## What is/are Propagation Artificial?

Propagation Artificial - In this study, backpropagation artificial neural network and particle swarm optimization combination used to predict the number of Chronic Kidney Disease patient.^{[1]}We demonstrate how a back-propagation artificial neural network can be trained to represent a potential energy surface (PES) in a formless manner with limited data points and exploited to predict interaction energies for configurations not included in the training set.

^{[2]}The Analysis-Back Propagation Artificial Neural Network (BP ANN) PTF that was established, with input variables that were, Si·SOM, BD·Si, ln2Cl, SOM2, and SOM·lnCl had a better predictive performance than published PTFs and MLR PTFs.

^{[3]}96 of the results were utilized to formulate a Levernberg-Marquardt backpropagation artificial neural network (ANN) for determining the shear strength of the concrete.

^{[4]}In order to make the production estimation, feed forward back propagation artificial neural network (ANN) was used due to its success in predicting linear nonlinear models, which are artificial intelligence applications and Adaptive Network Based Fuzzy Inference System (ANFIS).

^{[5]}This includes back-propagation artificial neural network, generalized regression neural network, radial basis neural network, radial kernel support vector machines and ANOVA kernel support vector machines.

^{[6]}In this paper, a back propagation artificial neural network (BP-ANN) optimized by genetic algorithm (GA) method, called GA-ANN, is presented for the prediction of shot peen forming parameters.

^{[7]}Two artificial intelligence (AI)-based models, namely feed-forward backpropagation artificial neural network (ANN) and multi-layer adaptive neuro-fuzzy inference system (ANFIS), were developed to estimate the biogas production in an up-flow anaerobic sludge blanket (UASB) reactor.

^{[8]}To solve the problem of slow convergence speed and easy to fall into the local minimum of the back propagation artificial neural network (BP-ANN), an improved BP-ANN algorithm based on additional momentum and Levenberg-Marquardt optimization is proposed based on the analysis of the existing improved methods, which improves the convergence speed and avoids the local minimum effectively.

^{[9]}Furthermore, the learning and identification process is carried out using the Backpropagation Artificial Neural Network (ANN) algorithm.

^{[10]}In this paper, the lithium-ion battery safety model under mechanical abuse conditions is proposed by the Back Propagation Artificial Neural Network (BP-ANN) optimized by the Genetic Algorithm (GA).

^{[11]}In this paper, Orthogonal Frequency Division Multiplexing with Subcarrier-Power Modulation and Space-Time Block Coding (OFDM-SPM-STBC) technique is compounded with Back Propagation Artificial Neural Network (BPANN) in a multiple-input-single-output (MISO) setup to study, investigate and quantify the wireless system's performance of their combination over a multi-path Rayleigh fading channel.

^{[12]}In this study, a strategy combining mass spectrometry-based untargeted metabolomics with backpropagation artificial neural network (BP-ANN)-based machine learning approach was proposed to screen Q-markers from JQJT preparation.

^{[13]}In Part 1, important parameters that should be taken into consideration and their parametrizations were given, as well as the details and mathematics of the computational engine of COVID-LIBERTY, a feed-forward, back-propagation Artificial Neural Network.

^{[14]}, backpropagation artificial neural network (BP-ANN), maximum entropy (MaxEnt), generalized linear model (GLM), as well as the ensemble technique along with measuring the landscape metrics and analyzing the relationship between the distribution of the suitable habitat of the species in different landform classes in Fars Province, southern Iran.

^{[15]}The constitutive relationship was established based on backpropagation artificial neural network (BP-ANN) algorithm.

^{[16]}Taking the experimental stress-strain data as samples, the support vector regression (SVR) model and back propagation artificial neural network (BPANN) model were established by cross-validation (CV) method to describe the nonlinear flow behaviors of BT22 alloy.

^{[17]}A predictive advection model was executed using a combination of observed Cd concentrations and predicted Cd concentrations from a genetic algorithm–backpropagation artificial neural network (GA–BPANN).

^{[18]}A back-propagation artificial neural network (BP-ANN) model was constructed to predict pharmacokinetics based on physiological factors and genetic polymorphism data.

^{[19]}In this article, a model based on a comprehensive optimal back propagation artificial neural network model is developed.

^{[20]}This work presents a self-adjusting PID controller based on a backpropagation artificial neural network.

^{[21]}In order to estimate the current-voltage characteristic of Shottky diode at different temperatures, a multi-layer perceptron, a feed-forward back-propagation artificial neural network was developed using 362 experimental data obtained.

^{[22]}Moreover, a back-propagation artificial neural network is designed to identify the key influencing factors in disaster–economy–ecology system.

