p) 2010 - 16 Documents

Permanent URI for this communityhttps://dspace.psgrkcw.com/handle/123456789/4789

Browse

Author: search.filters.author.Karpagavalli SHas files: Yes

Search Results

Now showing 1 - 2 of 2
  • Thumbnail Image
    Item
    SUPERVISED LEARNING APPROACH FOR PREDICTING THE QUALITY OF COTTON USING WEKA
    (Springer Link, 2010) Selvanayaki M; Vijaya M S; Jamuna K S; Karpagavalli S
    Cotton is the world’s most important natural fibre used in Textile manufacturing. Cotton fiber is processed into yarn and fabric. Yarn strength depends extremely on the quality of cotton. The physical characteristics 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 the quality of cotton. Hence determining the quality of cotton accurately is an essential task to make better raw material choices in textile industry which in turn will support better buying and selling decisions. In this work, cotton quality prediction is modeled as classification task and implemented using supervised learning algorithms namely Multilayer Perceptron, Naive Bayes, J48 Decision tree, k-nearest neighbor in WEKA environment on the cotton quality assessment dataset. The classification models have been trained using the data collected from a spinning mill. The prediction accuracy of the classifiers is evaluated using 10-fold cross validation and the results are compared. It is observed that the model based on decision tree classifier produces high predictive accuracy compared to other models.
  • No Thumbnail Available
    Item
    AN INTERACTIVE TOOL FOR YARN STRENGTH PREDICTION USING SUPPORT VECTOR REGRESSION
    (IEEE Xplore, 2010-05-06) Selvanayaki M; Vijaya M S; Jamuna K S; Karpagavalli S
    Cotton, 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.