Publications

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Conference Paper
Mostafa, A., M. A. Fattah, A. Fouad, A. E. Hassanien, and T. - H. Kim, "Region growing segmentation with iterative K-means for CT liver images", Advanced Information Technology and Sensor Application (AITS), 2015 4th International Conference on: IEEE, pp. 88–91, 2015. Abstract
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El Bakrawy, L. M., N. I. Ghali, A. E. Hassanien, and T. - H. Kim, "A rough k-means fragile watermarking approach for image authentication", Computer Science and Information Systems (FedCSIS), 2011 Federated Conference on: IEEE, pp. 19–23, 2011. Abstract
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El Bakrawy, L. M., N. I. Ghali, A. E. Hassanien, and T. - H. Kim, "A rough k-means fragile watermarking approach for image authentication", Computer Science and Information Systems (FedCSIS), 2011 Federated Conference on: IEEE, pp. 19–23, 2011. Abstract
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Elshazly, Hanaa, N.; Ghali, A. Korany, and A. E. Hassanien, "Rough sets and genetic algorithms: A hybrid approach to breast cancer classification", World Congress on Information and Communication Technologies (WICT), pp. 260 - 265 , India, Oct. 30 2012-Nov. Abstract

The use of computational intelligence systems such as rough sets, neural networks, fuzzy set, genetic algorithms, etc., for predictions and classification has been widely established. This paper presents a generic classification model based on a rough set approach and decision rules. To increase the efficiency of the classification process, boolean reasoning discretization algorithm is used to discretize the data sets. The approach is tested by a comparatif study of three different classifiers (decision rules, naive bayes and k-nearest neighbor) over three distinct discretization techniques (equal bigning, entropy and boolean reasoning). The rough set reduction technique is applied to find all the reducts of the data which contains the minimal subset of attributes that are associated with a class label for prediction. In this paper we adopt the genetic algorithms approach to reach reducts. Finally, decision rules were used as a classifier to evaluate the performance of the predicted reducts and classes. To evaluate the performance of our approach, we present tests on breast cancer data set. The experimental results obtained, show that the overall classification accuracy offered by the employed rough set approach and decision rules is high compared with other classification techniques including Bayes and k-nearest neighbor.

Elshazly, H. I., N. I. Ghali, A. M. E. Korany, and A. E. Hassanien, "Rough sets and genetic algorithms: A hybrid approach to breast cancer classification", Information and Communication Technologies (WICT), 2012 World Congress on: IEEE, pp. 260–265, 2012. Abstract
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Elshazly, H. I., N. I. Ghali, A. M. E. Korany, and A. E. Hassanien, "Rough sets and genetic algorithms: A hybrid approach to breast cancer classification", Information and Communication Technologies (WICT), 2012 World Congress on: IEEE, pp. 260–265, 2012. Abstract
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Elshazly, H. I., N. I. Ghali, A. M. E. Korany, and A. E. Hassanien, "Rough sets and genetic algorithms: A hybrid approach to breast cancer classification", Information and Communication Technologies (WICT), 2012 World Congress on: IEEE, pp. 260–265, 2012. Abstract
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Kotyk, T., S. Chakraborty, N. Dey, T. Gaber, A. E. Hassanien, and V. Snasel, "Semi-automated System for Cup to Disc Measurement for Diagnosing Glaucoma Using Classification Paradigm", Proceedings of the Second International Afro-European Conference for Industrial Advancement AECIA 2015: Springer International Publishing, pp. 653–663, 2016. Abstract
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Ali, M. A. S., M. I. Shaalan, A. E. Hassanien, and T. - H. Kim, "A Simple Approach for Segmentation and Removal of Interphase Cells from Chromosome Images", Computer and Computing Science (COMCOMS), 2015 3rd International Conference on: IEEE, pp. 3–8, 2015. Abstract
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Moustafa Zein, A. Adl, A. E. Hassanien, A. Badr, and T. - H. Kim, "A Social Relationship Modifiers Modeller", Computer, Information and Application (CIA), 2015 3rd International Conference on: IEEE, pp. 33–37, 2015. Abstract
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Inbarani, H. H., S. S. Kumar, A. T. Azar, and A. E. Hassanien, "Soft rough sets for heart valve disease diagnosis", International Conference on Advanced Machine Learning Technologies and Applications: Springer International Publishing, pp. 347–356, 2014. Abstract
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Kilany, M., A. Adl, A. E. Hassanien, and T. - H. Kim, "Towards a Computational Human Behavioral Model", Computer, Information and Application (CIA), 2015 3rd International Conference on: IEEE, pp. 42–45, 2015. Abstract
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Amin, I. I., S. K. Kassim, A. E. Hassanien, and H. A. Hefny, "Using formal concept analysis for mining hyomethylated genes among breast cancer tumors subtypes", Advances in Computing, Communications and Informatics (ICACCI), 2013 International Conference on: IEEE, pp. 521–526, 2013. Abstract
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Sami, M., N. El-Bendary, T. - H. Kim, and A. E. Hassanien, "Using Particle Swarm Optimization for Image Regions Annotation", Future Generation Information Technology (FGIT 2012),, 241--250. Springer, Heidelberg. Kangwondo, Korea , cember 16-19,, 2012. Abstract77090241.pdf

In this paper, we propose an automatic image annotation approach
for region labeling that takes advantage of both context and semantics present
in segmented images. The proposed approach is based on multi-class K-nearest
neighbor, k-means and particle swarm optimization (PSO) algorithms for feature
weighting, in conjunction with normalized cuts-based image segmentation technique.
This hybrid approach refines the output of multi-class classification that
is based on the usage of K-nearest neighbor classifier for automatically labeling
images regions from different classes. Each input image is segmented using the
normalized cuts segmentation algorithm then a descriptor created for each segment.
The PSO algorithm is employed as a search strategy for identifying an optimal
feature subset. Extensive experimental results demonstrate that the proposed
approach provides an increase in accuracy of annotation performance by about
40%, via applying PSO models, compared to having no PSO models applied, for
the used dataset.

