Publications

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Miscellaneous
Hassanien, A. - E., A. T. Azar, V. Snasel, J. Kacprzyk, and J. H. Abawajy, Big data in complex systems: challenges and opportunities, : Springer, 2015. Abstract
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Hassanien, A. E., T. - H. Kim, J. Kacprzyk, and A. I. Awad, Bio-inspiring Cyber Security and Cloud Services: Trends and Innovations, : Springer, 2014. Abstract
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Hassanien, A. E., and A. A. A. T. Az Ar, Brain-Computer Interfaces, : Springer International Publishing, 2015. Abstract
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Journal Article
Tharwat;, A., A. E. Hassanien;, and B. E. Elnaghi, "A BA-based algorithm for parameter optimization of support vector machine", Pattern recognition letter, 2017. AbstractWebsite

Support Vector Machine (SVM) parameters such as kernel parameter and penalty parameter (C) have a great impact on the complexity and accuracy of predicting model. In this paper, Bat algorithm (BA) has been proposed to optimize the parameters of SVM, so that the classification error can be reduced. To evaluate the proposed model (BA-SVM), the experiment adopted nine standard datasets which are obtained from UCI machine learning data repository. For verification, the results of the BA-SVM algorithm are compared with grid search, which is a conventional method of searching parameter values, and two well-known optimization algorithms: Genetic Algorithm (GA) and Particle Swarm Optimization (PSO). The experimental results proved that the proposed model is capable to find the optimal values of the SVM parameters and avoids the local optima problem. The results also demonstrated lower classification error rates compared with PSO and GA algorithms.

Alaa Tharwat, A. E. Hassanien, and B. E. Elnaghi, "A BA-based algorithm for parameter optimization of Support Vector Machine", Pattern Recognition Letters: North-Holland, 2016. Abstract
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el-hoseny, M., Alaa Tharwat, and A. E. Hassanien, "Bezier Curve Based Path Planning in a Dynamic Field using Modified Genetic Algorithm", Journal of Computational Science, 2017. Website
Salama, M. A., and A. E. Hassanien, "Binarization and Validation in Formal Concept Analysis", International Journal of Systems Biology and Biomedical Technologies, vol. 1, issue 4, pp. 17-28, 2012. AbstractWebsite

Representation and visualization of continuous data using the Formal Concept Analysis (FCA) became an
important requirement in real-life fields. Application of formal concept analysis (FCA) model on numerical
data, a scaling or Discretization / binarization procedures should be applied as preprocessing stage. The
Scaling procedure increases the complexity of computation of the FCA, while the binarization process leads to a distortion in the internal structure of the input data set. The proposed approach uses a binarization procedure prior to applying FCA model, and then applies a validation process to the generated lattice to measure or ensure its degree of accuracy. The introduced approach is based on the evaluation of each attribute according to the objects of its extent set. To prove the validity of the introduced approach, the technique is applied on two data sets in the medical field which are the Indian Diabetes and the Breast Cancer data sets. Both data sets show the generation of a valid lattice.

Salama, M. A., and A. E. Hassanien, "Binarization and validation in formal concept analysis", International Journal of Systems Biology and Biomedical Technologies (IJSBBT), vol. 1, no. 4: IGI Global, pp. 16–27, 2012. Abstract
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Salama, M. A., and A. E. Hassanien, "Binarization and validation in formal concept analysis", International Journal of Systems Biology and Biomedical Technologies (IJSBBT), vol. 1, no. 4: IGI Global, pp. 16–27, 2012. Abstract
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E. Emary, H. M. Zawbaa, and A. E. Hassanien, "Binary ant lion approaches for feature selection", Neurocomputing, vol. 213, 2016. AbstractWebsite

