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2009
Ajith Abraham, Aboul-Ella Hassanien, P. S. A. E., Foundations of Computational Intelligence Volume 3: Global Optimization, , Germany, Studies in Computational Intelligence, Springer Verlag, Vol. 203 , 2009. AbstractWebsite

Global optimization is a branch of applied mathematics and numerical analysis that deals with the task of finding the absolutely best set of admissible conditions to satisfy certain criteria / objective function(s), formulated in mathematical terms. Global optimization includes nonlinear, stochastic and combinatorial programming, multiobjective programming, control, games, geometry, approximation, algorithms for parallel architectures and so on. Due to its wide usage and applications, it has gained the attention of researchers and practitioners from a plethora of scientific domains. Typical practical examples of global optimization applications include: Traveling salesman problem and electrical circuit design (minimize the path length); safety engineering (building and mechanical structures); mathematical problems (Kepler conjecture); Protein structure prediction (minimize the energy function) etc.

Ajith Abraham, Aboul-Ella Hassanien, A. C., Foundations of Computational Intelligence Volume 4: Bio-Inspired Data Mining, , Germany, Studies in Computational Intelligence, Springer Verlag, 2009. AbstractWebsite

Computational tools or solutions based on intelligent systems are being used with great success in Data Mining applications. Nature has been very successful in providing clever and efficient solutions to different sorts of challenges and problems posed to organisms by ever-changing and unpredictable environments. It is easy to observe that strong scientific advances have been made when issues from different research areas are integrated. A particularly fertile integration combines biology and computing. Computational tools inspired on biological process can be found in a large number of applications. One of these applications is Data Mining, where computing techniques inspired on nervous systems; swarms, genetics, natural selection, immune systems and molecular biology have provided new efficient alternatives to obtain new, valid, meaningful and useful patterns in large datasets.

Aboul-Ella Hassanien, Ajith Abraham, V. S., Foundations of Computational Intelligence Volume 5: Function Approximation and Classification, , Germany, Studies in Computational Intelligence, Springer Verlag, Vol. 205 , 2009. AbstractWebsite

Approximation theory is that area of analysis which is concerned with the ability to approximate functions by simpler and more easily calculated functions. It is an area which, like many other fields of analysis, has its primary roots in the mathematics.The need for function approximation and classification arises in many branches of applied mathematics, computer science and data mining in particular.

Ajith Abraham, Aboul-Ella Hassanien, A. C. V. S., Foundations of Computational Intelligence Volume 6: Data Mining, , Germany, ISBN: 978-3-642-01090-3, Studies in Computational Intelligence, Springer Verlag, 2009. AbstractWebsite

Finding information hidden in data is as theoretically difficult as it is practically important. With the objective of discovering unknown patterns from data, the methodologies of data mining were derived from statistics, machine learning, and artificial intelligence, and are being used successfully in application areas such as bioinformatics, business, health care, banking, retail, and many others. Advanced representation schemes and computational intelligence techniques such as rough sets, neural networks; decision trees; fuzzy logic; evolutionary algorithms; artificial immune systems; swarm intelligence; reinforcement learning, association rule mining, Web intelligence paradigms etc. have proved valuable when they are applied to Data Mining problems. Computational tools or solutions based on intelligent systems are being used with great success in Data Mining applications. It is also observed that strong scientific advances have been made when issues from different research areas are integrated.

Hassanien, A. E., "Hybrid Learning Enhancement of RBF Network with Particle Swarm Optimization", Foundations of Computational Intelligence, Volume 1: Learning and Approximation, Volume 201/2009, 381-397, London, Springer-Verlag , 2009. Abstract

This study proposes RBF Network hybrid learning with Particle Swarm Optimization (PSO) for better convergence, error rates and classification results. In conventional RBF Network structure, different layers perform different tasks. Hence, it is useful to split the optimization process of hidden layer and output layer of the network accordingly. RBF Network hybrid learning involves two phases. The first phase is a structure identification, in which unsupervised learning is exploited to determine the RBF centers and widths. This is done by executing different algorithms such as k-mean clustering and standard derivation respectively. The second phase is parameters estimation, in which supervised learning is implemented to establish the connections weights between the hidden layer and the output layer. This is done by performing different algorithms such as Least Mean Squares (LMS) and gradient based methods. The incorporation of PSO in RBF Network hybrid learning is accomplished by optimizing the centers, the widths and the weights of RBF Network. The results for training, testing and validation of five datasets (XOR, Balloon, Cancer, Iris and Ionosphere) illustrates the effectiveness of PSO in enhancing RBF Network learning compared to conventional Backpropogation.

