Publications

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2009
Hassanien, A. - E., J. H. Abawajy, A. Abraham, and H. Hagras, Pervasive computing: innovations in intelligent multimedia and applications, : Springer Science & Business Media, 2009. Abstract
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Hassanien, A. - E., J. H. Abawajy, A. Abraham, and H. Hagras, Pervasive computing: innovations in intelligent multimedia and applications, : Springer Science & Business Media, 2009. Abstract
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Sahba, F., H. R. Tizhoosh, and M. M. A. Salama, "Reinforced Medical Image Segmentation", Computational Intelligence in Medical Imaging: Techniques and Applications: Chapman and Hall/CRC, pp. 327–345, 2009. Abstract
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Schaefer, G., H. Zhou, Qinghua Hu, and A. E. Hassanien, "Rough image colour quantisation", International Workshop on Rough Sets, Fuzzy Sets, Data Mining, and Granular-Soft Computing: Springer Berlin Heidelberg, pp. 217–222, 2009. Abstract
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Schaefer, G., H. Zhou, Qinghua Hu, and A. E. Hassanien, "Rough image colour quantisation", International Workshop on Rough Sets, Fuzzy Sets, Data Mining, and Granular-Soft Computing: Springer Berlin Heidelberg, pp. 217–222, 2009. Abstract
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Hassanien, A. E., A. Abraham, J. F. Peters, G. Schaefer, and C. Henry, "Rough sets and near sets in medical imaging: a review", IEEE Transactions on Information Technology in Biomedicine, vol. 13, no. 6: IEEE, pp. 955–968, 2009. Abstract
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Kacprzyk, J., J. F. Peters, A. Abraham, and A. E. Hassanien, "Rough Sets in Medical Imaging", Computational Intelligence in Medical Imaging: Techniques and Applications: Chapman and Hall/CRC, pp. 47–87, 2009. Abstract
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Hassanien, A. E., A. Abraham, J. F. Peters, and G. Schaefer, "Rough sets in medical informatics applications", Applications of soft computing: Springer Berlin Heidelberg, pp. 23–30, 2009. Abstract
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Chakraborty, M., A. - E. Hassanien, D. Slezak, and W. Zhu, Rough Sets, Fuzzy Sets, Data Mining and Granular Computing, : Springer-Verlag Berlin Heidelberg, 2009. Abstract
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Chakraborty, M., A. - E. Hassanien, D. Slezak, and W. Zhu, Rough Sets, Fuzzy Sets, Data Mining and Granular Computing, : Springer-Verlag Berlin Heidelberg, 2009. Abstract
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Hassanien, A. E., A. Abraham, and C. Grosan, "Spiking neural network and wavelets for hiding iris data in digital images", Soft Computing-A Fusion of Foundations, Methodologies and Applications, vol. 13, no. 4: Springer Berlin/Heidelberg, pp. 401–416, 2009. Abstract
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Hassanien, A. E., A. Abraham, and C. Grosan, "Spiking neural network and wavelets for hiding iris data in digital images", Soft Computing-A Fusion of Foundations, Methodologies and Applications, vol. 13, no. 4: Springer Berlin/Heidelberg, pp. 401–416, 2009. Abstract
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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: Springer Berlin Heidelberg, pp. 275–293, 2009. Abstract
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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: Springer Berlin Heidelberg, pp. 275–293, 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.

Grosan, C., A. Abraham, and A. - E. Hassanien, "Designing resilient networks using multicriteria metaheuristics", Telecommunication Systems , vol. 40, issue 1-2, pp. 75-88, 2009. AbstractWebsite

The paper deals with the design of resilient networks that are fault tolerant against link failures. Usually,
fault tolerance is achieved by providing backup paths, which are used in case of an edge failure on a primary path. We consider this task as a multiobjective optimization problem: to provide resilience in networks while minimizing the cost subject to capacity constraint. We propose a stochastic approach,
which can generate multiple Pareto solutions in a single run. The feasibility of the proposed method is illustrated by considering several network design problems using a single weighted average of objectives and a direct multiobjective optimization approach using the Pareto dominance concept.

Aboul-Ella Hassanien, Ajith Abraham, A. V. W. P., Foundations of Computational Intelligence Volume 1: Learning and Approximation, , Germany , Studies in Computational Intelligence, Springer Verlag, Vol. 201 , 2009. AbstractWebsite

Learning methods and approximation algorithms are fundamental tools that deal with computationally hard problems and problems in which the input is gradually disclosed over time. Both kinds of problems have a large number of applications arising from a variety of fields, such as algorithmic game theory, approximation classes, coloring and partitioning, competitive analysis, computational finance, cuts and connectivity, geometric problems, inapproximability results, mechanism design, network design, packing and covering, paradigms for design and analysis of approximation and online algorithms, randomization techniques, real-world applications, scheduling problems and so on. The past years have witnessed a large number of interesting applications using various techniques of Computational Intelligence such as rough sets, connectionist learning; fuzzy logic; evolutionary computing; artificial immune systems; swarm intelligence; reinforcement learning, intelligent multimedia processing etc.. In spite of numerous successful applications of Computational Intelligence in business and industry, it is sometimes difficult to explain the performance of these techniques and algorithms from a theoretical perspective. Therefore, we encouraged authors to present original ideas dealing with the incorporation of different mechanisms of Computational Intelligent dealing with Learning and Approximation algorithms and underlying processes.

Aboul-Ella Hassanien, Ajith Abraham, F. H., Foundations of Computational Intelligence Volume 2: Approximate Reasoning, , Germany, Studies in Computational Intelligence, Springer Verlag, Vol. 202 , 2009. AbstractWebsite

Human reasoning usually is very approximate and involves various types of uncertainties. Approximate reasoning is the computational modelling of any part of the process used by humans to reason about natural phenomena or to solve real world problems. The scope of this book includes fuzzy sets, Dempster-Shafer theory, multi-valued logic, probability, random sets, and rough set, near set and hybrid intelligent systems. Besides research articles and expository papers on theory and algorithms of approximation reasoning, papers on numerical experiments and real world applications were also encouraged. This Volume comprises of 12 chapters including an overview chapter providing an up-to-date and state-of-the research on the applications of Computational Intelligence techniques for approximation reasoning. The Volume is divided into 2 parts: Part-I: Approximate Reasoning – Theoretical Foundations and Part-II: Approximate Reasoning – Success Stories and Real World Applications

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.

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