Publications

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Journal Article
Hassanien, A. E., and J. M. H. Ali, "Enhanced rough sets rule reduction algorithm for classification digital mammography", Journal of Intelligent Systems, vol. 13: FREUND PUBLISHING HOUSE LTD., pp. 151–171, 2003. Abstract
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Hussein, H. K., A. - E. Hassanien, and M. Nakajima, "Escape-Time Modified Algorithm for Generating Fractal Images Based on Petri Net Reachability", IEICE TRANSACTIONS on Information and Systems, vol. 82, no. 7: The Institute of Electronics, Information and Communication Engineers, pp. 1101–1108, 1999. Abstract
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Sarkar, M., S. Banerjee, and A. E. Hassanien, "Evaluating the Degree of Trust Under Context Sensitive Relational Database Hierarchy Using Hybrid Intelligent Approach", International Journal of Rough Sets and Data Analysis (IJRSDA), vol. 2, no. 1: IGI Global, pp. 1–21, 2015. Abstract
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Zawbaa, H. M., N. El-Bendary, A. E. Hassanien, and T. - H. Kim, "Event detection based approach for soccer video summarization using machine learning", Int J Multimed Ubiquitous Eng, vol. 7, no. 2, pp. 63–80, 2012. Abstract
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Zawbaa, H. M., N. El-Bendary, A. E. Hassanien, and T. - H. Kim, "Event detection based approach for soccer video summarization using machine learning", Int J Multimed Ubiquitous Eng, vol. 7, no. 2, pp. 63–80, 2012. Abstract
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Kai, and A. ela Hassanien, "extraction and application of deformation-based feature in medical images", Neurocomputing, 2012.
Xiao, K., L. A. Liang, Haibing Guan, and A. E. Hassanien, "Extraction and application of deformation-based feature in medical images", Neurocomputing, vol. 120: Elsevier, pp. 177–184, 2013. Abstract
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Xiao, K., L. A. Liang, Haibing Guan, and A. E. Hassanien, "Extraction and application of deformation-based feature in medical images", Neurocomputing, vol. 120: Elsevier, pp. 177–184, 2013. Abstract
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Eid, H. F., M. A. Salama, and A. E. Hassanien, "A Feature Selection Approach for Network Intrusion Classification: The Bi-Layer Behavioral-Based", International Journal of Computer Vision and Image Processing (IJCVIP), vol. 3, no. 4: IGI Global, pp. 51–59, 2013. 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.

Hassanien, A. - E., and M. Nakajima, "Feature-specification algorithm based on snake model for facial image morphing", IEICE transactions on information and systems, vol. 82, no. 2: The Institute of Electronics, Information and Communication Engineers, pp. 439–446, 1999. 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.

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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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, 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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Ho, S. H., A. E. Hassanien, N. Van Du, Q. Salih, and H. Sooi, "FUZZY C-MEANS CLUSTERING WITH ADJUSTABLE FEATURE WEIGHTING DISTRIBUTION FOR BRAIN MRI VENTRICLES SEGMENTATION Kai Xiao1", Update, vol. 15, pp. 1, 2001. 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
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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Hassanien, A. E., "Fuzzy rough sets hybrid scheme for breast cancer detection", Image and Vision Computing, vol. 25, issue 2, pp. 172–183, 2007. AbstractWebsite

This paper introduces a hybrid scheme that combines the advantages of fuzzy sets and rough sets in conjunction with statistical feature extraction techniques. An application of breast cancer imaging has been chosen and hybridization scheme have been applied to see their ability and accuracy to classify the breast cancer images into two outcomes: cancer or non-cancer. The introduced scheme starts with fuzzy image processing as pre-processing techniques to enhance the contrast of the whole image; to extracts the region of interest and then to enhance the edges surrounding the region of interest. A subsequently extract features from the segmented regions of the interested regions using the gray-level co-occurrence matrix is presented. Rough sets approach for generation of all reducts that contains minimal number of attributes and rules is introduced. Finally, these rules can then be passed to a classifier for discrimination for different regions of interest to test whether they are cancer or non-cancer. To measure the similarity, a new rough set distance function is presented. The experimental results show that the hybrid scheme applied in this study perform well reaching over 98% in overall accuracy with minimal number of generated rules. (This paper was not presented at any IFAC meeting).

Hassanien, A. E., "Fuzzy rough sets hybrid scheme for breast cancer detection", Image and vision computing, vol. 25, no. 2: Elsevier, pp. 172–183, 2007. Abstract
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