Research article

Decision methods based on Bonferroni mean operators and EDAS for the classifications of circular pythagorean fuzzy Meta-analysis

  • Received: 20 August 2024 Revised: 24 September 2024 Accepted: 26 September 2024 Published: 29 September 2024
  • MSC : 03B52, 03E72, 03E73, 28E10, 94D05

  • Meta-analysis is a statistical technique used to process an overall summary estimation, and the technique of meta-analysis is mostly used in medicine, social science, and psychology. In this manuscript, we aimed to combine the techniques of the Bonferroni mean (BM) operator based on circular Pythagorean fuzzy (CPF) sets, called the CPF Bonferroni mean (CPFBM) operator, and CPF weighted Bonferroni mean (CPFWBM) operator and described their special cases with the help of two parameters, "s" and "t", and some describable properties of them are also proposed. Further, we present the evaluation technique based on distance from average solution (EDAS) technique and the proposed operators. Moreover, we use some examples to show the flexibility and dominance of the proposed operators by comparing the proposed methods with some existing techniques.

    Citation: Weiwei Jiang, Zeeshan Ali, Muhammad Waqas, Peide Liu. Decision methods based on Bonferroni mean operators and EDAS for the classifications of circular pythagorean fuzzy Meta-analysis[J]. AIMS Mathematics, 2024, 9(10): 28273-28294. doi: 10.3934/math.20241371

    Related Papers:

  • Meta-analysis is a statistical technique used to process an overall summary estimation, and the technique of meta-analysis is mostly used in medicine, social science, and psychology. In this manuscript, we aimed to combine the techniques of the Bonferroni mean (BM) operator based on circular Pythagorean fuzzy (CPF) sets, called the CPF Bonferroni mean (CPFBM) operator, and CPF weighted Bonferroni mean (CPFWBM) operator and described their special cases with the help of two parameters, "s" and "t", and some describable properties of them are also proposed. Further, we present the evaluation technique based on distance from average solution (EDAS) technique and the proposed operators. Moreover, we use some examples to show the flexibility and dominance of the proposed operators by comparing the proposed methods with some existing techniques.



