Publications

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Conference Paper
Abdelsalam, M., Mahmood A. Mahmood, Yasser Mahmoud Awad, M. Hazman, N. Elbendary, A. E. Hassanien, M. F. Tolba, and S. M. Saleh, "Climate recommender system for wheat cultivation in North Egyptian Sinai Peninsula", The 5th International Conference on Innovations in Bio-Inspired Computing and Applications (Springer) IBICA2014, Ostrava, Czech Republic., 22-24 June, 2013.
Abdelsalam, M., M. A. Mahmood, Yasser Mahmoud Awad, M. Hazman, N. Elbendary, A. E. Hassanien, M. F. Tolba, and S. M. Saleh, "Climate recommender system for wheat cultivation in North Egyptian Sinai Peninsula", Proceedings of the Fifth International Conference on Innovations in Bio-Inspired Computing and Applications IBICA 2014: Springer International Publishing, pp. 121–130, 2014. Abstract
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Soliman, H., M. A. Fattah, and A. E. Hassanien, "Cloud Computing Framework for Solving Virtiual College Educations", The Second International Conference on INformation systems Design and Intelligent Applications ((INDIA 15), Kalyani, India, January 8-9 , 2015.
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.

Hassan, E. A., A. I. Hafez, A. E. Hassanien, and A. A. Fahmy, "Community Detection Algorithm Based on Artificial Fish Swarm Optimization", IEEE Conf. on Intelligent Systems (2) 2014: , Poland - Warsaw , 24 -26 Sept. , 2014. Abstract

Community structure identification in complex networks has been an important research topic in recent years. Community detection can be viewed as an optimization problem in which an objective quality function that captures the intuition of a community as a group of nodes with better internal connectivity than external connectivity is chosen to be optimized. In this paper Artificial Fish Swarm optimization (AFSO) has been used as an effective optimization technique to solve the community detection problem with the advantage that the number of communities is automatically determined in the process. However, the algorithm performance is influenced directly by the quality function used in the optimization process. A comparison is conducted between different popular communities’ quality measures and other well-known methods. Experiments on real life networks show the capability of the AFSO to successfully find an optimized community structure based on the quality function used.

Hafez, A. I., A. E. Hassanien, A. Fahmy, and M. Tolba, "Community Detection in Social Networks by using Bayesian network and Expectation Maximization technique", 13th IEEE International Conference on Hybrid Intelligent Systems (HIS13) Tunisia, 4-6 Dec. pp. 201-215, 2013, Tunisia, , 4-6 Dec, 2013.
Hafez, A. I., A. E. Hassanien, A. A. Fahmy, and M. F. Tolba, "Community detection in social networks by using Bayesian network and Expectation Maximization technique", Hybrid Intelligent Systems (HIS), 2013 13th International Conference on: IEEE, pp. 209–214, 2013. Abstract
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Elbedwehy, M. N., M. E. Ghoneim, and A. E. Hassanien, "Computational model for artificial learning using fonnal concept analysis", Computer Engineering & Systems (ICCES), 2013 8th International Conference on: IEEE, pp. 9–14, 2013. Abstract
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Elbedwehy, M. N., M. E. Ghoneim, and A. E. Hassanien, "Computational model for artificial learning using fonnal concept analysis", Computer Engineering & Systems (ICCES), 2013 8th International Conference on: IEEE, pp. 9–14, 2013. Abstract
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Soliman, O. S., and A. E. Hassanien, "A Computer Aided Diagnosis System for Breast Cancer Using Support Vector Machine", International Conference on Rough Sets and Current Trends in Computing: Springer Berlin Heidelberg, pp. 106–115, 2012. Abstract
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Soliman, O. S., and A. E. Hassanien, "A Computer Aided Diagnosis System for Breast Cancer Using Support Vector Machine", International Conference on Rough Sets and Current Trends in Computing: Springer Berlin Heidelberg, pp. 106–115, 2012. Abstract
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Soliman, O. S., and A. E. Hassanien, "A Computer Aided Diagnosis System for Breast Cancer Using Support Vector Machine", International Conference on Rough Sets and Current Trends in Computing: Springer Berlin Heidelberg, pp. 106–115, 2012. Abstract
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Hassanien, A. E., O. S. Soliman, and N. El-Bendary, "Contrast enhancement of breast MRI images based on fuzzy type-II", Soft Computing Models in Industrial and Environmental Applications, 6th International Conference SOCO 2011: Springer Berlin Heidelberg, pp. 77–83, 2011. 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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Davendra, D., A. El-Atta, H. Ahmed, M. A. Abu ElSoud, M. Adamek, M. Adhikari, A. Adl, H. Aldosari, Abder-Rahman Ali, A. F. Ali, et al., "Cordeschi, Nicola 43 Couceiro, Micael 83, 131 Czopik, Jan 365 Dasgupta, Kousik 271", Proceedings of the Fifth International Conference on Innovations in Bio-Inspired Computing and Applications IBICA 2014, vol. 303: Springer, pp. 439, 2014. Abstract
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Torky, M., R. Baberse, R. Ibrahim, A. E. Hassanien, G. Schaefer, I. Korovin, and S. Y. Zhu, "Credibility investigation of newsworthy tweets using a visualising Petri net model", Systems, Man, and Cybernetics (SMC), 2016 IEEE International Conference on: IEEE, pp. 003894–003898, 2016. Abstract
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Hamdy, E., A. Adl, A. E. Hassanien, O. Hegazy, and T. - H. Kim, "Criminal Act Detection and Identification Model", Advanced Communication and Networking (ACN), 2015 Seventh International Conference on: IEEE, pp. 79–83, 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.

