Publications

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2017
Hassanin, M. F., A. M. Shoeb, and A. E. Hassanien, "Designing Multilayer Feedforward Neural Networks Using Multi-Verse Optimizer", Handbook of Research on Machine Learning Innovations and Trends: IGI Global, pp. 1076–1093, 2017. Abstract
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Oliva, D., and A. E. Hassanien, "Digital Images Segmentation Using a Physical-Inspired Algorithm", Handbook of Research on Machine Learning Innovations and Trends: IGI Global, pp. 975–996, 2017. Abstract
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Ahmed, K., A. E. Hassanien, and E. Ezzat, "An Efficient Approach for Community Detection in Complex Social Networks Based on Elephant Swarm Optimization Algorithm", Handbook of Research on Machine Learning Innovations and Trends: IGI Global, pp. 1062–1075, 2017. Abstract
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Osman, M. A., A. Darwish, A. E. Khedr, A. Z. Ghalwash, and A. E. Hassanien, "Enhanced Breast Cancer Diagnosis System Using Fuzzy Clustering Means Approach in Digital Mammography", Handbook of Research on Machine Learning Innovations and Trends: IGI Global, pp. 925–941, 2017. Abstract
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Sharif, M. M., Alaa Tharwat, A. E. Hassanien, and H. A. Hefny, "Enzyme Function Classification: Reviews, Approaches, and Trends", Handbook of Research on Machine Learning Innovations and Trends: IGI Global, pp. 161–186, 2017. Abstract
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Dey, N., A. S. Ashour, and A. E. Hassanien, "Feature Detectors and Descriptors Generations with Numerous Images and Video Applications: A Recap", Feature Detectors and Motion Detection in Video Processing: IGI Global, pp. 36–65, 2017. 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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Panda, M., A. E. Hassanien, and A. Abraham, "Hybrid Data Mining Approach for Image Segmentation Based Classification", Biometrics: Concepts, Methodologies, Tools, and Applications: IGI Global, pp. 1543–1561, 2017. 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", Computers and Electronics in Agriculture, vol. 136: Elsevier, pp. 86–96, 2017. Abstract
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El-Said, S. A., H. M. A. Atta, and A. E. Hassanien, "Interactive soft tissue modelling for virtual reality surgery simulation and planning", International Journal of Computer Aided Engineering and Technology, vol. 9, no. 1: Inderscience Publishers (IEL), pp. 38–61, 2017. Abstract
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Hassanien, A. E., "Machine Learning-Based Soccer Video Summarization System.", Multimedia, Computer Graphics and Broadcasting-International Conference, MulGraB 2011,, 2017. Abstract
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Sayed, G. I., and A. E. Hassanien, "Moth-flame swarm optimization with neutrosophic sets for automatic mitosis detection in breast cancer histology images", Applied Intelligence: Springer US, pp. 1–12, 2017. Abstract
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Issa, M., and A. E. Hassanien, "Multiple Sequence Alignment Optimization Using Meta-Heuristic Techniques", Handbook of Research on Machine Learning Innovations and Trends: IGI Global, pp. 409–423, 2017. Abstract
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Babers, R., and A. E. Hassanien, "A Nature-Inspired Metaheuristic Cuckoo Search Algorithm for Community Detection in Social Networks", International Journal of Service Science, Management, Engineering, and Technology (IJSSMET), vol. 8, no. 1: IGI Global, pp. 50–62, 2017. Abstract
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Sayed, G. I., and A. E. Hassanien, "Neuro-Imaging Machine Learning Techniques for Alzheimer's Disease Diagnosis", Handbook of Research on Machine Learning Innovations and Trends: IGI Global, pp. 522–540, 2017. Abstract
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Asad, A. H., A. T. Azar, and A. E. Hassanien, "A new heuristic function of ant colony system for retinal vessel segmentation", Medical Imaging: Concepts, Methodologies, Tools, and Applications: IGI Global, pp. 2063–2081, 2017. Abstract
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Darwish, A., M. M. El-Gendy, and A. E. Hassanien, "A New Hybrid Cryptosystem for Internet of Things Applications", Multimedia Forensics and Security: Springer International Publishing, pp. 365–380, 2017. Abstract
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Alaa Tharwat, T. Gaber, A. E. Hassanien, and B. E. Elnaghi, "Particle Swarm Optimization: A Tutorial", Handbook of Research on Machine Learning Innovations and Trends: IGI Global, pp. 614–635, 2017. Abstract
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Farouk, A., M. Elhoseny, J. Batle, M. Naseri, and A. E. Hassanien, "A Proposed Architecture for Key Management Schema in Centralized Quantum Network", Handbook of Research on Machine Learning Innovations and Trends: IGI Global, pp. 997–1021, 2017. Abstract
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El-Atta, A. A. H., and A. E. Hassanien, "Two-class support vector machine with new kernel function based on paths of features for predicting chemical activity", Information Sciences, vol. 403: Elsevier, pp. 42–54, 2017. Abstract
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Dey, N., A. S. Ashour, S. Chakraborty, S. Banerjee, E. Gospodinova, M. Gospodinov, and A. E. Hassanien, "Watermarking in Biomedical Signal Processing", Intelligent Techniques in Signal Processing for Multimedia Security: Springer International Publishing, pp. 345–369, 2017. Abstract
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2016
Torky, M., R. Babers, R. A. Ibrahim, A. E. Hassanien, G. Schaefer, I. Korovin, and S. Y. Zhu, " Credibility investigation of newsworthy tweets using a visualising Petri net model", 2016 IEEE International Conference on Systems, Man, and Cybernetics (SMC), , USA, 9-12 Oct. 2016. Abstract

