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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%.

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Tharwatd, A., T. Gaber, and A. E. Hassanien, " One-dimensional vs. two-dimensional based features: Plant identification approach, ", Journal of Applied Logic , vol. Available online 15 November 2017 , 2017. AbstractWebsite

The number of endangered species has been increased due to shifts in the agricultural production, climate change, and poor urban planning. This has led to investigating new methods to address the problem of plant species identification/classification. In this paper, a plant identification approach using 2D digital leaves images was proposed. The approach used two features extraction methods based on one-dimensional (1D) and two-dimensional (2D) and the Bagging classifier. For the 1D-based methods, Principal Component Analysis (PCA), Direct Linear Discriminant Analysis (DLDA), and PCA + LDA techniques were applied, while 2DPCA and 2DLDA algorithms were used for the 2D-based method. To classify the extracted features in both methods, the Bagging classifier, with the decision tree as a weak learner was used. The five variants, i.e. PCA, PCA + LDA, DLDA, 2DPCA, and 2DLDA, of the approach were tested using the Flavia public dataset which consists of 1907 colored leaves images. The accuracy of these variants was evaluated and the results showed that the 2DPCA and 2DLDA methods were much better than using the PCA, PCA + LDA, and DLDA. Furthermore, it was found that the 2DLDA method was the best one and the increase of the weak learners of the Bagging classifier yielded a better classification accuracy. Also, a comparison with the most related work showed that our approach achieved better accuracy under the same dataset and same experimental setup.

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El-Bendary, N., A. E. Hassanien, J. Sedano, O. S. Soliman, and N. I. Ghali, "$μ$TESLA-based secure routing protocol for wireless sensor networks", Proceedings of the First International Workshop on Security and Privacy Preserving in e-Societies: ACM, pp. 34–39, 2011. Abstract
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Hassanien, A. E., N. El-Bendary, J. Sedano, O. S. Soliman, and N. I. Ghali, "$μ$TESLA-based secure routing protocol for wireless sensor networks", The First ACM International Workshop on Security and Privacy Preserving in e-Societies, Lebanon , 7 June, 2011. Abstract

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El-Bendary, N., A. E. Hassanien, J. Sedano, O. S. Soliman, and N. I. Ghali, "$μ$TESLA-based secure routing protocol for wireless sensor networks", Proceedings of the First International Workshop on Security and Privacy Preserving in e-Societies: ACM, pp. 34–39, 2011. Abstract
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Hassanien, A. E., N. El-Bendary, J. Sedano, O. S. Soliman, and N. I. Ghali, "$μ$TESLA-based secure routing protocol for wireless sensor networks", The First ACM International Workshop on Security and Privacy Preserving in e-Societies, 2011. Abstract

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Moftah, H. M., A. T. Azar, E. T. Al-Shammari, N. I. Ghali, A. E. Hassanien, and M. Shoman, "Adaptive k-means clustering algorithm for MR breast image segmentation", Neural Computing and Applications, vol. 24, no. 7-8: Springer London, pp. 1917–1928, 2014. Abstract
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Moftah, H. M., A. T. Azar, E. T. Al-Shammari, N. I. Ghali, A. E. Hassanien, and M. Shoman, "Adaptive k-means clustering algorithm for MR breast image segmentation", Neural Computing and Applications, vol. 24, no. 7-8: Springer London, pp. 1917–1928, 2014. Abstract
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Soliman, M. M., A. E. Hassanien, N. I. Ghali, and H. M. Onsi, "An adaptive Watermarking Approach for Medical Imaging Using Swarm Intelligent", International Journal of Smart Home, (ISSN: 1975-4094), vol. 6, issue 1, pp. 37-45, 2012. AbstractIJSH_ 2012.pdfWebsite

In this paper we present a secure patient medical images and authentication scheme which enhances the security, confidentiality and integrity of medical images transmitted through the Internet. This paper proposes a watermarking by invoking particle swarm optimization (PSO) technique in adaptive quantization index modulation and singular value decomposition in conjunction with discrete wavelet transform (DWT) and discrete cosine transform (DCT). The proposed approach promotes the robustness and watermarked image quality. The experimental results show that the proposed algorithm yields a watermark which is invisible to human eyes, robust against a wide variety of common attacks and reliable enough for tracing colluders.

