Publications

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Journal Article
I.Ghali, N., R. Wahid, and A. E. Hassanien, "Heart Sounds Human Identification and Verification Approaches using Vector Quantization and Gaussian Mixture Models", International Journal of Systems Biology and Biomedical Technologies, , vol. 1, issue 4, pp. 75-88, 2012. Abstract

In this paper the possibility of using the human heart sounds as a human print is investigated. To evaluate the performance and the uniqueness of the proposed approach, tests using a high resolution auscultation digital stethoscope are done for nearly 80 heart sound samples. The verification approach consists of a robust feature extraction with a specified configuration in conjunction with Gaussian mixture modeling. The similarity of two samples is estimated by measuring the difference between their log-likelihood similarities of the features. The experimental results obtained show that the overall accuracy offered by the employed Gaussian mixture modeling reach up to 85%. The identification approach consists of a robust feature extraction with a specified configuration in conjunction with LBG-VQ. The experimental results obtained show that the overall accuracy offered by the employed LBG-VQ reach up to 88.7%

Ghali, N. I., R. Wahid, and A. E. Hassanien, "Heart Sounds Human Identification and Verification Approaches using Vector Quantization and Gaussian Mixture Models", International Journal of Systems Biology and Biomedical Technologies (IJSBBT), vol. 1, no. 4: IGI Global, pp. 74–87, 2012. Abstract
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Ghali, N. I., R. Wahid, and A. E. Hassanien, "Heart Sounds Human Identification and Verification Approaches using Vector Quantization and Gaussian Mixture Models", International Journal of Systems Biology and Biomedical Technologies (IJSBBT), vol. 1, no. 4: IGI Global, pp. 74–87, 2012. Abstract
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Hassanien, E. A., "Hiding iris data for authentication of digital images using wavelet theory", Pattern Recognition and Image Analysis, vol. 16, no. 4: MAIK Nauka/Interperiodica, pp. 637–643, 2006. Abstract
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Anter, A. M., M. A. El Souod, A. T. Azar, and A. E. Hassanien, "A hybrid approach to diagnosis of hepatic tumors in computed tomography images", International Journal of Rough Sets and Data Analysis (IJRSDA), vol. 1, no. 2: IGI Global, pp. 31–48, 2014. Abstract
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Panda, M., A. E. Hassanien, and A. Abraham, "Hybrid Data Mining Approach for Image Segmentation Based Classification", International Journal of Rough Sets and Data Analysis (IJRSDA), vol. 3, issue 2, 2016. AbstractWebsite

Evolutionary harmony search algorithm is used for its capability in finding solution space both locally and globally. In contrast, Wavelet based feature selection, for its ability to provide localized frequency information about a function of a signal, makes it a promising one for efficient classification. Research in this direction states that wavelet based neural network may be trapped to fall in a local minima whereas fuzzy harmony search based algorithm effectively addresses that problem and able to get a near optimal solution. In this, a hybrid wavelet based radial basis function (RBF) neural network (WRBF) and feature subset harmony search based fuzzy discernibility classifier (HSFD) approaches are proposed as a data mining technique for image segmentation based classification. In this paper, the authors use Lena RGB image; Magnetic resonance image (MR) and Computed Tomography (CT) Image for analysis. It is observed from the obtained simulation results that Wavelet based RBF neural network outperforms the harmony search based fuzzy discernibility classifiers.

