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New Extension of Fuzzy-Weighted Zero-Inconsistency and Fuzzy Decision by Opinion Score Method Based on Cubic Pythagorean Fuzzy Environment: A Benchmarking Case Study of Sign Language Recognition Systems

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Abstract

Many of the current fuzzy sets based on intuitionistic fuzzy concepts suffer from different limitations. Different approaches towards that end in the form of scientific works have been introduced, and many other fuzzy sets were extended to address such limitations. In this study, the Cubic Pythagorean fuzzy sets (CPFS) are introduced as one of the proposed solutions and among the most powerful tools to address uncertainty, especially in complex and difficult situations by using both interval-valued Pythagorean fuzzy set and Pythagorean fuzzy set to represent vagueness or ill-defined information. Considering all the merits of CPFS and its flexibility, this study aimed to integrate it with two profound multi-criteria decision-making methods. Fuzzy-weighted zero-inconsistency (FWZIC) and decision by opinion score method (FDOSM) are both extended based on CPFS, called CP-FWZIC and CP-FDOSM. Two methodological phases are performed in two phases. The first phase discusses all the necessary CP-FWZIC steps to determine the evaluation criteria weights, followed by alternatives ranking using CP-FDOSM. The second phase discusses a case study used in this research for sign language recognition systems. Finally, two methods were used to assess the extended multi-criteria decision-making methods, namely, systematic ranking assessment and sensitivity analysis. The assessment findings suggest that the ranking results are supported by both systematic ranking and high correlation results which were performed using different criteria weight-changing scenarios.

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References

  1. Almahdi, E., et al.: Mobile-based patient monitoring systems: a prioritisation framework using multi-criteria decision-making techniques. J. Med. Syst. 43(7), 219 (2019)

    Article  Google Scholar 

  2. Hamid, R.A., et al.: Dempster–Shafer theory for classification and hybridised models of multi-criteria decision analysis for prioritisation: a telemedicine framework for patients with heart diseases. J. Ambient Intell. Humaniz. Comput. (2021). https://doi.org/10.1007/s12652-021-03325-3

    Article  Google Scholar 

  3. Alamoodi, A.H., Zaidan, B.B., Zaidan, A.A., Albahri, O.S., Chen, J., Chyad, M.A., Aleesa, A.M.: Machine learning-based imputation soft computing approach for large missing scale and non-reference data imputation. Chaos Solitons Fractals 151, 111236 (2021)

    Article  Google Scholar 

  4. Khatari, M., et al.: Multidimensional benchmarking framework for AQMs of network congestion control based on AHP and group-TOPSIS. Int. J. Inf. Technol. Decis. Mak. (2021). https://doi.org/10.1142/S0219622021500127

    Article  Google Scholar 

  5. Tasrif, E., Saputra, H.K., Kurniadi, D., Hidayat, H., Mubai, A.: Designing website-based scholarship management application for teaching of analytical hierarchy process (AHP) in decision support systems (DSS) subjects. Int. J. Interact. Mob. Technol. 16(9), 179–191 (2021)

    Article  Google Scholar 

  6. Albahri, O., et al.: New mHealth hospital selection framework supporting decentralised telemedicine architecture for outpatient cardiovascular disease-based integrated techniques: Haversine-GPS and AHP-VIKOR. J. Ambient Intell. Humaniz. Comput. (2021). https://doi.org/10.1007/s12652-021-02897-4

    Article  Google Scholar 

  7. Mitra, A.: Grading of raw jute fibres using criteria importance through intercriteria correlation (CRITIC) and range of value (ROV) approach of multi-criteria decision making. J. Nat. Fibers (2021). https://doi.org/10.1080/15440478.2021.1951422

    Article  Google Scholar 

  8. Albahri, O., et al.: Systematic review of artificial intelligence techniques in the detection and classification of COVID-19 medical images in terms of evaluation and benchmarking: taxonomy analysis, challenges, future solutions and methodological aspects. J. Infect. Public Health 13(10), 1381–1396 (2020)

