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dc.contributor.authorVladik Kreinovichen_US
dc.contributor.authorRujira Ouncharoenen_US
dc.date.accessioned2018-09-04T10:11:44Z-
dc.date.available2018-09-04T10:11:44Z-
dc.date.issued2015-12-01en_US
dc.identifier.issn02184885en_US
dc.identifier.other2-s2.0-84954042264en_US
dc.identifier.other10.1142/S0218488515400061en_US
dc.identifier.urihttps://www.scopus.com/inward/record.uri?partnerID=HzOxMe3b&scp=84954042264&origin=inwarden_US
dc.identifier.urihttp://cmuir.cmu.ac.th/jspui/handle/6653943832/54312-
dc.description.abstract© 2015 World Scientific Publishing Company. In some practical situations - e.g., when treating a new illness - we do not have enough data to make valid statistical conclusions. In such situations, it is necessary to use expert knowledge - and thus, it is beneficial to use fuzzy techniques that were specifically designed to process such knowledge. At first glance, it may seem that in situations when we have large amounts of data, the relative importance of expert knowledge should decrease. However, somewhat surprisingly, it turns out that expert knowledge is still very useful in the current age of big data. In this paper, we explain how exactly (and why) expert knowledge is useful, and we overview efficient methods for processing this knowledge. This overview is illustrated by examples from environmental science, geosciences, engineering (in particular, aircraft maintenance and underwater robots), and medicine.en_US
dc.subjectComputer Scienceen_US
dc.subjectEngineeringen_US
dc.titleFuzzy (and Interval) techniques in the age of big data: An overview with applications to environmental science, geosciences, engineering, and medicineen_US
dc.typeJournalen_US
article.title.sourcetitleInternational Journal of Uncertainty, Fuzziness and Knowlege-Based Systemsen_US
article.volume23en_US
article.stream.affiliationsUniversity of Texas at El Pasoen_US
article.stream.affiliationsChiang Mai Universityen_US
Appears in Collections:CMUL: Journal Articles

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