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.
Wahid, R., N. I. Ghali, H. S. Own, T. - H. Kim, and A. E. Hassanien,
"A Gaussian mixture models approach to human heart signal verification using different feature extraction algorithms",
Computer Applications for Bio-technology, Multimedia, and Ubiquitous City: Springer Berlin Heidelberg, pp. 16–24, 2012.
Abstractn/a
Wahid, R., N. I. Ghali, H. S. Own, T. - H. Kim, and A. E. Hassanien,
"A Gaussian mixture models approach to human heart signal verification using different feature extraction algorithms",
Computer Applications for Bio-technology, Multimedia, and Ubiquitous City: Springer Berlin Heidelberg, pp. 16–24, 2012.
Abstractn/a
Hassanien, A. E., S. H. Basha, and A. S. Abdalla,
"Generalization of Fuzzy C-Means Based on Neutrosophic Logic",
Studies in Informatics and Control, vol. 27, issue 1, pp. 43-54, , 2018.
AbstractThis article presents a New Neutrosophic C-Means (NNCMs) method for clustering. It uses the neutrosophic logic (NL), to generalize the Fuzzy C-Means (FCM) clustering system. The NNCMs system assigns objects to clusters using three degrees of membership: a degree of truth, a degree of indeterminacy, and a degree of falsity, rather than only the truth degree used in the FCM. The indeterminacy degree, in the NL, helps in categorizing objects laying in the intersection and the boundary areas. Therefore, the NNCMs reaches more accurate results in clustering. These degrees are initialized randomly without any constraints. That is followed by calculating the clusters’ centers. Then, iteratively, the NNCMs updates the membership values of every object, and the clusters’ centers. Finally, it measures the accuracy and tests the objective function. The performance of the proposed system is tested on the six real-world databases: Iris, Wine, Wisconsin Diagnostic Breast Cancer, Seeds, Pima, and Statlog (Heart). The comparison between the two systems shows that the proposed NNCMs is more accurate.
Hassanien, A. E., H. Karam, H. Akter, and M. Nakajima:,
"Generation of Missing Medical Slices Using Morphing Technology",
Proceedings of IAPR Workshop on Machine Vision Applications, MVA 1998,, Chiba, Japan, November 17-19, 1998.
Haque, H., A. - E. Hassanien, and M. Nakajima,
"Generation of missing medical slices using morphing technology",
IEICE TRANSACTIONS on Information and Systems, vol. 83, no. 7: The Institute of Electronics, Information and Communication Engineers, pp. 1400–1407, 2000.
Abstractn/a
Haque, H., A. - E. Hassanien, and M. Nakajima,
"Generation of missing medical slices using morphing technology",
IEICE TRANSACTIONS on Information and Systems, vol. 83, no. 7: The Institute of Electronics, Information and Communication Engineers, pp. 1400–1407, 2000.
Abstractn/a
Aziz, A. S. A., A. T. Azar, M. A. Salama, A. E. Hassanien, and S. E. - O. Hanafy,
"Genetic algorithm with different feature selection techniques for anomaly detectors generation",
Computer Science and Information Systems (FedCSIS), 2013 Federated Conference on: IEEE, pp. 769–774, 2013.
Abstractn/a
Ahmed Ibrahim Hafez, N. Ghali, A. E. Hassanien, and A. Fahmy,
"Genetic Algorithms for Multi-Objective Community Detection in Complex Networks ",
IEEE International Conference on Intelligent Systems Design and Applications (ISDA) , Kochi, India, pp. 460 - 465, Nov. 27-29 2012.
AbstractCommunity detection in complex networks has attracted a lot of attention in recent years. Community detection can be viewed as an optimization problem, in which an objective 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. Many single-objective optimization techniques have been used to solve the problem however those approaches have its drawbacks since they try optimizing one objective function and this results to a solution with a particular community structure property. More recently researchers viewed the problem as a multi-objective optimization problem and many approaches have been proposed to solve it. However which objective functions could be used with each other is still under debated since many objective functions have been proposed over the past years and in somehow most of them are similar in definition. In this paper we use Genetic Algorithm (GA) as an effective optimization technique to solve the community detection problem as a single-objective and multi-objective problem, we use the most popular objectives proposed over the past years, and we show how those objective correlate with each other, and their performances when they are used in the single-objective Genetic Algorithm and the Multi-Objective Genetic Algorithm and the community structure properties they tend to produce.
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.
El-Hosseini, M. A., A. E. Hassanien, A. Abraham, and H. Al-Qaheri,
"Genetic annealing optimization: Design and real world applications",
Intelligent Systems Design and Applications, 2008. ISDA'08. Eighth International Conference on, vol. 1: IEEE, pp. 183–188, 2008.
Abstractn/a
El-Hosseini, M. A., A. E. Hassanien, A. Abraham, and H. Al-Qaheri,
"Genetic annealing optimization: Design and real world applications",
Intelligent Systems Design and Applications, 2008. ISDA'08. Eighth International Conference on, vol. 1: IEEE, pp. 183–188, 2008.
Abstractn/a
Elmasry, W. H., H. M. Moftah, N. El-Bendary, and A. E. Hassanien,
"Graph Partitioning based Automatic Segmentation Approach for CT Scan Liver Images",
IEEE Federated Conference on Computer Science and Information Systems, pp. 205–208, Wroclaw - Poland , 9-13 Sept, 2012.
AbstractManual segmentation of liver computerized tomography (CT) images is very time consuming, so it is desired to develop a computer-based approach for the analysis of liver
CT images that can precisely segment the liver without any human intervention. This paper presents normalized cuts graph partitioning approach for liver segmentation from CT images. To evaluate the performance of the presented approach, we present tests on different liver CT images. Experimental results obtained show that the overall accuracy offered by the employed normalized cuts technique is high compared to the well known K-means segmentation approach.
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