Publications

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Miscellaneous
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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Abraham, A., A. - E. Hassanien, V. Sná, and others, Foundations of Computational Intelligence Volume 5: Function Approximation and Classification, : Springer Science & Business Media, 2009. Abstract
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Abraham, A., A. - E. Hassanien, V. Sná, and others, Foundations of Computational Intelligence Volume 5: Function Approximation and Classification, : Springer Science & Business Media, 2009. Abstract
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Abraham, A., A. - E. Hassanien, V. Sná, and others, Foundations of Computational Intelligence: Volume 6: Data Mining, : Springer, 2009. Abstract
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Abraham, A., A. - E. Hassanien, V. Sná, and others, Foundations of Computational Intelligence: Volume 6: Data Mining, : Springer, 2009. Abstract
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Journal Article
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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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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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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Own, H. S., N. I. GHALL, and E. L. L. A. H. A. S. S. A. N. I. E. N. ABOUL, "Hybrid Dual-Tree Wavelet Transform and Adaptive Threshold for Image Denoising", International journal of imaging and robotics, vol. 9, no. 1: CESER Publications, pp. 17–25, 2013. Abstract
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Own, H. S., N. I. GHALL, and E. L. L. A. H. A. S. S. A. N. I. E. N. ABOUL, "Hybrid Dual-Tree Wavelet Transform and Adaptive Threshold for Image Denoising", International journal of imaging and robotics, vol. 9, no. 1: CESER Publications, pp. 17–25, 2013. Abstract
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Azar, A. T., A. E. Hassanien, and others, "Hybrid TRS-PSO clustering approach for Web2. 0 social tagging system", International Journal of Rough Sets and Data Analysis (IJRSDA), vol. 2, no. 1: IGI Global, pp. 22–37, 2015. Abstract
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Hassanien, A. E., I. El Henawy, and H. Own, "Multiresolution image denosing based on wavelet transform", Machine Graphics and Vision, vol. 10, no. 2, pp. 221–230, 2001. Abstract
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