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Hamdy, A., N. El-Bendary, A. Khodeir, M. M. M. Fouad, A. E. Hassanien, and H. Hefny, "Cardiac disorders detection approach based on local transfer function classifier", Computer Science and Information Systems (FedCSIS), 2013 Federated Conference on: IEEE, pp. 55–61, 2013. Abstract
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Hassan, E. A., A. I. Hafez, A. E. Hassanien, and A. A. Fahmy, "Community detection algorithm based on artificial fish swarm optimization", Intelligent Systems' 2014: Springer International Publishing, pp. 509–521, 2015. Abstract
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Hassan, E. A., A. I. Hafez, A. E. Hassanien, and A. A. Fahmy, "Community Detection Algorithm Based on Artificial Fish Swarm Optimization", IEEE Conf. on Intelligent Systems (2) 2014: , Poland - Warsaw , 24 -26 Sept. , 2014. Abstract

Community structure identification in complex networks has been an important research topic in recent years. Community detection can be viewed as an optimization problem in which an objective quality function that captures the intuition of a community as a group of nodes with better internal connectivity than external connectivity is chosen to be optimized. In this paper Artificial Fish Swarm optimization (AFSO) has been used as an effective optimization technique to solve the community detection problem with the advantage that the number of communities is automatically determined in the process. However, the algorithm performance is influenced directly by the quality function used in the optimization process. A comparison is conducted between different popular communities’ quality measures and other well-known methods. Experiments on real life networks show the capability of the AFSO to successfully find an optimized community structure based on the quality function used.

Hassanien, A. E., G. Schaefer, and A. Darwish, "Computational intelligence in speech and audio processing: recent advances", Soft Computing in Industrial Applications: Springer Berlin Heidelberg, pp. 303–311, 2010. Abstract
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Hassanien, A. - E., and A. Abraham, Computational intelligence in multimedia processing: recent advances, : Springer, 2008. Abstract
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Hassanien, A. E., E. T. Al-Shammari, and N. I. Ghali, "Computational intelligence techniques in bioinformatics", Computational biology and chemistry, vol. 47: Elsevier, pp. 37–47, 2013. Abstract
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Hassanien, A. E., "A Copyright Protection using Watermarking Algorithm", Informatica, vol. 17 , issue 2, pp. 187-198, 2006. AbstractWebsite

In this paper, a digital watermarking algorithm for copyright protection based on the concept of embed digital watermark and modifying frequency coefficients in discrete wavelet transform (DWT) domain is presented. We embed the watermark into the detail wavelet coefficients of the original image with the use of a key. This key is randomly generated and is used to select the exact locations in the wavelet domain in which to embed the watermark. The corresponding watermark detection algorithm is presented. A new metric that measure the objective quality of the image based on the detected watermark bit is introduced, which the original unmarked image is not required for watermark extraction. The performance of the proposed watermarking algorithm is robust to variety of signal distortions, such a JPEG, image cropping, geometric transformations and noises.

Hassanien, A. E., Computational Intelligence in Medical Imaging: Techniques and Applications, , USA, Chapman and Hall/CRC , 2009. AbstractWebsite

A compilation of the latest trends in the field, Computational Intelligence in Medical Imaging: Techniques and Applications explores how intelligent computing can bring enormous benefit to existing technology in medical image processing as well as improve medical imaging research. The contributors also cover state-of-the-art research toward integrating medical image processing with artificial intelligence and machine learning approaches.

Hassanien, A. - E., Computational Social Networks Analysis, , London, Computer Communications and Networks Series - Springer, 2010. AbstractWebsite

Social networks provide a powerful abstraction of the structure and dynamics of diverse kinds of people or people-to-technology interaction. Web 2.0 has enabled a new generation of web-based communities, social networks, and folksonomies to facilitate collaboration among different communities. This unique text/reference compares and contrasts the ethological approach to social behavior in animals with web-based evidence of social interaction, perceptual learning, information granulation, the behavior of humans and affinities between web-based social networks. An international team of leading experts present the latest advances of various topics in intelligent-social-networks and illustrates how organizations can gain competitive advantages by applying the different emergent techniques in real-world scenarios. The work incorporates experience reports, survey articles, and intelligence techniques and theories with specific network technology problems.

Hassanien, A. - E., and A. Abraham, Computational intelligence in multimedia processing: recent advances, : Springer, 2008. Abstract
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Hassanien, A. E., and A. Badr, "A comparative study on digital mamography enhancement algorithms based on fuzzy theory", Studies in informatics and control, vol. 12, no. 1: INFORMATICS AND CONTROL PUBLICATIONS, pp. 21–32, 2003. Abstract
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Hassanien, A. E., and J. M. Ali, "Classification and Retrieval of Images from Databases Using Rough Set Theory", Distributed Artificial Intelligence, Agent Technology, and Collaborative Applications: IGI Global, pp. 179–198, 2009. Abstract
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Hassanien, A. - E., A. Abraham, J. Kacprzyk, and J. F. Peters, "Computational intelligence in multimedia processing: foundation and trends", Computational Intelligence in Multimedia Processing: Recent Advances: Springer Berlin Heidelberg, pp. 3–49, 2008. Abstract
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Hassanien, A. E., "Clustering Time Series Data: An Evolutionary Approach ", Foundations of Computational Intelligence, Volume 206, pp.193-207: Springer , 2008. Abstract

Time series clustering is an important topic, particularly for similarity search amongst long time series such as those arising in bioinformatics, in marketing research, software engineering and management. This chapter discusses the state-of-the-art methodology for some mining time series databases and presents a new evolutionary algorithm for times series clustering an input time series data set. The data mining methods presented include techniques for efficient segmentation, indexing, and clustering time series.

