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Salama, M. A., A. E. Hassanien, and A. A. Fahmy, "Feature evaluation based fuzzy C-mean classification", Fuzzy Systems (FUZZ), 2011 IEEE International Conference on: IEEE, pp. 2534–2539, 2011. 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., O. S. Soliman, I. Maglogiannis, A. E. Hassanien, and A. A. Fahmy, "Rough set-based identification of heart valve diseases using heart sounds", Rough Sets and Intelligent Systems-Professor Zdzisław Pawlak in Memoriam: Springer Berlin Heidelberg, pp. 475–491, 2013. Abstract
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Salama, M. A., A. E. Hassanien, and A. A. Fahmy, "Pattern-based subspace classification model", Nature and Biologically Inspired Computing (NaBIC), 2010 Second World Congress on: IEEE, pp. 357–362, 2010. Abstract
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Salama, M. A., A. E. Hassanien, and A. A. Fahmy, "Reducing the influence of normalization on data classification", Computer Information Systems and Industrial Management Applications (CISIM), 2010 International Conference on: IEEE, pp. 609–613, 2010. Abstract
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Salama, M. A., O. S. Soliman, I. Maglogiannis, A. E. Hassanien, and A. A. Fahmy, "Rough set-based identification of heart valve diseases using heart sounds", Rough Sets and Intelligent Systems-Professor Zdzisław Pawlak in Memoriam: Springer Berlin Heidelberg, pp. 475–491, 2013. Abstract
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Salama, M. A., A. E. Hassanien, A. A. Fahmy, and T. - H. Kim, "Heart Sound Feature Reduction Approach for Improving the Heart Valve Diseases Identification", Signal Processing, Image Processing and Pattern Recognition: Springer Berlin Heidelberg, pp. 280–290, 2011. Abstract
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Salama, M. A., A. E. Hassanien, and A. A. Fahmy, "Uni-class pattern-based classification model", Intelligent Systems Design and Applications (ISDA), 2010 10th International Conference on: IEEE, pp. 1293–1297, 2010. Abstract
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Salama, M. A., and A. E. Hassanien, "Binarization and Validation in Formal Concept Analysis", International Journal of Systems Biology and Biomedical Technologies, vol. 1, issue 4, pp. 17-28, 2012. AbstractWebsite

Representation and visualization of continuous data using the Formal Concept Analysis (FCA) became an
important requirement in real-life fields. Application of formal concept analysis (FCA) model on numerical
data, a scaling or Discretization / binarization procedures should be applied as preprocessing stage. The
Scaling procedure increases the complexity of computation of the FCA, while the binarization process leads to a distortion in the internal structure of the input data set. The proposed approach uses a binarization procedure prior to applying FCA model, and then applies a validation process to the generated lattice to measure or ensure its degree of accuracy. The introduced approach is based on the evaluation of each attribute according to the objects of its extent set. To prove the validity of the introduced approach, the technique is applied on two data sets in the medical field which are the Indian Diabetes and the Breast Cancer data sets. Both data sets show the generation of a valid lattice.

Salama, M. A., and A. E. Hassanien, "Binarization and validation in formal concept analysis", International Journal of Systems Biology and Biomedical Technologies (IJSBBT), vol. 1, no. 4: IGI Global, pp. 16–27, 2012. Abstract
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Salama, M. A., A. E. Hassanien, A. A. Fahmy, and T. - H. Kim, "Heart Sound Feature Reduction Approach for Improving the Heart Valve Diseases Identification", Signal Processing, Image Processing and Pattern Recognition: Springer Berlin Heidelberg, pp. 280–290, 2011. Abstract
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Salama, M. A., N. El-Bendary, and A. E. Hassanien, "Towards secure mobile agent based e-cash system", Proceedings of the First International Workshop on Security and Privacy Preserving in e-Societies: ACM, pp. 1–6, 2011. Abstract
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Salama, M. A., M. M. M. Fouad, N. El-Bendary, and A. E. O. Hassanien, "Mutagenicity Analysis Based on Rough Set Theory and Formal Concept Analysis", Recent Advances in Intelligent Informatics: Springer International Publishing, pp. 265–273, 2014. Abstract
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Salama, M. A., A. E. Hassanien, and A. A. Fahmy, "Feature evaluation based fuzzy C-mean classification", Fuzzy Systems (FUZZ), 2011 IEEE International Conference on: IEEE, pp. 2534–2539, 2011. Abstract
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Salama, M., M. Panda, Y. Elbarawy, A. E. Hassanien, and A. Abraham, "Social Networks Security and Privacy: Basics,Threats and Case Study to Visualize Foreign Terrorist Network dataset", Computational Social Networks: Security and Privacy, London, Series in Computer Communications and Networks, Springer Verlag, , 2012. Abstract

