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

Kalantary, Saba; Jahani, Ali; Jahani, Reza

doi:10.1038/s41598-020-65121-x

Cited by 51 publications

(45 citation statements)

References 46 publications

(65 reference statements)

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“…As a part of this, the number of correctly classified data sets can be calculated, called accuracy (ACC), by using a CM, followed by error calculation (1‐ACC). In addition to that, precision, recall, and F‐Score are added to this calculation 25,26 . Good accuracy was achieved with all sets of classification.…”

Section: Resultsmentioning

confidence: 99%

“…20,21 The polysaccharide is a biocompatible biopolymer, possesses excellent mechanical properties. 22 There have been few attempts to model the fiber diameter and electrospinning process parameters [23][24][25][26][27][28] ; however, there is hardly any report to model the relationship between electrospinning process variables with fiber diameter and also the quality (poor, good, and fair) of polysaccharide. The fiber diameter and the quality depends on the several features, such as (i) the solution (e.g., conductivity, solvent volatility, viscosity, and surface tension); and (ii) electrospinning process parameters (e.g., collector rotating speed, flow rate, the distance between tip and collector, and applied voltage), and the temperature.…”

Section: Introductionmentioning

confidence: 99%

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Using artificial neural networks to model and interpret electrospun polysaccharide (Hylon VII starch) nanofiber diameter

Premasudha

Reddy

Lee

et al. 2020

J of Applied Polymer Sci

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Present work was aimed to develop an artificial neural networks (ANN) model to predict the polysaccharide-based biopolymer (Hylon VII starch) nanofiber diameter and classification of its quality (good, fair, and poor) as a function of polymer concentration, spinning distance, feed rate, and applied voltage during the electrospinning process. The relationship between diameter and its quality with process parameters is complex and nonlinear. The backpropagation algorithm was used to train the ANN model and achieved the classification accuracy, precision, and recall of 93.9%, 95.2%, and 95.2%, respectively. The average errors of the predicted fiber diameter for training and unseen testing data were found to be 0.05% and 2.6%, respectively. A stand-alone ANN software was designed to extract information on the electrospinning system from a small experimental database. It was successful in establishing the relationship between electrospinning process parameters and fiber quality and diameter. The yield of smaller diameter with good quality was favored by lower feed rate, lower polymer solution concentration, and higher applied voltage.

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Section: Resultsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Using artificial neural networks to model and interpret electrospun polysaccharide (Hylon VII starch) nanofiber diameter

Premasudha

Reddy

Lee

et al. 2020

J of Applied Polymer Sci

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“…The developed EDSS tool, such as other proposed EDSS tools for ANN models (Refer to Jahani et al 17 , 42 and Kalantary et al 52 ), is applicable in forest lands where the forest inventory data has been prepared. The new data will be based on forest inventory sample plots and forest managers use these sample plots data to identify susceptible trees for wind-damage.…”

Section: Discussionmentioning

confidence: 99%

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

Jahani¹,

Saffariha

2021

Sci Rep

Self Cite

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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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“…Next, performance evaluation of the ANN model was done by several math indexes such as mean squared error -MSE (Equation ( 1)), root mean squared error -RMSE (Equation ( 2)), mean absolute error -MAE (Equation ( 3)). [28][29][30] MSE =…”

Section: Model Selectionmentioning

confidence: 99%

Development of a decision support system tool to predict the pulmonary function using artificial neural network approach

Kalantary

Pourbabaki

Jahani³

et al. 2021

Concurrency and Computation

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The spirometry is considered a preclinical tool for the evaluation of the respiratory system. The formal lung volumes measurement and health status lung system are made using spirometry. Artificial neural network (ANN) has been introduced in solving complex problems in a large number of different settings, including medical diagnosis support system as predictive power. An objective of this research was intended to investigate the development of a new decision support system (DSS) using ANN modeling approaches and algorithms to predict pulmonary function in people. The spirometry data and general characteristics, anthropometric data, and body composition parameters (N = 130) were obtained from subjects. The classification of pulmonary function was performed by the multi-layer perceptron (MLP) model. Findings show that the MLP model is capable of classifying respiratory abnormalities in different people. The ANN model was totally 93.6%, 92.3%, 84.6%, and 91.5% successful in correctly classified in training, validation, test, and all data, respectively. Also, a DSS tool was created that allows the evaluation and classification of the results of spirometry data. It appears that ANNs are useful in classification pulmonary function.

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

Cited by 51 publications

References 46 publications

Using artificial neural networks to model and interpret electrospun polysaccharide (Hylon VII starch) nanofiber diameter

Using artificial neural networks to model and interpret electrospun polysaccharide (Hylon VII starch) nanofiber diameter

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

Development of a decision support system tool to predict the pulmonary function using artificial neural network approach

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