On Shadow soft relations for handling uncertainty in Decision-Making problems

Shadowed relation is a basic mathematical tool that can be applied to various real-life data. Motivated by this, the present study aims to extend the concept of shadowed relation to shadow soft relation by connecting two universal sets of information through a fixed set of parameters. The proposed framework studies several fundamental properties and establishes related theorems supported by proofs. Furthermore, we explore various results concerning shadow soft inverse relations on universal sets. Finally, we demonstrate an application by developing a decision-making algorithm based on shadow soft relations. The primary objective of this study is to provide a systematic framework that integrates the principles of shadowed relation and soft relation, thereby enhancing analytical methods for handling uncertainty. The study concludes that this framework represents an initial yet promising step toward the development of future developments in shadow soft relations.