A model for predicting drying time period of wool yarn bobbins using computational intelligence techniques

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.