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Babers, R., and A. E. Hassanien, " A Nature-Inspired Metaheuristic Cuckoo Search Algorithm for Community Detection in Social Networks", ", International Journal of Service Science, Management, Engineering, and Technology, IJSSMET , vol. 8, issue 1, pp. 50-, 2017. AbstractWebsite

In last few years many approaches have been proposed to detect communities in social networks using diverse ways. Community detection is one of the important researches in social networks and graph analysis. This paper presents a cuckoo search optimization algorithm with Lévy flight for community detection in social networks. Experimental on well-known benchmark data sets demonstrates that the proposed algorithm can define the structure and detect communities of complex networks with high accuracy and quality. In addition, the proposed algorithm is compared with some swarms algorithms including discrete bat algorithm, artificial fish swarm, discrete Krill Herd, ant lion algorithm and lion optimization algorithm and the results show that the proposed algorithm is competitive with these algorithms.

Babers, R., and A. E. Hassanien, "A Nature-Inspired Metaheuristic Cuckoo Search Algorithm for Community Detection in Social Networks", International Journal of Service Science, Management, Engineering, and Technology (IJSSMET), vol. 8, no. 1: IGI Global, pp. 50–62, 2017. Abstract
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Babers, R., N. I. Ghali, A. E. Hassanien, and N. M. Madbouly, "Optimal community detection approach based on Ant Lion Optimization", Computer Engineering Conference (ICENCO), 2015 11th International: IEEE, pp. 284–289, 2015. Abstract
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Babers, R., N. I. Ghali, and A. E. Hassanien, "Optimal Community Detection Approach based on Ant Lion Optimization", IEEE iInternational Computer Engineering Conference - ICENCO , Cairo, 30 Dec, 2015.
Babers, R., A. E. Hassanien, and N. I. Ghali, "A nature-inspired metaheuristic Lion Optimization Algorithm for community detection", Computer Engineering Conference (ICENCO), 2015 11th International: IEEE, pp. 217–222, 2015. Abstract
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Badr, Y., R. Chbeir, A. Abraham, and A. - E. Hassanien, Emergent web intelligence: Advanced semantic technologies, : Springer Science & Business Media, 2010. Abstract
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Badr, Y., R. Chbeir, A. Abraham, and A. - E. Hassanien, Emergent web intelligence: Advanced semantic technologies, : Springer Science & Business Media, 2010. Abstract
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Bakrawy, L. M. E., A. - E. " "Hassanien, and N. I. Ghali, Machine Learning in Image Authentication, , Cairo, Al-Azhar University, 2012. Abstract

In recent years image authentication has gained substantial attraction by the research community. It promises the solution to many problems such as content piracy, illicit manipulation of medical/legal documents, content security and so on. As watermark-based image authentication approaches are efficient and attractive, some types of watermarks such as logos, labels, trademark, or random sequence representing the author’s ownership, are mbedded into the desired digital image. Generally, a registration to the authentication center is necessary, which helps to solve ownership disputes by identifying the owner of the disputed media. If necessary, the embedded watermark in the digital image can be used to verify ownership Due to the open environment of Internet downloading, copyright protection introduces a new set of challenging problems regarding security and illegal distribution of privately owned images. One solution to these problems is digital watermarking, i.e., the insertion of information into the image data in such a way that the added information is not visible and yet resistant to image alterations. A watermarking technique is to prevent digital images that belong to rightful owners from being illegally commercialized or used, and it can verify the intellectual property right. The watermark should be robust and transparent, but the ways of pursuing transparency and robustness are conflict. For instance, if we would like to concentrate on the transparency issue, it is natural to embed the smallest modulation into images whenever possible. However, due to such small values in the embedded watermark, attacks can easily destroy the problems The first proposed solution is based on the associative watermarking and vector quantization. It achieves more effective against several images processing such as blurring, sharpening adding in Gaussian noise, cropping, and JPEG lossy compression especially in case of Gaussian noise and blurring. Also this technique is implemented to hide biometric data, fingerprint image, over three different types of medical images: CT, MRI and interventional images. It also achieves an effective resistance against several images processing such as JPEG lossy compression, sharpening, blurring and adding in Gaussian noise The second contribution in this thesis is strict authentication of multimodal biometric images using an improved secure hash function (ISHA-1) and near sets. It indicates that the proposed hash function is collision resistant and assures a good compression and preimage resistance. Also it reduces the time of implementation comparing to standard secure hash function. Moreover, the difference in time between SHA-1 and ISHA-1 increases by increasing the number of letters in message since the running time of implementation of ISHA-1 is limited compared to the running time of implementation of SHA-1. Also the proposed approach guarantees the security assurance and reduces the time of implementation. The third proposed contribution is fragile watermarking approach for image authentication based on rough k-means only and hybridization of rough k-means and particle swarm optimization. It can embed watermark without causing noticeable visual artifacts, and does not only achieve superior tamper detection in images accurately, it also recovers tampered regions effectively. In addition, it shows that the proposed approach can effectively thwart different attacks, such as the cut-and paste attack and collage attack, while sustaining superior tamper detection and localization accuracy. Moreover, the running time of implemented hybrid system is limited compared to the running time of the implemented rough k-means only. Especially, when we used exponential particle swarm optimization to optimize the parameters of rough k-means.

