1
National University of San Marcos, Lima, Peru
(jramonr@unmsm.edu.pe)
2
National University of San Marcos, Lima, Peru
(agallegosr@unmsm.edu.pe)
3
National University of San Marcos, Lima, Peru
(ccabrerac@unmsm.edu.pe)
4
National University of San Marcos, Lima, Peru
(vrodriguezc@unmsm.edu.pe)
Abstract :
Nowadays, Machine Learning techniques stand out, especially in the business sector, in predicting bankruptcies in small and medium-sized enterprises (SMEs). This reduces the probability of making bad investments when creating SMEs. Therefore, a systematic review of Machine Learning for predicting bankruptcies in SMEs was conducted to identify ideal articles. The search was conducted on Taylor & Francis Online, IEEE Xplore, ARDI, ScienceDirect, ACM Digital Library, Google Scholar, and ProQuest. As a result, information was collected from 84 definitive studies on determining bankruptcies in SMEs using Machine Learning. Therefore, this study aims to determine the state-of-the-art regarding determining bankruptcies in SMEs using Machine Learning. To obtain the results, the Saaty Neutrosophic AHP method was used to identify the most applied business sector and predict possible bankruptcy due to its broad nature of indeterminacy in that subset. The systematic review results have allowed for determining essential details regarding the state-of-the-art of determining bankruptcies in SMEs using Machine Learning.
Keywords :
Keywork one; Keywork two; Keywork three; Keyword four; ….
References :
[1] Sampedro Guaman, C.R., D.P. Palma Rivera, S.A. Machuca Vivar, and E.V. Arrobo Lapo, Digital Transformation of Marketing in Small and Medium Enterprises Through Social Networks: Plitogenic Decision-Making. Neutrosophic Sets & Systems, 2021. 44.
[2] Alshikho, M., M. Jdid, and S. Broumi, Artificial Intelligence and Neutrosophic Machine learning in the Diagnosis and Detection of COVID 19. Prospects for Applied Mathematics and Data Analysis, 2023. Vol. 1(No. 2): p. 17-27.
[3] Zuñiga, V.C., A.M. León, D.M. Nogueira, D.A. Valencia, and J.M. Romero, Validation of A Model for Knowledge Management in the Cocoa Producing Peasant Organizations of Vinces Using Neutrosophic Iadov Technique. Neutrosophic Sets and Systems, 2019. 30: p. 253-260.
[4] Arnaiz, N.V.Q., N.G. Arias, and L.C.C. Muñoz, Neutrosophic K-means Based Method for Handling Unlabeled Data. Neutrosophic Sets and Systems, 2020. 37: p. 308-315.
[5] Sharma, M., I. Kandasamy, and W. Vasantha, Comparison of neutrosophic approach to various deep learning models for sentiment analysis. Knowledge-Based Systems, 2021. 223: p. 107058.
[6] A. B. Bonilla Rodríguez , M.F.C.M., F. B. Morocho Quinchuela, Construction of Sanda Teaching risk assessment index system using neutrosophic AHP method. International Journal of Neutrosophic Science, 2022. 18(4): p. 334-343.
[7] Comas Rodríguez, R., J.M.D. Oca Sánchez, and V. Lucero Salcedo, Evaluation of Social Projects Using Neutrosophic AHP. International Journal of Neutrosophic Science, 2022. 19(1): p. 280-288.
[8] 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).
[9] Rashno, E., A. Akbari, and B. Nasersharif. A convolutional neural network model based on neutrosophy for noisy speech recognition. in 2019 4th International Conference on Pattern Recognition and Image Analysis (IPRIA). 2019. IEEE.
[10] Abdulaziz Shehab , Mahmood Mahmood, An Intelligent Bankruptcy Prediction Model based on an Enhanced Sparrow Search Algorithm, Journal of Intelligent Systems and Internet of Things, Vol. 6 , No. 1 , (2022) : 09-19 (Doi : https://doi.org/10.54216/JISIoT.060101).
[11] Kandasamy, I., W. Vasantha, J.M. Obbineni, and F. Smarandache, Sentiment analysis of tweets using refined neutrosophic sets. Computers in Industry, 2020. 115: p. 103180.
[12] Woodall, W.H., A.R. Driscoll, and D.C. Montgomery, A review and perspective on neutrosophic statistical process monitoring methods. IEEE Access, 2022.
[13] Smarandache, F., J.E. Ricardo, E.G. Caballero, M.Y.L. Vázquez, and N.B. Hernández, Delphi method for evaluating scientific research proposals in a neutrosophic environment. Infinite Study, 2020. 34: p. 204-213.
