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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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Ajith Abraham, Aboul-Ella Hassanien, A. C. V. S., Foundations of Computational Intelligence Volume 6: Data Mining, , Germany, ISBN: 978-3-642-01090-3, Studies in Computational Intelligence, Springer Verlag, 2009. AbstractWebsite

Finding information hidden in data is as theoretically difficult as it is practically important. With the objective of discovering unknown patterns from data, the methodologies of data mining were derived from statistics, machine learning, and artificial intelligence, and are being used successfully in application areas such as bioinformatics, business, health care, banking, retail, and many others. Advanced representation schemes and computational intelligence techniques such as rough sets, neural networks; decision trees; fuzzy logic; evolutionary algorithms; artificial immune systems; swarm intelligence; reinforcement learning, association rule mining, Web intelligence paradigms etc. have proved valuable when they are applied to Data Mining problems. Computational tools or solutions based on intelligent systems are being used with great success in Data Mining applications. It is also observed that strong scientific advances have been made when issues from different research areas are integrated.

Hassanien, A. - E., A. Abraham, A. V. Vasilakos, and W. Pedrycz, Foundations of Computational Intelligence: Volume 1: Learning and Approximation, : Springer, 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., A. - E. Hassanien, V. Sná, and others, Foundations of Computational Intelligence: Volume 6: Data Mining, : Springer, 2009. Abstract
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Karam, H., A. Hassanien, and M. Nakajima, "Fractal Animation Metamorphosis Based on Polar Decomposition", ICAT, vol. 98, pp. 40–46, 1998. Abstract
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Moustafa Zein, A. Adl, A. E. Hassanien, A. Badr, and T. - H. Kim, "Friendship Classification from Psychological Theories to Computational Model", 2015 Fourth International Conference on Information Science and Industrial Applications (ISI): IEEE, pp. 55–60, 2015. Abstract
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Kudelka, M., V. Snásel, Z. Horak, and A. E. Hassanien, "From Web Pages to Web Communities", Annual International Workshop on DAtabases, TExts, Specifications and Objects, Spindleruv Mlyn, Czech Republic , April 15-17, 2009. Abstract

In this paper we are looking for a relationship between the intent of Web pages, their architecture and the communities who take part in their usage and creation. From our point of view, the Web page is entity carrying information about these communities and this paper describes techniques, which can be used to extract mentioned information as well as tools usable in analysis of these information. Information about communities could be used in several ways thanks to our approach. Finally we present an experiment which illustrates the benefits of our approach.

Kudelka, M., V. Snásel, Z. Horák, and A. E. Hassanien, "From Web Pages to Web Communities.", DATESO, pp. 13–22, 2009. Abstract
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Kudelka, M., V. Snásel, Z. Horák, and A. E. Hassanien, "From Web Pages to Web Communities.", DATESO, pp. 13–22, 2009. Abstract
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Semary, N. A., Alaa Tharwat, Esraa Elhariri, and A. E. Hassanien, "Fruit-Based Tomato Grading System Using Features Fusion and Support Vector Machine", IEEE Conf. on Intelligent Systems (2) 2014: 401-410, Poland - Warsaw , 24 -26 Sept. , 2014. Abstract

Machine learning and computer vision techniques have applied for evaluating food quality as well as crops grading. In this paper, a new classification system has been proposed to classify infected/uninfected tomato fruits according to its external surface. The system is based on feature fusion method with color and texture features. Color moments, GLCM, and Wavelets energy and entropy have been used in the proposed system. Principle Component Analysis (PCA) technique has been used to reduce the feature vector obtained after fusion to avoid dimensionality problem and save time and cost. Support vector machine (SVM) was used to classify tomato images into 2 classes; infected/uninfected using Min-Max and Z-Score normalization methods. The dataset used in this research contains 177 tomato fruits each was captured from four faces (Top, Side1, Side2, and End). Using 70% of the total images for training phase and 30% for testing, our proposed system achieved accuracy 92%.