Sami, M., N. El-Bendary, T. - H. Kim, and A. E. Hassanien, "Using particle swarm optimization for image regions annotation", International Conference on Future Generation Information Technology: Springer Berlin Heidelberg, pp. 241–250, 2012. Abstract
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Sami, M., N. El-Bendary, T. - H. Kim, and A. E. Hassanien, "Using particle swarm optimization for image regions annotation", International Conference on Future Generation Information Technology: Springer Berlin Heidelberg, pp. 241–250, 2012. Abstract
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Karam, H., A. E. Hassanien, and M. Nakajima, "Visual simulation of texture/non-texture image synthesis", Computer Graphics International, 2000. Proceedings: IEEE, pp. 343–351, 2000. Abstract
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Karam, H., A. E. Hassanien, and M. Nakajima, "Visual simulation of texture/non-texture image synthesis", Computer Graphics International, 2000. Proceedings: IEEE, pp. 343–351, 2000. Abstract
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Amin, I. I., A. E. Hassanien, Hesham A. Hefny, and S. K. Kassim, "Visualizing and identifying the DNA methylation markers in breast cancer tumor subtypes", The 5th International Conference on Innovations in Bio-Inspired Computing and Applications (Springer) IBICA2014, Ostrava, Czech Republic., 22-24 June, 2014. Abstractibica2014_p14.pdf

DNA methylation is an epigenetic mechanism that cells use to control
gene expression. DNA methylation has become one of the hottest topics in cancer
research, especially for abnormally hypermethylated tumor suppressor genes
or hypomethylaed oncogenes research. The analysis of DNA methylation data
determines the differential hypermethlated or hypomethylated genes that are candidate
to be cancer biomarkers. Visualization the DNA methylation status may
lead to discover new relationships between hypomethylated and hypermethylated
genes, therefore this paper applied a mathematical modelling theory called formal
concept analysis for visualizing DNA methylation status.

Amin, I. I., A. E. Hassanien, H. A. Hefny, and S. K. Kassim, "Visualizing and identifying the DNA methylation markers in breast cancer tumor subtypes", Proceedings of the Fifth International Conference on Innovations in Bio-Inspired Computing and Applications IBICA 2014: Springer International Publishing, pp. 161–171, 2014. Abstract
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Kudelka, M., V. Snásel, Z. Horak, and A. E. Hassanien, "Web Communities Defined by Web Page Content", IEEE/WIC/ACM International Conference on Web Intelligence and International Conference on Intelligent Agent Technology , Sydney, NSW, Australia, pp.385-389 , 9-12 December, 2008. 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. For us, the Web page is entity carrying information about these communities. Our 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 proves the feasibility of our approach.

Samanta, S., D. Kundu, S. Chakraborty, N. Dey, T. Gaber, A. E. Hassanien, and T. - H. Kim, "Wooden Surface Classification based on Haralick and The Neural Networks", Information Science and Industrial Applications (ISI), 2015 Fourth International Conference on: IEEE, pp. 33–39, 2015. Abstract
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Conference Proceedings
Wahid, R., N. I. Ghali, H. S. Own, T. - H. Kim, and A. ella Hassanien., " A Gaussian Mixture Models Approach to Human Heart Signal Verification Using Different Feature Extraction Algorithms ", International Conference on Bio-Science and Bio-Technology (BSBT2012),, , Kangwondo, Korea, , Springer, Heidelberg , pp. pp. 16--24, 2012. Abstract3530016.pdf

In this paper the possibility of using the human heart signal
feature for human verification is investigated. The presented approach
consists of two different robust feature extraction algorithms with a specified
configuration in conjunction with Gaussian mixture modeling. The
similarity of two samples is estimated by measuring the difference between
their negative log-likelihood of the features. To evaluate the performance
and the uniqueness of the presented approach tests using a
high resolution auscultation digital stethoscope are done for nearly 80
heart sound samples. The experimental results obtained show that the
accuracy offered by the employed Gaussian mixture modeling reach up
to 100% for 7 samples using the first feature extraction algorithm and
6 samples using the second feature extraction algorithm and varies with
average 85%.

Amin, I. I., S. K. Kassim, A. E. Hassanien, and H. Hefny, "Formal concept analysis for mining hypermethylated genes in breast cancer tumor subtypes", 12th International Conference on Intelligent Systems Design and Applications (ISDA), , Kochi, India, pp. 764 - 769, 2012. Abstract

The main purpose of this paper is to show the use of formal concept analysis (FCA) as data mining approach for mining the common hypermethylated genes between breast cancer subtypes, by extracting formal concepts which representing sets of significant hypermethylated genes for each breast cancer subtypes, then the formal context is built which leading to construct a concept lattice which is composed of formal concepts. This lattice can be used as knowledge discovery and knowledge representation therefore, becoming more interesting for the biologists.

Karam, H., A. E. Hassanien, and M. Nakajima, "Visual Simulation of Texture/Non-Texture Image Synthesis.", IEEE International conference on Computer Graphics , Geneva, Switzerland, 19-24 June 2000 , pp. 343-351, 2000. Abstract

We propose a new and effective image modeling dual technique which is capable of simulating both texture image synthesis and non-texture images like fractals. The technique uses the algebraic approach of graph grammars theory as a new simulation tool for both texture and non-texture image synthesis via its graph production, derivation and double-pushout construction. Validation of our approach is given by discussion and an illustration of some experimental results. An investigation of the relationships between the generated patterns and their corresponding graph grammars is also discussed

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