[14] Aurelia Maria, C.B., I.Q. Janneth Ximena, and P.P. Alex Javier, Multi-Criteria Analysis of Pollution rivers. International Journal of Neutrosophic Science, 2022. 19(1): p. 306-313.
[15] Becerra Arévalo, N.P., M.F. Calles Carrasco, J.L. Toasa Espinoza, and M. Velasteguí Córdova, Neutrosophic AHP for the prioritization of requirements for a computerized facial recognition system. Neutrosophic Sets and Systems, 2020. 34(1): p. 21.
[16] Ortega, R.G., M.D.O. Rodríguez, M.L. Vázquez, J.E. Ricardo, J.A.S. Figueiredo, and F. Smarandache, 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.
[17] Noboa, M.F.O., O.E.P. Copa, and A.N.G. Eloísa, Comparative analysis of multicriteria methods based on single-valued neutrosophic numbers for the evaluation of medical technologies. International Journal of Neutrosophic Science, 2022. 18(4): p. 72-82.
[18] Bonilla Rodríguez, A.B., M.F. Cueva Moncayo, and F.B. Morocho Quinchuela, Construction of Sanda Teaching risk assessment index system using neutrosophic AHP method. International Journal of Neutrosophic Science, 2022. 18(4): p. 334-343.
[19] R. Comas Rodríguez , J.M.D.O.S., V. Lucero Salcedo, Evaluation of Social Projects Using Neutrosophic AHP. International Journal of Neutrosophic Science, 2022. Vol. 19(No. 1 ).
[20] Barragán, M.F.L., R.F.G. Montenegro, and L.L.C. María De, Application of Neutrosophic Techniques for the Selection of the in-Hospital Triage System. International Journal of Neutrosophic Science, 2022. 18(4): p. 116-124.
[21] Quemac, R.E.C., M.E.G. Santos, L.A.C. Ramos, and C.P.C. Zúñiga, Neutrosophic Analytic Hierarchy Process for the Analysis of Innovation in Latin America. Neutrosophic Sets and Systems, 2021. 44: p. 411-419.
| Style | # |
|---|---|
| MLA | J. Ramón R. de Vega, A. G. Ruiz Conejo, Carlos C. Carranza, Vladimir R. Cairo. "Analysis of Neutrosophic Elements in the Determination of Bankruptcies in SMEs Using Machine Learning." International Journal of Neutrosophic Science, Vol. 20, No. 4, 2023 ,PP. 152-163 (Doi : https://doi.org/10.54216/IJNS.200412) |
| APA | J. Ramón R. de Vega, A. G. Ruiz Conejo, Carlos C. Carranza, Vladimir R. Cairo. (2023). Analysis of Neutrosophic Elements in the Determination of Bankruptcies in SMEs Using Machine Learning. Journal of International Journal of Neutrosophic Science, 20 ( 4 ), 152-163 (Doi : https://doi.org/10.54216/IJNS.200412) |
| Chicago | J. Ramón R. de Vega, A. G. Ruiz Conejo, Carlos C. Carranza, Vladimir R. Cairo. "Analysis of Neutrosophic Elements in the Determination of Bankruptcies in SMEs Using Machine Learning." Journal of International Journal of Neutrosophic Science, 20 no. 4 (2023): 152-163 (Doi : https://doi.org/10.54216/IJNS.200412) |
| Harvard | J. Ramón R. de Vega, A. G. Ruiz Conejo, Carlos C. Carranza, Vladimir R. Cairo. (2023). Analysis of Neutrosophic Elements in the Determination of Bankruptcies in SMEs Using Machine Learning. Journal of International Journal of Neutrosophic Science, 20 ( 4 ), 152-163 (Doi : https://doi.org/10.54216/IJNS.200412) |
| Vancouver | J. Ramón R. de Vega, A. G. Ruiz Conejo, Carlos C. Carranza, Vladimir R. Cairo. Analysis of Neutrosophic Elements in the Determination of Bankruptcies in SMEs Using Machine Learning. Journal of International Journal of Neutrosophic Science, (2023); 20 ( 4 ): 152-163 (Doi : https://doi.org/10.54216/IJNS.200412) |
| IEEE | J. Ramón R. de Vega, A. G. Ruiz Conejo, Carlos C. Carranza, Vladimir R. Cairo, Analysis of Neutrosophic Elements in the Determination of Bankruptcies in SMEs Using Machine Learning, Journal of International Journal of Neutrosophic Science, Vol. 20 , No. 4 , (2023) : 152-163 (Doi : https://doi.org/10.54216/IJNS.200412) |