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American Journal of Applied Mathematics and Statistics. 2022, 10(1), 1-3
DOI: 10.12691/AJAMS-10-1-1
Original Research

Application of Soft Sets to Assessment Processes

Michael Gr. Voskoglou1,

1Department of Mathematical Sciences, Graduate Technological Educational Institute of Western Greece, Patras, Greece

Pub. Date: January 09, 2022
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Cite this paper

Michael Gr. Voskoglou. Application of Soft Sets to Assessment Processes. American Journal of Applied Mathematics and Statistics. 2022; 10(1):1-3. doi: 10.12691/AJAMS-10-1-1

Abstract

From the time that Zadeh introduced the concept of fuzzy set in 1965 a lot of research has been carried out for generalizing and extending the corresponding theory on the purpose of tackling more effectively the existing in real life uncertainty. One such generalization is the concept of soft set aiming, among others, to overcome the existing difficulty of defining properly the membership function of a fuzzy set. A new model using soft sets is presented in this paper for assessing human-machine performance in a parametric manner and examples are given to illustrate its applicability in practice. Such kind of models are very useful when the assessment has qualitative rather than quantitative characteristics.

Keywords

fuzzy sets, soft sets, fuzzy assessment methods

Copyright

Creative CommonsThis work is licensed under a Creative Commons Attribution 4.0 International License. To view a copy of this license, visit http://creativecommons.org/licenses/by/4.0/

References

[1]  Zadeh, L.A., Fuzzy Sets, Information and Control, 8, 338-353, 1965.
 
[2]  Voskoglou, M.Gr., Generalizations of Fuzzy Sets and Related Theories, in M. Voskoglou (Ed.), An Essential Guide to Fuzzy Systems, 345-352, Nova Publishers, N.Y., 2019.
 
[3]  Molodtsov, D., Soft Set Theory-First Results, Computers and Mathematics with Applications, 37(4-5), 19-31, 1999.
 
[4]  Tripathy, B.K., Arun, K.R., Soft Sets and Its Applications, in J.S. Jacob (Ed.), Handbook of Research on Generalized and Hybrid Set Structures and Applications for Soft Computing, 65-85, IGI Global, Hersey, PA, 2016.
 
[5]  Voskoglou, M.Gr., Finite Markov Chain and Fuzzy Logic Assessment Models: Emerging Research and Opportunities, Createspace.com – Amazon, Columbia, SC, 2017.
 
[6]  Voskoglou, M.Gr., Methods for Assessing Human-Machine Performance under Fuzzy Conditions, Mathematics, 7(3), article 230, 2019.
 
[7]  Voskoglou, M.Gr., Salem, A.-B., Analogy-Based and Case-Based Reasoning: Two Sides of the Same Coin, International Journal of Applications of Fuzzy Sets and Artificial Intelligence, 4, 5-51, 2014.