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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%.

A
Ali, M. A., A. Assefa, D. Assefa, L. Bal{\'ık, A. Basu, O. Berger, E. Berhan, B. Beshah, E. Birhan, T. Buriánek, et al., "Abraham, Ajith 183, 293,303, 315, 371 Ahmed, Nada 315 Aldosari, Hamoud M. 303 Alhamedi, Adel H. 303", Afro-European Conference for Industrial Advancement: Proceedings of the First International Afro-European Conference for Industrial Advancement AECIA 2014, vol. 334: Springer, pp. 383, 2014. Abstract
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Soliman, M. M., A. E. Hassanien, and H. M. Onsi, "An adaptive medical images watermarking using quantum particle swarm optimization", Telecommunications and Signal Processing (TSP), 2012 35th International Conference on: IEEE, pp. 735–739, 2012. Abstract
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Soliman, M. M., A. E. Hassanien, and H. M. Onsi, "An adaptive medical images watermarking using quantum particle swarm optimization", Telecommunications and Signal Processing (TSP), 2012 35th International Conference on: IEEE, pp. 735–739, 2012. Abstract
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Soliman, M. M., A. E. Hassanien, and H. M. Onsi, "An adaptive medical images watermarking using quantum particle swarm optimization", Telecommunications and Signal Processing (TSP), 2012 35th International Conference on: IEEE, pp. 735–739, 2012. Abstract
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Soliman, M. M., A. E. Hassanien, and H. M. Onsi, "An adaptive watermarking approach based on weighted quantum particle swarm optimization", Neural Computing and Applications, vol. 27, issue 2, pp. 469–481, 2016. AbstractWebsite

In this paper, we propose a novel optimal singular value decomposition (SVD)-based image watermarking approach that uses a new combination of weighted quantum particle swarm optimization (WQPSO) algorithm and a human visual system (HVS) model for both the hybrid discrete wavelet transform and discrete cosine transform (DCT). The proposed SVD-based watermarking approach initially decomposes the host image into sub-bands; afterwards, singular values of the DCT of the lower sub-band of the host image are quantized using a set of optimal quantization steps deduced from a combination of the WQPSO algorithm and the HVS model. To evaluate the performance of the proposed approach, we present tests on different images. The experimental results show that the proposed approach yields a watermarked image with good visual definition; at the same time, the embedded watermark was robust against a wide variety of common attacks, including JPEG compression, Gaussian noise, salt and pepper noises, Gaussian filters, median filters, image cropping, and image scaling. Moreover, the results of various experimental analyses demonstrated the superiority of the WQPSO approach over other optimization techniques, including classical PSO and QPSO in terms of local convergence speed, resulting in a better balance between global and local searches of the watermarking algorithm.

Soliman, M. M., A. E. Hassanien, and H. M. Onsi, "An adaptive watermarking approach based on weighted quantum particle swarm optimization", Neural Computing and Applications, vol. 27, no. 2: Springer London, pp. 469–481, 2016. 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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Atteya, W. A., L. E. M. Bakrawy, R. Batista, S. Bazzi, M. Beheshti, A. P. Bennett, A. Boulmakoul, S. Bureerat, L. Caggiani, N. S. Choubey, et al., "Akbarzadeh-T, Mohammad-R. 175 Antunes, Carlos Henggeler 13", Advances in Intelligent and Soft Computing 96, vol. 6, pp. 437, 2011. Abstract
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Hassanien, A. E., M. A. Fattah, S. MOHAMED, and others, "Art. 04–Volume 24• Issue 3• 2015", Studies in Informatics and Control-ICI Bucharest, 2015. Abstract
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Issaa, M., A. E. Hassanien, D. Oliva, A. Helmi, and I. Z. A. and Alzohairy, "ASCA-PSO: Adaptive sine cosine optimization algorithm integrated with particle swarm for pairwise local sequence alignment", Expert Systems with Applications, vol. 99, issue 1, pp. 56-70, 2018. AbstractWebsite

