Triangular Neutrosophic Cognitive Map for Multistage Sequential Decision-Making Problems

Al-Subhi, Salah Hasan Saleh; Papageorgiou, Elpiniki I.; Pérez, Pedro Yobanis Piñero; Mahdi, Gaafar Sadeq S.; Acuña, Luis Alvarado

doi:10.1007/s40815-020-01014-5

Cited by 8 publications

(3 citation statements)

References 35 publications

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“…Al-subhi et al [111] applied an enhanced fuzzy cognitive maps approach for project monitoring that integrates diagnosis, decision, and prediction during project evaluation. The validation of the proposed model is performed by evaluating project records related to the PM knowledge areas of scheduling, cost, resource, quality, and procurement from the Research Database Repository of the University of Informatics Sciences of Cuba.…”

Section: Measurement Pdmentioning

confidence: 99%

Artificial Intelligence Enabled Project Management: A Systematic Literature Review

et al. 2023

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In the Industry 5.0 era, companies are leveraging the potential of cutting-edge technologies such as artificial intelligence for more efficient and green human-centric production. In a similar approach, project management would benefit from artificial intelligence in order to achieve project goals by improving project performance, and consequently, reaching higher sustainable success. In this context, this paper examines the role of artificial intelligence in emerging project management through a systematic literature review; the applications of AI techniques in the project management performance domains are presented. The results show that the number of influential publications on artificial intelligence-enabled project management has increased significantly over the last decade. The findings indicate that artificial intelligence, predominantly machine learning, can be considerably useful in the management of construction and IT projects; it is notably encouraging for enhancing the planning, measurement, and uncertainty performance domains by providing promising forecasting and decision-making capabilities.

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Section: Measurement Pdmentioning

confidence: 99%

Artificial Intelligence Enabled Project Management: A Systematic Literature Review

et al. 2023

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“…SWOT analysis and NCMs were combined to analyze organic farming in India [25]. [26] proposed triangular NCMs and uses them in multistage decision-making with a use case of evaluation. NCMs and cloud data were used in [27] to detect violence, and several datasets were used.…”

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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“…Pocze ˛ta et al [51] presented how to optimize FCMs operation for better decision-making and prediction. Al-subhi et al [52] proposed an extension of FCMs, the neutrosophic cognitive map, that was successfully applied to model multistage sequential decision-making problems.…”

Section: Introductionmentioning

confidence: 99%

Improved Decision-Making through a DEMATEL and Fuzzy Cognitive Maps-Based Framework

Mazzuto

Stylios

Ciarapica

et al. 2022

Mathematical Problems in Engineering

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The decision-making process is highly demanding. There has been an increasing tendency to incorporate human thinking, individual experience about a problem, and pure mathematical approaches. Here, a novel integrated approach is investigated and proposed to develop an advanced hybrid decision-support system based on the decision-making trial and evaluation laboratory (DEMATEL) and fuzzy cognitive maps (FCMs). Indeed, knowledge acquisition and elicitation may present distortions and difficulties finding a consensus and an interpretation. Thus, the proposed combined approach aims to examine in depth the potential to improve FCMs’ outcomes by integrating FCM with the DEMATEL approach. The combined methodology achieves at avoiding some of the drawbacks, such as the lack of a standardized FCM theoretical model. Thus, it provides advanced comparative analysis and results in better interpretation of the decision-making process. It is highlighted that the traditional FCM approach does not allow distinguishing the whole number of defined scenarios, in contrast to the hybrid one presented here, which increases the ability of users to make correct decisions. Combining the two approaches provides new capabilities to FCMs in grouping experts’ knowledge, while the DEMATEL approach contributes to refining the strength of concepts’ connections.

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Triangular Neutrosophic Cognitive Map for Multistage Sequential Decision-Making Problems

Cited by 8 publications

References 35 publications

Artificial Intelligence Enabled Project Management: A Systematic Literature Review

Artificial Intelligence Enabled Project Management: A Systematic Literature Review

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

Improved Decision-Making through a DEMATEL and Fuzzy Cognitive Maps-Based Framework

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