Publications

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Alaa Tharwat, T. Gaber, M. K. Shahin, B. Refaat, and A. E. H. Ali, "SIFT-based Arabic Sign Language Recognition System", The 1st Afro-European Conference for Industrial Advancement, , Addis Ababa, Ethiopia, November 17-19, , 2014.
Alaa Tharwat, T. Gaber, and A. E. Hassanien, "Cattle identification based on muzzle images using gabor features and SVM classifier", International Conference on Advanced Machine Learning Technologies and Applications: Springer International Publishing, pp. 236–247, 2014. Abstract
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Alaa Tharwat, Hani Mahdi, Adel El Hennawy, and A. E. Hassanien, "Face sketch synthesis and recognition based on linear regression transformation and multi-classifier technique", The 1st International Conference on Advanced Intelligent System and Informatics (AISI2015), November 28-30, 2015, Beni Suef, Egypt: Springer International Publishing, pp. 183–193, 2016. Abstract
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Alaa Tharwat, T. Gaber, and A. E. Hassanien, "One-dimensional vs. two-dimensional based features: Plant identification approach", Journal of Applied Logic: Elsevier, 2016. Abstract
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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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Alaa Tharwat, T. Gaber, Abdelhameed Ibrahim, and A. E. Hassanien, "Linear Discriminant Analysis: A Detailed Tutorial", AI Communications, IOS press, 2017. linear_discreminate_analysisp_detailed_tutorails.pdf
Alaa Tharwat, Mahir M. Sharif, A. E. Hassanien, and H. A. Hefny, "Improving Enzyme Function Classification Performance Based on Score Fusion Method.", 10th International Conference Hybrid Artificial Intelligent System, Bilbao, Spain, 23 June, 2015.
Alaa Tharwat, A. E. Hassanien, and B. E. Elnaghi, "A BA-based algorithm for parameter optimization of Support Vector Machine", Pattern Recognition Letters: North-Holland, 2016. 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.

Ali, A. F., A. E. Hassanien, and Václav Snášel, "The Nelder-Mead Simplex Method with Variables Partitioning for Solving Large Scale Optimization Problems.", Innovations in Bio-inspired Computing and Applications. Advances in Intelligent Systems and Computing(Springer) , Czech republic , Volume 237, pp. 271-284, 2013.
Ali, A. F., A. E. Hassanien, Václav Snášel, and M. F. Tolba, "A New Hybrid Particle Swarm Optimization with Variable Neighborhood Search for Solving Unconstrained Global Optimization Problems", Proceedings of the Fifth International Conference on Innovations in Bio-Inspired Computing and Applications IBICA 2014: Springer International Publishing, pp. 151–160, 2014. Abstract
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Ali, A. F., A. E. Hassanien, and V. Snasel, "Memetic Artificial Bee Colony for Integer Programming", International Conference on Advanced Machine Learning Technologies and Applications: Springer International Publishing, pp. 268–277, 2014. Abstract
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Ali, J. M. H., and A. E. Hassanien, "A Human Iris Recognition Techniques to Enhance E-Security Environment Using Wavelet Trasform.", ICWI, pp. 572–579, 2003. 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 ", The 2nd International Conference on Advanced Machine Learning Technologies and Applications , Egypt, November 17-19, , 2014.
Ali, M. A. S., G. I. Sayed, T. Gaber, A. E. Hassanien, V. Snasel, and L. F. Silva, "Detection of breast abnormalities of thermograms based on a new segmentation method", Computer Science and Information Systems (FedCSIS), 2015 Federated Conference on: IEEE, pp. 255–261, 2015. Abstract
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Ali, J. M., and A. E. Hassanien, "An Iris Recognition System to Enhance E-Security, Advanced Modeling and Optimization", vol, vol. 5, pp. 93–104, 2003. Abstract

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Ali, M. A. S., M. I. Shaalan, A. E. Hassanien, and T. - H. Kim, "A Simple Approach for Segmentation and Removal of Interphase Cells from Chromosome Images", Computer and Computing Science (COMCOMS), 2015 3rd International Conference on: IEEE, pp. 3–8, 2015. Abstract
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Ali, A. F., A. E. Hassanien, and Václav Snášel, "The nelder-mead simplex method with variables partitioning for solving large scale optimization problems", Innovations in Bio-inspired Computing and Applications: Springer International Publishing, pp. 271–284, 2014. Abstract
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Ali, A. F., A. Mostafa, G. I. Sayed, M. A. Fattah, and A. E. Hassanien, "Nature Inspired Optimization Algorithms for CT Liver Segmentation", Medical Imaging in Clinical Applications: Springer International Publishing, pp. 431–460, 2016. Abstract
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Ali, A. F., and A. E. Hassanien, "Minimizing molecular potential energy function using genetic Nelder-Mead algorithm", Computer Engineering & Systems (ICCES), 2013 8th International Conference on: IEEE, pp. 177–183, 2013. Abstract
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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, A. F., and A. - E. Hassanien, "A Survey of Metaheuristics Methods for Bioinformatics Applications", Applications of Intelligent Optimization in Biology and Medicine: Springer International Publishing, pp. 23–46, 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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Ali, J. M. H., and A. E. Hassanien, "An iris recognition system to enhance e-security environment based on wavelet theory", AMO-Advanced Modeling and Optimization, vol. 5, no. 2, pp. 93–104, 2003. Abstract
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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.
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