Soliman, M. M., A. E. Hassanien, N. I. Ghali, and H. M. Onsi, "An adaptive watermarking approach for medical imaging using swarm intelligent", International Journal of Smart Home, vol. 6, no. 1, pp. 37–50, 2012. Abstract
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Soliman, M. M., A. E. Hassanien, N. I. Ghali, and H. M. Onsi, "An adaptive watermarking approach for medical imaging using swarm intelligent", International Journal of Smart Home, vol. 6, no. 1, pp. 37–50, 2012. Abstract
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Hassanien, A. - E., C. Grosan, and M. F. Tolba, Applications of Intelligent Optimization in Biology and Medicine Current Trends and Open Problems, , Germany , Springer , 2016. Website
El Bakrawy, L. M., N. I. Ghali, A. E. Hassanien, and A. Abraham, "An associative watermarking based image authentication scheme", Intelligent Systems Design and Applications (ISDA), 2010 10th International Conference on: IEEE, pp. 823–828, 2010. Abstract
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El Bakrawy, L. M., N. I. Ghali, A. E. Hassanien, and A. Abraham, "An associative watermarking based image authentication scheme", Intelligent Systems Design and Applications (ISDA), 2010 10th International Conference on: IEEE, pp. 823–828, 2010. Abstract
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Ghali, N. I., L. M. El Bakrawy, and A. E. Hassanien, "Associative Watermarking Scheme for Medical Image Authentication", International Symposium on Distributed Computing and Artificial Intelligence: Springer Berlin Heidelberg, pp. 43–50, 2011. Abstract
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Ghali, N. I., L. M. El Bakrawy, and A. E. Hassanien, "Associative Watermarking Scheme for Medical Image Authentication", International Symposium on Distributed Computing and Artificial Intelligence: Springer Berlin Heidelberg, pp. 43–50, 2011. Abstract
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Alaa Tharwat, H. M. Zawbaa, T. Gaber, A. E. Hassanien, and V. Snasel, "Automated zebrafish-based toxicity test using bat optimization and adaboost classifier", Computer Engineering Conference (ICENCO), 2015 11th International: IEEE, pp. 169–174, 2015. Abstract
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Alaa Tharwat, B. E. Elnaghi, A. M. Ghanem, and A. E. Hassanien, "Automatically Human Age Estimation Approach via Two-Dimensional Facial Image Analysis", International Conference on Advanced Intelligent Systems and Informatics: Springer International Publishing, pp. 491–501, 2016. Abstract
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Amin, R., T. Gaber, G. ElTaweel, and A. E. Hassanien, "Biometric and traditional mobile authentication techniques: Overviews and open issues", Bio-inspiring cyber security and cloud services: trends and innovations: Springer Berlin Heidelberg, pp. 423–446, 2014. Abstract
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Gaber, T., Alaa Tharwat, A. E. Hassanien, and V. Snasel, "Biometric cattle identification approach based on Weber’s Local Descriptor and AdaBoost classifier", Computers and Electronics in Agriculture, vol. 122 , issue March 2016 , pp. 55–66, 2016. Website
Gaber, T., Alaa Tharwat, A. E. Hassanien, and V. Snasel, "Biometric cattle identification approach based on weber’s local descriptor and adaboost classifier", Computers and Electronics in Agriculture, vol. 122: Elsevier, pp. 55–66, 2016. Abstract
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El Bakrawy, L. M., N. I. Ghali, T. - H. Kim, and A. E. Hassanien, "A Block-wise-based Fragile Watermarking Hybrid Approach using Rough Sets and Exponential Particle Swarm Optimization", International Journal of Future Generation Communication and Networking, vol. 4, no. 4, pp. 77–88, 2011. Abstract
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El Bakrawy, L. M., N. I. Ghali, T. - H. Kim, and A. E. Hassanien, "A Block-wise-based Fragile Watermarking Hybrid Approach using Rough Sets and Exponential Particle Swarm Optimization", International Journal of Future Generation Communication and Networking, vol. 4, no. 4, pp. 77–88, 2011. Abstract
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