Publications

Export 69 results:
Sort by: Author [ Title  (Asc)] Type Year
A [B] C D E F G H I J K L M N O P Q R S T U V W X Y Z   [Show ALL]
B
Hassan, G., A. E. Hassanien, N. El-Bendary, and A. Fahmy, "Blood vessel segmentation approach for extracting the vasculature on retinal fundus images using Particle Swarm Optimization", Computer Engineering Conference (ICENCO), 2015 11th International: IEEE, pp. 290–296, 2015. Abstract
n/a
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
n/a
Fouad, M. M., K. M. Amin, N. El-Bendary, and A. E. Hassanien, "Brain Computer Interface: A Review", Brain-Computer Interfaces: Springer International Publishing, pp. 3–30, 2015. Abstract
n/a
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
n/a
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
n/a
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
n/a
Xiao, K., A. E. Hassanien, Y. Sun, and E. K. K. Ng, "Brain mr image tumor segmentation with ventricular deformation", Image and Graphics (ICIG), 2011 Sixth International Conference on: IEEE, pp. 297–302, 2011. Abstract
n/a
Xiao, K., A. E. Hassanien, Y. Sun, and E. K. K. Ng, "Brain mr image tumor segmentation with ventricular deformation", Image and Graphics (ICIG), 2011 Sixth International Conference on: IEEE, pp. 297–302, 2011. Abstract
n/a
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
n/a
Hassanien, A. E., and A. A. A. T. Az Ar, Brain-Computer Interfaces, : Springer International Publishing, 2015. Abstract
n/a
Hassanien, A. E., N. El-Bendary, M. Kudělka, and Václav Snášel, "Breast cancer detection and classification using support vector machines and pulse coupled neural network", Proceedings of the Third International Conference on Intelligent Human Computer Interaction (IHCI 2011), Prague, Czech Republic, August, 2011: Springer Berlin Heidelberg, pp. 269–279, 2013. Abstract
n/a
Hassanien, A. E., N. El-Bendary, M. Kudělka, and Václav Snášel, "Breast cancer detection and classification using support vector machines and pulse coupled neural network", Proceedings of the Third International Conference on Intelligent Human Computer Interaction (IHCI 2011), Prague, Czech Republic, August, 2011: Springer Berlin Heidelberg, pp. 269–279, 2013. Abstract
n/a
Hassanien, A. E., N. El-Bendary, M. Kudělka, and Václav Snášel, "Breast cancer detection and classification using support vector machines and pulse coupled neural network", Proceedings of the Third International Conference on Intelligent Human Computer Interaction (IHCI 2011), Prague, Czech Republic, August, 2011: Springer Berlin Heidelberg, pp. 269–279, 2013. Abstract
n/a
Sayed, G. I., A. Darwish, A. E. Hassanien, and J. - S. Pan, "Breast Cancer Diagnosis Approach Based on Meta-Heuristic Optimization Algorithm Inspired by the Bubble-Net Hunting Strategy of Whales", International Conference on Genetic and Evolutionary Computing: Springer International Publishing, pp. 306–313, 2016. Abstract
n/a
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
n/a
Tourism