A novel probabilistic q-rung orthopair linguistic neutrosophic information-based method for rating nanoparticles in various sectors

. 2024 Mar 08 ; 14 (1) : 5738. [epub] 20240308

Status PubMed-not-MEDLINE Jazyk angličtina Země Velká Británie, Anglie Médium electronic

Typ dokumentu časopisecké články

Perzistentní odkaz   https://www.medvik.cz/link/pmid38459126
Odkazy

PubMed 38459126
PubMed Central PMC11319472
DOI 10.1038/s41598-024-55649-7
PII: 10.1038/s41598-024-55649-7
Knihovny.cz E-zdroje

The idea of probabilistic q-rung orthopair linguistic neutrosophic (P-QROLN) is one of the very few reliable tools in computational intelligence. This paper explores a significant breakthrough in nanotechnology, highlighting the introduction of nanoparticles with unique properties and applications that have transformed various industries. However, the complex nature of nanomaterials makes it challenging to select the most suitable nanoparticles for specific industrial needs. In this context, this research facilitate the evaluation of different nanoparticles in industrial applications. The proposed framework harnesses the power of neutrosophic logic to handle uncertainties and imprecise information inherent in nanoparticle selection. By integrating P-QROLN with AO, a comprehensive and flexible methodology is developed for assessing and ranking nanoparticles according to their suitability for specific industrial purposes. This research contributes to the advancement of nanoparticle selection techniques, offering industries a valuable tool for enhancing their product development processes and optimizing performance while minimizing risks. The effectiveness of the proposed framework are demonstrated through a real-world case study, highlighting its potential to revolutionize nanoparticle selection in HVAC (Heating, Ventilation, and Air Conditioning) industry. Finally, this study is crucial to enhance nanoparticle selection in industries, offering a sophisticated framework probabilistic q-rung orthopair linguistic neutrosophic quantification with an aggregation operator to meet the increasing demand for precise and informed decision-making.

Zobrazit více v PubMed

Munson, B. R., Okiishi, T. H., Huebsch, W. W. & Rothmayer, A. P. Fluid Mechanics (Wiley Singapore, 2013).

Choi, S. U. & Eastman, J. A. “Enhancing thermal conductivity of fluids with nanoparticles,” tech. rep., Argonne National Lab.(ANL), Argonne, IL (United States), (1995).

Xuan, Y. & Li, Q. Heat transfer enhancement of nanofluids. Int. J. Heat Fluid Flow21(1), 58–64 (2000).10.1016/S0142-727X(99)00067-3 DOI

Wang, X.-Q. & Mujumdar, A. S. A review on nanofluids-part II: Experiments and applications. Braz. J. Chem. Eng.25, 631–648 (2008).10.1590/S0104-66322008000400002 DOI

Heris, S. Z., Esfahany, M. N. & Etemad, S. G. Experimental investigation of convective heat transfer of al2o3/water nanofluid in circular tube. Int. J. Heat Fluid Flow28(2), 203–210 (2007).10.1016/j.ijheatfluidflow.2006.05.001 DOI

Prasad, A. R., Singh, S. & Nagar, H. A review on nanofluids: Properties and applications. Int. J. Adv. Res. Innov. Ideas Educ.3(3), 3185–3209 (2017).

Bashirnezhad, K. et al. Viscosity of nanofluids: A review of recent experimental studies. Int. Commun. Heat Mass Transf73, 114–123 (2016).10.1016/j.icheatmasstransfer.2016.02.005 DOI

Shahid, A., Zhou, Z., Hassan, M. & Bhatti, M. M. Computational study of magnetized blood flow in the presence of gyrotactic microorganisms propelled through a permeable capillary in a stretching motion. Int. J. Multiscale Comput. Eng.16(5), 409–426 (2018).

Clifford, A. A. & Williams, J. R. Introduction to Supercritical Fluids and Their Applications (Springer, 2000).

Chamsa-Ard, W., Brundavanam, S., Fung, C. C., Fawcett, D. & Poinern, G. Nanofluid types, their synthesis, properties and incorporation in direct solar thermal collectors: A review. Nanomaterials7(6), 131 (2017). 10.3390/nano7060131 PubMed DOI PMC

Klir, G. & Yuan, B. Fuzzy Sets and Fuzzy Logic Vol. 4 (Prentice Hall, 1995).

