Publications

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Abder-Rahman Ali, Micael Couceiro, A. M. Anter, and A. E. Hassanien, "Evaluating an Evolutionary Particle Swarm Optimization for Fast Fuzzy C-Means Clustering on Liver CT Images", Computer Vision and Image Processing in Intelligent Systems and Multimedia Technologies, USA, IGI, 2014. Abstract

An Evolutionary Particle Swarm Optimization based on the Fractional Order Darwinian method for
optimizing a Fast Fuzzy C-Means algorithm is proposed. This chapter aims at enhancing the performance
of Fast Fuzzy C-Means, both in terms of the overall solution and speed. To that end, the concept
of fractional calculus is used to control the convergence rate of particles, wherein each one of them
represents a set of cluster centers. The proposed solution, denoted as FODPSO-FFCM, is applied on
liver CT images, and compared with Fast Fuzzy C-Means and PSOFFCM, using Jaccard Index and
Dice Coefficient. The computational efficiency is achieved by using the histogram of the image intensities
during the clustering process instead of the raw image data. The experimental results based on the
Analysis of Variance (ANOVA) technique and multiple pair-wise comparison show that the proposed
algorithm is fast, accurate, and less time consuming.

Abdo, W., Evolutionary Computation in Cryptanalysis, , Cairo Egypt, Al Azhar University and Scientific Research Group in Egypt (SRGE), 2013. ppt_phd_thesis_on_EC_CA.pdfphd_thesis_EC_CA_2013.pdf
Ahmad. Taher Azar, A. E. Hassanien, and T. - H. Kim, "Expert System Based On Neural-Fuzzy Rules for Thyroid Diseases Diagnosis.", International Conference on Bio-Science and Bio-Technology (BSBT2012), , Kangwondo, Korea. pp. 94--105, December 16-19, 2012. Abstract3530094.pdf

The thyroid, an endocrine gland that secretes hormones in the blood, circulates its products to all tissues of the body, where they control vital functions in every cell. Normal levels of thyroid hormone help the brain, heart, intestines, muscles and reproductive system function normally. Thyroid hormones control the metabolism of the body. Abnormalities of thyroid function are usually related to production of too little thyroid hormone (hypothyroidism) or production of too much thyroid hormone (hyperthyroidism). Therefore, the correct diagnosis of these diseases is very important topic. In this study, Linguistic Hedges Neural-Fuzzy Classifier with Selected Features (LHNFCSF) is presented for diagnosis of thyroid diseases. The performance evaluation of this system is estimated by using classification accuracy and k-fold cross-validation. The results indicated that the classification accuracy without feature selection was 98.6047% and 97.6744% during training and testing phases, respectively with RMSE of 0.02335. After applying feature selection algorithm, LHNFCSF achieved 100% for all cluster sizes during training phase. However, in the testing phase LHNFCSF achieved 88.3721% using one cluster for each class, 90.6977% using two clusters, 91.8605% using three clusters and 97.6744% using four clusters for each class and 12 fuzzy rules. The obtained classification accuracy was very promising with regard to the other classification applications in literature for this problem.

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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Ahmed, K., and A. E. Hassanien, "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, USA, IGI, 2017. Abstract

Complex social networks analysis is an important research trend, which basically based on community detection. Community detection is the process of dividing the complex social network into a dynamic number of clusters based on their edges connectivity. This paper presents an efficient Elephant Swarm Optimization Algorithm for community detection problem (EESO) as an optimization approach. EESO can define dynamically the number of communities within complex social network. Experimental results are proved that EESO can handle the community detection problem and define the structure of complex networks with high accuracy and quality measures of NMI and modularity over four popular benchmarks such as Zachary Karate Club, Bottlenose Dolphin, American college football and Facebook. EESO presents high promised results against eight community detection algorithms such as discrete krill herd algorithm, discrete Bat algorithm, artificial fish swarm algorithm, fast greedy, label propagation, walktrap, Multilevel and InfoMap.

