Publications

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Journal Article
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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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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Jui, S. - L., S. Zhang, W. Xiong, F. Yu, M. Fu, D. Wang, and A. E. H. K. and Xiao, "Brain MR Image Tumor Segmentation with 3-Dimensional Intracranial Structure Deformation Features", IEEE Intelligent systems , issue Accepted , 2015. Abstract

Abstract—Extraction of relevant features is of significant importance for brain tumor segmentation systems. In this paper, with the objective of improving brain tumor segmentation accuracy, we present an improved feature extraction component to take advantage of the correlation between intracranial structure deformation and the compression from brain tumor growth. Using 3-dimensional non-rigid registration and deformation modeling techniques, the component is capable of measuring lateral ventricular (LaV) deformation in the volumetric magnetic resonance (MR) images. By verifying the location of the extracted LaV deformation feature data and applying the features on brain tumor segmentation with widely used classification algorithms, the proposed component is evaluated qualitatively and quantitatively with promising results on 11 datasets comprising real patient and simulated images.

Shang-Ling, S. Z. Jui, W. Xiong, F. Yu, M. Fu, D. Wang, A. E. Hassanien, and K. Xiao, "Brain MR Image Tumor Segmentation with 3-Dimensional Intracranial Structure Deformation Features", IEEE Intelligent Systems, vol. 31, pp. 66-76, 2016. AbstractWebsite

Extraction of relevant features is of significant importance for brain tumor segmentation systems. To improve brain tumor segmentation accuracy, the authors present an improved feature extraction component that takes advantage of the correlation between intracranial structure deformation and the compression resulting from brain tumor growth. Using 3D nonrigid registration and deformation modeling techniques, the component measures lateral ventricular (LaV) deformation in volumetric magnetic resonance images. By verifying the location of the extracted LaV deformation feature data and applying the features on brain tumor segmentation with widely used classification algorithms, the authors evaluate the proposed component qualitatively and quantitatively with promising results on 11 datasets comprising real and simulated patient images.

Jui, S. - L., S. Zhang, W. Xiong, F. Yu, M. Fu, D. Wang, A. E. Hassanien, and K. Xiao, "Brain MR image tumor segmentation with 3-Dimensional intracranial structure deformation features", IEEE Intell. Syst. submitted, under review, 2015. Abstract
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Jui, S. - L., S. Zhang, W. Xiong, F. Yu, M. Fu, D. Wang, A. E. Hassanien, and K. Xiao, "Brain MRI Tumor Segmentation with 3D Intracranial Structure Deformation Features", IEEE Intelligent Systems, vol. 31, no. 2: IEEE, pp. 66–76, 2016. Abstract
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Hassanien, A. E., and T. - H. Kim, "Breast cancer diagnosis system based on machine learning techniques", Applied Logic journal, vol. 10, issue 4, pp. 277–284, 2012. AbstractWebsite

This article introduces a hybrid approach that combines the advantages of fuzzy sets, pulse coupled neural networks (PCNNs), and support vector machine, in conjunction with wavelet-based feature extraction. An application of breast cancer MRI imaging has been chosen and hybridization approach has been applied to see their ability and accuracy to classify the breast cancer images into two outcomes: normal or non-normal. The introduced approach starts with an algorithm based on type-II fuzzy sets to enhance the contrast of the input images. This is followed by performing PCNN-based segmentation algorithm in order to identify the region of interest and to detect the boundary of the breast pattern. Then, wavelet-based features are extracted and normalized. Finally, a support vector machine classifier was employed to evaluate the ability of the lesion descriptors for discrimination of different regions of interest to determine whether they represent cancer or not. To evaluate the performance of presented approach, we present tests on different breast MRI images. The experimental results obtained, show that the overall accuracy offered by the employed machine learning techniques is high compared with other machine learning techniques including decision trees, rough sets, neural networks, and fuzzy artmap.

Hassanien, A. E., and T. - H. Kim, "Breast cancer MRI diagnosis approach using support vector machine and pulse coupled neural networks", Journal of Applied Logic, vol. 10, no. 4: Elsevier, pp. 277–284, 2012. Abstract
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Alaa Tharwat, and A. E. Hassanien, "Chaotic antlion algorithm for parameter optimization of support vector machine", Applied Intelligence, vol. 48, issue 3, pp. 670–686, 2018. AbstractWebsite

Support Vector Machine (SVM) is one of the well-known classifiers. SVM parameters such as kernel parameters and penalty parameter (C) significantly influence the classification accuracy. In this paper, a novel Chaotic Antlion Optimization (CALO) algorithm has been proposed to optimize the parameters of SVM classifier, so that the classification error can be reduced. To evaluate the proposed algorithm (CALO-SVM), the experiment adopted six standard datasets which are obtained from UCI machine learning data repository. For verification, the results of the CALO-SVM algorithm are compared with grid search, which is a conventional method of searching parameter values, standard Ant Lion Optimization (ALO) SVM, and three well-known optimization algorithms: Genetic Algorithm (GA), Particle Swarm Optimization (PSO), and Social Emotional Optimization Algorithm (SEOA). The experimental results proved that the proposed algorithm is capable of finding the optimal values of the SVM parameters and avoids the local optima problem. The results also demonstrated lower classification error rates compared with GA, PSO, and SEOA algorithms.