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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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Abder-Rahman Ali, Micael Couceiro, A. M. Anter, A. E. Hassanien, M. F. Tolba, and Václav Snášel, "Liver CT Image Segmentation with an Optimum Threshold Using Measure of Fuzziness", Proceedings of the Fifth International Conference on Innovations in Bio-Inspired Computing and Applications IBICA 2014: Springer International Publishing, pp. 83–92, 2014. Abstract
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Abder-Rahman Ali, Micael Couceiro, Ahmed M. Anter, A. E. Hassenian, M. F. Tolba, and V. Snasel, "Liver CT Image Segmentation with an Optimum Threshold using Measure of Fuzziness", The 5th International Conference on Innovations in Bio-Inspired Computing and Applications, 22-24 June 2014, , Ostrava, Czech Republic., 22-24 June , 2014.
Abder-Rahman Ali, Micael Couceiro, A. M. Anter, and A. E. Hassanien, "Evaluating an Evolutionary Particle Swarm Optimization for Fast Fuzzy C-Means Clustering on Liver CT Images", Computer Vision and Image Processing in Intelligent Systems and Multimedia Technologies, USA, IGI, 2014. Abstract

An Evolutionary Particle Swarm Optimization based on the Fractional Order Darwinian method for
optimizing a Fast Fuzzy C-Means algorithm is proposed. This chapter aims at enhancing the performance
of Fast Fuzzy C-Means, both in terms of the overall solution and speed. To that end, the concept
of fractional calculus is used to control the convergence rate of particles, wherein each one of them
represents a set of cluster centers. The proposed solution, denoted as FODPSO-FFCM, is applied on
liver CT images, and compared with Fast Fuzzy C-Means and PSOFFCM, using Jaccard Index and
Dice Coefficient. The computational efficiency is achieved by using the histogram of the image intensities
during the clustering process instead of the raw image data. The experimental results based on the
Analysis of Variance (ANOVA) technique and multiple pair-wise comparison show that the proposed
algorithm is fast, accurate, and less time consuming.

Abder-Rahman Ali, Micael S. Couceiro, and A. E. Hassenian, "PSilhOuette: Towards an Optimal Number of Clusters using a Nested Particle Swarm Approach for Liver CT Image Segmentation ", The 2nd International Conference on Advanced Machine Learning Technologies and Applications , Egypt, November 17-19, , 2014.
Abdo, W., Evolutionary Computation in Cryptanalysis, , Cairo Egypt, Al Azhar University and Scientific Research Group in Egypt (SRGE), 2013. ppt_phd_thesis_on_EC_CA.pdfphd_thesis_EC_CA_2013.pdf
Aboul-Ella, H., and M. Nakajima, "Image metamorphosis transformation of facial images based on elastic body splines ", Signal Processing , issue Volume 70, Issue 2,, pp. 129–137 , 1998. Abstracts01651684.gifWebsite

In this paper, we propose a new image metamorphosis algorithm which uses elastic body splines to generate warp functions for interpolating scattered data points. The spline is based on a partial differential equation proposed by Navier that describes the equilibrium displacement of an elastic body subjected to forces. The spline maps can be expressed as the linear combination of an affine transformation and a Navier spline. The proposed algorithm generates a smooth warp that reflects feature point correspondences. It is efficient in time complexity and smoothly interpolated morphed images with only a remarkably small number of specified feature points. The algorithm allows each feature point in the source image to be mapped to the corresponding feature point in the destination image. Once the images are warped to align the positions of features and their shapes, the in-between facial animation from two given facial images can be defined by cross dissolving the positions of correspondence features and their shapes and colors. We describe an efficient cross-dissolve algorithm for generating the in-between images

Aboul-Ella Hassanien, Ajith Abraham, A. V. W. P., Foundations of Computational Intelligence Volume 1: Learning and Approximation, , Germany , Studies in Computational Intelligence, Springer Verlag, Vol. 201 , 2009. AbstractWebsite

Learning methods and approximation algorithms are fundamental tools that deal with computationally hard problems and problems in which the input is gradually disclosed over time. Both kinds of problems have a large number of applications arising from a variety of fields, such as algorithmic game theory, approximation classes, coloring and partitioning, competitive analysis, computational finance, cuts and connectivity, geometric problems, inapproximability results, mechanism design, network design, packing and covering, paradigms for design and analysis of approximation and online algorithms, randomization techniques, real-world applications, scheduling problems and so on. The past years have witnessed a large number of interesting applications using various techniques of Computational Intelligence such as rough sets, connectionist learning; fuzzy logic; evolutionary computing; artificial immune systems; swarm intelligence; reinforcement learning, intelligent multimedia processing etc.. In spite of numerous successful applications of Computational Intelligence in business and industry, it is sometimes difficult to explain the performance of these techniques and algorithms from a theoretical perspective. Therefore, we encouraged authors to present original ideas dealing with the incorporation of different mechanisms of Computational Intelligent dealing with Learning and Approximation algorithms and underlying processes.

