Publications

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2016
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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2017
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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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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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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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.

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.
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