^{[23]}The experimental results were explored for the formulation of a feed-forward back-propagation artificial neural network (ANN) model to predict the removal efficiency of As from the contaminated water.

^{[24]}Moreover, error back propagation artificial neural network (BP-ANN) was used to quantitatively predict the concentration of formaldehyde in squids.

^{[25]}Our learning framework algorithmic architecture iteratively chains the features selection process and the backpropagation artificial neural network architecture design based on the data assessment accomplished by the RReliefF algorithm.

^{[26]}The decolorization process was modeled using backpropagation artificial neural network and optimized by genetic algorithm and particle swarm optimization.

^{[27]}Therefore, to answer the challenge, the purpose of this study was to develop a model of plant growth prediction using the resilient backpropagation Artificial Neural Network (ANN) method with environmental parameter input at the plant factory and evaluate the model.

^{[28]}Finally, cloud motion estimations are obtained using the Feed Forward Backpropagation Artificial Neural Network.

^{[29]}The information was used to develop and compare the prediction models for intramuscular (IMF) content prediction by partial least square regression (PLSR), support vector machines regression (SVMR), and backpropagation artificial neural network (BPANN).

^{[30]}The spectral data were used for determination of viscosity value according to back-propagation artificial neural network (BP-ANN) algorithm.

^{[31]}To find the best solution, various models were used: counter-propagation artificial neural networks (CP-ANNs), XY-fused Querynetworks (XY-Fs), supervised Kohonen networks (SKNs), neural networks with Rectangular Linear Activations (ReLU), extreme gradient boosting (XGBoost), support-vector machine (SVM), and different subsets of the independent variables in five vegetation periods.

^{[32]}This aims of this study is to predict particulate matter (PM 10 ) levels in Pekanbaru using back propagation artificial neural networks (ANN) based on weather factors.

^{[33]}To improve the accuracy of the modeling, machine learning with the backpropagation artificial neural network (BPNN) and support vector machine (SVM) was used to calculate the values of oxidation rates and scale deformation ratios, and genetic algorithm (GA) was employed to optimize the model parameters.

^{[34]}This study proposed a backpropagation artificial neural network (BPANN) optimized by a genetic algorithm (GA) to estimate the monthly SAT fields of the Antarctic continent for the period 1960–2019.

^{[35]}Initially, the mathematics and details of the computational engine of the framework, a feed-forward, back-propagation Artificial Neural Network are presented.

^{[36]}To enhance the classification rates, different chemometric algorithms including variable importance in projection (VIP), partial least square‐discriminant analysis (PLS‐DA), and counter propagation artificial neural networks (CPANN) were used.

^{[37]}On this basis, the back propagation artificial neural network (BP-ANN) was applied to the qualitative classification of waste textiles to complete the automatic identification of fabric components in the sorting process.

^{[38]}Using the experimental data obtained, a multi-layer perceptron feed-forward back-propagation artificial neural network and a new mathematical correlation have been developed in order to predict the viscosity of ZrO2/Water nanofluid.

^{[39]}Combined with the back propagation artificial neural network, the impedance spectral data also show a strong correlation with water content.

^{[40]}In this study, a method for accurately identifying the location and magnitude of the load causing plastic deformation of a fixed beam using a backpropagation artificial neural network (BP-ANN).

^{[41]}First order, logistic, gompertz, and back-propagation artificial neural network (BP-ANN) models were used to study cumulative biogas and methane yields.

^{[42]}Even though some published correlations present acceptable predictions for the tested flow and thermal performance of EG/water based ZnO nanofluid, the newly developed Backpropagation Artificial Neural Networks (BP-ANNs) show even better prediction accuracies for Nusselt number and friction factor with the MARDs of 0.

^{[43]}A backpropagation artificial neural network prediction model for damage locations was trained with acoustic emission data using six different training algorithms, and the prediction accuracies of six algorithms were evaluated using seven different accuracy metrics.

^{[44]}5 forecasting models were developed and applied in the Northern Xinjiang, China, through combining the back propagation artificial neural network (BPANN) and multiple linear regression (MLR) with another BPANN model.

^{[45]}Hue Min Max Diff (HMMD) color space is used to extract color histogram and texture features which were fed to Back Propagation Artificial Neural Network (BPANN) for classification.

^{[46]}Then, Tan-Sigmoid was selected as the transfer function of the hidden layer node, and the linear function was used as the output layer function to establish the back propagation artificial neural network (BP-ANN) model.

^{[47]}To achieve this goal, this study develops a nonlinear hybrid model, which integrates extreme-point symmetric mode decomposition (ESMD), back propagation artificial neural networks (BPANN) and weights connection method.

