Publications

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Journal Article
Grosan, C., A. Abraham, and A. - E. Hassanien, "Designing resilient networks using multicriteria metaheuristics", Telecommunication Systems , vol. 40, issue 1-2, pp. 75-88, 2009. AbstractWebsite

The paper deals with the design of resilient networks that are fault tolerant against link failures. Usually,
fault tolerance is achieved by providing backup paths, which are used in case of an edge failure on a primary path. We consider this task as a multiobjective optimization problem: to provide resilience in networks while minimizing the cost subject to capacity constraint. We propose a stochastic approach,
which can generate multiple Pareto solutions in a single run. The feasibility of the proposed method is illustrated by considering several network design problems using a single weighted average of objectives and a direct multiobjective optimization approach using the Pareto dominance concept.

Hassanien, A., J. Ali, and H. Nobuhara, "Detection of spiculated masses in Mammograms based on fuzzy image processing", Artificial Intelligence and Soft Computing-ICAISC 2004: Springer Berlin/Heidelberg, pp. 1002–1007, 2004. Abstract
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Hassanien, A., J. Ali, and H. Nobuhara, "Detection of spiculated masses in Mammograms based on fuzzy image processing", Artificial Intelligence and Soft Computing-ICAISC 2004: Springer Berlin/Heidelberg, pp. 1002–1007, 2004. Abstract
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Azar, A. T., and A. E. Hassanien, "Dimensionality reduction of medical big data using neural-fuzzy classifier", Soft computing, vol. 19, no. 4: Springer Berlin Heidelberg, pp. 1115–1127, 2015. Abstract
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Taher, A., and A. E. Hassanien, "Dimensionality reduction of medical big data using neural-fuzzy classifier", Soft Computing, 2014. Abstract
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Taher, A., and A. E. Hassanien, "Dimensionality reduction of medical big data using neural-fuzzy classifier", Soft Computing, vol. June 2014, 2014. AbstractWebsite

Massive and complex data are generated every day in many fields. Complex data refer to data sets that are so large that conventional database management and data analysis tools are insufficient to deal with them. Managing and analysis of medical big data involve many different issues regarding their structure, storage and analysis. In this paper, linguistic hedges neuro-fuzzy classifier with selected features (LHNFCSF) is presented for dimensionality reduction, feature selection and classification. Four real-world data sets are provided to demonstrate the performance of the proposed neuro-fuzzy classifier. The new classifier is compared with the other classifiers for different classification problems. The results indicated that applying LHNFCSF not only reduces the dimensions of the problem, but also improves classification performance by discarding redundant, noise-corrupted, or unimportant features. The results strongly suggest that the proposed method not only help reducing the dimensionality of large data sets but also can speed up the computation time of a learning algorithm and simplify the classification tasks.

