Publications

Export 16 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]
L
Eid, H. F., A. E. Hassanien, and T. - H. Kim, "Leaf plant identification system based on Hidden Na{\"ıve bays classifier", Advanced Information Technology and Sensor Application (AITS), 2015 4th International Conference on: IEEE, pp. 76–79, 2015. Abstract
n/a
Abdalla Zidan, N. I. Ghali, A. Hassanien, H. Hefny, and J. Hemanth, "Level set-based CT liver computer aided diagnosis system", Int. J. Imaging Robot, vol. 9, pp. 26–36, 2013. Abstract
n/a
Abdalla Zidan, N. I. Ghali, A. Hassanien, H. Hefny, and J. Hemanth, "Level set-based CT liver computer aided diagnosis system", Int. J. Imaging Robot, vol. 9, pp. 26–36, 2013. Abstract
n/a
Abdalla Zidan, N. I.Ghali, A. E. Hassanien, H. Hefny, and J. Hemanth, "Level Set-based CT Liver Computer Aided Diagnosis System. . ", International Journal of Imaging and Robotic Systems, , vol. 7, issue S13, 2013.
Abdalla Zidan, N. I. Ghali, A. ella Hassamen, and H. Hefny, "Level set-based CT liver image segmentation with watershed and artificial neural networks", Hybrid Intelligent Systems (HIS), 2012 12th International Conference on: IEEE, pp. 96–102, 2012. Abstract
n/a
Abdalla Zidan, N. Ghali, A. E. Hassanien, and H. Hefny, "Level Set-based CT Liver Image Segmentation with Watershed and Artificial Neural Networks.", The IEEE International Conference on Hybrid Intelligent Systems (HIS2012)., Pune. India. , 4-7 Dec. 2012,, pp. 96 - 102, 2012. Abstract

The objective of this paper is to evaluate a new combined approach intended for reliable CT liver image segmentation, to separate the liver from other organs, and segment the liver into a set of regions of interest (ROIs). The approach combines the level set with watershed approach used as post segmentation step to produce a reliable segmentation result. Features of first order statistics and grey-level cooccurrence matrix, are calculated and passed to an artificial neural network, to be trained and to classify infected regions. Filtering is used before the segmentation approach to enhance contrast, remove noise and emphasize certain features, as well as connecting ribs around the liver. To evaluate the performance of presented approach, we performed many tests on different CT liver images. The experimental results obtained, show that the overall accuracy offered by the proposed approach is 92.1% in segmenting CT liver images into set of regions even with noise, and 88.9% average accuracy for neural network classification.

Mostafa, A., A. E. Hassanien, N. I. Ghali, and H. Hefny, "Level Set-based Liver Image Segmentation with Watershed and ANN Classifier", The IEEE International Conference on Hybrid Intelligent Systems (HIS2012). , Pune. India. 4-7, Dec. 2012,. Abstract

n/a

Ayeldeen, H., A. E. Hassanien, and A. A. Fahmy, "Lexical similarity using fuzzy Euclidean distance", Engineering and Technology (ICET), 2014 International Conference on: IEEE, pp. 1–6, 2014. Abstract
n/a
Eid, H. F., A. E. Hassanien, T. - H. Kim, and S. Banerjee, "Linear correlation-based feature selection for network intrusion detection model", Advances in Security of Information and Communication Networks: Springer Berlin Heidelberg, pp. 240–248, 2013. Abstract
n/a
Alaa Tharwat, T. Gaber, Abdelhameed Ibrahim, and A. E. Hassanien, "Linear Discriminant Analysis: A Detailed Tutorial", AI Communications, IOS press, 2017. linear_discreminate_analysisp_detailed_tutorails.pdf
Abder-Rahman Ali, Micael Couceiro, Ahmed M. Anter, A. E. Hassenian, M. F. Tolba, and V. Snasel, "Liver CT Image Segmentation with an Optimum Threshold using Measure of Fuzziness", The 5th International Conference on Innovations in Bio-Inspired Computing and Applications, 22-24 June 2014, , Ostrava, Czech Republic., 22-24 June , 2014.
Abder-Rahman Ali, Micael Couceiro, A. M. Anter, A. E. Hassanien, M. F. Tolba, and Václav Snášel, "Liver CT Image Segmentation with an Optimum Threshold Using Measure of Fuzziness", Proceedings of the Fifth International Conference on Innovations in Bio-Inspired Computing and Applications IBICA 2014: Springer International Publishing, pp. 83–92, 2014. Abstract
n/a
Mostafa, A., A. E. Hassanien, and H. Hefney, "Liver segmentation in MRI images based on whale optimization algorithm,", Multimedia Tools and Applications, Springer, 2017.
Metawa, N., M. E.;, K. M. Hassan, and A. E. Hassanien, "Loan portfolio optimization using Genetic Algorithm: A case of credit constraints", 12th International Computer Engineering Conference (ICENCO),, Cairo, 28-29 Dec. , 2016. Abstract

With the increasing impact of capital regulation on banks financial decisions especially in competing environment with credit constraints, it comes the urge to set an optimal mechanism of bank lending decisions that will maximize the bank profit in a timely manner. In this context, we propose a self-organizing method for dynamically organizing bank lending decision using Genetic Algorithm (GA). Our proposed GA based model provides a framework to optimize bank objective when constructing the loan portfolio, which maximize the bank profit and minimize the probability of bank default in a search for an optimal, dynamic lending decision. Multiple factors related to loan characteristics, creditor ratings are integrated to GA chromosomes and validation is performed to ensure the optimal decision. GA uses random search to suggest the best appropriate design. We use this algorithm in order to obtain the most efficient lending decision. The reason for choosing GA is its convergence and its flexibility in solving multi-objective optimization problems such as credit assessment, portfolio optimization and bank lending decision.

Metawa, N., M. Elhoseny, K. M. Hassan, and A. E. Hassanien, "Loan portfolio optimization using genetic algorithm: A case of credit constraints", Computer Engineering Conference (ICENCO), 2016 12th International: IEEE, pp. 59–64, 2016. Abstract
n/a
Elshazly, H. I., A. M. Elkorany, and A. E. Hassanien, "Lymph diseases diagnosis approach based on support vector machines with different kernel functions", Computer Engineering & Systems (ICCES), 2014 9th International Conference on: IEEE, pp. 198–203, 2014. Abstract
n/a