Hala S. Own, N. I.Ghali, and A. E. Hassanien, "Hybrid Dual-Tree Wavelet Transform and Adaptive Threshold for Image Denoising", International Journal of Imaging and Robotic Systems, , vol. 7, issue S13, 2013.
Own, H. S., N. I. GHALL, and E. L. L. A. H. A. S. S. A. N. I. E. N. ABOUL, "Hybrid Dual-Tree Wavelet Transform and Adaptive Threshold for Image Denoising", International journal of imaging and robotics, vol. 9, no. 1: CESER Publications, pp. 17–25, 2013. Abstract
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Own, H. S., N. I. GHALL, and E. L. L. A. H. A. S. S. A. N. I. E. N. ABOUL, "Hybrid Dual-Tree Wavelet Transform and Adaptive Threshold for Image Denoising", International journal of imaging and robotics, vol. 9, no. 1: CESER Publications, pp. 17–25, 2013. Abstract
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Inbarani, H. H., S. Udhaya Kumar, A. T. Azar, and A. E. Hassanien, "Hybrid rough-bijective soft set classification system", Neural Computing and Applications, , vol. 29, issue 8, pp. 67–78., 2018. Abstract

In today’s medical world, the patient’s data with symptoms and diseases are expanding rapidly, so that analysis of all factors with updated knowledge about symptoms and corresponding new treatment is merely not possible by medical experts. Hence, the essential for an intelligent system to reflect the different issues and recognize an appropriate model between the different parameters is evident. In recent decades, rough set theory (RST) has been broadly applied in various fields such as medicine, business, education, engineering and multimedia. In this study, a hybrid intelligent system that combines rough set (RST) and bijective soft set theory (BISO) to build a robust classifier model is proposed. The aim of the hybrid system is to exploit the advantages of the constituent components while eliminating their limitations. The resulting approach is thus able to handle data inconsistency in datasets through rough sets, while obtaining high classification accuracy based on prediction using bijective soft sets. Toward estimating the performance of the hybrid rough-bijective soft set (RBISO)-based classification approach, six benchmark medical datasets (Wisconsin breast cancer, liver disorder, hepatitis, Pima Indian diabetes, echocardiogram data and thyroid gland) from the UCI repository of machine learning databases are utilized. Experimental results, based on evaluation in terms of sensitivity, specificity and accuracy, are compared with other well-known classification methods, and the proposed algorithm provides an effective method for medical data classification.

Inbarani, H., U. S. Kum, A. T. Azar, and A. E. Hassanien, "Hybrid Rough-Bijective Soft Set Classification system,", Neural Computing and Applications (NCAA) , pp. , pp, 1-21, 2017 , 2017. AbstractWebsite

In today’s medical world, the patient’s data with symptoms and diseases are expanding rapidly, so that analysis of all factors with updated knowledge about symptoms and corresponding new treatment is merely not possible by medical experts. Hence, the essential for an intelligent system to reflect the different issues and recognize an appropriate model between the different parameters is evident. In recent decades, rough set theory (RST) has been broadly applied in various fields such as medicine, business, education, engineering and multimedia. In this study, a hybrid intelligent system that combines rough set (RST) and bijective soft set theory (BISO) to build a robust classifier model is proposed. The aim of the hybrid system is to exploit the advantages of the constituent components while eliminating their limitations. The resulting approach is thus able to handle data inconsistency in datasets through rough sets, while obtaining high classification accuracy based on prediction using bijective soft sets. Toward estimating the performance of the hybrid rough-bijective soft set (RBISO)-based classification approach, six benchmark medical datasets (Wisconsin breast cancer, liver disorder, hepatitis, Pima Indian diabetes, echocardiogram data and thyroid gland) from the UCI repository of machine learning databases are utilized. Experimental results, based on evaluation in terms of sensitivity, specificity and accuracy, are compared with other well-known classification methods, and the proposed algorithm provides an effective method for medical data classification.

Sayed, G. I., and A. E. Hassanien, "A hybrid SA-MFO algorithm for function optimization and engineering design problems", Complex & Intelligent Systems, 2018. Abstract

This paper presents a hybrid algorithm based on using moth-flame optimization (MFO) algorithm with simulated annealing (SA), namely (SA-MFO). The proposed SA-MFO algorithm takes the advantages of both algorithms. It takes the ability to escape from local optima mechanism of SA and fast searching and learning mechanism for guiding the generation of candidate solutions of MFO. The proposed SA-MFO algorithm is applied on 23 unconstrained benchmark functions and four well-known constrained engineering problems. The experimental results show the superiority of the proposed algorithm. Moreover, the performance of SA-MFO is compared with well-known and recent meta-heuristic algorithms. The results show competitive results of SA-MFO concerning MFO and other meta-heuristic algorithms.

