Publications

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Anter, A. M., M. A. El Souod, A. T. Azar, and A. E. Hassanien, "A hybrid approach to diagnosis of hepatic tumors in computed tomography images", International Journal of Rough Sets and Data Analysis (IJRSDA), vol. 1, no. 2: IGI Global, pp. 31–48, 2014. Abstract
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Abdelazeem, M., E. Emary, and A. E. Hassanien, "A hybrid Bat-regularized Kaczmarz Algorithm to Solve Ill-posed Geomagnetic Inverse Problem", the 1st International Conference on Advanced Intelligent Systems and Informatics (AISI’15) Springer., Beni Suef University, Beni Suef, Eg, Nov. 28-30, 2015. Abstract

The aim of geophysical inverse problem is to determine the
spatial distribution and depths to buried targets at a variety of scales;
it ranges from few centimetres to many kilometres. To identify ore bodies,
extension of archaeological targets, old mines, unexploded ordnance
(UXO) and oil traps, the linear geomagnetic inverse problem resulted
from the Fredholm integral equation of the first kind is solved using
many strategies. The solution is usually affected by the condition of
the kernel matrix of the linear system and the noise level in the data
collected. In this paper, regularized Kaczmarz method is used to get a
regularized solution. This solution is taken as an initial solution to bat
swarm algorithm (BA) as a global swarm-based optimizer to refine the
quality and reach a plausible model. To test efficiency, the proposed hybrid
method is applied to different synthetic examples of different noise
levels and different dimensions and proved an advance over using the
Kaczmarz method.

Abdelazeem, M., Eid Emary, and A. E. Hassanien, "A hybrid Bat-regularized Kaczmarz algorithm to solve ill-posed geomagnetic inverse problem", The 1st International Conference on Advanced Intelligent System and Informatics (AISI2015), November 28-30, 2015, Beni Suef, Egypt: Springer International Publishing, pp. 263–272, 2016. Abstract
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Kareem Kamal A.Ghany, G. Hassan, G. Schaefer, A. E. Hassanien, M. A. R. Ahad, and H. A. Hefny, "A Hybrid Biometric Approach Embedding DNA Data in Fingerprint Images", 3rd Intl. Conf. on Informatics, Electronics & Vision (ICIEV2014), Dhaka - Bangladesh, 23-24 May, 2014.
Ghany, K. K. A., G. Hassan, A. E. Hassanien, H. A. Hefny, G. Schaefer, and M. A. R. Ahad, "A hybrid biometric approach embedding DNA data in fingerprint images", Informatics, Electronics & Vision (ICIEV), 2014 International Conference on: IEEE, pp. 1–5, 2014. Abstract
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Esraa Elhariri, N. El-Bendary, and A. E. Hassanien, "A Hybrid Classification Model for EMG signals using Grey Wolf Optimizer", the 1st International Conference on Advanced Intelligent Systems and Informatics (AISI’15) Springer, Beni Suef University, Beni Suef, Eg, Nov. 28-30, 2015.
Esraa Elhariri, N. El-Bendary, and A. E. Hassanien, "A Hybrid Classification Model for EMG Signals Using Grey Wolf Optimizer and SVMs", The 1st International Conference on Advanced Intelligent System and Informatics (AISI2015), November 28-30, 2015, Beni Suef, Egypt: Springer International Publishing, pp. 297–307, 2016. Abstract
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Panda, M., A. E. Hassanien, and A. Abraham, "Hybrid Data Mining Approach for Image Segmentation Based Classification", International Journal of Rough Sets and Data Analysis (IJRSDA), vol. 3, issue 2, 2016. AbstractWebsite

Evolutionary harmony search algorithm is used for its capability in finding solution space both locally and globally. In contrast, Wavelet based feature selection, for its ability to provide localized frequency information about a function of a signal, makes it a promising one for efficient classification. Research in this direction states that wavelet based neural network may be trapped to fall in a local minima whereas fuzzy harmony search based algorithm effectively addresses that problem and able to get a near optimal solution. In this, a hybrid wavelet based radial basis function (RBF) neural network (WRBF) and feature subset harmony search based fuzzy discernibility classifier (HSFD) approaches are proposed as a data mining technique for image segmentation based classification. In this paper, the authors use Lena RGB image; Magnetic resonance image (MR) and Computed Tomography (CT) Image for analysis. It is observed from the obtained simulation results that Wavelet based RBF neural network outperforms the harmony search based fuzzy discernibility classifiers.

Panda, M., A. E. Hassanien, and A. Abraham, "Hybrid Data Mining Approach for Image Segmentation Based Classification", Biometrics: Concepts, Methodologies, Tools, and Applications: IGI Global, pp. 1543–1561, 2017. Abstract
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Hala S. Own, N. I.Ghali, and A. E. Hassanien, "Hybrid Dual-Tree Wavelet Transform and Adaptive Threshold for Image Denoising", International Journal of Imaging and Robotic Systems, , vol. 7, issue S13, 2013.
Own, H. S., N. I. GHALL, and E. L. L. A. H. A. S. S. A. N. I. E. N. ABOUL, "Hybrid Dual-Tree Wavelet Transform and Adaptive Threshold for Image Denoising", International journal of imaging and robotics, vol. 9, no. 1: CESER Publications, pp. 17–25, 2013. Abstract
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Own, H. S., N. I. GHALL, and E. L. L. A. H. A. S. S. A. N. I. E. N. ABOUL, "Hybrid Dual-Tree Wavelet Transform and Adaptive Threshold for Image Denoising", International journal of imaging and robotics, vol. 9, no. 1: CESER Publications, pp. 17–25, 2013. Abstract
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Zawbaa, H. M., A. E. H. , and W. Y., E. Emary, "Hybrid flower pollination algorithm with rough sets for feature selection", IEEE iInternational Computer Engineering Conference - ICENCO , Cairo, 30 Dec, 2015.
Zawbaa, H. M., A. E. Hassanien, E. Emary, Waleed Yamany, and B. PARV, "Hybrid flower pollination algorithm with rough sets for feature selection", Computer Engineering Conference (ICENCO), 2015 11th International: IEEE, pp. 278–283, 2015. Abstract
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Hamad, A., E. H. Houssein, A. E. Hassanien, and A. A. Fahmy, "Hybrid Grasshopper Optimization Algorithm and Support Vector Machines for Automatic Seizure Detection in EEG Signals", AMLTA 2018: The International Conference on Advanced Machine Learning Technologies and Applications (AMLTA2018), Cairo, 23 fEB, 2018. Abstract

