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Zawbaa, H. M., M. Hazman, M. Abbass, and A. E. Hassanien, "Automatic fruit classification using random forest algorithm", Hybrid Intelligent Systems (HIS), 2014 14th International Conference on: IEEE, pp. 164–168, 2014. Abstract
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Zawbaa, H. M., M. Abbass, M. Hazman, and A. E. Hassenian, "Automatic Fruit Image Recognition System based on Shape and Color Features ", The 2nd International Conference on Advanced Machine Learning Technologies and Applications , Egypt, November 17-19, , 2014.
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.

Zawbaa, H. M., and A. E. Hassanien, Automatic Soccer Video Summarization, : Cairo Unversity, 2012. 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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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
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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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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Václav Snášel, A. Keprt, A. Abraham, and A. E. Hassanien, "Approximate string matching by fuzzy automata", Man-Machine Interactions: Springer Berlin Heidelberg, pp. 281–290, 2009. Abstract
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Václav Snášel, A. Keprt, A. Abraham, and A. E. Hassanien, "Approximate string matching by fuzzy automata", Man-Machine Interactions: Springer Berlin Heidelberg, pp. 281–290, 2009. 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, 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, 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, O. S., Jan Platoš, A. E. Hassanien, and Václav Snášel, "Automatic localization and boundary detection of retina in images using basic image processing filters", Proceedings of the Third International Conference on Intelligent Human Computer Interaction (IHCI 2011), Prague, Czech Republic, August, 2011: Springer Berlin Heidelberg, pp. 169–182, 2013. 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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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, O. S., Jan Platoš, A. E. Hassanien, and Václav Snášel, "Automatic localization and boundary detection of retina in images using basic image processing filters", Proceedings of the Third International Conference on Intelligent Human Computer Interaction (IHCI 2011), Prague, Czech Republic, August, 2011: Springer Berlin Heidelberg, pp. 169–182, 2013. 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, no. 2: Springer London, pp. 469–481, 2016. Abstract
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Soliman, O. S., Jan Platoš, A. E. Hassanien, and Václav Snášel, "Automatic localization and boundary detection of retina in images using basic image processing filters", Proceedings of the Third International Conference on Intelligent Human Computer Interaction (IHCI 2011), Prague, Czech Republic, August, 2011: Springer Berlin Heidelberg, pp. 169–182, 2013. 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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Smolinski, T. G., M. G. Milanova, and A. - E. Hassanien, Applications of Computational Intelligence in Biology: Current Trends and Open Problems, : Springer, 2008. Abstract
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Smolinski, T. G., M. G. Milanova, and A. - E. Hassanien, Applications of Computational Intelligence in Biology: Current Trends and Open Problems, : Springer, 2008. Abstract
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Sharif, M. M., Alaa Tharwat, A. E. Hassanien, and H. A. Hefeny, "Automated Enzyme Function Classification Based on Pairwise Sequence Alignment Technique", Intelligent Data Analysis and Applications: Springer International Publishing, pp. 499–510, 2015. Abstract
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Sayed, G. I., A. E. Hassanien, T. M. Nassef, and J. - S. Pan, "Alzheimer’s Disease Diagnosis Based on Moth Flame Optimization", International Conference on Genetic and Evolutionary Computing: Springer International Publishing, pp. 298–305, 2016. Abstract
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