Publications

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2009
Wu, Z. Q., J. Jiang, and Y. H. Peng, "Computational Intelligence on Medical Imaging with Artificial Neural Networks in", Computational intelligence in medical imaging techniques and applications, 2009. Abstract
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Watchareeruetai, U., T. Matsumoto, Y. Takeuchi, H. Kudo, and N. Ohnishi, "Efficient construction of image feature extraction programs by using linear genetic programming with fitness retrieval and intermediate-result caching", Foundations of Computational Intelligence Volume 4: Springer Berlin Heidelberg, pp. 355–375, 2009. Abstract
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Watchareeruetai, U., T. Matsumoto, Y. Takeuchi, H. Kudo, and N. Ohnishi, "Efficient construction of image feature extraction programs by using linear genetic programming with fitness retrieval and intermediate-result caching", Foundations of Computational Intelligence Volume 4: Springer Berlin Heidelberg, pp. 355–375, 2009. Abstract
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Tobin, K. W., E. Chaum, J. Gregor, T. P. Karnowski, J. R. Price, and J. Wall, "Image Informatics for Clinical and Preclinical Biomedical Analysis", Computational Intelligence in Medical Imaging: Techniques and Applications: CRC Press, pp. 239, 2009. Abstract
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2012
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. Abstract
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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. Abstract
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Ghali, N. I., R. Wahid, and A. E. Hassanien, "Heart Sounds Human Identification and Verification Approaches using Vector Quantization and Gaussian Mixture Models", International Journal of Systems Biology and Biomedical Technologies (IJSBBT), vol. 1, no. 4: IGI Global, pp. 74–87, 2012. Abstract
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Ghali, N. I., R. Wahid, and A. E. Hassanien, "Heart Sounds Human Identification and Verification Approaches using Vector Quantization and Gaussian Mixture Models", International Journal of Systems Biology and Biomedical Technologies (IJSBBT), vol. 1, no. 4: IGI Global, pp. 74–87, 2012. Abstract
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Li, T., H. S. Nguyen, G. Wang, J. W. Grzymala-Busse, R. Janicki, A. - E. Hassanien, and H. Yu, Rough Sets and Knowledge Technology: 7th International Conference, RSKT 2012, Chengdu, China, August 17-20, 2012, Proceedings, : Springer, 2012. Abstract
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Li, T., H. S. Nguyen, G. Wang, J. W. Grzymala-Busse, R. Janicki, A. - E. Hassanien, and H. Yu, Rough Sets and Knowledge Technology: 7th International Conference, RSKT 2012, Chengdu, China, August 17-20, 2012, Proceedings, : Springer, 2012. Abstract
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Wahid, R., N. I. Ghali, H. S. Own, T. - H. Kim, and A. ella Hassanien., " A Gaussian Mixture Models Approach to Human Heart Signal Verification Using Different Feature Extraction Algorithms ", International Conference on Bio-Science and Bio-Technology (BSBT2012),, , Kangwondo, Korea, , Springer, Heidelberg , pp. pp. 16--24, 2012. Abstract3530016.pdf

In this paper the possibility of using the human heart signal
feature for human verification is investigated. The presented approach
consists of two different robust feature extraction algorithms with a specified
configuration in conjunction with Gaussian mixture modeling. The
similarity of two samples is estimated by measuring the difference between
their negative log-likelihood of the features. To evaluate the performance
and the uniqueness of the presented approach tests using a
high resolution auscultation digital stethoscope are done for nearly 80
heart sound samples. The experimental results obtained show that the
accuracy offered by the employed Gaussian mixture modeling reach up
to 100% for 7 samples using the first feature extraction algorithm and
6 samples using the second feature extraction algorithm and varies with
average 85%.

Walaa Elmasry, Hossam Moftah, Walaa Elmasry, N. Elbendary, and A. E. Hassanien, " Performance Evaluation of Computed Tomography Liver Image Segmentation Approachers", The IEEE International Conference on Hybrid Intelligent Systems (HIS2012). , Pune. India., 4-7 Dec. 2012,, pp. 109 - 114, 2012. Abstract

This paper presents and evaluates the performance of two well-known segmentation approaches that were applied on liver computed tomography (CT) images. The two approaches are K-means and normalized cuts. An experiment was applied on ten liver CT scan images, with reference segmentations, in order to test the performance of the two approaches. Experimental results were compared using an evaluation measure that highlights segmentation accuracy. Based on the obtained results in this study, it has been observed that K-means clustering algorithm outperformed normalized cuts segmentation algorithm for cases where region of interest depicts a closed shape, while, normalized cuts algorithm obtained better results with non-circular clusters. Moreover, for K-means clustering, different initial partitions can result in different final clusters.

I.Ghali, N., R. Wahid, and A. E. Hassanien, "Heart Sounds Human Identification and Verification Approaches using Vector Quantization and Gaussian Mixture Models", International Journal of Systems Biology and Biomedical Technologies, , vol. 1, issue 4, pp. 75-88, 2012. Abstract

In this paper the possibility of using the human heart sounds as a human print is investigated. To evaluate the performance and the uniqueness of the proposed approach, tests using a high resolution auscultation digital stethoscope are done for nearly 80 heart sound samples. The verification approach consists of a robust feature extraction with a specified configuration in conjunction with Gaussian mixture modeling. The similarity of two samples is estimated by measuring the difference between their log-likelihood similarities of the features. The experimental results obtained show that the overall accuracy offered by the employed Gaussian mixture modeling reach up to 85%. The identification approach consists of a robust feature extraction with a specified configuration in conjunction with LBG-VQ. The experimental results obtained show that the overall accuracy offered by the employed LBG-VQ reach up to 88.7%

