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Ali, A. F., A. E. Hassanien, V. Snasel, and M. F.Tolba, "A new hybrid particle swarm optimization with variable neighborhood search for solving unconstrained global optimization problems", The 5th International Conference on Innovations in Bio-Inspired Computing and Applications (Springer) IBICA2014, Ostrava, Czech Republic., 22-24 June, 2014.
Ali, J. M. H., and A. E. Hassanien, "PCNN for detection of masses in digital mammogram", Neural Network World, vol. 16, no. 2: Institute of Computer Science, pp. 129, 2006. Abstract
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Ali, M. A. S., and A. E. Hassanien, "An observational study to identify the role of online communication in offline social networks", International Conference on Advanced Machine Learning Technologies and Applications: Springer International Publishing, pp. 509–522, 2014. Abstract
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Ali, J. M., and A. E. Hassanien, "Mathematical Morphology Approach for Enhancement Digital Mammography Images", IASTED, International Conference on Biomedical Engineering (BioMED2004) February, pp. 16–18, 2004. Abstract
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Ali, J., and A. E. Hassanien, "Rough Set Approach for Generation of Classification Rules of Breast Cancer Data.", Informatica, vol. 15, issue 1, pp. 23-38, 2004. Abstract

Extensive amounts of knowledge and data stored in medical databases require the development of specialized tools for storing, accessing, analysis, and effectiveness usage of stored knowledge and data. Intelligent methods such as neural networks, fuzzy sets, decision trees, and expert systems are, slowly but steadily, applied in the medical fields. Recently, rough set theory is a new intelligent technique was used for the discovery of data dependencies, data reduction, approximate set classification, and rule induction from databases.

In this paper, we present a rough set method for generating classification rules from a set of observed 360 samples of the breast cancer data. The attributes are selected, normalized and then the rough set dependency rules are generated directly from the real value attribute vector. Then the rough set reduction technique is applied to find all reducts of the data which contains the minimal subset of attributes that are associated with a class label for classification. Experimental results from applying the rough set analysis to the set of data samples are given and evaluated. In addition, the generated rules are also compared to the well-known IDS classifier algorithm. The study showed that the theory of rough sets seems to be a useful tool for inductive learning and a valuable aid for building expert systems.

Ali, M. A., A. Assefa, D. Assefa, L. Bal{\'ık, A. Basu, O. Berger, E. Berhan, B. Beshah, E. Birhan, T. Buriánek, et al., "Abraham, Ajith 183, 293,303, 315, 371 Ahmed, Nada 315 Aldosari, Hamoud M. 303 Alhamedi, Adel H. 303", Afro-European Conference for Industrial Advancement: Proceedings of the First International Afro-European Conference for Industrial Advancement AECIA 2014, vol. 334: Springer, pp. 383, 2014. Abstract
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Ali, A. F., and A. - E. Hassanien, "A Simplex Nelder Mead Genetic Algorithm for Minimizing Molecular Potential Energy Function", Applications of Intelligent Optimization in Biology and Medicine: Springer International Publishing, pp. 1–21, 2016. Abstract
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Ali, J. M., and A. E. Hassanien, "Mathematical Morphology Approach for Enhancement Digital Mammography Images", IASTED, International Conference on Biomedical Engineering (BioMED2004) February, pp. 16–18, 2004. Abstract
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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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Alshabrawy, O. S., M. E. Ghoneim, W. A. Awad, and A. E. Hassanien, "Underdetermined blind source separation based on fuzzy c-means and semi-nonnegative matrix factorization", Computer Science and Information Systems (FedCSIS), 2012 Federated Conference on: IEEE, pp. 695–700, 2012. Abstract
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Alshabrawy, O. S., A. E. Hassanien, W. A. Awad, and A. Salama, "Blind Separation of Underdetermined Mixtures with Additive White and Pink Noises", 13th IEEE International Conference on Hybrid Intelligent Systems (HIS13) Tunisia, pp. 306-312, 2013, Tunisia, 4-6 Dec, 2013.
Alshabrawy, O. S., M. E. Ghoneim, W. A. Awad, and A. E. Hassanien, "Underdetermined Blind Source Separation based on Fuzzy C-Means and Semi-Nonnegative Matrix Factorization", IEEE Federated Conference on Computer Science and Information Systems, pp. 723–728, Wroclaw - Poland, 9-13 Sept, 2012. Abstractunderdetermined_blind_source_separation_based_on_fuzzy.pdf

Conventional blind source separation is based on
over-determined with more sensors than sources but the underdetermined
is a challenging case and more convenient to actual
situation. Non-negative Matrix Factorization (NMF) has been
widely applied to Blind Source Separation (BSS) problems.
However, the separation results are sensitive to the initialization
of parameters of NMF. Avoiding the subjectivity of choosing
parameters, we used the Fuzzy C-Means (FCM) clustering
technique to estimate the mixing matrix and to reduce the requirement
for sparsity.Also, decreasing the constraints is regarded
in this paper by using Semi-NMF. In this paper we propose
a new two-step algorithm in order to solve the underdetermined
blind source separation. We show how to combine the FCM clustering technique with the gradient-based NMF with the multi-layer technique. The simulation results show that our proposed algorithm can separate the source signals with high signal-to-noise ratio and quite low cost time compared with some algorithms.

