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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.
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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Hafez, A. I., H. M. Zawbaa, E. Emary, H. A. Mahmoud, and A. E. Hassanien, "An innovative approach for feature selection based on chicken swarm optimization", Soft Computing and Pattern Recognition (SoCPaR), 2015 7th International Conference of: IEEE, pp. 19–24, 2015. Abstract
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Hafez, A. I., A. E. Hassanien, and A. A. Fahmy, "Testing community detection algorithms: A closer look at datasets", Social Networking: Springer International Publishing, pp. 85–99, 2014. Abstract
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Hafez, A. I., E. T. Al-Shammari, A. E. Hassanien, and A. A. Fahmy, "Genetic algorithms for multi-objective community detection in complex networks", Social Networks: A Framework of Computational Intelligence: Springer International Publishing, pp. 145–171, 2014. Abstract
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Hafez, A. I., A. E. Hassanien, A. A. Fahmy, and M. F. Tolba, "Community detection in social networks by using Bayesian network and Expectation Maximization technique", Hybrid Intelligent Systems (HIS), 2013 13th International Conference on: IEEE, pp. 209–214, 2013. Abstract
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Hafez, A. I., E. T. Al-Shammari, A. E. Hassanien, and A. A. Fahmy, "Community detection in social networks using logic-based probabilistic programming", International Journal of Social Network Mining, vol. 2, no. 2: Inderscience Publishers (IEL), pp. 158–172, 2015. Abstract
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Hafez, A. I., A. E. Hassanien, A. Fahmy, and M. Tolba, "Community Detection in Social Networks by using Bayesian network and Expectation Maximization technique", 13th IEEE International Conference on Hybrid Intelligent Systems (HIS13) Tunisia, 4-6 Dec. pp. 201-215, 2013, Tunisia, , 4-6 Dec, 2013.
Hafez, A. I., H. M. Zawbaa, E. Emary, and A. E. Hassanien, "Sine cosine optimization algorithm for feature selection", INnovations in Intelligent SysTems and Applications (INISTA), 2016 International Symposium on: IEEE, pp. 1–5, 2016. Abstract
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Hafez, A. I., E. T. Al-Shammari, A. E. Hassanien, and A. A. Fahmy:, "Genetic Algorithms for Multi-Objective Community Detection in Complex Networks.", Social Networks: A Framework of Computational Intelligence , London, Volume 526, pp 145-171, Springer, 2014.
Hafez, A. I., A. E. Hassanien, and H. M. Zawbaa, "Hybrid Swarm Intelligence Algorithms for Feature Selection: Monkey and Krill Herd Algorithms", IEEE iInternational Computer Engineering Conference - ICENCO , Cairo, 30 Dec, 2015.
Hafez, A. I., E. T. Al-Shammari, A. E. Hassanien, and A. A. Fahmy, "Genetic algorithms for multi-objective community detection in complex networks", Social Networks: A Framework of Computational Intelligence: Springer International Publishing, pp. 145–171, 2014. Abstract
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Hafez, A. I., Hossam M. Zawbaa, E. Emary, and A. E. H. Hamdi A. Mahmoud, "An innovative approach for feature selection based on chicken swarm optimization", 7th IEEE International Conference of Soft Computing and Pattern Recognition, , Kyushu University, Fukuoka, Japan,, November 13 - 1, 2015. Abstract

In this paper, a system for feature selection based
on chicken swarm optimization (CSO) algorithm is proposed.
Datasets ordinarily includes a huge number of attributes, with
irrelevant and redundant attribute. Commonly wrapper-based
approaches are used for feature selection but it always requires
an intelligent search technique as part of the evaluation function.
Chicken swarm optimization (CSO)is a new bio-inspired
algorithm mimicking the hierarchal order of the chicken
swarm and the behaviors of chicken swarm, including roosters,
hens and chicks, CSO can efficiently extract the chickens’
swarm intelligence to optimize problems. Therefore, CSO was
employed to feature selection in wrapper mode to search
the feature space for optimal feature combination maximizing
classification performance, while minimizing the number of
selected features. The proposed system was benchmarked
on 18 datasets drawn from the UCI repository and using
different evaluation criteria and proves advance over particle
swarm optimization (PSO) and genetic algorithms (GA) that
commonly used in optimization problems

Hafez, A. I., A. E. Hassanien, H. M. Zawbaa, and E. Emary, "Hybrid monkey algorithm with krill herd algorithm optimization for feature selection", Computer Engineering Conference (ICENCO), 2015 11th International: IEEE, pp. 273–277, 2015. Abstract
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Hafez, A. I., A. E. Hassanien, and A. A. Fahmy, "BNEM: a fast community detection algorithm using generative models", Social Network Analysis and Mining, vol. 4, no. 1: Springer Vienna, pp. 1–20, 2014. Abstract
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Hafez, A. I., N. I. Ghali, A. E. Hassanien, and A. A. Fahmy, "Genetic algorithms for community detection in social networks", Intelligent Systems Design and Applications (ISDA), 2012 12th International Conference on: IEEE, pp. 460–465, 2012. Abstract
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Hafez, A. I., N. I. Ghali, A. E. Hassanien, and A. A. Fahmy, "Genetic algorithms for community detection in social networks", Intelligent Systems Design and Applications (ISDA), 2012 12th International Conference on: IEEE, pp. 460–465, 2012. 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;, E. Emary;, and A. E. Hassanien, "Sine cosine optimization algorithm for feature selection ", 016 International Symposium on INnovations in Intelligent SysTems and Applications , Romania, 2-5 Aug., 2016. Abstract

