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Zhang, S., F. Hu, S. - L. Jui, A. E. Hassanien, and K. Xiao, "Unsupervised Brain MRI Tumor Segmentation with Deformation-Based Feature", the 1st International Conference on Advanced Intelligent Systems and Informatics (AISI’15) Springer, Beni Suef University, Beni Suef, Eg, Nov. 28-30, 2015.
Zhang, S., F. Hu, S. - L. Jui, A. E. Hassanien, and K. Xiao, "Unsupervised Brain MRI Tumor Segmentation with Deformation-Based Feature", The 1st International Conference on Advanced Intelligent System and Informatics (AISI2015), November 28-30, 2015, Beni Suef, Egypt: Springer International Publishing, pp. 173–181, 2016. 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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Salama, M. A., A. E. Hassanien, and A. A. Fahmy, "Uni-class pattern-based classification model", Intelligent Systems Design and Applications (ISDA), 2010 10th International Conference on: IEEE, pp. 1293–1297, 2010. Abstract
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Salama, M. A., A. E. Hassanien, and A. A. Fahmy, "Uni-class pattern-based classification model", Intelligent Systems Design and Applications (ISDA), 2010 10th International Conference on: IEEE, pp. 1293–1297, 2010. Abstract
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Ossama S. Alshabrawy, and A. E. Hassanien, "Underdetermined blind separation of mixtures of an unknown number of sources with additive white and pink noises", The 5th International Conference on Innovations in Bio-Inspired Computing and Applications (Springer) IBICA2014, Ostrava, Czech Republic., 22-24 June, 2014. Abstractibica2014_p29.pdf

In this paper we propose an approach for underdetermined
blind separation in the case of additive Gaussian white noise and pink
noise in addition to the most challenging case where the number of source
signals is unknown. In addition to that, the proposed approach is appli-
cable in the case of separating I +3 source signals from I mixtures with
an unknown number of source signals and the mixtures have additive two
kinds of noises. This situation is more challenging and also more suitable
to practical real world problems. Moreover, unlike to some traditional
approaches, the sparsity conditions are not imposed. Firstly, the number
of source signals is approximated and estimated using multiple source
detection, followed by an algorithm for estimating the mixing matrix
based on combining short time Fourier transform and rough-fuzzy clus-
tering. Then, the mixed signals are normalized and the source signals
are recovered using multi-layer modi ed Gradient descent Local Hier-
archical Alternating Least Squares Algorithm exploiting the number of
source signals estimated , and the mixing matrix obtained as an input
and initialized by multiplicative algorithm for matrix factorization based
on alpha divergence. The computer simulation results show that the pro-
posed approach can separate I + 3 source signals from I mixed signals,
and it has superior evaluation performance compared to some traditional
approaches in recent references.

N
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.

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El-Dahshan, E. - S. A., A. E. Hassanien, A. Radi, and S. Banerjee, "Ultrasound Biomicroscopy Glaucoma Images Analysis Based on Rough Set and Pulse Coupled Neural Network", Foundations of Computational Intelligence, Volume 2, pp. 275-293 , London, Springer , 2009. Abstract

The objective of this book chapter is to present the rough sets and pulse coupled neural network scheme for Ultrasound Biomicroscopy glaucoma images analysis. To increase the efficiency of the introduced scheme, an intensity adjustment process is applied first using the Pulse Coupled Neural Network (PCNN) with a median filter. This is followed by applying the PCNN-based segmentation algorithm to detect the boundary of the interior chamber of the eye image. Then, glaucoma clinical parameters have been calculated and normalized, followed by application of a rough set analysis to discover the dependency between the parameters and to generate set of reduct that contains minimal number of attributes. Finally, a rough confusion matrix is designed for discrimination to test whether they are normal or glaucomatous eyes. Experimental results show that the introduced scheme is very successful and has high detection accuracy.

El-Dahshan, E. - S. A., A. E. Hassanien, A. Radi, and S. Banerjee, "Ultrasound biomicroscopy glaucoma images analysis based on rough set and pulse coupled neural network", Foundations of Computational Intelligence Volume 2: Springer Berlin Heidelberg, pp. 275–293, 2009. Abstract
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El-Dahshan, E. - S. A., A. E. Hassanien, A. Radi, and S. Banerjee, "Ultrasound biomicroscopy glaucoma images analysis based on rough set and pulse coupled neural network", Foundations of Computational Intelligence Volume 2: Springer Berlin Heidelberg, pp. 275–293, 2009. 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
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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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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Alshabrawy, O. S., M. E. Ghoneim, A. A. Salama, and A. E. Hassanien, "Underdetermined blind separation of an unknown number of sources based on fourier transform and matrix factorization", Computer Science and Information Systems (FedCSIS), 2013 Federated Conference on: IEEE, pp. 19–25, 2013. Abstract
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Alshabrawy, O. S., M. E. Ghoneim, W. A. Awad, and A. E. Hassanien, "Underdetermined blind source separation based on fuzzy c-means and semi-nonnegative matrix factorization", Computer Science and Information Systems (FedCSIS), 2012 Federated Conference on: IEEE, pp. 695–700, 2012. Abstract
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Alshabrawy, O. S., M. E. Ghoneim, W. A. Awad, and A. E. Hassanien, "Underdetermined blind source separation based on fuzzy c-means and semi-nonnegative matrix factorization", Computer Science and Information Systems (FedCSIS), 2012 Federated Conference on: IEEE, pp. 695–700, 2012. Abstract
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Alshabrawy, O. S., M. E. Ghoneim, A. A. Salama, and A. E. Hassanien, "Underdetermined blind separation of an unknown number of sources based on fourier transform and matrix factorization", Computer Science and Information Systems (FedCSIS), 2013 Federated Conference on: IEEE, pp. 19–25, 2013. Abstract
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Alshabrawy, O. S., and A. E. Hassanien, "Underdetermined blind separation of mixtures of an unknown number of sources with additive white and pink noises", Proceedings of the Fifth International Conference on Innovations in Bio-Inspired Computing and Applications IBICA 2014: Springer International Publishing, pp. 241–250, 2014. Abstract
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Alshabrawy, O. S., M. E. Ghoneim, W. A. Awad, and A. E. Hassanien, "Underdetermined Blind Source Separation based on Fuzzy C-Means and Semi-Nonnegative Matrix Factorization", IEEE Federated Conference on Computer Science and Information Systems, pp. 723–728, Wroclaw - Poland, 9-13 Sept, 2012. Abstractunderdetermined_blind_source_separation_based_on_fuzzy.pdf

Conventional blind source separation is based on
over-determined with more sensors than sources but the underdetermined
is a challenging case and more convenient to actual
situation. Non-negative Matrix Factorization (NMF) has been
widely applied to Blind Source Separation (BSS) problems.
However, the separation results are sensitive to the initialization
of parameters of NMF. Avoiding the subjectivity of choosing
parameters, we used the Fuzzy C-Means (FCM) clustering
technique to estimate the mixing matrix and to reduce the requirement
for sparsity.Also, decreasing the constraints is regarded
in this paper by using Semi-NMF. In this paper we propose
a new two-step algorithm in order to solve the underdetermined
blind source separation. We show how to combine the FCM clustering technique with the gradient-based NMF with the multi-layer technique. The simulation results show that our proposed algorithm can separate the source signals with high signal-to-noise ratio and quite low cost time compared with some algorithms.

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