A Neutrosophic Cognitive Maps Approach for Pestle Analysis in Food Industry

Bhutani, Kanika; Gaur, Sanjay; Panwar, Punita; Garg, Sneha

doi:10.1007/978-981-99-1909-3_30

Cited by 1 publication

(1 citation statement)

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“…Dynamic NCMs [28], enhanced cuckoo search and ensemble classifier were presented for acquiring the profile of the gene expression, differentiating between the individuals affected with rheumatoid arthritis and possible control subjects. [29] proposed a technique combining pest analysis based on fuzzy and neutrosophic logic to analyze the food industry.…”

Section: Introductionmentioning

confidence: 99%

NCMPy: A modelling software for Neutrosophic Cognitive Maps (NCMs) based on Python package

Kandasamy,

Arumugam,

Rathore

et al. 2023

Preprint

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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 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, was 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 or 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 1 in this paper aids in visualizing the NCMs as a signed digraph with indeter-minacy that is directed signed neutrosophic graph. This package implements a sample case study using NCMs.

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Section: Introductionmentioning

confidence: 99%