Statistical Features and Traditional SA-SVM Classification Algorithm for Crack Detection

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ISSN Print: 2159-4465
ISSN Online: 2159-4481
www.scirp.org/journal/jsip
E-mail: jsip@scirp.org

"Statistical Features and Traditional SA-SVM Classification Algorithm for Crack Detection"
written by Azadeh Noori Hoshyar, Sergey Kharkovsky, Bijan Samali,
published by Journal of Signal and Information Processing, Vol.9 No.2, 2018

has been cited by the following article(s):

[1]	Machine Learning Techniques for Structural Health Monitoring of Concrete Structures: A Systematic Review
	Iranian Journal of Science …, 2023

[2]	Klasifikasi Keretakan Beton dengan Metode Convolutional Neural Network (CNN) dan Support Vector Machine (SVM)
	2022

[3]	Building crack identification and total quality management method based on deep learning
	2021

[4]	Crack Detection in Images of Masonry Using CNNs
	2021

[5]	Implementation of Improved SVM Algorithm in Tourism Economic Data Analysis
	2020

[6]	Algorithm Development for the non-destructive testing of structural damage
	2019

[7]	Análise de atributos de classificação para o diagnóstico de falhas em rolamentos baseado em SVM
	2019

[1]	Machine Learning Techniques for Structural Health Monitoring of Concrete Structures: A Systematic Review Iranian Journal of Science and Technology, Transactions of Civil Engineering, 2023 DOI:10.1007/s40996-023-01054-5

[2]	Machine Learning Techniques for Structural Health Monitoring of Concrete Structures: A Systematic Review Iranian Journal of Science and Technology, Transactions of Civil Engineering, 2023 DOI:10.1007/s40996-023-01054-5

[3]	Building Crack Identification and Total Quality Management Method Based on Deep Learning Pattern Recognition Letters, 2021 DOI:10.1016/j.patrec.2021.01.034

[4]	Implementation of Improved SVM Algorithm in Tourism Economic Data Analysis 2020 5th International Conference on Smart Grid and Electrical Automation (ICSGEA), 2020 DOI:10.1109/ICSGEA51094.2020.00113

[5]	Algorithm Development for the Non-Destructive Testing of Structural Damage Applied Sciences, 2019 DOI:10.3390/app9142810

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