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

Lv, Zhenzhen; Hu, Ming; Yang, Yixin; Makhdoumi, Jeren

doi:10.1155/2021/5368987

Cited by 4 publications

(1 citation statement)

References 37 publications

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“…Nonlinear SVM is learned using kernel functions and soft margin maximization. Ultimately, SVM achieves global optimization by constructing an optimal classification hyperplane in the attribute space using the principle of structural risk minimization [21].…”

Section: Svmmentioning

confidence: 99%

Enterprise Accounting Information Identification and Strategic Management under Data Mining Technology

Shao

Lai

Zheng

et al. 2022

Mobile Information Systems

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Present-day enterprise accounting solutions have been developed to a certain extent to provide authenticity of accounting information and to provide modules for billing, pay role, general ledger, and more, but they come with certain problems such as distortion of accounting information, incomplete selection of indicator variables, and the limited and single use of identification methods. Based on this, this study starts with two points. The first is to give the concepts of decision trees and support vector machine (SVM) in data mining. Then, the accounting distortion information identification model is constructed based on this, and the model effect is verified by setting experiments. The second is to establish a regression model on the relationship between enterprise strategy and accounting information quality to further explore the factors that affect the quality of enterprise accounting information. The following are the research results: (1) The accuracy rates of classification and identification of training set data, overall data, and test set data using the SVM-based identification model are 99.19%, 96.21%, and 94.8%, respectively. (2) The average identification rate of the sample data is 88.5% using the identification model based on the decision tree. (3) The regression coefficients of enterprise strategy and accounting information quality are −0.053 and −0.054, respectively without considering the industry and year variables and with considering the industry and year variables, both of which are negative at the 0.1 significance level. The purpose of this study is to use data mining to achieve high-quality identification of enterprise accounting information and provide some references for enterprises to choose or formulate relevant development strategies.

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Section: Svmmentioning

confidence: 99%