Implementation of support-vector machine algorithm to develop a model for electronic commerce energy regulatory system

Zhu, Mingxun

doi:10.1016/j.egyr.2021.05.009

Cited by 12 publications

(9 citation statements)

References 32 publications

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“…Shyamala et al proposed a SVM-based damage detection strategy, which is performed step-by-step by first locating and then determining the severity of the damage [ 7 ]. Zhu talked about the ongoing models of energy oversight frameworks at home and abroad, worked on the strategy in blend with the deficiencies of information mining, and afterward applied the help vector machine (SVM) calculation to the web-based business energy oversight model [ 8 ]. The drawback of these examinations, in any case, is that the contemplations are not adequately extensive to adjust to additional perplexing circumstances, and accuracy should be moved along.…”

Section: Related Workmentioning

confidence: 99%

[Retracted] Legal Guarantee of Smart City Pilot and Green and Low‐Carbon Development

Yan

2022

Journal of Environmental and Public Health

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Green and smart cities are based on clean energy and rely on information technology. They are the guarantee for the realization of efficient and intelligent urban development and green ecological transformation, the basis for sustainable social and economic development, and the inevitable trend of urban development. Therefore, the evaluation of the development level of green and smart cities is of great significance to the development of Chinese cities. This paper has aimed to study the issue of smart city pilots and legal guarantees for green and low-carbon development and introduced the concept of smart city line management, as well as the related theory of entropy weight method, cloud model, and support vector machine algorithm. Based on the sustainable development index system, this paper has combined the low-carbon concept to construct the low-carbon city evaluation index system and carried out an empirical analysis. The sustainable development index system, the research results of low-carbon city, and the current situation and characteristics of low-carbon city construction are studied and analyzed. On this premise, a low-carbon city assessment framework in view of reasonable improvement is built, including low-carbon economy, low-carbon society, low-carbon climate, and low-carbon component. The experimental results of this paper show that the low-carbon environment subsystem has the best coordinated development among the four subsystems, and the current state is the best. By 2021, the coordination degree value has reached 0.6656, which is in a relatively coordinated state.

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Section: Related Workmentioning

confidence: 99%

[Retracted] Legal Guarantee of Smart City Pilot and Green and Low‐Carbon Development

Yan

2022

Journal of Environmental and Public Health

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“…People have always regarded customers and suppliers as a separate subject. The SIPOC model studies customers, suppliers, and enterprises as a whole (Deng & Zhao, 2022;Zheng et al, 2021b;Zheng et al, 2022;Zhu, 2021).…”

Section: Relevant Theoretical Basismentioning

confidence: 99%

The Impact of SIPOC on Process Reengineering and Sustainability of Enterprise Procurement Management in E-Commerce Environments Using Deep Learning

Zhang

Fan

Chen

et al. 2022

Journal of Organizational and End User Computing

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In order to better promote the healthy and long-term development of enterprise procurement management process, under the background of e-commerce environment, Suppliers-Inputs-Process-Outputs-Customers (SIPOC) model, deep learning and related theories of enterprise procurement management are expounded and proposed. Then, D electric power enterprise is studied as samples. After understanding the current situation of procurement management of the enterprise, there are a series of problems in the enterprise, such as complex process, and no correlation between procurement management process and overall strategic planning. Finally, through the analysis of the early warning indicators of the enterprise by the deep learning algorithm, the procurement management process has caused certain risks to the financial management level of the enterprise, and the procurement management process of the enterprise needs to be adjusted. The material record and consumption scheme of the enterprise is optimized by using the SIPOC organizational system model.

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“…Zhu pointed out that the precise analysis of the data is essential to improve the reliability of the diagnostic method. erefore, based on solid-state micropore microfluidic impedance cytometer, he used the SVM algorithm to detect and enumerate cancer cells in red blood cells [10]. e above experts and scholars have fully exploited the advantages of SVMs, but they have not extended the algorithm to the financial field, and their research on the SVM algorithm is not very thorough.…”

Section: Related Workmentioning

confidence: 99%

Design and Implementation of Financial Service and Management Platform considering Support Vector Machine Algorithm

Tian

2022

Computational Intelligence and Neuroscience

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With the rapid economic development, the financial industry has quietly become the leader of industries, the core and lifeblood of promoting economic development. At the same time, various financial services and management platforms emerge one after another. However, the emergence of financial services and management platforms cannot effectively alleviate the current financial crisis. In the face of increasingly complex financial risks, traditional financial service and management platforms cannot achieve effective information sharing, which leads to continued low service and management efficiency and frequent financial risk problems. Support vector machine is a data classification algorithm based on supervision, which can realize data sharing and improve the efficiency of data processing. The article firstly readjusted the underlying architecture of the financial service and management platform to break through the barriers of data interaction. Then on this basis, the article further combines the support vector machine algorithm and extends it from binary data classification to multivariate classification. Finally, the paper redesigns the financial service and management platform considering support vector machines. After a series of experiments, it can be found that the financial service and management platform based on the support vector machine algorithm can reduce the financial risk by 17.2%, improve the financial service level by 30.2%, and improve the financial comprehensive service level by 45.2%. At the same time, thanks to information sharing and interaction, the financial service and management platform can effectively predict financial risks, and the accuracy of the prediction basically reaches 78.9%. This shows that a financial service and management platform that takes into account the support vector machine algorithm can effectively prevent financial risks and improve the efficiency of financial services and management.

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Implementation of support-vector machine algorithm to develop a model for electronic commerce energy regulatory system

Cited by 12 publications

References 32 publications

[Retracted] Legal Guarantee of Smart City Pilot and Green and Low‐Carbon Development

[Retracted] Legal Guarantee of Smart City Pilot and Green and Low‐Carbon Development

The Impact of SIPOC on Process Reengineering and Sustainability of Enterprise Procurement Management in E-Commerce Environments Using Deep Learning

Design and Implementation of Financial Service and Management Platform considering Support Vector Machine Algorithm

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