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Using principal component analysis and elastic net in logistic regression to identify the location of objects in EIT

cris.lastimport.scopus2024-09-18T01:31:31Z
dc.abstract.enThis paper presents the research results on the use of machine learning algorithms and electrical tomography to detect moisture in the tank. The article presents methods such as principal component analysis and elastic net in logistic regression, for identifying object locations. Tomographic methods show a spatial image of the interior, not individual points of the examined cross-section. Previous studies have shown that the choice of machine learning model has a significant impact on the quality of the results obtained. Machine learning is more likely to provide accurate tomogram reconstructions than traditional mathematical methods. In this study, linear regression models performed slightly worse than neural networks. A specially developed numerical model was used in this study. The characteristic feature of the analyzed solution is the partition of the modeled object into a set of elements using a specially developed mesh.
dc.affiliationTransportu i Informatyki
dc.contributor.authorKrzysztof Król
dc.contributor.authorTomasz Rymarczyk
dc.contributor.authorE Kozłowski
dc.contributor.authorKonrad Niderla
dc.date.accessioned2024-08-06T12:05:57Z
dc.date.available2024-08-06T12:05:57Z
dc.date.issued2022
dc.identifier.doi10.1088/1742-6596/2408/1/012025
dc.identifier.eissn1742-6596
dc.identifier.issn1742-6588
dc.identifier.urihttps://repo.akademiawsei.eu/handle/item/570
dc.pbn.affiliationinformation and communication technology
dc.relation.ispartofJournal of Physics: Conference Series
dc.rightsCC-BY
dc.subject.enelastic net
dc.subject.enlogistic regression
dc.subject.enidentify
dc.titleUsing principal component analysis and elastic net in logistic regression to identify the location of objects in EIT
dc.typeReviewArticleConference
dspace.entity.typePublication
oaire.citation.issue1
oaire.citation.volume2408