Publications

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Miscellaneous
Chbeir, R., Y. Badr, A. Abraham, and A. - E. Hassanien, Emergent web intelligence: advanced information retrieval, : Springer, 2010. Abstract
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Chbeir, R., Y. Badr, A. Abraham, and A. - E. Hassanien, Emergent web intelligence: advanced information retrieval, : Springer, 2010. Abstract
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Badr, Y., R. Chbeir, A. Abraham, and A. - E. Hassanien, Emergent web intelligence: Advanced semantic technologies, : Springer Science & Business Media, 2010. Abstract
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Badr, Y., R. Chbeir, A. Abraham, and A. - E. Hassanien, Emergent web intelligence: Advanced semantic technologies, : Springer Science & Business Media, 2010. Abstract
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Kacprzyk, J., A. Abraham, A. P. L. F. de Carvalho, and A. - E. Hassanien, Foundations of Computational Intelligence Volume 4: Bio-Inspired Data Mining, : Springer 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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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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Corchado, E. S., V. Snasel, J. Sedano, A. E. Hassanien, J. L. Calvo, and D. Slezak, Soft Computing Models in Industrial and Environmental Applications, 6th International Conference SOCO 2011, : Springer Science & Business Media, 2011. Abstract
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Corchado, E. S., V. Snasel, J. Sedano, A. E. Hassanien, J. L. Calvo, and D. Slezak, Soft Computing Models in Industrial and Environmental Applications, 6th International Conference SOCO 2011, : Springer Science & Business Media, 2011. Abstract
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Journal Article
Atteya, W. A., L. E. M. Bakrawy, R. Batista, S. Bazzi, M. Beheshti, A. P. Bennett, A. Boulmakoul, S. Bureerat, L. Caggiani, N. S. Choubey, et al., "Akbarzadeh-T, Mohammad-R. 175 Antunes, Carlos Henggeler 13", Advances in Intelligent and Soft Computing 96, vol. 6, pp. 437, 2011. Abstract
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Abder-Rahman Ali, M. S. Couceirob, A. E. Hassanie, and J. Hemanth, "Fuzzy C-Means based on Minkowski distance for liver CT image segmentation", Intelligent Decision Technologies , vol. 10, pp. 393–406 , 2016. AbstractWebsite

Abstract: This paper presents a Fuzzy C-Means based image segmentation approach that benefits from the Minkowski distance as the dissimilarity measure, denoted as FCM-M, instead of the traditional Euclidean distance, herein identified as FCM-E. The proposed approach was applied on Liver CT images, and a thorough comparison between both methods was carried out. FCM-M provided better accuracy when compared to the traditional FCM-E, with an area under the ROC curve of 85.44% and 47.96%, respectively. In terms of statistical significant analysis, a twofold benefit was obtained from using the proposed approach: the performance of the image segmentation procedure was maintained, or even slightly increased in some situations, while the CPU processing time was significantly decreased. The advantages inherent to the proposed FCM-M pave the way to a whole new chain of fully automatic segmentation methods.

Abder-Rahman Ali, M. S. Couceiro, A. E. Hassanien, and J. D. Hemanth, "Fuzzy C-Means based on Minkowski distance for liver CT image segmentation", Intelligent Decision Technologies, vol. 10, no. 4: IOS Press, pp. 393–406, 2016. Abstract
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Tobin, K. W., E. Chaum, J. Gregor, T. P. Karnowski, J. R. Price, and J. Wall, "Image Informatics for Clinical and Preclinical Biomedical Analysis", Computational Intelligence in Medical Imaging: Techniques and Applications: CRC Press, pp. 239, 2009. Abstract
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Schaefer, G., H. Zhou, E. M. Celebi, and A. E. Hassanien, "Rough colour quantisation", International Journal of Hybrid Intelligent Systems, vol. 8, no. 1: IOS Press, pp. 25–30, 2011. Abstract
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Schaefer, G., H. Zhou, E. M. Celebi, and A. E. Hassanien, "Rough colour quantisation", International Journal of Hybrid Intelligent Systems, vol. 8, no. 1: IOS Press, pp. 25–30, 2011. Abstract
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Hassanien, A. E., H. Sakai, D. Slezak, M. K. Chakraborty, and W. Zhu, "Special issue: Rough and Fuzzy Methods for Data Mining", International Journal of Hybrid Intelligent Systems, vol. 8, no. 1, pp. 1, 2011. Abstract
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Hassanien, A. E., H. Sakai, D. Slezak, M. K. Chakraborty, and W. Zhu, "Special issue: Rough and Fuzzy Methods for Data Mining", International Journal of Hybrid Intelligent Systems, vol. 8, no. 1, pp. 1, 2011. Abstract
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Conference Paper
El-Bendary, N., A. E. Hassanien, E. Corchado, and R. C. Berwick, "ARIAS: Automated retinal image analysis system", Soft Computing Models in Industrial and Environmental Applications, 6th International Conference SOCO 2011: Springer Berlin Heidelberg, pp. 67–76, 2011. Abstract
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El-Bendary, N., A. E. Hassanien, E. Corchado, and R. C. Berwick, "ARIAS: Automated retinal image analysis system", Soft Computing Models in Industrial and Environmental Applications, 6th International Conference SOCO 2011: Springer Berlin Heidelberg, pp. 67–76, 2011. Abstract
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Acharjee, S., S. Chakraborty, S. Samanta, A. T. Azar, A. E. Hassanien, and N. Dey, "Highly secured multilayered motion vector watermarking", The 2nd International Conference on Advanced Machine Learning Technologies and Applications , Egypt, 28-30 Nov. 2014.
Acharjee, S., S. Chakraborty, S. Samanta, A. T. Azar, A. E. Hassanien, and N. Dey, "Highly secured multilayered motion vector watermarking", International Conference on Advanced Machine Learning Technologies and Applications: Springer International Publishing, pp. 121–134, 2014. Abstract
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Schaefer, G., Bartosz Krawczyk, E. M. Celebi, H. Iyatomi, and A. E. Hassanien, "Melanoma Classification based on Ensemble Classification of Dermoscopy Image Features", The 2nd International Conference on Advanced Machine Learning Technologies and Applications , Egypt, November 17-19, , 2014.
Schaefer, G., Bartosz Krawczyk, E. M. Celebi, H. Iyatomi, and A. E. Hassanien, "Melanoma classification based on ensemble classification of dermoscopy image features", International Conference on Advanced Machine Learning Technologies and Applications: Springer International Publishing, pp. 291–298, 2014. Abstract
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