    Article  Google Scholar 

  9. Malik, R., et al.: Novel roadside unit positioning framework in the context of the vehicle-to-infrastructure communication system based on AHP—Entropy for weighting and borda—VIKOR for uniform ranking. Int. J. Inf. Technol. Decis. Mak. (2021). https://doi.org/10.1142/S0219622021500061

    Article  Google Scholar 

  10. Alaa, M., et al.: Assessment and ranking framework for the English skills of pre-service teachers based on fuzzy Delphi and TOPSIS methods. IEEE Access 7, 126201–126223 (2019)

    Article  Google Scholar 

  11. Napi, N.M., et al.: Medical emergency triage and patient prioritisation in a telemedicine environment: a systematic review. Health Technol. 9(5), 679–700 (2019)

    Article  Google Scholar 

  12. Mohammed, K., et al.: Novel technique for reorganisation of opinion order to interval levels for solving several instances representing prioritisation in patients with multiple chronic diseases. Comput. Methods Programs Biomed. 185, 105151 (2020)

    Article  Google Scholar 

  13. Manzoor, B., Othman, I., Durdyev, S., Ismail, S., Wahab, M.H.: Influence of artificial intelligence in civil engineering toward sustainable development—a systematic literature review. Appl. Syst. Innov. 4(3), 52 (2021)

    Article  Google Scholar 

  14. Ibrahim, N., et al.: Multi-criteria evaluation and benchmarking for young learners’ English language mobile applications in terms of LSRW skills. IEEE Access 7(7), 146620–146651 (2019)

    Article  Google Scholar 

  15. Mohammed, T.J., et al.: Convalescent-plasma-transfusion intelligent framework for rescuing COVID-19 patients across centralised/decentralised telemedicine hospitals based on AHP-group TOPSIS and matching component. Appl. Intell. 51(5), 2956–2987 (2021)

    Article  Google Scholar 

  16. Rezaei, J.: Best-worst multi-criteria decision-making method. Omega 53, 49–57 (2015)

    Article  Google Scholar 

  17. Albahri, O., et al.: Helping doctors hasten COVID-19 treatment: Towards a rescue framework for the transfusion of best convalescent plasma to the most critical patients based on biological requirements via ml and novel MCDM methods. Comput. Methods Programs Biomed. 196, 105617 (2020)

    Article  Google Scholar 

  18. Zavadskas, E.K., Turskis, Z.: Multiple criteria decision making (MCDM) methods in economics: an overview. Technol. Econ. Dev. Econ. 17(2), 397–427 (2011)

    Article  Google Scholar 

  19. Mohammed, K., et al.: A Uniform Intelligent Prioritisation for Solving Diverse and Big Data Generated From Multiple Chronic Diseases Patients Based on Hybrid Decision-Making and Voting Method. IEEE Access 8, 91521–91530 (2020)

    Article  Google Scholar 

  20. Albahri, A., et al.: Multi-Biological Laboratory Examination Framework for the Prioritization of Patients with COVID-19 Based on Integrated AHP and Group VIKOR Methods. Int. J. Inf. Technol. Decis. Mak. 19(05), 1247–1269 (2020)

    Article  Google Scholar 

  21. Riaz, M., Sałabun, W., Farid, H.M.A., Ali, N., Wątróbski, J.: A robust q-rung orthopair fuzzy information aggregation using Einstein operations with application to sustainable energy planning decision management. Energies 13(9), 2155 (2020)

    Article  Google Scholar 

  22. Enaizan, O., et al.: Electronic medical record systems: Decision support examination framework for individual, security and privacy concerns using multi-perspective analysis. Heal. Technol. 10(3), 795–822 (2020)

    Article  Google Scholar 

  23. Zaidan, A., et al.: Multi-agent learning neural network and Bayesian model for real-time IoT skin detectors: a new evaluation and benchmarking methodology. Neural Comput. Appl. 32(12), 8315–8366 (2020)

    Article  Google Scholar 

  24. Wang, H., Zhang, Y., Yao, J.: An extended VIKOR method based on q-rung orthopair shadowed set and its application to multi-attribute decision making. Symmetry 12(9), 1508 (2020)

