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Karam, H., A. E. Hassanien, and M. Nakajima, "極分解補間を用いた線形フラクタル形状のアニメーション", 映像情報メディア学会誌, vol. 53, no. 3: 一般社団法人 映像情報メディア学会, pp. 411–416, 1999. Abstract
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Hassanien, A. E., "الطريق الى جوائز البحث العلمى", جوائز البحث العلمى, 2015. Abstract

ستناقش المحاضرة اهمية البحث العلمى والابداع وانواع الجوائز العلمية وتصنيفاتها وانواعها وشروطها بداية من جوائز الجامعة (تشجيعية - تقديرية - تفوق) ثم الدولة (تشجيعية - تفوق - تقديرية - النيل) جوائز على مستوى العالم الاسلامى(الايسيسكووالمصطفى الايرانية) وجوائز على مستوى الدول العربية (جائزة عبد الحميد شومان) وجوائز الاتحاد الافريقى على مستوى القارة الافريقية والعالمية (فيصل ونوبل)

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Hossam M. Zawbaa, E. Emary, A. E. Hassanien, and B. PARV, "A wrapper approach for feature selection based on swarm optimization algorithm inspired from the behavior of social-spiders", 7th IEEE International Conference of Soft Computing and Pattern Recognition, , Kyushu University, Fukuoka, Japan,, November 13 - 1, 2015. Abstract

In this paper, a proposed system for feature selection
based on social spider optimization (SSO) is proposed. SSO is
used in the proposed system as searching method to find optimal
feature set maximizing classification performance and mimics
the cooperative behavior mechanism of social spiders in nature.
The proposed SSO algorithm considers two different search
agents (social members) male and female spiders, that simulate
a group of spiders with interaction to each other based on the
biological laws of the cooperative colony. Depending on spider
gender, each spider (individual) is simulating a set of different
evolutionary operators of different cooperative behaviors that are
typically found in the colony. The proposed system is evaluated
using different evaluation criteria on 18 different datasets, which
compared with two common search methods namely particle
swarm optimization (PSO), and genetic algorithm (GA). SSO
algorithm proves an advance in classification performance using
different evaluation indicators

Zawbaa, H. M., E. Emary, A. E. Hassanien, and B. PARV, "A wrapper approach for feature selection based on swarm optimization algorithm inspired from the behavior of social-spiders", Soft Computing and Pattern Recognition (SoCPaR), 2015 7th International Conference of: IEEE, pp. 25–30, 2015. Abstract
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Samanta, S., D. Kundu, S. Chakraborty, N. Dey, T. Gaber, A. E. Hassanien, and T. - H. Kim, "Wooden Surface Classification based on Haralick and The Neural Networks", Information Science and Industrial Applications (ISI), 2015 Fourth International Conference on: IEEE, pp. 33–39, 2015. Abstract
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Waleed Yamany, Eid Emary, and A. E. Hassanien, "Wolf search algorithm for attribute reduction in classification", Computational Intelligence and Data Mining (CIDM), 2014 IEEE Symposium on: IEEE, pp. 351–358, 2014. Abstract
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Waleed Yamany, E. Emary, and A. E. Hassanien, "Wolf Search Algorithm for Attribute Reduction", IEEE Symposium Series on Computational Intelligence (IEEE SSCI 2014),, Orlando, Florida, USA, 9-12 Dec., 2014. Abstract

Data sets ordinarily includes a huge number of attributes, with irrelevant and redundant attributes. Redundant and irrelevant attributes might minimize the classification accuracy because of the huge search space. The main goal of attribute reduction is choose a subset of relevant attributes from a huge number of available attributes to obtain comparable or even better classification accuracy than using all attributes. A system for feature selection is proposed in this paper using a modified version of the wolf search algorithm optimization. WSA is a bio-inspired heuristic optimization algorithm that imitates the way wolves search for food and survive by avoiding their enemies. The WSA can quickly search the feature space for optimal or near-optimal feature subset minimizing a given fitness function. The proposed fitness function used incorporate both classification accuracy and feature reduction size. The proposed system is applied on a set of the UCI machine learning data sets and proves good performance in comparison with the GA and PSO optimizers commonly used in this context.

Mostafa, A., M. A. Fattah, A. Fouad, A. E. Hassanien, and H. Hefny, "Wolf local thresholding approach for liver image segmentation in CT images", Proceedings of the Second International Afro-European Conference for Industrial Advancement AECIA 2015: Springer International Publishing, pp. 641–651, 2016. Abstract
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Fouad, M. M. M., N. El-Bendary, R. A. Ramadan, and A. E. Hassanien, "Wireless sensor Networks: a medical perspective", Wireless Sensor Networks: From Theory to Applications: CRC Press, 2013. Abstract
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Fouad, M. M. M., N. El-Bendary, R. A. Ramadan, and A. E. Hassanien, "Wireless sensor Networks: a medical perspective", Wireless Sensor Networks: From Theory to Applications: CRC Press, 2013. Abstract
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Fouad, M. M. M., N. El-Bendary, R. A. Ramadan, and A. E. Hassanien, "Wireless Sensor Networks, A Medical Perspective", Wireless Sensor Networks: Theory and Applications, pp. 713-732 , USA, CRC Press, Taylor and Francis Group, 2013. k15146_c024.pdf
Khairy, M., Alaa Tharwat, T. Gaber, and A. E. Hassanien, "A wheelchair control system using the human machine interaction: Single-modal and Multi-modal approaches", ournal of Intelligent Systems (JISYS), vol. In press, 2017.
abd elaziz, M., A. A. Ewees, and A. E. Hassanien, "Whale Optimization Algorithm and Moth-Flame Optimization for Multilevel Thresholding Image Segmentation", Expert Systems with Applications, 2017. AbstractWebsite

