Publications

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Schaefer, G., Bartosz Krawczyk, E. M. Celebi, H. Iyatomi, and A. E. Hassanien, "Melanoma classification based on ensemble classification of dermoscopy image features", International Conference on Advanced Machine Learning Technologies and Applications: Springer International Publishing, pp. 291–298, 2014. Abstract
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Ali, A. F., A. E. Hassanien, and V. Snasel, "Memetic Artificial Bee Colony for integer programming ", The 2nd International Conference on Advanced Machine Learning Technologies and Applications , Egypt, November 17-19, , 2014.
Ali, A. F., A. E. Hassanien, and V. Snasel, "Memetic Artificial Bee Colony for Integer Programming", International Conference on Advanced Machine Learning Technologies and Applications: Springer International Publishing, pp. 268–277, 2014. Abstract
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Alaa Tharwat, B. E. Elnaghi, and A. E. Hassanien, "Meta-Heuristic Algorithm Inspired by Grey Wolves for Solving Function Optimization Problems", International Conference on Advanced Intelligent Systems and Informatics: Springer International Publishing, pp. 480–490, 2016. Abstract
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Ali, A. F., and A. E. Hassanien, "Minimizing molecular potential energy function using genetic Nelder-Mead algorithm", Computer Engineering & Systems (ICCES), 2013 8th International Conference on: IEEE, pp. 177–183, 2013. Abstract
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Banerjee, S., H. Al-Qaheri, and A. E. Hassanien, "Mining social networks for viral marketing using fuzzy logic", Mathematical/Analytical Modelling and Computer Simulation (AMS), 2010 Fourth Asia International Conference on: IEEE, pp. 24–28, 2010. Abstract
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Banerjee, S., H. Al-Qaheri, and A. E. Hassanien, "Mining social networks for viral marketing using fuzzy logic", Mathematical/Analytical Modelling and Computer Simulation (AMS), 2010 Fourth Asia International Conference on: IEEE, pp. 24–28, 2010. Abstract
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Rouibah, K., "Mobile-commerce intention to use via SMS: The case of Kuwait", Emerging markets and e-commerce in developing economies: IGI Global, pp. 230–253, 2009. Abstract
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Soliman, O. S., A. E. Hassanien, N. I. Ghali, N. El-Bendary, and R. A. Sarker, "A model-based decision support tool using fuzzy optimization for climate change", International Conference on Rough Sets and Knowledge Technology: Springer Berlin Heidelberg, pp. 388–393, 2011. Abstract
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Soliman, O. S., A. E. Hassanien, N. I. Ghali, N. El-Bendary, and R. A. Sarker, "A model-based decision support tool using fuzzy optimization for climate change", International Conference on Rough Sets and Knowledge Technology: Springer Berlin Heidelberg, pp. 388–393, 2011. Abstract
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abd elaziz, M., and A. E. Hassanien, "Modified cuckoo search algorithm with rough sets for feature selection", Neural Computing and Applications: Springer London, pp. 1–10, 2016. Abstract
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abd elaziz, M., and A. E. Hassanien, "Modified cuckoo search algorithm with rough sets for feature selection", Download PDF Neural Computing and Applications, vol. 29, issue 4, pp. 925–934, 2018. AbstractWebsite

In this paper, a modified cuckoo search algorithm with rough sets is presented to deal with high dimensionality data through feature selection. The modified cuckoo search algorithm imitates the obligate brood parasitic behavior of some cuckoo species in combination with the Lévy flight behavior of some birds. The modified cuckoo search uses the rough sets theory to build the fitness function that takes the number of features in reduct set and the classification quality into account. The proposed algorithm is tested and validated benchmark on several benchmark datasets drawn from the UCI repository and using different evaluation criteria as well as a further analysis is carried out by means of the Analysis of Variance test. In addition, the proposed algorithm is experimentally compared with the existing algorithms on discrete datasets. Finally, two learning algorithms, namely K-nearest neighbors and support vector machines are used to evaluate the performance of the proposed approach. The results show that the proposed algorithm can significantly improve the classification performance.

abd elaziz, M., and A. E. Hassanien, "Modified cuckoo search algorithm with rough sets for feature selection,", Neural Computing and Applications,, pp. pp.1-10, 2017, 2017. AbstractWebsite

In this paper, a modified cuckoo search algorithm with rough sets is presented to deal with high dimensionality data through feature selection. The modified cuckoo search algorithm imitates the obligate brood parasitic behavior of some cuckoo species in combination with the Lévy flight behavior of some birds. The modified cuckoo search uses the rough sets theory to build the fitness function that takes the number of features in reduct set and the classification quality into account. The proposed algorithm is tested and validated benchmark on several benchmark datasets drawn from the UCI repository and using different evaluation criteria as well as a further analysis is carried out by means of the Analysis of Variance test. In addition, the proposed algorithm is experimentally compared with the existing algorithms on discrete datasets. Finally, two learning algorithms, namely K-nearest neighbors and support vector machines are used to evaluate the performance of the proposed approach. The results show that the proposed algorithm can significantly improve the classification performance.

