A COMPARITIVE STUDY OF FABRIC DETECTION AND CLASSIFICATION USING EFFICIENT HYBRID LEARNING ALGORITHM.
Textile industries are one among important industries that contribute to the GDP of a nation. Century after century there has been advancement in this particular industry. The revenue generated by this sector is decided by the quality of the fabric items. Any fabric that is defect less has a good re...
| Publicado en: | Turkish Journal of Physiotherapy Rehabilitation Vol. 32; no. 2; pp. 942 - 948 |
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| Autores principales: | , , , , |
| Formato: | equations & formulas pictorial research tables/charts Journal Article |
| Publicado: |
Turkish Journal of Physiotherapy & Rehabilitation
2021
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| Acceso en línea: | Ver este registro en EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=ccm&AN=151006053&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 151006053 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 13008757 YU1 jtl: Turkish Journal of Physiotherapy Rehabilitation issn: 13008757 maglogo: N pubinfo: dt: 2021 vid: 32 iid: 2 pid: 20392 pub: Turkish Journal of Physiotherapy & Rehabilitation place: Kizilay/ Ankara, <Blank> artinfo: ui: 151006053 151006053 151006053 151006053 ppf: 942 ppct: 6 formats: fmt: @attributes: type: P tig: atl: A COMPARITIVE STUDY OF FABRIC DETECTION AND CLASSIFICATION USING EFFICIENT HYBRID LEARNING ALGORITHM. aug: au: PAUL, ELDHO R. S., SABEENIAN M. E., PARAMASIVAM VALLIAPPAN, MURUGAPPAN KAVINKUMARK affil: Department of Electronics and Communication, Sona college of Technology, Salem sug: subj: Algorithms Learning Textiles Classification Textiles Evaluation Human Comparative Studies ab: Textile industries are one among important industries that contribute to the GDP of a nation. Century after century there has been advancement in this particular industry. The revenue generated by this sector is decided by the quality of the fabric items. Any fabric that is defect less has a good reception in the market and defective material produces only half of the production cost. Manual defect identification is a tiresome procedure where a individual is assigned to identify the defect in the running fabric. The success rate of such system is only 60%. An automated defect detection technique is the best solution for most of the textile industry as it produces around 96% efficiency in identifying the defect. The proposed algorithm is a new approach to embed with supervised and unsupervised learning classification techniques like support vector machine and K-Means clustering. The technique uses the GLCM texture features for the classification. The output depends on both of the classification results. The accuracy obtained in this embedded technique is 95%. pubtype: Academic Journal doctype: equations & formulas pictorial research tables/charts Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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