Differentiation of Agarwood oil quality using Support Vector Machine (SVM)

Differentiation of Agarwood oil quality using Support Vector Machine (SVM)

Ths research presents an Agarwood oil gradng system using Support Vector Machine (SVM). Agarwood is grown in tropical parts of Asia (including Malaysia) and is a valuable international commodity. It is used primarily in fragrance and medicine. Data collected from 96 Agarwood oil samples of dfferent...

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Published in:Journal of Engineering and Applied Sciences
Main Author: Jantan H.; Yassin I.M.; Zabidi A.; Ismail N.; Ali M.S.A.M.
Format: Article
Language:English
Published: Medwell Journals 2017
Online Access:https://www.scopus.com/inward/record.uri?eid=2-s2.0-85029111994&doi=10.3923%2fjeasci.2017.3810.3812&partnerID=40&md5=b54d8e887d0785a9d966decadd5ce7b8
id 2-s2.0-85029111994
spelling 2-s2.0-85029111994
Jantan H.; Yassin I.M.; Zabidi A.; Ismail N.; Ali M.S.A.M.
Differentiation of Agarwood oil quality using Support Vector Machine (SVM)
2017
Journal of Engineering and Applied Sciences
12
15
10.3923/jeasci.2017.3810.3812
https://www.scopus.com/inward/record.uri?eid=2-s2.0-85029111994&doi=10.3923%2fjeasci.2017.3810.3812&partnerID=40&md5=b54d8e887d0785a9d966decadd5ce7b8
Ths research presents an Agarwood oil gradng system using Support Vector Machine (SVM). Agarwood is grown in tropical parts of Asia (including Malaysia) and is a valuable international commodity. It is used primarily in fragrance and medicine. Data collected from 96 Agarwood oil samples of dfferent qualities were used to train several SVMs with dfferent Kernel functions. Implementation of the project was done using MATLAB v2010a. It was found that nonlinear Kernels were able to produce 100% accuracy, outperforming the linear Kernel (87.5% accuracy). © Medwell Journals, 2017.
Medwell Journals
1816949X
English
Article
author Jantan H.; Yassin I.M.; Zabidi A.; Ismail N.; Ali M.S.A.M.
spellingShingle Jantan H.; Yassin I.M.; Zabidi A.; Ismail N.; Ali M.S.A.M.
Differentiation of Agarwood oil quality using Support Vector Machine (SVM)
author_facet Jantan H.; Yassin I.M.; Zabidi A.; Ismail N.; Ali M.S.A.M.
author_sort Jantan H.; Yassin I.M.; Zabidi A.; Ismail N.; Ali M.S.A.M.
title Differentiation of Agarwood oil quality using Support Vector Machine (SVM)
title_short Differentiation of Agarwood oil quality using Support Vector Machine (SVM)
title_full Differentiation of Agarwood oil quality using Support Vector Machine (SVM)
title_fullStr Differentiation of Agarwood oil quality using Support Vector Machine (SVM)
title_full_unstemmed Differentiation of Agarwood oil quality using Support Vector Machine (SVM)
title_sort Differentiation of Agarwood oil quality using Support Vector Machine (SVM)
publishDate 2017
container_title Journal of Engineering and Applied Sciences
container_volume 12
container_issue 15
doi_str_mv 10.3923/jeasci.2017.3810.3812
url https://www.scopus.com/inward/record.uri?eid=2-s2.0-85029111994&doi=10.3923%2fjeasci.2017.3810.3812&partnerID=40&md5=b54d8e887d0785a9d966decadd5ce7b8
description Ths research presents an Agarwood oil gradng system using Support Vector Machine (SVM). Agarwood is grown in tropical parts of Asia (including Malaysia) and is a valuable international commodity. It is used primarily in fragrance and medicine. Data collected from 96 Agarwood oil samples of dfferent qualities were used to train several SVMs with dfferent Kernel functions. Implementation of the project was done using MATLAB v2010a. It was found that nonlinear Kernels were able to produce 100% accuracy, outperforming the linear Kernel (87.5% accuracy). © Medwell Journals, 2017.
publisher Medwell Journals
issn 1816949X
language English
format Article
accesstype
record_format scopus
collection Scopus
_version_ 1809677909036630016

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