Akademik Məlumat İdarəetmə Sistemi
International Journal of Neutrosophic Science, vol.25, no.2, pp.129-140, 2025 (Scopus)
Neutrosophic set (NS) and logic are powerful mathematical approaches for managing different uncertainties. Amongst different approaches for examining NS statistics, rough set theory (RST) offers a valuable instrument in the domain of NS statistics, and masses of researchers have been motivated by NS combination of RST. Recently, there have been no wide-ranging statistics and literature reviews of the universal RST and its applications. The Financial Crisis Prediction mechanism leverages cutting-edge computation methods to predict possible disruptions or economic downturns. By investigating past fiscal information, marketplace gauges, and macroeconomic features, the typical recognizes primary caution indications of imminent disasters. This practical method helps financial institutions, policymakers, and investors in applying pre-emptive procedures to alleviate fiscal marketplaces and threats. In this paper, we develop a Financial Crisis Prediction Model using Neutrosophic Fusion of Rough Set Theory (FCPM-NFRST) methodology. The suggested FCPM-NFRST method for financial crises incorporates numerous forward-thinking systems to improve predictive performance. It is initiated by the Firefly Algorithm (FFA) based feature selection to detect the fittest fiscal gauges. Consequently, the Neutrosophic Fusion of RST (NFRST) is exploited for strong cataloguing and successful management of vagueness and roughness in economic information. Lastly, the Whale Optimization Algorithm (WOA) is exploited for parameter fine-tuning, enhancing the system's accuracy. Investigational study displays that the FCPM-NFRST ensemble mechanism is more robust and superior than its complements. Accordingly, this study powerfully suggests that the suggested FCPM-NFRST method is very competitive than conventional and other existing algorithms.