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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.

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., A. E. Hassanien, and H. Hefney, "Liver segmentation in MRI images based on whale optimization algorithm,", Multimedia Tools and Applications, Springer, 2017.
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., 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. Houseni, N. Allam, A. E. Hassanien, H. Hefny, and P. - W. Tsai, "Antlion Optimization Based Segmentation for MRI Liver Images", International Conference on Genetic and Evolutionary Computing: Springer International Publishing, pp. 265–272, 2016. 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
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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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.

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 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, 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, 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, "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 Zeina, A. A. Fatma Yakouba, A. E. Hassanien, and V. Snasel, "Identifying Circles of Relations from Smartphone Photo Gallery", International Conference on Communications, management, and Information technology (ICCMIT'2015) Volume 65, 2015, Pages 582–591, Ostrava, Czech Republic, 2015. Abstract

Geotagged photos carry hidden data about the surrounding area, and the owner of the photo. Moreover; Geotagged photos have background information about the user, where the alternative resources of Geo-spatial data lack background information. In this study, we propose identification for the circles of relations of the smartphone user from Geotagged photos. The proposed solution mainly depends on a framework, which is based on smartphone photo gallery. The framework extracts a degree of relation between smartphone user and circles of relations entities. Circles of relations incorporate closest people, places, where the participant visits, and interests. The circles of relations are represented in a social graph, which shows the clusters of social relations and interests of smartphone user. The social graph clarifies the nature and the degree of the relations for the participants. The results of framework introduced the relation between the level of variety of participant social relations, and the degree of relations.

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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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.