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Amin, I. I., Samar K. Kassim, A. E. Hassanien, and H. A. Hefny, "Using formal concept analysis for mining hyomethylated genes among breast cancer tumors subtypes ", IEEE International Conference on Advances in Computing, Communications and Informatics (ICACCI-2013) , Mysore, India, August 22-25, 2013. pid2853599_3_2.pdf
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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and Nashwa El-Bendary, Esraa El hariri, A. E. H. A. B., "Using Machine Learning Techniques for Evaluating Tomato Ripeness", Expert Systems with Applications, issue Available online 13 October 2014, 2014. AbstractWebsite

Tomato quality is one of the most important factors that helps ensuring a consistent marketing of tomato fruit. As ripeness is the main indicator for tomato quality from customers perspective, the determination of tomato ripeness stages is a basic industrial concern regarding tomato production in order to get high quality product. Automatic ripeness evaluation of tomato is an essential research topic as it may prove benefits in ensuring optimum yield of high quality product, this will increase the income because tomato is one of the most important crops in the world. This article presents an automated multi-class classification approach for tomato ripeness measurement and evaluation via investigating and classifying the different maturity/ripeness stages. The proposed approach uses color features for classifying tomato ripeness stages. The approach proposed in this article uses Principal Components Analysis (PCA) in addition to Support Vector Machines (SVMs) and Linear Discriminant Analysis (LDA) algorithms for feature extraction and classification, respectively. Experiments have been conducted on a dataset of total 250 images that has been used for both training and testing datasets with 10-fold cross validation. Experimental results showed that the proposed classification approach has obtained ripeness classification accuracy of 90.80%, using one-against-one (OAO) multi-class SVMs algorithm with linear kernel function, ripeness classification accuracy of 84.80% using one-against-all (OAA) multi-class SVMs algorithm with linear kernel function, and ripeness classification accuracy of 84% using LDA algorithm.

El-Bendary, N., Esraa Elhariri, A. E. Hassanien, and A. Badr, "Using machine learning techniques for evaluating tomato ripeness", Expert Systems with Applications, vol. 42, no. 4: Pergamon, pp. 1892–1905, 2015. Abstract
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El-Bendary, N., Esraa Elhariri, A. E. Hassanien, and A. Badr, "Using machine learning techniques for evaluating tomato ripeness. Expert Syst. Appl. ", Expert Syst. Appl. , vol. 42, issue 4, pp. 1892-1905, 2015. Website
El-Bendary, N., Esraa Elhariri, A. E. Hassanien, and A. Badr, "Using machine learning techniques for evaluating tomato ripeness. Expert Syst. Appl. ", Expert Syst. Appl. , vol. 42, issue 4, pp. 1892-1905, 2015. Website
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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Ghali, N. I., O. Soluiman, N. El-Bendary, T. M. Nassef, S. A. Ahmed, Y. M. Elbarawy, and A. E. Hassanien, "Virtual reality technology for blind and visual impaired people: reviews and recent advances", Advances in Robotics and Virtual Reality: Springer Berlin Heidelberg, pp. 363–385, 2012. Abstract
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Ghali, N. I., O. Soluiman, N. El-Bendary, T. M. Nassef, S. A. Ahmed, Y. M. Elbarawy, and A. E. Hassanien, "Virtual reality technology for blind and visual impaired people: reviews and recent advances", Advances in Robotics and Virtual Reality: Springer Berlin Heidelberg, pp. 363–385, 2012. Abstract
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Ghali, N. I., O. Soluiman, N. El-Bendary, T. M. Nassef, S. A. Ahmed, Y. M. Elbarawy, and A. E. Hassanien, "Virtual reality technology for blind and visual impaired people: Reviews and Recent Advances,", Engineering Advances in Robotics and Virtual Reality, Germany, Intelligent Systems Reference Library - Springer, 2012. Abstract

Virtual reality technology enables people to become immersed in a computer-simulated and three-dimensional environment. In this chapter, we investigate the effects of the virtual reality technology on disabled people such as blind and visually impaired people (VIP) in order to enhance their computer skills and prepare them to make use of recent technology in their daily life. As well as, they need to advance their information technology skills beyond the basic computer training and skills. This chapter describes what best tools and practices in information technology to support disabled people such as deaf-blind and visual impaired people in their activities such as mobility systems, computer games, accessibility of e-learning, web-based information system, and wearable finger-braille interface for navigation of deaf-blind. Moreover, we will show how physical disabled people can benefits from the innovative virtual reality techniques and discuss some representative examples to illustrate how virtual reality technology can be utilized to address the information technology problem of blind and visual impaired people. Challenges to be addressed and an extensive bibliography are included.