In this paper, binary variants of the ant lion optimizer (ALO) are proposed and used to select the optimal feature subset for classification purposes in wrapper-mode. ALO is one of the recently bio-inspired optimization techniques that imitates the hunting process of ant lions. Moreover, ALO balances exploration and exploitation using a single operator that can adaptively searches the domain of solutions for the optimal solution. Binary variants introduced here are performed using two different approaches. The first approach takes only the inspiration of ALO operators and makes the corresponding binary operators. In the second approach, the native ALO is applied while its continuous steps are threshold using suitable threshold function after squashing them. The proposed approaches for binary ant lion optimizer (BALO) are utilized in the feature selection domain for finding feature subset that maximizing the classification performance while minimizing the number of selected features. The proposed binary algorithms were compared to three common optimization algorithms hired in this domain namely particle swarm optimizer (PSO), genetic algorithms (GAs), binary bat algorithm (BBA), as well as the native ALO. A set of assessment indicators is used to evaluate and compare the different methods over 21 data sets from the UCI repository. Results prove the capability of the proposed binary algorithms to search the feature space for optimal feature combinations regardless of the initialization and the used stochastic operators.

Emary, E., H. M. Zawbaa, and A. E. Hassanien, "Binary ant lion approaches for feature selection", Neurocomputing, vol. 213: Elsevier, pp. 54–65, 2016. Abstract
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Emarya, E., H. M. Zawbaab, and A. E. Hassanien, "Binary Gray Wolf Optimization Approaches for Feature Selection", Neurocomputing, 2015. AbstractWebsite

In this work, a novel binary version of the gray wolf optimization (GWO) is proposed and used to select optimal feature subset for classification purposes. Gray wolf optimizer (GWO) is one of the latest bio-inspired optimization techniques, which simulate the hunting process of gray wolves in nature. The binary version introduced here is performed using two different approaches. In the first approach, individual steps toward the first three best solutions are binarized and then stochastic crossover is performed among the three basic moves to find the updated binary gray wolf position. In the second approach, sigmoidal function is used to squash the continuous updated position, then stochastically threshold these values to find the updated binary gray wolf position. The two approach for binary gray wolf optimization (bGWO) are hired in the feature selection domain for finding feature subset maximizing the classification accuracy while minimizing the number of selected features. The proposed binary versions were compared to two of the common optimizers used in this domain namely particle swarm optimizer and genetic algorithms. A set of assessment indicators are used to evaluate and compared the different methods over 18 different datasets from the UCI repository. Results prove the capability of the proposed binary version of gray wolf optimization (bGWO) to search the feature space for optimal feature combinations regardless of the initialization and the used stochastic operators.

Emary, E., H. M. Zawbaa, and A. E. Hassanien, "Binary grey wolf optimization approaches for feature selection", Neurocomputing, vol. 172, issue 8, pp. 371–381, 2016. Website
Eid Emary, H. M. Zawbaa, and A. E. Hassanien, "Binary grey wolf optimization approaches for feature selection", Neurocomputing, vol. 172: Elsevier, pp. 371–381, 2016. Abstract
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Gaber, T., Alaa Tharwat, A. E. Hassanien, and V. Snasel, "Biometric cattle identification approach based on Weber’s Local Descriptor and AdaBoost classifier", Computers and Electronics in Agriculture, vol. 122 , issue March 2016 , pp. 55–66, 2016. Website
Gaber, T., Alaa Tharwat, A. E. Hassanien, and V. Snasel, "Biometric cattle identification approach based on weber’s local descriptor and adaboost classifier", Computers and Electronics in Agriculture, vol. 122: Elsevier, pp. 55–66, 2016. Abstract
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Mona M. Soliman, A. E. Hassanien, and H. M. Ons, "A Blind 3D Watermarking Approach for 3D Mesh Using Clustering Based Methods", IJCVIP - International Journal of Computer Vision and Image Processing, vol. 3, issue 2, pp. 43-53, 2013. Website
Soliman, M. M., A. E. Hassanien, and H. M. Onsi, "A Blind 3D Watermarking Approach for 3D Mesh Using Clustering Based Methods", International Journal of Computer Vision and Image Processing (IJCVIP), vol. 3, no. 2: IGI Global, pp. 43–53, 2013. Abstract
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Soliman, M. M., A. E. Hassanien, and H. M. Onsi, "A Blind 3D Watermarking Approach for 3D Mesh Using Clustering Based Methods", International Journal of Computer Vision and Image Processing (IJCVIP), vol. 3, no. 2: IGI Global, pp. 43–53, 2013. Abstract
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Lamiaa M. El Bakrawy, N. I.Ghali, T. - H. Kim, and A. E. Hassanien, "A Block-wise-based Fragile Watermarking Hybrid Approach using Rough Sets and Exponential Particle Swarm Optimization ", Journal of Future Generation Communication and Networking, , vol. 4, issue 4, 2011. Abstractblock-wise-based_fragile_watermarking.pdf

