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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.

El-Atta, A. A. H., M. I. Moussa, and A. E. Hassenian, " Predicting biological activity of 2,4,6-trisubstituted 1,3,5-triazines", 5ththe 5th International Conference on Innovations in Bio-Inspired Computing and Applications - IBICA2014 (Springer), Ostrava, Czech Republic., 22-24 June, 2013.
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Eid Emary, H. zawbaa, A. E. Hassanien, G. Schaefer, and A. T. Azar, " Retinal Blood Vessel Segmentation using Bee Colony Optimisation and Pattern Search ", The annual IEEE International Joint Conference on Neural Networks (IJCNN) – July 6-, Beijing, China, 6 July, 2014.
Eid Emary, H. zawbaa, A. E. Hassanien, G. Schaefer, and A. T. Azar, " Retinal Vessel Segmentation based on Possibilistic Fuzzy c-means Clustering Optimised with Cuckoo Search", The annual IEEE International Joint Conference on Neural Networks (IJCNN) – July 6-, Beijing, China, 6 July, 2014.
Al-Qaheri, H., S. Zamoon, A. E. Hassanien, and A. Abraham, " Rough Set Generating Prediction Rules for Stock Price Movement", The Second IEEE UKSIM European Symposium on Computer Modeling and Simulation, Liverpool, England, UK, pp.111-116 , 8-10 September , 2008. Abstract

This paper presents rough sets generating prediction rules scheme for stock price movement. The scheme was able to extract knowledge in the form of rules from daily stock movements. These rules then could be used to guide investors whether to buy, sell or hold a stock. To increase the efficiency of the prediction process, rough sets with Boolean reasoning discretization algorithm is used to discretize the data. Rough set reduction technique is applied to find all the reducts of the data. Finally, rough sets dependency rules are generated directly from all generated reducts. Rough confusion matrix is used to evaluate the performance of the predicted reducts and classes. A comparison between the obtained results using rough sets with decision tree and neural networks algorithms have been made. Rough sets show a higher overall accuracy rates reaching over 97% and generate more compact rules.

Al-Qaheri, H., S. Zamoon, A. E. Hassanien, and A. Abraham, " Rough Set Generating Prediction Rules for Stock Price Movement", The Second IEEE UKSIM European Symposium on Computer Modeling and Simulation, Liverpool, England, UK, pp.111-116 , 8-10 September , 2008. Abstract

This paper presents rough sets generating prediction rules scheme for stock price movement. The scheme was able to extract knowledge in the form of rules from daily stock movements. These rules then could be used to guide investors whether to buy, sell or hold a stock. To increase the efficiency of the prediction process, rough sets with Boolean reasoning discretization algorithm is used to discretize the data. Rough set reduction technique is applied to find all the reducts of the data. Finally, rough sets dependency rules are generated directly from all generated reducts. Rough confusion matrix is used to evaluate the performance of the predicted reducts and classes. A comparison between the obtained results using rough sets with decision tree and neural networks algorithms have been made. Rough sets show a higher overall accuracy rates reaching over 97% and generate more compact rules.

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El-Bendary, N., A. E. Hassanien, J. Sedano, O. S. Soliman, and N. I. Ghali, "$μ$TESLA-based secure routing protocol for wireless sensor networks", Proceedings of the First International Workshop on Security and Privacy Preserving in e-Societies: ACM, pp. 34–39, 2011. Abstract
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Hassanien, A. E., N. El-Bendary, J. Sedano, O. S. Soliman, and N. I. Ghali, "$μ$TESLA-based secure routing protocol for wireless sensor networks", The First ACM International Workshop on Security and Privacy Preserving in e-Societies, Lebanon , 7 June, 2011. Abstract

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El-Bendary, N., A. E. Hassanien, J. Sedano, O. S. Soliman, and N. I. Ghali, "$μ$TESLA-based secure routing protocol for wireless sensor networks", Proceedings of the First International Workshop on Security and Privacy Preserving in e-Societies: ACM, pp. 34–39, 2011. Abstract
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Hassanien, A. E., N. El-Bendary, J. Sedano, O. S. Soliman, and N. I. Ghali, "$μ$TESLA-based secure routing protocol for wireless sensor networks", The First ACM International Workshop on Security and Privacy Preserving in e-Societies, 2011. Abstract