^{[48]}The back propagation artificial neural network algorithm and cluster analysis revealed the color of different BFAR could be accurately divided into three categories; subsequently, the color range for the optimal degrees of stir-frying was established as follows: R[red value (105.

^{[49]}Furthermore, the learning and identification process is carried out using the Backpropagation Artificial Neural Network (ANN) algorithm.

^{[50]}

## support vector machine

This includes back-propagation artificial neural network, generalized regression neural network, radial basis neural network, radial kernel support vector machines and ANOVA kernel support vector machines.^{[1]}The information was used to develop and compare the prediction models for intramuscular (IMF) content prediction by partial least square regression (PLSR), support vector machines regression (SVMR), and backpropagation artificial neural network (BPANN).

^{[2]}To find the best solution, various models were used: counter-propagation artificial neural networks (CP-ANNs), XY-fused Querynetworks (XY-Fs), supervised Kohonen networks (SKNs), neural networks with Rectangular Linear Activations (ReLU), extreme gradient boosting (XGBoost), support-vector machine (SVM), and different subsets of the independent variables in five vegetation periods.

^{[3]}To improve the accuracy of the modeling, machine learning with the backpropagation artificial neural network (BPNN) and support vector machine (SVM) was used to calculate the values of oxidation rates and scale deformation ratios, and genetic algorithm (GA) was employed to optimize the model parameters.

^{[4]}Finally, the performance of the proposed model was compared with the Linear Regression model, the Support Vector Machine model, the Radial Basis Function Artificial Neural Network model, and the Back Propagation Artificial Neural Network model.

^{[5]}(5)- The classification of the lesions by applying five well known supervised machine learning classification algorithms: the back-propagation artificial neural network (ANN), the support vector machine (SVM), the K-nearest neighbours (K-NN), Relevance Vectors Machine (RVM), and the random forest (RF) compared to deep learning based on convolutional neural network (CNN).

^{[6]}Back-propagation artificial neural network (BP-ANN) and support vector machine (SVM) models were applied to predict elemental concentrations in aquatic products, using magnetic parameters as input.

^{[7]}Based on this database, five different machine learning models: Back Propagation Artificial Neural Network (BPANN) and Support Vector Machine (SVM), with hyperparameters optimised by Particle Swarm Optimisation Algorithm (PSO) and Genetic Algorithm (GA), respectively, and Extreme Learning Machine (ELM) optimised by Exhaustive Method, were adopt to assess the peak shear strength of soil-GDL interfaces.

^{[8]}, back-propagation artificial neural network, multi-gene genetic programming and support vector machine) with high accuracy.

^{[9]}Moreover, back propagation artificial neural network, support vector machine, principal component analysis combined with support vector machine and t-distributed stochastic neighbor embedding combined with support vector machine are established to make comparisons with the proposed model, respectively.

^{[10]}Error back propagation artificial neural networks and support vector machine models were established based on corrected versus original spectra.

^{[11]}

## feed forward back

In order to make the production estimation, feed forward back propagation artificial neural network (ANN) was used due to its success in predicting linear nonlinear models, which are artificial intelligence applications and Adaptive Network Based Fuzzy Inference System (ANFIS).^{[1]}In Part 1, important parameters that should be taken into consideration and their parametrizations were given, as well as the details and mathematics of the computational engine of COVID-LIBERTY, a feed-forward, back-propagation Artificial Neural Network.

^{[2]}The experimental results were explored for the formulation of a feed-forward back-propagation artificial neural network (ANN) model to predict the removal efficiency of As from the contaminated water.

^{[3]}Initially, the mathematics and details of the computational engine of the framework, a feed-forward, back-propagation Artificial Neural Network are presented.

^{[4]}The objective of this study was to model the removal efficiency of ternary adsorption system using feed-forward back propagation artificial neural network (FFBP-ANN).

^{[5]}Artificial Neural Network (ANN) was applied to predict the QWI by using the algorithm of feed forward back propagation artificial neural networks (BP ANN) for optimization.

^{[6]}Herein, a new technique based on the Feed-forward Back-propagation Artificial Neural Network (FBANN) model has been developed and used to predict both the hourly solar radiation and the wind speed simultaneously.

^{[7]}Speed Up Robust Features (SURF) algorithm has been used for features selection & extraction while Feed-forward Back-propagation Artificial Neural Network (FFBP ANN) has been used for classification.

^{[8]}In this paper, we present a new version of ATLASLang that is implemented using a feed-forward back-propagation Artificial Neural Network.

