Optimization of computational intelligence approach for the prediction of glutinous rice dehydration

Abstract

BackgroundFive computational intelligence approaches, namely: gaussian process regression (GPR), artificial neural network (ANN), decision tree (DT), ensemble of trees (EoT), and support vector machine (SVM) were used to describe the evolution of moisture during the dehydration process of glutinous rice. The hyperparameters of the models were optimized with three strategies i.e., Bayesian optimization, grid search and random search. To understand the parameters that facilitate intelligence model adaptation to the dehydration process, global sensitivity analysis (GSA) was used to compute the impact of the input variables on the model output.ResultThe result shows that the optimum computational intelligence techniques include the 3–9‐1 topology trained with Bayesian regulation function for ANN, gaussian kernel function for SVM, matern covariance function combined with zero mean function for the GPR, boosting method for EoT, and 4 minimum leaf size for DT models. The GPR has the highest performance with R2 of 100% and 99.71% during calibration and testing of the model respectively. The GSA reveals that all the models significantly rely on the variation in time as the main factor that affects the model outputs.ConclusionTherefore, the computational intelligence models especially the GPR can be applied for an effective description of moisture evolution during small‐scale and industrial dehydration of glutinous rice.This article is protected by copyright. All rights reserved.