Publications

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Conference Paper
Awad, A. I., H. M. Zawbaa, H. A. Mahmoud, E. H. H. A. Nabi, R. H. Fayed, and A. E. Hassanien, "A robust cattle identification scheme using muzzle print images", Computer Science and Information Systems (FedCSIS), 2013 Federated Conference on: IEEE, pp. 529–534, 2013. Abstract
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Mahmood, M. A., N. El-Bendary, A. E. Hassanien, and H. A. Hefny, "Rule Generation Approach for Granular Computing Using Rough Mereology", International Conference on Computer Research and Development, 5th (ICCRD 2013): ASME Press, 2013. Abstract
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Mahmoud, R., N. El-Bendary, H. M. O. Mokhtar, and A. E. Hassanien, "Similarity Measures based Recommender System for Rehabilitation of People with Disabilities", the 1st International Conference on Advanced Intelligent Systems and Informatics (AISI’15) Springer, Beni Suef University, Beni Suef, Eg, Nov. 28-30, 2015. Abstract

This paper proposes a recommender system to predict and suggest a
set of rehabilitation methods for patients with spinal cord injuries (SCI). The proposed
system automates, stores and monitors the heath conditions of SCI patients.
The International Classification of Functioning, Disability and Health classification
(ICF) is used to stores and monitors the progress in health status. A set of
similarity measures are utilized in order to get the similarity between patients and
predict the rehabilitation recommendations. Experimental results showed that the
proposed recommender system has obtained an accuracy of 98% via implementing
the cosine similarity measure.

Mahmoud, R., N. El-Bendary, H. M. O. Mokhtar, and A. E. Hassanien, "Similarity Measures Based Recommender System for Rehabilitation of People with Disabilities", The 1st International Conference on Advanced Intelligent System and Informatics (AISI2015), November 28-30, 2015, Beni Suef, Egypt: Springer International Publishing, pp. 523–533, 2016. Abstract
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Moustafa Zein, A. Adl, A. E. Hassanien, A. Badr, and T. - H. Kim, "A Social Relationship Modifiers Modeller", Computer, Information and Application (CIA), 2015 3rd International Conference on: IEEE, pp. 33–37, 2015. Abstract
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Mokhtar, U., A. E. Hassanien, and M. A. H. A. S. Hefny, "Tomato leaves diseases detection approach based on support vector machines", IEEE iInternational Computer Engineering Conference - ICENCO , Cairo, 30 Dec, 2015.
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.

Moftah, H. M., N. I. Ghali, A. E. Hassanien, and M. A. Ismail, "Volume identification and estimation of MRI brain tumor", Hybrid Intelligent Systems (HIS), 2012 12th International Conference on: IEEE, pp. 120–124, 2012. Abstract
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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, 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.

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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Mostafa, A., M. A. Fattah, A. Fouad, A. E. Hassanien, and H. Hefny, "Wolf local thresholding approach for liver image segmentation in CT images", Proceedings of the Second International Afro-European Conference for Industrial Advancement AECIA 2015: Springer International Publishing, pp. 641–651, 2016. Abstract
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Journal Article
Mukherjee, A., N. Dey, N. Kausar, A. S. Ashour, R. Taiar, and A. E. Hassanien, " A Disaster Management Specific Mobility Model for Flying Ad-hoc Network", International Journal of Rough Sets and Data Analysis (IJRSDA), vol. 3, issue 3, 2016. AbstractWebsite

The extended Mobile Ad-hoc Network architecture is a paramount research domain due to a wide enhancement of smart phone and open source Unmanned Aerial Vehicle (UAV) technology. The novelty of the current work is to design a disaster aware mobility modeling for a Flying Ad-hoc network infrastructure, where the UAV group is considered as nodes of such ecosystem. This can perform a collaborative task of a message relay, where the mobility modeling under a “Post Disaster” is the main subject of interest, which is proposed with a multi-UAV prototype test bed. The impact of various parameters like UAV node attitude, geometric dilution precision of satellite, Global Positioning System visibility, and real life atmospheric upon the mobility model is analyzed. The results are mapped with the realistic disaster situation. A cluster based mobility model using the map oriented navigation of nodes is emulated with the prototype test bed.

