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Wahid, R., N. I. Ghali, H. S. Own, T. - H. Kim, and A. ella Hassanien., " A Gaussian Mixture Models Approach to Human Heart Signal Verification Using Different Feature Extraction Algorithms ", International Conference on Bio-Science and Bio-Technology (BSBT2012),, , Kangwondo, Korea, , Springer, Heidelberg , pp. pp. 16--24, 2012. Abstract3530016.pdf

In this paper the possibility of using the human heart signal
feature for human verification is investigated. The presented approach
consists of two different robust feature extraction algorithms with a specified
configuration in conjunction with Gaussian mixture modeling. The
similarity of two samples is estimated by measuring the difference between
their negative log-likelihood of the features. To evaluate the performance
and the uniqueness of the presented approach tests using a
high resolution auscultation digital stethoscope are done for nearly 80
heart sound samples. The experimental results obtained show that the
accuracy offered by the employed Gaussian mixture modeling reach up
to 100% for 7 samples using the first feature extraction algorithm and
6 samples using the second feature extraction algorithm and varies with
average 85%.

I
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 ", 20th International Conference on Knowledge Based and Intelligent Information and Engineering Systems, KES2016,, , United Kingdom., 5-7 September , 2016. Abstract

Optimal search is a major challenge for wrapper-based attribute reduction. Rough sets have been used with much success, but current hill-climbing rough set approaches to attribute reduction are insufficient for finding optimal solutions. In this paper, we propose an innovative use of an intelligent optimisation method, namely the flower search algorithm (FSA), with rough sets for attribute reduction. FSA is a relatively recent computational intelligence algorithm, which is inspired by the pollination process of flowers. For many applications, the attribute space, besides being very large, is also rough with many different local minima which makes it difficult to converge towards an optimal solution. FSA can adaptively search the attribute space for optimal attribute combinations that maximise a given fitness function, with the fitness function used in our work being rough set-based classification. Experimental results on various benchmark datasets from the UCI repository confirm our technique to perform well in comparison with competing methods.

C
Elshazly, H. I., A. M. Elkorany, A. E. Hassanien, and M. Waly, " Chronic eye disease diagnosis using ensemble-based classifier", The second International Conference on Engineering and Technology (ICET 2014) , German Uni - Cairo Egypt, 19 Apr - 20 Apr , 2014.
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Walaa Elmasry, Hossam Moftah, Walaa Elmasry, N. Elbendary, and A. E. Hassanien, " Performance Evaluation of Computed Tomography Liver Image Segmentation Approachers", The IEEE International Conference on Hybrid Intelligent Systems (HIS2012). , Pune. India., 4-7 Dec. 2012,, pp. 109 - 114, 2012. Abstract

This paper presents and evaluates the performance of two well-known segmentation approaches that were applied on liver computed tomography (CT) images. The two approaches are K-means and normalized cuts. An experiment was applied on ten liver CT scan images, with reference segmentations, in order to test the performance of the two approaches. Experimental results were compared using an evaluation measure that highlights segmentation accuracy. Based on the obtained results in this study, it has been observed that K-means clustering algorithm outperformed normalized cuts segmentation algorithm for cases where region of interest depicts a closed shape, while, normalized cuts algorithm obtained better results with non-circular clusters. Moreover, for K-means clustering, different initial partitions can result in different final clusters.

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Waleed Yamany, H. M. Zawbaa, Eid Emary, and A. E. Hassanien, "Attribute reduction approach based on modified flower pollination algorithm", Fuzzy Systems (FUZZ-IEEE), 2015 IEEE International Conference on: IEEE, pp. 1–7, 2015. Abstract
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B
Jui, S. - L., S. Zhang, W. Xiong, F. Yu, M. Fu, D. Wang, and A. E. H. K. and Xiao, "Brain MR Image Tumor Segmentation with 3-Dimensional Intracranial Structure Deformation Features", IEEE Intelligent systems , issue Accepted , 2015. Abstract

Abstract—Extraction of relevant features is of significant importance for brain tumor segmentation systems. In this paper, with the objective of improving brain tumor segmentation accuracy, we present an improved feature extraction component to take advantage of the correlation between intracranial structure deformation and the compression from brain tumor growth. Using 3-dimensional non-rigid registration and deformation modeling techniques, the component is capable of measuring lateral ventricular (LaV) deformation in the volumetric magnetic resonance (MR) images. By verifying the location of the extracted LaV deformation feature data and applying the features on brain tumor segmentation with widely used classification algorithms, the proposed component is evaluated qualitatively and quantitatively with promising results on 11 datasets comprising real patient and simulated images.

Shang-Ling, S. Z. Jui, W. Xiong, F. Yu, M. Fu, D. Wang, A. E. Hassanien, and K. Xiao, "Brain MR Image Tumor Segmentation with 3-Dimensional Intracranial Structure Deformation Features", IEEE Intelligent Systems, vol. 31, pp. 66-76, 2016. AbstractWebsite

Extraction of relevant features is of significant importance for brain tumor segmentation systems. To improve brain tumor segmentation accuracy, the authors present an improved feature extraction component that takes advantage of the correlation between intracranial structure deformation and the compression resulting from brain tumor growth. Using 3D nonrigid registration and deformation modeling techniques, the component measures lateral ventricular (LaV) deformation in volumetric magnetic resonance images. By verifying the location of the extracted LaV deformation feature data and applying the features on brain tumor segmentation with widely used classification algorithms, the authors evaluate the proposed component qualitatively and quantitatively with promising results on 11 datasets comprising real and simulated patient images.

