Sequential minimal optimization algorithm with support vector machine for mosquito larvae identification

Sequential minimal optimization algorithm with support vector machine for mosquito larvae identification

Efficient and fast mosquito larvae identification is of important due to the cost, effort and time taken during manual processes. This paper evaluates the use of the sequential minimal optimization algorithm (SMO) employed with support vector machine (SVM) to improve the identification process that...

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Bibliographic Details
Published in:Advanced Science Letters
Main Author: Yusoff M.; Jefri N.J.; Shahar M.K.
Format: Article
Language:English
Published: American Scientific Publishers 2017
Online Access:https://www.scopus.com/inward/record.uri?eid=2-s2.0-85023776213&doi=10.1166%2fasl.2017.8246&partnerID=40&md5=22dfdb1ac8b4a12cbcb50c8b33d25371
id 2-s2.0-85023776213
spelling 2-s2.0-85023776213
Yusoff M.; Jefri N.J.; Shahar M.K.
Sequential minimal optimization algorithm with support vector machine for mosquito larvae identification
2017
Advanced Science Letters
23
5
10.1166/asl.2017.8246
https://www.scopus.com/inward/record.uri?eid=2-s2.0-85023776213&doi=10.1166%2fasl.2017.8246&partnerID=40&md5=22dfdb1ac8b4a12cbcb50c8b33d25371
Efficient and fast mosquito larvae identification is of important due to the cost, effort and time taken during manual processes. This paper evaluates the use of the sequential minimal optimization algorithm (SMO) employed with support vector machine (SVM) to improve the identification process that takes into account mosquito larva images. All images of Aedes and Culex are transformed to binary values with the application of pre-processing steps. The comparison results are based on computational experiments of the linear kernel, polynomial kernel, and Gaussian Radial Basis Function (RBF) kernel settings. The findings of RBF offer a better performance compared to linear and polynomial. © 2017 American Scientific Publishers All rights reserved.
American Scientific Publishers
19366612
English
Article
author Yusoff M.; Jefri N.J.; Shahar M.K.
spellingShingle Yusoff M.; Jefri N.J.; Shahar M.K.
Sequential minimal optimization algorithm with support vector machine for mosquito larvae identification
author_facet Yusoff M.; Jefri N.J.; Shahar M.K.
author_sort Yusoff M.; Jefri N.J.; Shahar M.K.
title Sequential minimal optimization algorithm with support vector machine for mosquito larvae identification
title_short Sequential minimal optimization algorithm with support vector machine for mosquito larvae identification
title_full Sequential minimal optimization algorithm with support vector machine for mosquito larvae identification
title_fullStr Sequential minimal optimization algorithm with support vector machine for mosquito larvae identification
title_full_unstemmed Sequential minimal optimization algorithm with support vector machine for mosquito larvae identification
title_sort Sequential minimal optimization algorithm with support vector machine for mosquito larvae identification
publishDate 2017
container_title Advanced Science Letters
container_volume 23
container_issue 5
doi_str_mv 10.1166/asl.2017.8246
url https://www.scopus.com/inward/record.uri?eid=2-s2.0-85023776213&doi=10.1166%2fasl.2017.8246&partnerID=40&md5=22dfdb1ac8b4a12cbcb50c8b33d25371
description Efficient and fast mosquito larvae identification is of important due to the cost, effort and time taken during manual processes. This paper evaluates the use of the sequential minimal optimization algorithm (SMO) employed with support vector machine (SVM) to improve the identification process that takes into account mosquito larva images. All images of Aedes and Culex are transformed to binary values with the application of pre-processing steps. The comparison results are based on computational experiments of the linear kernel, polynomial kernel, and Gaussian Radial Basis Function (RBF) kernel settings. The findings of RBF offer a better performance compared to linear and polynomial. © 2017 American Scientific Publishers All rights reserved.
publisher American Scientific Publishers
issn 19366612
language English
format Article
accesstype
record_format scopus
collection Scopus
_version_ 1809678485923299328

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