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Comparison of classifying the material mechanical properties by using k-Nearest Neighbor and Neural Network Backpropagation

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Main Authors: Intan Maisarah, Abd Rahim, Fauziah, Mat, Sazali, Yaacob, Prof. Dr.
Other Authors: umaisarah_138@yahoo.com
Format: Article
Language:English
Published: Science Academy 2011
Subjects:
k-Nearest Neighbor (k-NN)
Neural network
Material mechanical properties
Online Access:http://dspace.unimap.edu.my/xmlui/handle/123456789/12102
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spelling my.unimap-121022011-05-26T05:14:31Z Comparison of classifying the material mechanical properties by using k-Nearest Neighbor and Neural Network Backpropagation Intan Maisarah, Abd Rahim Fauziah, Mat Sazali, Yaacob, Prof. Dr. umaisarah_138@yahoo.com fauziah@unimap.edu.my sazali22@yahoo.com k-Nearest Neighbor (k-NN) Neural network Material mechanical properties Link to publisher's homepage at http://www.sciacademypublisher.com This paper present a development of a system with non-destructive testing on the material to define the mechanical properties of material. The experimental and testing of the material mechanical properties using vibration technique could determine the natural frequencies, the damping ratio and mode shapes of the structure. However, in this study, we only considering the natural frequencies and its amplitude of the material as the input data needed for training. As an extension for the study, the input data tested with various method of classifier. The k-Nearest Neighbor classifier and artificial neural network with Levenberg-Marquardt Backpropagation are developed to work as a system to classify the materials tested according to their mechanical properties. The result from the classification system shows that k-NN is giving the accuracy of 99.79783 % with the k value of 1 and in the other hand, Levenberg-Marquardt Backpropagation is giving the best classification rate of 99.86%. 2011-05-26T05:13:31Z 2011-05-26T05:13:31Z 2011-03 Article International Journal of Research and Reviews in Artificial Intelligence, vol. 1(1), 2011, pages 7-11 2046-5122 http://www.sciacademypublisher.com/journals/index.php/IJRRAI/article/view/46/39 http://hdl.handle.net/123456789/12102 en Science Academy
institution Universiti Malaysia Perlis
building UniMAP Library
collection Institutional Repository
continent Asia
country Malaysia
content_provider Universiti Malaysia Perlis
content_source UniMAP Library Digital Repository
url_provider http://dspace.unimap.edu.my/
language English
topic k-Nearest Neighbor (k-NN)
Neural network
Material mechanical properties
spellingShingle k-Nearest Neighbor (k-NN)
Neural network
Material mechanical properties
Intan Maisarah, Abd Rahim
Fauziah, Mat
Sazali, Yaacob, Prof. Dr.
Comparison of classifying the material mechanical properties by using k-Nearest Neighbor and Neural Network Backpropagation
description Link to publisher's homepage at http://www.sciacademypublisher.com
author2 umaisarah_138@yahoo.com
author_facet umaisarah_138@yahoo.com
Intan Maisarah, Abd Rahim
Fauziah, Mat
Sazali, Yaacob, Prof. Dr.
format Article
author Intan Maisarah, Abd Rahim
Fauziah, Mat
Sazali, Yaacob, Prof. Dr.
author_sort Intan Maisarah, Abd Rahim
title Comparison of classifying the material mechanical properties by using k-Nearest Neighbor and Neural Network Backpropagation
title_short Comparison of classifying the material mechanical properties by using k-Nearest Neighbor and Neural Network Backpropagation
title_full Comparison of classifying the material mechanical properties by using k-Nearest Neighbor and Neural Network Backpropagation
title_fullStr Comparison of classifying the material mechanical properties by using k-Nearest Neighbor and Neural Network Backpropagation
title_full_unstemmed Comparison of classifying the material mechanical properties by using k-Nearest Neighbor and Neural Network Backpropagation
title_sort comparison of classifying the material mechanical properties by using k-nearest neighbor and neural network backpropagation
publisher Science Academy
publishDate 2011
url http://dspace.unimap.edu.my/xmlui/handle/123456789/12102
_version_ 1643790302436130816
score 13.214268

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