Mukherjee, A., N. Dey, N. Kausar, A. S. Ashour, R. Taiar, and A. E. Hassanien, "A disaster management specific mobility model for flying ad-hoc network", International Journal of Rough Sets and Data Analysis (IJRSDA), vol. 3, no. 3: IGI Global, pp. 72–103, 2016. Abstract
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Al-Qaheri, H., A. E. Hassanien, and A. Abraham, "Discovering stock price prediction rules using rough sets", Neural Network World, vol. 18, no. 3: Institute of Computer Science, pp. 181, 2008. Abstract
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Al-Qaheri, H., A. E. Hassanien, and A. Abraham, "Discovering stock price prediction rules using rough sets", Neural Network World, vol. 18, no. 3: Institute of Computer Science, pp. 181, 2008. Abstract
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Azar, A. T., and A. E. Hassanien, "Editorial on: Fuzzy Logic in Biomedicine", Computers in biology and medicine, vol. 64: Elsevier Limited, pp. 321, 2015. Abstract
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Egiazarian, K., and A. E. Hassanien, "Editorial: special issue on soft computing in multimedia processing", Informatica, vol. 29, no. 3: Slovenian Society Informatika, pp. 251–253, 2005. Abstract
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Egiazarian, K., and A. E. Hassanien, "Editorial: special issue on soft computing in multimedia processing", Informatica, vol. 29, no. 3: Slovenian Society Informatika, pp. 251–253, 2005. Abstract
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Dadkhah, S., A. A. Manaf, Y. Hori, A. E. Hassanien, and S. Sadeghi, "An effective SVD-based image tampering detection and self-recovery using active watermarking", Signal Processing: Image Communication, vol. 29, no. 10: Elsevier, pp. 1197–1210, 2014. Abstract
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Hassanien, A. E., and M. Nakajima, "An efficient cross-dissolve transformation with alastic body spline warping interpolation for facial image morphing", Machine Graphics and Vision, vol. 7, no. 1/2, pp. 397–406, 1998. Abstract
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Helal, M. A., T. El-Arief, A. E. Hassanien, and N. El-Haggar, "An Efficient Texture Segmentation Algorithm for Isolating Iris Patterns Based on Wavelet Theory", PATTERN RECOGNITION AND IMAGE ANALYSIS C/C OF RASPOZNAVANIYE OBRAZOV I ANALIZ IZOBRAZHENII, vol. 14, no. 1: NAUKA/INTERPERIODICA PUBLISHING, pp. 97–103, 2004. Abstract
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Hassanien, A. - E., Emergent Web Intelligence: Advanced Information Retrieval, : Advanced Information and Knowledge Processing-Springer Verlag, 2010. Abstract
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Hassanien, A. - E., Emergent Web Intelligence: Advanced Semantic Technologies, : Advanced Information and Knowledge Processing-Springer Verlag, 2010. Abstract
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Salama, M. A., A. E. Hassanien, and K. Revett, "Employment of neural network and rough set in meta-learning", Memetic Computing Springer , 2013. AbstractWebsite

The selection of the optimal ensembles of classifiers in multiple-classifier selection technique is un-decidable in many cases and it is potentially subjected to a trial-and-error search. This paper introduces a quantitative meta-learning approach based on neural network and rough set theory in the selection of the best predictive model. This approach depends directly on the characteristic, meta-features of the input data sets. The employed meta-features are the degree of discreteness and the distribution of the features in the input data set, the fuzziness of these features related to the target class labels and finally the correlation and covariance between the different features. The experimental work that consider these criteria are applied on twenty nine data sets using different classification techniques including support vector machine, decision tables and Bayesian believe model. The measures of these criteria and the best result classification technique are used to build a meta data set. The role of the neural network is to perform a black-box prediction of the optimal, best fitting, classification technique. The role of the rough set theory is the generation of the decision rules that controls this prediction approach. Finally, formal concept analysis is applied for the visualization of the generated rules.

Salama, M. A., A. E. Hassanien, and K. Revett, "Employment of neural network and rough set in meta-learning", Memetic Computing, vol. 5, no. 3: Springer Berlin Heidelberg, pp. 165–177, 2013. Abstract
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Salama, M. A., A. E. Hassanien, and K. Revett, "Employment of neural network and rough set in meta-learning", Memetic Computing, vol. 5, no. 3: Springer Berlin Heidelberg, pp. 165–177, 2013. Abstract
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Salama, M. A., A. E. Hassanien, and K. Revett, "Employment of neural network and rough set in meta-learning.", Memetic Computing- Springer, vol. 5, issue 3, pp. 165-177, 2013. Website
Fouad, M. M., V. Snasel, and A. E. Hassanien, "Energy-Aware Sink Node Localization Algorithm for Wireless Sensor Networks", International Journal of Distributed Sensor Networks, , vol. 2015, 2015. Website
Fouad, M. M., V. Snasel, and A. E. Hassanien, "Energy-aware sink node localization algorithm for wireless sensor networks", International Journal of Distributed Sensor Networks, vol. 11, no. 7: SAGE Publications, pp. 810356, 2015. Abstract
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Zhou, X., K. Xiao, Alei Liang, Haibing Guan, and A. E. Hassanien, Energy-based Particle Swarm Optimization: Towards Energy Homeostasis in Social Autonomous Robots, , 2011. Abstract
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Zhou, X., K. Xiao, Alei Liang, Haibing Guan, and A. E. Hassanien, Energy-based Particle Swarm Optimization: Towards Energy Homeostasis in Social Autonomous Robots, , 2011. Abstract
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