A Symmetric TOPSIS Framework Using Trapezoidal-Valued Neutrosophic Fuzzy Aczel–Alsina Operators for AI-Based Hiring Evaluation

M. Kaviyarasu, R. Venitha, Mesfer H. Alqahtani and Amlak I. Alajlan

Journal of Mathematics, 2026, vol. 2026, 1-29

Abstract: In decision-making, problems like the evaluation of recruiting companies with artificial intelligence (AI)-based hiring systems have ambiguities, inconsistency, and indeterminacy. To overcome these difficulties, this study provides symmetric aggregation techniques that effectively capture truth, indeterminacy, and falsity information within the context of neutrosophic fuzzy numbers with trapezoidal values. The symmetric operational laws of the Aczel–Alsina (AA) operator, namely, direct sum, direct product, and scalar multiplication are established. The main mathematical features of the novel trapezoidal-valued neutrosophic fuzzy AA weighted average, ordered weighted average, hybrid average, weighted geometric, ordered weighted geometric, and hybrid geometric aggregation operators are provided. Additionally, by using the proposed symmetric AA aggregating operators for ranking alternatives, a modified TzVNF–TOPSIS technique is created. The model’s utility is demonstrated through an AI-based recruiting decision support system that selects the top companies. Sensitivity and comparative studies verify the durability, effectiveness, and superiority of the proposed work for decision-making under neutrosophic uncertainty.

Date: 2026
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Persistent link: https://EconPapers.repec.org/RePEc:hin:jjmath:7394542

DOI: 10.1155/jom/7394542

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