Abdallavi, D. Popa, C (2025).Adoption of artificial intelligence in agriculture. Bulletin of the University of Agricultural Sciences & Veterinary Medicine Cluj-Napoca. Agriculture 68(1):284–293.
Ahmadpour, A., & Shahsavari, M. (2014). Examination of managers’ powers in different stages of financial distress in companies listed on the Tehran Stock Exchange. Journal of Accounting Knowledge, 19, 27-50.
Aini Zadeh, M., & Gharib, H. (2021). Examining the impact of financial ratios and ownership structure on predicting financial distress, Accounting and Management Prospects, 4(41), 66-48.
Altman, E. I. (1968). Financial ratios discriminant analysis and the prediction of corporate bankruptcy. The Journal of Finance, 23(4), 589-609.
Beaver, W. H. (1966). Financial ratios as predictors of failure. Journal of Accounting Research, 4, 11-111.
Benjamin. S, A, Ayadi O, Boone B (2005). Artificial neural network and the financial markets: a survey. Manag Fin 26(12):32–45.
Brown, J. R., Martinsson, G., & Thomann, C. (2021). Government lending in a crisis. Journal of Corporate Finance, 71, 102116.
Campuzano, F., Mula, J., & Peidro, D. (2010). Fuzzy estimations and system dynamics for improving supply chains. Fuzzy Sets and Systems, 161, 1530-1542.
Desalles, D. C., Goncalves N., Armando C., Marujo, L. G. (2016). Using fuzzy logic to implement decision policies in system dynamics models. Expert Systems with Applications.
Elson, D. (2013). Economic crises from the 1980s to the 2010s: A gender analysis. Routledge.
Esmailzadeh Maghari, A., & Shakeri, H. (2015). Predicting financial distress of companies listed on the Tehran Stock Exchange using a simple Bayesian network and comparing it with data envelopment analysis. Journal of Financial Engineering and Securities Management, 22.
Falo Lebsanev, (1999) Adoption of artificial intelligence in agriculture. Bulletin of the University of Agricultural Sciences & Veterinary Medicine Cluj-Napoca. Agriculture 68(1):284–293.
Farazi, N. Z. R. (2024). Evaluating the impact of AI and blockchain on credit risk mitigation: A predictive analytic approach using machine learning. International Journal of Science and Research Archive, 13(1), 575-582.
Georgiadis & Bessou, (2008) Artificial intelligence in indian banking sector: challenges and opportunities. Int J Adv Res (IJAR), 7(5):1581–1587.
Georgiadis, P; Volachos, D; Iakovou, E. (2005). A system dynamics modeling framework for the strategic supply chain management of food chains. Journal of Food Engineering, 70, 351-364.
Horrigan, J. O. (1968). A short history of financial ratio analysi. The Accounting Review, 284-294.
Kazi, A. (1984) Artificial intelligence and financial services. IEEE Trans Knowl Data Eng 3(2):137–148.
Khoshtinat, M., & Qasouri, M. T. (2005). Comparing combined financial ratios based on cash flow statements and accrual items with ratios based solely on balance sheet and income statement items in predicting financial distress of companies. Quarterly Journal of Accounting Studies, 9, 43-61.
Komeijani, A., & Saadatfar, B. (2006). Determining the optimal conditional probability model for predicting corporate bankruptcy in Iran. Monthly Magazine of Mofid, 57.
Kunsch, P., & Springael, J. (2008). Simulation with system dynamics and fuzzy reasoning of a tax policy to reduce CO2 emissions in the residential sector. European Journal of Operational Research, 185, 1285–1299.
Kurdistani, G. R., Ghior, F., & Ashtab, A. (2011). Comparing the efficiency of financial ratios based on cash flow methods and accrual methods in predicting financial distress of companies listed on the Tehran Stock Exchange. Quarterly Journal of Stock Exchange, 15, 191-205.
Mehrabi, R. (2024). Unveiling the nature of entrepreneurial opportunities: A systematic exploration through conceptual framework development. European Journal of Business and Management Research, 9(3), 68-79.
Mehrani, S., Bahramfar, N., & Ghior, F. (2005). Examining the relationship between traditional liquidity ratios and those derived from cash flow statements for evaluating the continuation of companies’ activities. Accounting and Auditing Reviews, 40, 3-17.
Mehrani, S., Mehrani, K., & Karami, G. R. (2004). Using historical financial and non-financial information to distinguish between successful and unsuccessful companies. Accounting and Auditing Reviews, 11th Year.
