Publications

Export 95 results:
Sort by: [ Author  (Desc)] Title 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]
Z
Zhu, Z., Z. Wang, T. Li, X. Wang, H. Liu, A. E. Hassanien, and W. Yang, "Multi-knowledge extraction algorithm using Group Search Optimization for brain dataset analysis", Computing for Sustainable Global Development (INDIACom), 2015 2nd International Conference on: IEEE, pp. 1891–1896, 2015. Abstract
n/a
Zhu, Z., Z. Wang;, T. Li;, X. Wang, H. Liu, and A. E. Hassanien, "Multi-knowledge extraction algorithm using Group Search Optimization for brain dataset analysis", 2nd International Conference on Computing for Sustainable Global Development (INDIACom) 11-13 March, pp. 1891 – 1896, , India, 11 March, 2015.
Zawbaa, H. M., N. El-Bendary, A. E. Hassanien, and T. - H. Kim, "Machine learning-based soccer video summarization system", Multimedia, Computer Graphics and Broadcasting: Springer Berlin Heidelberg, pp. 19–28, 2011. Abstract
n/a
Zawbaa, H. M., N. El-Bendary, A. E. Hassanien, and T. - H. Kim, "Machine learning-based soccer video summarization system", Multimedia, Computer Graphics and Broadcasting: Springer Berlin Heidelberg, pp. 19–28, 2011. Abstract
n/a
Y
Yamanya, W., A. T. Mohammed Fawzy, and A. E. Hassanien, "Moth-Flame Optimization for Training Multi-layer Perceptrons", IEEE iInternational Computer Engineering Conference - ICENCO , Cairo, 30 Dec, 2015.
X
Xiao, K., A. E. Hassanien, and N. I. Ghali, "Medical image segmentation using information extracted from deformation", Computer Science and Information Systems (FedCSIS), 2011 Federated Conference on: IEEE, pp. 157–163, 2011. Abstract
n/a
Xiao, K., A. E. Hassanien, and N. I. Ghali, "Medical image segmentation using information extracted from deformation", Computer Science and Information Systems (FedCSIS), 2011 Federated Conference on: IEEE, pp. 157–163, 2011. Abstract
n/a
W
Waleed Yamany, N. El-Bendary, A. E. Hassanien, and Eid Emary, "Multi-Objective Cuckoo Search Optimization for Dimensionality Reduction", Procedia Computer Science, vol. 96: Elsevier, pp. 207–215, 2016. Abstract
n/a
Waleed Yamany, M. O. H. A. M. M. E. D. FAWZY, Alaa Tharwat, and A. E. Hassanien, "Moth-flame optimization for training multi-layer perceptrons", Computer Engineering Conference (ICENCO), 2015 11th International: IEEE, pp. 267–272, 2015. Abstract
n/a
S
Staelens, S., and I. Lemahieu, "Monte Carlo Based Image Reconstruction in Emission Tom ography", Computational Intelligence in Medical Imaging: Techniques and Applications: CRC Press, pp. 407, 2009. Abstract
n/a
Staelens, S., and I. Lemahieu, "Monte Carlo Based Image Reconstruction in Emission Tom ography", Computational Intelligence in Medical Imaging: Techniques and Applications: CRC Press, pp. 407, 2009. Abstract
n/a
Soliman, O. S., A. E. Hassanien, N. I. Ghali, N. El-Bendary, and R. A. Sarker, "A model-based decision support tool using fuzzy optimization for climate change", International Conference on Rough Sets and Knowledge Technology: Springer Berlin Heidelberg, pp. 388–393, 2011. Abstract
n/a
Soliman, O. S., A. E. Hassanien, N. I. Ghali, N. El-Bendary, and R. A. Sarker, "A model-based decision support tool using fuzzy optimization for climate change", International Conference on Rough Sets and Knowledge Technology: Springer Berlin Heidelberg, pp. 388–393, 2011. Abstract
n/a
Schaefer, G., Bartosz Krawczyk, E. M. Celebi, H. Iyatomi, and A. E. Hassanien, "Melanoma classification based on ensemble classification of dermoscopy image features", International Conference on Advanced Machine Learning Technologies and Applications: Springer International Publishing, pp. 291–298, 2014. Abstract
n/a
Schaefer, G., Bartosz Krawczyk, E. M. Celebi, H. Iyatomi, and A. E. Hassanien, "Melanoma Classification based on Ensemble Classification of Dermoscopy Image Features", The 2nd International Conference on Advanced Machine Learning Technologies and Applications , Egypt, November 17-19, , 2014.
Sayed, G. I., and A. E. Hassanien, "Moth-flame swarm optimization with neutrosophic sets for automatic mitosis detection in breast cancer histology images", Applied Intelligence: Springer US, pp. 1–12, 2017. Abstract
n/a
Sayed, G. I., M. Soliman, and A. E. Hassanien, "Modified Optimal Foraging Algorithm for Parameters Optimization of Support Vector Machine", International Conference on Advanced Machine Learning Technologies and Applications, Cairo, 23 Feb, 2018. Abstract

