A model for predicting drying time period of wool yarn bobbins using computational intelligence techniques
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Date
2015
Journal Title
Journal ISSN
Volume Title
Publisher
Sage Publications Ltd
Access Rights
info:eu-repo/semantics/closedAccess
Abstract
In this study, a predictive model has been developed using computational intelligence techniques for the prediction of drying time in the wool yarn bobbin drying process. The bobbin drying process is influenced by various drying parameters, 19 of which were used as input variables in the dataset. These parameters affect the drying time of yarn bobbins, which is considered as the target variable. The dataset, which consists of these input and target variables, was collected from an experimental yarn bobbin drying system. Firstly, the most effective input variables on the target variable, named as the best feature subset of the dataset, were investigated by using a filter-based feature selection method. As a result, the most important five parameters were obtained as the best feature subset. Afterwards, the most successful method that can predict the drying time of wool yarn bobbins with the highest accuracy was explored amongst the 16 computational intelligence methods for the best feature subset. Finally, the best performance has been found by the REP tree method, which achieved minimum error and time taken to build the model.
Description
Keywords
prediction of drying time, wool, bobbin, feature selection, machine learning regression method, REP tree method
Journal or Series
Textile Research Journal
WoS Q Value
Q2
Scopus Q Value
Q2
Volume
85
Issue
13
