Publications

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Journal Article
Alaa Tharwat, Y. S. Moemen, and A. E. Hassanien, "A Predictive Model for Toxicity Effects Assessment of Biotransformed Hepatic Drugs Using Iterative Sampling Method", Scientific Reports, vol. 6: Nature Publishing Group, 2016. 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., "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.

Yi Zhou, K. Xiao, Y. Wang, Alei Liang, and A. E. Hassanien, "A PSO-inspired Multi-Robot Map Exploration Algorithm Using Frontier-Based Strategy", International Journal of System Dynamics Applications,, vol. 2, issue 2, pp. 1-13, 2013. AbstractWebsite

Map exploration is a fundamental problem in mobile robots. This paper presents a distributed algorithm that coordinates a team of autonomous mobile robots to explore an unknown environment. The proposed strategy is based on frontiers which are the regions on the boundary between open and unexplored space. With this strategy, robots are guided to move constantly to the nearest frontier to reduce the size of unknown region. Based on the PSO model incorporated in the algorithm, robots are navigated towards remote frontier after exploring the local area. The exploration completes when there is no frontier cell in the environment. The experiments implemented on both simulated and real robot scenarios show that the proposed algorithm is capable of completing the exploration task. Compared to the conventional method of randomly selecting frontier, the proposed algorithm proves its efficiency by the decreased 60% exploration time at least. Additional experimental results show the decreased coverage time when the number of robots increases, which further suggests the validity, efficiency and scalability.

Yi Zhou, K. Xiao, Y. Wang, Alei Liang, and A. E. Hassanien, "A pso-inspired multi-robot map exploration algorithm using frontier-based strategy", International Journal of System Dynamics Applications (IJSDA), vol. 2, no. 2: IGI Global, pp. 1–13, 2013. Abstract
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Yi Zhou, K. Xiao, Y. Wang, Alei Liang, and A. E. Hassanien, "A pso-inspired multi-robot map exploration algorithm using frontier-based strategy", International Journal of System Dynamics Applications (IJSDA), vol. 2, no. 2: IGI Global, pp. 1–13, 2013. 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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Miscellaneous
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., K. Shaalan, T. Gaber, A. T. Azar, and F. Tolba, Proceedings of the International Conference on Advanced Intelligent Systems and Informatics 2016, : Springer, 2016. Abstract
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Abraham, A., K. Wegrzyn-Wolska, A. E. Hassanien, Václav Snášel, and A. M. Alimi, Proceedings of the Second International Afro-European Conference for Industrial Advancement AECIA 2015, : Springer, 2016. Abstract
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Youssef, A., A. Nitaj, and A. E. Hassanien, Progress in Cryptology-AFRICACRYPT 2013, : Springer Berlin Heidelberg, 2013. Abstract
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