^{[9]}

## neural network model

In this article, a model based on a comprehensive optimal back propagation artificial neural network model is developed.^{[1]}) Based on experimental data, the strain-compensated Arrhenius model (SCAM) and the back-propagation artificial neural network model (BP-ANN) were constructed for the prediction of the flow stress.

^{[2]}It consisted of a back-propagation artificial neural network model with a “6–10–2” structure that could effectively simulate and predict the value with more than 99% accuracy.

^{[3]}The results show that the early warning model can realize the prediction and early warning of the safety condition of the working face, and the performance of the neural network model optimized by genetic algorithm is better than the traditional back-propagation artificial neural network model, with higher prediction accuracy and convergence speed.

^{[4]}This paper presents two kinds of neural network model – Back-propagation Artificial Neural Networks (ANNs), and Adaptive Neuro-Fuzzy Inference System (ANFIS), developed to estimate temporary sea level variation caused by meteorologically driven forces related to storm wind and pressure in Quinhon basin in the Central region of Vietnam, a place which is frequently affected by tropical storms.

^{[5]}

## support vector regression

Taking the experimental stress-strain data as samples, the support vector regression (SVR) model and back propagation artificial neural network (BPANN) model were established by cross-validation (CV) method to describe the nonlinear flow behaviors of BT22 alloy.^{[1]}For performance comparison, shallow Bi-LSTM S2S, shallow LSTM S2S, deep LSTM S2S, Levenberg-Marquardt backpropagation artificial neural networks (LMBP-ANN), and medium Gaussian support vector regression (MG-SVR) forecasting models are also developed and tested.

^{[2]}The Pearson correlation coefficients (R) of leave-one-out cross validation (LOOCV) were adopted to compare the performances of different algorithms including gradient boosting regression (GBR), support vector regression (SVR), backpropagation artificial neural network (BPANN), and random forest (RF).

^{[3]}Prediction models are developed using multi-linear regression (MLR), back propagation artificial neural network (BP-ANN), and support vector regression (SVR).

^{[4]}To investigate the feasibility of using near-infrared (NIR) spectral technology to detect the soluble solids content (SSC) of

^{[5]}

## particle swarm optimization

In this study, backpropagation artificial neural network and particle swarm optimization combination used to predict the number of Chronic Kidney Disease patient.^{[1]}The decolorization process was modeled using backpropagation artificial neural network and optimized by genetic algorithm and particle swarm optimization.

^{[2]}A prediction model of pKa values of neutral and alkaline drugs based on particle swarm optimization algorithm and back propagation artificial neural network, called PSO–BP ANN, was established by Chen et al.

^{[3]}

## multi layer perceptron

In order to estimate the current-voltage characteristic of Shottky diode at different temperatures, a multi-layer perceptron, a feed-forward back-propagation artificial neural network was developed using 362 experimental data obtained.^{[1]}Using the experimental data obtained, a multi-layer perceptron feed-forward back-propagation artificial neural network and a new mathematical correlation have been developed in order to predict the viscosity of ZrO2/Water nanofluid.

^{[2]}In current investigation, a novel implementation of intelligent numerical computing solver based on multi-layer perceptron (MLP) feed-forward back-propagation artificial neural networks (ANN) with the Levenberg–Marquard algorithm is provided to interpret heat generation/absorption and radiation phenomenon in unsteady electrically conducting Williamson liquid flow along porous stretching surface.

^{[3]}

## adaptive neuro fuzzy

Two artificial intelligence (AI)-based models, namely feed-forward backpropagation artificial neural network (ANN) and multi-layer adaptive neuro-fuzzy inference system (ANFIS), were developed to estimate the biogas production in an up-flow anaerobic sludge blanket (UASB) reactor.^{[1]}The adaptive neuro-fuzzy inference system (ANFIS) and a feed-forward back-propagation artificial neural network (ANN) system are utilized to explore their effectiveness in the 1D (discrete values), 2D (cross sections), and 3D (complete bead) domains.

^{[2]}

## tunnel smooth blasting

By using the methods of index utilization rate statistics, gray correlation analysis, and principal component analysis, this paper primarily elects and selects the control indexes; establishes the tunnel smooth blasting quality control index system; constructs a comprehensive optimization control model of tunnel smooth blasting quality using back propagation artificial neural network (BP-ANN), Elman neural network (ENN), and adaptive neuro fuzzy inference systems (ANFIS); and studies the tunnel smooth blasting quality control system.^{[1]}

## Back Propagation Artificial

The Analysis-Back Propagation Artificial Neural Network (BP ANN) PTF that was established, with input variables that were, Si·SOM, BD·Si, ln2Cl, SOM2, and SOM·lnCl had a better predictive performance than published PTFs and MLR PTFs.^{[1]}In order to make the production estimation, feed forward back propagation artificial neural network (ANN) was used due to its success in predicting linear nonlinear models, which are artificial intelligence applications and Adaptive Network Based Fuzzy Inference System (ANFIS).

