Volume 24 , Issue 4 , PP: 39-49, 2024 | Cite this article as | XML | Html | PDF | Full Length Article
Tawfiq Hasanin 1 *
Doi: https://doi.org/10.54216/IJNS.240402
Neutrosophy is the study of neutralities and extends the discussion of the truth of opinions. Neutrosophic logic may be employed in any domain, for providing the solution for the ambiguity problems. Several real-time data experience problems such as indeterminacy, incompleteness, and inconsistency. A fuzzy set provides an uncertain solution, and intuitionistic fuzzy set handles incomplete data, but both fail to manage uncertain data. Before bankruptcy, financial distress is the early stage. Bankruptcies caused by financial problems can be seen in the financial statement of the company. The capability to predict financial problems became a crucial area of research since it provides earlier warning for the company. Moreover, predicting financial problems is advantageous for creditors and investors. In this article, we develop a new Dimensionality Reduction with Neutrosophic Net-RBF Neural Networks (DR-NSRBFNN) technique for FCP process. The DR-NSRBFNN technique concentrates on the predictive modelling of financial distress. In the DR-NSRBFNN technique, two major stages are involved. In the preliminary phase, the high dimensionality features can be reduced by the use of arithmetic optimization algorithm (AOA). In the second phase, the DR-NSRBFNN technique applies the NSRBFNN model to predict financial distress. The performance evaluation of the DR-NSRBFNN technique can be examined using distinct aspects. The widespread study stated the improved performance of the DR-NSRBFNN technique compared to other systems
Financial Distress Prediction , Neutrosophic Set , Arithmetic Optimization Algorithm , Fuzzy Set , Intuitionistic Fuzzy Sets
[1] Metawa, N., Pustokhina, I. V., Pustokhin, D. A., Shankar, K., & Elhoseny, M. (2021). Computational intelligence-based financial crisis prediction model using feature subset selection with optimal deep belief network. Big Data, 9(2), 100–115.
[2] Sun, J., Fujita, H., Chen, P., & Li, H. (2017). Dynamic financial distress prediction with concept drift based on time weighting combined with Adaboost support vector machine ensemble. Knowledge-Based Systems, 120, 4–14
[3] Zeng, S., Li, Y., Yang, W., & Li, Y. (2020). A financial distress prediction model based on sparse algorithm and support vector machine. Mathematical Problems in Engineering, 2020, 1–11.
[4] Huang, Y.-P., & Yen, M.-F. (2019). A new perspective of performance comparison among machine learning algorithms for financial distress prediction. Applied Soft Computing, 83, 105663.
[5] Sun, J., Li, H., Huang, Q.-H., & He, K.-Y. (2014). Predicting financial distress and corporate failure: A review from the state-of-the-art definitions, modeling, sampling, and featuring approaches. Knowledge-Based System, 57, 41–56.
[6] Bao, Y.L. and Yang, H.L., 2019. On single valued neutrosophic refined rough set model and its application. Fuzzy Multi-criteria Decision-Making Using Neutrosophic Sets, pp.107-143..
[7] Uthayakumar, J., Metawa, N., Shankar, K., & Lakshmanaprabu, S. K. (2020). Intelligent hybrid model for financial crisis prediction using machine learning techniques. Information Systems and e-Business Management, 18(4), 617–645.
[8] Bao, Y.L. and Yang, H.L., 2017. On single valued neutrosophic refined rough set model and its application. Journal of Intelligent & Fuzzy Systems, 33(2), pp.1235-1248..
[9] Zainal, S.N.A.M., Ab Ghani, A.T. and Abdullah, M.L., 2023. An effective rough neutrosophic based approach for data pre-processing. TEM Journal, 12(2), p.1048..
[10] Samitas, A., Kampouris, E., & Kenourgios, D. (2020). Machine learning as an early warning system to predict financial crisis. International Review of Financial Analysis, 71, 101507.
[11] Kadkhoda, S.T. and Amiri, B., 2024. A hybrid Network Analysis and Machine Learning model for Enhanced Financial Distress Prediction. IEEE Access.
[12] Zhao, Y. and Yang, G., 2023. Deep Learning-based Integrated Framework for stock price movement prediction. Applied Soft Computing, 133, p.109921.
[13] Elhoseny, M., Metawa, N., Sztano, G. and El-Hasnony, I.M., 2022. Deep learning-based model for financial distress prediction. Annals of Operations Research, pp.1-23.
[14] Lee, Y. and Kim, E., 2024. Deep Learning-based Delinquent Taxpayer Prediction: A Scientific Administrative Approach. KSII Transactions on Internet & Information Systems, 18(1).
[15] Alkhafaji, M.A., Ameer, S.A., Alawadi, A.H. and Sharif, H., 2023, July. Design of Hyperparameter Tuned Deep Reinforcement Learning Based Prediction Model for Financial Crisis. In 2023 6th International Conference on Engineering Technology and its Applications (IICETA) (pp. 287-293). IEEE.
[16] Zhang, X., Pan, Y., Xiong, Y., Zhang, Y., Tang, M., Dai, W., Liu, B. and Hou, L., 2024. Deep learning-based vibration stress and fatigue-life prediction of a battery-pack system. Applied Energy, 357, p.122481.
[17] Kalaivani, R. and Saravanan, A., 2023, November. Exploiting Pattern Recognition using Chimp Optimization Algorithm with Machine Learning for Financial Crisis Prediction. In 2023 International Conference on Sustainable Communication Networks and Application (ICSCNA) (pp. 944-950). IEEE.
[18] Yıldız, B.S., Kumar, S., Panagant, N., Mehta, P., Sait, S.M., Yildiz, A.R., Pholdee, N., Bureerat, S. and Mirjalili, S., 2023. A novel hybrid arithmetic optimization algorithm for solving constrained optimization problems. Knowledge-Based Systems, 271, p.110554.
[19] Rubio-Solis, A. and Panoutsos, G., 2014, July. Fuzzy uncertainty assessment in rbf neural networks using neutrosophic sets for multiclass classification. In 2014 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE) (pp. 1591-1598). IEEE.
