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Khairy, M., Alaa Tharwat, T. Gaber, and A. E. Hassanien, "A wheelchair control system using the human machine interaction: Single-modal and Multi-modal approaches", ournal of Intelligent Systems (JISYS), vol. In press, 2017.
Elshazly, H. I., A. F. Ali, H. Mahmoud, A. M. Elkorany, and A. E. Hassanien, "Weighted reduct selection metaheuristic based approach for rules reduction and visualization", Computing, Communication and Automation (ICCCA), 2016 International Conference on: IEEE, pp. 274–280, 2016. Abstract
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Kudělka, M., Václav Snášel, Z. Horák, A. E. Hassanien, and A. Abraham, "Web communities defined by web page content", Computational Social Network Analysis: Springer London, pp. 349–370, 2010. Abstract
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Kudělka, M., Václav Snášel, Z. Horák, A. E. Hassanien, and A. Abraham, "Web communities defined by web page content", Computational Social Network Analysis: Springer London, pp. 349–370, 2010. Abstract
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Dey, N., A. S. Ashour, S. Chakraborty, S. Banerjee, E. Gospodinova, M. Gospodinov, and A. E. Hassanien, "Watermarking in Biomedical Signal Processing", Intelligent Techniques in Signal Processing for Multimedia Security: Springer International Publishing, pp. 345–369, 2017. Abstract
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Asmaa Hashem Sweidan, N. El-Bendary, A. E. Hassanien, and O. M. H. A. E. -karim Mohamed, "Water Quality Classification Approach based on Bio-inspired Gray Wolf Optimization, ", 7th IEEE International Conference of Soft Computing and Pattern Recognition, , Kyushu University, Fukuoka, Japan, , , November 13 - 15, 2015. Abstract

Abstract—This paper presents a bio-inspired optimized classification approach for assessing water quality. As fish liver histopathology is a good biomarker for detecting water pollution, the proposed classification approach uses fish liver microscopic images in order to detect water pollution and determine water
quality. The proposed approach includes three phases; preprocessing, feature extraction, and classification phases. Color histogram and Gabor wavelet transform have been utilized for feature extraction phase. The Machine Learning (ML) Support Vector Machines (SVMs) classification algorithm has been employed,
along with the bio-inspired Gray Wolf Optimization (GWO) algorithm for optimizing SVMs parameters, in order to classify water pollution degree. Experimental results showed that the average accuracy achieved by the proposed GWO-SVMs classification approach exceeded 95% considering a variety of
water pollutants.

Asmaa Hashem Sweidan, N. El-Bendary, A. E. Hassanien, O. M. Hegazy, and A. E. -karim Mohamed, "Water quality classification approach based on bio-inspired Gray Wolf Optimization", Soft Computing and Pattern Recognition (SoCPaR), 2015 7th International Conference of: IEEE, pp. 1–6, 2015. Abstract
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Asmaa Hashem Sweidan, N. El-Bendary, O. M. Hegazy, A. E. Hassanien, and V. Snasel, "Water Pollution Detection System Based on Fish Gills as a Biomarker", International Conference on Communications, management, and Information technology (ICCMIT'2015), 2015. Abstract

This article presents an automatic system for assessing water quality based on fish gills microscopic images. As fish gills are a good biomarker for assessing water quality, the proposed system uses fish gills microscopic images in order to detect water pollution. The proposed system consists of three phases; namely pre-processing, feature extraction, and classification phases. Since the shape is the main characteristic of fish gills microscopic images, the proposed system uses shape feature based on edge detection and wavelets transform for classifying the water-quality degree. Furthermore, it implemented Principal Components Analysis (PCA) along with Support Vector Machines (SVMs) algorithms for feature extraction and water quality degree classification. The datasets used for experiments were constructed based on real sample images for fish gills. Training dataset is divided into four classes representing the different histopathological changes and the corresponding water quality degrees. Experimental results showed that the proposed classification system has obtained water quality classification accuracy of 95.41%, using the SVMs linear kernel function and 10-fold cross validation with 37 images per class for training.

Asmaa Hashem Sweidan, N. El-Bendary, O. M. Hegazy, A. E. Hassanien, and V. Snasel, "Water pollution detection system based on fish gills as a biomarker", Procedia Computer Science, vol. 65: Elsevier, pp. 601–611, 2015. Abstract
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Amin, I. I., A. E. Hassanien, Hesham A. Hefny, and S. K. Kassim, "Visualizing and identifying the DNA methylation markers in breast cancer tumor subtypes", The 5th International Conference on Innovations in Bio-Inspired Computing and Applications (Springer) IBICA2014, Ostrava, Czech Republic., 22-24 June, 2014. Abstractibica2014_p14.pdf

DNA methylation is an epigenetic mechanism that cells use to control
gene expression. DNA methylation has become one of the hottest topics in cancer
research, especially for abnormally hypermethylated tumor suppressor genes
or hypomethylaed oncogenes research. The analysis of DNA methylation data
determines the differential hypermethlated or hypomethylated genes that are candidate
to be cancer biomarkers. Visualization the DNA methylation status may
lead to discover new relationships between hypomethylated and hypermethylated
genes, therefore this paper applied a mathematical modelling theory called formal
concept analysis for visualizing DNA methylation status.

