A survey on the applications of artificial bee colony in signal, image, and video processing

Akay, Bahriye; Karaboğa, Derviş

doi:10.1007/s11760-015-0758-4

Cited by 134 publications

(62 citation statements)

References 94 publications

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“…The two famous SI algorithms are ant colony optimization (ACO) [265] and particle swarm optimization (PSO) [266]. Other popular SI algorithms include artificial bee colony (ABC) [267], firefly algorithm (FA) [268], and glowworm swarm optimization.…”

Section: Optimizationmentioning

confidence: 99%

Pathological Brain Detection by Artificial Intelligence in Magnetic Resonance Imaging Scanning (Invited Review)

Wang¹,

Zhang²,

Zhan³

et al. 2016

PIER

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Abstract-(Aim) Pathological brain detection (PBD) systems aim to assist and even replace neuroradiologists to make decisions for patients. This review offers a comprehensive and quantitative comparison for PBD systems by artificial intelligence in magnetic resonance imaging (MRI) scanning. (Method) We first investigated four categories of brain diseases, including neoplastic disease, neurodegenerative disease, cerebrovascular disease, and inflammation. Next, we introduced important MRI techniques, such as the shimming, water and fat suppression, and three advanced imaging modalities (functional MRI, diffusion tensor imaging, and magnetic resonance spectroscopic imaging). Then, we discussed four image preprocessing techniques (image denoising, slice selection, brain extraction, spatial normalization, and intensity normalization), seven feature representation techniques (shape, moment, wavelet, statistics, entropy, gray level co-occurrence matrix, and Fourier transform), and two dimension reduction techniques (feature selection and feature extraction). Afterwards, we studied classification related methods: six learning models (decision tree, extreme learning machine, k-nearest neighbors, naive Bayes classifier, support vector machine, feed-forward neural network), five kernel functions (linear, homogeneous and inhomogeneous polynomial, radial basis function, and sigmoid), and three types of optimization methods (evolutionary algorithm, stochastic optimization, and swarm intelligence). (Results) We introduced three benchmark datasets and used K-fold stratified cross validation to avoid overfitting. We presented a detailed quantitative comparison among 44 state-of-theart PBD algorithms and discussed their advantages and limitations. (Discussions) Artificial intelligence is now making stride in the PBD field and enjoys a fair amount of success. In the future, semi-supervised learning and transfer learning techniques may be potential breakthroughs to develop PBD systems.

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Section: Optimizationmentioning

confidence: 99%

Pathological Brain Detection by Artificial Intelligence in Magnetic Resonance Imaging Scanning (Invited Review)

Wang¹,

Zhang²,

Zhan³

et al. 2016

PIER

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“…The important activities of ABC are to share the indexes of the provided solutions, which are known as food sources. This process in the ABC is called information sharing (Karaboga, et al, 2014;Akay and Karaboga, 2015). However, a typical ABC cannot do this sharing efficiently.…”

Section: Introductionmentioning

confidence: 99%

A Hybrid of Artificial Bee Colony, Genetic Algorithm, and Neural Network for Diabetic Mellitus Diagnosing

Rashid

Abdullah

2018

ARO

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“…Many realistic problems contain simultaneous optimization of several objectives in which these objectives may conflict with each other and with other nonlinear constraints, if exist [1][2][3][4][5][6][7]. These types of problems are known as Multi-Objective Problems (MOPs).…”

Section: Introductionmentioning

confidence: 99%

Application of Multi-objective Charged System Search Algorithm for Optimization Problems

2018

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A survey on the applications of artificial bee colony in signal, image, and video processing

Cited by 134 publications

References 94 publications

Pathological Brain Detection by Artificial Intelligence in Magnetic Resonance Imaging Scanning (Invited Review)

Pathological Brain Detection by Artificial Intelligence in Magnetic Resonance Imaging Scanning (Invited Review)

A Hybrid of Artificial Bee Colony, Genetic Algorithm, and Neural Network for Diabetic Mellitus Diagnosing

Application of Multi-objective Charged System Search Algorithm for Optimization Problems

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