El-Hosseini, M. A., A. E. Hassanien, A. Abraham, and H. Al-Qaheri, "Cultural-Based Genetic Algorithm: Design and Real World Applications", Intelligent Systems Design and Applications, 2008. ISDA'08. Eighth International Conference on, vol. 3: IEEE, pp. 488–493, 2008. Abstract
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El-Hosseini, M. A., A. E. Hassanien, A. Abraham, and H. Al-Qaheri, "Cultural-Based Genetic Algorithm: Design and Real World Applications. ", Eighth International Conference on Intelligent Systems Design and Applications, ISDA 2008, Kaohsiung, Taiwan, pp.488-493 , 26-28 November, 2008. Abstract

Due to their excellent performance in solving combinatorial optimization problems, metaheuristics algorithms such as Genetic Algorithms GA [35], [18], [5], Simulated Annealing SA [34], [13] and Tabu Search TS make up another class of search methods that has been adopted to efficiently solve dynamic optimization problem. Most of these methods are confined to the population space and in addition the solutions of nonlinear problems become quite difficult especially when they are heavily constrained. They do not make full use of the historical information and lack prediction about the search space. Besides the knowledge that individuals inherited "genetic code" from their ancestors, there is another component called Culture. In this paper, a novel culture-based GA algorithm is proposed and is tested against multidimensional and highly nonlinear real world applications.

Book Chapter
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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Ayeldeen, H., O. Hegazy, and A. E. Hassanien, "Case selection strategy based on K-means clustering", Information Systems Design and Intelligent Applications: Springer India, pp. 385–394, 2015. Abstract
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Ayeldeen, H., O. Shaker, O. Hegazy, and A. E. Hassanien, "Case-Based Reasoning: A Knowledge Extraction Tool to Use", Information systems design and intelligent applications: Springer India, pp. 369–378, 2015. Abstract
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Awad, A. I., A. E. Hassanien, and H. M. Zawbaa, "A cattle identification approach using live captured muzzle print images", Advances in Security of Information and Communication Networks: Springer Berlin Heidelberg, pp. 143–152, 2013. Abstract
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Hassanien, A. E., and J. M. Ali, "Classification and Retrieval of Images from Databases Using Rough Set Theory", Distributed Artificial Intelligence, Agent Technology, and Collaborative Applications: IGI Global, pp. 179–198, 2009. Abstract
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