Nowadays, a dataset includes a huge number of features with irrelevant and redundant ones. Feature selection is required for a better machine-learning algorithms' performance. A system for feature selection is proposed in this work using a sine cosine algorithm (SCA). SCA is a new stochastic search algorithm for optimization problems. SCA optimization adaptively balances the exploration and exploitation to find the optimal solution quickly. The SCA can quickly explore the feature space for optimal or near-optimal feature subset minimizing a given fitness function. The proposed fitness function used incorporates both classification accuracy and feature size reduction. The proposed system was tested on 18 datasets and shows an advance over other search methods as particle swarm optimization (PSO) and genetic algorithm (GA) optimizers commonly used in this context using different evaluation indicators.

Hala S. Own, N. I.Ghali, and A. E. Hassanien, "Hybrid Dual-Tree Wavelet Transform and Adaptive Threshold for Image Denoising", International Journal of Imaging and Robotic Systems, , vol. 7, issue S13, 2013.
Hamad, A., E. H. Houssein, A. E. Hassanien, and A. A. Fahmy, "Hybrid Grasshopper Optimization Algorithm and Support Vector Machines for Automatic Seizure Detection in EEG Signals", AMLTA 2018: The International Conference on Advanced Machine Learning Technologies and Applications (AMLTA2018), Cairo, 23 fEB, 2018. Abstract

In this paper, a hybrid classification model using Grasshopper Optimization Algorithm (GOA) and support vector machines (SVMs) for automatic seizure detection in EEG is proposed called GOA-SVM approach. Various parameters were extracted and employed as the features to train the SVM with radial basis function (RBF) kernel function (SVM-RBF) classifiers. GOA was used for selecting the effective feature subset and the optimal settings of SVMs parameters in order to obtain a successful EEG classification. The experimental results confirmed that the proposed GOA-SVM approach, able to detect epileptic and could thus further enhance the diagnosis of epilepsy with accuracy 100% for normal subject data versus epileptic data. Furthermore, the proposed approach has been compared with Particle Swarm Optimization (PSO) with support vector machines (PSO-SVMs) and SVM using RBF kernel function. The computational results reveal that GOA-SVM approach achieved better classification accuracy outperforms both PSO-SVM and typical SVMs.

Hamad, A., E. H. Houssein, A. E. Hassanien, and A. A. Fahmy, "Feature extraction of epilepsy EEG using discrete wavelet transform", IEEE International Conference on Systems, Man, and Cybernetics (SMC), 9, Cairo, 28-29 Dec. , 2016. Abstract

Epilepsy is one of the most common a chronic neurological disorders of the brain that affect millions of the world's populations. It is characterized by recurrent seizures, which are physical reactions to sudden, usually brief, excessive electrical discharges in a group of brain cells. Hence, seizure identification has great importance in clinical therapy of epileptic patients. Electroencephalogram (EEG) is most commonly used in epilepsy detection since it includes precious physiological information of the brain. However, it could be a challenge to detect the subtle but critical changes included in EEG signals. Feature extraction of EEG signals is core trouble on EEG-based brain mapping analysis. This paper will extract ten features from EEG signal based on discrete wavelet transform (DWT) for epilepsy detection. These numerous features will help the classifiers to achieve a good accuracy when utilize to classify EEG signal to detect epilepsy. Subsequently, the results have illustrated that DWT has been adopted to extract various features i.e., Entropy, Min, Max, Mean, Median, Standard deviation, Variance, Skewness, Energy and Relative Wave Energy (RWE).

Hamad, A., E. H. Houssein, A. E. Hassanien, and A. A. Fahmy, "Feature extraction of epilepsy EEG using discrete wavelet transform", Computer Engineering Conference (ICENCO), 2016 12th International: IEEE, pp. 190–195, 2016. Abstract
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Hamdy, A., H. Hefny, M. A. Salama, A. E. Hassanien, and T. - H. Kim, "The importance of handling multivariate attributes in the identification of heart valve diseases using heart signals", Computer Science and Information Systems (FedCSIS), 2012 Federated Conference on: IEEE, pp. 75–79, 2012. Abstract
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Hamdy, A., H. Hefny, M. A. Salama, A. E. Hassanien, and T. - H. Kim, "The importance of handling multivariate attributes in the identification of heart valve diseases using heart signals", Computer Science and Information Systems (FedCSIS), 2012 Federated Conference on: IEEE, pp. 75–79, 2012. Abstract
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