    Article  Google Scholar 

  25. Alsalem, M.A., et al.: Rise of multiattribute decision-making in combating COVID-19: a systematic review of the state-of-the-art literature. Int. J. Intell. Syst. (2021). https://doi.org/10.1002/int.22699

    Article  Google Scholar 

  26. Zadeh, L.A.: Fuzzy sets. In: Zadeh, L.A. (ed.) Fuzzy Sets, Fuzzy Logic, and Fuzzy Systems: Selected Papers, pp. 394–432. World Scientific, Singapore (1996)

    Chapter  Google Scholar 

  27. Liu, P., Zhu, B., Wang, P., Shen, M.: An approach based on linguistic spherical fuzzy sets for public evaluation of shared bicycles in China. Eng. Appl. Artif. Intell. 87, 103295 (2020)

    Article  Google Scholar 

  28. Garg, H.: CN-q-ROFS: Connection number-based q-rung orthopair fuzzy set and their application to decision-making process. Int. J. Intell. Syst. 36(7), 3106–3143 (2021)

    Article  Google Scholar 

  29. Mahmood, T., Ur Rehman, U., Ali, Z., Mahmood, T.: Hybrid vector similarity measures based on complex hesitant fuzzy sets and their applications to pattern recognition and medical diagnosis. J. Intell. Fuzzy Syst. 40(1), 625–646 (2021)

    Article  Google Scholar 

  30. D. A. Pelta, M. T. Lamata, J. L. Verdegay, C. Cruz, and A. Salas, "Against Artificial Complexification: Crisp vs. Fuzzy Information in the TOPSIS Method," in 19th World Congress of the International Fuzzy Systems Association (IFSA), 12th Conference of the European Society for Fuzzy Logic and Technology (EUSFLAT), and 11th International Summer School on Aggregation Operators (AGOP), 2021, pp. 345–351: Atlantis Press.

  31. T. M. Al-Shami, "Bipolar soft sets: relations between them and ordinary points and their applications," Complexity, vol. 2021, 2021.

  32. Liu, S., Yu, W., Chan, F.T., Niu, B.: A variable weight-based hybrid approach for multi-attribute group decision making under interval-valued intuitionistic fuzzy sets. Int. J. Intell. Syst. 36(2), 1015–1052 (2021)

    Article  Google Scholar 

  33. Alsalem, M.A., Alsattar, H.A., Albahri, A.S., Mohammed, R.T., Albahri, O.S., Zaidan, A.A., Alnoor, A., Alamoodi, A.H., Qahtan, S., Zaidan, B.B., Aickelin, U.: Based on T-spherical fuzzy environment: a combination of FWZIC and FDOSM for prioritising COVID-19 vaccine dose recipients. J. Infect. Public Health 14(10), 1513–59 (2021)

    Article  Google Scholar 

  34. Albahri, A., et al.: Integration of fuzzy-weighted zero-inconsistency and fuzzy decision by opinion score methods under a q-rung orthopair environment: a distribution case study of COVID-19 vaccine doses. Comput. Stand. Interfaces 80, 103572 (2021)

    Article  Google Scholar 

  35. Verma, V., Anand, S., Aggarwal, A.G.: Intuitionistic fuzzy AHP based reliability allocation model for multi-software system. Int. J. Serv. Oper. Inf. 11(2–3), 240–259 (2021)

    Google Scholar 

  36. Hussian, Z., Yang, M.S.: Distance and similarity measures of Pythagorean fuzzy sets based on the Hausdorff metric with application to fuzzy TOPSIS. Int. J. Intell. Syst. 34(10), 2633–2654 (2019)

    Article  Google Scholar 

  37. Wan, S.-P., Li, S.-Q., Dong, J.-Y.: A three-phase method for Pythagorean fuzzy multi-attribute group decision making and application to haze management. Comput. Ind. Eng. 123, 348–363 (2018)

    Article  Google Scholar 

  38. Fahmi, A., Yaqoob, N., Chammam, W.: Maclaurin symmetric mean aggregation operators based on cubic Pythagorean linguistic fuzzy number. J. Ambient. Intell. Humaniz. Comput. 12(2), 1925–1942 (2021)

