Publications

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1998
Karam, H., A. E. Hassanien, and M. Nakajima, "Petri Net Modeling Methods For Generating Self-Similar Fractal Images", 映像情報メディア学会技術報告, vol. 22, no. 45: 一般社団法人映像情報メディア学会, pp. 13–18, 1998. Abstract
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Karam, H., A. E. Hassanien, and M. Nakajima, "Petri Net Modeling Methods for Generating Self-Similar Fractal Images (マルチメディア情報処理研究会)", 映像情報メディア学会誌: 映像情報メディア, vol. 52, no. 12: 一般社団法人映像情報メディア学会, pp. 1807, 1998. Abstract

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2006
Ali, J. M. H., and A. E. Hassanien, "PCNN for detection of masses in digital mammogram", Neural Network World, vol. 16, no. 2: Institute of Computer Science, pp. 129, 2006. Abstract
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Ali, J. M. H., and A. E. Hassanien, "PCNN for detection of masses in digital mammogram", Neural Network World, vol. 16, no. 2: Institute of Computer Science, pp. 129, 2006. Abstract
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Hassanien, A. E., "Pulse coupled neural network for detection of masses in digital mammogram", Neural network world journal, vol. 2, no. 6, pp. 129–141, 2006. Abstract
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Hassanien, A. E., "Pulse coupled neural network for detection of masses in digital mammogram", Neural network world journal, vol. 2, no. 6, pp. 129–141, 2006. Abstract
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2009
Toews, M., and T. Arbel, "Parts-Based Appearance Modeling of Medical Imagery", Computational Intelligence in Medical Imaging: Techniques and Applications: CRC Press, pp. 291, 2009. Abstract
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Hassanien, A. E., Pervasive Computing, : Springer Science & Business Media, 2009. Abstract
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Hassanien, A. - E., J. H. Abawajy, A. Abraham, and H. Hagras, Pervasive computing: innovations in intelligent multimedia and applications, : Springer Science & Business Media, 2009. Abstract
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Hassanien, A. - E., J. H. Abawajy, A. Abraham, and H. Hagras, Pervasive computing: innovations in intelligent multimedia and applications, : Springer Science & Business Media, 2009. Abstract
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Hassanien, A. E., "Proceedings of the 12th International Conference on Rough Sets, Fuzzy Sets, Data Mining and Granular Computing", Proceedings of the 12th International Conference on Rough Sets, Fuzzy Sets, Data Mining and Granular Computing, Berlin, Heidelberg, Springer-Verlag , 2009.
2010
Salama, M. A., A. E. Hassanien, and A. A. Fahmy, "Pattern-based subspace classification model", Nature and Biologically Inspired Computing (NaBIC), 2010 Second World Congress on: IEEE, pp. 357–362, 2010. Abstract
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Salama, M. A., A. E. Hassanien, and A. A. Fahmy, "Pattern-based subspace classification model", Nature and Biologically Inspired Computing (NaBIC), 2010 Second World Congress on: IEEE, pp. 357–362, 2010. Abstract
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Heba, E. F., A. Darwish, A. E. Hassanien, and A. Abraham, "Principle components analysis and support vector machine based intrusion detection system", Intelligent Systems Design and Applications (ISDA), 2010 10th International Conference on: IEEE, pp. 363–367, 2010. Abstract
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Heba, E. F., A. Darwish, A. E. Hassanien, and A. Abraham, "Principle components analysis and support vector machine based intrusion detection system", Intelligent Systems Design and Applications (ISDA), 2010 10th International Conference on: IEEE, pp. 363–367, 2010. Abstract
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Hassanien, A. E., G. Schaefer, and H. AlQaheri, "Prostate Boundary Detection in Ultrasound Images Based on Type-II Fuzzy Sets and Modified Fuzzy C-Means", Soft Computing in Industrial Applications: Springer Berlin Heidelberg, pp. 187–195, 2010. Abstract
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Hassanien, A. E., G. Schaefer, and H. AlQaheri, "Prostate Boundary Detection in Ultrasound Images Based on Type-II Fuzzy Sets and Modified Fuzzy C-Means", Soft Computing in Industrial Applications: Springer Berlin Heidelberg, pp. 187–195, 2010. Abstract
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Hassanien, A. E., Pervasive Computing : Innovations in Intelligent Multimedia and Applications, , London, Computer Communications and Networks - Springer , 2010. AbstractWebsite

