Special Issues

Neutrosophic Theories in Intelligent Decision Making, Management and Engineering

Submission Deadline: 22 November 2022 (closed) View: 43

Guest Editors

Dr. S. A. Edalatpanah, Ayandegan Institute of Higher Education, Iran.
Dr. Florentin Smarandache, University of New Mexico, United States.
Dr. Dragan Pamučar, University of Defence, Serbia.


Neutrosophic set is a generalization of fuzzy set and intuitionistic fuzzy set. The key distinction between the neutrosophic set and other types of sets is the introduction of

the degree of indeterminacy / neutrality (I) as independent component in the neutrosophic set.

In the neutrosophic set, the degree of membership-truth (T), the degree of indeterminacy (I), and the degree of non-membership-falsehood (F) are independent, therefore their sum (as single-valued numbers) can be up to 3. Neutrosophic set has been used in solving problems that involve indeterminacy, uncertainty, impreciseness, vagueness, inconsistent, incompleteness, etc. In the past years, the field of neutrosophic set, logic, measure, probability and statistics, pre-calculus and calculus, and their applications in multiple fields have been extended and applied in various fields. For more information, see the University of New Mexico’s website on neutrosophic at: http://fs. [1] unm.edu/neutrosophy.htm [2]. This special issue will provide a systematic overview and state-of-the-art research in the field of neutrosophic set, and will outline new and important developments in fundamentals, approaches, models, methodologies, intelligent decision support systems, and applications in the area of management and engineering.

Scope and Interests (included but not limited to)

• Neutrosophic logic

• Neutrosophic deep learning

• Neutrosophic transportation problems

• Neutrosophic Optimization

• Neutrosophic image processing

• Neutrosophic information processing

• Neutrosophic decision making

• Neutrosophic big data mining

• Neutrosophic decision support systems

• Neutrosophic computational modelling

• Neutrosophic medical diagnosis

• Neutrosophic fault diagnosis

• Artificial intelligence

• Plithogenic sets

• Hybrid neutrosophic sets (rough neutrosophic sets, neutrosophic soft sets…)


Neutrosophic sets, plithogenic sets, optimization, decision making, big data, manufacturing, deep learning, image processing.

Published Papers

  • Open Access


    Using Digital Twin to Diagnose Faults in Braiding Machinery Based on IoT

    Youping Lin, Huangbin Lin, Dezhi Wei
    Intelligent Automation & Soft Computing, Vol.37, No.2, pp. 1363-1379, 2023, DOI:10.32604/iasc.2023.038601
    (This article belongs to the Special Issue: Neutrosophic Theories in Intelligent Decision Making, Management and Engineering)
    Abstract The digital twin (DT) includes real-time data analytics based on the actual product or manufacturing processing parameters. Data from digital twins can predict asset maintenance requirements ahead of time. This saves money by decreasing operating expenses and asset downtime, which improves company efficiency. In this paper, a digital twin in braiding machinery based on IoT (DTBM-IoT) used to diagnose faults. When an imbalance fault occurs, the system gathers experimental data. After that, the information is sent into a digital win model of the rotor system to see whether it can quantify and locate imbalance for More >

  • Open Access


    Marketing Model Analysis of Fashion Communication Based on the Visual Analysis of Neutrosophic Systems

    Fangyu Ye, Xiaoshu Xu, Yunfeng Zhang, Yan Ye, Jingyu Dai
    Intelligent Automation & Soft Computing, Vol.37, No.2, pp. 1257-1274, 2023, DOI:10.32604/iasc.2023.037057
    (This article belongs to the Special Issue: Neutrosophic Theories in Intelligent Decision Making, Management and Engineering)
    Abstract In order to Improvement the Neutrosophic sets as effective tools to deal with uncertain and inconsistent information. The research takes methodology of combined single-valued neutrosophic rough set and multi-scale decision systems. This paper proposes the optimal scale selection and reduction algorithms based on multi-scale single-valued neutrosophic dominance rough set model. User requirements were analyzed using KJ method to construct a hierarchical model. According to the statistics of representative studies from China and the West, we found that, on the one hand, classical theory has been expanded and supplemented in fashion culture communication and marketing. The… More >

  • Open Access


    A Non-singleton Type-3 Fuzzy Modeling: Optimized by Square-Root Cubature Kalman Filter

    Aoqi Xu, Khalid A. Alattas, Nasreen Kausar, Ardashir Mohammadzadeh, Ebru Ozbilge, Tonguc Cagin
    Intelligent Automation & Soft Computing, Vol.37, No.1, pp. 17-32, 2023, DOI:10.32604/iasc.2023.036623
    (This article belongs to the Special Issue: Neutrosophic Theories in Intelligent Decision Making, Management and Engineering)
    Abstract In many problems, to analyze the process/metabolism behavior, a model of the system is identified. The main gap is the weakness of current methods vs. noisy environments. The primary objective of this study is to present a more robust method against uncertainties. This paper proposes a new deep learning scheme for modeling and identification applications. The suggested approach is based on non-singleton type-3 fuzzy logic systems (NT3-FLSs) that can support measurement errors and high-level uncertainties. Besides the rule optimization, the antecedent parameters and the level of secondary memberships are also adjusted by the suggested square More >

  • Open Access


    An Endogenous Feedback and Entropy Analysis in Machine Learning Model for Stock’s Return Forecast

    Edson Vinicius Pontes Bastos, Jorge Junio Moreira Antunes, Lino Guimarães Marujo, Peter Fernandes Wanke, Roberto Ivo da Rocha Lima Filho
    Intelligent Automation & Soft Computing, Vol.36, No.3, pp. 3175-3190, 2023, DOI:10.32604/iasc.2023.034582
    (This article belongs to the Special Issue: Neutrosophic Theories in Intelligent Decision Making, Management and Engineering)
    Abstract Stock markets exhibit Brownian movement with random, non-linear, uncertain, evolutionary, non-parametric, nebulous, chaotic characteristics and dynamism with a high degree of complexity. Developing an algorithm to predict returns for decision-making is a challenging goal. In addition, the choice of variables that will serve as input to the model represents a non-triviality, since it is possible to observe endogeneity problems between the predictor and the predicted variables. Thus, the goal is to analyze the endogenous origin of the stock return prediction model based on technical indicators. For this, we structure a feed-forward neural network. We evaluate More >

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