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Moustafa Zein, A. E. Hassanien, A. Badr, and T. - H. Kim, "Human Activity Classification Approach on Smartphone Using Monkey Search Algorithm", Advanced Communication and Networking (ACN), 2015 Seventh International Conference on: IEEE, pp. 84–88, 2015. Abstract
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Moustafa Zein, A. Adl, A. E. Hassanien, A. Badr, and T. - H. Kim, "Friendship Classification from Psychological Theories to Computational Model", 2015 Fourth International Conference on Information Science and Industrial Applications (ISI): IEEE, pp. 55–60, 2015. 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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Moustafa Zein, Ahmed Abdo, A. Adl, A. E. Hassanien, M. F. Tolba, and V. Snasel, "Orphan drug legislation with data fusion rules using multiple fingerprints measurements", The 5th International Conference on Innovations in Bio-Inspired Computing and Applications (Springer) IBICA2014, Ostrava, Czech Republic., 22-24 June, 2014. Abstractibica2014_p32.pdf

The orphan drug certification process from the European committee is
depending on experts opinions that it is not similar to any other drug, this stage is
very complicated and those opinions differ based on the expertise. So, this paper
introduces computational model that gives one accurate probability of similarity,
using multiple fingerprints measurements to similarity, and fuse these measurements
by data fusion rules, that give one probability of similarity helping experts
to determine that drug is similar to existing anyone or not.

Moustafa Zein, Ahmed Abdo, A. Adl, A. E. Hassanien, M. F. Tolba, and Václav Snášel, "Orphan Drug Legislation with Data Fusion Rules Using Multiple Fingerprints Measurements", Proceedings of the Fifth International Conference on Innovations in Bio-Inspired Computing and Applications IBICA 2014: Springer International Publishing, pp. 261–270, 2014. 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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Moustafa Ahmed, A. Hafez, M. Elwak, A. E. Hassanien, and E. Hassanien, "A Multi-Objective Genetic Algorithm for Community Detection in Multidimensional Social Network", the 1st International Conference on Advanced Intelligent Systems and Informatics (AISI’15) Springer, Beni Suef University, Beni Suef, Egypt , Nov. 28-30,, 2015. Abstract

Multidimensionality in social networks is a great issue that
came out into view as a result of that most social media sites such as
Facebook, Twitter, and YouTube allow people to interact with each other
through di erent social activities. The community detection in such mul-
tidimensional social networks has attracted a lot of attention in the recent
years. When dealing with these networks the concept of community de-
tection changes to be, the discovery of the shared group structure across
all network dimensions such that members in the same group interact
with each other more frequently than those outside the group. Most of
the studies presented on the topic of community detection assume that
there is only one kind of relation in the network. In this paper, we propose
a multi-objective approach, named MOGA-MDNet, to discover commu-
nities in multidimensional networks, by applying genetic algorithms. The
method aims to nd community structure that simultaneously maximizes
modularity, as an objective function, in all network dimensions. This
method does not need any prior knowledge about number of communi-
ties. Experiments on synthetic and real life networks show the capability
of the proposed algorithm to successfully detect the structure hidden
within these networks.

Mouhamed, M. R., A. Darwish, and A. E. Hassanien, "2D and 3D Intelligent Watermarking", Handbook of Research on Machine Learning Innovations and Trends: IGI Global, pp. 652–669, 2017. Abstract
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Mouhamed, M. R., H. M. Zawbaa, E. Al-Shammari, A. E. Hassanien, and V. Snasel, "Blind Watermark Approach for Map Authentication using Support Vector Machine", International conference on Advances in Security of Information and Communication Networks, (SecNet 2013) , Springer pp. 84–97, Cairo - Egypt, 3-5 Sept, 2013, . blind_watermark_approach_for_map_authentication_svm.pdf
Mouhamed, M. R., H. M. Zawbaa, E. T. Al-Shammari, A. E. Hassanien, and V. Snasel, "Blind watermark approach for map authentication using support vector machine", Advances in security of information and communication networks: Springer Berlin Heidelberg, pp. 84–97, 2013. Abstract
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Mouhamed, M. R., A. M. Rashad, and A. E. Hassanien, "Blind 2D vector data watermarking approach using random table and polar coordinates", Uncertainty Reasoning and Knowledge Engineering (URKE), 2012 2nd International Conference on: IEEE, pp. 67–70, 2012. Abstract
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Mostafa A. Salama, M. M. M. Fouad, N. El-Bendary, and A. E. Hassanien, "Mutagenicity analysis based on Rough Set Theory and Formal Concept Analysis", In Proceedings of the Second International Symposium on Intelligent Informatics (ISI'13), , Mysore, India, 23-24 August, 2, 2013. mutagenicity_analysis_based_on_roug_set_FCA.pdf
Mostafa, A., A. E. Hassanien, and H. A. Hefny, " Grey Wolf Optimization-Based Segmentation Approach for Abdomen CT Liver Images", Handbook of Research on Machine Learning Innovations and Trends, USA, IGI, 2017. Abstract

