Publications

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Chakraborty, S., S. Chatterjee, N. Dey, A. S. Ashour, and A. E. Hassanien, "Comparative Approach Between Singular Value Decomposition and Randomized Singular Value Decomposition-based Watermarking", Intelligent Techniques in Signal Processing for Multimedia Security: Springer International Publishing, pp. 133–149, 2017. 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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Aziz, A. S. A., E. L. Sanaa, and A. E. Hassanien, "Comparison of classification techniques applied for network intrusion detection and classification", Journal of Applied Logic: Elsevier, 2016. Abstract
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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.

Smolinski, T. G., M. G. Milanova, and A. - E. Hassanien, Computational Intelligence in Biomedicine and Bioinformatics: Current trends and applications, : Springer, 2009. Abstract
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Smolinski, T. G., M. G. Milanova, and A. - E. Hassanien, Computational Intelligence in Biomedicine and Bioinformatics: Current trends and applications, : Springer, 2009. Abstract
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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.

Schaefer, G., A. Hassanien, and J. Jiang, Computational Intelligence in Medical Imaging: Techniques and Applications, : CRC press, 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., 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., 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 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., and A. Abraham, Computational intelligence in multimedia processing: recent advances, : Springer, 2008. 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., "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., 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., 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., 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., 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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Wu, Z. Q., J. Jiang, and Y. H. Peng, "Computational Intelligence on Medical Imaging with Artificial Neural Networks in", Computational intelligence in medical imaging techniques and applications, 2009. Abstract
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Ahmed M. Anter, and A. E. Hassenian, "Computational intelligence optimization approach based on particle swarm optimizer and neutrosophic set for abdominal CT liver tumor segmentation", Journal of Computational Science, 2018. Abstract

In this paper, an improved segmentation approach for abdominal CT liver tumor based on neutrosophic sets (NS), particle swarm optimization (PSO), and fast fuzzy C-mean algorithm (FFCM) is proposed. To increase the contrast of the CT liver image, the intensity values and high frequencies of the original images were removed and adjusted firstly using median filter approach. It is followed by transforming the abdominal CT image to NS domain, which is described using three subsets namely; percentage of truth T, percentage of falsity F, and percentage of indeterminacy I. The entropy is used to evaluate indeterminacy in NS domain. Then, the NS image is passed to optimized FFCM using PSO to enhance, optimize clusters results and segment liver from abdominal CT. Then, these segmented livers passed to PSOFCM technique to cluster and segment tumors. The experimental results obtained based on the analysis of variance (ANOVA) technique, Jaccard Index and Dice Coefficient measures show that, the overall accuracy offered by neutrosophic sets is accurate, less time consuming and less sensitive to noise and performs well on non-uniform CT images.

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., 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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Elbedwehy, M. N., M. E. Ghoneim, A. E. Hassanien, and A. T. Azar, "A computational knowledge representation model for cognitive computers", Neural Computing and Application (Springer), vol. In press, 2014.