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1.
J. A. Goguen, “L. A. Zadeh. Fuzzy sets. Information and control, vol. 8 (1965), pp. 338–353. - L. A. Zadeh. Similarity relations and fuzzy orderings. Information sciences, vol. 3 (1971), pp. 177–200.,” J. Symb. Logic, vol. 38, no. 4, pp. 656–657, 1973. [Google Scholar] [Crossref]
2.
K. T. Atanassov, “Intuitionistic fuzzy sets,” Fuzzy Sets Syst., vol. 20, no. 1, pp. 87–96, 1986. [Google Scholar] [Crossref]
3.
K. Atanassov and G. Gargov, “Interval valued intuitionistic fuzzy sets,” Fuzzy Sets Syst., vol. 31, no. 3, pp. 343–349, 1989. [Google Scholar] [Crossref]
4.
F. E. Boran and D. Akay, “A biparametric similarity measure on intuitionistic fuzzy sets with applications to pattern recognition,” Inf. Sci., vol. 255, pp. 45–57, 2014. [Google Scholar] [Crossref]
5.
H. Nguyen, “A new knowledge-based measure for intuitionistic fuzzy sets and its application in multiple attribute group decision making,” Expert Syst. Appl., vol. 42, no. 22, pp. 8766–8774, 2015. [Google Scholar] [Crossref]
6.
J. Mahanta and S. Panda, “A novel distance measure for intuitionistic fuzzy sets with diverse applications,” Int. J. Intell. Syst., vol. 36, no. 2, pp. 615–627, 2021. [Google Scholar] [Crossref]
7.
R. R. Yager, “Pythagorean fuzzy subsets,” in 2013 Joint IFSA World Congress and NAFIPS Annual Meeting (IFSA/NAFIPS), Edmonton, AB, Canada, 2023, pp. 57–61. [Google Scholar] [Crossref]
8.
R. Ronald  Yager, “Generalized orthopair fuzzy sets,” IEEE Trans. Fuzzy Syst., vol. 25, no. 5, pp. 1222–1230, 2016. [Google Scholar]
9.
B. C. Cuong, “Picture fuzzy sets,” vol. 30, no. 4, pp. 409–420, 2015. [Google Scholar] [Crossref]
10.
B. C. Cuong and V. H. Pham, “Some fuzzy logic operators for picture fuzzy sets,” in 2015 Seventh International Conference on Knowledge and Systems Engineering (KSE), Ho Chi Minh City, Vietnam, 2015, pp. 132–137. [Google Scholar] [Crossref]
11.
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 Comput. Appl., vol. 31, no. 11, pp. 7041–7053, 2019. [Google Scholar]
12.
R. Khan, K. Ullah, D. Pamucar, and M. Bari, “Performance measure using a multi-attribute decision making approach based on complex T-spherical fuzzy power aggregation operators,” J. Comput. Cogn. Eng., 2022. [Google Scholar] [Crossref]
13.
T. Senapati, G. Chen, R. Mesiar, and R. Ronald  Yager, “Novel Aczel–Alsina operations-based interval-valued intuitionistic fuzzy aggregation operators and their applications in multiple attribute decision-making process,” Int. J. Intell. Syst., 2022. [Google Scholar] [Crossref]
14.
C. Jana, T. Senapati, and M. Pal, “Pythagorean fuzzy dombi aggregation operators and its applications in multiple attribute decision-making,” Int. J. Intell. Syst., vol. 34, no. 9, pp. 2019–2038, 2019. [Google Scholar] [Crossref]
15.
T. Senapati, “Approaches to multi-attribute decision-making based on picture fuzzy Aczel–Alsina average aggregation operators,” Comp. Appl. Math., vol. 41, no. 40, pp. 1–19, 2022. [Google Scholar] [Crossref]
16.
M. R. Khan, K. Ullah, D. Pamucar, and M. Bari, “P“Performance measure using a multi-attribute decision-making approach based on complex T-spherical fuzzy power aggregation operators,” J. Comput. Cogn. Eng., vol. 3, no. 1, pp. 138–146, 2022. [Google Scholar] [Crossref]
17.
T. Mahmood and Z. Ali, “Prioritized muirhead mean aggregation operators under the complex single-valued neutrosophic settings and their application in multi-attribute decision-making,” J. Comput. Cogn. Eng., vol. 2, no. 1, pp. 56–73, 2022. [Google Scholar] [Crossref]
18.
M. Riaz and H. M. A. Farid, “Picture fuzzy aggregation approach with application to third-party logistic provider selection process,” Rep. Mech. Eng., vol. 3, no. 1, pp. 318–327, 2022. [Google Scholar] [Crossref]
19.
M. Riaz, H. Garg, H. M. A. Farid, and R. Chinram, “Multi-Criteria Decision Making Based on Bipolar Picture Fuzzy Operators and New Distance Measures,” Comput. Model. Eng. Sci., vol. 127, no. 2, pp. 771–800, 2021. [Google Scholar] [Crossref]
20.
H. Garg, “Intuitionistic fuzzy hamacher aggregation operators with entropy weight and their applications to multi-criteria decision-making problems,” Iran. J. Sci. Technol. Trans. Electr. Eng., vol. 43, no. 3, pp. 597–613, 2019. [Google Scholar] [Crossref]
21.
A. Ashraf, K. Ullah, A. Hussain, and M. Bari, “Interval-valued picture fuzzy maclaurin symmetric mean operator with application in multiple attribute decision-making,” Rep. Mech. Eng., vol. 3, no. 1, pp. 301–317, 2022. [Google Scholar] [Crossref]
22.
