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https://hdl.handle.net/1822/50851
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Campo DC | Valor | Idioma |
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dc.contributor.author | Silva, Nuno Alberto Ribeiro | por |
dc.contributor.author | Soares, João Paulo Conceição | por |
dc.contributor.author | Shah, Vaibhav | por |
dc.contributor.author | Santos, Maribel Yasmina | por |
dc.contributor.author | Rodrigues, Helena | por |
dc.date.accessioned | 2018-02-22T11:55:13Z | - |
dc.date.available | 2018-02-22T11:55:13Z | - |
dc.date.issued | 2017 | - |
dc.identifier.citation | Nuno Silva, João Soares, Vaibhav Shah, Maribel Yasmina Santos, Helena Rodrigues, Anomaly Detection in Roads with a Data Mining Approach, Procedia Computer Science, Volume 121, 2017, Pages 415-422, ISSN 1877-0509 | por |
dc.identifier.issn | 1877-0509 | - |
dc.identifier.uri | https://hdl.handle.net/1822/50851 | - |
dc.description.abstract | Road condition has an important role in our daily live. Anomalies in road surface can cause accidents, mechanical failure, stress and discomfort in drivers and passengers. Governments spend millions each year in roads maintenance for maintaining roads in good condition. But extensive maintenance work can lead to traffic jams, causing frustration in road users. In way to avoid problems caused by road anomalies, we propose a system that can detect road anomalies using smartphone sensors. The approach is based in data-mining algorithms to mitigate the problem of hardware diversity. In this work we used scikit-learn, a python module, and Weka, as tools for data-mining. All cleaning data process was made using python language. The final results show that it is possible detect road anomalies using only a smartphone. | por |
dc.description.sponsorship | European Structural and Investment Funds in the FEDER component, through the Operational Competitiveness and Internationalization Programme (COMPETE 2020) | por |
dc.description.sponsorship | This research is sponsored by the Portugal Incentive System for Research and Technological Development. Project in co-promotion nº 002797/2015 (INNOVCAR 2015-2018) | por |
dc.language.iso | eng | por |
dc.publisher | Elsevier 1 | por |
dc.relation | Project in co-promotion POCI-01-0247-FEDER-002797 | por |
dc.rights | openAccess | por |
dc.subject | Road anomalies | por |
dc.subject | Data mining | por |
dc.subject | Data analytics | por |
dc.subject | Data Analitics | por |
dc.title | Anomaly detection in roads with a data mining approach | por |
dc.type | conferencePaper | por |
dc.peerreviewed | yes | por |
dc.relation.publisherversion | https://www.sciencedirect.com/science/article/pii/S1877050917322494 | por |
oaire.citationStartPage | 415 | por |
oaire.citationEndPage | 422 | por |
oaire.citationVolume | 121 | por |
dc.identifier.doi | 10.1016/j.procs.2017.11.056 | por |
dc.subject.fos | Engenharia e Tecnologia::Engenharia Eletrotécnica, Eletrónica e Informática | por |
dc.description.publicationversion | info:eu-repo/semantics/publishedVersion | por |
dc.subject.wos | Science & Technology | por |
sdum.journal | Procedia Computer Science | por |
sdum.conferencePublication | CENTERIS 2017 - International Conference on ENTERprise Information Systems | por |
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cs_centeris_silva.pdf | 619,2 kB | Adobe PDF | Ver/Abrir |