Publications

Export 268 results:
Sort by: [ Author  (Asc)] Title 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   [Show ALL]
A
Alaa Tharwat, and A. E. Hassanien, " Chaotic Antlion Algorithm for Parameter Optimization of Support Vector Machine", Applied Intelligence , vol. in press, 2017. AbstractWebsite

Support Vector Machine (SVM) is one of the well-known classifiers. SVM parameters such as kernel
parameters and penalty parameter (C) significantly influences the classification accuracy. In this
paper, a novel Chaotic Antlion Optimization (CALO) algorithm has been proposed to optimize the
parameters of SVM classifier, so that the classification error can be reduced. To evaluate the proposed
model (CALO-SVM), the experiment adopted six standard datasets which are obtained from UCI machine
learning data repository. For verification, the results of the CALO-SVM algorithm are compared
with grid search, which is a conventional method of searching parameter values, standard Ant Lion
Optimization (ALO) SVM, and two well-known optimization algorithms: Genetic algorithm (GA)
and Particle Swarm Optimization (PSO). The experimental results proved that the proposed model is
capable to find the optimal values of the SVM parameters and avoids the local optima problem. The
results also demonstrated lower classification error rates compared with GA and PSO algorithms

Alaa Tharwat, Hani Mahdi, Adel El Hennawy, and A. E. Hassanien, "Face sketch recognition using local invariant features", Soft Computing and Pattern Recognition (SoCPaR), 2015 7th International Conference of: IEEE, pp. 117–122, 2015. Abstract
n/a
Alaa Tharwat, Hani Mahdi, Adel El Hennawy, and A. E. Hassanien, "Face sketch synthesis and recognition based on linear regression transformation and multi-classifier technique", The 1st International Conference on Advanced Intelligent System and Informatics (AISI2015), November 28-30, 2015, Beni Suef, Egypt: Springer International Publishing, pp. 183–193, 2016. Abstract
n/a
Alaa Tharwat, T. Gaber, and A. E. Hassanien, "One-dimensional vs. two-dimensional based features: Plant identification approach", Journal of Applied Logic: Elsevier, 2016. Abstract
n/a
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
Alaa Tharwat, Mahir M. Sharif, A. E. Hassanien, and H. A. Hefny, "Improving Enzyme Function Classification Performance Based on Score Fusion Method.", 10th International Conference Hybrid Artificial Intelligent System, Bilbao, Spain, 23 June, 2015.
Alaa Tharwat, T. Gaber, Abdelhameed Ibrahim, and A. E. Hassanien, "Linear Discriminant Analysis: A Detailed Tutorial", AI Communications, IOS press, 2017. linear_discreminate_analysisp_detailed_tutorails.pdf
Alaa Tharwat, A. E. Hassanien, and B. E. Elnaghi, "A BA-based algorithm for parameter optimization of Support Vector Machine", Pattern Recognition Letters: North-Holland, 2016. 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.

Ali, A. F., A. E. Hassanien, and V. Snasel, "Memetic Artificial Bee Colony for Integer Programming", International Conference on Advanced Machine Learning Technologies and Applications: Springer International Publishing, pp. 268–277, 2014. Abstract
n/a
Ali, A. F., A. E. Hassanien, and Václav Snášel, "The Nelder-Mead Simplex Method with Variables Partitioning for Solving Large Scale Optimization Problems.", Innovations in Bio-inspired Computing and Applications. Advances in Intelligent Systems and Computing(Springer) , Czech republic , Volume 237, pp. 271-284, 2013.
Ali, A. F., A. E. Hassanien, Václav Snášel, and M. F. Tolba, "A New Hybrid Particle Swarm Optimization with Variable Neighborhood Search for Solving Unconstrained Global Optimization Problems", Proceedings of the Fifth International Conference on Innovations in Bio-Inspired Computing and Applications IBICA 2014: Springer International Publishing, pp. 151–160, 2014. Abstract
n/a
Ali, J. M. H., and A. E. Hassanien, "A Human Iris Recognition Techniques to Enhance E-Security Environment Using Wavelet Trasform.", ICWI, pp. 572–579, 2003. Abstract
n/a
Ali, M. A. S., and A. E. Hassanien, "An observational study to identify the role of online communication in offline social networks ", The 2nd International Conference on Advanced Machine Learning Technologies and Applications , Egypt, November 17-19, , 2014.
Ali, M. A. S., G. I. Sayed, T. Gaber, A. E. Hassanien, V. Snasel, and L. F. Silva, "Detection of breast abnormalities of thermograms based on a new segmentation method", Computer Science and Information Systems (FedCSIS), 2015 Federated Conference on: IEEE, pp. 255–261, 2015. Abstract
n/a
Ali, J. M., and A. E. Hassanien, "An Iris Recognition System to Enhance E-Security, Advanced Modeling and Optimization", vol, vol. 5, pp. 93–104, 2003. Abstract

n/a

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
n/a
Ali, A. F., A. E. Hassanien, and Václav Snášel, "The nelder-mead simplex method with variables partitioning for solving large scale optimization problems", Innovations in Bio-inspired Computing and Applications: Springer International Publishing, pp. 271–284, 2014. Abstract
n/a
Ali, A. F., A. Mostafa, G. I. Sayed, M. A. Fattah, and A. E. Hassanien, "Nature Inspired Optimization Algorithms for CT Liver Segmentation", Medical Imaging in Clinical Applications: Springer International Publishing, pp. 431–460, 2016. Abstract
n/a
Ali, A. F., and A. E. Hassanien, "Minimizing molecular potential energy function using genetic Nelder-Mead algorithm", Computer Engineering & Systems (ICCES), 2013 8th International Conference on: IEEE, pp. 177–183, 2013. Abstract
n/a
Ali, J. M. H., and A. E. Hassanien, "PCNN for detection of masses in digital mammogram", Neural Network World, vol. 16, no. 2: Institute of Computer Science, pp. 129, 2006. Abstract
n/a
Ali, A. F., and A. - E. Hassanien, "A Survey of Metaheuristics Methods for Bioinformatics Applications", Applications of Intelligent Optimization in Biology and Medicine: Springer International Publishing, pp. 23–46, 2016. Abstract
n/a
Ali, J. M., and A. E. Hassanien, "Mathematical Morphology Approach for Enhancement Digital Mammography Images", IASTED, International Conference on Biomedical Engineering (BioMED2004) February, pp. 16–18, 2004. Abstract
n/a
Ali, J. M. H., and A. E. Hassanien, "An iris recognition system to enhance e-security environment based on wavelet theory", AMO-Advanced Modeling and Optimization, vol. 5, no. 2, pp. 93–104, 2003. Abstract
n/a
Ali, A. F., A. E. Hassanien, and V. Snasel, "Memetic Artificial Bee Colony for integer programming ", The 2nd International Conference on Advanced Machine Learning Technologies and Applications , Egypt, November 17-19, , 2014.
Tourism