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Cited by 19Educational Technology and AssessmentEdcuational Technology SystemsStudent Assessment and Feedback

The DOI matches its Crossref record; 1 of the 1 references it cites were found in a registry or an index.

Fuzzy descriptive evaluation system: real, complete and fair evaluation of students

Open access
Annabestani, Mohsen, Rowhanimanesh, Alireza, Mizani, Aylar, Rezaei, Akram·2020·Soft Computing
DOI 10.1007/s00500-019-04078-0
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Crossref · 10.1007/s00500-019-04078-0

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Citations

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Research works printed but not linked: 29

  1. Annabestani M, Naghavi N (2014a) Nonlinear identification of IPMC actuators based on ANFIS–NARX paradigm. Sens Actuators A 209:140–148 · DOI
  2. Annabestani M, Naghavi N (2014b) Nonuniform deformation and curvature identification of ionic polymer metal composite actuators. J Intell Mater Syst Struct 26(6):1–17
  3. Annabestani M, Naghavi N, Maymandi-Nejad M (2016a) Non-autoregressive nonlinear identification of IPMC in large deformation situations using generalized Volterra-based approach. IEEE Trans Instrum Meas PP(99):1–7
  4. Annabestani M, Maymandi-Nejad M, Naghavi N (2016b) Restraining IPMC back relaxation in large bending displacements: applying non-feedback local gaussian disturbance by patterned electrodes. IEEE Trans Electron Devices 63(4):1689–1695 · DOI
  5. Annabestani M, Naghavi N, Maymandi-Nejad M (2018) From modeling to implementation of a method for restraining back relaxation in ionic polymer–metal composite soft actuators. J Intell Mater Syst Struct 29(15):3124–3135 · DOI
  6. Annabestani M, Rowhanimanesh A, Rezaei A, Avazpour L, Sheikhhasani F (2019) A knowledge-based intelligent system for control of dirt recognition process in the smart washing machines. arXiv:1905.00607 [cs.AI] · arXiv
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  9. Choy SK, Lam SY, Yu KW, Lee WY, Leung KT (2017) Fuzzy model-based clustering and its application in image segmentation. Pattern Recognit 68:141–157 · DOI
  10. Das P, Mukherjee S (2017) Designing a fuzzy approach for modelling the performance evaluation of education service providers. Int J Serv Oper Manag 26(1):49–67
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  14. Hatami-Marbini A, Kangi F (2017) An extension of fuzzy TOPSIS for a group decision making with an application to Tehran stock exchange. Appl Soft Comput 52:1084–1097 · DOI
  15. Ingoley SN, Bakal (2012) Students performance evaluation using fuzzy logic. Presented at the proceedings of IEEE Nirma University international conference on engineering (NUICONE), Ahmadabad · DOI
  16. Jamsandekar S, Mudholkar R (2013) Performance evaluation by fuzzy inference technique. Int J Soft Comput Eng 3(2):158–164
  17. Jyothi G, Parvathi C, Srinivas P, Rahaman SA (2014) Fuzzy expert model for evaluation of faculty performance in technical educational institutions. Int J Eng Res Appl 4(5):41–50
  18. Kharola A, Kunwar S, Choudhury GB (2015) Students performance evaluation: a fuzzy logic reasoning approach. PM World J 4(9):1–11
  19. Liu X, Gao Z, Chen MZQ (2017) Takagi–Sugeno fuzzy model based fault estimation and signal compensation with application to wind turbines. IEEE Trans Ind Electron 64(7):5678–5689 · DOI
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  21. Nagy MT, Kos ZA, Biro D, Vicsek TS (2010) Hierarchical group dynamics in pigeon flocks. Nature 464:890–894 · DOI · Paper page
  22. Nunes M, O’Neill H (2011) Team performance evaluation using fuzzy logic. In: Fuzzy logic and applications, pp 130–146. https://doi.org/10.1007/978-3-642-23713-3_18 · DOI
  23. Pangaro LN (2000) Investing in descriptive evaluation: a vision for the future of assessment. Med Teach 22(5):478–481 · DOI
  24. Rasmani KA, Shen Q (2006) Data-driven fuzzy rule generation and its application for student academic performance evaluation. Appl Intell 25(3):305–319 · DOI
  25. Wang L-X (1996) A course in fuzzy systems and control, 1st edn. Prentice Hall, Upper Saddle River
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  27. Yadav RS, Soni AK, Pal S (2014) A study of academic performance evaluation using fuzzy logic techniques. Presented at the proceedings of IEEE international conference on computing for sustainable global development (INDIACom) · DOI
  28. Yildiz Z, Baba AF (2014) Evaluation of student performance in laboratory applications using fuzzy decision support system model. Presented at the proceedings of IEEE global engineering education conference (EDUCON), Istanbul · DOI
  29. Zimmermann H-J (2001) Fuzzy set theory—and its applications. Springer, Netherlands · DOI

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Metadata

Authors
Annabestani, M., Rowhanimanesh, A., Mizani, A., & Rezaei, A.
Affiliations
  • Sharif University of Technology (IR)
  • University of Neyshabur (IR)
  • Kharazmi University (IR)
  • Islamic Azad University, Mashhad (IR)
Corresponding author
Annabestani, Mohsen
ORCID
Venue
Soft Computing
Publisher
Springer Science and Business Media LLC
Published
2019-05-20
Volume
24
Issue
4
Pages
3025-3035
ISSN
  • 1432-7643 (ISSN-L)
  • 1433-7479
DOI
10.1007/s00500-019-04078-0
OpenAlex
W2946139234
Cited by
19
References
1
Citations by year
2025: 2 · 2023: 4 · 2022: 4 · 2021: 5 · 2020: 4
FWCI
3.84
Type
journal article
Language
en
Open access
Licence
Locations
  • Soft Computing (publishedVersion)
  • arXiv (Cornell University) (submittedVersion)
  • arXiv (Cornell University)
Topics
  • Physical Sciences › Computer Science › Information Systems › Educational Technology and Assessment
  • Physical Sciences › Computer Science › Artificial Intelligence › Edcuational Technology Systems
  • Social Sciences › Education › Student Assessment and Feedback
Keywords
student assessment, fuzzy logic, infinite-valued logic, approximate reasoning, grading fairness, primary school mathematics, third grade, Iranian education system

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