Decision-Making With Unknown Weights for the Performance of Digital Devices in Healthcare Systems Based on Interval Valued T-Spherical Fuzzy Information

The multi-attribute group decision-making (MAGDM) problem is used to evaluate suitable options based on specific attributes or characteristics of an object. The potential of the MAGDM problem is demonstrated in every field of life, like artificial intelligence, social and environmental science, networking, medical diagnosis, and many other domains. The essential components of the MAGDM technique are aggregation operators (AOs), which several mathematicians develop under the system of different fuzzy domains. In this article, we express the notion of an interval-valued t-spherical fuzzy (IVT-SF) set (IVT-SFS), which is an expanded form of an intuitionistic fuzzy set and picture fuzzy set with four characteristics of human opinions. An IVT-SFS is a well-known aggregation model that can deal with unpredictable and redundant information about human opinions. To mitigate the influence of redundant information, we illustrate some robust operations of Aczel Alsina aggregation tools in light of IVT-SF information. Power operators are used to reduce the impact of redundant information impact and define correlation among different input arguments. By combining two different concepts like Aczel Alsina aggregation tools and power operators, we developed a class of new approaches based on IVT-SF information, namely IVT-SF Aczel Alsina power weighted average (IVT-SFAAPWA) and IVT-SF Aczel Alsina power weighted geometric (IVT-SFAAPWG) operators. In order to illustrate the effectiveness of the proposed approaches, some notable characteristics and properties are also presented. To resolve different real-life applications, we established an algorithm for the MAGDM problem in light of IVTSF information. We illustrate a numerical example to evaluate an appropriate optimal option for digital devices in the healthcare system. The advantages and consistency of derived approaches are verified by comparing the findings of existing methodologies with currently discussed AOs.