Pervasive computing (also referred to as ubiquitous computing or ambient intelligence) aims to create environments where computers are invisibly and seamlessly integrated and connected into our everyday environment. Pervasive computing and intelligent multimedia technologies are becoming increasingly important, although many potential applications have not yet been fully realized. These key technologies are creating a multimedia revolution that will have significant impact across a wide spectrum of consumer, business, healthcare, and governmental domains.

Salama, M., A. E. Hassanien, and A. A. Fahmy, "Pattern-based Subspace Classification Approach", The Second IEEE World Congress on Nature and Biologically Inspired Computing (NaBIC2010), Kitakyushu- Japan, 15 Dec, 2010. Abstract

The use of patterns in predictive models has received a lot of attention in recent years. This paper presents a pattern-based classification model which extracts the patterns that have similarity among all objects in a specific class. This introduced model handles the problem of the dependence on a user-defined threshold that appears in the pattern-based subspace clustering. The experimental results obtained, show that the overall pattern-based classification accuracy is high compared with other machine learning techniques including Support vector machine, Bayesian Network, multi-layer perception and decision trees.

2011
El-Bendary, N., H. M. Zawbaa, A. E. Hassanien, and V. Snasel, "PCA-based home videos annotation system", International Journal of Reasoning-based Intelligent Systems, vol. 3, no. 2: Inderscience Publishers, pp. 71–79, 2011. Abstract
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El-Bendary, N., H. M. Zawbaa, A. E. Hassanien, and V. Snasel, "PCA-based home videos annotation system", International Journal of Reasoning-based Intelligent Systems, vol. 3, no. 2: Inderscience Publishers, pp. 71–79, 2011. Abstract
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Hassanien, A. E., H. Al-Qaheri, and E. - S. A. El-Dahshan, "Prostate boundary detection in ultrasound images using biologically-inspired spiking neural network", Applied Soft Computing, vol. 11, no. 2: Elsevier, pp. 2035–2041, 2011. Abstract
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Hassanien, A. E., H. Al-Qaheri, and E. - S. A. El-Dahshan, "Prostate boundary detection in ultrasound images using biologically-inspired spiking neural network", Applied Soft Computing, vol. 11, no. 2: Elsevier, pp. 2035–2041, 2011. Abstract
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Hassanien, A. E., "Proceeding of the 6th International Conference on Soft Computing Models in Industrial and Environmental Applications", The 6th International Conference on Soft Computing Models in Industrial and Environmental Applications SOCO 2011 , Spain, Advances in Intelligent and Soft Computing, Vol. 87 , 2011.
Hassanien, A. E., "Prostate boundary detection in ultrasound images using biologically-inspired spiking neural network.", Appl. Soft Computing, vol. 11, issue 2, pp. 2035-2041, 2011. AbstractCU-PDF.pdfWebsite

Pulse-coupled neural networks (PCNNs) are a biologically inspired type of neural networks. It is a simplified model of the cat's visual cortex with local connections to other neurons. PCNN has the ability to extract edges, segments and texture information from images. Only a few changes to the PCNN parameters are necessary for effective operation on different types of data. This is an advantage over published image processing algorithms that generally require information about the target before they are effective. The main aim of this paper is to provide an accurate boundary detection algorithm of the prostate ultrasound images to assist radiologists in making their decisions. To increase the contrast of the ultrasound prostate image, the intensity values of the original images were adjusted firstly using the PCNN with median filter. It is followed by the PCNN segmentation algorithm to detect the boundary of the image. Combining adjusting and segmentation enable us to eliminate PCNN sensitivity to the setting of the various PCNN parameters whose optimal selection can be difficult and can vary even for the same problem. The experimental results obtained show that the overall boundary detection overlap accuracy offered by the employed PCNN approach is high compared with other machine learning techniques including Fuzzy C-mean and Fuzzy Type-II.