Publications

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2016
Waleed Yamany, Eid Emary, A. E. Hassanien, G. Schaefer, and S. Y. Zhu, " An Innovative Approach for Attribute Reduction using Rough Sets and Flower Pollination Optimisation ", 20th International Conference on Knowledge Based and Intelligent Information and Engineering Systems, KES2016,, , United Kingdom., 5-7 September , 2016. Abstract

Optimal search is a major challenge for wrapper-based attribute reduction. Rough sets have been used with much success, but current hill-climbing rough set approaches to attribute reduction are insufficient for finding optimal solutions. In this paper, we propose an innovative use of an intelligent optimisation method, namely the flower search algorithm (FSA), with rough sets for attribute reduction. FSA is a relatively recent computational intelligence algorithm, which is inspired by the pollination process of flowers. For many applications, the attribute space, besides being very large, is also rough with many different local minima which makes it difficult to converge towards an optimal solution. FSA can adaptively search the attribute space for optimal attribute combinations that maximise a given fitness function, with the fitness function used in our work being rough set-based classification. Experimental results on various benchmark datasets from the UCI repository confirm our technique to perform well in comparison with competing methods.

Shang-Ling, S. Z. Jui, W. Xiong, F. Yu, M. Fu, D. Wang, A. E. Hassanien, and K. Xiao, "Brain MR Image Tumor Segmentation with 3-Dimensional Intracranial Structure Deformation Features", IEEE Intelligent Systems, vol. 31, pp. 66-76, 2016. AbstractWebsite

Extraction of relevant features is of significant importance for brain tumor segmentation systems. To improve brain tumor segmentation accuracy, the authors present an improved feature extraction component that takes advantage of the correlation between intracranial structure deformation and the compression resulting from brain tumor growth. Using 3D nonrigid registration and deformation modeling techniques, the component measures lateral ventricular (LaV) deformation in volumetric magnetic resonance images. By verifying the location of the extracted LaV deformation feature data and applying the features on brain tumor segmentation with widely used classification algorithms, the authors evaluate the proposed component qualitatively and quantitatively with promising results on 11 datasets comprising real and simulated patient images.

Jui, S. - L., S. Zhang, W. Xiong, F. Yu, M. Fu, D. Wang, A. E. Hassanien, and K. Xiao, "Brain MRI Tumor Segmentation with 3D Intracranial Structure Deformation Features", IEEE Intelligent Systems, vol. 31, no. 2: IEEE, pp. 66–76, 2016. Abstract
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Waleed Yamany, Eid Emary, A. E. Hassanien, G. Schaefer, and S. Y. Zhu, "An Innovative Approach for Attribute Reduction Using Rough Sets and Flower Pollination Optimisation", Procedia Computer Science, vol. 96: Elsevier, pp. 403–409, 2016. Abstract
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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
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Waleed Yamany, Eid Emary, and A. E. Hassanien, "New Rough Set Attribute Reduction Algorithm Based on Grey Wolf Optimization", The 1st International Conference on Advanced Intelligent System and Informatics (AISI2015), November 28-30, 2015, Beni Suef, Egypt: Springer International Publishing, pp. 241–251, 2016. Abstract
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Abraham, A., K. Wegrzyn-Wolska, A. E. Hassanien, Václav Snášel, and A. M. Alimi, Proceedings of the Second International Afro-European Conference for Industrial Advancement AECIA 2015, : Springer, 2016. Abstract
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2015
Waleed Yamany, Eid Emary, and A. E. Hassanien, "New Rough Set Attribute Reduction Algorithm based on Grey Wolf Optimization,", the 1st International Conference on Advanced Intelligent Systems and Informatics (AISI’15) Springer, Beni Suef University, Beni Suef, Egypt , Nov. 28-30, , 2015. Abstract