Alaa Tharwat, Abdelhameed Ibrahim, A. E. Hassanien, and G. Schaefer, "Ear recognition using block-based principal component analysis and decision fusion", International Conference on Pattern Recognition and Machine Intelligence: Springer International Publishing, pp. 246–254, 2015. Abstract
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Alaa Tharwat, Abdelhameed Ibrahim, A. E. Hassanien, and G. Schaefer, "Ear Recognition Using Block-Based Principal Component Analysis and Decision Fusion", 6th International Conference Pattern Recognition and Machine Intelligence (PReMI 2015:), Warsaw, Poland, 2 July, 2015.
Asmaa Osamaa, S. A. El-Said, and A. E. Hassanien, "Energy-Efficient Routing Techniques for Wireless Sensors Networks", Handbook of Research on Emerging Technologies for Electrical Power Planning, Analysis, and Optimization: IGI Global, pp. 37–62, 2016. Abstract
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Awad, A. I., and A. E. Hassanien, "Erratum: Impact of Some Biometric Modalities on Forensic Science", Computational Intelligence in Digital Forensics: Forensic Investigation and Applications: Springer International Publishing, pp. E1–E1, 2014. Abstract
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Ayeldeen, H., A. E. Hassanien, and A. Fahmy, "Evaluation of Semantic Similarity across MeSH Ontology: A Cairo University Thesis Mining Case Study", 12th Mexican International Conference on Artificial Intelligence, , Mexico City, Mexico, pp. 139-144, 2013.
Ayeldeen, H., A. E. Hassanien, and A. A. Fahmy, "Evaluation of semantic similarity across MeSH ontology: a Cairo University thesis mining case study", Artificial Intelligence (MICAI), 2013 12th Mexican International Conference on: IEEE, pp. 139–144, 2013. Abstract
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Ayeldeen, H., M. A. Mahmood, and A. E. Hassanien, "Effective Classification and Categorization for Categorical Sets: Distance Similarity Measures", Information Systems Design and Intelligent Applications: Springer India, pp. 359–368, 2015. Abstract
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Azar, A. T., A. E. Hassanien, T. - H. Kim, and others, "Expert system based on neural-fuzzy rules for thyroid diseases diagnosis", Computer Applications for Bio-Technology, Multimedia, and Ubiquitous City: Springer Berlin Heidelberg, pp. 94–105, 2012. Abstract
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Azar, A. T., A. E. Hassanien, T. - H. Kim, and others, "Expert system based on neural-fuzzy rules for thyroid diseases diagnosis", Computer Applications for Bio-Technology, Multimedia, and Ubiquitous City: Springer Berlin Heidelberg, pp. 94–105, 2012. Abstract
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Azar, A. T., and A. E. Hassanien, "Editorial on: Fuzzy Logic in Biomedicine", Computers in biology and medicine, vol. 64: Elsevier Limited, pp. 321, 2015. Abstract
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Azar, A. T., A. E. Hassanien, T. - H. Kim, and others, "Expert system based on neural-fuzzy rules for thyroid diseases diagnosis", Computer Applications for Bio-Technology, Multimedia, and Ubiquitous City: Springer Berlin Heidelberg, pp. 94–105, 2012. Abstract
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Badr, Y., R. Chbeir, A. Abraham, and A. - E. Hassanien, Emergent web intelligence: Advanced semantic technologies, : Springer Science & Business Media, 2010. Abstract
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Badr, Y., R. Chbeir, A. Abraham, and A. - E. Hassanien, Emergent web intelligence: Advanced semantic technologies, : Springer Science & Business Media, 2010. Abstract
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Belem, B., and P. Plassmann, "Early Detection of Wound Inflammation by Color Analysis", Computational Intelligence in Medical Imaging: Techniques and Applications: Chapman and Hall/CRC, pp. 89–111, 2009. Abstract
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Chbeir, R., Y. Badr, A. Abraham, and A. - E. Hassanien, Emergent web intelligence: advanced information retrieval, : Springer, 2010. Abstract
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Chbeir, R., Y. Badr, A. Abraham, and A. - E. Hassanien, Emergent web intelligence: advanced information retrieval, : Springer, 2010. 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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Egiazarian, K., and A. E. Hassanien, "Editorial: special issue on soft computing in multimedia processing", Informatica, vol. 29, no. 3: Slovenian Society Informatika, pp. 251–253, 2005. Abstract
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Egiazarian, K., and A. E. Hassanien, "Editorial: special issue on soft computing in multimedia processing", Informatica, vol. 29, no. 3: Slovenian Society Informatika, pp. 251–253, 2005. Abstract
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El-Bendary, N., V. Snasel, G. Adam, F. Mansour, N. I. Ghali, O. S. Soliman, and A. E. Hassanien, "E-Contract Securing System Using Digital Signature Approach", Advanced Communication and Networking: Springer Berlin Heidelberg, pp. 183–189, 2011. Abstract
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Tourism