Ragab A. El-Sehiemy, Mostafa Abdelkhalik El-hosseini, and A. E. Hassanien, " Multiobjective Real-Coded Genetic Algorithm for Economic/Environmental Dispatch Problem, ", Studies in Informatics and Control, , vol. 22, issue 2, pp. 113-122, 2013. Website
Hassanien, A. E., A. Abraham, F. Marcelloni, H. Hagras, M. Antonelli, and T. - P. Hong, 2010 10th International Conference on Intelligent Systems Design and Applications, , 2010. Abstract
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Hassanien, A. E., A. Abraham, F. Marcelloni, H. Hagras, M. Antonelli, and T. - P. Hong, 2010 10th International Conference on Intelligent Systems Design and Applications, , 2010. Abstract
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Hassanien, A. E., A. Abraham, F. Marcelloni, H. Hagras, M. Antonelli, and T. - P. Hong, 2010 10th InternaƟonal Conference on, , 2010. Abstract
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Hassanien, A. E., A. Abraham, F. Marcelloni, H. Hagras, M. Antonelli, and T. - P. Hong, 2010 10th InternaƟonal Conference on, , 2010. Abstract
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M.Moftah, H., A. T. Azar, E. T. Al-Shammari, N. I.Ghali, A. E. Hassanien, and M. Shoman, "Adaptive K-Means Clustering Algorithm for MR Breast Image Segmentation", Neural Computing and Applications (Springer), 2013. Abstract

Image segmentation is vital for meaningful analysis and interpretation
of medical images. The most popular method for clustering is k-means
clustering. This article presents a new approach intended to provide more reliable
Magnetic Resonance (MR) breast image segmentation that is based on
adaptation to identify target objects through an optimization methodology
that maintains the optimum result during iterations. The proposed approach
improves and enhances the effectiveness and efficiency of the traditional kmeans
clustering algorithm. The performance of the presented approach was
evaluated using various tests and different MR breast images. The experimental
results demonstrate that the overall accuracy provided by the proposed
adaptive k-means approach is superior to the standard k-means clustering
technique.

Moftah, H. M., A. T. Azar, E. T. Al-Shammari, N. I. Ghali, A. E. Hassanien, and M. Shoman, "Adaptive k-means clustering algorithm for MR breast image segmentation", Neural Computing and Applications, vol. 24, no. 7-8: Springer London, pp. 1917–1928, 2014. Abstract
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Moftah, H. M., A. T. Azar, E. T. Al-Shammari, N. I. Ghali, A. E. Hassanien, and M. Shoman, "Adaptive k-means clustering algorithm for MR breast image segmentation", Neural Computing and Applications, vol. 24, no. 7-8: Springer London, pp. 1917–1928, 2014. Abstract
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Hassanien, A. E., M. A. Fattah, S. MOHAMED, and others, "Art. 04–Volume 24• Issue 3• 2015", Studies in Informatics and Control-ICI Bucharest, 2015. Abstract
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Mona M. Soliman, A. E. Hassanien, and H. M. Ons, "A Blind 3D Watermarking Approach for 3D Mesh Using Clustering Based Methods", IJCVIP - International Journal of Computer Vision and Image Processing, vol. 3, issue 2, pp. 43-53, 2013. Website
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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Mostafa, A., A. Fouad, M. A. Fattah, A. E. Hassanien, H. Hefny, S. Y. Zhu, and G. Schaefer, "CT liver segmentation using artificial bee colony optimisation", Procedia Computer Science, vol. 60: Elsevier, pp. 1622–1630, 2015. Abstract
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