In this paper, a hybrid classification model using Grasshopper Optimization Algorithm (GOA) and support vector machines (SVMs) for automatic seizure detection in EEG is proposed called GOA-SVM approach. Various parameters were extracted and employed as the features to train the SVM with radial basis function (RBF) kernel function (SVM-RBF) classifiers. GOA was used for selecting the effective feature subset and the optimal settings of SVMs parameters in order to obtain a successful EEG classification. The experimental results confirmed that the proposed GOA-SVM approach, able to detect epileptic and could thus further enhance the diagnosis of epilepsy with accuracy 100% for normal subject data versus epileptic data. Furthermore, the proposed approach has been compared with Particle Swarm Optimization (PSO) with support vector machines (PSO-SVMs) and SVM using RBF kernel function. The computational results reveal that GOA-SVM approach achieved better classification accuracy outperforms both PSO-SVM and typical SVMs.

Mostafa, A., A. Fouad, M. Houseni, N. Allam, A. E. Hassanien, H. Hefny, and I. Aslanishvili, "A Hybrid Grey Wolf Based Segmentation with Statistical Image for CT Liver Images", International Conference on Advanced Intelligent Systems and Informatics: Springer International Publishing, pp. 846–855, 2016. Abstract
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Sami, M., N. El-Bendary, A. E. Hassanien, and G. Schaefer, "Hybrid intelligent automatic image annotation using machine learning", The 2011 Online Conference on Soft Computing in Industrial Applications WWW (WSC16), 2011. Abstract
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Sami, M., N. El-Bendary, A. E. Hassanien, and G. Schaefer, "Hybrid intelligent automatic image annotation using machine learning", The 2011 Online Conference on Soft Computing in Industrial Applications WWW (WSC16), 2011. Abstract
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Salama, M. A., H. F. Eid, R. A. Ramadan, A. Darwish, and A. E. Hassanien, "Hybrid intelligent intrusion detection scheme", Soft computing in industrial applications: Springer Berlin Heidelberg, pp. 293–303, 2011. Abstract
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Ahmed, K., A. A. Ewees, M. abd elaziz, A. E. Hassanien, T. Gaber, P. - W. Tsai, and J. - S. Pan, "A Hybrid Krill-ANFIS Model for Wind Speed Forecasting", International Conference on Advanced Intelligent Systems and Informatics: Springer International Publishing, pp. 365–372, 2016. Abstract
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Hassanien, A. E., "Hybrid Learning Enhancement of RBF Network with Particle Swarm Optimization", Foundations of Computational Intelligence, Volume 1: Learning and Approximation, Volume 201/2009, 381-397, London, Springer-Verlag , 2009. Abstract

This study proposes RBF Network hybrid learning with Particle Swarm Optimization (PSO) for better convergence, error rates and classification results. In conventional RBF Network structure, different layers perform different tasks. Hence, it is useful to split the optimization process of hidden layer and output layer of the network accordingly. RBF Network hybrid learning involves two phases. The first phase is a structure identification, in which unsupervised learning is exploited to determine the RBF centers and widths. This is done by executing different algorithms such as k-mean clustering and standard derivation respectively. The second phase is parameters estimation, in which supervised learning is implemented to establish the connections weights between the hidden layer and the output layer. This is done by performing different algorithms such as Least Mean Squares (LMS) and gradient based methods. The incorporation of PSO in RBF Network hybrid learning is accomplished by optimizing the centers, the widths and the weights of RBF Network. The results for training, testing and validation of five datasets (XOR, Balloon, Cancer, Iris and Ionosphere) illustrates the effectiveness of PSO in enhancing RBF Network learning compared to conventional Backpropogation.

Noman, S., S. M. Shamsuddin, and A. E. Hassanien, "Hybrid learning enhancement of RBF network with particle swarm optimization", Foundations of Computational, Intelligence Volume 1: Springer Berlin Heidelberg, pp. 381–397, 2009. Abstract
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Noman, S., S. M. Shamsuddin, and A. E. Hassanien, "Hybrid learning enhancement of RBF network with particle swarm optimization", Foundations of Computational, Intelligence Volume 1: Springer Berlin Heidelberg, pp. 381–397, 2009. Abstract
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Noman, S., S. M. Shamsuddin, and A. E. Hassanien, "Hybrid learning enhancement of RBF network with particle swarm optimization", Foundations of Computational, Intelligence Volume 1: Springer Berlin Heidelberg, pp. 381–397, 2009. Abstract
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Hafez, A. I., A. E. Hassanien, H. M. Zawbaa, and E. Emary, "Hybrid monkey algorithm with krill herd algorithm optimization for feature selection", Computer Engineering Conference (ICENCO), 2015 11th International: IEEE, pp. 273–277, 2015. Abstract
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