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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.

S
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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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.

Sato, M., A. E. Hassanien, and M. Nakajima, "Nonlinear registration of medical images using Cauchy-Navier spline transformation", Medical Imaging'99: International Society for Optics and Photonics, pp. 774–781, 1999. Abstract
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Sato, M., A. E. Hassanien, and M. Nakajima, "Non-Linear Image Registration: Combining Viscous Fluid Deformations and Elastic Body Splines", 映像情報メディア学会技術報告, vol. 22, no. 45: 一般社団法人映像情報メディア学会, pp. 1–6, 1998. 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
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Kudelka, M., V. Snasel, Z. Horak, A. E. Hassanien, A. Abraham, and J. D. Velásquez, "A novel approach for comparing web sites by using MicroGenres", Engineering Applications of Artificial Intelligence, vol. 35: Pergamon, pp. 187–198, 2014. Abstract
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Kudelka, M., V. Snasel, Z. Horak, A. E. Hassanien, A. Abraham, and J. D. Velásquez, "A novel approach for comparing web sites by using MicroGenres", Engineering Applications of Artificial Intelligence,, vol. 35, pp. 178-198, 2014. AbstractWebsite

In this paper, a novel approach is introduced to compare web sites by analysing their web page content. Each web page can be expressed as a set of entities called MicroGenres, which in turn are abstractions about design patterns and genres for representing the page content. This description is useful for web page and web site classification and for a deeper insight into the web site׳s social context.

The web site comparison is useful for extracting patterns which can be used for improving Web search engine effectiveness, the identification of best practices in web site design and of course in the organization of web page content to personalize the web user experience on a web site.

The effectiveness of the proposed approach was tested in a real world case, with e-shop web sites showing that a web site can be represented in a high level of abstraction by using MicroGenres, the contents of which can then be compared and given a measure corresponding to web site similarity. This measure is very useful for detecting web communities on the Web, i.e., a group of web sites sharing similar contents, and the result is essential in performing a focused and effective information search as well as minimizing web page retrieval.

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Ismail, F. H., A. F. Ali, S. Esmat, and A. E. Hassanien, "Newcastle Disease Virus Clustering Based on Swarm Rapid Centroid Estimation", Advances in Nature and Biologically Inspired Computing: Springer International Publishing, pp. 359–367, 2016. Abstract
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Ismael, G., A. E. Hassanien, and A. Darwish, "new chaotic whale optimization algorithm for features selection", Journal of Classification (In review), vol. Springer, 2017.
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Hassanien, A. E., M. A. Fattah, K. M. AMIN, and S. MOHAMED, "A Novel Hybrid Binarization Technique for Images of Historical Arabic Manuscripts", Studies in Informatics and Control, , vol. 24, issue 3, pp. 271-282, 2015. AbstractWebsite

In this paper, a novel binarization approach based on neutrosophic sets and sauvola’s approach is presented.
This approach is used for historical Arabic manuscript images which have problems with types of noise. The input RGB image is changed into the NS domain, which is shown using three subsets, namely, the percentage of indeterminacy in a subset, the percentage of falsity in a subset and the percentage of truth in a subset. The entropy in NS is used for evaluating the indeterminacy with the most important operation ”λ mean” operation in order to minimize indeterminacy which can be used to reduce noise. Finally, the manuscript is binarized using an adaptive thresholding technique. The main advantage of the proposed approach is that it preserves weak connections and provides smooth and continuous strokes. The performance of the proposed approach is evaluated both objectively and subjectively against standard databases and manually collected data base. The proposed method gives high results compared with other famous binarization approaches

Hassanien, A. E., M. A. Fattah, K. M. AMIN, and S. MOHAMED, "A novel hybrid binarization technique for images of historical Arabic manuscripts", Studies in Informatics and Control, vol. 24, no. 3, pp. 271–282, 2015. Abstract
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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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Gaber, T., N. Zhang, and A. E. Hassanien, "A novel approach to allow multiple resales of DRM-protected contents", Computer Engineering & Systems (ICCES), 2013 8th International Conference on: IEEE, pp. 86–91, 2013. 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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D
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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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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A
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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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., 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.