Please use this identifier to cite or link to this item: http://dspace.unimap.edu.my:80/xmlui/handle/123456789/33162
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dc.contributor.authorHariharan, Muthusamy, Dr.-
dc.contributor.authorKemal, Polatb-
dc.contributor.authorSindhu, Ravindran-
dc.contributor.authorSazali, Yaacob, Prof. Dr.-
dc.date.accessioned2014-03-27T06:47:45Z-
dc.date.available2014-03-27T06:47:45Z-
dc.date.issued2013-
dc.identifier.citationApplied Soft Computing Journal, vol. 13(10), 2013, pages 4148-4161en_US
dc.identifier.issn1568-4946-
dc.identifier.urihttp://www.sciencedirect.com/science/article/pii/S1568494613001932?via=ihub-
dc.identifier.urihttp://dspace.unimap.edu.my:80/dspace/handle/123456789/33162-
dc.descriptionLink to publisher's homepage at https://www.elsevier.com/en_US
dc.description.abstractAcoustical parameters extracted from the recorded voice samples are actively pursued for accurate detection of vocal fold pathology. Most of the system for detection of vocal fold pathology uses high quality voice samples. This paper proposes a hybrid expert system approach to detect vocal fold pathology using the compressed/low quality voice samples which includes feature extraction using wavelet packet transform, clustering based feature weighting and classification. In order to improve the robustness and discrimination ability of the wavelet packet transform based features (raw features), we propose clustering based feature weighting methods including k-means clustering (KMC), fuzzy c-means (FCM) clustering and subtractive clustering (SBC). We have investigated the effectiveness of raw and weighted features (obtained after applying feature weighting methods) using four different classifiers: Least Square Support Vector Machine (LS-SVM) with radial basis kernel, k-means nearest neighbor (kNN) classifier, probabilistic neural network (PNN) and classification and regression tree (CART). The proposed hybrid expert system approach gives a promising classification accuracy of 100% using the feature weighting methods and also it has potential application in remote detection of vocal fold pathology.en_US
dc.language.isoenen_US
dc.publisherElsevier B.V.en_US
dc.subjectClassificationen_US
dc.subjectCompressed voice samplesen_US
dc.subjectFeature extractionen_US
dc.subjectFeature weightingen_US
dc.subjectVocal fold pathologyen_US
dc.titleA hybrid expert system approach for telemonitoring of vocal fold pathologyen_US
dc.typeArticleen_US
dc.contributor.urlhari@unimap.edu.myen_US
dc.contributor.urls.yaacob@unimap.edu.myen_US
Appears in Collections:Hariharan Muthusamy, Dr.
Sazali Yaacob, Prof. Dr.
School of Microelectronic Engineering (Articles)
School of Mechatronic Engineering (Articles)

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