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dc.creatorRajendran, D.
dc.creatorSasilatha, T.
dc.creatorRajendran, S.
dc.creatorSelvaraj, S.K.
dc.creatorLacnjevac, C.
dc.creatorPrabha, S.S.
dc.creatorRathish, R.J.
dc.date.accessioned2022-09-20T10:30:50Z
dc.date.available2022-09-20T10:30:50Z
dc.date.issued2022
dc.identifier.issn0351-9465
dc.identifier.urihttp://aspace.agrif.bg.ac.rs/handle/123456789/6171
dc.description.abstractArtificial intelligence is a branch of science concerned with teaching machines to think and act like humans. Machine learning is concerned with enabling computers to perform tasks without the need for explicit programming. Machine Learning enables computers to learn without the need for explicit programming. Machine Learning is a broad field that encompasses a wide range of machine learning operations such as clustering, classification, and the development of predictive models. Machine Learning (ML) and Deep Learning (DL) research is now finding a home in both industry and academia. Machine Learning technologies are increasingly being used in medical imaging. To detect tumours and other malignant growths in the human body. Deep Learning is making significant contributions to the advancement of industrial robotics. Machine learning algorithms are used in the self-driving car industry to guide the vehicle to its destination. Deep Learning and Machine Learning are also used in corrosion science and engineering. They are used to choose the inhibitor molecules from a large pool of available molecules. © 2022 Authors.
dc.languageEnglish
dc.rightsopenAccess
dc.rights.urihttps://creativecommons.org/licenses/by-nd/4.0/
dc.sourceMaterials Protection
dc.sourceMaterials Protection
dc.subjectAlgorithms
dc.subjectArtificial Intelligence
dc.subjectDeep learning
dc.subjectMachine learning
dc.subjectNeural Networks
dc.subjectThe Hidden Layer and The Output Layer
dc.subjectThe Input Layer
dc.titleApplication of machine learning in corrosion inhibition study
dc.typearticleen
dc.rights.licenseBY-ND
dc.citation.epage290
dc.citation.issue3
dc.citation.rankM52
dc.citation.spage280
dc.citation.volume63
dc.identifier.doi10.5937/zasmat2203280R
dc.identifier.fulltexthttp://aspace.agrif.bg.ac.rs/bitstream/id/24026/Application_of_machine_pub_2022.pdf
dc.type.versionpublishedVersion


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