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Karam, H., A. E. Hassanien, and M. Nakajima, "15-1 Polar Decomposition Interpolations for Linear Fractal Metamorphosis", 映像情報メディア学会年次大会講演予稿集, no. 1998: 一般社団法人映像情報メディア学会, pp. 200–201, 1998. Abstract
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Hassanien, A. E., H. Karam, and M. Nakajima, "15-2 Image Metamorphosis for Inter Slice Interpolation of Medical Images", 映像情報メディア学会年次大会講演予稿集, no. 1998: 一般社団法人映像情報メディア学会, pp. 202–203, 1998. Abstract
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Hassanien, A. E., A. Abraham, F. Marcelloni, H. Hagras, M. Antonelli, and T. - P. Hong, 2010 10th International Conference on Intelligent Systems Design and Applications, , 2010. Abstract
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Hassanien, A. E., A. Abraham, F. Marcelloni, H. Hagras, M. Antonelli, and T. - P. Hong, 2010 10th International Conference on Intelligent Systems Design and Applications, , 2010. Abstract
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Hassanien, A. E., A. Abraham, F. Marcelloni, H. Hagras, M. Antonelli, and T. - P. Hong, 2010 10th InternaƟonal Conference on, , 2010. Abstract
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Hassanien, A. E., A. Abraham, F. Marcelloni, H. Hagras, M. Antonelli, and T. - P. Hong, 2010 10th InternaƟonal Conference on, , 2010. Abstract
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Alshabrawy, O. S., A. E. Hassanien, W. A. Awad, and A. A. Salama, 2013 13th International Conference on Hybrid Intelligent Systems (HIS), , 2013. Abstract

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Mouhamed, M. R., A. Darwish, and A. E. Hassanien, "2D and 3D Intelligent Watermarking", Handbook of Research on Machine Learning Innovations and Trends: IGI Global, pp. 652–669, 2017. Abstract
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Hossam, M. M., A. E. Hassanien, and M. Shoman, "3D brain tumor segmentation scheme using K-mean clustering and connected component labeling algorithms", Intelligent Systems Design and Applications (ISDA), 2010 10th International Conference on: IEEE, pp. 320–324, 2010. Abstract
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Hossam, M. M., A. E. Hassanien, and M. Shoman, "3D brain tumor segmentation scheme using K-mean clustering and connected component labeling algorithms", Intelligent Systems Design and Applications (ISDA), 2010 10th International Conference on: IEEE, pp. 320–324, 2010. Abstract
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Soliman, M. M., and A. E. Hassanien, "3D Watermarking Approach Using Particle Swarm Optimization Algorithm", Handbook of Research on Machine Learning Innovations and Trends, USA, IGI, 2017. Abstract

This work proposes a watermarking approach by utilizing the use of Bio-Inspired techniques such as swarm intelligent in optimizing watermarking algorithms for 3D models. In this proposed work we present an approach of 3D mesh model watermarking by introducing a new robust 3D mesh watermarking authentication methods by ensuring a minimal surface distortion at the same time ensuring a high robustness of extracted watermark. In order to achieve these requirements this work proposes the use of Particle Swarm Optimization (PSO) as Bio-Inspired optimization techniques. The experiments were executed using different sets of 3D models. In all experimental results we consider two important factors: imperceptibility and robustness. The experimental results show that the proposed approach yields a watermarked object with good visual definition; at the same time, the embedded watermark was robust against a wide variety of common attacks.

Soliman, M. M., and A. E. Hassanien, "3D Watermarking Approach Using Particle Swarm Optimization Algorithm", Handbook of Research on Machine Learning Innovations and Trends: IGI Global, pp. 582–613, 2017. Abstract
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Hassanien, A. E., and M. Nakajima, "7) Image Morphing by using Thin Plate Spline Transformation and Snake Model", 映像情報メディア学会誌: 映像情報メディア, vol. 52, no. 6: 一般社団法人映像情報メディア学会, pp. 833, 1998. Abstract
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Sayed, G. I., and A. E. Hassanien, "Abdominal CT Liver Parenchyma Segmentation Based on Particle Swarm Optimization", The 1st International Conference on Advanced Intelligent System and Informatics (AISI2015), November 28-30, 2015, Beni Suef, Egypt: Springer International Publishing, pp. 219–228, 2016. Abstract
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Tourism