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A spherical fuzzy correlation coefficient based on statistical viewpoint with its applications in classification and bidirectional approximate reasoning

Treść / Zawartość
Identyfikatory
Warianty tytułu
Języki publikacji
EN
Abstrakty
EN
Spherical fuzzy sets are more powerful in modelling the uncertain situations than picture fuzzy sets, fermatean fuzzy sets, Pythagorean fuzzy sets, intuitionistic fuzzy sets, and fuzzy sets. In this paper, we first define the variance and covariance of spherical fuzzy sets. Then, using variance and covariance, we define the unique spherical fuzzy set correlation metric in line with the statistical coefficient of correlation. Two spherical fuzzy sets are correlated in both direction and strength using the provided measure of correlation. We discussed its many characteristics. We compared the measure of correlation with the current ones through linguistic variables. We established its validity by showing its application in bidirectional approximate reasoning. We also resolve a pattern identification issue in the spherical fuzzy environment using the provided correlation function, and we compare the results with several current measurements.
Rocznik
Strony
63--82
Opis fizyczny
Bibliogr. 28 poz., rys., tab., wzory
Twórcy
  • Department of Mathematics, National Institute of Technology, Warangal 506004, Telangana, India
  • Department of Mathematics, National Institute of Technology, Warangal 506004, Telangana, India.
Bibliografia
  • [1] E.P. Augustine: Novel correlation coefficient for intuitionistic fuzzy sets and its application to multi-criteria decision-making problems. International Journal of Fuzzy System Applications, 10(2), (2021), 39-58, DOI: 10.4018/IJFSA.2021040103
  • [2] A. Aydogdu and S. Gul: A novel entropy proposition for spherical fuzzy sets and its application in multiple attribute decision-making. International Journal of Intelligent Systems, 35(9), (2020), 1354-1374, DOI: 10.1002/int.22256
  • [3] S.M. Chen, W.H. Hsiao and W.T. Jong: Bidirectional approximate reasoning based on interval-valued fuzzy sets. Fuzzy Sets and Systems, 91(3), (1997), 339-353, DOI: 10.1016/S0165-0114(97)86594-310.1016/ S0165-0114(97)86594-3
  • [4] D.A. Chiang and N.P. Lin: Correlation of fuzzy sets. Fuzzy Sets and Systems, 102(2), (1999), 221-226, DOI: 10.1016/S0165-0114(97)00127-9
  • [5] B.C. Cuong and V. Kreinovich: Picture fuzzy sets-a new concept for computational intelligence problems. In 2013 third World Congress on Information and Communication Technologies (WICT 2013), 1-6. IEEE, Hanoi, Vietnam (2013), DOI: 10.1109/WICT.2013.7113099
  • [6] N.V. Dinh and N.X. Thao: Some measures of picture fuzzy sets and their application in multi-attribute decision-making. International Journal of Mathematical Sciences and Computing, 4(3), (2018), 23-41. DOI: 10.5815/ijmsc.2018.03.03
  • [7] P. Dutta: Medical diagnosis based on distance measures between picture fuzzy sets. International Journal of Fuzzy System Applications, 7(4), (2018), 15-36. DOI: 10.4018/IJFSA.2018100102
  • [8] P.A. Ejegwa: Generalized triparametric correlation coefficient for Pythagorean fuzzy sets with application to MCDM problems. Granular Computing, 6(3), (2021), 557-566. DOI: 10.1007/s41066-020-00215-5
  • [9] K.G. Fatma and K. Cengiz: A novel VIKOR method using spherical Fuzzy Sets and its application to warehouse site selection. Journal of Intelligent & Fuzzy Systems, 37(1), (2019), 1197-1211. DOI: 10.3233/JIFS-182651
  • [10] A.H. Ganie: Applicability of a novel Pythagorean fuzzy correlation coefficient in medical diagnosis, clustering, and classification problems. Computational and Applied Mathematics, 41(8), (2022), 410. DOI: 10.1007/s40314-022-02108-6
  • [11] A.H. Ganie, S. Singh and P.K. Bhatia: Some new correlation coefficients of picture fuzzy sets with applications. Neural Computing and Applications, 32(16), (2020), 12609-12625. DOI: 10.1007/s00521-020-04715-y
