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ORCID

Ilanthenral Kandasamy: 0000-0003-4826-9466

Divakar Arumugam: 0009-0007-1413-4332

Aryan Rathore: 0009-0004-1111-0675

Ateeth Arun: 0009-0002-7193-6845

Manan Jain: 0009-0007-2884-8774

Vasantha .W.B: 0000-0001-9832-1475

Florentin Smarandache: 0000-0002-5560-5926

Article Type

Original Study

Abstract

Cognitive maps are a vital tool that can be used for knowledge representation and reasoning. Fuzzy Cognitive Maps (FCMs) are popular soft computing techniques used to model large and complex systems, and they can aid in explainable artificial intelligence (AI). FCMs, however, cannot model the indeterminacy that arises in a system due to various uncertainties. Neutrosophic Cognitive Maps (NCMs), upgraded FCMs that could model indeterminacy, were introduced to address this issue. NCMs are a generalization of FCMs, a field of cognitive science firmly based on neural networks. NCMs have been used to solve a wide range of problems. NCMs were introduced in 2002, and even after 20 years, NCMs do not have any supportive software, package, toolbox, or visualization software like FCMs. The main reason for the absence of dedicated software is due to the indeterminacy concept 'I' and how it has to be handled. This paper presents the dedicated Python package created for handling the functioning of NCMs. The modelling software presented in this paper aids in visualizing the NCMs as a signed digraph with indeterminacy that is a directed signed neutrosophic graph. This package implements a sample case study using NCMs.

Creative Commons License

Creative Commons Attribution 4.0 International License
This work is licensed under a Creative Commons Attribution 4.0 International License.

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