Investigating information credibility is an important problem in online social networks such as Twitter. Since misleading information can get easily propagated in Twitter, ranking tweets according to their credibility can help to detect rumors and identify misinformation. In this paper, we propose a Petri net model to visualise tweet credibility in Twitter. We consider the uniform resource locator (URL) as an effective feature in evaluating tweet credibility since it is used to identify the source of tweets, especially for newsworthy tweets. We perform an experimental evaluation on about 1000 tweets, and show that the proposed model is effective for assigning tweets to two classes: credible and incredible tweets, which each class being further divided into two sub-classes (“credible” and “seem credible” and “doubtful” and “incredible” tweets, respectively) based on appropriate features.

Hassanien, A. E., M. A. Fattah, S. Aboulenin, G. Schaefer, S. Y. Zhu, and I. Korovin, " Historic handwritten manuscript binarisation using whale optimization, Systems", IEEE International Conference on Systems, Man, and Cybernetics (SMC), 9, 9-12 Oct. 2016. Abstract

Preserving the content of historic handwritten manuscripts is important for a variety of reasons. On the other hand, digital libraries are rapidly expanding and thus facilitate to store this information directly in digital form. For digitising text documents, a crucial step is to binarise the captured images to separate the text from the background. In this paper, we propose an effective approach for binarisation of handwritten Arabic manuscripts which employs a whale optimisation algorithm, incorporating a fuzzy c-means objective function, to obtain optimal thresholds. Experimental results confirm the effectiveness of the proposed approach compared to earlier methods.

Elhoseny, M., N. Metawa, and A. E. Hassanien, "An automated information system to ensure quality in higher education institutions", 2016 12th International Computer Engineering Conference (ICENCO), , Cairo, 28-29 Dec. 2016. Abstract

Despite the great efforts to assure quality in higher education institutions, the ambiguity of its related concepts and requirements constitute a big challenge when trying to implement it as an automated information system. The present work introduces a framework for an automated information system that manages the quality assurance in higher educations institutions. The aim of designing such a system is to provide an automation tool that avoids unnecessary and redundant tasks associated to quality in higher education institutions. In addition, the proposed system helps all higher education stockholders to handle and monitor their tasks. Moreover, it aims to help the quality assurance center in a higher education institution to apply its qualitys standards, and to make sure that they are being maintained and enhanced. This information system contains a core module and 17 sub-modules, which are described in this paper.

Ahmed, M. M., M. M. Elwakil, A. E. Hassanien, and E. Hassanien, "Discrete Group Search Optimizer for community detection in multidimensional social network", 2016 12th International Computer Engineering Conference (ICENCO), , Cairo, 28-29 Dec. , 2016. Abstract

Multidimensionality is a distinctive aspect of real world social networks. Multidimensional social networks appeared as a result of that most social media sites such as Facebook, Twitter, and YouTube enable people to interact with each other through different social activities, reflecting different kinds of relationships between them. Recently, studying community structures hidden in multidimensional social networks has attracted a lot of attention. When dealing with these networks, the concept of community detection problem changes to be the discovery of the shared group structure across all network dimensions, such that members in the same group are tightly connected with each other, but are loosely connected with others outside the group. Studies in community detection topic have traditionally focused on networks that represent one type of interactions or one type of relationships between network entities. In this paper, we propose Discrete Group Search Optimizer (DGSO-MDNet) to solve the community detection problem in Multidimensional social networks, without any prior knowledge about the number of communities. The method aims to find community structure that maximizes multi-slice modularity, as an objective function. The proposed DGSO-MDNet algorithm adopts the locus-based adjacency representation and several discrete operators. Experiments on synthetic and real life networks show the capability of the proposed algorithm to successfully detect the structure hidden within these networks compared with other high performance algorithms in the literature.