Semiring-valued fuzzy rough sets and colour segmentation
The result's identifiers
Result code in IS VaVaI
<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F61988987%3A17610%2F22%3AA2302DNQ" target="_blank" >RIV/61988987:17610/22:A2302DNQ - isvavai.cz</a>
Result on the web
<a href="https://link.springer.com/chapter/10.1007/978-3-031-13448-7_4" target="_blank" >https://link.springer.com/chapter/10.1007/978-3-031-13448-7_4</a>
DOI - Digital Object Identifier
<a href="http://dx.doi.org/10.1007/978-3-031-13448-7_4" target="_blank" >10.1007/978-3-031-13448-7_4</a>
Alternative languages
Result language
angličtina
Original language name
Semiring-valued fuzzy rough sets and colour segmentation
Original language description
Many of the new fuzzy structures with complete $MV$-algebras as value sets, such as hesitant, intuitionistic, neutrosophic, or fuzzy soft sets, can be transformed into one common type of fuzzy sets with values in special semirings. We use this transformation of fuzzy structures to unify the theory of $R$-fuzzy rough sets with these new fuzzy structures. For this purpose, we use the $({cal R}_2,{cal R}_2^*)$-fuzzy rough set defined for fuzzy soft set and $({cal R}_1,{cal R}_1^*)$-fuzzy rough sets defined for intuitionistic fuzzy sets. We also show how this general theory can be used to determine the upper and lower approximations of a colour segment corresponding to a particular colour.
Czech name
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Czech description
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Classification
Type
D - Article in proceedings
CEP classification
—
OECD FORD branch
10102 - Applied mathematics
Result continuities
Project
<a href="/en/project/EF17_049%2F0008414" target="_blank" >EF17_049/0008414: Centre for the development of Artificial Intelligence Methods for the Automotive Industry of the region</a><br>
Continuities
P - Projekt vyzkumu a vyvoje financovany z verejnych zdroju (s odkazem do CEP)
Others
Publication year
2022
Confidentiality
S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů
Data specific for result type
Article name in the collection
Torra, V., Narukawa, Y. (eds) Modeling Decisions for Artificial Intelligence. MDAI 2022. Lecture Notes in Computer Science(), vol 13408
ISBN
978-3-031-13447-0
ISSN
0302-9743
e-ISSN
1611-3349
Number of pages
13
Pages from-to
38-50
Publisher name
Springer
Place of publication
Cham
Event location
Sant Cugat, Catalonia, Spain
Event date
Aug 30, 2022
Type of event by nationality
WRD - Celosvětová akce
UT code for WoS article
000877020800004