Aboul-Ella Hassanien, Ajith Abraham, V. S., Foundations of Computational Intelligence Volume 5: Function Approximation and Classification, , Germany, Studies in Computational Intelligence, Springer Verlag, Vol. 205 , 2009. AbstractWebsite

Approximation theory is that area of analysis which is concerned with the ability to approximate functions by simpler and more easily calculated functions. It is an area which, like many other fields of analysis, has its primary roots in the mathematics.The need for function approximation and classification arises in many branches of applied mathematics, computer science and data mining in particular.

Aboul-Ella Hassanien, Ajith Abraham, F. H., Foundations of Computational Intelligence Volume 2: Approximate Reasoning, , Germany, Studies in Computational Intelligence, Springer Verlag, Vol. 202 , 2009. AbstractWebsite

Human reasoning usually is very approximate and involves various types of uncertainties. Approximate reasoning is the computational modelling of any part of the process used by humans to reason about natural phenomena or to solve real world problems. The scope of this book includes fuzzy sets, Dempster-Shafer theory, multi-valued logic, probability, random sets, and rough set, near set and hybrid intelligent systems. Besides research articles and expository papers on theory and algorithms of approximation reasoning, papers on numerical experiments and real world applications were also encouraged. This Volume comprises of 12 chapters including an overview chapter providing an up-to-date and state-of-the research on the applications of Computational Intelligence techniques for approximation reasoning. The Volume is divided into 2 parts: Part-I: Approximate Reasoning – Theoretical Foundations and Part-II: Approximate Reasoning – Success Stories and Real World Applications

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.

Abraham, A., A. - E. Hassanien, V. Sná, and others, Computational social network analysis: Trends, tools and research advances, : Springer Science & Business Media, 2009. Abstract
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Abraham, A., A. - E. Hassanien, V. Sná, and others, Foundations of Computational Intelligence: Volume 6: Data Mining, : Springer, 2009. Abstract
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Abraham, A., K. Wegrzyn-Wolska, A. E. Hassanien, Václav Snášel, and A. M. Alimi, Proceedings of the Second International Afro-European Conference for Industrial Advancement AECIA 2015, : Springer, 2016. Abstract
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Abraham, A., A. - E. Hassanien, P. Siarry, and A. Engelbrecht, Foundations of Computational Intelligence Volume 3: Global Optimization, : Springer, 2009. Abstract
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Abraham, A., H. Liu, and A. E. Hassanien, "Multi swarms for neighbor selection in peer-to-peer overlay networks", Telecommunication Systems, vol. 46, no. 3: Springer Netherlands, pp. 195–208, 2011. Abstract
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Abraham, A., and A. - E. Hassanien, Computational social networks: Tools, perspectives and applications, : Springer Science & Business Media, 2012. Abstract
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Abraham, A., A. - E. Hassanien, V. Sná, and others, Foundations of Computational Intelligence Volume 5: Function Approximation and Classification, : Springer Science & Business Media, 2009. Abstract
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Abraham, A., A. - E. Hassanien, P. Siarry, and A. Engelbrecht, Foundations of Computational Intelligence Volume 3: Global Optimization, : Springer, 2009. Abstract
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Abraham, A., K. Wegrzyn-Wolska, A. E. Hassanien, V. Snasel, and A. M. Alimi, Second International Afro-European Conference for Industrial Advancement AECIA 2015, , 2015. Abstract

This volume contains accepted papers presented at AECIA2014, the First International Afro-European Conference for Industrial Advancement. The aim of AECIA was to bring together the foremost experts as well as excellent young researchers from Africa, Europe, and the rest of the world to disseminate latest results from various fields of engineering, information, and communication technologies. The first edition of AECIA was organized jointly by Addis Ababa Institute of Technology, Addis Ababa University, and VSB - Technical University of Ostrava, Czech Republic and took place in Ethiopia's capital, Addis Ababa.

Abraham, A., A. - E. Hassanien, P. Siarry, and A. Engelbrecht, Foundations of Computational Intelligence Volume 3: Global Optimization, : Springer, 2009. Abstract
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Abraham, A., H. Liu, and A. E. Hassanien, "Multi swarms for neighbor selection in peer-to-peer overlay networks", Telecommunication Systems, vol. 46, no. 3: Springer Netherlands, pp. 195–208, 2011. Abstract
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Abraham, A., A. - E. Hassanien, V. Sná, and others, Foundations of Computational Intelligence Volume 5: Function Approximation and Classification, : Springer Science & Business Media, 2009. Abstract
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Abraham, A., A. - E. Hassanien, V. Sná, and others, Foundations of Computational Intelligence: Volume 6: Data Mining, : Springer, 2009. Abstract
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