Grosan, C., and A. E. Hassanien, "Hybrid Self Organizing Neurons and Evolutionary Algorithms for Global Optimization", Journal of Computational and Theoretical Nanoscience, vol. 9, issue 2, pp. 304-309, 2012. AbstractWebsite

In this work a new algorithm inspired by the self organizing maps combined with evolutionary algorithms is lined up. A neuron in the map is not evolving by itself but it is the result of the application of an evolutionary algorithm during a set of iterations. This idea really helps to increasing the performance of both self organizing maps and evolutionary algorithms while considered individually. The experiments performed in this research envisage test functions having a single criteria but a high number of dimensions. Comparisons with four other well known metaheuristics for optimization (such as differential evolution, particle swarm optimization, simulated annealing) show the performance and efficiency of the proposed approach.

Grosan, C., and A. E. Hassanien, "Hybrid Self Organizing Neurons and Evolutionary Algorithms for Global Optimization", Journal of Computational and Theoretical Nanoscience, vol. 9, no. 2: American Scientific Publishers, pp. 304–309, 2012. Abstract
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Grosan, C., and A. E. Hassanien, "Hybrid Self Organizing Neurons and Evolutionary Algorithms for Global Optimization", Journal of Computational and Theoretical Nanoscience, vol. 9, no. 2: American Scientific Publishers, pp. 304–309, 2012. Abstract
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Elshazly, H. I., H. I. Elshazly, A. E. Hassanien, and A. M. Elkorany, "Hybrid System based on Rough Sets and Genetic Algorithms for Medical Data Classifications", International Journal of Fuzzy System Applications (IJFSA) , vol. 3, issue 4, 2013.
Elshazly, H. I., A. T. Azar, A. E. Hassanien, and A. M. Elkorany, "Hybrid system based on rough sets and genetic algorithms for medical data classifications", International Journal of Fuzzy System Applications (IJFSA), vol. 3, no. 4: IGI Global, pp. 31–46, 2013. Abstract
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Azar, A. T., A. E. Hassanien, and others, "Hybrid TRS-PSO clustering approach for Web2. 0 social tagging system", International Journal of Rough Sets and Data Analysis (IJRSDA), vol. 2, no. 1: IGI Global, pp. 22–37, 2015. Abstract
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Inbarani, H., S. Kumar, A. E. Hassanien, and A. T. Azar, "Hybrid TRS-PSO Clustering Approach for Web2.0 Social Tagging System. ", International Journal of Rough Sets and Data Analysis (IJRSDA) , vol. 2, issue 1, 2015. AbstractWebsite

Social tagging is one of the important characteristics of WEB2.0. The challenge of Web 2.0 is a huge amount of data generated over a short period. Tags are widely used to interpret and classify the web 2.0 resources. Tag clustering is the process of grouping the similar tags into clusters. The tag clustering is very useful for searching and organizing the web2.0 resources and also important for the success of Social Bookmarking systems. In this paper, the authors proposed a hybrid Tolerance Rough Set Based Particle Swarm optimization (TRS-PSO) clustering algorithm for clustering tags in social systems. Then the proposed method is compared to the benchmark algorithm K-Means clustering and Particle Swarm optimization (PSO) based Clustering technique. The experimental analysis illustrates the effectiveness of the proposed approach.

