Volume 20 , Issue 3 , PP: 137-149, 2023 | Cite this article as | XML | Html | PDF | Full Length Article
Julia Juro-Barrios 1 * , Javier Gamboa-Cruzado 2 , Alfonso Romero Baylon 3 , C. del Valle Jurado 4
Doi: https://doi.org/10.54216/IJNS.200313
Micro and small enterprises (MSEs) have generated great opportunities for the growth of countries in the Latin American region. Unfortunately, as a result of the global crisis caused by the Sar-Cov-2, MSEs were severely affected. The main objective of this investigation is to validate in a practical way in a neutrosophic environment the use of a predictive Machine Learning technique that demonstrates the probability of the return on investment that a candidate investor will obtain with respect to a given business plan. With them it is expected that the investor can make the decision to finance a MSE, with the positive decision will close gaps in the growth of micro and small enterprises in Peru. The research is descriptive and predictive, with a research design of post-test only and control group. Neutrosophic TOPSIS was used as a technique. NEBS turns out to be efficient for the applicability of Machine Learning by obtaining statistical evidence to accept the hypotheses proposed for the finance sector in micro and small en-terprises in Peru. The results showed that the use of Machine Learning is validated, and its implementation increases the amount of financing obtained, decreases the evaluation time of requirements, reduces the number of complaints, and increases the number of formal sources used. Machine Learning research should be continued due to the complexity of this technology, which is con-stantly evolving.…
Machine learning , Financing , SMEs , Neutrosophic TOPSIS
[1] Başhan, V., H. Demirel, and M. Gul, An FMEA-based TOPSIS approach under single valued neutrosophic sets for maritime risk evaluation: the case of ship navigation safety. Soft Computing, 2020. 24(24): p. 18749-18764.
[2] Salamai, A.A., Neutrosophic-based machine learning context for the trustworthiness of devices in the internet of things. Journal of Neutrosophic and Fuzzy Systems, 2023. 1(1): p. 59-9-68.
[3] Abdel-Basset, M., et al., Utilising neutrosophic theory to solve transition difficulties of IoT-based enterprises. Enterprise Information Systems, 2020. 14(9-10): p. 1304-1324.
[4] Wang, P., Y. Lin, and Z. Wang, An Integrated Decision-Making Model Based on Plithogenic-Neutrosophic Rough Number for Sustainable Financing Enterprise Selection. Sustainability, 2022. 14(19): p. 12473.
[5] Boloș, M.-I., I.-A. Bradea, and C. Delcea, Modeling the Covariance of Financial Assets Using Neutrosophic Fuzzy Numbers. Symmetry, 2023. 15(2): p. 320.
[6] Elhassouny, A., S. Idbrahim, and F. Smarandache, Machine learning in Neutrosophic Environment: A Survey. Neutrosophic Sets and Systems, 2019. 28: p. 58-69.
[7] Praveena, N.J.P., et al., Nonagonal Neutrosophic Number and its Application in Optimization Technique. International Journal of Neutrosophic Science, 2022. 19(2): p. 66-79.
[8] Fernández, A.R., G.A. Gómez, and C.G. Armijos, Neutrosophic K-means for market segmentation. International Journal of Neutrosophic Science, 2022. 19(1): p. 272-279.
[9] Ortega, R.G., et al., Pestel analysis based on neutrosophic cognitive maps and neutrosophic numbers for the sinos river basin management. Neutrosophic Sets and Systems, 2019. 26: p. 105-113.
[10] Smarandache, F., et al., Delphi method for evaluating scientific research proposals in a neutrosophic environment. Infinite Study, 2020. 34: p. 204-213.
[11] Leyva-Vázquez, M., et al., A new model for the selection of information technology project in a neutrosophic environment. Neutrosophic Sets and Systems, 2020. 32(1): p. 344-360.
[12] Leyva-Vázquez, M., et al. The Extended Hierarchical Linguistic Model in Fuzzy Cognitive Maps. in International Conference on Technologies and Innovation. 2016. Springer.
[13] Pástor, B.A.C., et al. Proposal of a technological ergonomic model for people with disabilities in the public transport system in Guayaquil. in International Conference on Applied Human Factors and Ergonomics. 2019. Springer.
[14] Bibhas, C.G., U.M. Mahatab, and B. Pranab, Método TOPSIS para la toma de decisiones multiatributo difusa vacilante neutrosófica. Informática, 2020. 31(1): p. 35-63.
[15] Gómez, G.A.Á., M.Y.L. Vázquez, and J.E. Ricardo, Application of Neutrosophy to the Analysis of Open Government, its Implementation and Contribution to the Ecuadorian Judicial System. Neutrosophic Sets and Systems, 2022. 52: p. 215-224.
[16] Zulqarnain, R.M., et al., An integrated model of Neutrosophic TOPSIS with application in Multi-Criteria Decision-Making Problem. Neutrosophic Sets and Systems, 2021. 40: p. 253-269.
[17] Umer-Farooq, M., M. Saqlain, and Zaka-ur-Rehman, The Selection of LASER as Surgical Instrument in Medical using Neutrosophic Soft Set with Generalized Fuzzy TOPSIS, WSM and WPM along with MATLAB Coding. Neutrosophic Sets and Systems, 2021. 40: p. 29-44.
[18] Kumar Tiwari, R. and R. Kumar, A framework for prioritizing cloud services in neutrosophic environment. Journal of King Saud University - Computer and Information Sciences, 2022. 34(6, Part B): p. 3151-3166.
[19] Corinne Celant , Irina V. Pustokhina, Future trends and Italian SMEs, American Journal of Business and Operations Research, Vol. 1 , No. 1 , (2020) : 52-59.
[20] Abdel-Basset, M., A. Atef, and F. Smarandache, A hybrid neutrosophic multiple criteria group decision making approach for project selection. Cognitive Systems Research, 2019. 57: p. 216-227.
[21] Supciller, A.A. and F. Toprak, Selection of wind turbines with multi-criteria decision making techniques involving neutrosophic numbers: A case from Turkey. Energy, 2020. 207: p. 118237.
[22] Abdel Nasser H. Zaied , Shaimaa Mohmed, ERP Implementation Road Map for Small and Medium Size Enterprises (SMEs), Journal of Intelligent Systems and Internet of Things, Vol. 2 , No. 1 , (2020) : 14-25 (Doi : https://doi.org/10.54216/JISIoT.020102).