In this paper, we propose a fragile watermarking hybrid approach using rough set kmeans and exponential particle swarm optimization (EPSO) systems. It is based on a block-wise dependency mechanism which can detect any alterations made to the protected image. Initially, the input image is divided into blocks with equal size in order to improve image tamper localization precision. Then feature sequence is generated by applying rough k-means and EPSO clustering to create the relationship between all image blocks and cluster
all of them since EPSO is used to optimize the parameters of rough k-means. Both feature sequence and generated secret key are used to construct the authentication data. Each resultant 8-bit authentication data is embedded into the eight least significant bits (LSBs) of the corresponding image block. We gives experimental results which show the feasibility of using these optimization algorithms for the fragile watermarking and demonstrate the
accuracy of the proposed approach. The performance comparison of the approach was also realized. The performance of a fragile watermarking approach has been improved in this paper by using exponential particle swarm optimization (EPSO) to optimize the rough kmean parameters. The proposed approach can embed watermark without causing noticeable visual artifacts, and does not only achieve superior tamper detection in images accurately,
it also recovers tampered regions effectively. In addition, the results show that the proposed approach can effectively thwart different attacks, such as the cut-and paste attack and collage attack, while sustaining superior tamper detection and localization accuracy.

El Bakrawy, L. M., N. I. Ghali, T. - H. Kim, and A. E. Hassanien, "A Block-wise-based Fragile Watermarking Hybrid Approach using Rough Sets and Exponential Particle Swarm Optimization", International Journal of Future Generation Communication and Networking, vol. 4, no. 4, pp. 77–88, 2011. Abstract
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El Bakrawy, L. M., N. I. Ghali, T. - H. Kim, and A. E. Hassanien, "A Block-wise-based Fragile Watermarking Hybrid Approach using Rough Sets and Exponential Particle Swarm Optimization", International Journal of Future Generation Communication and Networking, vol. 4, no. 4, pp. 77–88, 2011. Abstract
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Ahmed I. Hafez, AE Hassanien, F. A. A., "BNEM - A Fast Community Detection Algorithm using generative models", Social Network Analysis and Mining, , vol. 4(, issue 1, pp. 1-20,, 2014. AbstractWebsite

Actors in social networks tend to form community groups based on common location, interests, occupation, etc. Communities play special roles in the structure–function relationship; therefore, detecting such communities can be a way to describe and analyze such networks. However, the size of those networks has grown tremendously with the increase of computational power and data storage. While various methods have been developed to extract community structures, their computational cost or the difficulty to parallelize existing algorithms make partitioning real networks into communities a challenging problem. In this paper, we introduce a generative process to model the interactions between social network’s actors. Through unsupervised learning using expectation maximization, we derive an efficient and fast community detection algorithm based on Bayesian network and expectation maximization (BNEM). We show that BNEM algorithm can infer communities within directed or undirected networks, and within weighted or un-weighted networks. We also show that the algorithm is easy to parallelize. We then explore and analyze the result of the BNEM method. Finally, we conduct a comparative analysis with other well-known methods in the fields of community detection.

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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Xiao, K., S. H. Ho, and A. E. Hassanien, "Brain magnetic resonance image lateral ventricles deformation analysis and tumor prediction", Malaysian Journal of Computer Science, vol. 20, no. 2: Faculty of Computer Science and Information Technology, pp. 115, 2007. Abstract
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