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Farouk, A., M. Elhoseny, and A. E. Hassanien, "A Proposed Architecture for Key Management Schema in Centralized Quantum Network", Handbook of Research on Machine Learning Innovations and Trends, USA, IGI, 2017. Abstract

Most existing realizations of quantum key distribution (QKD) are point-to-point systems with one source transferring to only one destination. Growth of these single-receiver systems has now achieved a reasonably sophisticated point. However, many communication systems operate in a point-to-multi-point (Multicast) configuration rather than in point-to-point mode, so it is crucial to demonstrate compatibility with this type of network in order to maximize the application range for QKD. Therefore, this chapter proposed architecture for implementing a multicast quantum key distribution Schema. The proposed architecture is designed as a Multicast Centralized Key Management Scheme Using Quantum Key Distribution and Classical Symmetric Encryption. In this architecture, a secured key generation and distribution solution has been proposed for a single host sending to two or more (N) receivers using centralized Quantum Multicast Key Distribution Centre and classical symmetric encryption.

Farouk, A., M. Elhoseny, J. Batle, M. Naseri, and A. E. Hassanien, "A Proposed Architecture for Key Management Schema in Centralized Quantum Network", Handbook of Research on Machine Learning Innovations and Trends: IGI Global, pp. 997–1021, 2017. Abstract
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Fatma Yakoub, Moustafa Zein, K. Y. A. A. A. E. H., "Predicting Personality Traits and Social Context Based on Mining the Smartphones SMS Data", Proceedings of the Second Euro-China Conference on Intelligent Data Analysis and Applications, ECC 2015, , Ostrava, Czech Republic, , June 29 - July , 2015. Abstract

Reality Mining is one of the first efforts that have been exerted to utilize smartphone’s data; to analyze human behavior. The smartphone data are used to identify human behavior and discover more attributes about smartphone users, such as their personality traits and their relationship status. Text messages and SMS logs are two of the main data resources from the smartphones. In this paper, The proposed system define the user personality by observing behavioral characteristics derived from smartphone logs and the language used in text messages. Hence, The supervised machine learning methods (K-nearest nighbor (KNN), support vector machine, and Naive Bayes) and text mining techniques are used in studying the textual matter messages. From this study, The correlation between text messages and predicate users personality traits is broken down. The results provided an overview on how text messages and smartphone logs represent the user behavior; as they chew over the user personality traits with accuracy up to 70 %.

