-objective optimization K-mean segmentation approach for MRI Breast Images, , 2012. Abstract
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Elmasry, W. H., H. M. Moftah, N. El-Bendary, and A. E. Hassanien, "Performance evaluation of computed tomography liver image segmentation approaches", Hybrid Intelligent Systems (HIS), 2012 12th International Conference on: IEEE, pp. 109–114, 2012. Abstract
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Moftah, H. M., N. I. Ghali, A. E. Hassanien, and M. A. Ismail, "Volume identification and estimation of MRI brain tumor", Hybrid Intelligent Systems (HIS), 2012 12th International Conference on: IEEE, pp. 120–124, 2012. Abstract
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M.Moftah, H., A. E. Hassanien, N. Ghali, and M. Shoman, Multi-objective optimization K-mean segmentation approach for MRI Breast Images, , 2012. Abstract

The objective of this paper is to evaluate a new approach intended for reliable MRI breast image segmentation. It is based on the concepts of multi-objective and adaptation to identify target objects through an optimization methodology which keeps the optimum result during its iterations. The proposed approach were used to improve and enhance the traditional k-means clustering algorithm to be more effective and efficient. The clustering and breast cancer segmentation are implemented in the proposed approach at the same time by using the concept of multiobjective, and adaptation continually, in each iteration and then maintaining the best results. To evaluate performance of the presented approach, we run tests over different MRI breast images. The experimental results show that the overall accuracy offered by the multiobjective proposed k-means is high compared with standard K-mean clustering technique.

Elmasry, W. H., H. M. Moftah, N. El-Bendary, and A. E. Hassanien, "Graph Partitioning based Automatic Segmentation Approach for CT Scan Liver Images", IEEE Federated Conference on Computer Science and Information Systems, pp. 205–208, Wroclaw - Poland , 9-13 Sept, 2012. Abstractgraph_partitioning_based_automatic_segmentation.pdf

Manual segmentation of liver computerized tomography (CT) images is very time consuming, so it is desired to develop a computer-based approach for the analysis of liver
CT images that can precisely segment the liver without any human intervention. This paper presents normalized cuts graph partitioning approach for liver segmentation from CT images. To evaluate the performance of the presented approach, we present tests on different liver CT images. Experimental results obtained show that the overall accuracy offered by the employed normalized cuts technique is high compared to the well known K-means segmentation approach.

Mostafa, A., A. E. Hassanien, N. I. Ghali, and H. Hefny, "Level Set-based Liver Image Segmentation with Watershed and ANN Classifier", The IEEE International Conference on Hybrid Intelligent Systems (HIS2012). , Pune. India. 4-7, Dec. 2012,. Abstract

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2013
Moftah, H. M., A. E. Hassanien, A. M. Alimi, H. Karray, and M. F. Tolba, "Ant-based clustering algorithm for magnetic resonance breast image segmentation", Hybrid Intelligent Systems (HIS), 2013 13th International Conference on: IEEE, pp. 161–166, 2013. Abstract
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Mouhamed, M. R., H. M. Zawbaa, E. T. Al-Shammari, A. E. Hassanien, and V. Snasel, "Blind watermark approach for map authentication using support vector machine", Advances in security of information and communication networks: Springer Berlin Heidelberg, pp. 84–97, 2013. Abstract
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Mahmoud, S., N. El-Bendary, M. A. Mahmood, and A. E. Hassanien, "An intelligent recommender system for drinking water quality", Hybrid Intelligent Systems (HIS), 2013 13th International Conference on: IEEE, pp. 285–290, 2013. Abstract
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El-Sehiemy, R. A., Mostafa Abdelkhalik El-hosseini, and A. E. Hassanien, "Multiobjective real-coded genetic algorithm for economic/environmental dispatch problem", Studies in Informatics and Control, vol. 22, no. 2, pp. 113–122, 2013. Abstract
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El-Sehiemy, R. A., Mostafa Abdelkhalik El-hosseini, and A. E. Hassanien, "Multiobjective real-coded genetic algorithm for economic/environmental dispatch problem", Studies in Informatics and Control, vol. 22, no. 2, pp. 113–122, 2013. Abstract
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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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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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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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El-Sehiemy, R. A., Mostafa Abdelkhalik El-hosseini, and A. E. Hassanien, "Volume 22• Issue 2• 2013", Studies in Informatics and Control-ICI Bucharest, 2013. Abstract
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Ragab A. El-Sehiemy, Mostafa Abdelkhalik El-hosseini, and A. E. Hassanien, " Multiobjective Real-Coded Genetic Algorithm for Economic/Environmental Dispatch Problem, ", Studies in Informatics and Control, , vol. 22, issue 2, pp. 113-122, 2013. Website
M.Moftah, H., A. T. Azar, E. T. Al-Shammari, N. I.Ghali, A. E. Hassanien, and M. Shoman, "Adaptive K-Means Clustering Algorithm for MR Breast Image Segmentation", Neural Computing and Applications (Springer), 2013. Abstract