    加载中


    [1] S. P. Whelton, A. Chin, X. Xin, J. He, Effect of aerobic exercise on blood pressure: a meta-analysis of randomized, controlled trials, Ann. Int. Med., 136 (2002), 493–503.
    [2] H. S. Sacks, J. Berrier, D. Reitman, V. A. Ancona-Berk, T. C. Chalmers, Meta-analyses of randomized controlled trials, New England J. Med., 316 (1987), 450–455.
    [3] G. Ellis, M. A. Whitehead, D. Robinson, D. Neill, P. Langhorne, Meta-analysis of randomised controlled trials, Consensus Statement, 303 (1991), 1385–1387.
    [4] L. A. Zadeh, Fuzzy sets, Inform. Control, 8 (4965), 338–353.
    [5] K. Atanassov, Intuitionistic fuzzy sets, Berlin: Springer, 1999.
    [6] K. Atanassov, More on intuitionistic fuzzy sets, Fuzzy Sets Syst., 33 (1989), 37–45. https://doi.org/10.1016/0165-0114(89)90215-7 doi: 10.1016/0165-0114(89)90215-7
    [7] K. Hayat, Z. Tariq, E. Lughofer, M. F. Aslam, New aggregation operators on group-based generalized intuitionistic fuzzy soft sets, Soft Comput., 25 (2021), 13353–13364. https://doi.org/10.1007/s00500-021-06181-7 doi: 10.1007/s00500-021-06181-7
    [8] Y. Xue, Y. Deng, Decision making under measure-based granular uncertainty with intuitionistic fuzzy sets, Appl. Intel., 51 (2021), 6224–6233. https://doi.org/10.1007/s10489-021-02216-6 doi: 10.1007/s10489-021-02216-6
    [9] H. Garg, K. Kumar, A novel exponential distance and its based TOPSIS method for interval-valued intuitionistic fuzzy sets using connection number of SPA theory, Artif. Intell. Rev., 53 (2020), 595–624. https://doi.org/10.1007/s10462-018-9668-5 doi: 10.1007/s10462-018-9668-5
    [10] A. K. Das, C. Granados, IFP-intuitionistic multi fuzzy N-soft set and its induced IFP-hesitant N-soft set in decision-making, J. Ambient Intell. Human. Comput., 14 (2023), 10143–10152. https://doi.org/10.1007/s12652-021-03677-w doi: 10.1007/s12652-021-03677-w
    [11] A. İlbaş, A. Gürdere, F. E. Boran, An integrated intuitionistic fuzzy set and stochastic multi-criteria acceptability analysis approach for supplier selection, Neural Comput. Appl., 35 (2023), 3937–3953. https://doi.org/10.1007/s00521-022-07919-6 doi: 10.1007/s00521-022-07919-6
    [12] X. He, Y. Wu, Global research trends of intuitionistic fuzzy set: a bibliometric analysis, J. Intell. Syst., 28 (2019), 621–631. https://doi.org/10.1515/jisys-2017-0240 doi: 10.1515/jisys-2017-0240
    [13] R. R. Yager, Pythagorean fuzzy subsets, In: 2013 joint IFSA world congress and NAFIPS annual meeting (IFSA/NAFIPS), IEEE, 57–61, 2013.
    [14] M. Deveci, L. Eriskin, M. Karatas, A survey on recent applications of pythagorean fuzzy sets: a state-of-the-art between 2013 and 2020, In: Garg, H. (eds) Pythagorean Fuzzy Sets, Springer, 2021, 3–38. https://doi.org/10.1007/978-981-16-1989-2_1
    [15] P. Mandal, A. S. Ranadive, Decision‐theoretic rough sets under Pythagorean fuzzy information, Int. J. Intell. Syst., 33 (2018), 818–835. https://doi.org/10.1002/int.21969 doi: 10.1002/int.21969
    [16] L. Pérez-Dominguez, S. N. A. Durán, R. R. López, I. J. C. Pérez-Olguin, D. Luviano-Cruz, J. A. A. H. Gómez, Assessment urban transport service and Pythagorean Fuzzy Sets CODAS method: a case of study of Ciudad Juárez, Sustainability, 13 (2021), 1281. https://doi.org/10.3390/su13031281 doi: 10.3390/su13031281
    [17] N. Alkan, C. Kahraman, CODAS extension using novel decomposed Pythagorean fuzzy sets: Strategy selection for IOT based sustainable supply chain system, Expert Syst. Appl., 237 (2024), 121534. https://doi.org/10.1016/j.eswa.2023.121534 doi: 10.1016/j.eswa.2023.121534
    [18] G. Sun, M. Wang, Pythagorean fuzzy information processing based on centroid distance measure and its applications, Expert Syst. Appl., 236 (2024), 121295. https://doi.org/10.1016/j.eswa.2023.121295 doi: 10.1016/j.eswa.2023.121295
    [19] A. Çalık, A novel Pythagorean fuzzy AHP and fuzzy TOPSIS methodology for green supplier selection in the Industry 4.0 era, Soft Comput., 25 (2021), 2253–2265. https://doi.org/10.1007/s00500-020-05294-9 doi: 10.1007/s00500-020-05294-9
    [20] K. T. Atanassov, Circular intuitionistic fuzzy sets, J. Intell. Fuzzy Syst., 39 (2020), 5981–5986. https://doi.org/10.3233/JIFS-189072 doi: 10.3233/JIFS-189072
    [21] E. Çakır, M. A. Taş, Circular intuitionistic fuzzy decision making and its application, Expert Syst. Appl., 225 (2023), 120076. https://doi.org/10.1016/j.eswa.2023.120076 doi: 10.1016/j.eswa.2023.120076
