Integrating Neutrosophic Logic with Fuzzy Inference  Systems: A Comprehensive Survey on Enhancing  Decision-Making under Uncertainty

A.A. Salama; Huda E. Khalid; Ahmed K. Essa; Ibrahim Yasser; Aya A. Abd El-Khalek

Authors

A.A. Salama Dept. of Math and Computer Sci., Faculty of Science, Port Said Univ., Egypt
Huda E. Khalid University of Telafer, The Administration Assistant for the President of the Telafer University, Telafer, Iraq;
Ahmed K. Essa University of Telafer, The Administration Assistant for the President of the Telafer University, Telafer, Iraq;
Ibrahim Yasser Electronics and Communication Engineering Department, Faculty of Engineering, Mansoura University, Mansoura, 35516, Egypt.
Aya A. Abd El-Khalek Communications and Electronics Engineering Department, Nile Higher Institute for Engineering and Technology, Mansoura, Egypt

Keywords:

Neutrosophic Logic, Fuzzy Logic, Inference Systems, Uncertainty Modeling, Indeterminacy, Decision Making, Fish Quality Assessment, Artificial Intelligence.

Abstract

In this study, we combined neutrosophic logic with fuzzy logic inference systems
to handle uncertainties related to influencing factors in decision making processes. Fuzzy
logic based reasoning useful for dealing with partial truth but it does not allow for
indeterminacy, which is a key factor in modeling real world problems. While classical and
fuzzy logics mainly focus on truth, indeterminacy and falsity are excluded in their
representations of uncertainty, thus limiting their approach; neutrosophic logic provides a
more complete solution because it explicitly deals with Truth, Indeterminacy, Falsity. This
paper also describes the main concepts and techniques related with the combination of both
paradigms such as neutrosophic fuzzy sets, rules, and inference mechanisms. Example of
such a fish quality assessment system it also discusses the advantages of this integration,
including enhanced expressiveness and decision-making ability. Finally, we present the
challenges and future research directions regarding computational complexity, data
collection and standardization.

DOI: 10.5281/zenodo.16887985