    Article  Google Scholar 

  39. Hussain, A., Irfan Ali, M., Mahmood, T.: Covering based q-rung orthopair fuzzy rough set model hybrid with TOPSIS for multi-attribute decision making. J. Intell. Fuzzy Syst. 37(1), 981–993 (2019)

    Article  Google Scholar 

  40. Mahmood, T., Ali, Z.: Entropy measure and TOPSIS method based on correlation coefficient using complex q-rung orthopair fuzzy information and its application to multi-attribute decision making. Soft. Comput. 25(2), 1249–1275 (2021)

    Article  MATH  Google Scholar 

  41. Chen, K., Luo, Y.: Generalized orthopair linguistic Muirhead mean operators and their application in multi-criteria decision making. J. Intell. Fuzzy Syst. 37(1), 797–809 (2019)

    Article  Google Scholar 

  42. Talukdar, P., Dutta, P.: Distance measures for cubic Pythagorean fuzzy sets and its applications to multicriteria decision making. Granul. Comput. 6(2), 267–284 (2021)

    Article  Google Scholar 

  43. Khan, M.S.A., Khan, F., Lemley, J., Abdullah, S., Hussain, F.: Extended topsis method based on Pythagorean cubic fuzzy multi-criteria decision making with incomplete weight information. J. Intell. Fuzzy Syst. 38(2), 2285–2296 (2020)

    Article  Google Scholar 

  44. Wang, F., Zhao, X.: Prospect-theory and geometric distance measure-based Pythagorean cubic fuzzy multicriteria decision-making. Int. J. Intell. Syst. (2021). https://doi.org/10.1002/int.22453

    Article  Google Scholar 

  45. Abbas, S.Z., Ali Khan, M.S., Abdullah, S., Sun, H., Hussain, F.: Cubic Pythagorean fuzzy sets and their application to multi-attribute decision making with unknown weight information. J. Intell. Fuzzy Syst. 37(1), 1529–1544 (2019)

    Article  Google Scholar 

  46. Salih, M.M., Zaidan, B., Zaidan, A.: Fuzzy decision by opinion score method. Appl. Soft Comput. 96, 106595 (2020)

    Article  Google Scholar 

  47. Albahri, O.S., et al.: Multidimensional benchmarking of the active queue management methods of network congestion control based on extension of fuzzy decision by opinion score method. Int. J. Intell. Syst. 36(2), 796–831 (2021)

    Article  Google Scholar 

  48. Salih, M.M., Albahri, O., Zaidan, A., Zaidan, B., Jumaah, F., Albahri, A.: Benchmarking of AQM methods of network congestion control based on extension of interval type-2 trapezoidal fuzzy decision by opinion score method. Telecommun. Syst. 77(3), 493–522 (2021)

    Article  Google Scholar 

  49. Albahri, O., et al.: Novel dynamic fuzzy decision-making framework for COVID-19 vaccine dose recipients. J. Adv. Res. (2021). https://doi.org/10.1016/j.jare.2021.08.009

    Article  Google Scholar 

  50. Mohammed, R.T., et al.: Determining importance of many-objective optimisation competitive algorithms evaluation criteria based on a novel fuzzy-weighted zero-inconsistency method. Int. J. Inf. Technol. Decis. Mak. (2021). https://doi.org/10.1142/S0219622021500140

    Article  Google Scholar 

  51. Krishnan, E., et al.: Interval type 2 trapezoidal-fuzzy weighted with zero inconsistency combined with VIKOR for evaluating smart e-tourism applications. Int. J. Intell. Syst. (2021). https://doi.org/10.1002/int.22489

    Article  Google Scholar 

  52. Akram, M., Dudek, W.A., Ilyas, F.: Group decision-making based on pythagorean fuzzy TOPSIS method. Int. J. Intell. Syst. 34(7), 1455–1475 (2019)

    Article  Google Scholar 

  53. Salih, M.M., Zaidan, B., Zaidan, A.: Fuzzy decision by opinion score method. Appl. Soft Comput. 96, 106595 (2020)

    Article  Google Scholar 

  54. Ahmed, M., Zaidan, B., Zaidan, A., Salih, M.M., Al-qaysi, Z., Alamoodi, A.: Based on wearable sensory device in 3D-printed humanoid: a new real-time sign language recognition system. Measurement 168, 108431 (2021)

