MLR and ANN Approaches for Prediction of Synthetic/Natural Nanofibers Diameter in the Environmental and Medical Applications

Saba Kalantary,Ali Jahani,Reza Jahani

doi:10.1038/s41598-020-65121-x

Abstract

Fiber diameter plays an important role in the properties of electrospinning of nanofibers. However, one major problem is the lack of a comprehensive method that can link processing parameters to nanofibers’ diameter. The objective of this study is to develope an artificial neural network (ANN) modeling and multiple regression (MLR) analysis approaches to predict the diameter of nanofibers. Processing parameters, including weight ratio, voltage, injection rate, and distance, were considered as independent variables and the nanofiber diameter as the dependent variable of the ANN model. The results of ANN modeling, especially its high accuracy (R2 = 0.959) in comparison with MLR results (R2 = 0.564), introduced the prediction the diameter of nanofibers model (PDNFM) as a comparative model for predicting the diameter of poly (3-caprolactone) (PCL)/gelatin (Gt) nanofibers. According to the result of sensitivity analysis of the model, the values of weight ratio, distance, injection rate, and voltage, respectively, were identified as the most significant parameters which influence PDNFM.

Highlights

Www.nature.com/scientificreports increases by using multiple regression (MLR) while it declines when independent variables increase
The objective of this research is to compare the classical regression method with a multilayer perceptron artificial neural network (MLP) for predicting the diameter of PCL and gelatin (PCL/Gt) nanofibers electrospinning and developing a probabilistic model to predict the diameter of PCL/Gt nanofibers (PDNF) using objective criteria
The results obtained of the artificial neural network (ANN) modeling, especially its high accuracy (R2 = 0.959) in comparison with MLR results (R2 = 0.564), introduced PDNFMmlp as a comparative model for predict the diameter of PCL/Gt nanofibers

Summary

Introduction

Www.nature.com/scientificreports increases by using MLR while it declines when independent variables increase. In recent years ANN approach as one of the most popular artificial intelligence approaches has been used to model the electrospinning technique, mostly aimed at predicting the diameter of nanofibers electrospinning[16,20]. The accuracy of multilayer perceptron artificial neural network (MLP) in comparison with other ANN techniques such as Radial Basis Function (RBF) and Support Vector Machine (SVM) in nanofibers diameter prediction has been proved in recent researches. The capability of ANN techniques, in nanofibers diameter prediction, has not been compared with classic regression methods such as MLR. The objective of this research is to compare the classical regression method with a multilayer perceptron artificial neural network (MLP) for predicting the diameter of PCL/Gt nanofibers electrospinning and developing a probabilistic model to predict the diameter of PCL/Gt nanofibers (PDNF) using objective criteria

Objectives

Methods

Results

Discussion

Conclusion