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Xiao, K., J. Li, S. Xiao, Haibing Guan, F. Fang, and A. E. Hassanien, "Fuzzy Clustering with Multi-Resolution Bilateral Filtering for Medical Image Segmentation", International Journal of Fuzzy System Applications (IJFSA), vol. 3, no. 4: IGI Global, pp. 47–59, 2013. Abstract
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Xia, K., J. Li, H. G. Shuangjiu Xiao, F. Fang, and A. E. Hassanien, "Fuzzy Clustering with Multi-resolution Bilateral Filtering for Medical Image Segmentation", International Journal of Fuzzy System Applications (IJFSA), vol. 3, issue 4, 2013. fuzzy_clustering_with_multi-resolution_bilateral_filtering_for_medical_image_segmentation-revision.pdf
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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", Intelligent Systems' 2014: Springer International Publishing, pp. 401–410, 2015. 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%.

Salama, M. A., A. E. Hassanien, and A. A. Fahmy, "Feature evaluation based fuzzy C-mean classification", Fuzzy Systems (FUZZ), 2011 IEEE International Conference on: IEEE, pp. 2534–2539, 2011. Abstract
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Salama, M. A., A. E. Hassanien, and A. A. Fahmy, "Feature evaluation based fuzzy C-mean classification", Fuzzy Systems (FUZZ), 2011 IEEE International Conference on: IEEE, pp. 2534–2539, 2011. Abstract
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Salama, M., A. E. Hassanien, and Adel Alimi, "Formal concept analysis approach for comparison between mutagenicity and carcinogenicity in Cheminformatics. ", 13th IEEE International Conference on Hybrid Intelligent Systems |(HIS13) Tunisia, 4-6 Dec. pp. 268-273, 2013, Tunisia, , 4-6 Dec, 2013.
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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Salama, M. A., A. E. Hassanien, and A. M. Alimi, "Formal concept analysis approach for comparison between Mutagenicity and Carcinogenicity in Cheminformatics", Hybrid Intelligent Systems (HIS), 2013 13th International Conference on: IEEE, pp. 267–272, 2013. 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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Radhwan, A., M. Kamel, M. Y. Dahab, and A. E. Hassanien, "Forecasting Exchange Rates: A Chaos-Based Regression Approach", International Journal of Rough Sets and Data Analysis (IJRSDA), vol. 2, no. 1: IGI Global, pp. 38–57, 2015. Abstract
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Radhwan, A., M. Kamel, M. Y. Dahab, and A. E. Hassanien, "Forecasting Exchange Rates: A Chaos-Based Regression Approach. Intelligent Approach.", International Journal of Rough Sets and Data Analysis (IJRSDA) , vol. 2, issue 1, 2015. AbstractWebsite

Accurate forecasting for future events constitutes a fascinating challenge for theoretical and for applied researches. Foreign Exchange market (FOREX) is selected in this research to represent an example of financial systems with a complex behavior. Forecasting a financial time series can be a very hard task due to the inherent uncertainty nature of these systems. It seems very difficult to tell whether a series is stochastic or deterministic chaotic or some combination of these states. More generally, the extent to which a non-linear deterministic process retains its properties when corrupted by noise is also unclear. The noise can affect a system in different ways even though the equations of the system remain deterministic. Since a single reliable statistical test for chaoticity is not available, combining multiple tests is a crucial aspect, especially when one is dealing with limited and noisy data sets like in economic and financial time series. In this research, the authors propose an improved model for forecasting exchange rates based on chaos theory that involves phase space reconstruction from the observed time series and the use of support vector regression (SVR) for forecasting.Given the exchange rates of a currency pair as scalar observations, observed time series is first analyzed to verify the existence of underlying nonlinear dynamics governing its evolution over time. Then, the time series is embedded into a higher dimensional phase space using embedding parameters.In the selection process to find the optimal embedding parameters,a novel method based on the Differential Evolution (DE) geneticalgorithm(as a global optimization technique) was applied. The authors have compared forecasting accuracy of the proposed model against the ordinary use of support vector regression. The experimental results demonstrate that the proposed method, which is based on chaos theory and genetic algorithm,is comparable with the existing approaches.

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Nadi, M., N. El-Bendary, A. E. Hassanien, and T. - H. Kim, "Falling Detection System Based on Machine Learning", Advanced Information Technology and Sensor Application (AITS), 2015 4th International Conference on: IEEE, pp. 71–75, 2015. Abstract
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Nadi, M., N. El-Bendary, H. Mahmoud, and A. E. Hassanien, "Fall detection system of elderly people based on integral image and histogram of oriented gradient feature", Hybrid Intelligent Systems (HIS), 2014 14th International Conference on: IEEE, pp. 23–29, 2014. 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. 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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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.

Karam, H., A. Hassanien, and M. Nakajima, "Feature-based image metamorphosis optimization algorithm", Virtual Systems and Multimedia, 2001. Proceedings. Seventh International Conference on: IEEE, pp. 555–564, 2001. 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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Karam, H., A. Hassanien, and M. Nakajima, "Feature-based image metamorphosis optimization algorithm", Virtual Systems and Multimedia, 2001. Proceedings. Seventh International Conference on: IEEE, pp. 555–564, 2001. 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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Kacprzyk, J., A. Abraham, A. - E. Hassanien, and F. Herrera, Foundations of Computational Intelligence Volume 2: Approximate Reasoning, : Springer Berlin Heidelberg, 2009. Abstract
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Jui, S. - L., C. Lin, Haibing Guan, A. Abraham, A. E. Hassanien, and K. Xiao, "Fuzzy c-means with wavelet filtration for MR image segmentation", Nature and Biologically Inspired Computing (NaBIC), 2014 Sixth World Congress on: IEEE, pp. 12–16, 2014. Abstract
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Ismael, G., A. E. H. and, and A. Taher, "Feature selection via a novel chaotic crow search algorithm,", Neural Computing and Applications , 2017. AbstractWebsite

Crow search algorithm (CSA) is a new natural inspired algorithm proposed by Askarzadeh in 2016. The main inspiration of CSA came from crow search mechanism for hiding their food. Like most of the optimization algorithms, CSA suffers from low convergence rate and entrapment in local optima. In this paper, a novel meta-heuristic optimizer, namely chaotic crow search algorithm (CCSA), is proposed to overcome these problems. The proposed CCSA is applied to optimize feature selection problem for 20 benchmark datasets. Ten chaotic maps are employed during the optimization process of CSA. The performance of CCSA is compared with other well-known and recent optimization algorithms. Experimental results reveal the capability of CCSA to find an optimal feature subset which maximizes the classification performance and minimizes the number of selected features. Moreover, the results show that CCSA is superior compared to CSA and the other algorithms. In addition, the experiments show that sine chaotic map is the appropriate map to significantly boost the performance of CSA.

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