Volume 25 , Issue 4 , PP: 282-294, 2025 | Cite this article as | XML | Html | PDF | Full Length Article
Khaled A. Hassan Mohmmed 1 * , Hiba Awad Alla Ali Hussin 2 , Nadia Bushra Mohammed Ali 3 , Abdelsamie Eltayeb Tayfor 4
Doi: https://doi.org/10.54216/IJNS.250424
Using three machine knowledge models that utilise Neutrosophic Logic (NL)—Linear Regression, Random Forest, and Gradient Increasing—this study studies the possibilities of refining financial result forecast. The cognitive behind this is that NL recovers the prediction power of these models across dissimilar organisations by accounting for the inherent uncertainty, unpredictability, and lack of sureness in financial numbers. In this study, the models' presentation is evaluated using a variety of financial factors, including interest rates and stock prices. F1 score, recall, correctness, and exactness are some of the metrics used by this drive. When likened to other models, NL with Gradient Cumulative consistently outperforms them in terms of correctness and robustness. You might think of Abu Dhabi Islamic Bank and the National Bank of Bahrain as two such examples. Companies like Emirates Islamic Bank reap some benefits from Chance Forest's combination of cheap computation with precision, but only to a lower degree. Complex datasets used by businesses like Al Rajhi Bank are beyond the capabilities of Linear Reversion, even when combined with NL. By proving that cooperative techniques combined with NL positively reduce financial data volatility, our results lay the groundwork for improved financial forecasting and decision-making. The exercise has demonstrated that NL has great potential to enhance financial prediction models, which could have future applications in investment planning and risk organization.
Decision-Making , Ensemble Methods , Machine Learning , Linear Regression , Financial Data Uncertainty , Gradient Boosting , Neutrosophic Logic
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