In this paper, we propose a new attribute reduction strat-
egy based on rough sets and grey wolf optimization (GWO). Rough sets
have been used as an attribute reduction technique with much success,
but current hill-climbing rough set approaches to attribute reduction are
inconvenient at nding optimal reductions as no perfect heuristic can
guarantee optimality. Otherwise, complete searches are not feasible for
even medium sized datasets. So, stochastic approaches provide a promis-
ing attribute reduction technique. Like Genetic Algorithms, GWO is a
new evolutionary computation technique, mimics the leadership hierar-
chy and hunting mechanism of grey wolves in nature. The grey wolf
optimization nd optimal regions of the complex search space through
the interaction of individuals in the population. Compared with GAs,
GWO does not need complex operators such as crossover and mutation,
it requires only primitive and easy mathematical operators, and is com-
putationally inexpensive in terms of both memory and runtime. Experi-
mentation is carried out, using UCI data, which compares the proposed
algorithm with a GA-based approach and other deterministic rough set
reduction algorithms. The results show that GWO is ecient for rough
set-based attribute reduction.

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.
Jui, S. - L., S. Zhang, W. Xiong, F. Yu, M. Fu, D. Wang, and A. E. H. K. and Xiao, "Brain MR Image Tumor Segmentation with 3-Dimensional Intracranial Structure Deformation Features", IEEE Intelligent systems , issue Accepted , 2015. Abstract

Abstract—Extraction of relevant features is of significant importance for brain tumor segmentation systems. In this paper, with the objective of improving brain tumor segmentation accuracy, we present an improved feature extraction component to take advantage of the correlation between intracranial structure deformation and the compression from brain tumor growth. Using 3-dimensional non-rigid registration and deformation modeling techniques, the component is capable of measuring lateral ventricular (LaV) deformation in the volumetric magnetic resonance (MR) images. By verifying the location of the extracted LaV deformation feature data and applying the features on brain tumor segmentation with widely used classification algorithms, the proposed component is evaluated qualitatively and quantitatively with promising results on 11 datasets comprising real patient and simulated images.

E. Emary, Waleed Yamany, A. E. Hassanien, and V. Snasel, "Multi-Objective Gray-Wolf Optimization for Attribute Reduction", International Conference on Communications, management, and Information technology (ICCMIT'2015), 2015. Abstract

Feature sets are always dependent, redundant and noisy in almost all application domains. These problems in The data always declined the performance of any given classifier as it make it difficult for the training phase to converge effectively and it affect also the running time for classification at operation and training time. In this work a system for feature selection based on multi-objective gray wolf optimization is proposed. The existing methods for feature selection either depend on the data description; filter-based methods, or depend on the classifier used; wrapper approaches. These two main approaches lakes of good performance and data description in the same system. In this work gray wolf optimization; a swarm-based optimization method, was employed to search the space of features to find optimal feature subset that both achieve data description with minor redundancy and keeps classification performance. At the early stages of optimization gray wolf uses filter-based principles to find a set of solutions with minor redundancy described by mutual information. At later stages of optimization wrapper approach is employed guided by classifier performance to further enhance the obtained solutions towards better classification performance. The proposed method is assessed against different common searching methods such as particle swarm optimization and genetic algorithm and also was assessed against different single objective systems. The proposed system achieves an advance over other searching methods and over the other single objective methods by testing over different UCI data sets and achieve much robustness and stability.

Abraham, A., K. Wegrzyn-Wolska, A. E. Hassanien, V. Snasel, and A. M. Alimi, Second International Afro-European Conference for Industrial Advancement AECIA 2015, , 2015. Abstract

This volume contains accepted papers presented at AECIA2014, the First International Afro-European Conference for Industrial Advancement. The aim of AECIA was to bring together the foremost experts as well as excellent young researchers from Africa, Europe, and the rest of the world to disseminate latest results from various fields of engineering, information, and communication technologies. The first edition of AECIA was organized jointly by Addis Ababa Institute of Technology, Addis Ababa University, and VSB - Technical University of Ostrava, Czech Republic and took place in Ethiopia's capital, Addis Ababa.