Hassanien, A. E., "Proceedings of the 12th International Conference on Rough Sets, Fuzzy Sets, Data Mining and Granular Computing", Proceedings of the 12th International Conference on Rough Sets, Fuzzy Sets, Data Mining and Granular Computing, Berlin, Heidelberg, Springer-Verlag , 2009.
Hassanien, A. E., A. Abraham, J. F. Peters, and J. Kacprzyk, "Rough Sets in Medical Imaging: Foundations and Trends", Computational Intelligence in Medical Imaging: Techniques and Applications, USA, CRC, 2009. Abstract

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AboulElla, H., A. Abraham, J. F. Peters, and G. Schaefer, "Rough Sets in Medical Informatics Applications", Applications of Soft Computing - Advances in Intelligent and Soft Computing, pp 23-30, Berlin , Springer Berlin Heidelberg (ISSN: 978-3-540-89618-0), 2009. Abstract

Rough sets offer an effective approach of managing uncertainties and can be employed for tasks such as data dependency analysis, feature identification, dimensionality reduction, and pattern classification. As these tasks are common in many medical applications it is only natural that rough sets, despite their relative ‘youth’ compared to other techniques, provide a suitable method in such applications. In this paper, we provide a short summary on the use of rough sets in the medical informatics domain, focussing on applications of medical image segmentation, pattern classification and computer assisted medical decision making.

El-Dahshan, E. - S. A., A. E. Hassanien, A. Radi, and S. Banerjee, "Ultrasound Biomicroscopy Glaucoma Images Analysis Based on Rough Set and Pulse Coupled Neural Network", Foundations of Computational Intelligence, Volume 2, pp. 275-293 , London, Springer , 2009. Abstract

The objective of this book chapter is to present the rough sets and pulse coupled neural network scheme for Ultrasound Biomicroscopy glaucoma images analysis. To increase the efficiency of the introduced scheme, an intensity adjustment process is applied first using the Pulse Coupled Neural Network (PCNN) with a median filter. This is followed by applying the PCNN-based segmentation algorithm to detect the boundary of the interior chamber of the eye image. Then, glaucoma clinical parameters have been calculated and normalized, followed by application of a rough set analysis to discover the dependency between the parameters and to generate set of reduct that contains minimal number of attributes. Finally, a rough confusion matrix is designed for discrimination to test whether they are normal or glaucomatous eyes. Experimental results show that the introduced scheme is very successful and has high detection accuracy.

Václav Snášel, A. Keprt, A. Abraham, and A. E. Hassanien, "Approximate string matching by fuzzy automata", Man-Machine Interactions: Springer Berlin Heidelberg, pp. 281–290, 2009. Abstract
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Václav Snášel, A. Keprt, A. Abraham, and A. E. Hassanien, "Approximate string matching by fuzzy automata", Man-Machine Interactions: Springer Berlin Heidelberg, pp. 281–290, 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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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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Schaefer, G., A. Hassanien, and J. Jiang, Computational Intelligence in Medical Imaging: Techniques and Applications, : CRC press, 2009. 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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Abraham, A., A. - E. Hassanien, V. Sná, and others, Computational social network analysis: Trends, tools and research advances, : Springer Science & Business Media, 2009. Abstract
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Abraham, A., A. - E. Hassanien, V. Sná, and others, Computational social network analysis: Trends, tools and research advances, : Springer Science & Business Media, 2009. Abstract
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Terzopoulos, D., C. McIntosh, T. McInerney, and G. Hamarneh, "Deformable Organisms", Computational Intelligence in Medical Imaging: Techniques and Applications: Chapman and Hall/CRC, pp. 433–474, 2009. 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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Watchareeruetai, U., T. Matsumoto, Y. Takeuchi, H. Kudo, and N. Ohnishi, "Efficient construction of image feature extraction programs by using linear genetic programming with fitness retrieval and intermediate-result caching", Foundations of Computational Intelligence Volume 4: Springer Berlin Heidelberg, pp. 355–375, 2009. Abstract
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Watchareeruetai, U., T. Matsumoto, Y. Takeuchi, H. Kudo, and N. Ohnishi, "Efficient construction of image feature extraction programs by using linear genetic programming with fitness retrieval and intermediate-result caching", Foundations of Computational Intelligence Volume 4: Springer Berlin Heidelberg, pp. 355–375, 2009. Abstract
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