Tracing the Evolution of Max-Min Aggregation and Fuzzy Systems in AI: A Bibliometric Review

Nguyen Van Han

doi:10.4108/eetcasa.9750

Authors

Nguyen Van Han Thuyloi University https://orcid.org/0000-0002-4054-565X

DOI:

https://doi.org/10.4108/eetcasa.9750

Keywords:

Max-Min Aggregation, Explainable AI, Bibliometric Analysis, Artificial Intelligence, Fuzzy Systems, Neuro-Fuzzy Models

Abstract

This paper presents a bibliometric review of Max-Min aggregation functions and fuzzy systems in artificial intelligence (AI) from 1990 to 2024. Drawing on data from Scopus and analyzed using Bibliometrix and VOSviewer, we map publication trends, key contributors, thematic developments, and emerging research areas. The findings reveal growing interest in interpretable AI, neuro-fuzzy models, and hybrid systems. We highlight the integration of Max-Min aggregation in explainable AI and identify key research gaps. This review provides a structured overview of the field’s evolution and offers guidance for future research directions.

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