Support Vector Machine (SVM) is one of the widely used algorithms for classification and regression problems. In SVM, penalty parameter C and kernel parameters can have a significant impact on the complexity and performance of SVM. In this paper, an Optimal Foraging Algorithm (OFA) is proposed to optimize the main parameters of SVM and reduce the classification error. Six public benchmark datasets were employed for evaluating the proposed (OFA-SVM). Also, five well-known and recently optimization algorithms are used for evaluation. These algorithms are Artificial Bee Colony (ABC), Genetic Algorithm (GA), Chicken Swarm Optimization (CSO), Particle Swarm Optimization (PSO) and Bat Algorithm (BA). The experimental results show that the proposed OFA-SVM obtained superior results. Also, the results demonstrate the capability of the proposed OFA-SVM to find optimal values of SVM parameters.

Sami, M., N. El-Bendary, and A. E. Hassanien, "Multi-Class Image Annotation Approach using Particle Swarm Optimization.", The IEEE International Conference on Hybrid Intelligent Systems (HIS2012).. , Pune. India, 4-7 Dec. 2012,, pp. 103 - 108., 2012. Abstract

This paper presents an automatic image annotation approach for region labeling. The proposed approach is based on multi-class k-nearest neighbor, K-means, and particle swarm optimization algorithms for feature weighting, in conjunction with normalized cuts based image segmentation technique. This hybrid approach refines the output of multi-class classification that is based on the usage of k-nearest neighbor classifier for automatically labeling image regions from different classes. Each input image is segmented using the normalized cuts segmentation algorithm in order to subsequently create a descriptor for each segment. Particle swarm optimization algorithm is employed as a search strategy to identify an optimal feature subset. Experimental results and comparative performance evaluation, for results obtained from the proposed particle swarm optimization based approach and another support vector machine based approach presented in previous work, demonstrate that the proposed particle swarm optimization based approach outperforms the support vector machine based one, regarding annotation accuracy, for the used dataset.

Sami, M., N. El-Bendary, and A. E. Hassanien, "Multi-class image annotation approach using particle swarm optimization", Hybrid Intelligent Systems (HIS), 2012 12th International Conference on: IEEE, pp. 103–108, 2012. Abstract
n/a
Saleh Esmate Aly, H. I. Elshazly, A. F. Ali, H. A. Hussein, G. Schaefer, and M. A. R. Ahad, "Molecular classification of Newcastle disease virus based on degree of virulence", The 3rd Intl. Conf. on Informatics, Electronics & Vision. (ICIEV2014), Dhaka - Bangladesh, 23-24 May , 2014.
Saleh Esmate Aly, H. I. Elshazly, A. F. Ali, H. A. Hussein, A. E. Hassanien, G. Schaefer, and M. A. R. Ahad, "Molecular classification of Newcastle disease virus based on degree of virulence", Informatics, Electronics & Vision (ICIEV), 2014 International Conference on: IEEE, pp. 1–5, 2014. Abstract
n/a
Salama, M. A., M. M. M. Fouad, N. El-Bendary, and A. E. O. Hassanien, "Mutagenicity Analysis Based on Rough Set Theory and Formal Concept Analysis", Recent Advances in Intelligent Informatics: Springer International Publishing, pp. 265–273, 2014. Abstract
n/a
R
Rouibah, K., "Mobile-commerce intention to use via SMS: The case of Kuwait", Emerging markets and e-commerce in developing economies: IGI Global, pp. 230–253, 2009. Abstract
n/a
Rehab Mahmoud, Nashwa El-Bendary, H. M. A. E. H. H. S. M. O. A., "Machine Learning-Based Measurement System for Spinal Cord Injuries Rehabilitation Length of Stay", Proceedings of the Second Euro-China Conference on Intelligent Data Analysis and Applications, ECC 2015, , Ostrava, Czech Republic, , June 29 - July , 2015. Abstract

Disabilities, specially Spinal Cord Injuries (SCI), affect people behaviors, their response, and the participation in daily activities. People with SCI need long care, cost, and time to improve their heath status. So, the rehabilitation of people with SCI on different period of times is required. In this paper, we proposed an automated system to estimate the rehabilitation length of stay of patients with SCI. The proposed system is divided into three phases; (1) pre-processing phase, (2) classification phase, and (3) rehabilitation length of stay measurement phase. The proposed system is automating International Classification of Functioning, Disability and Health classification (ICF) coding process, monitoring progress in patient status, and measuring the rehabilitation time based on support vector machines algorithm. The proposed system used linear and radial basis (RBF) kernel functions of support vector machines (SVMs) classification algorithm to classify data. The accuracy obtained was full match on training and testing data for linear kernel function and 93.3 % match for RBF kernel function.

O
Own, H. S., and A. E. Hassanien, "Multiresolution image registration algorithm in wavelet transform domain", Digital Signal Processing, 2002. DSP 2002. 2002 14th International Conference on, vol. 2: IEEE, pp. 889–892, 2002. Abstract
n/a