Banerjee, S., H. Al-Qaheri, and A. E. Hassanien, "Mining social networks for viral marketing using fuzzy logic", Mathematical/Analytical Modelling and Computer Simulation (AMS), 2010 Fourth Asia International Conference on: IEEE, pp. 24–28, 2010. Abstract
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Banerjee, S., H. Al-Qaheri, E. - S. A. El-Dahshan, and A. E. Hassanien, "Rough set approach in ultrasound biomicroscopy glaucoma analysis", Advances in Computer Science and Information Technology: Springer Berlin Heidelberg, pp. 491–498, 2010. Abstract
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Banerjee, S., N. El-Bendary, A. E. Hassanien, and T. - H. Kim, "A modified pheromone dominant ant colony algorithm for computer virus detection", IEEE 14th International on Multitopic Conference (INMIC), pp. 35-40, Packistan, , 22-24 Dec., 2011. Abstract

This paper proposes an elementary pattern detection approach for viruses propagated through e-mail and address books using the non-uniform pheromone deposition mechanism of ant colony. The local temporary tabu memory has been used to learn the pattern and it can combine known information from past viruses with a type of prediction for future viruses. This is achieved through certain generated test signature of viruses associated with e-mail over landscape. A non-uniform and non-decreasing time function for pheromone deposition and evaporation ensures that subsequent ants who are close enough to a previously selected trial solution will follow the trajectory or test landscape. They are capable to examine gradually thicker deposition of pheromone over the trajectory. It is empirically shown that the proposed modified pheromone learning mechanism can be an alternative approach to detect virus pattern for e-mail messages.

Banerjee, S., N. El-Bendary, A. E. Hassanien, and T. - H. Kim, "A modified pheromone dominant ant colony algorithm for computer virus detection", 2011 IEEE 14th International Multitopic Conference (INMIC), PP. 35-40 , Karachi, Pakistan , 22-24 Dec. 2011. Abstract

This paper proposes an elementary pattern detection approach for viruses propagated through e-mail and address books using the non-uniform pheromone deposition mechanism of ant colony. The local temporary tabu memory has been used to learn the pattern and it can combine known information from past viruses with a type of prediction for future viruses. This is achieved through certain generated test signature of viruses associated with e-mail over landscape. A non-uniform and non-decreasing time function for pheromone deposition and evaporation ensures that subsequent ants who are close enough to a previously selected trial solution will follow the trajectory or test landscape. They are capable to examine gradually thicker deposition of pheromone over the trajectory. It is empirically shown that the proposed modified pheromone learning mechanism can be an alternative approach to detect virus pattern for e-mail messages.

Banerjee, S., H. Al-Qaheri, E. - S. A. El-Dahshan, and A. E. Hassanien, "Rough set approach in ultrasound biomicroscopy glaucoma analysis", Advances in Computer Science and Information Technology: Springer Berlin Heidelberg, pp. 491–498, 2010. Abstract
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Banerjee, S., N. Elbendary, A. E. Hassanien, and M. Tolba, "Decision Support System for Customer Churn Reduction Approach", 13th IEEE International Conference on Hybrid Intelligent Systems |(HIS13) Tunisia, 4-6 Dec. pp.229-234, 2013, Tunisia, , 4-6 Dec, 2013.
Banerjee, S., N. El-Bendary, A. E. Hassanien, and T. - H. Kim, "A modified pheromone dominant ant colony algorithm for computer virus detection", Multitopic Conference (INMIC), 2011 IEEE 14th International: IEEE, pp. 35–40, 2011. Abstract
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Banerjee, S., N. Ghali, and A. E. Hassanien, Investigating Optimization in Retail Inventory: A Bio-inspired Perspective towards Retail Recommender System, , 2012. Abstract

Interaction with different person leads to different kinds of ideas and sharing or some nourishing effects which might influence others to believe or trust or even join some association and subsequently become the member of that community. This will facilitate to enjoy all kinds of social privileges. These concepts of grouping similar objects can be experienced as well as could be implemented on any social networks. The concept of homophily
could assist to design the affiliation graph with similar and close similar entities of every member of any social network which tends identifying the most popular community. This paper propose and discuss a novel data-mining algorithm from the perspective of graph properties of a social network such as
embeddedness, betweenness and graph occupancy. Finally, the implication of homophily graph for cultivating leading community of social network has also been solicited.

Banerjee, S., N. El-Bendary, A. E. Hassanien, and M. F. Tolba, "Decision support system for customer churn reduction approach", Hybrid Intelligent Systems (HIS), 2013 13th International Conference on: IEEE, pp. 228–233, 2013. Abstract
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Banerjee, S., N. El-Bendary, A. E. Hassanien, and T. - H. Kim, "A modified pheromone dominant ant colony algorithm for computer virus detection", Multitopic Conference (INMIC), 2011 IEEE 14th International: IEEE, pp. 35–40, 2011. Abstract
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Banerjee, S., H. Al-Qaheri, and A. E. Hassanien, "Mining social networks for viral marketing using fuzzy logic", Mathematical/Analytical Modelling and Computer Simulation (AMS), 2010 Fourth Asia International Conference on: IEEE, pp. 24–28, 2010. Abstract
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Banu, P. K. N., H. H. Inbarani, A. T. Azar, H. S. Own, and A. E. Hassanien, "Rough set based feature selection for egyptian neonatal jaundice", International Conference on Advanced Machine Learning Technologies and Applications: Springer International Publishing, pp. 367–378, 2014. Abstract
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Basha, S. H., A. S. Abdalla, and A. E. Hassanien, "GNRCS: Hybrid Classification System based on Neutrosophic Logic and Genetic Algorithm", Computer Engineering Conference (ICENCO), 2016 12th International: IEEE, pp. 53–58, 2016. Abstract
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Belem, B., and P. Plassmann, "Early Detection of Wound Inflammation by Color Analysis", Computational Intelligence in Medical Imaging: Techniques and Applications: Chapman and Hall/CRC, pp. 89–111, 2009. Abstract
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