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Soliman, M. M., A. E. Hassanien, and H. M. Onsi, "The way of improving PSO performance: medical imaging watermarking case study", International Conference on Rough Sets and Current Trends in Computing: Springer Berlin Heidelberg, pp. 237–242, 2012. Abstract
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Soliman, M. M., A. E. Hassanien, and H. M. Onsi, "The way of improving PSO performance: medical imaging watermarking case study", International Conference on Rough Sets and Current Trends in Computing: Springer Berlin Heidelberg, pp. 237–242, 2012. Abstract
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Soliman, M. M., A. E. Hassanien, and H. M. Onsi, "The way of improving PSO performance: medical imaging watermarking case study", International Conference on Rough Sets and Current Trends in Computing: Springer Berlin Heidelberg, pp. 237–242, 2012. Abstract
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Soliman, M. M., A. E. Hassanien, and H. M. Onsi, "Watermarking 3D Triangular Mesh Models Using Intelligent Vertex Selection", Proceedings of the Second International Afro-European Conference for Industrial Advancement AECIA 2015: Springer International Publishing, pp. 617–627, 2016. Abstract
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Ossama S. Alshabrawy, and A. E. Hassanien, "Underdetermined blind separation of mixtures of an unknown number of sources with additive white and pink noises", The 5th International Conference on Innovations in Bio-Inspired Computing and Applications (Springer) IBICA2014, Ostrava, Czech Republic., 22-24 June, 2014. Abstractibica2014_p29.pdf

In this paper we propose an approach for underdetermined
blind separation in the case of additive Gaussian white noise and pink
noise in addition to the most challenging case where the number of source
signals is unknown. In addition to that, the proposed approach is appli-
cable in the case of separating I +3 source signals from I mixtures with
an unknown number of source signals and the mixtures have additive two
kinds of noises. This situation is more challenging and also more suitable
to practical real world problems. Moreover, unlike to some traditional
approaches, the sparsity conditions are not imposed. Firstly, the number
of source signals is approximated and estimated using multiple source
detection, followed by an algorithm for estimating the mixing matrix
based on combining short time Fourier transform and rough-fuzzy clus-
tering. Then, the mixed signals are normalized and the source signals
are recovered using multi-layer modi ed Gradient descent Local Hier-
archical Alternating Least Squares Algorithm exploiting the number of
source signals estimated , and the mixing matrix obtained as an input
and initialized by multiplicative algorithm for matrix factorization based
on alpha divergence. The computer simulation results show that the pro-
posed approach can separate I + 3 source signals from I mixed signals,
and it has superior evaluation performance compared to some traditional
approaches in recent references.

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Skowron, J. P. A. F., V. M. E. W. Orłowska, and R. S. W. Ziarko, Transactions on Rough Sets VII, , 2007. Abstract
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Skowron, J. P. A. F., V. M. E. W. Orłowska, and R. S. W. Ziarko, Transactions on Rough Sets VII, , 2007. 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, 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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Hassanien, A. E., and H. Own, "Rough sets for prostate patient analysis", Proceedings of International Conference on Modeling and Simulation (MS2006), 2006. Abstract
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Hassanien, A. E., and H. Own, "Rough sets for prostate patient analysis", Proceedings of International Conference on Modeling and Simulation (MS2006), 2006. 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. 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.

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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P. K. Nizar Banu, H. H. Inbarani, A. T. Azar, H. S. Own, and A. E. Hassanien, "Rough Set Based Feature Selection for Egyptian Neonatal Jaundice ", The 2nd International Conference on Advanced Machine Learning Technologies and Applications , Egypt, November 17-19, , 2014.
Banu, P. K. N., H. H. Inbarani, A. T. Azar, H. S. Own, and A. E. Hassanien, "Rough set based feature selection for egyptian neonatal jaundice", International Conference on Advanced Machine Learning Technologies and Applications: Springer International Publishing, pp. 367–378, 2014. Abstract
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Ella Hassanien, A., M. E. Abdelhafez, and H. S. Own, "Rough set analysis in knowledge discovery: a case of Kuwaiti diabetic children patients", Advances in Fuzzy Systems, pp. 1–13, 2007. Abstract
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Ślęzak, D., and others, "Rough neural intelligent approach for image classification: A case of patients with suspected breast cancer", International Journal of Hybrid Intelligent Systems, vol. 3, no. 4: IOS Press, pp. 205–218, 2006. Abstract
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Ślęzak, D., and others, "Rough neural intelligent approach for image classification: A case of patients with suspected breast cancer", International Journal of Hybrid Intelligent Systems, vol. 3, no. 4: IOS Press, pp. 205–218, 2006. Abstract
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Soliman, M. M., A. E. Hassanien, and H. M. Onsi, "Robust watermarking approach for 3D triangular mesh using self organization map", Computer Engineering & Systems (ICCES), 2013 8th International Conference on: IEEE, pp. 99–104, 2013. Abstract
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Soliman, M. M., A. E. Hassanien, and H. M. Onsi, "A Robust 3D Mesh Watermarking Approach Using Genetic Algorithms", IEEE Intelligent Systems'2014, Poland - Warsaw , 24 -26 Sept. , 2014. Abstract

This paper proposes a new approach of 3D watermarking by ensuring the optimal preservation of mesh surfaces. The minimal surface distortion is enforced during watermark embedding stage using Genetic Algorithm (GA) optimization. The watermark embedding is performed only on set of selected vertices come out from k-means clustering technique. These vertices are used as candidates for watermark carriers that will hold watermark bits stream. A 3D surface preservation function is defined according to the distance of a vertex displaced by watermarking to the original surface. A study of the proposed methodology has high robustness against the common mesh attacks while preserving the original object surface during watermarking.

Soliman, M. M., A. E. Hassanien, and H. M. Onsi, "A Robust 3D Mesh Watermarking Approach Using Genetic Algorithms", Intelligent Systems' 2014: Springer International Publishing, pp. 731–741, 2015. Abstract
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Hassan, G., N. El-Bendary, A. E. Hassanien, A. Fahmy, V. Snasel, and others, "Retinal blood vessel segmentation approach based on mathematical morphology", Procedia Computer Science, vol. 65: Elsevier, pp. 612–622, 2015. Abstract
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