^{[2]}In this paper, a back propagation artificial neural network (BP-ANN) optimized by genetic algorithm (GA) method, called GA-ANN, is presented for the prediction of shot peen forming parameters.

^{[3]}To solve the problem of slow convergence speed and easy to fall into the local minimum of the back propagation artificial neural network (BP-ANN), an improved BP-ANN algorithm based on additional momentum and Levenberg-Marquardt optimization is proposed based on the analysis of the existing improved methods, which improves the convergence speed and avoids the local minimum effectively.

^{[4]}In this paper, the lithium-ion battery safety model under mechanical abuse conditions is proposed by the Back Propagation Artificial Neural Network (BP-ANN) optimized by the Genetic Algorithm (GA).

^{[5]}In this paper, Orthogonal Frequency Division Multiplexing with Subcarrier-Power Modulation and Space-Time Block Coding (OFDM-SPM-STBC) technique is compounded with Back Propagation Artificial Neural Network (BPANN) in a multiple-input-single-output (MISO) setup to study, investigate and quantify the wireless system's performance of their combination over a multi-path Rayleigh fading channel.

^{[6]}Taking the experimental stress-strain data as samples, the support vector regression (SVR) model and back propagation artificial neural network (BPANN) model were established by cross-validation (CV) method to describe the nonlinear flow behaviors of BT22 alloy.

^{[7]}In this article, a model based on a comprehensive optimal back propagation artificial neural network model is developed.

^{[8]}Moreover, error back propagation artificial neural network (BP-ANN) was used to quantitatively predict the concentration of formaldehyde in squids.

^{[9]}This aims of this study is to predict particulate matter (PM 10 ) levels in Pekanbaru using back propagation artificial neural networks (ANN) based on weather factors.

^{[10]}On this basis, the back propagation artificial neural network (BP-ANN) was applied to the qualitative classification of waste textiles to complete the automatic identification of fabric components in the sorting process.

^{[11]}Combined with the back propagation artificial neural network, the impedance spectral data also show a strong correlation with water content.

^{[12]}5 forecasting models were developed and applied in the Northern Xinjiang, China, through combining the back propagation artificial neural network (BPANN) and multiple linear regression (MLR) with another BPANN model.

^{[13]}Hue Min Max Diff (HMMD) color space is used to extract color histogram and texture features which were fed to Back Propagation Artificial Neural Network (BPANN) for classification.

^{[14]}Then, Tan-Sigmoid was selected as the transfer function of the hidden layer node, and the linear function was used as the output layer function to establish the back propagation artificial neural network (BP-ANN) model.

^{[15]}To achieve this goal, this study develops a nonlinear hybrid model, which integrates extreme-point symmetric mode decomposition (ESMD), back propagation artificial neural networks (BPANN) and weights connection method.

^{[16]}The back propagation artificial neural network algorithm and cluster analysis revealed the color of different BFAR could be accurately divided into three categories; subsequently, the color range for the optimal degrees of stir-frying was established as follows: R[red value (105.

^{[17]}Based on the relationship between the ZHD and ZTD, the new model, GZHD, was developed using the back propagation artificial neural network (BP-ANN) method which took the ZTD as an input variable.

^{[18]}In this study, we develop a nonlinear hybrid model, which integrates extreme-point symmetric mode decomposition (ESMD), back propagation artificial neural networks (BPANN) and weights connection method, to represent the relationships between different drivers and runoff.

^{[19]}As a result, on the ground of conventional well logs, vast kinds of methods, for example, back propagation artificial neural network (BPANN), have been introduced to solve this problem.

^{[20]}Based on the obtained flow stress curves, four flow stress models, namely, strain-compensated, physically based, back propagation artificial neural network (BP-ANN), and modified BP-ANN based on a genetic algorithm (GA-BP-ANN), were established to predict the flow stress.

^{[21]}The outcomes of interest were lower extremity deep vein thrombosis group and Non-lower extremity deep vein thrombosis group were determined by univariate analysis, and SPSS was used to establish the back propagation artificial neural network prediction model.

^{[22]}Finally, the performance of the proposed model was compared with the Linear Regression model, the Support Vector Machine model, the Radial Basis Function Artificial Neural Network model, and the Back Propagation Artificial Neural Network model.

^{[23]}In this paper, a back propagation artificial neural network (BP-ANN) is proposed to predict the slagging potential for both ZDc and other steam coals.

