Developing a Novel Method for Estimating the Speed of Sound in Biodiesel Known as Grey Wolf Optimizer Support Vector Machine Algorithm

In the current study, our goal was to obtain a robust model to predict the speed of sound in biodiesel. For this purpose, an extensive databank has been extracted from previously published papers. Then, a Support Vector Machine (SVM) has been optimized by Grey Wolf Optimization (GWO) method to analy...

Full description

Bibliographic Details
Main Authors: Zhenzhen Lv, Ming Hu, Yixin Yang, Jeren Makhdoumi
Format: Article
Language:English
Published: Hindawi Limited 2021-01-01
Series:BioMed Research International
Online Access:http://dx.doi.org/10.1155/2021/5368987
id doaj-cd63b20ee6a34d0c848b8cbe4600d791
record_format Article
spelling doaj-cd63b20ee6a34d0c848b8cbe4600d7912021-07-12T02:13:01ZengHindawi LimitedBioMed Research International2314-61412021-01-01202110.1155/2021/5368987Developing a Novel Method for Estimating the Speed of Sound in Biodiesel Known as Grey Wolf Optimizer Support Vector Machine AlgorithmZhenzhen Lv0Ming Hu1Yixin Yang2Jeren Makhdoumi3School of Electrical and Information EngineeringSchool of Electrical and Information EngineeringSchool of Electrical and Information EngineeringDepartment of Educational ScienceIn the current study, our goal was to obtain a robust model to predict the speed of sound in biodiesel. For this purpose, an extensive databank has been extracted from previously published papers. Then, a Support Vector Machine (SVM) has been optimized by Grey Wolf Optimization (GWO) method to analyze these data and determine the correlation between speed of sound in biodiesel and its related properties including pressure, temperature, molecular weight, and normal melting point. The results were very satisfactory because the values of statistical parameters R2 and RMSE were obtained 1 and 1.4024, respectively. Here, this is the first time that the sensitivity analysis is used to estimate this target value. This analysis shows that the pressure widely affects the output values with relevancy factor 87.92. Also, our proposed method is highly accurate than other machine learning methods used in papers employed for this objective.http://dx.doi.org/10.1155/2021/5368987
collection DOAJ
language English
format Article
sources DOAJ
author Zhenzhen Lv
Ming Hu
Yixin Yang
Jeren Makhdoumi
spellingShingle Zhenzhen Lv
Ming Hu
Yixin Yang
Jeren Makhdoumi
Developing a Novel Method for Estimating the Speed of Sound in Biodiesel Known as Grey Wolf Optimizer Support Vector Machine Algorithm
BioMed Research International
author_facet Zhenzhen Lv
Ming Hu
Yixin Yang
Jeren Makhdoumi
author_sort Zhenzhen Lv
title Developing a Novel Method for Estimating the Speed of Sound in Biodiesel Known as Grey Wolf Optimizer Support Vector Machine Algorithm
title_short Developing a Novel Method for Estimating the Speed of Sound in Biodiesel Known as Grey Wolf Optimizer Support Vector Machine Algorithm
title_full Developing a Novel Method for Estimating the Speed of Sound in Biodiesel Known as Grey Wolf Optimizer Support Vector Machine Algorithm
title_fullStr Developing a Novel Method for Estimating the Speed of Sound in Biodiesel Known as Grey Wolf Optimizer Support Vector Machine Algorithm
title_full_unstemmed Developing a Novel Method for Estimating the Speed of Sound in Biodiesel Known as Grey Wolf Optimizer Support Vector Machine Algorithm
title_sort developing a novel method for estimating the speed of sound in biodiesel known as grey wolf optimizer support vector machine algorithm
publisher Hindawi Limited
series BioMed Research International
issn 2314-6141
publishDate 2021-01-01
description In the current study, our goal was to obtain a robust model to predict the speed of sound in biodiesel. For this purpose, an extensive databank has been extracted from previously published papers. Then, a Support Vector Machine (SVM) has been optimized by Grey Wolf Optimization (GWO) method to analyze these data and determine the correlation between speed of sound in biodiesel and its related properties including pressure, temperature, molecular weight, and normal melting point. The results were very satisfactory because the values of statistical parameters R2 and RMSE were obtained 1 and 1.4024, respectively. Here, this is the first time that the sensitivity analysis is used to estimate this target value. This analysis shows that the pressure widely affects the output values with relevancy factor 87.92. Also, our proposed method is highly accurate than other machine learning methods used in papers employed for this objective.
url http://dx.doi.org/10.1155/2021/5368987
work_keys_str_mv AT zhenzhenlv developinganovelmethodforestimatingthespeedofsoundinbiodieselknownasgreywolfoptimizersupportvectormachinealgorithm
AT minghu developinganovelmethodforestimatingthespeedofsoundinbiodieselknownasgreywolfoptimizersupportvectormachinealgorithm
AT yixinyang developinganovelmethodforestimatingthespeedofsoundinbiodieselknownasgreywolfoptimizersupportvectormachinealgorithm
AT jerenmakhdoumi developinganovelmethodforestimatingthespeedofsoundinbiodieselknownasgreywolfoptimizersupportvectormachinealgorithm
_version_ 1721308054445621248