Semary, N. A., Alaa Tharwat, Esraa Elhariri, and A. E. Hassanien, "Fruit-based tomato grading system using features fusion and support vector machine", Intelligent Systems' 2014: Springer International Publishing, pp. 401–410, 2015. Abstract
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Alaa Tharwat, T. Gaber, A. E. Hassanien, G. Schaefer, and J. - S. Pan, "A Fully-Automated Zebra Animal Identification Approach Based on SIFT Features", International Conference on Genetic and Evolutionary Computing: Springer International Publishing, pp. 289–297, 2016. Abstract
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Reham Gharbia, Ali Hassan El Baz, A. E. Hassanien, G. Schaefer, T. Nakashima, and A. T. Azar, "Fusion of multi-spectral and panchromatic satellite images using principal component analysis and fuzzy logic", Fuzzy Systems (FUZZ-IEEE), 2014 IEEE International Conference on: IEEE, pp. 1118–1122, 2014. Abstract
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Salama, M. A., and A. E. Hassanien, "Fuzzification of Euclidean Space Approach in Machine Learning Techniques", International Journal of Service Science, Management, Engineering, and Technology (IJSSMET), vol. 5, no. 4: IGI Global, pp. 29–43, 2014. Abstract
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Horàk, Z., Václav Snášel, A. Abraham, and A. E. Hassanien, "Fuzzified Aho-Corasick Search Automata", Information Assurance and Security (IAS), 2010 Sixth International Conference on: IEEE, pp. 338–342, 2010. Abstract
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Horàk, Z., Václav Snášel, A. Abraham, and A. E. Hassanien, "Fuzzified Aho-Corasick Search Automata", Information Assurance and Security (IAS), 2010 Sixth International Conference on: IEEE, pp. 338–342, 2010. Abstract
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Azar, A. T., S. A. El-Said, and A. E. Hassanien, "Fuzzy and hard clustering analysis for thyroid disease", Computer Methods and Programs in Biomedicine (Elsiver), vol. Available online 26 January 2013, 2013. Website
Azar, A. T., S. A. El-Said, and A. E. Hassanien, "Fuzzy and hard clustering analysis for thyroid disease", Computer Methods and Programs in Biomedicine (Elsiver), vol. Available online 26 January 2013, 2013. Website
Azar, A. T., S. A. El-Said, and A. E. Hassanien, "Fuzzy and hard clustering analysis for thyroid disease", Computer methods and programs in biomedicine, vol. 111, no. 1: Elsevier, pp. 1–16, 2013. Abstract
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Azar, A. T., S. A. El-Said, and A. E. Hassanien, "Fuzzy and hard clustering analysis for thyroid disease", Computer methods and programs in biomedicine, vol. 111, no. 1: Elsevier, pp. 1–16, 2013. Abstract
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Abder-Rahman Ali, Micael Couceiro, A. E. Hassenian, M. F. Tolba, and V. Snasel, "Fuzzy C-Means Based Liver CT Image Segmentation with Optimum Number of Clusters", The 5th International Conference on Innovations in Bio-Inspired Computing and Applications (Springer) IBICA2014, Ostrava, Czech Republic., 22-24 June, 2014. Abstractibica2014_p10.pdf

In this paper, we investigate the e ect of using an optimum
number of clusters with Fuzzy C-Means clustering, for Liver CT image
segmentation. The optimum number of clusters to be used was measured
using the average silhouette value. The evaluation was carried out using
the Jaccard index, in which we concluded that using the optimum number
of clusters may not necessarily lead to the best segmentation results.

Abder-Rahman Ali, Micael Couceiro, A. E. Hassanien, M. F. Tolba, and Václav Snášel, "Fuzzy c-means based liver ct image segmentation with optimum number of clusters", Proceedings of the Fifth International Conference on Innovations in Bio-Inspired Computing and Applications IBICA 2014: Springer International Publishing, pp. 131–139, 2014. 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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