^{[24]}The overall water pollution situation was evaluated with back propagation artificial neural network (BP-ANN) analysis.

^{[25]}The objective of this study was to model the removal efficiency of ternary adsorption system using feed-forward back propagation artificial neural network (FFBP-ANN).

^{[26]}Prediction models are developed using multi-linear regression (MLR), back propagation artificial neural network (BP-ANN), and support vector regression (SVR).

^{[27]}Artificial Neural Network (ANN) was applied to predict the QWI by using the algorithm of feed forward back propagation artificial neural networks (BP ANN) for optimization.

^{[28]}The automatic classification is based on multilayer back propagation artificial neural networks (ANN) algorithm.

^{[29]}Based on this database, five different machine learning models: Back Propagation Artificial Neural Network (BPANN) and Support Vector Machine (SVM), with hyperparameters optimised by Particle Swarm Optimisation Algorithm (PSO) and Genetic Algorithm (GA), respectively, and Extreme Learning Machine (ELM) optimised by Exhaustive Method, were adopt to assess the peak shear strength of soil-GDL interfaces.

^{[30]}By using the methods of index utilization rate statistics, gray correlation analysis, and principal component analysis, this paper primarily elects and selects the control indexes; establishes the tunnel smooth blasting quality control index system; constructs a comprehensive optimization control model of tunnel smooth blasting quality using back propagation artificial neural network (BP-ANN), Elman neural network (ENN), and adaptive neuro fuzzy inference systems (ANFIS); and studies the tunnel smooth blasting quality control system.

^{[31]}Taking Hexi area, a typical arid area in northwest China, as the study area, the comprehensive evaluation model of soil quality based on back propagation artificial neural network (BP - ANN) was proposed.

^{[32]}In this study, the performance of an integrated desiccant air conditioning system (IDACS) activated by solar energy is evaluated by back propagation artificial neural network (BP-ANN).

^{[33]}Objective To investigate the predictive value of clinical variables on the poor prognosis at 90-day follow-up from acute stroke onset, and compare the diagnostic performance between back propagation artificial neural networks (BP ANNs) and Logistic regression (LR) models in predicting the prognosis.

^{[34]}A prediction model of pKa values of neutral and alkaline drugs based on particle swarm optimization algorithm and back propagation artificial neural network, called PSO–BP ANN, was established by Chen et al.

^{[35]}The back propagation artificial neural network approach is employed to approximate CMM measurements of the circular features of the aluminum workpieces machined with milling process.

^{[36]}Moreover, back propagation artificial neural network, support vector machine, principal component analysis combined with support vector machine and t-distributed stochastic neighbor embedding combined with support vector machine are established to make comparisons with the proposed model, respectively.

^{[37]}To investigate the feasibility of using near-infrared (NIR) spectral technology to detect the soluble solids content (SSC) of

^{[38]}Error back propagation artificial neural networks and support vector machine models were established based on corrected versus original spectra.

^{[39]}Thus, with the help of entropy generation (sg) combination, which is caused by the viscous flow of crude in the pipeline and the back propagation artificial neural networks (ANN) optimized by a genetic algorithm, a prediction model was developed to determine the viscosity of PPD treated waxy crude oil, which was affected by shear.

^{[40]}A back propagation artificial neural network (BPANN) model was constructed for predicting the sensory quality of garlic from four different areas in China.

^{[41]}

## Counter Propagation Artificial

To enhance the classification rates, different chemometric algorithms including variable importance in projection (VIP), partial least square‐discriminant analysis (PLS‐DA), and counter propagation artificial neural networks (CPANN) were used.^{[1]}Potential lipid biomarkers were screened and validated using the partial least squares discriminant analysis (PLS-DA), orthogonal partial least squares-discriminant analysis (OPLS-DA), random forest, binary logistic regression (BLR), receiver operating characteristic (ROC) and counter propagation artificial neural network (CP-ANN) analysis.

^{[2]}This paper proposes a sensitive, sample preparation-free, rapid, and low-cost method for the detection of the B-rapidly accelerated fibrosarcoma (BRAF) gene mutation involving a substitution of valine to glutamic acid at codon 600 (V600E) in colorectal cancer (CRC) by near-infrared (NIR) spectroscopy in conjunction with counter propagation artificial neural network (CP-ANN).

^{[3]}

## propagation artificial neural

In this study, backpropagation artificial neural network and particle swarm optimization combination used to predict the number of Chronic Kidney Disease patient.^{[1]}We demonstrate how a back-propagation artificial neural network can be trained to represent a potential energy surface (PES) in a formless manner with limited data points and exploited to predict interaction energies for configurations not included in the training set.

