Publications

Export 1250 results:
Sort by: Author [ Title  (Desc)] Type Year
A B C D E F G H I J K L M N O P Q R S T U V W X Y Z 
U
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
n/a
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
n/a
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
n/a
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.

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
n/a
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
n/a
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.

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
n/a
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
n/a
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
n/a
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
n/a
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
n/a
T
HASSAN, A. H. M. E. D., and A. E. Hassanien, "Two-Class Support Vector Machine with New Kernel Function Based on Paths of Features for Predicting Chemical Activity", Information Sciences, 2017. AbstractWebsite

Information and computer science fields such as machine learning and graph theory are implemented in chemoinformatics to discover the properties of chemical compounds. This paper presents a new algorithm based on the two-class support vector machine (SVM) model, which has new kernel functions for paths of features, enabling the prediction of chemical compound activity. Initially, we extract all paths of features (star subgraphs) with certain lengths, and we encode them depending on their structure in the graphs. Then, we use these codes to construct two relationship matrices between those paths. These matrices contain common and different sub-paths between paths of stars. The number of sub-paths/paths for each compound is passed to the proposed kernel functions in the two-class SVM to predict the activity of chemical compounds. The relationship matrices created by the proposed algorithm help to reduce the number of features, which improves prediction accuracy. We apply the proposed algorithm with and without feature selection using two benchmark datasets, specifically, the monoamine oxidase (MAO) dataset and the AIDS antiviral screen database of active compound dataset, which have 68 and 2000 chemical compounds, respectively. We perform comparative experiments for the proposed kernel functions and many other two-class SVM prediction methods, and the results before feature selection show prediction accuracies of 94% and 99.5% for MAO and AIDS, respectively. After selection, the prediction accuracies are 96% and 99.5% for MAO and AIDS, respectively.

El-Atta, A. A. H., and A. E. Hassanien, "Two-class support vector machine with new kernel function based on paths of features for predicting chemical activity", Information Sciences, vol. 403: Elsevier, pp. 42–54, 2017. Abstract
n/a
Alaa Tharwat, T. Gaber, and A. E. Hassanien, "Two biometric approaches for cattle identification based on features and classifiers fusion", International Journal of Image Mining, vol. 1, no. 4: Inderscience Publishers (IEL), pp. 342–365, 2015. Abstract
n/a
Skowron, J. P. A. F., V. M. E. W. Orłowska, and R. S. W. Ziarko, Transactions on Rough Sets VII, , 2007. Abstract
n/a
Skowron, J. P. A. F., V. M. E. W. Orłowska, and R. S. W. Ziarko, Transactions on Rough Sets VII, , 2007. Abstract
n/a
Sahlol, A. T., A. A. Ewees, A. M. H.;, and A. E. Hassanien, "Training feedforward neural networks using Sine-Cosine algorithm to improve the prediction of liver enzymes on fish farmed on nano-selenite", 12th International Computer Engineering Conference (ICENCO),, Cairo, 28-29 Dec, 2016. Abstract

Analytical prediction of oxidative stress biomarkers in ecosystem provides an expressive result for many stressors. These oxidative stress biomarkers including superoxide dismutase, glutathione peroxidase and catalase activity in fish liver tissue were analyzed within feeding different levels of selenium nanoparticles. Se-nanoparticles represent a salient defense mechanism in oxidative stress within certain limits; however, stress can be engendered from toxic levels of these nanoparticles. For instance, prediction of the level of pollution and/or stressors was elucidated to be improved with different levels of selenium nanoparticles using the bio-inspired Sine-Cosine algorithm (SCA). In this paper, we improved the prediction accuracy of liver enzymes of fish fed by nano-selenite by developing a neural network model based on SCA, that can train and update the weights and the biases of the network until reaching the optimum value. The performance of the proposed model is better and achieved more efficient than other models.

Sahlol, A. T., A. A. Ewees, A. M. Hemdan, and A. E. Hassanien, "Training feedforward neural networks using Sine-Cosine algorithm to improve the prediction of liver enzymes on fish farmed on nano-selenite", Computer Engineering Conference (ICENCO), 2016 12th International: IEEE, pp. 35–40, 2016. Abstract
n/a
Salama, M. A., N. El-Bendary, and A. E. Hassanien, "Towards secure mobile agent based e-cash system", Proceedings of the First International Workshop on Security and Privacy Preserving in e-Societies: ACM, pp. 1–6, 2011. Abstract
n/a
Salama, M. A., N. El-Bendary, and A. E. Hassanien, "Towards secure mobile agent based e-cash system", Proceedings of the First International Workshop on Security and Privacy Preserving in e-Societies: ACM, pp. 1–6, 2011. Abstract
n/a
Alaa Tharwataf, Tarek Gaberb, V. S. Mohamed Mostaf Fouadc, and Aboul Ella Hassaniene, "Towards an Automated Zebrafish-based Toxicity Test Model Using Machine Learning", International Conference on Communications, management, and Information technology (ICCMIT'2015) Volume 65, 2015, Pages 643–651, Check Republica, 2015. Abstract

Zebrafish animal is considered as one of the most suitable animals to test toxicity of compounds due many features such as transparency and a large number of embryos produced in each mating. The main problem of the zebrafish-based toxicity test is the manual inspection of thousands of animals images in different phases and this is not feasible enough for the analysis, i.e. it is slow and may be inaccurate process. To help addressing this problem, in this paper, an automated classification of alive (healthy) and coagulant (died because of toxic compounds) zebrafish embryos are proposed. The embryos’ images are used to extract some features using the Segmentation-based Fractal Texture Analysis (SFTA) technique. The Rotation Forest classifier is then used to match between testing and training features (i.e. to classify alive and coagulant embryos). The experiments have proved that choosing threshold value of SFTA technique and the size of the rotation forest classifier have a great impact on the classification accuracy. With accuracy around 99.98%, the experimental results have showed that the proposed model is a very promising step toward a fully automated toxicity test during drug discovery.

Alaa Tharwat, T. Gaber, M. M. Fouad, V. Snasel, and A. E. Hassanien, "Towards an automated zebrafish-based toxicity test model using machine learning", Procedia Computer Science, vol. 65: Elsevier, pp. 643–651, 2015. Abstract
n/a