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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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Mahmood, M. A., N. El-Bendary, A. E. Hassanien, and H. A. Hefny, "Classification Approach based on Rough Mereology", In Proceedings of the Second International Symposium on Intelligent Informatics (ISI'13), , Mysore, India, 23-24 August, 20, 2013. isi2013-india-classification_approach_based_on_rough_mereology.pdf
Mahmood, M. A., N. El-Bendary, A. E. Hassanien, and H. A. Hefny, "Classification Approach Based on Rough Mereology", Recent Advances in Intelligent Informatics: Springer International Publishing, pp. 175–184, 2014. Abstract
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Alaa Tharwat, Yasmine S. Moemen, and A. E. Hassanien, "Classification of toxicity effects of biotransformed hepatic drugs using whale optimized support vector machines", Journal of Biomedical Informatics, vol. 68, pp. 132-149 , 2017. AbstractWebsite

Measuring toxicity is an important step in drug development. Nevertheless, the current experimental methods used to estimate the drug toxicity are expensive and time-consuming, indicating that they are not suitable for large-scale evaluation of drug toxicity in the early stage of drug development. Hence, there is a high demand to develop computational models that can predict the drug toxicity risks. In this study, we used a dataset that consists of 553 drugs that biotransformed in liver. The toxic effects were calculated for the current data, namely, mutagenic, tumorigenic, irritant and reproductive effect. Each drug is represented by 31 chemical descriptors (features). The proposed model consists of three phases. In the first phase, the most discriminative subset of features is selected using rough set-based methods to reduce the classification time while improving the classification performance. In the second phase, different sampling methods such as Random Under-Sampling, Random Over-Sampling and Synthetic Minority Oversampling Technique (SMOTE), BorderLine SMOTE and Safe Level SMOTE are used to solve the problem of imbalanced dataset. In the third phase, the Support Vector Machines (SVM) classifier is used to classify an unknown drug into toxic or non-toxic. SVM parameters such as the penalty parameter and kernel parameter have a great impact on the classification accuracy of the model. In this paper, Whale Optimization Algorithm (WOA) has been proposed to optimize the parameters of SVM, so that the classification error can be reduced. The experimental results proved that the proposed model achieved high sensitivity to all toxic effects. Overall, the high sensitivity of the WOA + SVM model indicates that it could be used for the prediction of drug toxicity in the early stage of drug development.

Hassanien, A. E., and J. M. H. Ali, "Classification of digital mammography algorithm based on rough set theory", Automatic Control and Computer Sciences, vol. 37, no. 6: ALLERTON PRESS INC 18 WEST 27TH ST, NEW YORK, NY 10001 USA, pp. 64–71, 2003. Abstract
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Schaefer, G., Niraj P. Doshi, Qinghua Hu, and A. E. Hassanien, "Classification of HEp-2 Cell Images using Compact Multi-Scale Texture Information and Margin Distribution Based Bagging ", The 2nd International Conference on Advanced Machine Learning Technologies and Applications , Egypt, November 17-19, , 2014.
Schaefer, G., N. P. Doshi, Qinghua Hu, and A. E. Hassanien, "Classification of HEp-2 Cell Images Using Compact Multi-Scale Texture Information and Margin Distribution Based Bagging", International Conference on Advanced Machine Learning Technologies and Applications: Springer International Publishing, pp. 299–308, 2014. Abstract
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Alaa Tharwat, Y. S. Moemen, and A. E. Hassanien, "Classification of toxicity effects of biotransformed hepatic drugs using whale optimized support vector machines", Journal of Biomedical Informatics, vol. 68: Academic Press, pp. 132–149, 2017. Abstract
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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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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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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.
Alnashar, H. S., M. A. Fattah, M. M. Mosbah, and A. E. Hassanien, "Cloud computing framework for solving virtual college educations: A case of egyptian virtual university", Information Systems Design and Intelligent Applications: Springer India, pp. 395–407, 2015. Abstract
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Hassanien, A. E., "Clustering Time Series Data: An Evolutionary Approach ", Foundations of Computational Intelligence, Volume 206, pp.193-207: Springer , 2008. Abstract

Time series clustering is an important topic, particularly for similarity search amongst long time series such as those arising in bioinformatics, in marketing research, software engineering and management. This chapter discusses the state-of-the-art methodology for some mining time series databases and presents a new evolutionary algorithm for times series clustering an input time series data set. The data mining methods presented include techniques for efficient segmentation, indexing, and clustering time series.

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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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.

Mohamed Tahoun, Abd El Rahman Shabayek, R. Reulke, and A. E. Hassanien, "Co-registration of Satellite Images Based on Invariant Local Features", Intelligent Systems' 2014: Springer International Publishing, pp. 653–660, 2015. Abstract
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Abdelhameed Ibrahim, T. Horiuchi, S. Tominaga, and A. E. Hassanien, "Color Invariant Representation and Applications", Handbook of Research on Machine Learning Innovations and Trends: IGI Global, pp. 1041–1061, 2017. Abstract
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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.

Hassan, E. A., A. I. Hafez, A. E. Hassanien, and A. A. Fahmy, "Community detection algorithm based on artificial fish swarm optimization", Intelligent Systems' 2014: Springer International Publishing, pp. 509–521, 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.
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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ella and A. I. Hafez, E. T. Al-Shammari, A. H. F. A. A., "Community Detection in Social Networks Using Logic-Based Probabilistic Programming, ", Int. J. of Social Network Mining (IJSNM), , vol. 2, issue 3, 2014.