^{[2]}The Analysis-Back Propagation Artificial Neural Network (BP ANN) PTF that was established, with input variables that were, Si·SOM, BD·Si, ln2Cl, SOM2, and SOM·lnCl had a better predictive performance than published PTFs and MLR PTFs.

^{[3]}96 of the results were utilized to formulate a Levernberg-Marquardt backpropagation artificial neural network (ANN) for determining the shear strength of the concrete.

^{[4]}In order to make the production estimation, feed forward back propagation artificial neural network (ANN) was used due to its success in predicting linear nonlinear models, which are artificial intelligence applications and Adaptive Network Based Fuzzy Inference System (ANFIS).

^{[5]}This includes back-propagation artificial neural network, generalized regression neural network, radial basis neural network, radial kernel support vector machines and ANOVA kernel support vector machines.

^{[6]}In this paper, a back propagation artificial neural network (BP-ANN) optimized by genetic algorithm (GA) method, called GA-ANN, is presented for the prediction of shot peen forming parameters.

^{[7]}Two artificial intelligence (AI)-based models, namely feed-forward backpropagation artificial neural network (ANN) and multi-layer adaptive neuro-fuzzy inference system (ANFIS), were developed to estimate the biogas production in an up-flow anaerobic sludge blanket (UASB) reactor.

^{[8]}To solve the problem of slow convergence speed and easy to fall into the local minimum of the back propagation artificial neural network (BP-ANN), an improved BP-ANN algorithm based on additional momentum and Levenberg-Marquardt optimization is proposed based on the analysis of the existing improved methods, which improves the convergence speed and avoids the local minimum effectively.

^{[9]}Furthermore, the learning and identification process is carried out using the Backpropagation Artificial Neural Network (ANN) algorithm.

^{[10]}In this paper, the lithium-ion battery safety model under mechanical abuse conditions is proposed by the Back Propagation Artificial Neural Network (BP-ANN) optimized by the Genetic Algorithm (GA).

^{[11]}In this paper, Orthogonal Frequency Division Multiplexing with Subcarrier-Power Modulation and Space-Time Block Coding (OFDM-SPM-STBC) technique is compounded with Back Propagation Artificial Neural Network (BPANN) in a multiple-input-single-output (MISO) setup to study, investigate and quantify the wireless system's performance of their combination over a multi-path Rayleigh fading channel.

^{[12]}In this study, a strategy combining mass spectrometry-based untargeted metabolomics with backpropagation artificial neural network (BP-ANN)-based machine learning approach was proposed to screen Q-markers from JQJT preparation.

^{[13]}In Part 1, important parameters that should be taken into consideration and their parametrizations were given, as well as the details and mathematics of the computational engine of COVID-LIBERTY, a feed-forward, back-propagation Artificial Neural Network.

^{[14]}, backpropagation artificial neural network (BP-ANN), maximum entropy (MaxEnt), generalized linear model (GLM), as well as the ensemble technique along with measuring the landscape metrics and analyzing the relationship between the distribution of the suitable habitat of the species in different landform classes in Fars Province, southern Iran.

^{[15]}The constitutive relationship was established based on backpropagation artificial neural network (BP-ANN) algorithm.

^{[16]}Taking the experimental stress-strain data as samples, the support vector regression (SVR) model and back propagation artificial neural network (BPANN) model were established by cross-validation (CV) method to describe the nonlinear flow behaviors of BT22 alloy.

^{[17]}A predictive advection model was executed using a combination of observed Cd concentrations and predicted Cd concentrations from a genetic algorithm–backpropagation artificial neural network (GA–BPANN).

^{[18]}A back-propagation artificial neural network (BP-ANN) model was constructed to predict pharmacokinetics based on physiological factors and genetic polymorphism data.

^{[19]}In this article, a model based on a comprehensive optimal back propagation artificial neural network model is developed.

^{[20]}This work presents a self-adjusting PID controller based on a backpropagation artificial neural network.

^{[21]}In order to estimate the current-voltage characteristic of Shottky diode at different temperatures, a multi-layer perceptron, a feed-forward back-propagation artificial neural network was developed using 362 experimental data obtained.

^{[22]}Moreover, a back-propagation artificial neural network is designed to identify the key influencing factors in disaster–economy–ecology system.

^{[23]}The experimental results were explored for the formulation of a feed-forward back-propagation artificial neural network (ANN) model to predict the removal efficiency of As from the contaminated water.

^{[24]}Moreover, error back propagation artificial neural network (BP-ANN) was used to quantitatively predict the concentration of formaldehyde in squids.

