Publications

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2003
Hassanien, A. E., "Classification and feature selection of breast cancer data based on decision tree algorithm", Studies in Informatics and Control, vol. 12, no. 1: INFORMATICS AND CONTROL PUBLICATIONS, pp. 33–40, 2003. Abstract
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Hassanien, A. E., "Classification and feature selection of breast cancer data based on decision tree algorithm", Studies in Informatics and Control, vol. 12, no. 1: INFORMATICS AND CONTROL PUBLICATIONS, pp. 33–40, 2003. 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., 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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2006
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., "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.

2008
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., 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., 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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El-Hosseini, M. A., A. E. Hassanien, A. Abraham, and H. Al-Qaheri, "Cultural-Based Genetic Algorithm: Design and Real World Applications", Intelligent Systems Design and Applications, 2008. ISDA'08. Eighth International Conference on, vol. 3: IEEE, pp. 488–493, 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., 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.

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., "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.

El-Hosseini, M. A., A. E. Hassanien, A. Abraham, and H. Al-Qaheri, "Cultural-Based Genetic Algorithm: Design and Real World Applications. ", Eighth International Conference on Intelligent Systems Design and Applications, ISDA 2008, Kaohsiung, Taiwan, pp.488-493 , 26-28 November, 2008. Abstract

Due to their excellent performance in solving combinatorial optimization problems, metaheuristics algorithms such as Genetic Algorithms GA [35], [18], [5], Simulated Annealing SA [34], [13] and Tabu Search TS make up another class of search methods that has been adopted to efficiently solve dynamic optimization problem. Most of these methods are confined to the population space and in addition the solutions of nonlinear problems become quite difficult especially when they are heavily constrained. They do not make full use of the historical information and lack prediction about the search space. Besides the knowledge that individuals inherited "genetic code" from their ancestors, there is another component called Culture. In this paper, a novel culture-based GA algorithm is proposed and is tested against multidimensional and highly nonlinear real world applications.

2009
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., 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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Chiş, M., S. Banerjee, and A. E. Hassanien, "Clustering time series data: an evolutionary approach", Foundations of Computational, IntelligenceVolume 6: Springer Berlin Heidelberg, pp. 193–207, 2009. Abstract
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Chiş, M., S. Banerjee, and A. E. Hassanien, "Clustering time series data: an evolutionary approach", Foundations of Computational, IntelligenceVolume 6: Springer Berlin Heidelberg, pp. 193–207, 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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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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