Mesaki, M. (2010). Analysis of cash flow models. Journal of Contemporary Accounting Research, 14, 601-626.
Mohseni, R., Aghababai, R., & Mohammadi, V. (2013). Predicting financial distress by utilizing efficiency as a predictor variable. Quarterly Journal of Economic Research and Policies, 65, 123-146.
Moshaiehi. S & Ganji, A. (2014). Economic policy for artificial intelligence. National Bureau of Economic Research, University of Chicago Press, Toranto, pp 39–159.
Mousavi Shiri, M., & Tabrizani, M. R. (2012). Predicting financial distress using a diagnostic analysis-based model and evaluating the impact of efficiency variables on improving the model. Iranian Management Research, 1, 149-165.
Nasirzadeh, F., Khanzadi, M., & Alipour, M. (2015). Economic evaluation of BOT projects by integrating system dynamics simulation and fuzzy logic methods. Scientific Journal of Structural Engineering and Construction, 4.
Nguyen, T. N. Q., Nguyen, D. T., Le, H. A., & Le, D. L. (2022). Corporate governance and financial stability: The case of commercial banks in Vietnam. Journal of Risk and Financial Management, 15(11), 514.
Noor, C., Popa, C., & Rupali, B. (2021) Adoption of artificial intelligence in agriculture. Bulletin of the University of Agricultural Sciences & Veterinary Medicine Cluj-Napoca. Agriculture, 68(1):284–293.
Orji, I. J., Wei, S. (2015). An innovative integration of fuzzy logic and system dynamics in sustainable supplier selection: A case on manufacturing industry. Computers & Industrial Engineering, 5, 1-12.
Pankaj, S., & Sushil, K. (1994). A fuzzy set theoretic approach to qualitative analysis of causal loops system dynamics. European Journal of Operational Research, 78, 380-393
Pirayesh, R., Mansouri, A., & Amjadian, S. (2009). A mathematical model based on cash flows for predicting financial distress in companies listed on the Tehran Stock Exchange. Journal of Development and Capital, 4, 73-94.
Raaei, R., & Fallahpour, S. (2008). Application of Support Vector Machines in predicting financial distress of companies using financial ratios. Accounting and Auditing Reviews, 53, 17-34.
Rostami, M. R., Fallah Shams, M. F., & Eskandari, F. (2011). Assessing financial distress of companies listed on the Tehran Stock Exchange: A comparative study between Data Envelopment Analysis and Logistic Regression. Iranian Management Research, 3, 129-147.
Saidi, A., & Aghaei, A. (2009). Predicting financial distress of companies listed on the Tehran Stock Exchange using Bayesian Networks. Accounting and Auditing Reviews, 4, 59-78.
Sang, A., & zhang, Y. (2015). Artificial intelligence in healthcare. Nat Biomed Eng, 2:719–731.
Shokohzadeh, E., & Zafari, S. (2021). Application of artificial neural networks in predicting bankruptcy of companies listed on the Tehran Stock Exchange. 2nd National Conference on Modern Management and Startups, Shiraz.
Sterman, A., Ivanov, SH., Webster, C., & Berezina, K. (2020). Adoption of robots and service automation by tourism and hospitality companies. Revista Turismo and Desenvolvimento, 27(28):1501–1517.
Taghavi, S. M., Ghezavati, V., Mohammadi Bidhandi, H., & Mirzapour Al-e-Hashem, S. M. J. (2024). Sustainable and resilient supplier selection, order allocation, and production scheduling problem under disruption utilizing conditional value at risk. Journal of Modelling in Management, 19(2), 658-692.
Wallace, W. A. (2004). The economic role of the audit in free and regulated markets: A look back and a look forward. Research in Accounting Regulation, 17, 267-298.
Wanda, W. (2004). Risk assessment by internal auditors using past research on bankruptcy. Journal of Finance, 36, 427-465.
Weston, J., Fred, E., & Copeland, T. (1992). Managerial finance. Dryden Press.
Zhang, Y., Liang, C., & Jin, D. (2020). A novel insurance system for associated loans: the realization of reducing bankruptcy cost. Management Decision, 58(1), 146-163.
Zhang, Y., Liang, C., & Jin, D. (2023) Artificial intelligence: accelerator or panacea for financial crime?, J Fin Crim, 26(2):634–646.