Determining the optimal thresholding for image segmentation has got more attention in recent years since it has many applications. There are several methods used to find the optimal thresholding values such as Otsu and Kapur based methods. These methods are suitable for bi-level thresholding case and they can be easily extended to the multilevel case, however, the process of determining the optimal thresholds in the case of multilevel thresholding is time-consuming. To avoid this problem, this paper examines the ability of two nature inspired algorithms namely: Whale Optimization Algorithm (WOA) and Moth-Flame Optimization (MFO) to determine the optimal multilevel thresholding for image segmentation. The MFO algorithm is inspired from the natural behavior of moths which have a special navigation style at night since they fly using the moonlight, whereas, the WOA algorithm emulates the natural cooperative behaviors of whales. The candidate solutions in the adapted algorithms were created using the image histogram, and then they were updated based on the characteristics of each algorithm. The solutions are assessed using the Otsu’s fitness function during the optimization operation. The performance of the proposed algorithms has been evaluated using several of benchmark images and has been compared with five different swarm algorithms. The results have been analyzed based on the best fitness values, PSNR, and SSIM measures, as well as time complexity and the ANOVA test. The experimental results showed that the proposed methods outperformed the other swarm algorithms; in addition, the MFO showed better results than WOA, as well as provided a good balance between exploration and exploitation in all images at small and high threshold numbers.

Elshazly, H. I., A. F. Ali, H. Mahmoud, A. M. Elkorany, and A. E. Hassanien, "Weighted reduct selection metaheuristic based approach for rules reduction and visualization", Computing, Communication and Automation (ICCCA), 2016 International Conference on: IEEE, pp. 274–280, 2016. Abstract
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Kudelka, M., V. Snásel, Z. Horak, and A. E. Hassanien, "Web Communities Defined by Web Page Content", IEEE/WIC/ACM International Conference on Web Intelligence and International Conference on Intelligent Agent Technology , Sydney, NSW, Australia, pp.385-389 , 9-12 December, 2008. Abstract

In this paper we are looking for a relationship between the intent of Web pages, their architecture and the communities who take part in their usage and creation. For us, the Web page is entity carrying information about these communities. Our paper describes techniques, which can be used to extract mentioned information as well as tools usable in analysis of these information. Information about communities could be used in several ways thanks to our approach. Finally we present an experiment which proves the feasibility of our approach.

Kudělka, M., Václav Snášel, Z. Horák, A. E. Hassanien, and A. Abraham, "Web communities defined by web page content", Computational Social Network Analysis: Springer London, pp. 349–370, 2010. Abstract
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Kudělka, M., Václav Snášel, Z. Horák, A. E. Hassanien, and A. Abraham, "Web communities defined by web page content", Computational Social Network Analysis: Springer London, pp. 349–370, 2010. Abstract
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Darwish, A., and A. E. Hassanien, "Wearable and implantable wireless sensor network solutions for healthcare monitoring", Sensors, vol. 11, no. 6: Molecular Diversity Preservation International, pp. 5561–5595, 2011. Abstract
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Darwish, A., and A. E. Hassanien, "Wearable and implantable wireless sensor network solutions for healthcare monitoring", Sensors, vol. 11, no. 6: Molecular Diversity Preservation International, pp. 5561–5595, 2011. Abstract
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Soliman, M. M., A. E. Hassanien, and H. M. Onsi, "The way of improving PSO performance: medical imaging watermarking case study", International Conference on Rough Sets and Current Trends in Computing: Springer Berlin Heidelberg, pp. 237–242, 2012. Abstract
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Soliman, M. M., A. E. Hassanien, and H. M. Onsi, "The way of improving PSO performance: medical imaging watermarking case study", International Conference on Rough Sets and Current Trends in Computing: Springer Berlin Heidelberg, pp. 237–242, 2012. Abstract
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Soliman, M. M., A. E. Hassanien, and H. M. Onsi, "The way of improving PSO performance: medical imaging watermarking case study", International Conference on Rough Sets and Current Trends in Computing: Springer Berlin Heidelberg, pp. 237–242, 2012. Abstract
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Dey, N., A. S. Ashour, S. Chakraborty, S. Banerjee, E. Gospodinova, M. Gospodinov, and A. E. Hassanien, "Watermarking in Biomedical Signal Processing", Intelligent Techniques in Signal Processing for Multimedia Security: Springer International Publishing, pp. 345–369, 2017. Abstract
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Tourism