Publications

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2009
Staelens, S., and I. Lemahieu, "Monte Carlo Based Image Reconstruction in Emission Tom ography", Computational Intelligence in Medical Imaging: Techniques and Applications: CRC Press, pp. 407, 2009. Abstract
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Staelens, S., and I. Lemahieu, "Monte Carlo Based Image Reconstruction in Emission Tom ography", Computational Intelligence in Medical Imaging: Techniques and Applications: CRC Press, pp. 407, 2009. Abstract
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Toews, M., and T. Arbel, "Parts-Based Appearance Modeling of Medical Imagery", Computational Intelligence in Medical Imaging: Techniques and Applications: CRC Press, pp. 291, 2009. Abstract
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Hassanien, A. E., Pervasive Computing, : 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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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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2008
Kudelka, M., V. Snásel, Z. Horak, and A. E. Hassanien, "Web Communities Defined by Web Page Content", IEEE/WIC/ACM International Conference on Web Intelligence and International Conference on Intelligent Agent Technology , Sydney, NSW, Australia, pp.385-389 , 9-12 December, 2008. Abstract

In this paper we are looking for a relationship between the intent of Web pages, their architecture and the communities who take part in their usage and creation. For us, the Web page is entity carrying information about these communities. Our paper describes techniques, which can be used to extract mentioned information as well as tools usable in analysis of these information. Information about communities could be used in several ways thanks to our approach. Finally we present an experiment which proves the feasibility of our approach.

Al-Qaheri, H., S. Zamoon, A. E. Hassanien, and A. Abraham, " Rough Set Generating Prediction Rules for Stock Price Movement", The Second IEEE UKSIM European Symposium on Computer Modeling and Simulation, Liverpool, England, UK, pp.111-116 , 8-10 September , 2008. Abstract

This paper presents rough sets generating prediction rules scheme for stock price movement. The scheme was able to extract knowledge in the form of rules from daily stock movements. These rules then could be used to guide investors whether to buy, sell or hold a stock. To increase the efficiency of the prediction process, rough sets with Boolean reasoning discretization algorithm is used to discretize the data. Rough set reduction technique is applied to find all the reducts of the data. Finally, rough sets dependency rules are generated directly from all generated reducts. Rough confusion matrix is used to evaluate the performance of the predicted reducts and classes. A comparison between the obtained results using rough sets with decision tree and neural networks algorithms have been made. Rough sets show a higher overall accuracy rates reaching over 97% and generate more compact rules.

Al-Qaheri, H., S. Zamoon, A. E. Hassanien, and A. Abraham, " Rough Set Generating Prediction Rules for Stock Price Movement", The Second IEEE UKSIM European Symposium on Computer Modeling and Simulation, Liverpool, England, UK, pp.111-116 , 8-10 September , 2008. Abstract

This paper presents rough sets generating prediction rules scheme for stock price movement. The scheme was able to extract knowledge in the form of rules from daily stock movements. These rules then could be used to guide investors whether to buy, sell or hold a stock. To increase the efficiency of the prediction process, rough sets with Boolean reasoning discretization algorithm is used to discretize the data. Rough set reduction technique is applied to find all the reducts of the data. Finally, rough sets dependency rules are generated directly from all generated reducts. Rough confusion matrix is used to evaluate the performance of the predicted reducts and classes. A comparison between the obtained results using rough sets with decision tree and neural networks algorithms have been made. Rough sets show a higher overall accuracy rates reaching over 97% and generate more compact rules.

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

Both simulated annealing (SA) and the genetic algorithms (GA) are stochastic and derivative-free optimization technique. SA operates on one solution at a time, while the GA maintains a large population of solutions, which are optimized simultaneously. Thus, the genetic algorithm takes advantage of the experience gained in the past exploration of the solution space. Since SA operates on one solution at a time, it has very little history to use in learning from past trials. SA has the ability to escape from any local point; even it is a global optimization technique. On the other hand, there is no guarantee that the GA algorithm will succeeded in escaping from any local minima, thus it makes sense to hybridize the genetic algorithm and the simulated annealing technique. In this paper, a novel genetically annealed algorithm is proposed and is tested against multidimensional and highly nonlinear cases; Fed-batch fermentor for Penicillin production, and isothermal continuous stirred tank reactor CSTR. It is evident from the results that the proposed algorithm gives good performance.

Hassanien, A. E., A. Abraham, J. F. Peters, and G. Schaefer, "An overview of rough-hybrid approaches in image processing.", IEEE International Conference on Fuzzy Systems (ISBN 978-1-4244-1818-3), Hong Kong, China, pp, 2135 - 2142 , 1-6 June, , 2008. Abstract

Rough set theory offers a novel approach to manage uncertainty that has been used for the discovery of data dependencies, importance of features, patterns in sample data, feature space dimensionality reduction, and the classification of objects. Consequently, rough sets have been successfully employed for various image processing tasks including image segmentation, enhancement and classification. Nevertheless, while rough sets on their own provide a powerful technique, it is often the combination with other computational intelligence techniques that results in a truly effective approach. In this paper we show how rough sets have been combined with various other methodologies such as neural networks, wavelets, mathematical morphology, fuzzy sets, genetic algorithms, Bayesian approaches, swarm optimization, and support vector machines in the image processing domain.