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Risk Level Determination of Science and Technology Service Supply Chain with PA-BP Integrated Model

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The 2020 International Conference on Machine Learning and Big Data Analytics for IoT Security and Privacy (SPIOT 2020)

Part of the book series: Advances in Intelligent Systems and Computing ((AISC,volume 1283))

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Abstract

It is advantageous to predict the risk for scientific and technological services enterprises by making the science and technology service supply chain risk early warning, and help them to take effective actions to reduce or avoid risk timely to improve the decision level. On the basis of analyzing the connotation of science and technology service supply chain risk, this paper finds out the main risk factors influencing the science and technology service supply chain operation, and establishes the model on the science and technology service supply chain risk early warning by making the effective integration of BP neural networks and principal component analyzing method. Finally, the results show that it has high applicability and reliability of the risk early warning model of science and technology service supply chain with BP neural network, which can realize the accurate early warning, detection and analysis for the risk of scientific and technical service supply chain.

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References

  1. Yi-zhou, E., Yu-yang, Q.I.A.O., Zi-chao, L.I.U.: A study on the evolution and risk identification of internet financial models. East Chin. Econ. Manage. 30(3), 91–96 (2016)

    Google Scholar 

  2. Liu, H.-H.: Study on supply risk identification and assessment of the critical power batteries in NEVs. Sci. Technol. Manage. Res. 36(24), 189–195 (2016)

    Google Scholar 

  3. Guang, Z.H.U., Min-ning, F.E.N.G., Shuo, L.I.U.: Research on security risk identification and countermeasures of big data flow-based on the perspective of information life cycle. Res. Libr. Sci. 9, 84–90 (2017)

    Google Scholar 

  4. Zhou, Z., HU, C.-P.: Research on the risk identification of academic information system based on the comprehensive weighting method. Inf. Sci. 8, 159–163 (2017)

    Google Scholar 

  5. Wang, Z.-H., Liu, Z., ZHU, D.: Safety risk identification and prevention path of the production link in pig supply chain. Chin. Popul. Res. Environ. 27(12), 174–182 (2017)

    Google Scholar 

  6. Wang, H.H., Huang, X., Chen, R.: Research on risk recognition of supply chain for large aircraft collaborative development based on SCOR model. J. Jinling Inst. Technol. (2015)

    Google Scholar 

  7. Zou, X.-H., Tian, L.-X.: Comprehensive evaluation of CCS project based on improved integrated cloud model. Stat. Decis. Making 3, 182–185 (2016)

    Google Scholar 

  8. Rodríguez, A., Ortega, F., Concepción, R.: A method for the evaluation of risk in IT projects. Expert Syst. Appl. 45, 273–285 (2016)

    Article  Google Scholar 

  9. Wu, Y., Li, L., Xu, R., et al.: Risk assessment in straw-based power generation public-private partnership projects in China: a fuzzy synthetic evaluation analysis. J. Cleaner Prod. 161, 977–990 (2017)

    Article  Google Scholar 

  10. Jian, L.-L.: Research on risk assessment of logistics strategic alliance based on BP neural network. Sci. Technol. Manage. Res. 24, 165–167 (2011)

    Google Scholar 

Download references

Acknowledgments

This work is supported by the Humanity and Social Science Youth Foundation of Ministry of Education of China (Grant No. 19YJCZH054), the Science and Technology Research program of Chongqing Municipal Education Commission (Grant No. KJQN201801145) and the Chongqing Social Science Planning Project (Grant No. 2015YBSH051).

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Authors and Affiliations

  1. Chongqing University of Technology, 69 Hongguang Avenue, Chongqing, China

    JinHua Sun, Linlin Xu & Xiang Guo

Authors
  1. JinHua Sun
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  2. Linlin Xu
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  3. Xiang Guo
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Corresponding author

Correspondence to JinHua Sun .

Editor information

Editors and Affiliations

  1. David Goldman Informatics Centre, University of Sunderland, Sunderland, UK

    John MacIntyre

  2. University of Shanghai for Science and Technology, Shanghai, China

    Jinghua Zhao

  3. Shenzhen University, Shenzen, Guangdong, China

    Xiaomeng Ma

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Sun, J., Xu, L., Guo, X. (2021). Risk Level Determination of Science and Technology Service Supply Chain with PA-BP Integrated Model. In: MacIntyre, J., Zhao, J., Ma, X. (eds) The 2020 International Conference on Machine Learning and Big Data Analytics for IoT Security and Privacy. SPIOT 2020. Advances in Intelligent Systems and Computing, vol 1283. Springer, Cham. https://doi.org/10.1007/978-3-030-62746-1_43

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