Publications

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2009
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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2008
Hassanien, A. E., Rough Computing: Theories, Technologies, and Applications, , USA, IGI Global USA, 2008. AbstractWebsite

Rough set theory is a new soft computing tool which deals with vagueness and uncertainty. It has attracted the attention of researchers and practitioners worldwide, and has been successfully applied to many fields such as knowledge discovery, decision support, pattern recognition, and machine learning. Rough Computing: Theories, Technologies and Applications offers the most comprehensive coverage of key rough computing research, surveying a full range of topics from granular computing to pansystems theory. With its unique coverage of the defining issues of the field, this commanding research collection provides libraries with a single, authoritative reference to this highly advanced technological topic.

Hassanien, A. E., and A. Abraham, "Rough Morphology Hybrid Approach for Mammography Image Classification and Prediction", International Journal of Computational Intelligence and Applications , vol. 7, issue 1, pp. 17-42 , 2008. AbstractWebsite

The objective of this research is to illustrate how rough sets can be successfully integrated with mathematical morphology and provide a more effective hybrid approach to resolve medical imaging problems. Hybridization of rough sets and mathematical morphology techniques has been applied to depict their ability to improve the classification of breast cancer images into two outcomes: malignant and benign cancer. Algorithms based on mathematical morphology are first applied to enhance the contrast of the whole original image; to extract the region of interest (ROI) and to enhance the edges surrounding that region. Then, features are extracted characterizing the underlying texture of the ROI by using the gray-level co-occurrence matrix. The rough set approach to attribute reduction and rule generation is further presented. Finally, rough morphology is designed for discrimination of different ROI to test whether they represent malignant cancer or benign cancer. To evaluate performance of the presented rough morphology approach, we tested different mammogram images. The experimental results illustrate that the overall performance in locating optimal orientation offered by the proposed approach is high compared with other hybrid systems such as rough-neural and rough-fuzzy systems.

Hassanien, A. E., M. E. Abdelhafez, and H. S. Own, "Rough Sets Data Analysis in Knowledge Discovery: A Case of Kuwaiti Diabetic Children Patients", Advances in Fuzzy Systems,, vol. 2008, issue 1, pp. 13, 2008. AbstractWebsite

The main goal of this study is to investigate the relationship between psychosocial variables and diabetic children patients and to obtain a classifier function with which it was possible to classify the patients on the basis of assessed adherence level. The rough set theory is used to identify the most important attributes and to induce decision rules from 302 samples of Kuwaiti diabetic children patients aged 7–13 years old. To increase the efficiency of the classification process, rough sets with Boolean reasoning discretization algorithm is introduced to discretize the data, then the rough set reduction technique is applied to find all reducts of the data which contains the minimal subset of attributes that are associated with a class label for classification. Finally, the 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, neural networks, and statistical discriminate analysis classifier algorithms has been made. Rough sets show a higher overall accuracy rates and generate more compact rules.

Own, H. S., and A. E. Hassanien, "Rough Wavelet Hybrid Image Classification Scheme", Journal of Convergence Information Technology, vol. 3, issue 4, pp. 65-75, 2008. AbstractWebsite

This paper introduces a new computer-aided classification system for detection of prostate cancer in
Transrectal Ultrasound images (TRUS). To increase the efficiency of the computer aided classification
process, an intensity adjustment process is applied first, based on the Pulse Coupled Neural Network
(PCNN) with a median filter. This is followed by applying a PCNN-based segmentation algorithm to
detect the boundary of the prostate image. Combining the adjustment and segmentation enable to eliminate PCNN sensitivity to the setting of the various PCNN parameters whose optimal selection can be difficult and can vary even for the same problem. Then, wavelet based features have been extracted and
normalized, followed by application of a rough set analysis to discover the dependency between the
attributes and to generate a set of reduct that contains a minimal number of attributes. Finally, a rough
confusion matrix is designed that contain information about actual and predicted classifications done by a
classification system. Experimental results show that the introduced system is very successful and has high detection accuracy

Hassanien, A. E., and A. Abraham, "Rough Morphology Hybrid Approach for Mammography Image Classification and Prediction", International Journal of Computational Intelligence and Applications, vol. 7, no. 01: Imperial College Press, pp. 17–42, 2008. Abstract
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Hassanien, A. E., and A. Abraham, "Rough Morphology Hybrid Approach for Mammography Image Classification and Prediction", International Journal of Computational Intelligence and Applications, vol. 7, no. 01: Imperial College Press, pp. 17–42, 2008. Abstract
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Al-Qaheri, H., S. Zamoon, A. E. Hassanien, and A. Abraham, "Rough set generating prediction rules for stock price movement", Computer Modeling and Simulation, 2008. EMS'08. Second UKSIM European Symposium on: IEEE, pp. 111–116, 2008. Abstract
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Al-Qaheri, H., S. Zamoon, A. E. Hassanien, and A. Abraham, "Rough set generating prediction rules for stock price movement", Computer Modeling and Simulation, 2008. EMS'08. Second UKSIM European Symposium on: IEEE, pp. 111–116, 2008. Abstract
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Hassanien, A. E., M. E. Abdelhafez, and H. S. Own, "Rough sets data analysis in knowledge discovery: A case of kuwaiti diabetic children patients", Advances in fuzzy Systems, vol. 8: Hindawi Publishing Corp., pp. 2, 2008. Abstract
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Hassanien, A. E., M. E. Abdelhafez, and H. S. Own, "Rough sets data analysis in knowledge discovery: A case of kuwaiti diabetic children patients", Advances in fuzzy Systems, vol. 8: Hindawi Publishing Corp., pp. 2, 2008. Abstract
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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, pp. 47–87, 2008. Abstract
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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, pp. 47–87, 2008. Abstract
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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, pp. 47–87, 2008. Abstract
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Own, H. S., and A. E. Hassanien, "Rough wavelet hybrid image classification scheme", Journal of Convergence Information Technology, vol. 3, no. 4, pp. 65–75, 2008. Abstract
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Own, H. S., and A. E. Hassanien, "Rough wavelet hybrid image classification scheme", Journal of Convergence Information Technology, vol. 3, no. 4, pp. 65–75, 2008. Abstract
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Own, H. S., and A. E. Hassanien, "Rough wavelet hybrid image classification scheme", Journal of Convergence Information Technology, vol. 3, no. 4, pp. 65–75, 2008. Abstract
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