In the recent days, a great deal of researches is interested in segmentation of different organs in medical images. Segmentation of liver is as an initial phase in liver diagnosis, it is also a challenging task due to its similarity with other organs intensity values. This paper aims to propose a grey wolf optimization based approach for segmenting liver from the abdomen CT images. The proposed approach combines three parts to achieve this goal. It combines the usage of grey wolf optimization, statistical image of liver, simple region growing and Mean shift clustering technique. The initial cleaned image is passed to Grey Wolf (GW) optimization technique. It calculated the centroids of a predefined number of clusters. According to each pixel intensity value in the image, the pixel is labeled by the number of the nearest cluster. A binary statistical image of liver is used to extract the potential area that liver might exist in. It is multiplied by the clustered image to get an initial segmented liver. Then region growing (RG) is used to enhance the segmented liver. Finally, mean shift clustering technique is applied to extract the regions of interest in the segmented liver. A set of 38 images, taken in pre-contrast phase, was used for liver segmentation and testing the proposed approach. For evaluation, similarity index measure is used to validate the success of the proposed approach. The experimental results of the proposed approach showed that the overall accuracy offered by the proposed approach, results in 94.08% accuracy.

Mostafa, A., H. Hefny, N. I. Ghali, A. E. Hassanien, and G. Schaefer, "Evaluating the effects of image filters in CT liver CAD system", Biomedical and Health Informatics (BHI), 2012 IEEE-EMBS International Conference on: IEEE, pp. 448–451, 2012. Abstract
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Mostafa, A., M. A. Fattah, A. Fouad, A. E. Hassanien, and T. - H. Kim, "Region growing segmentation with iterative K-means for CT liver images", Advanced Information Technology and Sensor Application (AITS), 2015 4th International Conference on: IEEE, pp. 88–91, 2015. Abstract
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Mostafa, A., A. E. Hassanien, and H. A. Hefny, "Grey Wolf Optimization-Based Segmentation Approach for Abdomen CT Liver Images", Handbook of Research on Machine Learning Innovations and Trends: IGI Global, pp. 562–581, 2017. Abstract
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Mostafa, A., A. Fouad, M. A. Fattah, A. E. Hassanien, and H. Hefny, "Artificial Bee Colony Based Segmentation for CT Liver Images", Medical Imaging in Clinical Applications: Springer International Publishing, pp. 409–430, 2016. Abstract
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Mostafa, A., M. A. Fattah, A. Fouad, A. E. Hassanien, and H. Hefny, "Enhanced region growing segmentation for CT liver images", The 1st International Conference on Advanced Intelligent System and Informatics (AISI2015), November 28-30, 2015, Beni Suef, Egypt: Springer International Publishing, pp. 115–127, 2016. Abstract
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Mostafa, A., H. Hefny, N. I. Ghali, A. E. Hassanien, and G. Schaefer, "Evaluating the effects of image filters in CT liver CAD system", Biomedical and Health Informatics (BHI), 2012 IEEE-EMBS International Conference on: IEEE, pp. 448–451, 2012. 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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Mostafa, A., A. Fouad, M. Houseni, N. Allam, A. E. Hassanien, H. Hefny, and I. Aslanishvili, "A Hybrid Grey Wolf Based Segmentation with Statistical Image for CT Liver Images", International Conference on Advanced Intelligent Systems and Informatics: Springer International Publishing, pp. 846–855, 2016. Abstract
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Mostafa, A., H. Hefny, N. I. Ghali, A. E. Hassanien, and G. Schaefer, "Evaluating the effects of image filters in CT liver CAD system", Biomedical and Health Informatics (BHI), 2012 IEEE-EMBS International Conference on: IEEE, pp. 448–451, 2012. 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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Mostafa, A., A. E. Hassanien, N. I. Ghali, and H. Hefny, "Level Set-based Liver Image Segmentation with Watershed and ANN Classifier", The IEEE International Conference on Hybrid Intelligent Systems (HIS2012). , Pune. India. 4-7, Dec. 2012,. Abstract

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Mostafa, A., M. A. Fattah, A. E. Hassanien, H. Hefny, and G. S. Shao Ying Zhu, "CT Liver Segmentation Using Artificial Bee Colony Optimisation", 19th International Conference on Knowledge Based and Intelligent Information and Engineering Systems, Procedia Computer Science , Singapore, September, 2015. Abstract

The automated segmentation of the liver area is an essential phase in liver diagnosis from medical images. In this paper, we propose an artificial bee colony (ABC) optimisation algorithm that is used as a clustering technique to segment the liver in CT images. In our algorithm, ABC calculates the centroids of clusters in the image together with the region corresponding to each cluster. Using mathematical morphological operations, we then remove small and thin regions, which may represents flesh regions around the liver area, sharp edges of organs or small lesions inside the liver. The extracted regions are integrated to give an initial estimate of the liver area. In a final step, this is further enhanced using a region growing approach. In our experiments, we employed a set of 38 images, taken in pre-contrast phase, and the similarity index calculated to judge the performance of our proposed approach. This experimental evaluation confirmed our approach to afford a very good segmentation accuracy of 93.73% on the test dataset.

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