    [22] M. J. Khan, W. Kumam, N. A. Alreshidi, Divergence measures for circular intuitionistic fuzzy sets and their applications, Eng. Appl. Artif. Intell., 116 (2022), 105455. https://doi.org/10.1016/j.engappai.2022.105455 doi: 10.1016/j.engappai.2022.105455
    [23] K. Atanassov, E. Marinov, Four distances for circular intuitionistic fuzzy sets, Mathematics, 9 (2021), 1121. https://doi.org/10.3390/math9101121 doi: 10.3390/math9101121
    [24] N. A. Alreshidi, Z. Shah, M. J. Khan, Similarity and entropy measures for circular intuitionistic fuzzy sets, Eng. Appl. Artif. Intell., 131 (2024), 107786. https://doi.org/10.1016/j.engappai.2023.107786 doi: 10.1016/j.engappai.2023.107786
    [25] M. C. Bozyiğit, M. Olgun, M. Ünver, Circular Pythagorean fuzzy sets and applications to multi-criteria decision making, Informatica, 34 (2023), 713–742.
    [26] Z. Ali, M. S. Yang, Circular Pythagorean fuzzy Hamacher aggregation operators with application in the assessment of goldmines, IEEE Access, 12 (2024), 13070–13087. https://doi.org/10.1109/ACCESS.2024.3354823 doi: 10.1109/ACCESS.2024.3354823
    [27] M. Keshavarz Ghorabaee, E. K. Zavadskas, L. Olfat, Z. Turskis, Multi-criteria inventory classification using a new method of evaluation based on distance from average solution (EDAS), Informatica, 26 (2015), 435–451.
    [28] E. P. Klement, R. Mesiar, E. Pap, Triangular norms, Tatra Mount. Math. Publ., 13 (1997), 169–193.
    [29] P. Liu, Some Hamacher aggregation operators based on the interval-valued intuitionistic fuzzy numbers and their application to group decision making, IEEE Trans. Fuzzy Syst., 22 (2014), 83–97. https://doi.org/10.1109/TFUZZ.2013.2248736 doi: 10.1109/TFUZZ.2013.2248736
    [30] Z. Xu, R. R. Yager, Intuitionistic fuzzy Bonferroni means, IEEE Trans. Syst. Man Cybernet., Part B (Cybernetics), 41 (2010), 568–578. https://doi.org/10.1109/TSMCB.2010.2072918 doi: 10.1109/TSMCB.2010.2072918
    [31] M. Xia, Z. Xu, B. Zhu, Generalized intuitionistic fuzzy Bonferroni means, Int. J. Intell. Syst., 27 (2012), 23–47. https://doi.org/10.1002/int.20515 doi: 10.1002/int.20515
    [32] D. Liang, Y. Zhang, Z. Xu, A. P. Darko, Pythagorean fuzzy Bonferroni mean aggregation operator and its accelerative calculating algorithm with the multithreading, Int. J. Intell. Syst., 33 (2018), 615–633. https://doi.org/10.1002/int.21960 doi: 10.1002/int.21960
    [33] Y. Yang, K. S. Chin, H. Ding, H. X. Lv, Y. L. Li, Pythagorean fuzzy Bonferroni means based on T‐norm and its dual T‐conorm, Int. J. Intell. Syst., 34 (2019), 1303–1336. https://doi.org/10.1002/int.22097 doi: 10.1002/int.22097
    [34] W. Yang, Y. Pang, New q-rung orthopair fuzzy Bonferroni mean Dombi operators and their application in multiple attribute decision making, IEEE Access, 8 (2020), 50587–50610. https://doi.org/10.1109/ACCESS.2020.2979780 doi: 10.1109/ACCESS.2020.2979780
    [35] W. Yang, Y. Pang, T-spherical fuzzy Bonferroni mean operators and their application in multiple attribute decision making, Mathematics, 10 (2022), 988. https://doi.org/10.3390/math10060988 doi: 10.3390/math10060988
    [36] Y. Pang, W. Yang, Some T-Spherical hesitant fuzzy Shapley Bonferroni mean operators and their applications, IEEE Access, 12 (2022), 60185–60205. https://doi.org/10.1109/ACCESS.2024.3392293 doi: 10.1109/ACCESS.2024.3392293
    [37] P. Zhang, T. Li, Z. Yuan, Z. Deng, G. Wang, D. Wang, et al., A possibilistic information fusion-based unsupervised feature selection method using information quality measures, IEEE Trans. Fuzzy Syst., 31 (2023), 2975–2988. https://doi.org/10.1109/TFUZZ.2023.3238803 doi: 10.1109/TFUZZ.2023.3238803
    [38] P. Zhang, D. Wang, Z. Yu, Y. Zhang, T. Jiang, T. Li, A multi-scale information fusion-based multiple correlations for unsupervised attribute selection, Inform. Fusion, 106 (2024), 102276. https://doi.org/10.1016/j.inffus.2024.102276 doi: 10.1016/j.inffus.2024.102276
    [39] G. Zhang, J. Hu, P. Zhang, Leveraging local density decision labeling and fuzzy dependency for semi-supervised feature selection, Int. J. Fuzzy Syst., 2024. https://doi.org/10.1007/s40815-024-01740-0
  • Reader Comments
  • © 2024 the Author(s), licensee AIMS Press. This is an open access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0)
通讯作者: 陈斌, bchen63@163.com
  • 1. 

    沈阳化工大学材料科学与工程学院 沈阳 110142

  1. 本站搜索
  2. 百度学术搜索
  3. 万方数据库搜索
  4. CNKI搜索

Metrics

Article views(411) PDF downloads(30) Cited by(0)

Article outline

Figures and Tables

Tables(8)

Other Articles By Authors

/

DownLoad:  Full-Size Img  PowerPoint
Return
Return

Catalog