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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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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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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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Tobin, K. W., E. Chaum, J. Gregor, T. P. Karnowski, J. R. Price, and J. Wall, "Image Informatics for Clinical and Preclinical Biomedical Analysis", Computational Intelligence in Medical Imaging: Techniques and Applications: CRC Press, pp. 239, 2009. Abstract
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Sayed, G. I., and A. E. Hassanien, "Interphase cells removal from metaphase chromosome images based on meta-heuristic Grey Wolf Optimizer", Computer Engineering Conference (ICENCO), 2015 11th International: IEEE, pp. 261–266, 2015. Abstract
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Sayed, G. I., and A. E. Hassanien, "Interphase cells removal from metaphase chromosome images based on meta-heuristic grey wolf optimizer", IEEE iInternational Computer Engineering Conference - ICENCO , Cairo, 30 Dec, 2015.
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.

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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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. 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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and ella S. Udhaya Kumar, H. Hannah Inbarani, A. T. A. A. H., "Identification of Heart Valve Disease using Bijective, Soft sets Theory ", International Journal of Rough Sets and Data Analysis, vol. 1, issue 2, pp. , 1(2), 1-13, 2014. Abstract

Major complication of heart valve diseases is congestive heart valve failure. The heart is of essential significance to human beings. Auscultation with a stethoscope is considered as one of the techniques used in the analysis of heart diseases. Heart auscultation is a difficult task to determine the heart condition and requires some superior training of medical doctors. Therefore, the use of computerized techniques in the diagnosis of heart sounds may help the doctors in a clinical environment. Hence, in this study computer-aided heart sound diagnosis is performed to give support to doctors in decision making. In this study, a novel hybrid Rough-Bijective soft set is developed for the classification of heart valve diseases. A rough set (Quick Reduct) based feature selection technique is applied before classification for increasing the classification accuracy. The experimental results demonstrate that the overall classification accuracy offered by the employed Improved Bijective soft set approach (IBISOCLASS) provides higher accuracy compared with other classification techniques including hybrid Rough-Bijective soft set (RBISOCLASS), Bijective soft set (BISOCLASS), Decision table (DT), Naïve Bayes (NB) and J48.

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Reham Gharbia, A. T. Azar, A. E. Baz, and A. E. Hassanien, "Image fusion techniques in remote sensing", arXiv preprint arXiv:1403.5473, 2014. Abstract
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Moustafa Zeina, A. A. Fatma Yakouba, A. E. Hassanien, and V. Snasel, "Identifying Circles of Relations from Smartphone Photo Gallery", International Conference on Communications, management, and Information technology (ICCMIT'2015) Volume 65, 2015, Pages 582–591, Ostrava, Czech Republic, 2015. Abstract

Geotagged photos carry hidden data about the surrounding area, and the owner of the photo. Moreover; Geotagged photos have background information about the user, where the alternative resources of Geo-spatial data lack background information. In this study, we propose identification for the circles of relations of the smartphone user from Geotagged photos. The proposed solution mainly depends on a framework, which is based on smartphone photo gallery. The framework extracts a degree of relation between smartphone user and circles of relations entities. Circles of relations incorporate closest people, places, where the participant visits, and interests. The circles of relations are represented in a social graph, which shows the clusters of social relations and interests of smartphone user. The social graph clarifies the nature and the degree of the relations for the participants. The results of framework introduced the relation between the level of variety of participant social relations, and the degree of relations.

Moustafa Zein, F. Yakoub, A. Adl, A. E. Hassanien, and V. Snasel, "Identifying Circles of Relations from Smartphone Photo Gallery", Procedia Computer Science, vol. 65: Elsevier, pp. 582–591, 2015. Abstract
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Mokhtar, U., M. A. S. Ali, A. E. Hassanien, and H. Hefny, "Identifying two of tomatoes leaf viruses using support vector machine", Information Systems Design and Intelligent Applications: Springer India, pp. 771–782, 2015. Abstract
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Mahmoud, S., N. El-Bendary, M. A. Mahmood, and A. E. Hassanien, "An intelligent recommender system for drinking water quality", Hybrid Intelligent Systems (HIS), 2013 13th International Conference on: IEEE, pp. 285–290, 2013. Abstract
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Mahmoud, S., N. El-Bendary, M. A. Mahmood, and A. E. Hassanien, "An Intelligent Recommender system for drinking water quality", . 13th IEEE International Conference on Hybrid Intelligent Systems |(HIS13) . pp. 286-291, 2013, Tunisia, , 4-6 Dec, 2013.
Mahmoud, R., N. El-Bendary, H. M. O. Mokhtar, and A. E. Hassanien, "ICF based automation system for spinal cord injuries rehabilitation", Computer Engineering & Systems (ICCES), 2014 9th International Conference on: IEEE, pp. 192–197, 2014. Abstract
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Mahmood A. Mahmood, N. El-Bendary, Jan Platoš, A. E. Hassanien, and H. A. Hefny, "An Intelligent Multi-agent Recommender System. ", Innovations in Bio-inspired Computing and Applications. Advances in Intelligent Systems and Computing(Springer) , Czech republic , 2013.
Mahmood, M. A., N. El-Bendary, Jan Platoš, A. E. Hassanien, and H. A. Hefny, "An Intelligent Multi-agent Recommender System", Innovations in Bio-inspired Computing and Applications: Springer International Publishing, pp. 201–213, 2014. Abstract
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Mahmood, M. A., N. El-Bendary, A. E. Hassanien, and H. A. Hefny, "An Intelligent Multi-Agent Recommender System using Rough Mereology", In Proceedings of the 4th International Conference on Innovations in Bio-inspired Computing and Applications. Advances in Intelligent Systems and Computing (Springer) Volume 237, pp 201-213, 2013. Abstract

This article presents a Multi-Agent approach for handling the problem of recommendation. The proposed system works via two main agents; namely, the matching agent and the recommendation agent. Experimental results showed that the proposed rough mereology based Multi-agent system for solving the recommendation problem is scalable and has possibilities for future modification and adaptability to other problem domains. Moreover, it succeeded in reducing the information overload while recommending relevant decisions to users. The system achieved high accuracy in ranking using users profile and information system profiles. The resulted value of the Mean Absolute Error (MAE) is acceptable compared to other recommender systems applied other computational intelligence approaches.

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Liu, H., Y. Ji, and A. E. Hassanien, "Image Color Transfer Approach by Analogy with Taylor Expansion", International Journal of System Dynamics Applications (IJSDA), vol. 2, no. 2: IGI Global, pp. 43–54, 2013. Abstract
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Liu, H., Y. Ji, and A. E. Hassanien, "Image Color Transfer Approach by Analogy with Taylor Expansion. vol. 2 issue 2, 2013", International Journal of System Dynamics Applications,, vol. 2, issue 2, pp. 43-54, 2013. AbstractWebsite

The Taylor expansion has shown in many fields to be an extremely powerful tool. In this paper, we investigated image features and their relationships by analogy with Taylor expansion. The kind of expansion could be helpful for us to analyze image feature and engraftment, such as transferring color between images. By analogy with Taylor expansion, we designed the image color transfer algorithm by the first and second-order information. The luminance histogram represents the first-order information of image, and the co-occurrence matrix represents the second-order information of image. Some results illustrate our algorithm is effective. In our study, each polynomial in our analogy Taylor expansion of images is considered as one of image features, which makes us re-understand images and its features. It provided us a cue that the features of image, such as color, texture, dimension, time series, would be not isolated but mutual relational based on image expansion.