The continuous self-growing nature of social networks makes it hard to define a line of safety around these networks. Users in social networks are not interacting with the web only, but also with trusted groups that may contain enemies. There are different kinds of attacks on these networks including causing damage to the computer systems and steeling information about users. These attacks are not affecting individuals only, but also the organizations they are belonging to. Protection from these attacks should be performed by the users and security experts of the network. Advices should be provided to users of these social networks. Also security-experts should be sure that the contents transmitted through the network do not contain malicious or harmful data. This chapter shows the security risks and the tasks applied to minimize those risks. Explain the most famous ways that attackers and malicious use. Then show the security measures for each way. Also present a security guide and a social network security and privacy made in 2011, and finally a case study about the list of Foreign Terrorist Network dataset.

Salama, M., M. Panda, Y. Elbarawy, A. E. Hassanien, and A. Abraham, "Computational Social Networks: Security and Privacy", Computational Social Networks: Springer London, pp. 3–21, 2012. Abstract
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Salama, M. A., N. El-Bendary, A. E. Hassanien, K. Revett, and A. A. Fahmy, "Interval-based attribute evaluation algorithm", Computer Science and Information Systems (FedCSIS), 2011 Federated Conference on: IEEE, pp. 153–156, 2011. Abstract
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Salama, M. A., A. E. Hassanien, and A. A. Fahmy, "Deep belief network for clustering and classification of a continuous data", Signal Processing and Information Technology (ISSPIT), 2010 IEEE International Symposium on: IEEE, pp. 473–477, 2010. Abstract
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Salama, M. A., and A. E. Hassanien, "Binarization and validation in formal concept analysis", International Journal of Systems Biology and Biomedical Technologies (IJSBBT), vol. 1, no. 4: IGI Global, pp. 16–27, 2012. Abstract
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Salama, M. A., A. E. Hassanien, and A. A. Fahmy, "Pattern-based subspace classification model", Nature and Biologically Inspired Computing (NaBIC), 2010 Second World Congress on: IEEE, pp. 357–362, 2010. Abstract
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Salama, M., A. E. Hassanien, and A. A. Fahmy, "Pattern-based Subspace Classification Approach", The Second IEEE World Congress on Nature and Biologically Inspired Computing (NaBIC2010), Kitakyushu- Japan, 15 Dec, 2010. Abstract

The use of patterns in predictive models has received a lot of attention in recent years. This paper presents a pattern-based classification model which extracts the patterns that have similarity among all objects in a specific class. This introduced model handles the problem of the dependence on a user-defined threshold that appears in the pattern-based subspace clustering. The experimental results obtained, show that the overall pattern-based classification accuracy is high compared with other machine learning techniques including Support vector machine, Bayesian Network, multi-layer perception and decision trees.

Salama, M. A., and A. E. Hassanien, "Fuzzification of Euclidean Space Approach in Machine Learning Techniques", International Journal of Service Science, Management, Engineering, and Technology (IJSSMET), vol. 5, no. 4: IGI Global, pp. 29–43, 2014. Abstract
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Salama, M. A., A. E. Hassanien, and A. Mostafa, "The prediction of virus mutation using neural networks and rough set techniques", EURASIP Journal on Bioinformatics and Systems Biology, vol. 2016, no. 1: Springer International Publishing, pp. 1–11, 2016. Abstract
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Salama, M. A., H. F. Eid, R. A. Ramadan, A. Darwish, and A. E. Hassanien, "Hybrid intelligent intrusion detection scheme", Soft computing in industrial applications: Springer Berlin Heidelberg, pp. 293–303, 2011. 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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