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Ali, A. F., A. Mostafa, G. I. Sayed, M. A. Fattah, and A. E. Hassanien, "Nature Inspired Optimization Algorithms for CT Liver Segmentation", Medical Imaging in Clinical Applications: Springer International Publishing, pp. 431–460, 2016. Abstract
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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, no. 1: IGI Global, pp. 50–62, 2017. Abstract
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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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Aziz, A. S. A., A. T. Azar, A. E. Hassanien, and S. E. - O. Hanafy, "Negative Selection Approach Application in Network Intrusion Detection Systems", arXiv preprint arXiv:1403.2716, 2014. Abstract
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Ali, A. F., A. E. Hassanien, and Václav Snášel, "The nelder-mead simplex method with variables partitioning for solving large scale optimization problems", Innovations in Bio-inspired Computing and Applications: Springer International Publishing, pp. 271–284, 2014. Abstract
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Ali, A. F., A. E. Hassanien, and Václav Snášel, "The Nelder-Mead Simplex Method with Variables Partitioning for Solving Large Scale Optimization Problems.", Innovations in Bio-inspired Computing and Applications. Advances in Intelligent Systems and Computing(Springer) , Czech republic , Volume 237, pp. 271-284, 2013.
Hafez, A. I., Hossam M. Zawbaa, A. E. Hassanien, and A. A. Fahmy, "Networks community detection using artificial bee colony swarm optimization", The 5th International Conference on Innovations in Bio-Inspired Computing and Applications (Springer) IBICA2014, Ostrava, Czech Republic., 22-24 June, 2014. Abstractibica2014_p29.pdfibica2014_p27.pdf

Community structure identification in complex networks has been an
important research topic in recent years. Community detection can be viewed as
an optimization problem in which an objective quality function that captures the
intuition of a community as a group of nodes with better internal connectivity
than external connectivity is chosen to be optimized. In this work Artificial bee
colony (ABC) optimization has been used as an effective optimization technique
to solve the community detection problem with the advantage that the number of
communities is automatically determined in the process. However, the algorithm
performance is influenced directly by the quality function used in the optimization
process. A comparison is conducted between different popular communities’
quality measures when used as an objective function within ABC. Experiments
on real life networks show the capability of the ABC to successfully find an optimized
community structure based on the quality function used.

Hafez, A. I., H. M. Zawbaa, A. E. Hassanien, and A. A. Fahmy, "Networks community detection using artificial bee colony swarm optimization", Proceedings of the Fifth International Conference on Innovations in Bio-Inspired Computing and Applications IBICA 2014: Springer International Publishing, pp. 229–239, 2014. Abstract
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Sayed, G. I., and A. E. Hassanien, "Neuro-Imaging Machine Learning Techniques for Alzheimer's Disease Diagnosis ", Handbook of Research on Machine Learning Innovations and Trends, USA, IGI, 2017. Abstract

Alzheimer's disease (AD) is considered one of the most common dementia's forms affecting senior's age staring from 65 and over. The standard method for identifying AD are usually based on behavioral, neuropsychological and cognitive tests and sometimes followed by a brain scan. Advanced medical imagining modalities such as MRI and pattern recognition techniques are became good tools for predicting AD. In this chapter, an automatic AD diagnosis system from MRI images based on using machine learning tools is proposed. A bench mark dataset is used to evaluate the performance of the proposed system. The adopted dataset consists of 20 patients for each diagnosis case including cognitive impairment, Alzheimer's disease and normal. Several evaluation measurements are used to evaluate the robustness of the proposed diagnosis system. The experimental results reveal the good performance of the proposed system.