Edwards, W. The theory of decision making. Psychol. Bull.51(4), 380 (1954). 10.1037/h0053870 PubMed DOI

Zimmermann, H.-J. Fuzzy Set Theory-and Its Applications (Springer Science & Business Media, 2011).

De, S. K., Biswas, R. & Roy, A. R. An application of intuitionistic fuzzy sets in medical diagnosis. Fuzzy Sets Syst.117(2), 209–213 (2001).10.1016/S0165-0114(98)00235-8 DOI

Torra, V. Hesitant fuzzy sets. Int. J. Intell. Syst.25(6), 529–539 (2010).

Zadeh, L. A. The concept of a linguistic variable and its application to approximate reasoning-i. Inf. Sci.8(3), 199–249 (1975).10.1016/0020-0255(75)90036-5 DOI

Maiers, J. & Sherif, Y. S. Applications of fuzzy set theory. IEEE Trans. Syst. Man Cybern.1, 175–189 (1985).10.1109/TSMC.1985.6313408 DOI

Khan, M. J., Kumam, P. & Shutaywi, M. Knowledge measure for the q-rung orthopair fuzzy sets. Int. J. Intell. Syst.36(2), 628–655 (2021).10.1002/int.22313 DOI

Ejegwa, P. A. New q-rung orthopair fuzzy distance-similarity operators with applications in investment analysis, pattern recognition, clustering analysis, and selection of robot for smart manufacturing. Soft Comput. 1–20. 10.1007/s00500-023-08799-1 (2023).

Ejegwa, P. A. & Davvaz, B. An improved composite relation and its application in deciding patients medical status based on a q-rung orthopair fuzzy information. Comput. Appl. Math.41(7), 303 (2022).10.1007/s40314-022-02005-y DOI

Ejegwa, P. A. & Sarkar, A. Novel correlation measure for generalized orthopair fuzzy sets and its decision-making applications. In Operations Research Forum, vol. 4, 32 (Springer, 2023).

Ejegwa, P. A. Decision-making on patients’ medical status based on a q-rung orthopair fuzzy max-min-max composite relation. In q-Rung Orthopair Fuzzy Sets: Theory and Applications, 47–66 (Springer, 2022).

Joshi, B. P., Singh, A., Bhatt, P. K. & Vaisla, K. S. Interval valued q-rung orthopair fuzzy sets and their properties. J. Intell. Fuzzy Syst.35(5), 5225–5230 (2018).10.3233/JIFS-169806 DOI

Salama, A. & Smarandache, F. Neutrosophic crisp set theory. Neutrosophic Sets Syst.5, 27–35 (2014).

Saeed, M., Wahab, A., Ali, J. & Bonyah, E. A robust algorithmic framework for the evaluation of international cricket batters in odi format based on q-rung linguistic neutrosophic quantification. Heliyon. 9(11), 1–20. 10.1016/j.heliyon.2023.e21429 (2023). PubMed PMC

El-Hefenawy, N., Metwally, M. A., Ahmed, Z. M. & El-Henawy, I. M. A review on the applications of neutrosophic sets. J. Comput. Theor. Nanosci.13(1), 936–944 (2016).10.1166/jctn.2016.4896 DOI

Bhaumik, A., Roy, S. K. & Weber, G. W. Multi-objective linguistic-neutrosophic matrix game and its applications to tourism management. J. Dyn. Games8(2), 101–118 (2021).10.3934/jdg.2020031 DOI

Das, S., Roy, B. K., Kar, M. B., Kar, S. & Pamučar, D. Neutrosophic fuzzy set and its application in decision making. J. Ambient. Intell. Humaniz. Comput.11, 5017–5029 (2020).10.1007/s12652-020-01808-3 DOI

Xing, Y., Zhang, R., Zhu, X. & Bai, K. q-rung orthopair fuzzy uncertain linguistic choquet integral operators and their application to multi-attribute decision making. J. Intell. Fuzzy Syst.37(1), 1123–1139 (2019).10.3233/JIFS-182581 DOI

