Application of ANN modeling techniques in the prediction of the diameter of PCL/gelatin nanofibers in environmental and medical studies

Kalantary, Saba; Jahani, Ali; Pourbabaki, Reza; Beigzadeh, Zahra

doi:10.1039/c9ra04927d

Cited by 57 publications

(35 citation statements)

References 58 publications

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“…In general, decreasing fiber size is due to the fact that the surface of charge on the jet at higher voltage or field increased. This observation is similar to the previously published reports 21,48,49 . Figure 6(c) provides the effect of the injection rate on the diameter of nanofibers.…”

Section: Discussionsupporting

confidence: 93%

“…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. Researchers declared that the reliable results of the ANN in nanofiber studies are in the complex interactions between the variables which are influencing nanofiber formation 21 . However, the capability of ANN techniques, in nanofibers diameter prediction, has not been compared with classic regression methods such as MLR.…”

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confidence: 99%

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

Kalantary

Jahani

2020

Sci Rep

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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 (R 2 = 0.959) in comparison with MLR results (R 2 = 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.

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Section: Discussionsupporting

confidence: 93%

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confidence: 99%

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

Kalantary

Jahani

2020

Sci Rep

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“…Considering two classes of trees failure (0 and 1) in this research, we have two output layers in the structure of RBFNN. Gaussian function is the most frequently used function in the hidden layers of RBFNN 13 , 27 . The Gaussian function can find the center of circular classifiers successfully.…”

Section: Methodsmentioning

confidence: 99%

Modeling of trees failure under windstorm in harvested Hyrcanian forests using machine learning techniques

Jahani¹,

Saffariha

2021

Sci Rep

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In managed forests, windstorm disturbances reduce the yield of timber by imposing the costs of unscheduled clear-cutting or thinning operations. Hyrcanian forests are affected by permanent winds, with more than 100 km/h which cause damage forest trees and in result of the tree harvesting and gap creation in forest stands, many trees failure accidents happen annually. Using machine learning approaches, we aimed to compare the multi-layer perceptron (MLP) neural network, radial basis function neural network (RBFNN) and support vector machine (SVM) models for identifying susceptible trees in windstorm disturbances. Therefore, we recorded 15 variables in 600 sample plots which are divided into two categories: 1. Stand variables and 2.Tree variables. We developed the tree failure model (TFM) by artificial intelligence techniques such as MLP, RBFNN, and SVM. The MLP model represents the highest accuracy of target trees classification in training (100%), test (93.3%) and all data sets (97.7%). The values of the mean of trees height, tree crown diameter, target tree height are prioritized respectively as the most significant inputs which influence tree susceptibility in windstorm disturbances. The results of MLP modeling defined TFMmlp as a comparative impact assessment model in susceptible tree identification in Hyrcanian forests where the tree failure is in result of the susceptibility of remained trees after wood harvesting. The TFMmlp is applicable in Hyrcanian forest management planning for wood harvesting to decrease the rate of tree failure after wood harvesting and a tree cutting plan could be modified based on designed environmental decision support system tool to reduce the risk of trees failure in wind circulations.

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“…Model accuracy was estimated based on the following indices: coefficient of determination (R 2 ), mean absolute error (MAE), and mean square error (MSE) (Eqs. 5 to 7 [ 50 – 52 ]). …”

Section: Methodsmentioning

confidence: 99%

“…Model accuracy was estimated based on the following indices: coefficient of determination (R 2 ), mean absolute error (MAE), and mean square error (MSE) (Eqs. 5 to 7 [50][51][52]). In these equations: O i : measured data, P i : predicted data, O ave : mean measured data, P ave : the average of predicted data and n: number of the samples.…”

Section: Modeling S Limbata Seed Germinationmentioning

confidence: 99%

Seed germination prediction of Salvia limbata under ecological stresses in protected areas: an artificial intelligence modeling approach

Saffariha

Jahani²,

Potter

2020

BMC Ecol

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Background Salvia is a large, diverse, and polymorphous genus of the family Lamiaceae, comprising about 900 ornamentals, medicinal species with almost cosmopolitan distribution in the world. The success of Salvia limbata seed germination depends on a numerous ecological factors and stresses. We aimed to analyze Salvia limbata seed germination under four ecological stresses of salinity, drought, temperature and pH, with application of artificial intelligence modeling techniques such as MLR (Multiple Linear Regression), and MLP (Multi-Layer Perceptron). The S.limbata seeds germination was tested in different combinations of abiotic conditions. Five different temperatures of 10, 15, 20, 25 and 30 °C, seven drought treatments of 0, −2, −4, −6, −8, −10 and −12 bars, eight treatments of salinity containing 0, 50, 100.150, 200, 250, 300 and 350 mM of NaCl, and six pH treatments of 4, 5, 6, 7, 8 and 9 were tested. Indeed 228 combinations were tested to determine the percentage of germination for model development. Results Comparing to the MLR, the MLP model represents the significant value of R2 in training (0.95), validation (0.92) and test data sets (0.93). According to the results of sensitivity analysis, the values of drought, salinity, pH and temperature are respectively known as the most significant variables influencing S. limbata seed germination. Areas with high moisture content and low salinity in the soil have a high potential to seed germination of S. limbata. Also, the temperature of 18.3 °C and pH of 7.7 are proposed for achieving the maximum number of germinated S. limbata seeds. Conclusions Multilayer perceptron model helps managers to determine the success of S.limbata seed planting in agricultural or natural ecosystems. The designed graphical user interface is an environmental decision support system tool for agriculture or rangeland managers to predict the success of S.limbata seed germination (percentage) in different ecological constraints of lands.

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Application of ANN modeling techniques in the prediction of the diameter of PCL/gelatin nanofibers in environmental and medical studies

Cited by 57 publications

References 58 publications

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

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

Modeling of trees failure under windstorm in harvested Hyrcanian forests using machine learning techniques

Seed germination prediction of Salvia limbata under ecological stresses in protected areas: an artificial intelligence modeling approach

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