Publications

Export 71 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
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
n/a
Zawbaa, H. M., Eid Emary, A. E. Hassanien, and M. F. Tolba, "Hajj human event classification system using machine learning techniques", Hybrid Intelligent Systems (HIS), 2013 13th International Conference on: IEEE, pp. 191–196, 2013. Abstract
n/a
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.
S
Shehab, A., M. Elhoseny, and A. E. Hassanien, "A hybrid scheme for Automated Essay Grading based on LVQ and NLP techniques", 2016 12th International Computer Engineering Conference (ICENCO), , Cairo, 28-29 Dec, 2016. Abstract

This paper presents a hybrid approach to an Automated Essay Grading System (AEGS) that provides automated grading and evaluation of student essays. The proposed system has two complementary components: Writing Features Analysis tools, which rely on natural language processing (NLP) techniques and neural network grading engine, which rely on a set of pre-graded essays to judge the student answer and assign a grade. By this way, students essays could be evaluated with a feedback that would improve their writing skills. The proposed system is evaluated using datasets from computer and information sciences college students' essays in Mansoura University. These datasets was written as part of mid-term exams in introduction to information systems course and Systems analysis and design course. The obtained results shows an agreement with teachers' grades in between 70% and nearly 90% with teachers' grades. This indicates that the proposed might be useful as a tool for automatic assessment of students' essays, thus leading to a considerable reduction in essay grading costs.

Shehab, A., M. Elhoseny, and A. E. Hassanien, "A hybrid scheme for Automated Essay Grading based on LVQ and NLP techniques", Computer Engineering Conference (ICENCO), 2016 12th International: IEEE, pp. 65–70, 2016. Abstract
n/a
Sayed, G. I., A. E. Hassanien, M. A. Ali, and T. Gaber, "A Hybrid segmentation approach based on Neutrosophic sets and modified watershed: A case of abdominal CT liver parenchyma", IEEE iInternational Computer Engineering Conference - ICENCO , Cairo, 30 Dec, 2015.
Sayed, G. I., M. A. Ali, T. Gaber, A. E. Hassanien, and V. Snasel, "A hybrid segmentation approach based on neutrosophic sets and modified watershed: a case of abdominal CT Liver parenchyma", Computer Engineering Conference (ICENCO), 2015 11th International: IEEE, pp. 144–149, 2015. Abstract
n/a
Sayed, G. I., and A. E. Hassanien, "A hybrid SA-MFO algorithm for function optimization and engineering design problems", Complex & Intelligent Systems, 2018. Abstract

This paper presents a hybrid algorithm based on using moth-flame optimization (MFO) algorithm with simulated annealing (SA), namely (SA-MFO). The proposed SA-MFO algorithm takes the advantages of both algorithms. It takes the ability to escape from local optima mechanism of SA and fast searching and learning mechanism for guiding the generation of candidate solutions of MFO. The proposed SA-MFO algorithm is applied on 23 unconstrained benchmark functions and four well-known constrained engineering problems. The experimental results show the superiority of the proposed algorithm. Moreover, the performance of SA-MFO is compared with well-known and recent meta-heuristic algorithms. The results show competitive results of SA-MFO concerning MFO and other meta-heuristic algorithms.

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
n/a
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
n/a
Salama, M. A., A. E. Hassanien, A. A. Fahmy, and T. - H. Kim, "Heart Sound Feature Reduction Approach for Improving the Heart Valve Diseases Identification", Signal Processing, Image Processing and Pattern Recognition: Springer Berlin Heidelberg, pp. 280–290, 2011. Abstract
n/a
Salama, M. A., A. E. Hassanien, A. A. Fahmy, and T. - H. Kim, "Heart Sound Feature Reduction Approach for Improving the Heart Valve Diseases Identification", Signal Processing, Image Processing and Pattern Recognition: Springer Berlin Heidelberg, pp. 280–290, 2011. Abstract
n/a
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
n/a
P
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
n/a
O
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
n/a
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
n/a
N
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
n/a
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
n/a
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
n/a
M
Moustafa Zein, A. E. Hassanien, A. Badr, and T. - H. Kim, "Human Activity Classification Approach on Smartphone Using Monkey Search Algorithm", Advanced Communication and Networking (ACN), 2015 Seventh International Conference on: IEEE, pp. 84–88, 2015. Abstract
n/a
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
n/a
K
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.
I
Inbarani, H., U. S. Kum, A. T. Azar, and A. E. Hassanien, "Hybrid Rough-Bijective Soft Set Classification system,", Neural Computing and Applications (NCAA) , pp. , pp, 1-21, 2017 , 2017. AbstractWebsite

In today’s medical world, the patient’s data with symptoms and diseases are expanding rapidly, so that analysis of all factors with updated knowledge about symptoms and corresponding new treatment is merely not possible by medical experts. Hence, the essential for an intelligent system to reflect the different issues and recognize an appropriate model between the different parameters is evident. In recent decades, rough set theory (RST) has been broadly applied in various fields such as medicine, business, education, engineering and multimedia. In this study, a hybrid intelligent system that combines rough set (RST) and bijective soft set theory (BISO) to build a robust classifier model is proposed. The aim of the hybrid system is to exploit the advantages of the constituent components while eliminating their limitations. The resulting approach is thus able to handle data inconsistency in datasets through rough sets, while obtaining high classification accuracy based on prediction using bijective soft sets. Toward estimating the performance of the hybrid rough-bijective soft set (RBISO)-based classification approach, six benchmark medical datasets (Wisconsin breast cancer, liver disorder, hepatitis, Pima Indian diabetes, echocardiogram data and thyroid gland) from the UCI repository of machine learning databases are utilized. Experimental results, based on evaluation in terms of sensitivity, specificity and accuracy, are compared with other well-known classification methods, and the proposed algorithm provides an effective method for medical data classification.

Inbarani, H., S. Kumar, A. E. Hassanien, and A. T. Azar, "Hybrid TRS-PSO Clustering Approach for Web2.0 Social Tagging System. ", International Journal of Rough Sets and Data Analysis (IJRSDA) , vol. 2, issue 1, 2015. AbstractWebsite

Social tagging is one of the important characteristics of WEB2.0. The challenge of Web 2.0 is a huge amount of data generated over a short period. Tags are widely used to interpret and classify the web 2.0 resources. Tag clustering is the process of grouping the similar tags into clusters. The tag clustering is very useful for searching and organizing the web2.0 resources and also important for the success of Social Bookmarking systems. In this paper, the authors proposed a hybrid Tolerance Rough Set Based Particle Swarm optimization (TRS-PSO) clustering algorithm for clustering tags in social systems. Then the proposed method is compared to the benchmark algorithm K-Means clustering and Particle Swarm optimization (PSO) based Clustering technique. The experimental analysis illustrates the effectiveness of the proposed approach.

Tourism