Alshabrawy, O. S., M. E. Ghoneim, A. A. Salama, and A. E. Hassanien, "Underdetermined blind separation of an unknown number of sources based on fourier transform and matrix factorization", Computer Science and Information Systems (FedCSIS), 2013 Federated Conference on: IEEE, pp. 19–25, 2013. Abstract
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Alshabrawy, O. S., A. E. Hassanien, W. A. Awad, and A. A. Salama, "Blind separation of underdetermined mixtures with additive white and pink noises", Hybrid Intelligent Systems (HIS), 2013 13th International Conference on: IEEE, pp. 305–311, 2013. Abstract
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Alshabrawy, O. S., M. E. Ghoneim, W. A. Awad, and A. E. Hassanien, "Underdetermined blind source separation based on fuzzy c-means and semi-nonnegative matrix factorization", Computer Science and Information Systems (FedCSIS), 2012 Federated Conference on: IEEE, pp. 695–700, 2012. Abstract
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Alshabrawy, O. S., A. E. Hassanien, W. A. Awad, and A. A. Salama, 2013 13th International Conference on Hybrid Intelligent Systems (HIS), , 2013. Abstract

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Alshabrawy, O. S., M. E. Ghoneim, A. A. Salama, and A. E. Hassanien, "Underdetermined blind separation of an unknown number of sources based on fourier transform and matrix factorization", Computer Science and Information Systems (FedCSIS), 2013 Federated Conference on: IEEE, pp. 19–25, 2013. Abstract
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Alshabrawy, O. S., and A. E. Hassanien, "Underdetermined blind separation of mixtures of an unknown number of sources with additive white and pink noises", Proceedings of the Fifth International Conference on Innovations in Bio-Inspired Computing and Applications IBICA 2014: Springer International Publishing, pp. 241–250, 2014. Abstract
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Amin, I. I., S. K. Kassim, A. E. Hassanien, and H. A. Hefny, "Using formal concept analysis for mining hyomethylated genes among breast cancer tumors subtypes", Advances in Computing, Communications and Informatics (ICACCI), 2013 International Conference on: IEEE, pp. 521–526, 2013. Abstract
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Amin, I. I., S. K. Kassim, A. E. Hassanien, and H. A. Hefny, "Applying formal concept analysis for visualizing DNA methylation status in breast cancer tumor subtypes", Computer Engineering Conference (ICENCO), 2013 9th International: IEEE, pp. 37–42, 2013. Abstract
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Amin, I. I., S. K. Kassim, A. E. Hassanien, and H. A. Hefny, "Formal concept analysis for mining hypermethylated genes in breast cancer tumor subtypes", Intelligent Systems Design and Applications (ISDA), 2012 12th International Conference on: IEEE, pp. 764–769, 2012. Abstract
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Amin, I. I., S. K. Kassim, A. E. Hassanien, and H. A. Hefny, "Formal concept analysis for mining hypermethylated genes in breast cancer tumor subtypes", Intelligent Systems Design and Applications (ISDA), 2012 12th International Conference on: IEEE, pp. 764–769, 2012. Abstract
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Amin, I. I., S. K. Kassim, A. E. Hassanien, and H. Hefny, "Formal concept analysis for mining hypermethylated genes in breast cancer tumor subtypes", 12th International Conference on Intelligent Systems Design and Applications (ISDA), , Kochi, India, pp. 764 - 769, 2012. Abstract

The main purpose of this paper is to show the use of formal concept analysis (FCA) as data mining approach for mining the common hypermethylated genes between breast cancer subtypes, by extracting formal concepts which representing sets of significant hypermethylated genes for each breast cancer subtypes, then the formal context is built which leading to construct a concept lattice which is composed of formal concepts. This lattice can be used as knowledge discovery and knowledge representation therefore, becoming more interesting for the biologists.

Amin, K. M., M. A. Fattah, A. E. Hassanien, and G. Schaefer, "A binarization algorithm for historical arabic manuscript images using a neutrosophic approach", Computer Engineering & Systems (ICCES), 2014 9th International Conference on: IEEE, pp. 266–270, 2014. Abstract
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Amin, I. I., A. E. Hassanien, H. A. Hefny, and S. K. Kassim, "Visualizing and identifying the DNA methylation markers in breast cancer tumor subtypes", Proceedings of the Fifth International Conference on Innovations in Bio-Inspired Computing and Applications IBICA 2014: Springer International Publishing, pp. 161–171, 2014. Abstract
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