A comprehensive intuitionistic fuzzy cognitive map with multi-criteria decision-making system for post-COVID challenges

A comprehensive intuitionistic fuzzy cognitive map with multi-criteria decision-making system for post-COVID challenges

연구 분야: Verification

학회: Iran Journal of Computer Science

The world was unprepared for the sudden rise of the COVID pandemic. As an immediate countermeasure, mask was widely mandated to prevent the spread of the virus. However, in India, this practice led to several challenges during the post-pandemic period. To examine these difficulties, an intuitionistic fuzzy cognitive map (FCM)-based MACBETH–CRITIC approach is developed. In this framework, the measuring attractiveness using a categorical-based evaluation technique (MACBETH) method is employed to assess various alternatives against predefined criteria. These criteria are assigned weights using both subjective and objective techniques—FCM determines subjective weights, while the criteria importance through intercriteria correlation (CRITIC) method identifies objective weights. To address the uncertainty in the data, hexagonal intuitionistic fuzzy numbers (HeIFNs) are used to represent the vagueness between alternatives and criteria. A novel defuzzification approach converting hexagonal intuitionistic fuzzy into crisp score (CHeIFCS) algorithm is introduced to transform fuzzy numbers into crisp values. In addition, an aggregation operator is proposed for HeIFN to combine data. The applicability of the designed method is analyzed through one of the post-COVID challenges related to discomfort in wearing masks. The findings highlight "respiratory discomfort" as the most significant issue affecting individuals across all age groups. The robustness of the outcomes is further corroborated using sensitivity and comparative analyses. The existing MCDM method and the relative importance of criteria are evaluated in these analyses.