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Hassanien, A. E., "Intelligent data analysis of breast cancer based on rough set theory", International Journal on Artificial Intelligence Tools, vol. 12, no. 04: World Scientific Publishing Company, pp. 465–479, 2003. Abstract
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Alaa Tharwat, M. Elhoseny, A. E. Hassanien, and T. G. A. and Kumar, "Intelligent Bézier curve-based path planning model using Chaotic Particle Swarm Optimization algorithm", Cluster Computing, 2018. Abstract

Path planning algorithms have been used in different applications with the aim of finding a suitable collision-free path which satisfies some certain criteria such as the shortest path length and smoothness; thus, defining a suitable curve to describe path is essential. The main goal of these algorithms is to find the shortest and smooth path between the starting and target points. This paper makes use of a Bézier curve-based model for path planning. The control points of the Bézier curve significantly influence the length and smoothness of the path. In this paper, a novel Chaotic Particle Swarm Optimization (CPSO) algorithm has been proposed to optimize the control points of Bézier curve, and the proposed algorithm comes in two variants: CPSO-I and CPSO-II. Using the chosen control points, the optimum smooth path that minimizes the total distance between the starting and ending points is selected. To evaluate the CPSO algorithm, the results of the CPSO-I and CPSO-II algorithms are compared with the standard PSO algorithm. The experimental results proved that the proposed algorithm is capable of finding the optimal path. Moreover, the CPSO algorithm was tested against different numbers of control points and obstacles, and the CPSO algorithm achieved competitive results.

Fattah, M. A., N. Elbendary, M. A. Elsoud, H. Aboul Ella, and M. Tolba, "An Intelligent Approach for Galaxies Images Classification.", 13th IEEE International Conference on Hybrid Intelligent Systems (HIS13) Tunisia, pp. 168-173, 2013, Tunisia, , 4-6 Dec., 2013.
Fattah, M. A., N. Elbendary, M. A. Elsoud, H. Aboul Ella, and M. Tolba, "An Intelligent Approach for Galaxies Images Classification.", 13th IEEE International Conference on Hybrid Intelligent Systems (HIS13) Tunisia, pp. 168-173, 2013, Tunisia, , 4-6 Dec., 2013.
Fattah, M. A., N. El-Bendary, M. A. A. ELsoud, A. E. Hassanien, and M. F. Tolba, "An intelligent approach for galaxies images classification", Hybrid Intelligent Systems (HIS), 2013 13th International Conference on: IEEE, pp. 167–172, 2013. Abstract
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Hassanien, A. E., H. Al-Qaheri, G. Schaefer, and S. Banerjee, "Intelligent analysis of prostate ultrasound images", Nature & Biologically Inspired Computing, 2009. NaBIC 2009. World Congress on: IEEE, pp. 263–268, 2009. Abstract
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Hassanien, A. E., H. Al-Qaheri, G. Schaefer, and S. Banerjee, "Intelligent analysis of prostate ultrasound images", Nature & Biologically Inspired Computing, 2009. NaBIC 2009. World Congress on: IEEE, pp. 263–268, 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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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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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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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

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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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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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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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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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, 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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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
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