ArticleScientific reports2026
An advanced hybrid intuitionistic fuzzy Z-number framework for IT impact analysis in E-learning systems.
Article in Scientific reports, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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Abstract
The Aggregation operators (AOs) play an essential part in decision-making, particularly when there are other things to consider. The current study's objective is to construct AOs and operational rules within an intuitionistic fuzzy Z-number ([Formula: see text]) framework. In order to accomplish this, we create special operational laws that give an acceptable or equitable solution for [Formula: see text] through the use of the idea of proportionate distribution. In this article, we are introduced to [Formula: see text] based on fairly operators which is probably a new concept in fuzzy theory. The proposed study aims to provide a set of weighted AOs using [Formula: see text]. In this research, we looked at fairly operators for the [Formula: see text]. Additionally, multi-attribute group decision-making challenges are resolved by applying the suggested fairness operators for the [Formula: see text]. With numerical examples of group decision-making difficulties, we present the proposed models. Likewise, a fairly multi-criteria decision-making (MCDM) technique is established by using recommended reasonable AOs with assessments from numerous decision-makers (DMs) and partially weighting the data under [Formula: see text]. We assert that the new technique is far more accurate and efficient than the current model. Likewise, an extensive execution of the suggested methodology is furnished that demonstrates the efficient utilization of the assigned tasks. Furthermore, Grey Relational Analysis (GRA) is incorporated as a ranking validation mechanism to evaluate the relational closeness of alternatives under the proposed [Formula: see text] environment. Comparative analysis demonstrates that the proposed [Formula: see text] fairly aggregation framework provides reliable and consistent decision-making results under uncertain group assessment information.
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