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Problems and Countermeasures of Financial Risk in Project Management Based on Convolutional Neural Network

Under the background of market economy, engineering projects are faced with a lot of financial risks. If we cannot prevent them effectively, it will undoubtedly bring serious negative impact to the entire engineering management work. Therefore, it is particularly important to actively manage risks,...

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Detalles Bibliográficos
Autores principales: Wei, Ran, Ding, Dewen
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Hindawi 2022
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8956410/
https://www.ncbi.nlm.nih.gov/pubmed/35341195
http://dx.doi.org/10.1155/2022/1978415
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author Wei, Ran
Ding, Dewen
author_facet Wei, Ran
Ding, Dewen
author_sort Wei, Ran
collection PubMed
description Under the background of market economy, engineering projects are faced with a lot of financial risks. If we cannot prevent them effectively, it will undoubtedly bring serious negative impact to the entire engineering management work. Therefore, it is particularly important to actively manage risks, identify and evaluate risks in a timely and correct manner, manage risks efficiently, and minimize risk losses. At the same time, the development of wireless communication technology has brought many new branches of engineering project management. Some problems in the process of risk management are often not handled by traditional empirical calculation or mathematical methods, so it is necessary to find an appropriate way to define and describe the nonlinear relationship between a large number of uncertain causes and risk losses. In order to match the changes in the background of the development of wireless communication technology, this paper studies the financial risk problems and countermeasures in the engineering management of convolutional neural networks. The financial risk prediction model in network engineering management is constructed, and the volume neural network algorithm referenced by it is tested. The test results are highly consistent with the expert assessment. In the research process, the combination of questionnaire survey and mathematical analysis method was adopted, the extreme value of risk factors was determined by questionnaire survey, and then the accuracy of prediction was verified by a mathematical model. After many calculations, it has been proved that the convolutional neural network simulation system based on the scientific node selection method has greatly improved the accuracy of risk assessment.
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spelling pubmed-89564102022-03-26 Problems and Countermeasures of Financial Risk in Project Management Based on Convolutional Neural Network Wei, Ran Ding, Dewen Comput Intell Neurosci Research Article Under the background of market economy, engineering projects are faced with a lot of financial risks. If we cannot prevent them effectively, it will undoubtedly bring serious negative impact to the entire engineering management work. Therefore, it is particularly important to actively manage risks, identify and evaluate risks in a timely and correct manner, manage risks efficiently, and minimize risk losses. At the same time, the development of wireless communication technology has brought many new branches of engineering project management. Some problems in the process of risk management are often not handled by traditional empirical calculation or mathematical methods, so it is necessary to find an appropriate way to define and describe the nonlinear relationship between a large number of uncertain causes and risk losses. In order to match the changes in the background of the development of wireless communication technology, this paper studies the financial risk problems and countermeasures in the engineering management of convolutional neural networks. The financial risk prediction model in network engineering management is constructed, and the volume neural network algorithm referenced by it is tested. The test results are highly consistent with the expert assessment. In the research process, the combination of questionnaire survey and mathematical analysis method was adopted, the extreme value of risk factors was determined by questionnaire survey, and then the accuracy of prediction was verified by a mathematical model. After many calculations, it has been proved that the convolutional neural network simulation system based on the scientific node selection method has greatly improved the accuracy of risk assessment. Hindawi 2022-03-18 /pmc/articles/PMC8956410/ /pubmed/35341195 http://dx.doi.org/10.1155/2022/1978415 Text en Copyright © 2022 Ran Wei and Dewen Ding. https://creativecommons.org/licenses/by/4.0/This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
spellingShingle Research Article
Wei, Ran
Ding, Dewen
Problems and Countermeasures of Financial Risk in Project Management Based on Convolutional Neural Network
title Problems and Countermeasures of Financial Risk in Project Management Based on Convolutional Neural Network
title_full Problems and Countermeasures of Financial Risk in Project Management Based on Convolutional Neural Network
title_fullStr Problems and Countermeasures of Financial Risk in Project Management Based on Convolutional Neural Network
title_full_unstemmed Problems and Countermeasures of Financial Risk in Project Management Based on Convolutional Neural Network
title_short Problems and Countermeasures of Financial Risk in Project Management Based on Convolutional Neural Network
title_sort problems and countermeasures of financial risk in project management based on convolutional neural network
topic Research Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8956410/
https://www.ncbi.nlm.nih.gov/pubmed/35341195
http://dx.doi.org/10.1155/2022/1978415
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