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dc.contributor.authorNorasmadi, Abdul Rahim
dc.contributor.authorPandian, Paulraj Murugesa, Prof. Dr.
dc.contributor.authorAbd Hamid, Adom, Prof. Dr.
dc.date.accessioned2014-02-28T01:21:33Z
dc.date.available2014-02-28T01:21:33Z
dc.date.issued2013
dc.identifier.citationProcedia Engineering, vol. 53, 2013, page 728en_US
dc.identifier.isbn978-162748634-7
dc.identifier.issn1877-7058
dc.identifier.urihttp://www.sciencedirect.com/science/article/pii/S1877705813010497
dc.identifier.urihttp://dspace.unimap.edu.my:80/dspace/handle/123456789/32208
dc.descriptionLink to publisher's homepage at http://www.elsevier.com/en_US
dc.description.abstractProfoundly hearing impaired community (PHIC) cannot moderate wisely an acoustic noise ema- nated from moving vehicle in outdoor. They are not able to distinguish either type or distance of moving vehicle approaching from behind. Therefore, the PHIC encounter risky situation while they are in outdoor. In this paper, a simple system has been proposed to identify the type and distance of a moving vehicle using adaptive boosting (AdaBoost) ensemble method. One-third-octave filter band approach has been used for extracting the significant features from the noise emanated by the moving vehicle. The extracted features were associated with the type and distance of the moving vehicle. A support vector machines (SVM) has been used as a weak classifer during the AdaBoost classification. The AdaBoost classification system outperforms the single classifier system in terms of classification accuracy.en_US
dc.language.isoenen_US
dc.publisherElsevier Ltden_US
dc.subjectMoving vehicleen_US
dc.subjectAdaptive boostingen_US
dc.subjectSupport vector machineen_US
dc.subjectOne-third-octaveen_US
dc.titleErratum: Adaptive boosting with SVM classifier for moving vehicle classificationen_US
dc.typeArticleen_US
dc.contributor.urlnorasmadi@unimap.edu.myen_US
dc.contributor.urlpaul@unimap.edu.myen_US
dc.contributor.urlabdhamid@unimap.edu.myen_US


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