Kuo, T. Interval multiplicative pairwise comparison matrix: Consistency, indeterminacy and normality. Inf. Sci.517, 244–253 (2020).10.1016/j.ins.2019.12.066 DOI

Xu, Y., Chen, L., Rodríguez, R. M., Herrera, F. & Wang, H. Deriving the priority weights from incomplete hesitant fuzzy preference relations in group decision making. Knowl.-Based Syst.99, 71–78 (2016).10.1016/j.knosys.2016.01.047 DOI

Kamacı, H. Linguistic single-valued neutrosophic soft sets with applications in game theory. Int. J. Intell. Syst.36(8), 3917–3960 (2021).10.1002/int.22445 DOI

Saeed, M., Wahab, A., Ali, M., Ali, J. & Bonyah, E. An innovative approach to passport quality assessment based on the possibility q-rung ortho-pair fuzzy hypersoft set. Heliyon. 9(9), 1–18. 10.1016/j.heliyon.2023.e19379 (2023). PubMed PMC

Pennington, N. & Hastie, R. Evidence evaluation in complex decision making. J. Pers. Soc. Psychol.51(2), 242 (1986).10.1037/0022-3514.51.2.242 DOI

Chai, J., Liu, J. N. & Ngai, E. W. Application of decision-making techniques in supplier selection: A systematic review of literature. Expert Syst. Appl.40(10), 3872–3885 (2013).10.1016/j.eswa.2012.12.040 DOI

Herrera, F. & Herrera-Viedma, E. Aggregation operators for linguistic weighted information. IEEE Trans. Syst. Man Cybern. Part A Syst. Hum.27(5), 646–656 (1997).10.1109/3468.618263 DOI

Senapati, T. & Yager, R. R. Fermatean fuzzy weighted averaging/geometric operators and its application in multi-criteria decision-making methods. Eng. Appl. Artif. Intell.85, 112–121 (2019).10.1016/j.engappai.2019.05.012 DOI

Zadeh, L. A., Klir, G. J. & Yuan, B. Fuzzy Sets, Fuzzy Logic, and Fuzzy Systems: Selected Papers Vol. 6 (World Scientific, 1996).

Zadeh, L. A. Fuzzy sets and information granularity. In Fuzzy Sets, Fuzzy Logic, and Fuzzy Systems: Selected Papers, 433–448 (1979).

Mizumoto, M. & Tanaka, K. Some properties of fuzzy sets of type 2. Inf. Control31(4), 312–340 (1976).10.1016/S0019-9958(76)80011-3 DOI

Ali, M. I. Another view on q-rung orthopair fuzzy sets. Int. J. Intell. Syst.33(11), 2139–2153 (2018).10.1002/int.22007 DOI

Oh, H., Kim, H., Kim, H. & Kim, C. A method for improving the multiplicative inconsistency based on indeterminacy of an intuitionistic fuzzy preference relation. Inf. Sci.602, 1–12 (2022).10.1016/j.ins.2022.03.086 DOI

Alblowi, S., Salama, A. & Eisa, M. New concepts of neutrosophic sets. Infinite Study, (2014).

Mallick, R. & Pramanik, S. Pentapartitioned neutrosophic set and its properties, vol. 36. Infinite Study, (2020).

Khalil, A. M., Cao, D., Azzam, A., Smarandache, F. & Alharbi, W. R. Combination of the single-valued neutrosophic fuzzy set and the soft set with applications in decision-making. Symmetry12(8), 1361 (2020).10.3390/sym12081361 DOI

Smarandache, F. Neutrosophic set is a generalization of intuitionistic fuzzy set, inconsistent intuitionistic fuzzy set (picture fuzzy set, ternary fuzzy set), pythagorean fuzzy set, spherical fuzzy set, and q-rung orthopair fuzzy set, while neutrosophication is a generalization of regret theory, grey system theory, and three-ways decision (revisited). J. New Theory29, 1–31 (2019).

