International Journal of Neutrosophic Science

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https://doi.org/10.54216/IJNS

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2690-6805ISSN (Online) 2692-6148ISSN (Print)

Volume 23 , Issue 4 , PP: 308-322, 2024 | Cite this article as | XML | Html | PDF | Full Length Article

Fuzzy Parameterized Single-Valued Neutrosophic Subset based Artificial Intelligence for Sustainable Financial Crisis Prediction and Green Finance

Adam Mohamed Omer 1 * , Fadoua Kouki 2 , Adeeb Alhebri 3 , Radwan Hussien Alkebssi 4 , Ebrahim Mohammed Al-Matari 5

  • 1 Accounting Program, Applied College at Muhyle, King Khalid University, Kingdom of Saudi Arabia - (amahmeed@kku.edu.sa)
  • 2 Applied Management Program, Applied College at Muhyle, King Khalid University, Kingdom of Saudi Arabia - (falkoki@kku.edu.sa)
  • 3 Accounting Program, Applied College at Muhyle, King Khalid University, Kingdom of Saudi Arabia - (aalhebri@kku.edu.sa)
  • 4 Assistant Professor of Accounting, Business School, Xi’an International studies university, China - (Ralkebsee@xisu.edu.cn)
  • 5 Accounting Department, College of Business, jouf University, Kingdom of Saudi Arabia - (Ibrahim_matri7@yahoo.com)
  • Doi: https://doi.org/10.54216/IJNS.230424

    Received: June 15, 2023 Revised: January 22, 2024 Accepted: March 11, 2024
    Abstract

    Predicting sustainable financial crises and promoting green finance are paramount in fast developing economic landscape. Leveraging advanced AI-driven technologies, such as Neutrosophic logic, enables a nuanced understanding of complex sustainability factors influencing financial markets. By incorporating these advanced technologies, organizations can proactively mitigate and identify risks related to unsustainable practices while fostering investment aligned with environmental, social, and governance (ESG) principles. This proactive stance improves financial resilience and contributes to the transition towards a resilient and more sustainable financial ecosystem. We can navigate future challenges with foresight and responsibility through the synergy of sustainable financial crisis prediction and green finance initiatives, which ensures a prosperous and environmentally conscious financial future for the generation to come. This study develops a new optimal Fuzzy Parameterized Single-Valued Neutrosophic Subset for financial crisis prediction and green finance (OFPSVNS-FCPGF) technique. The OFPSVNS-FCPGF technique intends to recognize the presence of the financial disaster in the sustainable and green finance sector. In the OFPSVNS-FCPGF technique, Z-score normalization is primarily used to measure the economic data into a beneficial layout. For the procedure of prediction, the OFPSVNS-FCPGF approach designs the FPSVNS approach which detects the occurrence of financial crises or not. Furthermore, the parameter tuning of the FPSVNS technique takes place utilizing the grasshopper optimization algorithm (GOA). To illustrate the improved FCP outcomes of the OFPSVNS-FCPGF model, a series of simulations were involved. An wide comparison study specified that the OFPSVNS-FCPGF method gains significant outcomes in the green finance sector.

    Keywords :

    Green Finance , Financial Crisis Prediction , Grasshopper Optimization Algorithm , Neutrosophic Subset , Artificial Intelligence

    References

    [1]     Tyagi, S.K.S.; Boyang, Q. An intelligent internet of things aided financial crisis prediction model in fintech. IEEE Internet Things J. 2021, 10, 2183–2193.

    [2]     Muthukumaran, K.; Hariharanath, K. Deep Learning Enabled Financial Crisis Prediction Model for Small-Medium Sized Industries. Intell. Autom. Soft Comput. 2022, 35, 521–536.

    [3]     Koˇcišová, K.; Mišanková, M. Discriminant analysis as a tool for forecasting company’s financial health. Procedia-Soc. Behav. Sci. 2014, 110, 1148–1157.

    [4]     Bluwstein, K.; Buckmann, M.; Joseph, A.; Kapadia, S.; ¸Sim¸sek, Ö. Credit growth, the yield curve and financial crisis prediction: Evidence from a machine learning approach. J. Int. Econ. 2023, 103773.

    [5]     Balmaseda, V.; Coronado, M.; de Cadenas-Santiagoc, G. Predicting Systemic Risk in Financial Systems Using Deep Graph Learning. Intell. Syst. Appl. 2023, 19, 200240.

    [6]     Sankhwar, S., Gupta, D., Ramya, K.C. et al. Improved grey wolf optimization-based feature subset selection with fuzzy neural classifier for financial crisis prediction. Soft Comput 24, 101–110 (2020). https://doi.org/10.1007/s00500-019-04323-6

    [7]     Liu, L.; Chen, C.; Wang, B. Predicting financial crises with machine learning methods. J. Forecast. 2022, 41, 871–910.

    [8]     Al Duhayyim, M.; Alsolai, H.; Al-Wesabi, F.N.; Nemri, N.; Mahgoub, H.; Hilal, A.M.; Hamza, M.A.; Rizwanullah, M. Optimized stacked autoencoder for IoT enabled financial crisis prediction model. CMC-Comput. Mater. Contin. 2022, 71, 1079–1094.

    [9]     Venkateswarlu, Y.; Baskar, K.; Wongchai, A.; Gauri Shankar, V.; Paolo Martel Carranza, C.; Gonzáles, J.L.A.; Murali Dharan, A.R. An efficient outlier detection with deep learning-based financial crisis prediction model in big data environment. Comput. Intell. Neurosci. 2022, 2022, 4948947.

