Publications

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Thesis
Zawbaa, H. M., and A. E. Hassanien, Automatic Soccer Video Summarization, , Cairo, Cairo Unversity, 2012. Abstract

This thesis presents an automatic soccer video summarization system using machine learning (ML) techniques. The proposed system is composed of ve phases. Namely; in the pre-processing phase, the system segments the whole video stream into small video shots. Then, in the shot processing
phase, it applies two types of classi cation (shot type classi cation and play / break classification) to the video shots resulted from the pre-processing phase. Afterwards, in the replay detection phase, the proposed system applies two machine learning algorithms, namely; support vector machine (SVM) and arti cial neural network (ANN), for emphasizing important segments with championship logo appearance. Also, in the excitement event detection phase, the proposed system uses both machine learning algorithms for detecting the scoreboard which contain an information about the score of the game. The proposed system also uses k-means algorithm and Hough line transform for detecting vertical goal posts and Gabor lter for detecting goal net. Finally, in the event detection and summarization phase, the proposed system highlights the most important events during the match. Experiments on real soccer videos demonstrate encouraging results. The event detection and summarization has attained recall 94% and precision 97.3% for soccer match videos from ve international soccer championships.

Miscellaneous
Journal Article
Xiao, K., S. H. Ho, and A. E. Hassanien, "Aboul Ella Hassanien: Automatic Unsupervised Segmentation Methods for MRI Based on Modified Fuzzy C-Means", Fundamenta Informaticae, vol. 87, issue 3-4, pp. 465-481, 2008. Website
Azar, A. T., and A. E. Hassanien, "Aboul Ella Hassanien: Dimensionality reduction of medical big data using neural-fuzzy classifier.", soft computing , vol. 19, issue 4, pp. 1115-1127, 2015. Website
M.Moftah, H., 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 (Springer), 2013. Abstract

Image segmentation is vital for meaningful analysis and interpretation
of medical images. The most popular method for clustering is k-means
clustering. This article presents a new approach intended to provide more reliable
Magnetic Resonance (MR) breast image segmentation that is based on
adaptation to identify target objects through an optimization methodology
that maintains the optimum result during iterations. The proposed approach
improves and enhances the effectiveness and efficiency of the traditional kmeans
clustering algorithm. The performance of the presented approach was
evaluated using various tests and different MR breast images. The experimental
results demonstrate that the overall accuracy provided by the proposed
adaptive k-means approach is superior to the standard k-means clustering
technique.

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, 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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Awad, A. I., A. E. Hassanien, and K. Baba, "Advances in Security of Information and Communication Networks", Communications in Computer and Information Science, vol. 381, 2013. 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., T. - H. Kim, P. S. Rajan, and K. K. K. Hari, "Analysis of Energy Utilization through Mobile Ad Hoc Network with AODV", Proc. of the Intl. Conf. on Computer Applications, vol. 1, 2012. Abstract

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Karam, H., A. - E. Hassanien, and M. Nakajima, "Animation of linear fractal shapes using polar decomposistion interpolation", Journal of ITE, vol. 53, no. 3, 1999. 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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El-said, S. A., and A. E. Hassanien, "Artificial eye vision using wireless sensor networks", Wireless sensor networks: theory and applications. CRC Press, Taylor and Francis Group, Boca Raton, 2013. Abstract
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Aziz, A. S. A., M. A. Salama, A. E. Hassanien, and S. E. - O. Hanafi, "Artificial Immune System Inspired Intrusion Detection System Using Genetic Algorithm.", Informatica (Slovenia), vol. 36, no. 4, pp. 347–357, 2012. Abstract
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Aziz, A. S. A., M. A. Salama, A. E. Hassanien, and S. E. - O. Hanafi, "Artificial Immune System Inspired Intrusion Detection System Using Genetic Algorithm.", Informatica (Slovenia), vol. 36, no. 4, pp. 347–357, 2012. Abstract
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