Optimizing New Technology Implementation Through Fuzzy Hypersoft Set: A Framework Incorporating Entropy, Similarity Measure, and TOPSIS Techniques - Muhammad Ahsan; Muhammad Amad Sarwar; Showkat Ahmad Lone; Salmeh A. Almutlak; Sadia Anwer (IEEE Access, 2023)
As each day passes by the world’s NT requirements increase due to increasing population and technological advancements. Currently, traditional technologies are inadequate to support the requirement. It is vital to investigate cost-effective and suitable green environmental technologies as a response. Future connectivity(5G, 6G), programming, artificial intelligence and new technologies might be a resolution to this resource crisis in this setting. Now, choosing amongst the most suitable option present itself as a Multi-Criteria Decision Making (MCDM) challenge in which a judgment must be made in terms of a wide variety of characteristics. In this paper, the extended MCDM strategies are proposed to optimizing new technologies implementation. The novelty of the Fuzzy Hypersoft (FHS) set is discussed, which can deal with uncertainties, vagueness, and unclear data. This framework is more flexible than the structures found in literature as it can deal with the information where the attributes can be further sub-partitioned into attribute values for a better understanding. It may not always be possible to analyze these criteria using precise figures; instead, an assessment must be made using human and expert judgments for a more adaptable and sensitive review. The adaptive MCDM design with fuzzy edges incorporates Entropy (EN), Similarity Measure (SIM), and TOPSIS techniques rely on FHS. The conveyed frameworks are better for probing NT issues because they analyze a more expansive range of attributes, which can handle a component with multiple different sub-attribute values. Expert ratings are used to demonstrate a practical application to highlight the relevance of the proposed approach. In addition, a sensitivity analysis is done to investigate the impact of primary criterion weights in sorting.