Multiple attribute group decision-making based on cubic linguistic Pythagorean fuzzy sets and power Hamy mean

Xu, Wuhuan; Shang, Xiaopu; Wang, Jun

doi:10.1007/s40747-020-00255-z

Cited by 21 publications

(4 citation statements)

References 51 publications

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“…Therefore, it is necessary to study the quality evaluation of physical education based on fuzzy multi-attribute decision-making [6][7][8][9][10][11]. Then, how to make the final judgment and ranking of the commodities in the commodity selection of the e-commerce platform, it is necessary to apply the fuzzy MADM method [12][13][14][15][16][17]. The fuzzy MADM method is mainly divided into two steps: one is to use the fuzzy MADM method to find out the fuzzy utility value of each scheme; the second is to use the fuzzy sorting method to deal with the fuzzy utility [18][19][20][21][22].…”

Section: Introductionmentioning

confidence: 99%

A novel MADM algorithm for physical education teaching quality evaluation based on 2-tuple linguistic neutrosophic numbers power heronian mean operators

Rao

Xiao

2023

PLoS ONE

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Classroom teaching quality evaluation is an important link in the curriculum quality assurance system. It has important guiding significance for the timely feedback of classroom teaching effects, the achievement of teachers’ teaching goals, and the implementation of teaching plans. The evaluation system is scientific, objective and accurate. The classroom teaching quality evaluation is an important way to improve the level of teacher education and teaching and then determine the quality of talent training in various majors. At present, although the evaluation work has played a positive role, the backwardness of the evaluation system has seriously restricted the effectiveness of teaching feedback. The classroom teaching quality evaluation of college basketball training is viewed as the multi-attribute decision-making (MADM). In this article, we combine the generalized Heronian mean (GHM) operator and power average (PA) with 2-tuple linguistic neutrosophic sets (2TLNSs) to propose the generalized 2-tuple linguistic neutrosophic power HM (G2TLNPHM) operator. The G2TLNPHM operator is built for MADM. Finally, an example for classroom teaching quality evaluation of college basketball training is used to show the proposed methods.

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Section: Introductionmentioning

confidence: 99%

A novel MADM algorithm for physical education teaching quality evaluation based on 2-tuple linguistic neutrosophic numbers power heronian mean operators

Rao

Xiao

2023

PLoS ONE

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“…Thus, the best alternative is Θ 4 . Next, we make a comparison between the proposed 2TLCq-ROFWDPMM operator and the weighted dual PMSM (WDPMSM) operator based on the CLPyFS [49]. Accordingly, we are able to get the ranking as: Θ 5 > Θ 4 > Θ 3 > Θ 2 > Θ 7 > Θ 1 > Θ 6 .…”

Section: Comparison With Weighted Pmsm and Weighted Dual Pmsm Operatorsmentioning

confidence: 99%

MAGDM for selecting machine learning techniques: A novel approach utilizing the 2-tuple linguistic cubic q-rung orthopair fuzzy set

Naz

Akram

Sagheer

et al. 2023

Preprint

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Machine learning is a powerful technology for both current and future information management and it is already being used in a variety of domains. The use of machine learning in cyber security is still in its early stages, emphasizing the significant gap between research and practice. The q-rung orthopair fuzzy set (q-ROFS) has been demonstrated to be a valuable tool in describing decision-makers (DMs) assessment values in multi-attribute group decision-making (MAGDM). In this paper, we introduce a new tool called the 2-tuple linguistic cubic q-rung orthopair fuzzy set (2TLCq-ROFS) based on the combination of q-ROFS with interval-valued q-ROFS under the 2-tuple linguistic scenario in order to capture DMs assessment information in the complex MAGDM problems in a more efficient way. We investigate machine learning based MAGDM problems in cyber security in which the DM’s preference information is expressed as the 2TLCq-ROF numbers (2TLCqROFNs). Based on the combination of power average and power geometric operators with Muirhead mean, we propose a family of new aggregation operators including: the 2TLCq-ROF power Muirhead mean (2TLCq-ROFPMM), the 2TLCq-ROF dual power Muirhead mean (2TLCq-ROFDPMM), the 2TLCq-ROF weighted power Muirhead mean (2TLCq-ROFWPMM), and the 2TLCq-ROF weighted dual power Muirhead mean (2TLCq-ROFWDPMM) operators under the 2TLCq-ROF environment, which are more precise than the current aggregation operators and take into account the interconnection of the 2TLCq-ROFNs. Then, a versatile 2TLCq-ROF MAGDM approach is established and a case study on the evaluation of machine learning techniques in cyber security is given to show the method’s effectiveness and validity. Moreover, a parameter analysis is carried out to analyze the influence of parameters on ranking results. The comparative analysis further confirms the effectiveness and feasibility of the proposed approach to show why DMs should select our suggested strategy over several others. In the end, some conclusions of this paper are determined and future directions are demonstrated.

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“…The aggregation mechanism indicates the suitability of PA operator to balance the impact of extreme values and produce more reasonable aggregated result, and thus, PA has been widely studied in MAGDM (Kamal and Chen 2022;Liu et al 2020b;Xu et al 2021b). Particularly, Feng et al (2021) combined PA and Hamy mean to cope with MAGDM under IVq-RDHFSs information.…”

Section: Introductionmentioning

confidence: 99%

A novel group decision-making method for complex fuzzy information using extended power average operator and Frank operation

Zhong

Wang

2022

Preprint

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The interval-valued q-rung dual hesitant fuzzy sets (IVq-RDHFSs) has been proposed for effectively representing complex fuzzy information. IVq-RDHFSs can describe the membership degree and non-membership degree respectively through interval value set, and can flexibly adjust the space of information expression, which makes them surpass most existing fuzzy sets. Nevertheless, the main shortage of the existing multi-attribute group decision making (MAGDM) methods based on IVq-RDHFSs is that the functions of operation rules and aggregation operators (AOs) are very limited. Therefore, this paper investigates a new MAGDM under IVq-RDHFSs, established on the powerful Frank t-norm and t-conorm (FTT) operation and extended power average (EPA) operator. With the help of FTT, the basic operation of IVq-RDHFSs is redefined, then the interval-valued q-rung dual hesitant fuzzy Frank extended power average operator and the interval-valued q-rung dual hesitant fuzzy Frank weighted extended power average (IVq-RDHFFWEPA) operator are developed by combining FTT and EPA. Likewise, the desirable properties and special cases of the new AOs are explored. Afterwards, a novel MAGDM framework is constructed on the foundation of IVq-RDHFFWEPA operator. Compared with most existing approach, the proposed MAGDM in this paper possesses prominent ability in controlling the effect of extreme evaluation as well as modeling the risk attitude of decision-makers, so it is more appropriate for practical application. Finally, diverse experiments are devised to analyze the use and advantages of our method.

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Multiple attribute group decision-making based on cubic linguistic Pythagorean fuzzy sets and power Hamy mean

Cited by 21 publications

References 51 publications

A novel MADM algorithm for physical education teaching quality evaluation based on 2-tuple linguistic neutrosophic numbers power heronian mean operators

A novel MADM algorithm for physical education teaching quality evaluation based on 2-tuple linguistic neutrosophic numbers power heronian mean operators

MAGDM for selecting machine learning techniques: A novel approach utilizing the 2-tuple linguistic cubic q-rung orthopair fuzzy set

A novel group decision-making method for complex fuzzy information using extended power average operator and Frank operation

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