Image segmentation is vital for meaningful analysis and interpretation
of medical images. The most popular method for clustering is k-means
clustering. This article presents a new approach intended to provide more reliable
Magnetic Resonance (MR) breast image segmentation that is based on
adaptation to identify target objects through an optimization methodology
that maintains the optimum result during iterations. The proposed approach
improves and enhances the effectiveness and efficiency of the traditional kmeans
clustering algorithm. The performance of the presented approach was
evaluated using various tests and different MR breast images. The experimental
results demonstrate that the overall accuracy provided by the proposed
adaptive k-means approach is superior to the standard k-means clustering
technique.

Mona M. Soliman, A. E. Hassanien, and H. M. Ons, "A Blind 3D Watermarking Approach for 3D Mesh Using Clustering Based Methods", IJCVIP - International Journal of Computer Vision and Image Processing, vol. 3, issue 2, pp. 43-53, 2013. Website
Mahmood, M. A., N. El-Bendary, A. E. Hassanien, and H. A. Hefny, "An Intelligent Multi-Agent Recommender System using Rough Mereology", In Proceedings of the 4th International Conference on Innovations in Bio-inspired Computing and Applications. Advances in Intelligent Systems and Computing (Springer) Volume 237, pp 201-213, 2013. Abstract

This article presents a Multi-Agent approach for handling the problem of recommendation. The proposed system works via two main agents; namely, the matching agent and the recommendation agent. Experimental results showed that the proposed rough mereology based Multi-agent system for solving the recommendation problem is scalable and has possibilities for future modification and adaptability to other problem domains. Moreover, it succeeded in reducing the information overload while recommending relevant decisions to users. The system achieved high accuracy in ranking using users profile and information system profiles. The resulted value of the Mean Absolute Error (MAE) is acceptable compared to other recommender systems applied other computational intelligence approaches.

Mahmood A. Mahmood, N. El-Bendary, Jan Platoš, A. E. Hassanien, and H. A. Hefny, "An Intelligent Multi-agent Recommender System. ", Innovations in Bio-inspired Computing and Applications. Advances in Intelligent Systems and Computing(Springer) , Czech republic , 2013.
Esraa Elhariri, N. El-Bendary, Mohamed Mostafa M. Fouad, Jan Platoš, A. E. Hassanien, and A. M. M. Hussein., "Multi-class SVM Based Classification Approach for Tomato Ripeness, ", Innovations in Bio-inspired Computing and Applications. Advances in Intelligent Systems and Computing(Springer) , Czech republic , 2013.
Mohamed Abd. Elfattah, N. El-Bendary, M. A. A. Elsoud, Jan Platoš, and A. E. Hassanien, "Principal Component Analysis Neural Network Hybrid Classification Approach for Galaxies Images.", Innovations in Bio-inspired Computing and Applications. Advances in Intelligent Systems and Computing(Springer) , Czech republic , 2013.
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