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Wu, Z. Q., J. Jiang, and Y. H. Peng, "Computational Intelligence on Medical Imaging with Artificial Neural Networks in", Computational intelligence in medical imaging techniques and applications, 2009. Abstract
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Watchareeruetai, U., T. Matsumoto, Y. Takeuchi, H. Kudo, and N. Ohnishi, "Efficient construction of image feature extraction programs by using linear genetic programming with fitness retrieval and intermediate-result caching", Foundations of Computational Intelligence Volume 4: Springer Berlin Heidelberg, pp. 355–375, 2009. Abstract
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Watchareeruetai, U., T. Matsumoto, Y. Takeuchi, H. Kudo, and N. Ohnishi, "Efficient construction of image feature extraction programs by using linear genetic programming with fitness retrieval and intermediate-result caching", Foundations of Computational Intelligence Volume 4: Springer Berlin Heidelberg, pp. 355–375, 2009. Abstract
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Waleed Yamany, N. El-Bendary, H. M. Zawbaa, A. E. Hassanien, and Václav Snášel, "Rough Power Set Tree for Feature Selection and Classification: Case Study on MRI Brain Tumor", Innovations in Bio-inspired Computing and Applications: Springer International Publishing, pp. 259–270, 2014. 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 ", 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.

Waleed Yamany, N. El-Bendary, Hossam M. Zawbaa, A. E. Hassanien, and Václav Snášel, "Rough Power Set Tree for Feature Selection and Classification: Case Study on MRI Brain Tumor", Innovations in Bio-inspired Computing and Applications. Advances in Intelligent Systems and Computing(Springer) , Czech republic , 2013.
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, 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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Waleed Yamany, Eid Emary, and A. E. Hassanien, "Wolf search algorithm for attribute reduction in classification", Computational Intelligence and Data Mining (CIDM), 2014 IEEE Symposium on: IEEE, pp. 351–358, 2014. 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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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.

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.

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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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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Walaa Elmasry, Hossam Moftah, Walaa Elmasry, N. Elbendary, and A. E. Hassanien, " Performance Evaluation of Computed Tomography Liver Image Segmentation Approachers", The IEEE International Conference on Hybrid Intelligent Systems (HIS2012). , Pune. India., 4-7 Dec. 2012,, pp. 109 - 114, 2012. Abstract

This paper presents and evaluates the performance of two well-known segmentation approaches that were applied on liver computed tomography (CT) images. The two approaches are K-means and normalized cuts. An experiment was applied on ten liver CT scan images, with reference segmentations, in order to test the performance of the two approaches. Experimental results were compared using an evaluation measure that highlights segmentation accuracy. Based on the obtained results in this study, it has been observed that K-means clustering algorithm outperformed normalized cuts segmentation algorithm for cases where region of interest depicts a closed shape, while, normalized cuts algorithm obtained better results with non-circular clusters. Moreover, for K-means clustering, different initial partitions can result in different final clusters.

Wahid, R., N. I. Ghali, H. S. Own, T. - H. Kim, and A. E. Hassanien, "A Gaussian mixture models approach to human heart signal verification using different feature extraction algorithms", Computer Applications for Bio-technology, Multimedia, and Ubiquitous City: Springer Berlin Heidelberg, pp. 16–24, 2012. Abstract
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Wahid, R., N. I. Ghali, H. S. Own, T. - H. Kim, and A. E. Hassanien, "A Gaussian mixture models approach to human heart signal verification using different feature extraction algorithms", Computer Applications for Bio-technology, Multimedia, and Ubiquitous City: Springer Berlin Heidelberg, pp. 16–24, 2012. Abstract
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Wahid, R., N. I. Ghali, H. S. Own, T. - H. Kim, and A. ella Hassanien., " A Gaussian Mixture Models Approach to Human Heart Signal Verification Using Different Feature Extraction Algorithms ", International Conference on Bio-Science and Bio-Technology (BSBT2012),, , Kangwondo, Korea, , Springer, Heidelberg , pp. pp. 16--24, 2012. Abstract3530016.pdf

In this paper the possibility of using the human heart signal
feature for human verification is investigated. The presented approach
consists of two different robust feature extraction algorithms with a specified
configuration in conjunction with Gaussian mixture modeling. The
similarity of two samples is estimated by measuring the difference between
their negative log-likelihood of the features. To evaluate the performance
and the uniqueness of the presented approach tests using a
high resolution auscultation digital stethoscope are done for nearly 80
heart sound samples. The experimental results obtained show that the
accuracy offered by the employed Gaussian mixture modeling reach up
to 100% for 7 samples using the first feature extraction algorithm and
6 samples using the second feature extraction algorithm and varies with
average 85%.

W. Ghonaim, N. I.Ghali, A. E. Hassanien, and S. Banerjee:, "An improvement of chaos-based hash function in cryptanalysis approach: An experience with chaotic neural networks and semi-collision attack", Memetic Computing Springer, vol. 5, issue 3, pp. 179-185, 2013. Website
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