International Journal of Neutrosophic Science IJNS 2690-6805 2692-6148 10.54216/IJNS https://www.americaspg.com/journals/show/3189 2020 2020 Data Engineering Lead, Pursuign PhD,MS in Data Science, Indiana University, USA Vishwanadham Vishwanadham AI/ML Risk Lead, University of Connecticut, USA Rajiv Avacharmal SEI Investment Company, Sr. Cloud Solution Engineer, USA Pradeep Chintale Enterprise Architect, Bits Pilani, USA Rajesh Kumar Malviya Corporate Financial Reporting and Transformations expert, UBS, IL, 60502, USA Manoj Kumar Vandanapu Technical Support Engineer, Microsoft, USA Venkata Nagesh Boddapati As a generalization of fuzzy set (FS) and intuitionistic FS (IFS), neutrosophic sets (NS) were proposed to signify imprecise, uncertain, inconsistent and imperfect data present in real-time. Moreover, the interval NS (INSs) were developed just to find out the problems with an array of statistics in the actual unit interval. Then, there are least consistent processes for INSs, along with the decision-making process and INS aggregation operator. The vital operations are presented on n-valued interval NSs like intersection, union, multiplication, addition, scalar division, scalar multiplication, false-favorite and truth favorite. Bankruptcy prediction was a major concern in the areas of finance and management science that appealed to the attention of practitioners and researchers. With the great progress of up-to-date information technology, it has been developed to utilize machine learning (ML) or deep learning (DL) techniques to perform the prediction, from the primary analysis of financial statements. If ML methods have adequate interpretability, they might be employed as effectual analytical methods in bankruptcy calculation. This manuscript presents a Bankruptcy Prediction using Cutting-Edge N-Valued Interval Neutrosophic Sets (BP-CENVINS) mechanism. The projected BP-CENVINS method is a complicated approach to bankruptcy forecast that affects radical data preprocessing, classification, and hyper parameter optimization approaches. Initially, the Z-score normalization regularizes the fiscal details to increase the comparability and stability throughout the information. Next, it employs the CENVINS for the classification, skillfully detecting the subtle communication amongst variables to differentiate between creditworthy and bankrupt organizations. Finally, the Grasshopper Optimization Algorithm (GOA) is applied for parameter tuning to improve the predictive outcomes of the CENVINS classifiers, systematically purifying design parameters to achieve finest efficiency. An extensive experiments is made to illustrate the betterment of the BP-CENVINS technique. The simulation outcomes of the BP-CENVINS method have exhibited better performances than other existing methodologies. 2025 2025 303 312 10.54216/IJNS.250226 https://www.americaspg.com/articleinfo/21/show/3189