^{[25]}Our learning framework algorithmic architecture iteratively chains the features selection process and the backpropagation artificial neural network architecture design based on the data assessment accomplished by the RReliefF algorithm.

^{[26]}The decolorization process was modeled using backpropagation artificial neural network and optimized by genetic algorithm and particle swarm optimization.

^{[27]}Therefore, to answer the challenge, the purpose of this study was to develop a model of plant growth prediction using the resilient backpropagation Artificial Neural Network (ANN) method with environmental parameter input at the plant factory and evaluate the model.

^{[28]}Finally, cloud motion estimations are obtained using the Feed Forward Backpropagation Artificial Neural Network.

^{[29]}The information was used to develop and compare the prediction models for intramuscular (IMF) content prediction by partial least square regression (PLSR), support vector machines regression (SVMR), and backpropagation artificial neural network (BPANN).

^{[30]}The spectral data were used for determination of viscosity value according to back-propagation artificial neural network (BP-ANN) algorithm.

^{[31]}To find the best solution, various models were used: counter-propagation artificial neural networks (CP-ANNs), XY-fused Querynetworks (XY-Fs), supervised Kohonen networks (SKNs), neural networks with Rectangular Linear Activations (ReLU), extreme gradient boosting (XGBoost), support-vector machine (SVM), and different subsets of the independent variables in five vegetation periods.

^{[32]}This aims of this study is to predict particulate matter (PM 10 ) levels in Pekanbaru using back propagation artificial neural networks (ANN) based on weather factors.

^{[33]}To improve the accuracy of the modeling, machine learning with the backpropagation artificial neural network (BPNN) and support vector machine (SVM) was used to calculate the values of oxidation rates and scale deformation ratios, and genetic algorithm (GA) was employed to optimize the model parameters.

^{[34]}This study proposed a backpropagation artificial neural network (BPANN) optimized by a genetic algorithm (GA) to estimate the monthly SAT fields of the Antarctic continent for the period 1960–2019.

^{[35]}Initially, the mathematics and details of the computational engine of the framework, a feed-forward, back-propagation Artificial Neural Network are presented.

^{[36]}To enhance the classification rates, different chemometric algorithms including variable importance in projection (VIP), partial least square‐discriminant analysis (PLS‐DA), and counter propagation artificial neural networks (CPANN) were used.

^{[37]}On this basis, the back propagation artificial neural network (BP-ANN) was applied to the qualitative classification of waste textiles to complete the automatic identification of fabric components in the sorting process.

^{[38]}Using the experimental data obtained, a multi-layer perceptron feed-forward back-propagation artificial neural network and a new mathematical correlation have been developed in order to predict the viscosity of ZrO2/Water nanofluid.

^{[39]}Combined with the back propagation artificial neural network, the impedance spectral data also show a strong correlation with water content.

^{[40]}In this study, a method for accurately identifying the location and magnitude of the load causing plastic deformation of a fixed beam using a backpropagation artificial neural network (BP-ANN).

^{[41]}First order, logistic, gompertz, and back-propagation artificial neural network (BP-ANN) models were used to study cumulative biogas and methane yields.

^{[42]}Even though some published correlations present acceptable predictions for the tested flow and thermal performance of EG/water based ZnO nanofluid, the newly developed Backpropagation Artificial Neural Networks (BP-ANNs) show even better prediction accuracies for Nusselt number and friction factor with the MARDs of 0.

^{[43]}A backpropagation artificial neural network prediction model for damage locations was trained with acoustic emission data using six different training algorithms, and the prediction accuracies of six algorithms were evaluated using seven different accuracy metrics.

^{[44]}5 forecasting models were developed and applied in the Northern Xinjiang, China, through combining the back propagation artificial neural network (BPANN) and multiple linear regression (MLR) with another BPANN model.

^{[45]}Hue Min Max Diff (HMMD) color space is used to extract color histogram and texture features which were fed to Back Propagation Artificial Neural Network (BPANN) for classification.

^{[46]}Then, Tan-Sigmoid was selected as the transfer function of the hidden layer node, and the linear function was used as the output layer function to establish the back propagation artificial neural network (BP-ANN) model.

^{[47]}To achieve this goal, this study develops a nonlinear hybrid model, which integrates extreme-point symmetric mode decomposition (ESMD), back propagation artificial neural networks (BPANN) and weights connection method.

^{[48]}The back propagation artificial neural network algorithm and cluster analysis revealed the color of different BFAR could be accurately divided into three categories; subsequently, the color range for the optimal degrees of stir-frying was established as follows: R[red value (105.

^{[49]}Furthermore, the learning and identification process is carried out using the Backpropagation Artificial Neural