Publications

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Thesis
Kareem Kamal A.Ghany, and A. E. Hassanien, An Intelligent Hybrid Biometrics System, , Cairo, EGYPT , Cairo University , 2014. thesis_presentation.pdf
Journal Article
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 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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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., 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.

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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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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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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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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