  • [12] T. Gerstenkorn and J. Manko: Correlation of intuitionistic fuzzy sets. Fuzzy Sets and Systems, 44(1), (1991), 39-43. DOI: 10.1016/0165-0114(91)90031-K
  • [13] F.K. Gundogdu and C. Kahraman: A novel spherical fuzzy analytic hierarchy proces and its renewable energy application. Soft Computing, 24(6), (2020), 4607-4621. DOI: 10.1007/s00500-019-04222-w
  • [14] A.G. Hatzimichailidis, G.A. Papakostas and V.G. Kaburlasos: A novel distance measure of intuitionistic fuzzy sets and its application to pattern recognition problems. International Journal of Intelligent Systems, 27(4), (2012), 396-409. DOI: 10.1002/int.21529
  • [15] T. Mahmood, K. Ullah, Q. Khan and N. Jan: An approach toward decision-making and medical diagnosis problems using the concept of spherical fuzzy sets. Neural Computing and Applications, 31(11), (2019), 7041-7053. DOI: 10.1007/s00521-018-3521-2
  • [16] S. Sharma and S. Singh: On some generalized correlation coefficients of the fuzzy sets and fuzzy soft sets with application in cleanliness ranking of public health centres. Journal of Intelligent & Fuzzy Systems, 36(4), (2019), 3671-3683. DOI: 10.3233/JIFS-181838
  • [17] S.A. Shishavan, F.K. Gündogdu, E. Farrokhizadeh, Y. Donyatalab and C. Kahraman: Novel similarity measures in spherical fuzzy environment and their applications. Engineering Applications of Artificial Intelligence, 94 (2020), 103837. DOI: 10.1016/j.engappai.2020.103837
  • [18] S. Singh and A.H. Ganie: On some correlation coefficients in Pythagorean fuzzy environment with applications. International Journal of Intelligent Systems, 35(4), (2020), 682-717. DOI: 10.1002/int.22222
  • [19] S. Singh and A.H. Ganie: On a new picture fuzzy correlation coefficient with its applications to pattern recognition and identification of an investment sector. Computational and Applied Mathematics, 41(1), (2022), 8. DOI: 10.1007/s40314-021-01699-w
  • [20] S. Singh and A.H. Ganie: Some novel q-rung orthopair fuzzy correlation coefficients based on the statistical viewpoint with their applications. Journal of Ambient Intelligence and Humanized Computing, 13(4), (2022), 2227-2252. DOI: 10.1007/s12652-021-02983-7
  • [21] P. Singh, N.K. Mishra, M. Kumar, S. Saxena and V. Singh: Risk analysis of flood disaster based on similarity measures in picture fuzzy environment. Afrika Matematika, 29(7-8), (2018), 1019-1038. DOI: 10.1007/s13370-018-0597-x
  • [22] P. Singh: Correlation coefficients for picture fuzzy sets. Journal of Intelligent & Fuzzy Systems, 28(2), (2015), 591-604. DOI: 10.3233/IFS-141338
  • [23] L.H. Son: Measuring analogousness in picture fuzzy sets: from picture distance measures to picture association measures. Fuzzy Optimization and Decision Making, 16(3), (2017), 359-378. DOI: 10.1007/s10700-016-9249-5
  • [24] N.X. Thao, M. Ali and F. Smarandache: An intuitionistic fuzzy clustering algorithm based on a new correlation coefficient with application in medical diagnosis. Journal of Intelligent & Fuzzy Systems, 36(1), (2019), 189-198. DOI: 10.3233/JIFS-181084
  • [25] K. Ullah, H. Garg, T. Mahmood, N. Jan and Z. Ali: Correlation coefficients for T-spherical fuzzy sets and their applications in clustering and multi-attribute decision making. Soft Computing, 24 (2020), 1647-1659. DOI: 10.1007/s00500-019-03993-6
  • [26] G. Wei, J. Wang, M. Lu, J. Wu and C. Wei: Similarity measures of spherical fuzzy sets based on cosine function and their applications. IEEE Access, 7 (2019), 159069-159080. DOI: 10.1109/ACCESS.2019.2949296
  • [27] L.A. Zadeh: Fuzzy sets. Information and Control, 8(3), (1965), 338-353. DOI: 10.1016/S0019-9958(65)90241-X
  • [28] H. Zhang, Q. Cai and G. Wei: Spherical fuzzy power partitioned Maclaurin symmetric mean operators and their application in multiple attribute group decision making. Archives of Control sciences, 33(1), (2023), 179-238. DOI: 10.24425/acs.2023.145119
Typ dokumentu
Bibliografia
Identyfikator YADDA
bwmeta1.element.baztech-defd4934-1459-4499-9725-fffa45667786
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