Optimal cloud service provider selection: An MADM framework on correlation-based TOPSIS with interval-valued q-rung orthopair fuzzy soft set

The interaction between two factors significantly measures the results' exactness. But, acquiring information for statistical computation is frequent, and the data obtained could be challenging to interpret. To predict how a particular factor will fluctuate regarding another as well, the correlation coefficient (CC) is usually employed. But this approach is rarely utilized for interval-valued q-rung orthopair fuzzy soft set (IVq-ROFSS). The situation in which IVq-ROFSS grows as it is modern, along with a broad depiction of the q-rung orthopair fuzzy soft set (q-ROFSS), engaging for a more reflective and precise assessment. The current study explores the CC and weighted correlation coefficient (WCC) for IVq-ROFSS and their fundamental characteristics. This research is designed to improve the prioritization technique for order preference by similarity to the ideal solution (TOPSIS) with expanded measures. Also, to check the linearity of the intended approach, we integrated mathematical formu-lations of correlation constrictions. This study demonstrates that the suggested methodology is a robust multi-attribute decision-making (MADM) tool for intricate information set interpretation and prioritizing. We pre-sented a numerical illustration demonstrating the actual application of our recommended decision-making strategy for choosing Cloud Service Providers (CSPs) in cloud service management. The approach developed in this research is superior to conventional models in maintaining the precise structure of the determined studies. Thus, the algorithm produces more reliable and consistent decisions. The influence of our studies grows within the scope of this research, as the originated algorithm can enhance the ability to analyze realities and make informed decisions in light of the information provided. Therefore, this study can significantly impact data analysis and decision-making by revealing the importance of the proposed TOPSIS approach and the vitality of perpetual development in methods for making decisions to get more reliable and precise outcomes.