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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Ghali, N. I., O. Soluiman, N. El-Bendary, T. M. Nassef, S. A. Ahmed, Y. M. Elbarawy, and A. E. Hassanien, "Virtual reality technology for blind and visual impaired people: Reviews and Recent Advances,", Engineering Advances in Robotics and Virtual Reality, Germany, Intelligent Systems Reference Library - Springer, 2012. Abstract

Virtual reality technology enables people to become immersed in a computer-simulated and three-dimensional environment. In this chapter, we investigate the effects of the virtual reality technology on disabled people such as blind and visually impaired people (VIP) in order to enhance their computer skills and prepare them to make use of recent technology in their daily life. As well as, they need to advance their information technology skills beyond the basic computer training and skills. This chapter describes what best tools and practices in information technology to support disabled people such as deaf-blind and visual impaired people in their activities such as mobility systems, computer games, accessibility of e-learning, web-based information system, and wearable finger-braille interface for navigation of deaf-blind. Moreover, we will show how physical disabled people can benefits from the innovative virtual reality techniques and discuss some representative examples to illustrate how virtual reality technology can be utilized to address the information technology problem of blind and visual impaired people. Challenges to be addressed and an extensive bibliography are included.

Ghali, N. I., O. Soluiman, N. El-Bendary, T. M. Nassef, S. A. Ahmed, Y. M. Elbarawy, and A. E. Hassanien, "Virtual reality technology for blind and visual impaired people: reviews and recent advances", Advances in Robotics and Virtual Reality: Springer Berlin Heidelberg, pp. 363–385, 2012. Abstract
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Ghali, N. I., O. Soluiman, N. El-Bendary, T. M. Nassef, S. A. Ahmed, Y. M. Elbarawy, and A. E. Hassanien, "Virtual reality technology for blind and visual impaired people: reviews and recent advances", Advances in Robotics and Virtual Reality: Springer Berlin Heidelberg, pp. 363–385, 2012. Abstract
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Amin, I. I., Samar K. Kassim, A. E. Hassanien, and H. A. Hefny, "Using formal concept analysis for mining hyomethylated genes among breast cancer tumors subtypes ", IEEE International Conference on Advances in Computing, Communications and Informatics (ICACCI-2013) , Mysore, India, August 22-25, 2013. pid2853599_3_2.pdf
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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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, 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., 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., 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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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., 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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Alaa Tharwat, T. Gaber, and A. E. Hassanien, "Two biometric approaches for cattle identification based on features and classifiers fusion", International Journal of Image Mining, vol. 1, no. 4: Inderscience Publishers (IEL), pp. 342–365, 2015. Abstract
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Alaa Tharwataf, Tarek Gaberb, V. S. Mohamed Mostaf Fouadc, and Aboul Ella Hassaniene, "Towards an Automated Zebrafish-based Toxicity Test Model Using Machine Learning", International Conference on Communications, management, and Information technology (ICCMIT'2015) Volume 65, 2015, Pages 643–651, Check Republica, 2015. Abstract

Zebrafish animal is considered as one of the most suitable animals to test toxicity of compounds due many features such as transparency and a large number of embryos produced in each mating. The main problem of the zebrafish-based toxicity test is the manual inspection of thousands of animals images in different phases and this is not feasible enough for the analysis, i.e. it is slow and may be inaccurate process. To help addressing this problem, in this paper, an automated classification of alive (healthy) and coagulant (died because of toxic compounds) zebrafish embryos are proposed. The embryos’ images are used to extract some features using the Segmentation-based Fractal Texture Analysis (SFTA) technique. The Rotation Forest classifier is then used to match between testing and training features (i.e. to classify alive and coagulant embryos). The experiments have proved that choosing threshold value of SFTA technique and the size of the rotation forest classifier have a great impact on the classification accuracy. With accuracy around 99.98%, the experimental results have showed that the proposed model is a very promising step toward a fully automated toxicity test during drug discovery.

Alaa Tharwat, T. Gaber, M. M. Fouad, V. Snasel, and A. E. Hassanien, "Towards an automated zebrafish-based toxicity test model using machine learning", Procedia Computer Science, vol. 65: Elsevier, pp. 643–651, 2015. Abstract
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