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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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El-Baz, A. H., A. E. Hassanien, and G. Schaefer, "Identification of Diabetes Disease Using Committees of Neural Network-Based Classifiers", Machine Intelligence and Big Data in Industry: Springer International Publishing, pp. 65–74, 2016. Abstract
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Kumar, U. S., H. H. Inbarani, A. T. Azar, and A. E. Hassanien, "Identification of heart valve disease using bijective soft sets theory", International Journal of Rough Sets and Data Analysis (IJRSDA), vol. 1, no. 2: IGI Global, pp. 1–14, 2014. 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.

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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Hassanien, A. E., and J. M. H. Ali, "Image classification and retrieval algorithm based on rough set theory", South African Computer Journal, vol. 2003, no. 30: South African Computer Society (SAICSIT), pp. 9–16, 2003. Abstract
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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.

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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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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Hassanien, A. E., and H. T. A. K. A. H. A. S. H. I. M. NAKAJIMA, "Image Metamorphosis based on Elastic Body Spline and Snake Model", 映像情報メディア学会技術報告, vol. 21, no. 42: 一般社団法人映像情報メディア学会, pp. 25–30, 1997. Abstract
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Aboul-Ella, H., and M. Nakajima, "Image metamorphosis transformation of facial images based on elastic body splines ", Signal Processing , issue Volume 70, Issue 2,, pp. 129–137 , 1998. Abstracts01651684.gifWebsite

In this paper, we propose a new image metamorphosis algorithm which uses elastic body splines to generate warp functions for interpolating scattered data points. The spline is based on a partial differential equation proposed by Navier that describes the equilibrium displacement of an elastic body subjected to forces. The spline maps can be expressed as the linear combination of an affine transformation and a Navier spline. The proposed algorithm generates a smooth warp that reflects feature point correspondences. It is efficient in time complexity and smoothly interpolated morphed images with only a remarkably small number of specified feature points. The algorithm allows each feature point in the source image to be mapped to the corresponding feature point in the destination image. Once the images are warped to align the positions of features and their shapes, the in-between facial animation from two given facial images can be defined by cross dissolving the positions of correspondence features and their shapes and colors. We describe an efficient cross-dissolve algorithm for generating the in-between images

Hassanien, A. E., and M. Nakajima, "Image Morphing by using Thin Plate Spline Transformation and Snake Model", 電子情報通信学会技術研究報告. EID, 電子ディスプレイ, vol. 97, no. 527: 一般社団法人電子情報通信学会, pp. 43–48, 1998. Abstract
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Hassanien, A. E., and M. Nakajima:, "Image Morphing of Facial Images Transformation based on Navier Elastic Body Splines", IEEE Computer Animation (CA'98) , Philadelphia, Pennsylvania, USA,, 8-10 June,, 1998. Abstract

We propose an image morphing algorithm which uses Navier elastic body splines to generate warp functions for interpolating scattered data points. The spline is based on a partial differential equation proposed by Navier that describes the equilibrium displacement of an elastic body subjected to forces. The spline maps can be expressed as the linear combination of an affine transformation and a Navier interpolation spline. The proposed algorithm generates a smooth warp that reflects feature point correspondences. It is efficient in time complexity and smoothly interpolated morphed images with only a remarkably small number of specified feature points. The algorithm allows each feature point in the source image to be mapped to the corresponding feature point in the destination image. Once the images are warped to align the positions of features and their shapes, the in-between facial animation from two given facial images can be defined by cross dissolving the positions of correspondence features and their shapes and colors. We describe an efficient cross dissolve algorithm for generating the in-between images

Hassanien, A., and M. Nakajima, "Image morphing of facial images transformation based on navier elastic body splines", Computer Animation 98. Proceedings: IEEE, pp. 119–125, 1998. Abstract
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Hassanien, A. E., and M. Nakajima, "Image morphing with snake model and thin-plate spline interpolation", Voice, Video, and Data Communications: International Society for Optics and Photonics, pp. 407–416, 1997. Abstract
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Houssein, E. H., M. A. S. Ali, and A. E. Hassanien, "An image steganography algorithm using Haar Discrete Wavelet Transform with Advanced Encryption System", Computer Science and Information Systems (FedCSIS), 2016 Federated Conference on: IEEE, pp. 641–644, 2016. Abstract
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Awad, A. I., and A. E. Hassanien, "Impact of some biometric modalities on forensic science", Computational Intelligence in Digital Forensics: Forensic Investigation and Applications: Springer International Publishing, pp. 47–62, 2014. Abstract
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Hamdy, A., H. Hefny, M. A. Salama, A. E. Hassanien, and T. - H. Kim, "The importance of handling multivariate attributes in the identification of heart valve diseases using heart signals", Computer Science and Information Systems (FedCSIS), 2012 Federated Conference on: IEEE, pp. 75–79, 2012. Abstract
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Hamdy, A., H. Hefny, M. A. Salama, A. E. Hassanien, and T. - H. Kim, "The importance of handling multivariate attributes in the identification of heart valve diseases using heart signals", Computer Science and Information Systems (FedCSIS), 2012 Federated Conference on: IEEE, pp. 75–79, 2012. Abstract
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Hamdy, A., H. Hefny, M. A. Salama, A. E. Hassanien, and T. - H. Kim, "The importance of handling multivariate attributes in the identification of heart valve diseases using heart signals", Computer Science and Information Systems (FedCSIS), 2012 Federated Conference on: IEEE, pp. 75–79, 2012. Abstract
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Asad, A. H., A. T. Azar, M. M. M. Fouad, and A. E. Hassanien, "An improved ant colony system for retinal blood vessel segmentation", Computer Science and Information Systems (FedCSIS), 2013 Federated Conference on: IEEE, pp. 199–205, 2013. Abstract
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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", Journal of Computers and Electronics in Agriculture, vol. 136, issue 15, pp. 86-96 , 2017. AbstractWebsite

Plant diseases is one of the major bottlenecks in agricultural production that have bad effects on the economic of any country. Automatic detection of such disease could minimize these effects. Features selection is a usual pre-processing step used for automatic disease detection systems. It is an important process for detecting and eliminating noisy, irrelevant, and redundant data. Thus, it could lead to improve the detection performance. In this paper, an improved moth-flame approach to automatically detect tomato diseases was proposed. The moth-flame fitness function depends on the rough sets dependency degree and it takes into a consideration the number of selected features. The proposed algorithm used both of the power of exploration of the moth flame and the high performance of rough sets for the feature selection task to find the set of features maximizing the classification accuracy which was evaluated using the support vector machine (SVM). The performance of the MFORSFS algorithm was evaluated using many benchmark datasets taken from UCI machine learning data repository and then compared with feature selection approaches based on Particle Swarm Optimization (PSO) and Genetic Algorithms (GA) with rough sets. The proposed algorithm was then used in a real-life problem, detecting tomato diseases (Powdery mildew and early blight) where a real dataset of tomato disease were manually built and a tomato disease detection approach was proposed and evaluated using this dataset. The experimental results showed that the proposed algorithm was efficient in terms of Recall, Precision, Accuracy and F-Score, as long as feature size reduction and execution time.