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KAMAL, K. A. R. E. E. M., A. GHANY, M. A. Moneim, N. I. Ghali, A. E. Hassanien, and H. A. Hefny, A symmetric bio-hash function based on fingerprint minutiae and principal curves approach, , 2011. Abstract
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KAMAL, K. A. R. E. E. M., A. GHANY, M. A. Moneim, N. I. Ghali, A. E. Hassanien, and H. A. Hefny, A symmetric bio-hash function based on fingerprint minutiae and principal curves approach, , 2011. Abstract
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Ahmed, K., A. E. Hassanien, E. Ezzat, and Siddhartha Bhattacharyya, "Swarming Behaviors of Chicken for Predicting Posts on Facebook Branding Pages", AMLTA 2018: The International Conference on Advanced Machine Learning Technologies and Applications (AMLTA2018), Cairo, 23 fEB , 2018. Abstract

The rapid increase in social networks data and users present an urgent need for predicting the performance of posted data over these networks. It helps in many industrial aspects such as election, public opinion detection and advertising or branding over social networks. This paper presents a new posts’ prediction system for Facebook’s branding pages concerning the user’s attention and interaction. CSO is utilized to optimize the ANFIS parameters for accurate prediction. CSO-ANFIS is compared with several methods including ANFIS, particle swarm optimization, genetic algorithm and krill herd optimization.

Hassanien, A. E., and E. Alamry, Swarm Intelligence: Principles, Advances, and Applications, , New yourk, CRC – Taylor & Francis Group, 2015. AbstractWebsite

warm Intelligence: Principles, Advances, and Applications delivers in-depth coverage of bat, artificial fish swarm, firefly, cuckoo search, flower pollination, artificial bee colony, wolf search, and gray wolf optimization algorithms. The book begins with a brief introduction to mathematical optimization, addressing basic concepts related to swarm intelligence, such as randomness, random walks, and chaos theory. The text then: Describes the various swarm intelligence optimization methods, standardizing the variants, hybridizations, and algorithms whenever possible
Discusses variants that focus more on binary, discrete, constrained, adaptive, and chaotic versions of the swarm optimizers, Depicts real-world applications of the individual optimizers, emphasizing variable selection and fitness function design, Details the similarities, differences, weaknesses, and strengths of each swarm optimization method and Draws parallels between the operators and searching manners of the different algorithms

Hassanien, A. E., and Eid Emary, Swarm intelligence: principles, advances, and applications, : CRC Press, 2016. Abstract
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Reham Gharbia, and A. E. Hassanien, "Swarm Intelligence Based on Remote Sensing Image Fusion: Comparison between the Particle Swarm Optimization and the Flower Pollination Algorithm ", Handbook of Research on Machine Learning Innovations and Trends, USA, IGI, 2017. Abstract

This chapter presents a remote sensing image fusion based on swarm intelligence. Image fusion is combining multi-sensor images in a single image that has most informative. Remote sensing image fusion is an effective way to extract a large volume of data from multisource images. However, traditional image fusion approaches cannot meet the requirements of applications because they can lose spatial information or distort spectral characteristics. The core of the image fusion is image fusion rules. The main challenge is getting suitable weight of fusion rule. This chapter proposes swarm intelligence to optimize the image fusion rule. Swarm intelligence algorithms are a family of global optimizers inspired by swarm phenomena in nature and have shown better performance. In this chapter, two remote sensing image fusion based on swarm intelligence algorithms, Particle Swarm Optimization (PSO) and flower pollination algorithm are presented to get an adaptive image fusion rule and comparative between them.

Zawbaa, H. M., N. El-Bendary, A. E. Hassanien, and A. Abraham, "SVM-based soccer video summarization system", Nature and Biologically Inspired Computing (NaBIC), 2011 Third World Congress on: IEEE, pp. 7–11, 2011. Abstract
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Zawbaa, H. M., N. El-Bendary, A. E. Hassanien, and A. Abraham, "SVM-based soccer video summarization system", Nature and Biologically Inspired Computing (NaBIC), 2011 Third World Congress on: IEEE, pp. 7–11, 2011. Abstract
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Ali, A. F., and A. - E. Hassanien, "A Survey of Metaheuristics Methods for Bioinformatics Applications", Applications of Intelligent Optimization in Biology and Medicine: Springer International Publishing, pp. 23–46, 2016. Abstract
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Saad, O., A. Darwish, and R. Faraj, "A survey of machine learning techniques for Spam filtering", International Journal of Computer Science and Network Security (IJCSNS), vol. 12, no. 2: International Journal of Computer Science and Network Security, pp. 66, 2012. Abstract
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Zawbaa, H. M., N. El-Bendary, A. E. Hassanien, G. Schaefer, and S. - S. Yeo, "Support Vector Machine based Logo Detection in Broadcast Soccer Videos", The 16th Online World Conference on Soft Computing in Industrial Applications (WSC16) Advances in Intelligent and Soft Computing, , 5-6 Dec, 2011. Abstract

