Publications

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2018
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.

Inbarani, H. H., S. Udhaya Kumar, A. T. Azar, and A. E. Hassanien, "Hybrid rough-bijective soft set classification system", Neural Computing and Applications, , vol. 29, issue 8, pp. 67–78., 2018. Abstract

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.

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.

2017
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.

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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2016
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.

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.

Hassaniena, A. E., N. El-Bendary, Asmaa Hashem Sweidan, and A. E. -karim Mohamed, "Hybrid-biomarker case-based reasoning system for water pollution assessment in Abou Hammad Sharkia, Egypt", Applied Soft Computing, vol. 46, issue 1, pp. 1043–1055, 2016. AbstractWebsite

Water pollution by organic materials or metals is one of the problems that threaten humanity, both nowadays and over the next decades. Morphological changes in Nile Tilapia “Oreochromis niloticus” fish liver and gills can also represent the adaptation strategies to maintain some physiological functions or to assess acute and chronic exposure to chemicals found in water and sediments. This paper presents an automatic system for assessing water quality, in Sharkia Governorate – Egypt, based on microscopic images of fish gills and liver. The proposed system used fish gills and liver as hybrid-biomarker in order to detect water pollution. It utilized case-based reasoning (CBR) for indicating the degree of water quality based on the different histopathological changes in fish gills and liver microscopic images. Various performance evaluation metrics namely, retrieval accuracy, receiver operating characteristic (ROC) curves, F-measure, and G-mean have been used in order to objectively indicate the true performance of the system considering the unbalanced data. Experimental results showed that the proposed hybrid-biomarker CBR based system achieved water quality prediction accuracy of 97.9% using cosine distance similarity measure. Also, it outperformed both SVMs and LDA classifiers for the tested microscopic images dataset.

Fattah, M. A., S. Abuelenin, A. E. Hassanien, and J. - S. Pan, "Handwritten Arabic Manuscript Image Binarization Using Sine Cosine Optimization Algorithm", International Conference on Genetic and Evolutionary Computing: Springer International Publishing, pp. 273–280, 2016. Abstract
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Hassanien, A. E., M. A. Fattah, S. Aboulenin, G. Schaefer, S. Y. Zhu, and I. Korovin, "Historic handwritten manuscript binarisation using whale optimisation", Systems, Man, and Cybernetics (SMC), 2016 IEEE International Conference on: IEEE, pp. 003842–003846, 2016. Abstract
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Abdelhameed Ibrahim, T. Gaber, T. Horiuchi, V. Snasel, and A. E. Hassanien, "Human Thermal Face Extraction Based on SuperPixel Technique", The 1st International Conference on Advanced Intelligent System and Informatics (AISI2015), November 28-30, 2015, Beni Suef, Egypt: Springer International Publishing, pp. 163–172, 2016. Abstract
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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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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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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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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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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
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abd elaziz, M., A. A. Ewees, and A. E. Hassanien, "Hybrid Swarms Optimization Based Image Segmentation", Hybrid Soft Computing for Image Segmentation: Springer International Publishing, pp. 1–21, 2016. Abstract
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Hassanien, A. E., N. El-Bendary, Asmaa Hashem Sweidan, A. E. -karim Mohamed, and O. M. Hegazy, "Hybrid-biomarker case-based reasoning system for water pollution assessment in Abou Hammad Sharkia, Egypt", Applied Soft Computing, vol. 46: Elsevier, pp. 1043–1055, 2016. Abstract
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2015
Abdelhameed Ibrahim, T. Gaber, T. Horiuchi, V. Snasel, and A. E. Hassanien, "Human Thermal Face Extraction Based on SuperPixel Technique ", the 1st International Conference on Advanced Intelligent Systems and Informatics (AISI’15) Springer. , Beni Suef University, Beni Suef, Egypt , Nov. 28-30, 2015. Abstract

Face extraction is considered a very important step in devel-
oping a recognition system. It is a challenging task as there are di erent
face expressions, rotations, and artifacts including glasses and hats. In
this paper, a face extraction model is proposed for thermal IR human face
images based on superpixel technique. Superpixels can improve the com-
putational eciency of algorithms as it reduces hundreds of thousands of
pixels to at most a few thousand superpixels. Superpixels in this paper
are formulated using the quick-shift method. The Quick-Shift's superpix-
els and automatic thresholding using a simple Otsu's thresholding help
to produce good results of extracting faces from the thermal images. To
evaluate our approach, 18 persons with 22,784 thermal images were used
from the Terravic Facial IR Database. The Experimental results showed
that the proposed model was robust against image illumination, face
rotations, and di erent artifacts in many cases compared to the most
related work.

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.

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.
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.
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.
Hafez, A. I., A. E. Hassanien, and H. M. Zawbaa, "Hybrid Swarm Intelligence Algorithms for Feature Selection: Monkey and Krill Herd Algorithms", IEEE iInternational Computer Engineering Conference - ICENCO , Cairo, 30 Dec, 2015.
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