Jui, S. - L., S. Zhang, W. Xiong, F. Yu, M. Fu, D. Wang, A. E. Hassanien, and K. Xiao, "Brain MR image tumor segmentation with 3-Dimensional intracranial structure deformation features", IEEE Intell. Syst. submitted, under review, 2015. Abstract
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Jui, S. - L., S. Zhang, W. Xiong, F. Yu, M. Fu, D. Wang, A. E. Hassanien, and K. Xiao, "Brain MRI Tumor Segmentation with 3D Intracranial Structure Deformation Features", IEEE Intelligent Systems, vol. 31, no. 2: IEEE, pp. 66–76, 2016. Abstract
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C
Elshazly, H. I., M. Waly, A. M. Elkorany, and A. E. Hassanien, "Chronic eye disease diagnosis using ensemble-based classifier", Engineering and Technology (ICET), 2014 International Conference on: IEEE, pp. 1–6, 2014. Abstract
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Wu, Z. Q., J. Jiang, and Y. H. Peng, "Computational Intelligence on Medical Imaging with Artificial Neural Networks in", Computational intelligence in medical imaging techniques and applications, 2009. Abstract
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E
Watchareeruetai, U., T. Matsumoto, Y. Takeuchi, H. Kudo, and N. Ohnishi, "Efficient construction of image feature extraction programs by using linear genetic programming with fitness retrieval and intermediate-result caching", Foundations of Computational Intelligence Volume 4: Springer Berlin Heidelberg, pp. 355–375, 2009. Abstract
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Watchareeruetai, U., T. Matsumoto, Y. Takeuchi, H. Kudo, and N. Ohnishi, "Efficient construction of image feature extraction programs by using linear genetic programming with fitness retrieval and intermediate-result caching", Foundations of Computational Intelligence Volume 4: Springer Berlin Heidelberg, pp. 355–375, 2009. Abstract
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G
Wahid, R., N. I. Ghali, H. S. Own, T. - H. Kim, and A. E. Hassanien, "A Gaussian mixture models approach to human heart signal verification using different feature extraction algorithms", Computer Applications for Bio-technology, Multimedia, and Ubiquitous City: Springer Berlin Heidelberg, pp. 16–24, 2012. Abstract
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Wahid, R., N. I. Ghali, H. S. Own, T. - H. Kim, and A. E. Hassanien, "A Gaussian mixture models approach to human heart signal verification using different feature extraction algorithms", Computer Applications for Bio-technology, Multimedia, and Ubiquitous City: Springer Berlin Heidelberg, pp. 16–24, 2012. Abstract
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H
I.Ghali, N., R. Wahid, and A. E. Hassanien, "Heart Sounds Human Identification and Verification Approaches using Vector Quantization and Gaussian Mixture Models", International Journal of Systems Biology and Biomedical Technologies, , vol. 1, issue 4, pp. 75-88, 2012. Abstract

In this paper the possibility of using the human heart sounds as a human print is investigated. To evaluate the performance and the uniqueness of the proposed approach, tests using a high resolution auscultation digital stethoscope are done for nearly 80 heart sound samples. The verification approach consists of a robust feature extraction with a specified configuration in conjunction with Gaussian mixture modeling. The similarity of two samples is estimated by measuring the difference between their log-likelihood similarities of the features. The experimental results obtained show that the overall accuracy offered by the employed Gaussian mixture modeling reach up to 85%. The identification approach consists of a robust feature extraction with a specified configuration in conjunction with LBG-VQ. The experimental results obtained show that the overall accuracy offered by the employed LBG-VQ reach up to 88.7%

Ghali, N. I., R. Wahid, and A. E. Hassanien, "Heart Sounds Human Identification and Verification Approaches using Vector Quantization and Gaussian Mixture Models", International Journal of Systems Biology and Biomedical Technologies (IJSBBT), vol. 1, no. 4: IGI Global, pp. 74–87, 2012. Abstract
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Ghali, N. I., R. Wahid, and A. E. Hassanien, "Heart Sounds Human Identification and Verification Approaches using Vector Quantization and Gaussian Mixture Models", International Journal of Systems Biology and Biomedical Technologies (IJSBBT), vol. 1, no. 4: IGI Global, pp. 74–87, 2012. Abstract
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Zawbaa, H. M., A. E. Hassanien, E. Emary, Waleed Yamany, and B. PARV, "Hybrid flower pollination algorithm with rough sets for feature selection", Computer Engineering Conference (ICENCO), 2015 11th International: IEEE, pp. 278–283, 2015. 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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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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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
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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Waleed Yamany, M. O. H. A. M. M. E. D. FAWZY, Alaa Tharwat, and A. E. Hassanien, "Moth-flame optimization for training multi-layer perceptrons", Computer Engineering Conference (ICENCO), 2015 11th International: IEEE, pp. 267–272, 2015. Abstract
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Dai, G., Z. Wang, C. Yang, H. Liu, A. E. Hassanien, and W. Yang, "A multi-granularity rough set algorithm for attribute reduction through particles particle swarm optimization", Computer Engineering Conference (ICENCO), 2015 11th International: IEEE, pp. 303–307, 2015. Abstract
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