A Hybrid Fuzzy Factor Analysis Model for Evaluation of Fiscal Proficiency

Poonam; Monika Mangla; Nonita Sharma; Mohamed Sirajudeen Yoosuf

doi:10.4108/eetsis.3973

Authors

Poonam J.C. Bose University of Science & Technology, YMCA
Monika Mangla Dwarkadas J. Sanghvi College of Engineering
Nonita Sharma Indira Gandhi Delhi Technical University for Women
Mohamed Sirajudeen Yoosuf Vellore Institute of Technology University

DOI:

https://doi.org/10.4108/eetsis.3973

Keywords:

Fiscal proficiency, Fuzzy Modelling, Mamdani approach, Fiscal Planning

Abstract

Fiscal Proficiency is one of the most significant priority for mankind as it has a key role in the escalation of the lifestyle. Hence, it plays an important role in the growth of individual, family and finally leads to the growth of the national economy. Here in this manuscript, authors present a fuzzy factor analysis model to determine and evaluate the factors that influence the fiscal proficiency. The application of fuzzy concepts to the statistical analysis deemed appropriate while investigating a nondeterministic report. Resultantly, authors present a Mamdani-based fuzzy model to evaluate the fiscal proficiency through various factors. The proposed model is proved to be an effective model and hence can be widely implemented in real life. Further, authors also recommend that the regulatory authorities should take efforts to promote fiscal proficiency that will lead towards escalation of national economy.

References

Ahmad, F. A., White, A. J., Hiller, K. M., Amini, R., & Jeffe, D. B. (2017). An assessment of residents’ and fellows’ personal finance literacy: an unmet medical education need. International journal of medical education, 8, 192.

References Agarwal, M., Kureel, R. And Yadav, D. (2017), A Study on Future Plan for Increasing Financial Literacy Among People, Global Journal of Finance and Management, Vol 9, pp 29-38.

Alqaydi, F. And Ibrahim, M. (2013),Financial Literacy, Personal Financial Attitude, and Forms of Personal Debt among Residents of the UAE , International Journal of Economics and Finance; Vol. 5, pp 126-138.

Banik, S., Sharma, N., Mangla, M., Mohanty, S. N., & Shitharth, S. (2021). LSTM based decision support system for swing trading in stock market. Knowledge-Based Systems, 107994.

Greenfield, J. S. (2015). Challenges and opportunities in the pursuit of college finance literacy. The High School Journal, 98(4), 316-336.

Mangla, M., Sharma, N., Yadav, S., Mehta, V., Kakkar, D., & Kandukuri, P. (2021). Multivariate economic analysis of the government policies and COVID-19 on financial sector. International Journal of Computer Applications in Technology, 66(3-4), 294-302.

Archana Remane Dhore, S. (2020). The Importance of Financial Literacy During the COVID-19 Pandemic. https://www.shrm.org/resourcesandtools/hr-topics/behavioral-competencies/pages/the-importance-of-financial-literacy-during-the-covid-19-pandemic.aspx.

Arianti, B. (2018),The influence of financial literacy, financial behaviour and income on investment decisions, Economics and Accounting General, Vol.1, pp 1-10.

Gharleghi, B. & Albeerdy. (2015), Determinants of the Financial Literacy among College Students in Malaysia , International Journal of Business Administration, Vol 6, pp 15-24.

Gowri, M. & Sekar, M. (2015), A Study on Financial Literacy and its Determinants among Gen Y Employees in Coimbatore City, Vol 9, pp 35-45.

Gupta, S. (2017), To measure the levels of Financial literacy among individuals of Delhi, Indian Journal of Research, Vol 6, Issue 1,pp 833-837.

Satpathy, S., Mangla, M., Sharma, N., Deshmukh, H., & Mohanty, S. (2021). Predicting mortality rate and associated risks in COVID-19 patients. Spatial Information Research, 29(4), 455-464.

Mangla, M., Sharma, N., & Mittal, P. (2021). A fuzzy expert system for predicting the mortality of COVID'19. Turkish Journal of Electrical Engineering & Computer Sciences, 29(3), 1628-1642.

Chai, Y., Jia, L. and Zhang, Z., 2009. Mamdani model based adaptive neural fuzzy inference system and its application. International Journal of Computational Intelligence, 5(1), pp.22-29.

P. Mittal, C. K. Nagpal, S. Gupta, and K. Garg, “A Fuzzy Logic based Efficient Routing Strategy for Ad hoc Cognitive Radio Network,” Int. J. Futur. Gener. Commun. Netw., vol. 10, no. 10, pp. 1–22, 2017, doi: 10.14257/ijfgcn.2017.10.10.01.

M. Mangla and N. Sharma, “Fuzzy Modelling of Clinical and Epidemiological Factors for COVID-19.

Poonam, Nagpal, C. K., & Gupta, S. (2017). A novel routing strategy for cognitive radio ad hoc network based on Sugeno fuzzy logic. International Journal of Fuzzy Computation and Modelling, 2(2), 87-115.