Hassanien, A. E., and J. M. Ali, "Classification and Retrieval of Images from Databases Using Rough Set Theory", Distributed Artificial Intelligence, Agent Technology, and Collaborative Applications: IGI Global, pp. 179–198, 2009. Abstract
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Hassanien, A. E., Computational Intelligence in Multimedia Processing: Recent Advances, , USA, Studies in Computational Intelligence, Springer Vol. 96 , 2008. AbstractWebsite

For the last decades Multimedia processing has emerged as an important technology to generate content based on images, video, audio, graphics, and text. Furthermore, the recent new development represented by High Definition Multimedia content and Interactive television will generate a huge volume of data and important computing problems connected with the creation, processing and management of Multimedia content. "Computational Intelligence in Multimedia Processing: Recent Advances" is a compilation of the latest trends and developments in the field of computational intelligence in multimedia processing. This edited book presents a large number of interesting applications to intelligent multimedia processing of various Computational Intelligence techniques, such as rough sets, Neural Networks; Fuzzy Logic; Evolutionary Computing; Artificial Immune Systems; Swarm Intelligence; Reinforcement Learning and evolutionary computation.

Hassanien, A. - E., M. G. Milanova, T. G. Smolinski, and A. Abraham, "Computational intelligence in solving bioinformatics problems: Reviews, perspectives, and challenges", Computational Intelligence in Biomedicine and Bioinformatics: Springer Berlin Heidelberg, pp. 3–47, 2008. Abstract
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Hassanien, A. E., "A copyright protection using watermarking algorithm", Informatica, vol. 17, no. 2: Institute of Mathematics and Informatics, pp. 187–198, 2006. Abstract
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Hassanien, A. E., and J. M. H. Ali, "Classification of digital mammography algorithm based on rough set theory", Automatic Control and Computer Sciences, vol. 37, no. 6: ALLERTON PRESS INC 18 WEST 27TH ST, NEW YORK, NY 10001 USA, pp. 64–71, 2003. Abstract
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Hassanien, A. E., E. T. Al-Shammari, and N. I. Ghali, "Computational intelligence techniques in bioinformatics", Computational biology and chemistry, vol. 47: Elsevier, pp. 37–47, 2013. Abstract
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Hassanien, A. E., O. S. Soliman, and N. El-Bendary, "Contrast enhancement of breast MRI images based on fuzzy type-II", Soft Computing Models in Industrial and Environmental Applications, 6th International Conference SOCO 2011: Springer Berlin Heidelberg, pp. 77–83, 2011. Abstract
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Hassanien, A. - E., A. Abraham, J. Kacprzyk, and J. F. Peters, "Computational intelligence in multimedia processing: foundation and trends", Computational Intelligence in Multimedia Processing: Recent Advances: Springer Berlin Heidelberg, pp. 3–49, 2008. Abstract
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Hassanien, A. - E., Computational Social Networks Analysis, : Computer Communications and Networks Series-Springer, 2010. Abstract
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Hassanien, A. E., "Computational Intelligence in Solving Bioinformatics Problems: Reviews, Perspectives Computational Intelligence in Solving Bioinformatics Problems: Reviews, Perspectives, and Challenges", Computational Intelligence in Biomedicine and Bioinformatics , London, Studies in Computational Intelligence,Springer, Volume 151/2008, 3-47, 2008. Abstract

This chapter presents a broad overview of Computational Intelligence (CI) techniques including Artificial Neural Networks (ANN), Particle Swarm Optimization (PSO), Genetic Algorithms (GA), Fuzzy Sets (FS), and Rough Sets (RS). We review a number of applications of computational intelligence to problems in bioinformatics and computational biology, including gene expression, gene selection, cancer classification, protein function prediction, multiple sequence alignment, and DNA fragment assembly. We discuss some representative methods to provide inspiring examples to illustrate how CI could be applied to solve bioinformatic problems and how bioinformatics could be analyzed, processed, and characterized by computational intelligence. Challenges to be addressed and future directions of research are presented. An extensive bibliography is also included.

Hassanien, A. E., Computational Intelligence in Biomedicine and Bioinformatics, , Germany, Studies in Computational Intelligence, Springer Vol. 151 , 2008. AbstractWebsite

The purpose of this book is to provide an overview of powerful state-of-the-art methodologies that are currently utilized for biomedicine and/ or bioinformatics-oriented applications, so that researchers working in those fields could learn of new methods to help them tackle their problems. On the other hand, the CI community will find this book useful by discovering a new and intriguing area of applications. In order to help fill the gap between the scientists on both sides of this spectrum, the editors have solicited contributions from researchers actively applying computational intelligence techniques to important problems in biomedicine and bioinformatics.

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