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Erkan Beşdok
Researcher at Erciyes University
Publications - 58
Citations - 1728
Erkan Beşdok is an academic researcher from Erciyes University. The author has contributed to research in topics: Noise & Salt-and-pepper noise. The author has an hindex of 17, co-authored 54 publications receiving 1475 citations.
Papers
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A conceptual comparison of the Cuckoo-search, particle swarm optimization, differential evolution and artificial bee colony algorithms
Pinar Civicioglu,Erkan Beşdok +1 more
TL;DR: Empirical results reveal that the problem solving success of the CK algorithm is very close to the DE algorithm and the run-time complexity and the required function-evaluation number for acquiring global minimizer by theDE algorithm is generally smaller than the comparison algorithms.
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A Comparison of RBF Neural Network Training Algorithms for Inertial Sensor Based Terrain Classification
Tuba Kurban,Erkan Beşdok +1 more
TL;DR: A comparison of training algorithms of radial basis function (RBF) neural networks for classification purposes and results show that the use of the ABC algorithm results in better learning than those of others.
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Comparison of evolutionary and swarm based computational techniques for multilevel color image thresholding
TL;DR: The comparison of evolutionary and swarm-based optimization algorithms for multilevel color image thresholding problem, which is a process used for segmentation of an image into different regions, exposed that evolutionary algorithms are faster than swarm based algorithms in terms of CPU running times.
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A simple neuro-fuzzy impulse detector for efficient blur reduction of impulse noise removal operators for digital images
Mehmet Emin Yuksel,Erkan Beşdok +1 more
TL;DR: Experimental results show that the proposed detector significantly reduces the distortion effects of any impulse noise removal operator even if the operator already has its own noise detector.
Journal ArticleDOI
Weighted differential evolution algorithm for numerical function optimization: a comparative study with cuckoo search, artificial bee colony, adaptive differential evolution, and backtracking search optimization algorithms
TL;DR: WDE’s success in solving CEC’ 2013 problems was compared to 4 different EAs statistically and results showed that, in general, problem-solving success of WDE is statistically better than the comparison algorithms that have been used in this paper.