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All issues Volume 28 / No 2 (April 2023) Wuhan Univ. J. Nat. Sci., 28 2 (2023) 117-128 References
Open Access
Issue
Wuhan Univ. J. Nat. Sci.
Volume 28, Number 2, April 2023
Page(s) 117 - 128
DOI https://doi.org/10.1051/wujns/2023282117
Published online 23 May 2023
  1. Yang G, Wang R, Wang S N, et al. Research on the impact of bank competition on credit risk of listed companies [J]. China Soft Science, 2021(10): 103-114(Ch). [Google Scholar]
  2. Ma X, Wei C,Han J. Credit risk assessment of Chinese listed companies based on SVM improved by shuffled frog leaping algorithm [C]//2021 33rd Chinese Control and Decision Conference (CCDC). New York: IEEE Press, 2021: 2462-2467. [Google Scholar]
  3. Wang C P, Li C L. Credit risk measurement of listed companies based on modified KMV model [J]. Friends of Accounting, 2018(13): 93-99(Ch). [Google Scholar]
  4. Wang R. AHP -entropy method credit risk assessment based on Python[C]// 2021 Asia-Pacific Conference on Communications Technology and Computer Science (ACCTCS). New York: IEEE Press, 2021: 17-20. [Google Scholar]
  5. Machado M R, Karray S. Assessing credit risk of commercial customers using hybrid machine learning algorithms [J]. Expert Systems with Applications, 2022, 200: 116889. [Google Scholar]
  6. Hu X, Hu J, Chen L, et al. Credit risk assessment model for small, medium and micro enterprises based on RS-PSO-SVM integration[C]// 2021 IEEE 6th International Conference on Cloud Computing and Big Data Analytics (ICCCBDA). New York: IEEE Press, 2021: 342-345. [Google Scholar]
  7. Tezerjan M Y, Samghabadi A S, Memariani A. ARF: A hybrid model for credit scoring in complex systems[J]. Expert Systems with Applications, 2021, 185(7): 115634. [CrossRef] [Google Scholar]
  8. Shen F, Zhao X, Kou G, et al. A new deep learning ensemble credit risk evaluation model with an improved synthetic minority oversampling technique[J]. Applied Soft Computing, 2021, 98(1): 106852. [Google Scholar]
  9. Liu J M, Zhang S C, Fan H Y. A two-stage hybrid credit risk prediction model based on XGBoost and graph-based deep neural network[J]. Expert Systems with Applications, 2022, 195: 116624. [Google Scholar]
  10. Hu Y, Su J. Research on credit risk evaluation of commercial banks based on artificial neural network model[J]. Procedia Computer Science, 2022, 199: 1168-1176. [CrossRef] [Google Scholar]
  11. Zhang K, Shi S, Liu S, et al. Research on DBN-based evaluation of distribution network reliability[C]// 7th International Conference on Renewable Energy Technologies (ICRET 2021). Kuala Lumpur: EDP Sciences, 2021, 242: 03004. [Google Scholar]
  12. Zhu J. Research of enterprise financial management capability system based on EFA method and intelligent data clustering model[C]// 2021 2nd International Conference on Smart Electronics and Communication (ICOSEC). New York: IEEE Press, 2021: 1342-1345. [Google Scholar]
  13. Liebana-Cabanillas F, Marinkovicet V, Kalinic Z, et al. Predicting the determinants of mobile payment acceptance: A hybrid SEM-neural network approach[J]. Technological Forecasting and Social Change, 2018, 129: 117-130. [CrossRef] [Google Scholar]

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