Fattah, M. A., M. A. A. ELsoud, A. E. Hassanien, and T. - H. Kim, "Automated classification of galaxies using invariant moments", International Conference on Future Generation Information Technology: Springer Berlin Heidelberg, pp. 103–111, 2012. Abstract
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Fattah, M. A., N. Elbendary, H. K. Elminir, M. A. A. El-Soud, and A. E. Hassanien, "Galaxies image classification using empirical mode decomposition and machine learning techniques", Engineering and Technology (ICET), 2014 International Conference on: IEEE, pp. 1–5, 2014. Abstract
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Fattah, M. A., N. El-Bendary, M. A. A. Elsoud, Jan Platoš, and A. E. Hassanien, "Principal component analysis neural network hybrid Classification Approach for Galaxies Images", Innovations in Bio-inspired Computing and Applications: Springer International Publishing, pp. 225–237, 2014. Abstract
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Fattah, M. A., S. Abuelenin, A. E. Hassanien, and J. - S. Pan, "Handwritten Arabic Manuscript Image Binarization Using Sine Cosine Optimization Algorithm", International Conference on Genetic and Evolutionary Computing: Springer International Publishing, pp. 273–280, 2016. Abstract
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Fattah, M. A., N. Elbendary, H. K. Elminir, M. A. A. El-Soud, and A. E. Hassanien, "Galaxies image classification using empirical mode decomposition and machine learning techniques ", The second International Conference on Engineering and Technology (ICET 2014) , German Uni - Cairo Egypt, 19 Apr - 20 Apr , 2014.
Fattah, M. A., N. Elbendary, M. A. Elsoud, H. Aboul Ella, and M. Tolba, "An Intelligent Approach for Galaxies Images Classification.", 13th IEEE International Conference on Hybrid Intelligent Systems (HIS13) Tunisia, pp. 168-173, 2013, Tunisia, , 4-6 Dec., 2013.
Fattah, M. A., N. El-Bendary, M. A. A. ELsoud, A. E. Hassanien, and M. F. Tolba, "An intelligent approach for galaxies images classification", Hybrid Intelligent Systems (HIS), 2013 13th International Conference on: IEEE, pp. 167–172, 2013. Abstract
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Fattah, M. A., M. I. Waly, M. A. A. ELsoud, A. E. Hassanien, M. F. Tolba, J. Platos, and G. Schaefer, "An improved prediction approach for progression of ocular hypertension to primary open angle glaucoma", Proceedings of the Fifth International Conference on Innovations in Bio-Inspired Computing and Applications IBICA 2014: Springer International Publishing, pp. 405–412, 2014. Abstract
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Fattah, M. A., M. A. A. ELsoud, A. E. Hassanien, and T. - H. Kim, "Automated classification of galaxies using invariant moments", International Conference on Future Generation Information Technology: Springer Berlin Heidelberg, pp. 103–111, 2012. Abstract
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Fattah, M. A., A. E. H. , and A. F. A. Abdalla Mostafa, "Artificial Bee Colony Optimizer for Historical Arabic Manuscript Images Binarization", IEEE iInternational Computer Engineering Conference - ICENCO , Cairo, 30 Dec, 2015.
Fattah, M. A., A. E. Hassanien, A. Mostafa, A. F. Ali, K. M. Amin, and S. MOHAMED, "Artificial bee colony optimizer for historical Arabic manuscript images binarization", Computer Engineering Conference (ICENCO), 2015 11th International: IEEE, pp. 251–255, 2015. Abstract
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Fattah, M. A., N. Elbendary, M. A. Elsoud, H. Aboul Ella, and M. Tolba, "An Intelligent Approach for Galaxies Images Classification.", 13th IEEE International Conference on Hybrid Intelligent Systems (HIS13) Tunisia, pp. 168-173, 2013, Tunisia, , 4-6 Dec., 2013.
Fouad, M. M. M., N. El-Bendary, R. A. Ramadan, and A. E. Hassanien, "Wireless sensor Networks: a medical perspective", Wireless Sensor Networks: From Theory to Applications: CRC Press, 2013. Abstract
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Fouad, M. M., A. I. Hafez, A. E. Hassanien, and V. Snasel, "Grey Wolves Optimizer-based Localization Approach in WSNs", IEEE iInternational Computer Engineering Conference - ICENCO , Bilbao, Spain, 30 Dec, 2015.
Fouad, M. M., A. I. Hafez, A. E. Hassanien, and V. Snasel, "Grey Wolves Optimizer-based localization approach in WSNs", Computer Engineering Conference (ICENCO), 2015 11th International: IEEE, pp. 256–260, 2015. Abstract
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Fouad, M. M., H. M. Zawbaa, T. Gaber, V. Snasel, and A. E. Hassanien, "A Fish Detection Approach Based on BAT Algorithm", The 1st International Conference on Advanced Intelligent System and Informatics (AISI2015), November 28-30, 2015, Beni Suef, Egypt: Springer International Publishing, pp. 273–283, 2016. Abstract
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Fouad, M. M. M., H. zawbaa, N. Elbendary, and H. Aboul Ella, "Automatic Nile Tilapia Fish Classification Approach using Machine Learning Techniques", 13th IEEE International Conference on Hybrid Intelligent Systems |(HIS13) Tunisia, 4-6 Dec. pp. 173-179, , Tunisia, , 4-6 Dec, 2013.
Fouad, M. M. M., N. El-Bendary, R. A. Ramadan, and A. E. Hassanien, "Wireless Sensor Networks, A Medical Perspective", Wireless Sensor Networks: Theory and Applications, pp. 713-732 , USA, CRC Press, Taylor and Francis Group, 2013. k15146_c024.pdf
Fouad, M. M. M., H. M. Zawbaa, N. El-Bendary, and A. E. Hassanien, "Automatic nile tilapia fish classification approach using machine learning techniques", Hybrid Intelligent Systems (HIS), 2013 13th International Conference on: IEEE, pp. 173–178, 2013. Abstract
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Fouad, M. M. M., N. El-Bendary, R. A. Ramadan, and A. E. Hassanien, "Wireless sensor Networks: a medical perspective", Wireless Sensor Networks: From Theory to Applications: CRC Press, 2013. Abstract
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Fouad, M. M., H. M. Zawbaa, T. Gaber, V. Snasel, and A. E. Hassanien, "A Fish Detection Approach Based on BAT Algorithm", the 1st International Conference on Advanced Intelligent Systems and Informatics (AISI’15) Springer, Beni Suef University, Beni Suef, Eg, Nov. 28-30, 2015. Abstract

Fish detection and identi cation are important steps towards
monitoring sh behavior. The importance of such monitoring step comes
from the need for better understanding of the sh ecology and issuing
conservative actions for keeping the safety of this vital food resource.
The recent advances in machine learning approaches allow many appli-
cations to easily analyze and detect a number of sh species. The main
competence between these approaches is based on two main detection
parameters: the time and the accuracy measurements. Therefore, this
paper proposes a sh detection approach based on BAT optimization
algorithm (BA). This approach aims to reduce the classi cation time
within the sh detection process. The performance of this system was
evaluated by a number of well-known machine learning classi ers, KNN,
ANN, and SVM. The approach was tested with 151 images to detect the
Nile Tilapia sh species and the results showed that k-NN can achieve
high accuracy 90%, with feature reduction ratio

Fouad, M. M., V. Snasel, and A. E. Hassanien, "An Adaptive PSO-Based Sink Node Localization Approach for Wireless Sensor Networks", Proceedings of the Second International Afro-European Conference for Industrial Advancement AECIA 2015: Springer International Publishing, pp. 679–688, 2016. Abstract
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