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Improving the tomographic image by enhancing the machine learning algorithm

cris.lastimport.scopus2024-09-18T01:30:59Z
dc.abstract.enHyperparameter optimization in machine learning models may help enhance the efficiency of obtaining high-quality tomographic pictures, the purpose of this paper. In the discipline of electrical impedance tomography, machine learning techniques are utilized to translate voltage measurements into reconstruction pictures. Because of this, the so-called "inverse problem" arises, whereby the optimal answer must be sought. Effective machine learning relies heavily on the appropriate choice of model coefficients (hyperparameters). As a consequence, the strategies used to improve this choice have an indirect effect on the final reconstruction. The k-nearest neighbors strategy may be utilized to improve a machine learning model based on linear regression and classification models, as we show in this paper. Electrical tomography, a technology that analyses flood embankments from the interior to measure their structural integrity, makes use of the methods outlined above. The data gathered shows that the suggested solutions work.
dc.affiliationTransportu i Informatyki
dc.contributor.authorTomasz Rymarczyk
dc.contributor.authorG Kłosowski
dc.contributor.authorE Kozłowski
dc.contributor.authorJan Sikora
dc.contributor.authorPrzemysław Adamkiewicz
dc.date.accessioned2024-04-11T07:31:20Z
dc.date.available2024-04-11T07:31:20Z
dc.date.issued2022
dc.description.abstract<jats:title>Abstract</jats:title> <jats:p>Hyperparameter optimization in machine learning models may help enhance the efficiency of obtaining high-quality tomographic pictures, the purpose of this paper. In the discipline of electrical impedance tomography, machine learning techniques are utilized to translate voltage measurements into reconstruction pictures. Because of this, the so-called "inverse problem" arises, whereby the optimal answer must be sought. Effective machine learning relies heavily on the appropriate choice of model coefficients (hyperparameters). As a consequence, the strategies used to improve this choice have an indirect effect on the final reconstruction. The <jats:italic>K</jats:italic>-nearest neighbors strategy may be utilized to improve a machine learning model based on linear regression and classification models, as we show in this paper. Electrical tomography, a technology that analyses flood embankments from the interior to measure their structural integrity, makes use of the methods outlined above. The data gathered shows that the suggested solutions work.</jats:p>
dc.identifier.doi10.1088/1742-6596/2408/1/012020
dc.identifier.issn1742-6588
dc.identifier.issn1742-6596
dc.identifier.urihttps://repo.akademiawsei.eu/handle/item/181
dc.languageen
dc.pbn.affiliationinformation and communication technology
dc.relation.ispartofJournal of Physics: Conference Series
dc.rightsCC-BY
dc.subject.entomographic image
dc.subject.enmachine learning algorithm
dc.titleImproving the tomographic image by enhancing the machine learning algorithm
dc.typeReviewArticle
dspace.entity.typePublication
oaire.citation.issue1
oaire.citation.volume2408