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Hassanien, A. E., T. Gaber, U. Mokhtar, and H. Hefny, "An improved moth flame optimization algorithm based on rough sets for tomato diseases detection", Computers and Electronics in Agriculture, vol. 136: Elsevier, pp. 86–96, 2017. Abstract
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Fattah, M. A., M. I. Waly, M. A. A. ELsoud, A. E. Hassanien, M. F. Tolba, J. Platos, and G. Schaefer, "An improved prediction approach for progression of ocular hypertension to primary open angle glaucoma", Proceedings of the Fifth International Conference on Innovations in Bio-Inspired Computing and Applications IBICA 2014: Springer International Publishing, pp. 405–412, 2014. Abstract
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Eid, H. F., A. T. Azar, and A. E. Hassanien, "Improved real-time discretize network intrusion detection system", Proceedings of seventh international conference on bio-inspired computing: theories and applications (BIC-TA 2012): Springer India, pp. 99–109, 2013. Abstract
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Eid, H. F., A. T. Azar, and A. E. Hassanien, "Improved real-time discretize network intrusion detection system", Proceedings of seventh international conference on bio-inspired computing: theories and applications (BIC-TA 2012): Springer India, pp. 99–109, 2013. Abstract
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abd elaziz, M., and A. E. Hassanien, "An improved social spider optimization algorithm based on rough sets for solving minimum number attribute reduction problem,", Neural Computing and Applications, 2017 , 2017. AbstractWebsite

The minimum number attribute reduction problem is an important issue when dealing with huge amounts of data. The problem of minimum attribute reduction is formally known to be as an NP complete nonlinearly constrained optimization problem. Social spider optimization algorithm is a new meta-heuristic algorithm of the swarm intelligence field to global solution. The social spider optimization algorithm is emulates the behavior of cooperation between spiders based on the biological laws of the cooperative colony. Inspired by the social spiders, in this paper, an improved social spider algorithm for the minimal reduction problem was proposed. In the proposed algorithm, the fitness function depends on the rough sets dependency degree and it takes into a consideration the number of selected features. For each spider, the fitness function is computed and compared with the global best fitness value. If the current value is better, then the global best fitness is replaced with it and its position became the reduct set. Then, the position of each spider is updated according to its type. This process is repeated until the stopping criterion is satisfied. To validate the proposed algorithm, several real clinical medical datasets which are available from the UCI Machine Learning Repository were used to compute the performance of the proposed algorithm. The experimental results illustrate that the proposed algorithm is superior to state-of-the-art swarm-based in terms of classification accuracy while limiting number of features.

W. Ghonaim, N. I.Ghali, A. E. Hassanien, and S. Banerjee:, "An improvement of chaos-based hash function in cryptanalysis approach: An experience with chaotic neural networks and semi-collision attack", Memetic Computing Springer, vol. 5, issue 3, pp. 179-185, 2013. Website
Ghonaim, W., N. I. Ghali, A. E. Hassanien, and S. Banerjee, "An improvement of chaos-based hash function in cryptanalysis approach: An experience with chaotic neural networks and semi-collision attack", Memetic Computing, vol. 5, no. 3: Springer Berlin Heidelberg, pp. 179–185, 2013. Abstract
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Ghonaim, W., N. I. Ghali, A. E. Hassanien, and S. Banerjee, "An improvement of chaos-based hash function in cryptanalysis approach: An experience with chaotic neural networks and semi-collision attack", Memetic Computing, vol. 5, no. 3: Springer Berlin Heidelberg, pp. 179–185, 2013. Abstract
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Alaa Tharwat, M. M. Sharif, A. E. Hassanien, and H. A. Hefeny, "Improving Enzyme Function Classification Performance Based on Score Fusion Method", International Conference on Hybrid Artificial Intelligence Systems: Springer International Publishing, pp. 530–542, 2015. Abstract
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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.
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., 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., 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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Waleed Yamany, Eid Emary, A. E. Hassanien, G. Schaefer, and S. Y. Zhu, "An Innovative Approach for Attribute Reduction Using Rough Sets and Flower Pollination Optimisation", Procedia Computer Science, vol. 96: Elsevier, pp. 403–409, 2016. Abstract
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Hafez, A. I., Hossam M. Zawbaa, E. Emary, and A. E. H. Hamdi A. Mahmoud, "An innovative approach for feature selection based on chicken swarm optimization", 7th IEEE International Conference of Soft Computing and Pattern Recognition, , Kyushu University, Fukuoka, Japan,, November 13 - 1, 2015. Abstract

