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Ghoneim, A., M. Barlow, and H. Abbass, "Rounds Effect in Evolutionary Games", The Third Australian Conference on Artificial Life , Gold Coast, Australia., December 4-6, 2007.
Fahmy, H. A. H., and M. J. Flynn, "Rounding in Redundant Digit Floating Point Systems", {The International Symposium on Optical Science and Technology, SPIE's 48th annual meeting (Arithmetic session), San Diego, California, USA}, aug, 2003. Abstract
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Fahmy, H. A. H., and M. J. Flynn, "Rounding in Redundant Digit Floating Point Systems", {The International Symposium on Optical Science and Technology, SPIE's 48th annual meeting (Arithmetic session), San Diego, California, USA}, aug, 2003. Abstract
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Fahmy, H. A. H., and M. J. Flynn, "Rounding in Redundant Digit Floating Point Systems", {The International Symposium on Optical Science and Technology, SPIE's 48th annual meeting (Arithmetic session), San Diego, California, USA}, aug, 2003. Abstract
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Eskander, G. S., A. F. Atiya, K. T. Chong, H. Kim, and S. G. Yoo, "Round Trip Time Prediction Using the Symbolic Function Network Approach", Computing Research Repository, vol. abs/0806.3, 2008. Abstract
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Eskander, G. S., A. Atiya, K. T. Chong, H. Kim, and S. G. Yoo, Round Trip Time Prediction Using the Symbolic Function Network Approach, , 2008. Abstract
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Eskander, G. S., A. Atiya, K. T. Chong, H. Kim, and S. G. Yoo, "Round Trip Time Prediction Using the Symbolic Function Network Approach", International Symposium on Information Technology Convergence, 2007. Abstract
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Sarhan, S., R. Bahgat, and A. Tolba, "Rough-Neuro Model for Improving Student State Diagnosis in Intelligent Tutoring System", Egyptian Rough Computing Journal, 2009.
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

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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Ahmed, Z., M. A. Salama, H. Hefny, and A. E. Hassanien, "Rough Sets-Based Rules Generation Approach: A Hepatitis C Virus Data Sets.", Advanced Machine Learning Technologies and Applications (AMLTA), Cairo Egypt, 8-10 Dec. , 2012. Abstract3220052.pdf

The risk of hepatitis-C virus is considered as a challenge in
the field of medicine. Applying feature reduction technique and generating
rules based on the selected features were considered as an important
step in data mining. It is needed by medical experts to analyze the generated
rules to find out if these rules are important in real life cases.
This paper presents an application of a rough set analysis to discover
the dependency between the attributes, and to generate a set of reducts
consisting of a minimal number of attributes. The experimental results
obtained, show that the overall accuracy offered by the rough sets is high.

zaki, A., M. A. Salama, H. Hefny, and A. E. Hassanien, "Rough Sets-Based Rules Generation Approach: A Hepatitis C Virus Data Sets", First International Conference, AMLTA 2012, 2012.
zaki, A., M. A. Salama, H. Hefny, and A. E. Hassanien, "Rough sets-based rules generation approach: A hepatitis c virus data sets", International Conference on Advanced Machine Learning Technologies and Applications: Springer Berlin Heidelberg, pp. 52–59, 2012. Abstract
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zaki, A., M. A. Salama, H. Hefny, and A. E. Hassanien, "Rough sets-based rules generation approach: A hepatitis c virus data sets", International Conference on Advanced Machine Learning Technologies and Applications: Springer Berlin Heidelberg, pp. 52–59, 2012. Abstract
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Salama, M. A., A. E. Hassanien, J. Platos, A. A. Fahmy, and V. Snasel, "Rough Sets-based Identification of Heart Valve Diseases using Heart Sounds", Conference on Intelligent Human Computer Interaction , 29-31Aug, 2011.
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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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.

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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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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