Sayed, G. I., and A. E. Hassanien, "Neuro-Imaging Machine Learning Techniques for Alzheimer's Disease Diagnosis", Handbook of Research on Machine Learning Innovations and Trends: IGI Global, pp. 522–540, 2017. Abstract
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Ahmed M. Anter, A. E. Hassenian, M. A. Elsoud, and M. F.Tolba, "Neutrosophic sets and fuzzy C-means clustering for improving CT liver image segmentation", The 5th International Conference on Innovations in Bio-Inspired Computing and Applications (Springer) IBICA2014, Ostrava, Czech Republic., 22-24 June, 2014.
Anter, A. M., A. E. Hassanien, M. A. A. ELsoud, and M. F. Tolba, "Neutrosophic sets and fuzzy c-means clustering for improving ct liver image segmentation", Proceedings of the Fifth International Conference on Innovations in Bio-Inspired Computing and Applications IBICA 2014: Springer International Publishing, pp. 193–203, 2014. Abstract
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Emary, E., Waleed Yamany, and A. E. Hassanien, "New approach for feature selection based on rough set and bat algorithm", Computer Engineering & Systems (ICCES), 2014 9th International Conference on: IEEE, pp. 346–353, 2014. Abstract
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Rizk-Allah, R. M., and A. E. Hassanien, "New binary bat algorithm for solving 0–1 knapsack problem", Complex & Intelligent Systems, 2017. Website
Ismael, G., A. E. Hassanien, and A. Darwish, "new chaotic whale optimization algorithm for features selection", Journal of Classification (In review), vol. Springer, 2017.
Asad, A. H., Eid Elamry, A. E. Hassanien, and M. F. Tolba, "New global update mechanism of ant colony system for retinal vessel segmentation", Hybrid Intelligent Systems (HIS), 2013 13th International Conference on: IEEE, pp. 221–227, 2013. Abstract
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Asad, A. H., Eid Elamry, A. E. Hassanien, and M. Tolba, "New Global Update Mechanism of Ant Colony System for Retinal Vessel Segmentation,", 13th IEEE International Conference on Hybrid Intelligent Systems |(HIS13) Tunisia, 4-6 Dec. pp. 222-228, 2013, Tunisia, , 4-6 Dec, 2013.
Asad, A. H., A. T. Azar, and A. E. Hassanien, "A New Heuristic Function of Ant Colony System for Retinal Vessel Segmentation", International Journal of Rough Sets and Data Analysis, vol. 1, issue 2, pp. 14-31, 2014.
Asad, A. H., A. T. Azar, and A. E. Hassanien, "A new heuristic function of ant colony system for retinal vessel segmentation", Medical Imaging: Concepts, Methodologies, Tools, and Applications: IGI Global, pp. 2063–2081, 2017. Abstract
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Darwish, A., M. M. El-Gendy, and A. E. Hassanien, "A New Hybrid Cryptosystem for Internet of Things Applications", Multimedia Forensics and Security: Springer International Publishing, pp. 365–380, 2017. Abstract
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Ali, A. F., A. E. Hassanien, V. Snasel, and M. F.Tolba, "A new hybrid particle swarm optimization with variable neighborhood search for solving unconstrained global optimization problems", The 5th International Conference on Innovations in Bio-Inspired Computing and Applications (Springer) IBICA2014, Ostrava, Czech Republic., 22-24 June, 2014.
Ali, A. F., A. E. Hassanien, Václav Snášel, and M. F. Tolba, "A New Hybrid Particle Swarm Optimization with Variable Neighborhood Search for Solving Unconstrained Global Optimization Problems", Proceedings of the Fifth International Conference on Innovations in Bio-Inspired Computing and Applications IBICA 2014: Springer International Publishing, pp. 151–160, 2014. Abstract
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Waleed Yamany, Alaa Tharwat, M. F. Hassanin, T. Gaber, A. E. Hassanien, and T. - H. Kim, "A new multi-layer perceptrons trainer based on ant lion optimization algorithm", Information Science and Industrial Applications (ISI), 2015 Fourth International Conference on: IEEE, pp. 40–45, 2015. Abstract
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Waleed Yamany, Eid Emary, and A. E. Hassanien, "New Rough Set Attribute Reduction Algorithm Based on Grey Wolf Optimization", The 1st International Conference on Advanced Intelligent System and Informatics (AISI2015), November 28-30, 2015, Beni Suef, Egypt: Springer International Publishing, pp. 241–251, 2016. Abstract
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Waleed Yamany, Eid Emary, and A. E. Hassanien, "New Rough Set Attribute Reduction Algorithm based on Grey Wolf Optimization,", the 1st International Conference on Advanced Intelligent Systems and Informatics (AISI’15) Springer, Beni Suef University, Beni Suef, Egypt , Nov. 28-30, , 2015. Abstract

In this paper, we propose a new attribute reduction strat-
egy based on rough sets and grey wolf optimization (GWO). Rough sets
have been used as an attribute reduction technique with much success,
but current hill-climbing rough set approaches to attribute reduction are
inconvenient at nding optimal reductions as no perfect heuristic can
guarantee optimality. Otherwise, complete searches are not feasible for
even medium sized datasets. So, stochastic approaches provide a promis-
ing attribute reduction technique. Like Genetic Algorithms, GWO is a
new evolutionary computation technique, mimics the leadership hierar-
chy and hunting mechanism of grey wolves in nature. The grey wolf
optimization nd optimal regions of the complex search space through
the interaction of individuals in the population. Compared with GAs,
GWO does not need complex operators such as crossover and mutation,
it requires only primitive and easy mathematical operators, and is com-
putationally inexpensive in terms of both memory and runtime. Experi-
mentation is carried out, using UCI data, which compares the proposed
algorithm with a GA-based approach and other deterministic rough set
reduction algorithms. The results show that GWO is ecient for rough
set-based attribute reduction.