<abstract><p>Many real-world decision-making issues frequently involve competing sets of criteria, uncertainty, and inaccurate information. Some of these require the involvement of a group of decision-makers, where it is necessary to reduce the various available individual preferences to a single collective preference. To enhance the effectiveness of multi-criteria decisions, multi-criteria decision-making is a popular decision-making technique that makes the procedure more precise, reasonable, and efficient. The "Technique for Order Preference by Similarity to Ideal Solution (TOPSIS)" and "Elimination and Choice Transforming Reality (ELECTRE)" are prominent ranking methods and widely used in the multi-criteria decision-making to solve complicated decision-making problems. In this study, two $ m $-polar fuzzy set-based ranking methods are proposed by extending the ELECTRE-Ⅰ and TOPSIS approaches equipped with cubic $ m $-polar fuzzy (C$ m $PF) sets, where the experts provide assessment results on feasible alternatives through a C$ m $PF decision matrix. The first proposed method, C$ m $PF-TOPSIS, focuses on the alternative that is closest to a C$ m $PF positive ideal solution and farthest away from the C$ m $PF negative ideal solution. The Euclidean and normalized Euclidean distances are used to determine the proximity of an alternative to ideal solutions. In contrast, the second developed method is C$ m $PF-ELECTRE-Ⅰ which uses an outranking directed decision graph to determine the optimal alternative, which entirely depends on the C$ m $PF concordance and discordance sets. Furthermore, a practical case study is carried out in the diagnosis of impulse control disorders to illustrate the feasibility and applicability of the proposed methods. Finally, a comparative analysis is performed to demonstrate the veracity, superiority, and effectiveness of the proposed methods.</p></abstract>

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Nowadays, several fuzzy extensions of ELECTRE (Elimination and Selection Transforming Reality) and TOPSIS (The Order Preference by Similarity to Positive Ideal Solution) methods are emerging as powerful mathematical tools for dealing with uncertainties in different multi-criteria decision-making (MCDM) situations of medical and engineering domains. Since cubic bipolar fuzzy (CBF) set theory is also becoming an effective mathematical tool for such kinds of problems. In this study, we present an improved CBF-TOPSIS method using the concepts of normalized Euclidean distance and revised closeness index because in the existing CBF-TOPSIS method the notions of Euclidean distance and relative closeness index were used to reach the final decision. Then, we rectify the shortcomings of the existing CBF-ELECTRE-I method, that is, there was a problem in the discordance formula. To modify the existing algorithm based on the CBF-ELECTRE-I model, we add a new step to find the exact linear ranking order of alternatives, which is purely based on concordance and discordance outranking coefficients. Second, we explore two new applications to validate the applicability and feasibility of the improved CBF-ELECTRE-I and CBF-TOPSIS methods in medical and engineering. Finally, to demonstrate the veracity of the modified CBF-ELECTRE-I and CBF-TOPSIS methods, we perform a brief sensitivity analysis between them and other existing models.