The sine cosine algorithm (SCA), a recently proposed population-based optimization algorithm, is based on the use of sine and cosine trigonometric functions as operators to update the movements of the search agents. To optimize performance, different parameters on the SCA must be appropriately tuned. Setting such parameters is challenging because they permit the algorithm to escape from local optima and avoid premature convergence. The main drawback of the SCA is that the parameter setting only affects the exploitation of the prominent regions. However, the SCA has good exploration capabilities. This article presents an enhanced version of the SCA by merging it with particle swarm optimization (PSO). PSO exploits the search space better than the operators of the standard SCA. The proposed algorithm, called ASCA-PSO, has been tested over several unimodal and multimodal benchmark functions, which show its superiority over the SCA and other recent and standard meta-heuristic algorithms. Moreover, to verify the capabilities of the SCA, the SCA has been used to solve the real-world problem of a pairwise local alignment algorithm that tends to find the longest consecutive substrings between two biological sequences. Experimental results provide evidence of the good performance of the ASCA-PSO solutions in terms of accuracy and computational time.

Own, H., and A. E. Hassanien, "Automatic Image Registration Algorithm Based on Multiresolution Local Contrast Entropy and Mutual Information", International Journal of Computers and Their Applications, vol. 12, issue 1, pp. 9-15, 2005.
Xiao, K., S. H. Ho, and others, "Automatic unsupervised segmentation methods for mri based on modified fuzzy c-means", Fundamenta Informaticae, vol. 87, no. 3-4: IOS Press, pp. 465–481, 2008. Abstract
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Xiao, K., S. H. Ho, and others, "Automatic unsupervised segmentation methods for mri based on modified fuzzy c-means", Fundamenta Informaticae, vol. 87, no. 3-4: IOS Press, pp. 465–481, 2008. Abstract
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B
Soliman, M. M., A. E. Hassanien, and H. M. Onsi, "Bio-inspiring Techniques in Watermarking Medical Images: A Review", Bio-inspiring Cyber Security and Cloud Services: Trends and Innovations: Springer Berlin Heidelberg, pp. 93–114, 2014. Abstract
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Mona M. Soliman, A. E. Hassanien, and H. M. Ons, "A Blind 3D Watermarking Approach for 3D Mesh Using Clustering Based Methods", IJCVIP - International Journal of Computer Vision and Image Processing, vol. 3, issue 2, pp. 43-53, 2013. Website
Soliman, M. M., A. E. Hassanien, and H. M. Onsi, "A Blind 3D Watermarking Approach for 3D Mesh Using Clustering Based Methods", International Journal of Computer Vision and Image Processing (IJCVIP), vol. 3, no. 2: IGI Global, pp. 43–53, 2013. Abstract
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Soliman, M. M., A. E. Hassanien, and H. M. Onsi, "A Blind 3D Watermarking Approach for 3D Mesh Using Clustering Based Methods", International Journal of Computer Vision and Image Processing (IJCVIP), vol. 3, no. 2: IGI Global, pp. 43–53, 2013. Abstract
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Soliman, M. M., A. E. Hassanien, and H. M. Onsi, "A Blind Robust 3D-Watermarking Scheme Based on Progressive Mesh and Self Organization Maps", Advances in Security of Information and Communication Networks: Springer Berlin Heidelberg, pp. 131–142, 2013. Abstract
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Soliman, M. M., A. E. Hassanien, and H. M. Onsi, "A Blind Roubst Watermarking Scheme based on Progresive Mesh and Self Organization Maps", International conference on Advances in Security of Information and Communication Networks, (SecNet 2013) , Egypt, Springer , pp. 3-5 Sept, 2013, 2013. blind_compressed_wm.pdf
C
Abraham, A., A. - E. Hassanien, V. Sná, and others, Computational social network analysis: Trends, tools and research advances, : Springer Science & Business Media, 2009. Abstract
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Abraham, A., A. - E. Hassanien, V. Sná, and others, Computational social network analysis: Trends, tools and research advances, : Springer Science & Business Media, 2009. Abstract
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Ashour, A. S., S. Samanta, N. Dey, N. Kausar, W. B. Abdessalemkaraa, A. E. Hassanien, and others, "Computed tomography image enhancement using cuckoo search: a log transform based approach", Journal of Signal and Information Processing, vol. 6, no. 03: Scientific Research Publishing, pp. 244, 2015. Abstract
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