H. Garg, “Confidence levels based pythagorean fuzzy aggregation operators and its application to decision-making process,” Comput. Math. Organ. Theory, vol. 23, no. 4, pp. 546–571, 2017. [Google Scholar] [Crossref]
23.
D. Pamucar, A. E. Torkayesh, and S. Biswas, “Supplier selection in healthcare supply chain management during the COVID-19 pandemic: A novel fuzzy rough decision-making approach,” Ann. Oper. Res., pp. 1–43, 2022. [Google Scholar] [Crossref]
24.
M. Sarfraz, K. Ullah, M. Akram, D. Pamucar, and D. Božanić, “Prioritized aggregation operators for intuitionistic fuzzy information based on Aczel–Alsina T-norm and T-conorm and their applications in group decision-making,” Symmetry, vol. 14, no. 12, 2022. [Google Scholar] [Crossref]
25.
A. Hussain and D. Pamucar, “Multi-attribute group decision-making based on pythagorean fuzzy rough set and novel Schweizer-sklar T-norm and T-conorm,” J. Innov. Res. Math. Comput. Sci., vol. 1, no. 2, pp. 1–17, 2022. [Google Scholar]
26.
A. Hussain, X. Zhu, K. Ullah, M. Sarfaraz, S. Yin, and D. Pamucar, “Multi-attribute group decision-making based on pythagorean fuzzy rough Aczel-Alsina aggregation operators and its applications to medical diagnosis,” Heliyon, vol. 9, no. 12, p. e23067, 2023. [Google Scholar] [Crossref]
27.
M. Sarfraz, “Multi-attribute decision-making for T-spherical fuzzy information utilizing schweizer-sklar prioritized aggregation operators for recycled water,” vol. 2, no. 1, pp. 105–128, 2024. [Google Scholar] [Crossref]
28.
K. Ullah, M. Sarfraz, M. Akram, and Z. Ali, “Identification and classification of prioritized Aczel-Alsina aggregation operators based on complex intuitionistic fuzzy information and their applications in decision-making problem,” in Fuzzy Optimization, Decision-making and Operations Research: Theory and Applications, C. Jana, M. Pal, G. Muhiuddin, and P. Liu, Eds., Springer International Publishing, 2023, pp. 377–398. [Google Scholar] [Crossref]
29.
W. S. Du and B. Q Hu, “Aggregation distance measure and its Induced similarity measure between intuitionistic fuzzy sets,” vol. 60, pp. 65–71, 2015. [Google Scholar] [Crossref]
30.
Y. Donyatalab, E. Farrokhizadeh, and S. A. S. Shishavan, “Similarity measures of q-Rung orthopair fuzzy sets based on square root cosine similarity function,” in International Conference on Intelligent and Fuzzy Systems, Springer, 2020, pp. 475–483. [Google Scholar] [Crossref]
31.
W. R. W. Mohd and L. Abdullah, “Similarity measures of pythagorean fuzzy sets based on combination of cosine similarity measure and euclidean distance measure,” in AIP Conference Proceedings, AIP Publishing LLC, 2018, p. 030017. [Google Scholar] [Crossref]
32.
G. Wei, “Some cosine similarity measures for picture fuzzy sets and their applications to strategic decision making,” Informatica, vol. 28, no. 3, pp. 547–564, 2017. [Google Scholar] [Crossref]
33.
N. Van Dinh and N. Xuân Thảo, “Some measures of picture fuzzy sets and their application in multi-attribute decision making,” Int. J. Math. Sci. Comput., vol. 4, pp. 23–41, 2018. [Google Scholar] [Crossref]
34.
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, vol. 29, pp. 1019–1038, 2018. [Google Scholar] [Crossref]
35.
M. Luo and Y. Zhang, “A new similarity measure between picture fuzzy sets and its application,” Eng. Appl. Artif. Intell., vol. 96, p. 103956, 2020. [Google Scholar] [Crossref]
36.
M. Rafiq, S. Ashraf, S. Abdullah, T. Mahmood, and S. Muhammad, “The cosine similarity measures of spherical fuzzy sets and their applications in decision making,” J. Intell. Fuzzy Syst., vol. 36, no. 6, pp. 6059–6073, 2019. [Google Scholar] [Crossref]
37.
R. R. Zhao, M. X. Luo, S. G. Li, and L. N. Ma, “A parametric similarity measure between picture fuzzy sets and its applications in multi-attribute decision-making,” Iranian J. Fuzzy Syst., vol. 20, no. 1, pp. 87–102, 2023. [Google Scholar] [Crossref]
38.
X. Shen, S. Sakhi, K. Ullah, M. N. Abid, and Y. Jin, “Information measures based on T-spherical fuzzy sets and their applications in decision making and pattern recognition,” Axioms, vol. 11, no. 7, p. 302, 2022. [Google Scholar] [Crossref]
39.
K. Ullah, T. Mahmood, and N. Jan, “Similarity measures for T-spherical fuzzy sets with applications in pattern recognition,” Symmetry, vol. 10, no. 6, p. 193, 2018. [Google Scholar] [Crossref]
40.
Y. Jin, M. Hussain, K. Ullah, and A. Hussain, “A new correlation coefficient based on T-spherical fuzzy information with its applications in medical diagnosis and pattern recognition,” Symmetry, vol. 14, no. 11, p. 2317, 2022. [Google Scholar] [Crossref]
41.
K. Ullah, T. Mahmood, and N. Jan, ““Similarity measures for T-spherical fuzzy sets with applications in pattern recognition,” Symmetry, vol. 10, no. 6, p. 193, 2018. [Google Scholar] [Crossref]
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