Traditional and innovative approaches for detecting hazardous liquids

Basit Öğe Kaydı
dc.authorscopusid57323751100
dc.authorscopusid48862103700
dc.contributor.authorEfeoglu, E.
dc.contributor.authorTuna, G.
dc.date.accessioned2024-06-13T20:16:05Z
dc.date.available2024-06-13T20:16:05Z
dc.date.issued2021
dc.departmentİstanbul Gedik Üniversitesi
dc.description.abstractIn this chapter, traditional and innovative approaches used in hazardous liquid detection are reviewed, and a novel approach for the detection of hazardous liquids is presented. The proposed system is based on electromagnetic response measurements of liquids in the microwave frequency band. Thanks to this technique, liquid classification can be made quickly without pouring the liquid from its bottle and without opening the lid of its bottle. The system can detect solutions with hazardous liquid concentrations of 70% or more, as well as pure hazardous liquids. Since it relies on machine learning methods and the success of all machine learning methods depends on provided data type and dataset, a performance evaluation study has been carried out to find the most suitable method. In the performance evaluation study naive Bayes and sequential minimal optimization has been evaluated, and the results have shown that naive Bayes is more suitable for liquid classification. © 2021 by IGI Global.
dc.identifier.doi10.4018/978-1-7998-6870-5.ch020
dc.identifier.endpage309
dc.identifier.isbn9781799868729
dc.identifier.scopus2-s2.0-85118521630
dc.identifier.scopusqualityN/A
dc.identifier.startpage290
dc.identifier.urihttps://doi.org/10.4018/978-1-7998-6870-5.ch020
dc.identifier.urihttps://hdl.handle.net/11501/1034
dc.indekslendigikaynakScopus
dc.language.isoen
dc.publisherIGI Global
dc.relation.ispartofHandbook of Research on Innovations and Applications of AI, IoT, and Cognitive Technologies
dc.relation.publicationcategoryKitap Bölümü - Uluslararası
dc.rightsinfo:eu-repo/semantics/closedAccess
dc.titleTraditional and innovative approaches for detecting hazardous liquids
dc.typeBook Chapter

Dosyalar

Koleksiyon

Scopus İndeksli Yayınlar Koleksiyonu