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Artificial intelligence elements in data mining from remote experiments

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dc.title Artificial intelligence elements in data mining from remote experiments en
dc.contributor.author Pálka, Lukáš
dc.contributor.author Schauer, František
dc.contributor.author Zelinka, Ivan
dc.relation.ispartof Advances in Intelligent Systems and Computing
dc.identifier.issn 2194-5357 Scopus Sources, Sherpa/RoMEO, JCR
dc.identifier.isbn 978-3-319-07400-9
dc.date.issued 2014
utb.relation.volume 289
dc.citation.spage 421
dc.citation.epage 428
dc.event.title International conference on prediction, modeling and analysis of complex systems, NOSTRADAMUS 2014
dc.event.location Ostrava
utb.event.state-en Czech Republic
utb.event.state-cs Česká republika
dc.event.sdate 2012-06-23
dc.event.edate 2012-06-25
dc.type conferenceObject
dc.language.iso en
dc.publisher Springer-Verlag
dc.identifier.doi 10.1007/978-3-319-07401-6_42
dc.relation.uri https://link.springer.com/chapter/10.1007/978-3-319-07401-6_42
dc.subject Analysis data en
dc.subject Data mining en
dc.subject ISES en
dc.subject Measureserver en
dc.subject Remote experiment en
dc.description.abstract In spite of the fact that remote laboratories have been existing for at least three decades, virtually no attention has been devoted to the accumulated data analysis of this new means of education. The paper deals with the data analysis, gathered in the Datacentre (DTC) implemented with the Laboratory Management System (RLMS), connected in turn to remote laboratories and remote experiments. In particular, we concentrate and describe a new model of experiment data analysis, based on the principles of artificial intelligence, based on the criterion function in need. The leading idea of the model functioning is during the procedure of rig(s) recognition i.e Data weighting: Data recognition: Data preparation: Phenomenon modelling: Model and measurement data comparison: Result deployment, where the artificial intellingence is integrated with steps of Data weighting by association and regression using neuron network. Benefit of the suggested method is its speed and efficiency and thus using it for the optimization of individual remote experiments and ther efficiency. Paper may serve as an inspiring source for the development in the field of remote laboratories, but it may influence in the similar areas of data mining. © Springer International Publishing Switzerland 2014 en
utb.faculty Faculty of Applied Informatics
dc.identifier.uri http://hdl.handle.net/10563/1004564
utb.identifier.scopus 2-s2.0-84927591608
utb.source d-scopus
dc.date.accessioned 2015-05-28T11:39:22Z
dc.date.available 2015-05-28T11:39:22Z
utb.contributor.internalauthor Pálka, Lukáš
utb.contributor.internalauthor Schauer, František
utb.contributor.internalauthor Zelinka, Ivan
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