Picture fuzzy interactional partitioned Heronian mean aggregation operators: an application to MADM process

Lin, Mingwei; Li, Xinmei; Chen, Riqing; Fujita, Hamido; Lin, Jian

doi:10.1007/s10462-021-09953-7

Cited by 96 publications

(39 citation statements)

References 51 publications

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“…Jan et al [ 36 , 37 ] initiated the innovative concepts of CIFRs and IVCIFRs and proposed the applications of said concepts in the analysis of cyber-securities, cyber threats in petroleum sectors, and other industries. Lin et al [ 38 – 40 ] worked comprehensively on decision-making techniques in the environment of picture fuzzy information. Xu et al [ 41 ] followed an approach based rough FS to detect the frauds in telecommunication, Pithani and Sethi [ 42 ] worked on a fuzzy set delay representation for computer network routing algorithms, Fu et al [ 43 ] assessed the information systems security risk on FS, Biswas et al [ 44 ] proposed the intuitionistic fuzzy real time multigraphs for communication networks, and Gao and Feng [ 45 ] used rough FS for risk evaluation of the electric power communication network.…”

Section: Introductionmentioning

confidence: 99%

[Retracted] Analysis of Communication and Network Securities Using the Concepts of Complex Picture Fuzzy Relations

Nasir

Jan

Khan

et al. 2021

Computational Intelligence and Neuroscience

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In our lives, we cannot avoid the uncertainty. Randomness, rough knowledge, and vagueness lead us to uncertainty. In mathematics, the fuzzy set (FS) theory and logics are used to model uncertain events. This article defines a new concept of complex picture fuzzy relation (CPFR) in the field of FS theory. In addition, the types of CPFRs are also discussed to make the paper more fruitful. Today’s complex network architecture faces the ever-changing threats. The cyber-attackers are always trying to discover, catch, and exploit the weaknesses in the networks. So, the security measures are essential to avoid and dismantle such threats. The CPFR has a vast structure composed of levels of membership, abstinence, and nonmembership which models uncertainty better than any other structures in the theory. Moreover, a CPFR has the ability to cope with multivariable problems. Therefore, this article proposes modeling techniques based on the complex picture fuzzy information which are used to study the effectiveness and ineffectiveness of different network securities against several threats and cyber-attack practices. Moreover, the strength and preeminence of the proposed methods are verified by studying their comparison with the existing methods.

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Section: Introductionmentioning

confidence: 99%

[Retracted] Analysis of Communication and Network Securities Using the Concepts of Complex Picture Fuzzy Relations

Nasir

Jan

Khan

et al. 2021

Computational Intelligence and Neuroscience

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“…The knowledge base, ruleset, and rule count all develop in lockstep with the size of the input variable and membership function. As a result, the fuzzy model must be modified regardless of whether classical or artificial intelligence approaches are utilized [26].…”

Section: Simulation Resultsmentioning

confidence: 99%

“…In classical set theory, an element is either a member of or not a member of a given set. As a result, the degree of membership in that set is its crisp value [26]. However, in fuzzy set theory, an element's degree of membership can be modified continually from the realm of discourse, a fuzzy set maps to the near interval 0, 1.…”

Section: Simulation Resultsmentioning

confidence: 99%

“…Weekday and weekend parameters of both fuzzy logic systems were intended to be identical [25]. The fuzzy logic system's input and output sets used symmetrical Gaussian distributed membership functions, with variables and parameterized throughout the evolutionary algorithm parameterization loop [26]. There are no discontinuous spots in the Gaussian distribution where the optimization may fail [24].…”

Section: The Fuzzy Logic Systemsmentioning

confidence: 99%

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A Study of Load Demand Forecasting Models in Electricity using Artificial Neural Networks and Fuzzy Logic Model

2022

IJE

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Since load time series are very changeable, demand forecasting of the short-term load is challenging based on hourly, daily, weekly, and monthly load forecast demand. As a result, the Turkish Electricity Transmission Company (TEA) load forecasting is proposed in this paper using artificial neural networks (ANN) and fuzzy logic (FL). Load forecasting enables utilities to purchase and generate electricity, load shift, and build infrastructure. A load forecast was classified into three sorts (hourly, weekly and monthly). Over time, forecasting power loads with artificial neural networks and fuzzy logic reveals a massive decrease in ANN and a progressive increase in FL from 24 to 168 hours. As illustrated, fuzzy logic and artificial neural networks outperform regression algorithms. This study has the highest growth and means absolute percentage error (MAPE) rates compared to FL and ANN. Although regression has the highest prediction growth rate, it is less precise than FL and ANN due to their lower MAPE percentage. Artificial Neural Networks and Fuzzy Logic are emerging technologies capable of forecasting and mitigating demand volatility. Future research can forecast various Turkish states using the same approach.

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“…Ayub et al [43] constructed several cubic fuzzy HM Dombi aggregation operators, which were based on combining the Dombi operational laws and HM operators. Lin et al [44] presented a MADM algorithm with picture fuzzy interactional partitioned HM aggregation operators. Tian et al [45] developed an MCDM method with the partitioned HM (PHM) operator and the Shapley fuzzy measure with a single-valued neutrosophic set.…”

Section: Introductionmentioning

confidence: 99%

Interval-Valued Hesitant Fuzzy Linguistic Multiattribute Decision-Making Method Based on Three-Parameter Heronian Mean Operators

Wang

2021

Journal of Mathematics

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Numerous variants have been proposed for sets of linguistic terms and the interval-valued hesitant fuzzy set (IVHFS). In particular, the interval-valued hesitant fuzzy linguistic set (IVHFLS) is more suitable for defining the hesitancy and inconsistency inherent in the human cognitive processes of decision making. A key aggregation operator is Heronian mean (HM), based on which the correlation among aggregated arguments can be captured. However, the existing HM operators partially overlook the correlation among more than two arguments and lack the properties of idempotency and reducibility. In this work, the limitations of HM operators are first analyzed. Then, two new HM variants are introduced: three-parameter weighted Heronian mean (TPWHM) and three-parameter weighted geometric Heronian mean (TPWGHM). Thus, the reducibility, idempotency, monotonicity, and boundedness properties are proven for the two computational procedures, and unique situations are mentioned. Furthermore, two more elaborate operators are also introduced which are called the interval-valued hesitant fuzzy linguistic TPWHM (IVHFLTPWHM) and the interval-valued hesitant fuzzy linguistic TPWGHM (IVHFLTPWGHM). The main properties, as well as unique situations of these two computational procedures, are discussed. Finally, the introduced methods are clarified by illustrative examples. In addition, the parameter effects on the decision-making outcomes are discussed and comparisons with other reference methods are made.

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Picture fuzzy interactional partitioned Heronian mean aggregation operators: an application to MADM process

Cited by 96 publications

References 51 publications

[Retracted] Analysis of Communication and Network Securities Using the Concepts of Complex Picture Fuzzy Relations

[Retracted] Analysis of Communication and Network Securities Using the Concepts of Complex Picture Fuzzy Relations

A Study of Load Demand Forecasting Models in Electricity using Artificial Neural Networks and Fuzzy Logic Model

Interval-Valued Hesitant Fuzzy Linguistic Multiattribute Decision-Making Method Based on Three-Parameter Heronian Mean Operators

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