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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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Sami, M., N. El-Bendary, and A. E. Hassanien, "Multi-Class Image Annotation Approach using Particle Swarm Optimization.", The IEEE International Conference on Hybrid Intelligent Systems (HIS2012).. , Pune. India, 4-7 Dec. 2012,, pp. 103 - 108., 2012. Abstract

This paper presents an automatic image annotation approach for region labeling. The proposed approach is based on multi-class k-nearest neighbor, K-means, and particle swarm optimization 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 image regions from different classes. Each input image is segmented using the normalized cuts segmentation algorithm in order to subsequently create a descriptor for each segment. Particle swarm optimization algorithm is employed as a search strategy to identify an optimal feature subset. Experimental results and comparative performance evaluation, for results obtained from the proposed particle swarm optimization based approach and another support vector machine based approach presented in previous work, demonstrate that the proposed particle swarm optimization based approach outperforms the support vector machine based one, regarding annotation accuracy, for the used dataset.

Sami, M., N. El-Bendary, and A. E. Hassanien, "Automatic image annotation via incorporating Naive Bayes with particle swarm optimization", Information and Communication Technologies (WICT), 2012 World Congress on: IEEE, pp. 790–794, 2012. Abstract
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Sami, M., A. E. Hassanien, N. El-Bendary, and R. C. Berwick, "Incorporating random forest trees with particle swarm optimization for automatic image annotation", Computer Science and Information Systems (FedCSIS), 2012 Federated Conference on: IEEE, pp. 763–769, 2012. Abstract
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Sami, M., N. El-Bendary, R. C. Berwick, and A. E. Hassanien, "Incorporating Random Forest Trees with Particle Swarm Optimization for Automatic Image Annotation", IEEE Federated Conference on Computer Science and Information Systems, pp. 791–797, Wroclaw - Poland, 9-13 Sept, 2012. Abstractincorporating_random_forest_trees_with.pdf

This paper presents an automatic image annotation approach that integrates the random forest classifier with particle swarm optimization algorithm for classes’ scores weighting.
The proposed hybrid approach refines the output of multiclass classification that is based on the usage of random forest classifier for automatically labeling images with a number of
words. Each input image is segmented using the normalized cuts segmentation algorithm in order to create a descriptor for each segment. Images feature vectors are clustered into K clusters and a random forest classifier is trained for each cluster. Particle swarm optimization algorithm is employed as a search strategy to identify an optimal weighting for classes’ scores from random forest classifiers. The proposed approach has been applied on Corel5K benchmark dataset. Experimental results and comparative performance evaluation, for results obtained from the proposed approach and other related researches, demonstrate that the proposed approach outperforms the performance
of other approaches, considering annotation accuracy, for the
experimented dataset.

Sami, M., N. El-Bendary, A. E. Hassanien, and G. Schaefer, "Hybrid intelligent automatic image annotation using machine learning", The 2011 Online Conference on Soft Computing in Industrial Applications WWW (WSC16), 2011. Abstract
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Sami, M., N. El-Bendary, and A. E. Hassanien, "Multi-class image annotation approach using particle swarm optimization", Hybrid Intelligent Systems (HIS), 2012 12th International Conference on: IEEE, pp. 103–108, 2012. Abstract
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Sami, M., N. El-Bendary, and A. E. Hassanien, "Automatic image annotation via incorporating Naive Bayes with particle swarm optimization", Information and Communication Technologies (WICT), 2012 World Congress on: IEEE, pp. 790–794, 2012. Abstract
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Sami, M., N. El-Bendary, and A. E. Hassanien, "Automatic image annotation via incorporating Naive Bayes with particle swarm optimization", Information and Communication Technologies (WICT), 2012 World Congress on: IEEE, pp. 790–794, 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", 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., A. E. Hassanien, N. El-Bendary, and R. C. Berwick, "Incorporating random forest trees with particle swarm optimization for automatic image annotation", Computer Science and Information Systems (FedCSIS), 2012 Federated Conference on: IEEE, pp. 763–769, 2012. Abstract
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Sami, M., N. El-Bendary, and A. E. Hassanien, "Automatic image annotation via incorporating Naive Bayes with particle swarm optimization ", World Congress on Information and Communication Technologies (WICT), pp. 790 - 794, India, Oct. 30 2012-Nov. Abstract

This paper presents an automatic image annotation approach that integrates the Naive Bayes classifier with particle swarm optimization algorithm for classes' probabilities weighting. The proposed hybrid approach refines the output of multi-class classification that is based on the usage of Naive Bayes classifier for automatically labeling images with a number of words. Each input image is segmented using the normalized cuts segmentation algorithm in order to create a descriptor for each segment. One Naive Bayes classifier is trained for all the classes. Particle swarm optimization algorithm is employed as a search strategy in order to identify an optimal weighting for classes probabilities from Naive Bayes classifier. The proposed approach has been applied on Corel5K benchmark dataset. Experimental results and comparative performance evaluation, for results obtained from the proposed approach and other related researches, demonstrate that the proposed approach outperforms the performance of the other approaches, considering annotation accuracy, for the experimented dataset.