    [10]   Sankhwar, S.; Gupta, D.; Ramya, K.C.; Sheeba Rani, S.; Shankar, K.; Lakshmanaprabu, S.K. Improved grey wolf optimization-based feature subset selection with fuzzy neural classifier for financial crisis prediction. Soft Comput. 2020, 24, 101–110.

    [11]   Wang, Y., 2024. Abnormal behavior identification of enterprise cloud platform financial system based on artificial neural network. Computers and Electrical Engineering, 115, p.109110.

    [12]   Katib, I., Assiri, F.Y., Althaqafi, T., AlKubaisy, Z.M., Hamed, D. and Ragab, M., 2023. Hybrid Hunter–Prey Optimization with Deep Learning-Based Fintech for Predicting Financial Crises in the Economy and Society. Electronics, 12(16), p.3429.

    [13]   Elhoseny, M., Metawa, N. and El-Hasnony, I.M., 2022. A new metaheuristic optimization model for financial crisis prediction: Towards sustainable development. Sustainable Computing: Informatics and Systems, 35, p.100778.

    [14]   Chandok, G.A., Rexy, V., Basha, H.A. and Selvi, H., 2024. Enhancing Bankruptcy Prediction with White Shark Optimizer and Deep Learning: A Hybrid Approach for Accurate Financial Risk Assessment. International Journal of Intelligent Engineering & Systems, 17(1).

    [15]   Sun, S., Zhang, X., Dong, L., Fan, L. and Liu, X., 2023. Research on the Impact of Green Technology Innovation on Enterprise Financial Information Management Based on Compound Neural Network. Journal of Organizational and End User Computing (JOEUC), 35(3), pp.1-13.

    [16]   Ramesh, R. and Jeyakarthic, M., 2024. Enhancing credit risk prediction with hybrid deep learning and sand cat swarm feature selection. Multimedia Tools and Applications, pp.1-21.

    [17]   Balachander, T., Akhlaq, N., Bansal, R., Vasani, S.A., Singh, K. and Mannar, B.R., 2023, March. Financial Crisis Prediction using Feature Subset Selection with Quantum Deep Neural Network. In 2023 Second International Conference on Electronics and Renewable Systems (ICEARS) (pp. 885-889). IEEE.

    [18]   Kumar, S., 2014. Efficient k-mean clustering algorithm for large datasets using data mining standard score normalization. Int. J. Recent Innov. Trends Comput. Commun, 2(10), pp.3161-3166.

    [19]   Ihsan, M., Saeed, M. and Rahman, A.U., 2023. Optimizing hard disk selection via a fuzzy parameterized single-valued neutrosophic soft set approach. J Oper Strateg Anal, 1(2), pp.62-69.

    [20]   Das, S., Roy, B.K., Kar, M.B., Kar, S. and Pamučar, D., 2020. Neutrosophic fuzzy set and its application in decision making. Journal of Ambient Intelligence and Humanized Computing11, pp.5017-5029.

    [21]   Han, J. and Vartosh, A., 2023. Multi-objective grasshopper optimization algorithm for optimal energy scheduling by considering heat as integrated demand response. Applied Thermal Engineering, 234, p.121242.

    [22]   https://archive.ics.uci.edu/ml/datasets/statlog+(german+credit+data)

    [23]   http://archive.ics.uci.edu/ml/datasets/statlog+(australian+credit+approval)

    Cite This Article As :
    Mohamed, Adam. , Kouki, Fadoua. , Alhebri, Adeeb. , Hussien, Radwan. , Mohammed, Ebrahim. Fuzzy Parameterized Single-Valued Neutrosophic Subset based Artificial Intelligence for Sustainable Financial Crisis Prediction and Green Finance. International Journal of Neutrosophic Science, vol. , no. , 2024, pp. 308-322. DOI: https://doi.org/10.54216/IJNS.230424
    Mohamed, A. Kouki, F. Alhebri, A. Hussien, R. Mohammed, E. (2024). Fuzzy Parameterized Single-Valued Neutrosophic Subset based Artificial Intelligence for Sustainable Financial Crisis Prediction and Green Finance. International Journal of Neutrosophic Science, (), 308-322. DOI: https://doi.org/10.54216/IJNS.230424
    Mohamed, Adam. Kouki, Fadoua. Alhebri, Adeeb. Hussien, Radwan. Mohammed, Ebrahim. Fuzzy Parameterized Single-Valued Neutrosophic Subset based Artificial Intelligence for Sustainable Financial Crisis Prediction and Green Finance. International Journal of Neutrosophic Science , no. (2024): 308-322. DOI: https://doi.org/10.54216/IJNS.230424
    Mohamed, A. , Kouki, F. , Alhebri, A. , Hussien, R. , Mohammed, E. (2024) . Fuzzy Parameterized Single-Valued Neutrosophic Subset based Artificial Intelligence for Sustainable Financial Crisis Prediction and Green Finance. International Journal of Neutrosophic Science , () , 308-322 . DOI: https://doi.org/10.54216/IJNS.230424
    Mohamed A. , Kouki F. , Alhebri A. , Hussien R. , Mohammed E. [2024]. Fuzzy Parameterized Single-Valued Neutrosophic Subset based Artificial Intelligence for Sustainable Financial Crisis Prediction and Green Finance. International Journal of Neutrosophic Science. (): 308-322. DOI: https://doi.org/10.54216/IJNS.230424
    Mohamed, A. Kouki, F. Alhebri, A. Hussien, R. Mohammed, E. "Fuzzy Parameterized Single-Valued Neutrosophic Subset based Artificial Intelligence for Sustainable Financial Crisis Prediction and Green Finance," International Journal of Neutrosophic Science, vol. , no. , pp. 308-322, 2024. DOI: https://doi.org/10.54216/IJNS.230424