In this paper, a system for feature selection based
on chicken swarm optimization (CSO) algorithm is proposed.
Datasets ordinarily includes a huge number of attributes, with
irrelevant and redundant attribute. Commonly wrapper-based
approaches are used for feature selection but it always requires
an intelligent search technique as part of the evaluation function.
Chicken swarm optimization (CSO)is a new bio-inspired
algorithm mimicking the hierarchal order of the chicken
swarm and the behaviors of chicken swarm, including roosters,
hens and chicks, CSO can efficiently extract the chickens’
swarm intelligence to optimize problems. Therefore, CSO was
employed to feature selection in wrapper mode to search
the feature space for optimal feature combination maximizing
classification performance, while minimizing the number of
selected features. The proposed system was benchmarked
on 18 datasets drawn from the UCI repository and using
different evaluation criteria and proves advance over particle
swarm optimization (PSO) and genetic algorithms (GA) that
commonly used in optimization problems

Hafez, A. I., H. M. Zawbaa, E. Emary, H. A. Mahmoud, and A. E. Hassanien, "An innovative approach for feature selection based on chicken swarm optimization", Soft Computing and Pattern Recognition (SoCPaR), 2015 7th International Conference of: IEEE, pp. 19–24, 2015. Abstract
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Sara Yassen, T. Gaber, and A. E. Hassanien, "Integer Wavelet Transform for Thermal Image Authentication", 7th IEEE International Conference of Soft Computing and Pattern Recognition, , Kyushu University, Fukuoka, Japan, , November 13 - 15, 2015. Abstract

Thermal imaging is a technology with property of
seeing objects in the darkness. Such property makes this technology
very important tool for security and surveillance applications.
In this paper, a thermal image authentication technique using
hash function is proposed. In this technique, the thermal images
are used as cover images and bits from secret data (i.e. messages
or images) are then hidden in the cover images. This is achieved
by using the hash function and IntegerWavelet Transform (IWT).
1, 2 and 3 bits per bytes have been hidden in both horizontal
and vertical components of wavelet transform. The proposed
technique has been evaluated based on mean square error (MSE),
peak signal to noise ratio (PSNR), image fidelity (IF) and standard
deviation (SD). The results have shown better performance of the
proposed technique comparing with the most related work.

Yassen, S., T. Gaber, and A. E. Hassanien, "Integer wavelet transform for thermal image authentication", Soft Computing and Pattern Recognition (SoCPaR), 2015 7th International Conference of: IEEE, pp. 13–18, 2015. Abstract
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Ahmed H. Asad, A. T. Azar, and A. E. Hassanien, "Integrated Features Based on Gray-Level and Hu Moment Invariants with Ant Colony System for Retinal Blood Vessels Segmentation", International Journal of Systems Biology and Biomedical Technologies, , vol. 1, issue 4, pp. 61-74, 2012. AbstractWebsite

Abnormality detection plays an important role in many real-life applications. Retinal vessel segmentation
algorithms are the critical components of circulatory blood vessel Analysis systems for detecting the various
abnormalities in retinal images. Traditionally, the vascular network is mapped by hand in a time-consuming
process that requires both training and skill. Automating the process allows consistency, and most importantly, frees up the time that a skilled technician or doctor would normally use for manual screening. Several studies were carried out on the segmentation of blood vessels in general; however, only a small number of them were associated to retinal blood vessels. In this paper, an approach for segmenting retinal blood vessels is
proposed using only ant colony system. Eight features are selected for the developed system; four are based on gray-level and the other features on Hu moment-invariants. The features are directly computed from values of image pixels, so they take about 90 seconds in computation. The performance of the proposed structure is evaluated in terms of accuracy, sensitivity and specificity. The results showed that the overall accuracy and sensitivity of the presented approach achieved 90.28% and 74%, respectively

Asad, A. H., A. T. Azar, and A. E. Hassanien, "Integrated features based on gray-level and hu moment-invariants with ant colony system for retinal blood vessels segmentation", International Journal of Systems Biology and Biomedical Technologies (IJSBBT), vol. 1, no. 4: IGI Global, pp. 60–73, 2012. Abstract
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Asad, A. H., A. T. Azar, and A. E. Hassanien, "Integrated features based on gray-level and hu moment-invariants with ant colony system for retinal blood vessels segmentation", International Journal of Systems Biology and Biomedical Technologies (IJSBBT), vol. 1, no. 4: IGI Global, pp. 60–73, 2012. Abstract
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Hassanien, A. E., "Intelligence techniques for prostate ultrasound image analysis", International Journal of Hybrid Intelligent Systems, vol. 6, no. 3: IOS Press, pp. 155–167, 2009. Abstract
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Hassanien, A. E., "Intelligence techniques for prostate ultrasound image analysis", International Journal of Hybrid Intelligent Systems, vol. 6, no. 3: IOS Press, pp. 155–167, 2009. Abstract
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El Emary, I. M. M., and A. E. Hassanien, "Intelligent agent in telecommunication systems", Telecommunication Systems, vol. 46, no. 3: Springer, pp. 191–193, 2011. Abstract
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El Emary, I. M. M., and A. E. Hassanien, "Intelligent agent in telecommunication systems", Telecommunication Systems, vol. 46, no. 3: Springer, pp. 191–193, 2011. Abstract
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