Sayed, G. I., M. Soliman, and A. E. Hassanien, "Modified Optimal Foraging Algorithm for Parameters Optimization of Support Vector Machine", International Conference on Advanced Machine Learning Technologies and Applications, Cairo, 23 Feb, 2018. Abstract

Support Vector Machine (SVM) is one of the widely used algorithms for classification and regression problems. In SVM, penalty parameter C and kernel parameters can have a significant impact on the complexity and performance of SVM. In this paper, an Optimal Foraging Algorithm (OFA) is proposed to optimize the main parameters of SVM and reduce the classification error. Six public benchmark datasets were employed for evaluating the proposed (OFA-SVM). Also, five well-known and recently optimization algorithms are used for evaluation. These algorithms are Artificial Bee Colony (ABC), Genetic Algorithm (GA), Chicken Swarm Optimization (CSO), Particle Swarm Optimization (PSO) and Bat Algorithm (BA). The experimental results show that the proposed OFA-SVM obtained superior results. Also, the results demonstrate the capability of the proposed OFA-SVM to find optimal values of SVM parameters.

Banerjee, S., N. El-Bendary, A. E. Hassanien, and T. - H. Kim, "A modified pheromone dominant ant colony algorithm for computer virus detection", 2011 IEEE 14th International Multitopic Conference (INMIC), PP. 35-40 , Karachi, Pakistan , 22-24 Dec. 2011. Abstract

This paper proposes an elementary pattern detection approach for viruses propagated through e-mail and address books using the non-uniform pheromone deposition mechanism of ant colony. The local temporary tabu memory has been used to learn the pattern and it can combine known information from past viruses with a type of prediction for future viruses. This is achieved through certain generated test signature of viruses associated with e-mail over landscape. A non-uniform and non-decreasing time function for pheromone deposition and evaporation ensures that subsequent ants who are close enough to a previously selected trial solution will follow the trajectory or test landscape. They are capable to examine gradually thicker deposition of pheromone over the trajectory. It is empirically shown that the proposed modified pheromone learning mechanism can be an alternative approach to detect virus pattern for e-mail messages.

Banerjee, S., N. El-Bendary, A. E. Hassanien, and T. - H. Kim, "A modified pheromone dominant ant colony algorithm for computer virus detection", IEEE 14th International on Multitopic Conference (INMIC), pp. 35-40, Packistan, , 22-24 Dec., 2011. Abstract

This paper proposes an elementary pattern detection approach for viruses propagated through e-mail and address books using the non-uniform pheromone deposition mechanism of ant colony. The local temporary tabu memory has been used to learn the pattern and it can combine known information from past viruses with a type of prediction for future viruses. This is achieved through certain generated test signature of viruses associated with e-mail over landscape. A non-uniform and non-decreasing time function for pheromone deposition and evaporation ensures that subsequent ants who are close enough to a previously selected trial solution will follow the trajectory or test landscape. They are capable to examine gradually thicker deposition of pheromone over the trajectory. It is empirically shown that the proposed modified pheromone learning mechanism can be an alternative approach to detect virus pattern for e-mail messages.

Banerjee, S., N. El-Bendary, A. E. Hassanien, and T. - H. Kim, "A modified pheromone dominant ant colony algorithm for computer virus detection", Multitopic Conference (INMIC), 2011 IEEE 14th International: IEEE, pp. 35–40, 2011. Abstract
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Banerjee, S., N. El-Bendary, A. E. Hassanien, and T. - H. Kim, "A modified pheromone dominant ant colony algorithm for computer virus detection", Multitopic Conference (INMIC), 2011 IEEE 14th International: IEEE, pp. 35–40, 2011. Abstract
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Saleh Esmate Aly, H. I. Elshazly, A. F. Ali, H. A. Hussein, G. Schaefer, and M. A. R. Ahad, "Molecular classification of Newcastle disease virus based on degree of virulence", The 3rd Intl. Conf. on Informatics, Electronics & Vision. (ICIEV2014), Dhaka - Bangladesh, 23-24 May , 2014.
Saleh Esmate Aly, H. I. Elshazly, A. F. Ali, H. A. Hussein, A. E. Hassanien, G. Schaefer, and M. A. R. Ahad, "Molecular classification of Newcastle disease virus based on degree of virulence", Informatics, Electronics & Vision (ICIEV), 2014 International Conference on: IEEE, pp. 1–5, 2014. Abstract
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Staelens, S., and I. Lemahieu, "Monte Carlo Based Image Reconstruction in Emission Tom ography", Computational Intelligence in Medical Imaging: Techniques and Applications: CRC Press, pp. 407, 2009. Abstract
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Staelens, S., and I. Lemahieu, "Monte Carlo Based Image Reconstruction in Emission Tom ography", Computational Intelligence in Medical Imaging: Techniques and Applications: CRC Press, pp. 407, 2009. Abstract
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Yamanya, W., A. T. Mohammed Fawzy, and A. E. Hassanien, "Moth-Flame Optimization for Training Multi-layer Perceptrons", IEEE iInternational Computer Engineering Conference - ICENCO , Cairo, 30 Dec, 2015.
Waleed Yamany, M. O. H. A. M. M. E. D. FAWZY, Alaa Tharwat, and A. E. Hassanien, "Moth-flame optimization for training multi-layer perceptrons", Computer Engineering Conference (ICENCO), 2015 11th International: IEEE, pp. 267–272, 2015. Abstract
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Sayed, G. I., and A. E. Hassanien, "Moth-flame swarm optimization with neutrosophic sets for automatic mitosis detection in breast cancer histology images", Applied Intelligence: Springer US, pp. 1–12, 2017. Abstract
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