scholarly journals The effect of crystallization level of silica nanoparticles on cell proliferation of MRC-5 cell line and its prediction using artificial neural network

2020 ◽  
Author(s):  
Fariba Abbasi ◽  
Mohammad Reza Samaei ◽  
Hassan Hashemi ◽  
Amir Savardashtaki ◽  
Abolfazl Azhdarpoor ◽  
...  
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Cell Proliferation ◽  
Silica Nanoparticles ◽  
Feed Rate ◽  
Laboratory Data ◽  
Maximum Level ◽  
Calcination Time ◽  
Marquardt Algorithm ◽  
Artificial Neural

Abstract Background: This study investigated the effect of the level of silica nanoparticles (SiO2NPs) crystallization on the cell proliferation of MRC-5 cells and its prediction using an artificial neural network (ANN).Methods: Variables studied included temperature (70-1000°C), calcination time (2, 12 and 24 hours), and catalyst feed rate (0.01, 0.05 and 0.1mL/min). Cell proliferation was determined by the MTT test after 24 hours of exposure, and results were analyzed using the t-test in MATLAB.Results: the synthesized particles size was less than 50nm, and the XRD peak varied from 30 to 21° during the increase in calcination temperature. The maximum level of crystallization was at 800°C (58% relative to amorphous) with the lowest cell viability. Cell proliferation decreased with increasing concentration of nanoparticles (p<0.05) and increasing feed rate. There was also a positive relationship between increased crystallization and decreased cell proliferation (R2=0.78), but no such association was observed for calcination time. Cell proliferation of MRC-5 was slightly correlated with the linear regression model (MSE>0.12), while ANN was well predicted by the Levenberg–Marquardt algorithm. The suggested structure in this study was 4:10:1 with R2all=0.97, R2test=0.97, RMSE=0.25 and MSE=0.003. The correlation between laboratory results and ANN prediction was 0.94, and the minimum and maximum OD level in the laboratory data and predicted ANN were attributed to 20 and 13 runs.Conclusion: changes in the degree of crystallization of SiO2NPs, an increase in concentration, and the rate of catalyst feed during crystallization of SiO2NPs were practical factors in increasing cytotoxicity.

scholarly journals An optimal feed-forward artificial neural network model and a new empirical correlation for prediction of the relative viscosity of Al2O3-engine oil nanofluid

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Mohammad Hemmat Esfe ◽  
Davood Toghraie
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Volume Fraction ◽  
Laboratory Data ◽  
Relative Viscosity ◽  
Optimal Structure ◽  
Engine Oil ◽  
Marquardt Algorithm ◽  
Optimal Feed ◽  
Artificial Neural

AbstractThis study presents the design of an artificial neural network (ANN) to evaluate and predict the viscosity behavior of Al2O3/10W40 nanofluid at different temperatures, shear rates, and volume fraction of nanoparticles. Nanofluid viscosity ($${\mu }_{nf}$$ μ nf ) is evaluated at volume fractions ($$\varphi$$ φ =0.25% to 2%) and temperature range of 5 to 55 °C. For modeling by ANN, a multilayer perceptron (MLP) network with the Levenberg–Marquardt algorithm (LMA) is used. The main purpose of this study is to model and predict the $${\mu }_{nf}$$ μ nf of Al2O3/10W40 nanofluid through ANN, select the best ANN structure from the set of predicted structures and manage time and cost by predicting the ANN with the least error. To model the ANN, $$\varphi$$ φ , temperature, and shear rate are considered as input variables, and $${\mu }_{nf}$$ μ nf is considered as output variable. From 400 different ANN structures for Al2O3/10W40 nanofluid, the optimal structure consisting of two hidden layers with the optimal structure of 6 neurons in the first layer and 4 neurons in the second layer is selected. Finally, the R regression coefficient and the MSE are 0.995838 and 4.14469E−08 for the optimal structure, respectively. According to all data, the margin of deviation (MOD) is in the range of less than 2% < MOD < + 2%. Comparison of the three data sets, namely laboratory data, correlation output, and ANN output, shows that the ANN estimates laboratory data more accurately.