Drossos, C. A. Generalized t-norm structures. Fuzzy Sets Syst.104(1), 53–59 (1999).10.1016/S0165-0114(98)00258-9 DOI

Murofushi, T. & Sugeno, M. Fuzzy t-conorm integral with respect to fuzzy measures: generalization of sugeno integral and choquet integral. Fuzzy Sets Syst.42(1), 57–71 (1991).10.1016/0165-0114(91)90089-9 DOI

Jenei, S. On Archimedean triangular norms. Fuzzy Sets Syst.99(2), 179–186 (1998).10.1016/S0165-0114(97)00021-3 DOI

Li, Z., Zhao, C. & Zheng, P. Operations on hesitant linguistic terms sets induced by Archimedean triangular norms and conorms. Int. J. Comput. Intell. Syst.11(1), 514 (2018).10.2991/ijcis.11.1.38 DOI

Kleijnen, J. P. & Rubinstein, R. Y. Optimization and sensitivity analysis of computer simulation models by the score function method. Eur. J. Oper. Res.88(3), 413–427 (1996).10.1016/0377-2217(95)00107-7 DOI

Kliegl, R., Maayr, U. & Krampe, R. T. Time-accuracy functions for determining process and person differences: An application to cognitive aging. Cogn. Psychol.26(2), 134–164 (1994). 10.1006/cogp.1994.1005 PubMed DOI

Kokoç, M. & Ersöz, S. New score and accuracy function for IVIF sets and their applications to AHP for MCGDM. Cybern. Syst.53(3), 257–281 (2022).10.1080/01969722.2021.1949519 DOI

Ali, J., Naeem, M. & Mahmood, W. Generalized q-rung picture linguistic aggregation operators and their application in decision making. J. Intell. Fuzzy Syst. 1–25 (2023).

Keikha, A. Archimedean t-norm and t-conorm-based aggregation operators of HFNs, with the approach of improving education. Int. J. Fuzzy Syst.24(1), 310–321 (2022).10.1007/s40815-021-01137-3 DOI

Liu, P. The aggregation operators based on Archimedean t-conorm and t-norm for single-valued neutrosophic numbers and their application to decision making. Int. J. Fuzzy Syst.18(5), 849–863 (2016).10.1007/s40815-016-0195-8 DOI

Chatterjee, A., Mukherjee, S. & Kar, S. A rough approximation of fuzzy soft set-based decision-making approach in supplier selection problem. Fuzzy Inf. Eng.10(2), 178–195 (2018).10.1080/16168658.2018.1517973 DOI

Zeng, S., Ali, S., Mahmood, M. K., Smarandache, F. & Ahmad, D. Decision-making problems under the environment of m-polar diophantine neutrosophic n-soft set. Comput. Model. Eng. Sci.130, 581–606 (2022).

Awang, A., Ali, M. & Abdullah, L. Hesitant bipolar-valued neutrosophic set: Formulation, theory and application. IEEE Access7, 176099–176114 (2019).10.1109/ACCESS.2019.2946985 DOI

Zadeh, L. Fuzzy sets. Inform. Control8, 338–353 (1965).10.1016/S0019-9958(65)90241-X DOI

Atanassov, K. T. & Atanassov, K. T. Intuitionistic Fuzzy Sets (Springer, 1999).

Yager, R. R. Pythagorean fuzzy subsets. In 2013 Joint IFSA World Congress and NAFIPS Annual Meeting (IFSA/NAFIPS), 57–61 (IEEE, 2013).

Yager, R. R. Generalized orthopair fuzzy sets. IEEE Trans. Fuzzy Syst.25(5), 1222–1230 (2016).10.1109/TFUZZ.2016.2604005 DOI

Smarandache, F. Neutrosophic set—a generalization of the intuitionistic fuzzy set. Int. J. Pure Appl. Math.24(3), 287 (2005).

Bhowmik, M. & Pal, M. Intuitionistic neutrosophic set. Infinite Study, (2009).

Jansi, R., Mohana, K. & Smarandache, F. Correlation measure for pythagorean neutrosophic sets with t and f as dependent neutrosophic components. Infinite Study, (2019).

Najít záznam

Citační ukazatele

Nahrávání dat ...

Možnosti archivace

Nahrávání dat ...