2013
Yi Zhou, K. Xiao, Y. Wang, Alei Liang, and A. E. Hassanien, "A pso-inspired multi-robot map exploration algorithm using frontier-based strategy", International Journal of System Dynamics Applications (IJSDA), vol. 2, no. 2: IGI Global, pp. 1–13, 2013. Abstract
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Yi Zhou, K. Xiao, Y. Wang, Alei Liang, and A. E. Hassanien, "A pso-inspired multi-robot map exploration algorithm using frontier-based strategy", International Journal of System Dynamics Applications (IJSDA), vol. 2, no. 2: IGI Global, pp. 1–13, 2013. Abstract
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W. Ghonaim, N. I.Ghali, A. E. Hassanien, and S. Banerjee:, "An improvement of chaos-based hash function in cryptanalysis approach: An experience with chaotic neural networks and semi-collision attack", Memetic Computing Springer, vol. 5, issue 3, pp. 179-185, 2013. Website
Yi Zhou, K. Xiao, Y. Wang, Alei Liang, and A. E. Hassanien, "A PSO-inspired Multi-Robot Map Exploration Algorithm Using Frontier-Based Strategy", International Journal of System Dynamics Applications,, vol. 2, issue 2, pp. 1-13, 2013. AbstractWebsite

Map exploration is a fundamental problem in mobile robots. This paper presents a distributed algorithm that coordinates a team of autonomous mobile robots to explore an unknown environment. The proposed strategy is based on frontiers which are the regions on the boundary between open and unexplored space. With this strategy, robots are guided to move constantly to the nearest frontier to reduce the size of unknown region. Based on the PSO model incorporated in the algorithm, robots are navigated towards remote frontier after exploring the local area. The exploration completes when there is no frontier cell in the environment. The experiments implemented on both simulated and real robot scenarios show that the proposed algorithm is capable of completing the exploration task. Compared to the conventional method of randomly selecting frontier, the proposed algorithm proves its efficiency by the decreased 60% exploration time at least. Additional experimental results show the decreased coverage time when the number of robots increases, which further suggests the validity, efficiency and scalability.

Waleed Yamany, N. El-Bendary, Hossam M. Zawbaa, A. E. Hassanien, and Václav Snášel, "Rough Power Set Tree for Feature Selection and Classification: Case Study on MRI Brain Tumor", Innovations in Bio-inspired Computing and Applications. Advances in Intelligent Systems and Computing(Springer) , Czech republic , 2013.
2014
Elshazly, H. I., M. Waly, A. M. Elkorany, and A. E. Hassanien, "Chronic eye disease diagnosis using ensemble-based classifier", Engineering and Technology (ICET), 2014 International Conference on: IEEE, pp. 1–6, 2014. Abstract
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Fattah, M. A., M. I. Waly, M. A. A. ELsoud, A. E. Hassanien, M. F. Tolba, J. Platos, and G. Schaefer, "An improved prediction approach for progression of ocular hypertension to primary open angle glaucoma", Proceedings of the Fifth International Conference on Innovations in Bio-Inspired Computing and Applications IBICA 2014: Springer International Publishing, pp. 405–412, 2014. Abstract
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Emary, E., Waleed Yamany, and A. E. Hassanien, "New approach for feature selection based on rough set and bat algorithm", Computer Engineering & Systems (ICCES), 2014 9th International Conference on: IEEE, pp. 346–353, 2014. Abstract
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Waleed Yamany, N. El-Bendary, H. M. Zawbaa, A. E. Hassanien, and Václav Snášel, "Rough Power Set Tree for Feature Selection and Classification: Case Study on MRI Brain Tumor", Innovations in Bio-inspired Computing and Applications: Springer International Publishing, pp. 259–270, 2014. Abstract
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Waleed Yamany, Eid Emary, and A. E. Hassanien, "Wolf search algorithm for attribute reduction in classification", Computational Intelligence and Data Mining (CIDM), 2014 IEEE Symposium on: IEEE, pp. 351–358, 2014. Abstract
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Elshazly, H. I., A. M. Elkorany, A. E. Hassanien, and M. Waly, " Chronic eye disease diagnosis using ensemble-based classifier", The second International Conference on Engineering and Technology (ICET 2014) , German Uni - Cairo Egypt, 19 Apr - 20 Apr , 2014.
Waleed Yamany, E. Emary, and A. E. Hassanien, "Wolf Search Algorithm for Attribute Reduction", IEEE Symposium Series on Computational Intelligence (IEEE SSCI 2014),, Orlando, Florida, USA, 9-12 Dec., 2014. Abstract

Data sets ordinarily includes a huge number of attributes, with irrelevant and redundant attributes. Redundant and irrelevant attributes might minimize the classification accuracy because of the huge search space. The main goal of attribute reduction is choose a subset of relevant attributes from a huge number of available attributes to obtain comparable or even better classification accuracy than using all attributes. A system for feature selection is proposed in this paper using a modified version of the wolf search algorithm optimization. WSA is a bio-inspired heuristic optimization algorithm that imitates the way wolves search for food and survive by avoiding their enemies. The WSA can quickly search the feature space for optimal or near-optimal feature subset minimizing a given fitness function. The proposed fitness function used incorporate both classification accuracy and feature reduction size. The proposed system is applied on a set of the UCI machine learning data sets and proves good performance in comparison with the GA and PSO optimizers commonly used in this context.

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