scholarly journals Investigating the Electronic Coefficients in Ne-Hg Mixtures: Comparing a Monte Carlo and Artificial Neural Network Model

2020 ◽  
Vol 55 (2) ◽  
Author(s):  
Rafid Abbas Ali ◽  
Faten Sajet Mater ◽  
Asmaa Satar Jeeiad Al-Ragehey
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Monte Carlo Simulation ◽  
Monte Carlo ◽  
Network Architecture ◽  
Vapor Concentration ◽  
Neural Network Architecture ◽  
Marquardt Algorithm ◽  
Artificial Neural ◽  
New Applications

Electron coefficients such as drift velocity, ionization coefficient, mean electron energy and Townsend energy for different concentrations of Hg 0.1%, 1%, 10% and 50% in the Ne-Hg mixture at a reduced electric field were calculated using two approaches taking into account inelastic collisions: The Monte Carlo simulation, and an artificial neural network. The effect of Hg vapor concentration on the electron coefficients showed that insignificant additions of mercury atom impurities to Neon, starting from fractions of a percent, affect the characteristics of inelastic processes and discharge, respectively. The aim of this paper is to explore the new applications of neural networks. The Levenberg-Marquardt algorithm and artificial neural network architecture employed was presented in this work to calculate the electron coefficients for different concentrations of Hg in Ne-Hg mixtures. The artificial neural network has been trained with four models (M1, M2, M3, M4), and analysis of the regression between the values of an artificial neural network and Monte Carlo simulation indicates that the M2 output provided the best perfect correlation at 100 Epochs, and the output data obtained was closest to the target data required through using the different stages of artificial neural network development starting with design, training and testing.

scholarly journals Artificial Neural Network Modeling of Greenhouse Tomato Yield and Aerial Dry Matter

Agriculture ◽  
2020 ◽  
Vol 10 (4) ◽  
pp. 97 ◽  
Author(s):  
Kelvin López-Aguilar ◽  
Adalberto Benavides-Mendoza ◽  
Susana González-Morales ◽  
Antonio Juárez-Maldonado ◽  
Pamela Chiñas-Sánchez ◽  
...  
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Dry Matter ◽  
Fruit Yield ◽  
Fresh Fruit ◽  
Neural Network Modeling ◽  
Peat Moss ◽  
Tomato Crop ◽  
Marquardt Algorithm ◽  
Artificial Neural

Non-linear systems, such as biological systems, can be simulated by artificial neural network (ANN) techniques. This research aims to use ANN to simulate the accumulated aerial dry matter (leaf, stem, and fruit) and fresh fruit yield of a tomato crop. Two feed-forward backpropagation ANNs, with three hidden layers, were trained and validated by the Levenberg–Marquardt algorithm for weights and bias adjusted. The input layer consisted of the leaf area, plant height, fruit number, dry matter of leaves, stems and fruits, and the growth degree-days at 136 days after transplanting (DAT); these were obtained from a tomato crop, a hybrid, EL CID F1, with indeterminate growth habits, grown with a mixture of peat moss and perlite 1:1 (v/v) (substrate) and calcareous soil (soil). Based on the experimentation of the ANNs with one, two and three hidden layers, with MSE values less than 1.55, 0.94 and 0.49, respectively, the ANN with three hidden layers was chosen. The 7-10-7-5-2 and 7-10-8-5-2 topologies showed the best performance for the substrate (R = 0.97, MSE = 0.107, error = 12.06%) and soil (R = 0.94, MSE = 0.049, error = 13.65%), respectively. These topologies correctly simulated the aerial dry matter and the fresh fruit yield of the studied tomato crop.

scholarly journals Estimation of Contact Tip to Work Distance (CTWD) using Artificial Neural Network (ANN) in GMAW