    Article  Google Scholar 

  55. Mohammed, A.Z., Al-Samarraaya, S., Albahri, O.S., Pamucar, D., AlSattar, H.A., Alamoodi, A.H., Zaidan, B.B., Albahri, A.S.: Extension of interval-valued pythagorean FDOSM for evaluating and benchmarking real-time SLRSs based on multidimensional criteria of hand gesture recognition and sensor glove perspectives (2021).

  56. Albahri, A., et al.: Based multiple heterogeneous wearable sensors: A smart real-time health monitoring structured for hospitals distributor. IEEE Access 7, 37269–37323 (2019)

    Article  Google Scholar 

  57. Albahri, O., et al.: Fault-tolerant mHealth framework in the context of IoT-based real-time wearable health data sensors. IEEE Access 7, 50052–50080 (2019)

    Article  Google Scholar 

  58. Alsalem, M., et al.: Multiclass benchmarking framework for automated acute Leukaemia detection and classification based on BWM and group-VIKOR. J. Med. Syst. 43(7), 212 (2019)

    Article  Google Scholar 

  59. Abdulkareem, K.H., et al.: A new standardisation and selection framework for real-time image dehazing algorithms from multi-foggy scenes based on fuzzy Delphi and hybrid multi-criteria decision analysis methods. Neural Comput. Appl. 33, 1029–1054 (2020)

    Article  Google Scholar 

  60. Abdulkareem, K.H., et al.: A novel multi-perspective benchmarking framework for selecting image Dehazing intelligent algorithms based on BWM and group VIKOR techniques. Int. J. Inf. Technol. Decis. Mak. 19(3), 909–957 (2020)

    Article  MathSciNet  Google Scholar 

  61. Albahri, A.S., Hamid, R.A., Albahri, O.S., Zaidan, A.A.: Detection-based prioritisation: framework of multi-laboratory characteristics for asymptomatic COVID-19 carriers based on integrated entropy–TOPSIS methods. Artif. Intell. Med. 111, 101983 (2021)

    Article  Google Scholar 

  62. Zughoul, O.: Novel triplex procedure for ranking the ability of software engineering students based on two levels of AHP and group TOPSIS techniques. Int. J. Inf. Technol. Decis. Mak. 20(01), 67–135 (2020)

    Article  Google Scholar 

  63. Pamucar, D., Yazdani, M., Obradovic, R., Kumar, A., Torres-Jiménez, M.: A novel fuzzy hybrid neutrosophic decision-making approach for the resilient supplier selection problem. Int. J. Intell. Syst. 35(12), 1934–1986 (2020)

    Article  Google Scholar 

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Authors and Affiliations

  1. Future Technology Research Center, National Yunlin University of Science and Technology, 123 University Road, Section 3, Douliou, Yunlin, 64002, Taiwan

    A. H. Alamoodi, O. S. Albahri, H. A. AlSattar, Mohamed A. Ahmed, B. B. Zaidan, A. S. Albahri & Mohammed S. Mahmoud

  2. Informatics Institute for Postgraduate Studies (IIPS), Iraqi Commission for Computers and Informatics (ICCI), Baghdad, Iraq

    A. A. Zaidan

  3. Department of Computer Science, Computer Science and Mathematics College, Tikrit University, Tikrit, Iraq

    Dragan Pamucar

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  1. A. H. Alamoodi
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  2. O. S. Albahri
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  3. A. A. Zaidan
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  4. H. A. AlSattar
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Alamoodi, A.H., Albahri, O.S., Zaidan, A.A. et al. New Extension of Fuzzy-Weighted Zero-Inconsistency and Fuzzy Decision by Opinion Score Method Based on Cubic Pythagorean Fuzzy Environment: A Benchmarking Case Study of Sign Language Recognition Systems. Int. J. Fuzzy Syst. 24, 1909–1926 (2022). https://doi.org/10.1007/s40815-021-01246-z

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