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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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

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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El-Sehiemy, R. A., Mostafa Abdelkhalik El-hosseini, and A. E. Hassanien, "Volume 22• Issue 2• 2013", Studies in Informatics and Control-ICI Bucharest, 2013. Abstract
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Moftah, H. M., N. I. Ghali, A. E. Hassanien, and M. A. Ismail, "Volume identification and estimation of MRI brain tumor", Hybrid Intelligent Systems (HIS), 2012 12th International Conference on: IEEE, pp. 120–124, 2012. Abstract
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Hossam Moftah, Walaa Elmasry, N. Ghali, A. E. Hassanien, and M. Showman, "Volume Identification and Estimation of MRI Brain Tumor.", The IEEE International Conference on Hybrid Intelligent Systems (HIS2012)., Pune. India, 4-7 Dec. 2012, , pp. 120 - 124, 2012. Abstract

This paper deals with two dimensional magnetic resonance imaging (MRI) sequence of brain slices which include many objects to identify and estimate the volume of the brain tumors. More than twenty five features based on shape, color and texture was extracted to obtain feature vector for each object to characterize the tumor and identify it. Experimental results show that the accuracy of the estimation of tissue volumes is very high.

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Asmaa Hashem Sweidan, N. El-Bendary, O. M. Hegazy, A. E. Hassanien, and V. Snasel, "Water Pollution Detection System Based on Fish Gills as a Biomarker", International Conference on Communications, management, and Information technology (ICCMIT'2015), 2015. Abstract

This article presents an automatic system for assessing water quality based on fish gills microscopic images. As fish gills are a good biomarker for assessing water quality, the proposed system uses fish gills microscopic images in order to detect water pollution. The proposed system consists of three phases; namely pre-processing, feature extraction, and classification phases. Since the shape is the main characteristic of fish gills microscopic images, the proposed system uses shape feature based on edge detection and wavelets transform for classifying the water-quality degree. Furthermore, it implemented Principal Components Analysis (PCA) along with Support Vector Machines (SVMs) algorithms for feature extraction and water quality degree classification. The datasets used for experiments were constructed based on real sample images for fish gills. Training dataset is divided into four classes representing the different histopathological changes and the corresponding water quality degrees. Experimental results showed that the proposed classification system has obtained water quality classification accuracy of 95.41%, using the SVMs linear kernel function and 10-fold cross validation with 37 images per class for training.

Asmaa Hashem Sweidan, N. El-Bendary, O. M. Hegazy, A. E. Hassanien, and V. Snasel, "Water pollution detection system based on fish gills as a biomarker", Procedia Computer Science, vol. 65: Elsevier, pp. 601–611, 2015. Abstract
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Asmaa Hashem Sweidan, N. El-Bendary, A. E. Hassanien, O. M. Hegazy, and A. E. -karim Mohamed, "Water quality classification approach based on bio-inspired Gray Wolf Optimization", Soft Computing and Pattern Recognition (SoCPaR), 2015 7th International Conference of: IEEE, pp. 1–6, 2015. Abstract
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Asmaa Hashem Sweidan, N. El-Bendary, A. E. Hassanien, and O. M. H. A. E. -karim Mohamed, "Water Quality Classification Approach based on Bio-inspired Gray Wolf Optimization, ", 7th IEEE International Conference of Soft Computing and Pattern Recognition, , Kyushu University, Fukuoka, Japan, , , November 13 - 15, 2015. Abstract

Abstract—This paper presents a bio-inspired optimized classification approach for assessing water quality. As fish liver histopathology is a good biomarker for detecting water pollution, the proposed classification approach uses fish liver microscopic images in order to detect water pollution and determine water
quality. The proposed approach includes three phases; preprocessing, feature extraction, and classification phases. Color histogram and Gabor wavelet transform have been utilized for feature extraction phase. The Machine Learning (ML) Support Vector Machines (SVMs) classification algorithm has been employed,
along with the bio-inspired Gray Wolf Optimization (GWO) algorithm for optimizing SVMs parameters, in order to classify water pollution degree. Experimental results showed that the average accuracy achieved by the proposed GWO-SVMs classification approach exceeded 95% considering a variety of
water pollutants.

Soliman, M. M., A. E. Hassanien, and H. M. Onsi, "Watermarking 3D Triangular Mesh Models Using Intelligent Vertex Selection", Proceedings of the Second International Afro-European Conference for Industrial Advancement AECIA 2015: Springer International Publishing, pp. 617–627, 2016. Abstract
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Tourism