Publications

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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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Aziz, A. S. A., A. T. Azar, A. E. Hassanien, and S. E. - O. Hanafy, "Continuous features discretization for anomaly intrusion detectors generation", Soft computing in industrial applications: Springer International Publishing, pp. 209–221, 2014. Abstract
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Aziz, A. S. A., A. T. Azar, A. E. Hassanien, and S. E. - O. Hanafy, "Continuous features discretization for anomaly intrusion detectors generation", Soft computing in industrial applications: Springer International Publishing, pp. 209–221, 2014. Abstract
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Aziz, A. S. A., A. T. Azar, A. E. Hassanien, and S. E. - O. Hanafy, "Continuous features discretization for anomaly intrusion detectors generation", Soft computing in industrial applications: Springer International Publishing, pp. 209–221, 2014. Abstract
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Aziz, A. S. A., E. L. Sanaa, and A. E. Hassanien, "Comparison of classification techniques applied for network intrusion detection and classification", Journal of Applied Logic: Elsevier, 2016. Abstract
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Aziz, A. S. A., M. M. Fouad, and A. E. Hassanien, "Cloud Computing Forensic Analysis: Trends and Challenges", Multimedia Forensics and Security: Springer International Publishing, pp. 3–23, 2017. Abstract
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Chakraborty, S., S. Chatterjee, N. Dey, A. S. Ashour, and A. E. Hassanien, "Comparative Approach Between Singular Value Decomposition and Randomized Singular Value Decomposition-based Watermarking", Intelligent Techniques in Signal Processing for Multimedia Security: Springer International Publishing, pp. 133–149, 2017. Abstract
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Chiş, M., S. Banerjee, and A. E. Hassanien, "Clustering time series data: an evolutionary approach", Foundations of Computational, IntelligenceVolume 6: Springer Berlin Heidelberg, pp. 193–207, 2009. Abstract
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Chiş, M., S. Banerjee, and A. E. Hassanien, "Clustering time series data: an evolutionary approach", Foundations of Computational, IntelligenceVolume 6: Springer Berlin Heidelberg, pp. 193–207, 2009. 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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El-Bendary, N., Esraa Elhariri, M. Hazman, S. M. Saleh, and A. E. Hassanien, "Cultivation-time recommender system based on climatic conditions for newly reclaimed lands in Egypt", Procedia Computer Science, vol. 96: Elsevier, pp. 110–119, 2016. Abstract
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El-Bendary, N., Esraa Elhariri, M. Hazman, S. M. Saleh, and A. E. Hassanien, "Cultivation-time Recommender System Based on Climatic Conditions for Newly Reclaimed Lands in Egypt", Procedia Computer Science, vol. Volume 96, , pp. Pages 110-119, 2016. AbstractWebsite

This research proposes cultivation-time recommender system for predicting the best sowing dates for winter cereal crops in the newly reclaimed lands in Farafra Oasis, The Egyptian Western Desert. The main goal of the proposed system is to support the best utilization of farm resources. In this research, predicting the best sowing dates for the aimed crops is based on weather conditions prediction along with calculating the seasonal accumulative growing degree days (GDD) fulfillment duration for each crop. Various Machine Learning (ML) regression algorithms have been used for predicting the daily minimum and maximum air temperature based on historical weather conditions data for twenty-five growing seasons (1990/91 to 2014/15). Experimental results showed that using the M5P and IBk ML regression algorithms have outperformed the other implemented regression algorithms for predicting the daily minimum and maximum air temperature based on historical weather conditions data. That has been measured based on the calculated mean absolute error (MAE). Also, obtained experimental results obviously indicated that the best cultivation-time prediction by the proposed recommender system has been achieved by the M5P algorithm, based on the seasonal accumulative GDD fulfillment duration, for the coming five growing seasons (2016/17 to 2019/20).

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.

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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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, A. E. Hassanien, and A. T. Azar, "A computational knowledge representation model for cognitive computers", Neural Computing and Applications, vol. 25, no. 7-8: Springer London, pp. 1517–1534, 2014. Abstract
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Elbedwehy, M. N., M. E. Ghoneim, A. E. Hassanien, and A. T. Azar, "A computational knowledge representation model for cognitive computers", Neural Computing and Application , vol. June 2014, 2014. AbstractWebsite

The accumulating data are easy to store but the ability of understanding and using it does not keep track with its growth. So researches focus on the nature of knowledge processing in the mind. This paper proposes a semantic model (CKRMCC) based on cognitive aspects that enables cognitive computer to process the knowledge as the human mind and find a suitable representation of that knowledge. In cognitive computer, knowledge processing passes through three major stages: knowledge acquisition and encoding, knowledge representation, and knowledge inference and validation. The core of CKRMCC is knowledge representation, which in turn proceeds through four phases: prototype formation phase, discrimination phase, generalization phase, and algorithm development phase. Each of those phases is mathematically formulated using the notions of real-time process algebra. The performance efficiency of CKRMCC is evaluated using some datasets from the well-known UCI repository of machine learning datasets. The acquired datasets are divided into training and testing data that are encoded using concept matrix. Consequently, in the knowledge representation stage, a set of symbolic rule is derived to establish a suitable representation for the training datasets. This representation will be available in a usable form when it is needed in the future. The inference stage uses the rule set to obtain the classes of the encoded testing datasets. Finally, knowledge validation phase is validating and verifying the results of applying the rule set on testing datasets. The performances are compared with classification and regression tree and support vector machine and prove that CKRMCC has an efficient performance in representing the knowledge using symbolic rules.

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, A. E. Hassanien, and A. T. Azar, "A computational knowledge representation model for cognitive computers", Neural Computing and Application (Springer), vol. In press, 2014.
Elshazly, H. I., M. Waly, A. M. Elkorany, and A. E. Hassanien, "Chronic eye disease diagnosis using ensemble-based classifier", Engineering and Technology (ICET), 2014 International Conference on: IEEE, pp. 1–6, 2014. Abstract
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H
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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Hafez, A. I., E. T. Al-Shammari, A. E. Hassanien, and A. A. Fahmy, "Community detection in social networks using logic-based probabilistic programming", International Journal of Social Network Mining, vol. 2, no. 2: Inderscience Publishers (IEL), pp. 158–172, 2015. Abstract
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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.
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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