Waleed Yamany, H. M. Zawbaa, Eid Emary, and A. E. Hassanien, "Attribute reduction approach based on modified flower pollination algorithm", Fuzzy Systems (FUZZ-IEEE), 2015 IEEE International Conference on: IEEE, pp. 1–7, 2015. Abstract
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Jui, S. - L., S. Zhang, W. Xiong, F. Yu, M. Fu, D. Wang, A. E. Hassanien, and K. Xiao, "Brain MR image tumor segmentation with 3-Dimensional intracranial structure deformation features", IEEE Intell. Syst. submitted, under review, 2015. Abstract
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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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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
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Dai, G., Z. Wang, C. Yang, H. Liu, A. E. Hassanien, and W. Yang, "A multi-granularity rough set algorithm for attribute reduction through particles particle swarm optimization", Computer Engineering Conference (ICENCO), 2015 11th International: IEEE, pp. 303–307, 2015. Abstract
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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
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Emary, E., Waleed Yamany, A. E. Hassanien, and V. Snasel, "Multi-objective gray-wolf optimization for attribute reduction", Procedia Computer Science, vol. 65: Elsevier, pp. 623–632, 2015. Abstract
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Waleed Yamany, Alaa Tharwat, M. F. Hassanin, T. Gaber, A. E. Hassanien, and T. - H. Kim, "A new multi-layer perceptrons trainer based on ant lion optimization algorithm", Information Science and Industrial Applications (ISI), 2015 Fourth International Conference on: IEEE, pp. 40–45, 2015. Abstract
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2014
Waleed Yamany, E. Emary, and A. E. Hassanien, "Wolf Search Algorithm for Attribute Reduction", IEEE Symposium Series on Computational Intelligence (IEEE SSCI 2014),, Orlando, Florida, USA, 9-12 Dec., 2014. Abstract

Data sets ordinarily includes a huge number of attributes, with irrelevant and redundant attributes. Redundant and irrelevant attributes might minimize the classification accuracy because of the huge search space. The main goal of attribute reduction is choose a subset of relevant attributes from a huge number of available attributes to obtain comparable or even better classification accuracy than using all attributes. A system for feature selection is proposed in this paper using a modified version of the wolf search algorithm optimization. WSA is a bio-inspired heuristic optimization algorithm that imitates the way wolves search for food and survive by avoiding their enemies. The WSA can quickly search the feature space for optimal or near-optimal feature subset minimizing a given fitness function. The proposed fitness function used incorporate both classification accuracy and feature reduction size. The proposed system is applied on a set of the UCI machine learning data sets and proves good performance in comparison with the GA and PSO optimizers commonly used in this context.

Elshazly, H. I., A. M. Elkorany, A. E. Hassanien, and M. Waly, " Chronic eye disease diagnosis using ensemble-based classifier", The second International Conference on Engineering and Technology (ICET 2014) , German Uni - Cairo Egypt, 19 Apr - 20 Apr , 2014.
Elshazly, H. I., M. Waly, A. M. Elkorany, and A. E. Hassanien, "Chronic eye disease diagnosis using ensemble-based classifier", Engineering and Technology (ICET), 2014 International Conference on: IEEE, pp. 1–6, 2014. Abstract
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Fattah, M. A., M. I. Waly, M. A. A. ELsoud, A. E. Hassanien, M. F. Tolba, J. Platos, and G. Schaefer, "An improved prediction approach for progression of ocular hypertension to primary open angle glaucoma", Proceedings of the Fifth International Conference on Innovations in Bio-Inspired Computing and Applications IBICA 2014: Springer International Publishing, pp. 405–412, 2014. Abstract
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Emary, E., Waleed Yamany, and A. E. Hassanien, "New approach for feature selection based on rough set and bat algorithm", Computer Engineering & Systems (ICCES), 2014 9th International Conference on: IEEE, pp. 346–353, 2014. Abstract
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