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El Bakrawy, L. M., N. I. Ghali, A. E. Hassanien, and J. F. Peters, "Strict authentication of multimodal biometric images using near sets", Soft Computing in Industrial Applications: Springer Berlin Heidelberg, pp. 249–258, 2011. Abstract
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El Bakrawy, L. M., N. I. Ghali, A. E. Hassanien, and J. F. Peters, "Strict authentication of multimodal biometric images using near sets", Soft Computing in Industrial Applications: Springer Berlin Heidelberg, pp. 249–258, 2011. Abstract
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Hassanien, A. E., A. Abraham, and C. Grosan, "Spiking neural network and wavelets for hiding iris data in digital images", Soft Computing-A Fusion of Foundations, Methodologies and Applications, vol. 13, no. 4: Springer Berlin/Heidelberg, pp. 401–416, 2009. Abstract
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Hassanien, A. E., A. Abraham, and C. Grosan, "Spiking neural network and wavelets for hiding iris data in digital images", Soft Computing-A Fusion of Foundations, Methodologies and Applications, vol. 13, no. 4: Springer Berlin/Heidelberg, pp. 401–416, 2009. Abstract
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Abdelhameed Ibrahim, T. Horiuchi, S. Tominaga, and A. E. Hassanien, "Spectral Reflectance Images and Applications", Image Feature Detectors and Descriptors: Springer International Publishing, pp. 227–254, 2016. Abstract
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Egiazarian, K., and A. E. Hassanien, "Special Issue: Soft Computing in Multimedia Processing", Informatica, vol. 29, issue 3, 2005. Abstractspecial_issue_soft_computing_in_multimedia_process.pdf

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Hassanien, A. E., H. Sakai, D. Slezak, M. K. Chakraborty, and W. Zhu, "Special issue: Rough and Fuzzy Methods for Data Mining", International Journal of Hybrid Intelligent Systems, vol. 8, no. 1, pp. 1, 2011. Abstract
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Hassanien, A. E., H. Sakai, D. Slezak, M. K. Chakraborty, and W. Zhu, "Special issue: Rough and Fuzzy Methods for Data Mining", International Journal of Hybrid Intelligent Systems, vol. 8, no. 1, pp. 1, 2011. Abstract
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Liang Chen, S. Feng, W. Zhang, A. E. Hassanien, and H. Liu, "Sparse ICA based on Infinite Norm for fMRI Analysis ", The 2nd International Conference on Advanced Machine Learning Technologies and Applications , Egypt, November 17-19, , 2014.
Liang Chen, S. Feng, W. Zhang, A. E. Hassanien, and H. Liu, "Sparse ICA Based on Infinite Norm for fMRI Analysis", International Conference on Advanced Machine Learning Technologies and Applications: Springer International Publishing, pp. 379–388, 2014. Abstract
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H. Hannah Inbarani, S. Senthil Kumar, A. E. Hassanien, and A. T. Azar, "Soft Rough Sets for Heart Valve Disease Diagnosis ", The 2nd International Conference on Advanced Machine Learning Technologies and Applications , Egypt, November 17-19, , 2014.
Inbarani, H. H., S. S. Kumar, A. T. Azar, and A. E. Hassanien, "Soft rough sets for heart valve disease diagnosis", International Conference on Advanced Machine Learning Technologies and Applications: Springer International Publishing, pp. 347–356, 2014. Abstract
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Corchado, E. S., V. Snasel, J. Sedano, A. E. Hassanien, J. L. Calvo, and D. Slezak, Soft Computing Models in Industrial and Environmental Applications, 6th International Conference SOCO 2011, : Springer Science & Business Media, 2011. Abstract
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