Publications

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Miscellaneous
Smolinski, T. G., M. G. Milanova, and A. - E. Hassanien, Applications of Computational Intelligence in Biology: Current Trends and Open Problems, : Springer, 2008. Abstract
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Smolinski, T. G., M. G. Milanova, and A. - E. Hassanien, Applications of Computational Intelligence in Biology: Current Trends and Open Problems, : Springer, 2008. Abstract
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Smolinski, T. G., M. G. Milanova, and A. - E. Hassanien, Computational Intelligence in Biomedicine and Bioinformatics: Current trends and applications, : Springer, 2009. Abstract
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Smolinski, T. G., M. G. Milanova, and A. - E. Hassanien, Computational Intelligence in Biomedicine and Bioinformatics: Current trends and applications, : Springer, 2009. Abstract
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Hassanien, A. E., M. M. Fouad, A. A. Manaf, M. Zamani, R. Ahmad, and J. Kacprzyk, Multimedia Forensics and Security: Foundations, Innovations, and Applications, : Springer, 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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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, no. 3: IGI Global, pp. 72–103, 2016. Abstract
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Dadkhah, S., A. A. Manaf, Y. Hori, A. E. Hassanien, and S. Sadeghi, "An effective SVD-based image tampering detection and self-recovery using active watermarking", Signal Processing: Image Communication, vol. 29, no. 10: Elsevier, pp. 1197–1210, 2014. Abstract
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Asmaa Hashem Sweidan, E. - B. Nashwa Mamdouh, A. E. Hassanien, O. M. Hegazy, and A. E. -karim Mohamed, "Hybrid-Biomarker Case-Based Reasoning System for Water Pollution Assessment in Abou Hammad Sharkia, Egypt", Applied Soft computing , pp. Accepted, 2015. AbstractWebsite

Water pollution by organic materials or metals is one of the
problems that threaten humanity, both nowadays and over the next decades.
Morphological changes in Nile Tilapia "Oreochromis niloticus'' fish liver
and gills can also represent the adaptation strategies to maintain some
physiological functions or to assess acute and chronic exposure to
chemicals found in water and sediments. This paper presents an automatic
system for assessing water quality; in Sharkia Governorate - Egypt, based
on microscopic images of fish gills and liver. The proposed system used
fish gills and liver as hybrid-biomarker in order to detect water
pollution. It utilized case-based reasoning (CBR) for indicating the
degree of water quality based on the different histopathological changes
in fish gills and liver microscopic images. Various performance
evaluation metrics; namely, retrieval accuracy, Receiver Operating
Characteristic (ROC) curves, F-measure, and G-mean, have been used in
order to objectively indicate the true performance of the system
considering the unbalanced data. Experimental results showed that the
proposed hybrid-biomarker CBR based system achieved water quality
prediction accuracy of 97.9% using cosine distance similarity measure.
Also, it outperformed both SVMs and LDA classifiers for the tested
microscopic images dataset.

Hassaniena, A. E., N. El-Bendary, Asmaa Hashem Sweidan, and A. E. -karim Mohamed, "Hybrid-biomarker case-based reasoning system for water pollution assessment in Abou Hammad Sharkia, Egypt", Applied Soft Computing, vol. 46, issue 1, pp. 1043–1055, 2016. AbstractWebsite

Water pollution by organic materials or metals is one of the problems that threaten humanity, both nowadays and over the next decades. Morphological changes in Nile Tilapia “Oreochromis niloticus” fish liver and gills can also represent the adaptation strategies to maintain some physiological functions or to assess acute and chronic exposure to chemicals found in water and sediments. This paper presents an automatic system for assessing water quality, in Sharkia Governorate – Egypt, based on microscopic images of fish gills and liver. The proposed system used fish gills and liver as hybrid-biomarker in order to detect water pollution. It utilized case-based reasoning (CBR) for indicating the degree of water quality based on the different histopathological changes in fish gills and liver microscopic images. Various performance evaluation metrics namely, retrieval accuracy, receiver operating characteristic (ROC) curves, F-measure, and G-mean have been used in order to objectively indicate the true performance of the system considering the unbalanced data. Experimental results showed that the proposed hybrid-biomarker CBR based system achieved water quality prediction accuracy of 97.9% using cosine distance similarity measure. Also, it outperformed both SVMs and LDA classifiers for the tested microscopic images dataset.

Hassanien, A. E., N. El-Bendary, Asmaa Hashem Sweidan, A. E. -karim Mohamed, and O. M. Hegazy, "Hybrid-biomarker case-based reasoning system for water pollution assessment in Abou Hammad Sharkia, Egypt", Applied Soft Computing, vol. 46: Elsevier, pp. 1043–1055, 2016. Abstract
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Moustafa Zein, F. Yakoub, A. Adl, A. E. Hassanien, and V. Snasel, "Identifying Circles of Relations from Smartphone Photo Gallery", Procedia Computer Science, vol. 65: Elsevier, pp. 582–591, 2015. Abstract
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Hassanien, A. E., T. Gaber, U. Mokhtar, and H. Hefny, "An Improved Moth Flame Optimization Algorithm based on Rough Sets for Tomato Diseases Detection", Journal of Computers and Electronics in Agriculture, vol. 136, issue 15, pp. 86-96 , 2017. AbstractWebsite

Plant diseases is one of the major bottlenecks in agricultural production that have bad effects on the economic of any country. Automatic detection of such disease could minimize these effects. Features selection is a usual pre-processing step used for automatic disease detection systems. It is an important process for detecting and eliminating noisy, irrelevant, and redundant data. Thus, it could lead to improve the detection performance. In this paper, an improved moth-flame approach to automatically detect tomato diseases was proposed. The moth-flame fitness function depends on the rough sets dependency degree and it takes into a consideration the number of selected features. The proposed algorithm used both of the power of exploration of the moth flame and the high performance of rough sets for the feature selection task to find the set of features maximizing the classification accuracy which was evaluated using the support vector machine (SVM). The performance of the MFORSFS algorithm was evaluated using many benchmark datasets taken from UCI machine learning data repository and then compared with feature selection approaches based on Particle Swarm Optimization (PSO) and Genetic Algorithms (GA) with rough sets. The proposed algorithm was then used in a real-life problem, detecting tomato diseases (Powdery mildew and early blight) where a real dataset of tomato disease were manually built and a tomato disease detection approach was proposed and evaluated using this dataset. The experimental results showed that the proposed algorithm was efficient in terms of Recall, Precision, Accuracy and F-Score, as long as feature size reduction and execution time.

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