Please use this identifier to cite or link to this item: https://rfos.fon.bg.ac.rs/handle/123456789/1262
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dc.creatorVukićević, Milan
dc.creatorRadovanović, Sandro
dc.creatorMilovanović, Miloš
dc.creatorMinović, Miroslav
dc.date.accessioned2023-05-12T10:47:21Z-
dc.date.available2023-05-12T10:47:21Z-
dc.date.issued2014
dc.identifier.issn1537-744X
dc.identifier.urihttps://rfos.fon.bg.ac.rs/handle/123456789/1262-
dc.description.abstractRapid growth and storage of biomedical data enabled many opportunities for predictive modeling and improvement of healthcare processes. On the other side analysis of such large amounts of data is a difficult and computationally intensive task for most existing data mining algorithms. This problem is addressed by proposing a cloud based system that integrates metalearning framework for ranking and selection of best predictive algorithms for data at hand and open source big data technologies for analysis of biomedical data.en
dc.publisherHindawi Ltd, London
dc.relationinfo:eu-repo/grantAgreement/MESTD/Integrated and Interdisciplinary Research (IIR or III)/41008/RS//
dc.relationinfo:eu-repo/grantAgreement/MESTD/Technological Development (TD or TR)/32013/RS//
dc.rightsopenAccess
dc.rights.urihttps://creativecommons.org/licenses/by/4.0/
dc.sourceScientific World Journal
dc.titleCloud Based Metalearning System for Predictive Modeling of Biomedical Dataen
dc.typearticle
dc.rights.licenseBY
dc.citation.rankM21
dc.identifier.doi10.1155/2014/859279
dc.identifier.fulltexthttp://prototype2.rcub.bg.ac.rs/bitstream/id/148/1258.pdf
dc.identifier.pmid24892101
dc.identifier.rcubconv_1635
dc.identifier.scopus2-s2.0-84900023007
dc.identifier.wos000335048400001
dc.type.versionpublishedVersion
item.cerifentitytypePublications-
item.fulltextWith Fulltext-
item.grantfulltextopen-
item.openairetypearticle-
item.openairecristypehttp://purl.org/coar/resource_type/c_18cf-
Appears in Collections:Radovi istraživača / Researchers’ publications
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