Please use this identifier to cite or link to this item: http://localhost:8080/xmlui/handle/123456789/2189
Full metadata record
DC FieldValueLanguage
dc.contributor.authorSelvanayaki M-
dc.contributor.authorVijaya M S-
dc.contributor.authorJamuna K S-
dc.date.accessioned2020-10-13T04:55:11Z-
dc.date.available2020-10-13T04:55:11Z-
dc.date.issued2010-
dc.identifier.isbnPrint:978-1-4244-6006-9-
dc.identifier.isbnOnline:978-1-4244-6007-6-
dc.identifier.urihttps://ieeexplore.ieee.org/document/5460714-
dc.identifier.urihttp://localhost:8080/xmlui/handle/123456789/2189-
dc.description.abstractCotton, popularly known as White Gold has been an important commercial crop of National significance due to the immense influence of its rural economy. Transfer of technology to identify the quality of fibre is gaining importance. The physical characteristics of cotton such as fiber length, length distribution, trash value, color grade, strength, shape, tenacity, density, moisture absorption, dimensional stability, resistance, thermal reaction, count, etc., contributes to determine the quality of cotton and in turn yarn strength. In this paper yarn strength prediction has been modeled using regression. Support Vector regression, the supervised machine learning technique has been employed for predicting the yarn strength. The trained model was evaluated based on mean squared error and correlation coefficient and was found that the prediction accuracy of SVR based model, the intelligence reasoning method is higher compared with the traditional statistical regression, the least square regression model.en_US
dc.language.isoenen_US
dc.publisherCPS and indexed in Thompson CSIen_US
dc.subjectSupport Vector Machineen_US
dc.subjectSupport Vector Regressionen_US
dc.subjectYarn Strengthen_US
dc.subjectLeast Square Regressionen_US
dc.titleAN INTERACTIVE TOOL FOR YARN STRENGTH PREDICTION USING SUPPORT VECTOR REGRESSIONen_US
dc.title.alternativeMachine Learning Computingen_US
dc.typeBooken_US
Appears in Collections:International Conference

Files in This Item:
File Description SizeFormat 
AN INTERACTIVE TOOL FOR YARN STRENGTH PREDICTION USING SUPPORT VECTOR REGRESSION.docx10.46 kBMicrosoft Word XMLView/Open


Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.