Asmaa Hashem Sweidan, E. - B. Nashwa Mamdouh, A. E. Hassanien, O. M. Hegazy, and A. E. -karim Mohamed, "Hybrid-Biomarker Case-Based Reasoning System for Water Pollution Assessment in Abou Hammad Sharkia, Egypt", Applied Soft computing , pp. Accepted, 2015. AbstractWebsite

Water pollution by organic materials or metals is one of the
problems that threaten humanity, both nowadays and over the next decades.
Morphological changes in Nile Tilapia "Oreochromis niloticus'' fish liver
and gills can also represent the adaptation strategies to maintain some
physiological functions or to assess acute and chronic exposure to
chemicals found in water and sediments. This paper presents an automatic
system for assessing water quality; in Sharkia Governorate - Egypt, based
on microscopic images of fish gills and liver. The proposed system used
fish gills and liver as hybrid-biomarker in order to detect water
pollution. It utilized case-based reasoning (CBR) for indicating the
degree of water quality based on the different histopathological changes
in fish gills and liver microscopic images. Various performance
evaluation metrics; namely, retrieval accuracy, Receiver Operating
Characteristic (ROC) curves, F-measure, and G-mean, have been used in
order to objectively indicate the true performance of the system
considering the unbalanced data. Experimental results showed that the
proposed hybrid-biomarker CBR based system achieved water quality
prediction accuracy of 97.9% using cosine distance similarity measure.
Also, it outperformed both SVMs and LDA classifiers for the tested
microscopic images dataset.

Hassaniena, A. E., N. El-Bendary, Asmaa Hashem Sweidan, and A. E. -karim Mohamed, "Hybrid-biomarker case-based reasoning system for water pollution assessment in Abou Hammad Sharkia, Egypt", Applied Soft Computing, vol. 46, issue 1, pp. 1043–1055, 2016. AbstractWebsite

Water pollution by organic materials or metals is one of the problems that threaten humanity, both nowadays and over the next decades. Morphological changes in Nile Tilapia “Oreochromis niloticus” fish liver and gills can also represent the adaptation strategies to maintain some physiological functions or to assess acute and chronic exposure to chemicals found in water and sediments. This paper presents an automatic system for assessing water quality, in Sharkia Governorate – Egypt, based on microscopic images of fish gills and liver. The proposed system used fish gills and liver as hybrid-biomarker in order to detect water pollution. It utilized case-based reasoning (CBR) for indicating the degree of water quality based on the different histopathological changes in fish gills and liver microscopic images. Various performance evaluation metrics namely, retrieval accuracy, receiver operating characteristic (ROC) curves, F-measure, and G-mean have been used in order to objectively indicate the true performance of the system considering the unbalanced data. Experimental results showed that the proposed hybrid-biomarker CBR based system achieved water quality prediction accuracy of 97.9% using cosine distance similarity measure. Also, it outperformed both SVMs and LDA classifiers for the tested microscopic images dataset.

Hassanien, A. E., N. El-Bendary, Asmaa Hashem Sweidan, A. E. -karim Mohamed, and O. M. Hegazy, "Hybrid-biomarker case-based reasoning system for water pollution assessment in Abou Hammad Sharkia, Egypt", Applied Soft Computing, vol. 46: Elsevier, pp. 1043–1055, 2016. Abstract
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Conference Paper
Hossam Zawbaee, Eid Emary, A. E. Hassanien, and M. Tolba, "Hajj Human Event Classification System using Machine Learning Techniques", 13th IEEE International Conference on Hybrid Intelligent Systems |(HIS13) Tunisia, 4-6 Dec. pp. 192-197, 2013, Tunisia, , 4-6 Dec, 2013.
Zawbaa, H. M., Eid Emary, A. E. Hassanien, and M. F. Tolba, "Hajj human event classification system using machine learning techniques", Hybrid Intelligent Systems (HIS), 2013 13th International Conference on: IEEE, pp. 191–196, 2013. Abstract
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Fattah, M. A., S. Abuelenin, A. E. Hassanien, and J. - S. Pan, "Handwritten Arabic Manuscript Image Binarization Using Sine Cosine Optimization Algorithm", International Conference on Genetic and Evolutionary Computing: Springer International Publishing, pp. 273–280, 2016. Abstract
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Tourism