Sami, M., N. El-Bendary, A. E. Hassanien, and G. Schaefer, "Hybrid intelligent automatic image annotation using machine learning", The 2011 Online Conference on Soft Computing in Industrial Applications WWW (WSC16), 2011. Abstract
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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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Saleh Esmate Aly, H. I. Elshazly, A. F. Ali, H. A. Hussein, A. E. Hassanien, G. Schaefer, and M. A. R. Ahad, "Molecular classification of Newcastle disease virus based on degree of virulence", Informatics, Electronics & Vision (ICIEV), 2014 International Conference on: IEEE, pp. 1–5, 2014. Abstract
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Saleh Esmate Aly, H. I. Elshazly, A. F. Ali, H. A. Hussein, G. Schaefer, and M. A. R. Ahad, "Molecular classification of Newcastle disease virus based on degree of virulence", The 3rd Intl. Conf. on Informatics, Electronics & Vision. (ICIEV2014), Dhaka - Bangladesh, 23-24 May , 2014.
Salama, M. A., A. E. Hassanien, and K. Revett, "Employment of neural network and rough set in meta-learning.", Memetic Computing- Springer, vol. 5, issue 3, pp. 165-177, 2013. Website
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
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Salama, M. A., N. El-Bendary, A. E. Hassanien, K. Revett, and A. A. Fahmy, "Interval-based attribute evaluation algorithm", Computer Science and Information Systems (FedCSIS), 2011 Federated Conference on: IEEE, pp. 153–156, 2011. Abstract
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Salama, M., M. Panda, Y. Elbarawy, A. E. Hassanien, and A. Abraham, "Computational Social Networks: Security and Privacy", Computational Social Networks: Springer London, pp. 3–21, 2012. Abstract
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Salama, M. A., A. E. Hassanien, and A. A. Fahmy, "Reducing the influence of normalization on data classification", Computer Information Systems and Industrial Management Applications (CISIM), 2010 International Conference on: IEEE, pp. 609–613, 2010. Abstract
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Salama, M. A., A. E. Hassanien, and A. M. Alimi, "Formal concept analysis approach for comparison between Mutagenicity and Carcinogenicity in Cheminformatics", Hybrid Intelligent Systems (HIS), 2013 13th International Conference on: IEEE, pp. 267–272, 2013. Abstract
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Salama, M. A., A. E. Hassanien, and A. A. Fahmy, "Deep belief network for clustering and classification of a continuous data", Signal Processing and Information Technology (ISSPIT), 2010 IEEE International Symposium on: IEEE, pp. 473–477, 2010. Abstract
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Salama, M. A., A. E. Hassanien, and K. Revett, "Employment of neural network and rough set in meta-learning", Memetic Computing, vol. 5, no. 3: Springer Berlin Heidelberg, pp. 165–177, 2013. Abstract
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Salama, M. A., A. Mostafa, and A. E. Hassanien, "The prediction of virus mutation using neural networks and rough set techniques", . EURASIP J. Bioinformatics and Systems Biology , vol. 10, 2016. AbstractWebsite

Viral evolution remains to be a main obstacle in the effectiveness of antiviral treatments. The ability to predict this evolution will help in the early detection of drug-resistant strains and will potentially facilitate the design of more efficient antiviral treatments. Various tools has been utilized in genome studies to achieve this goal. One of these tools is machine learning, which facilitates the study of structure-activity relationships, secondary and tertiary structure evolution prediction, and sequence error correction. This work proposes a novel machine learning technique for the prediction of the possible point mutations that appear on alignments of primary RNA sequence structure. It predicts the genotype of each nucleotide in the RNA sequence, and proves that a nucleotide in an RNA sequence changes based on the other nucleotides in the sequence. Neural networks technique is utilized in order to predict new strains, then a rough set theory based algorithm is introduced to extract these point mutation patterns. This algorithm is applied on a number of aligned RNA isolates time-series species of the Newcastle virus. Two different data sets from two sources are used in the validation of these techniques. The results show that the accuracy of this technique in predicting the nucleotides in the new generation is as high as 75 %. The mutation rules are visualized for the analysis of the correlation between different nucleotides in the same RNA sequence.