2019 ◽  
Vol 269 ◽  
pp. 04004
Author(s):  
Fuad Mahfudianto ◽  
Eakkachai Warinsiriruk ◽  
Sutep Joy-A-Ka
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Moving Average ◽  
Back Propagation ◽  
Welding Current ◽  
Travel Speed ◽  
Mean Square ◽  
Wire Electrode ◽  
Marquardt Algorithm ◽  
Artificial Neural

A method for optimizing monitoring by using Artificial Neural Network (ANN) technique was proposed based on instability of arc voltage signal and welding current signal of solid wire electrode (GMAW). This technique is not only for effective process modeling, but also to illustrate the correlation between the input and output parameters responses. The algorithms of monitoring were developed in time domain by carrying out the Moving Average (M.A) and Root Mean Square (RMS) based on the welding experiment parameters such as travel speed, thickness of specimen, feeding speed, and wire electrode diameter to detect and estimate with a satisfactory sample size. Experiment data was divided into three subsets: train (70%), validation (15%), and test (15%). Error back-propagation of Levenberg-Marquardt algorithm was used to train for this algorithm. The proposed algorithms on this paper were used to estimate the variety the Contact Tip to Work Distance (CTWD) through Mean Square Error (MSE). Based on the results, the algorithms have shown that be able to detect changes in CTWD automatically and real time with takes 0.147 seconds (MSE 0.0087).

A Comparison of Artificial Neural Network Learning Algorithms for Vibration-Based Damage Detection

2010 ◽  
Vol 163-167 ◽  
pp. 2756-2760 ◽  
Author(s):  
Goh Lyn Dee ◽  
Norhisham Bakhary ◽  
Azlan Abdul Rahman ◽  
Baderul Hisham Ahmad
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Damage Detection ◽  
Conjugate Gradient ◽  
Learning Algorithms ◽  
Mode Shapes ◽  
Neural Network Learning ◽  
Marquardt Algorithm ◽  
Levenberg Marquardt ◽  
Artificial Neural

This paper investigates the performance of Artificial Neural Network (ANN) learning algorithms for vibration-based damage detection. The capabilities of six different learning algorithms in detecting damage are studied and their performances are compared. The algorithms are Levenberg-Marquardt (LM), Resilient Backpropagation (RP), Scaled Conjugate Gradient (SCG), Conjugate Gradient with Powell-Beale Restarts (CGB), Polak-Ribiere Conjugate Gradient (CGP) and Fletcher-Reeves Conjugate Gradient (CGF) algorithms. The performances of these algorithms are assessed based on their generalisation capability in relating the vibration parameters (frequencies and mode shapes) with damage locations and severities under various numbers of input and output variables. The results show that Levenberg-Marquardt algorithm provides the best generalisation performance.

scholarly journals The Analysis of Performace Model Tiered Artificial Neural Network for Assessment of Coronary Heart Disease

2017 ◽  
Vol 7 (4) ◽  
pp. 2183
Author(s):  
Wiharto Wiharto ◽  
Harianto Herianto ◽  
Hari Kusnanto
Keyword(s):  
Neural Network ◽  
Coronary Heart Disease ◽  
Artificial Neural Network ◽  
Heart Disease ◽  
Assessment Model ◽  
Model Assessment ◽  
Marquardt Algorithm ◽  
One Step ◽  
Artificial Neural ◽  
Artificial Neural Network Ann

<p>The assessment model of coronary heart disease is so much developed in line with the development of information technology, particularly the field of artificial intelligence. Unfortunately, the assessment models developed mostly do not use such an approach made by the clinician, the tiered approach. This study aims to analyze the performance of a tiered model assessment. The method used for each level is, preprocessing, building architecture artificial neural network (ANN), conduct training using the Levenberg-Marquardt algorithm and one step secant, as well as testing the system. The study is divided into the terms of the stages in the examination procedure. The test results showed the influence of each level, both when the output level of the previous positive or negative, were tested back at the next level. The performance evaluation may indicate that the top level provides performance improvement and or reinforce the previous level. </p>

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