## What is/are Back Propagation Artificial?

Back Propagation Artificial - 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).^{[1]}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.

^{[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]}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.

^{[4]}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.

^{[5]}To investigate the feasibility of using near-infrared (NIR) spectral technology to detect the soluble solids content (SSC) of

^{[6]}Error back propagation artificial neural networks and support vector machine models were established based on corrected versus original spectra.

^{[7]}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.

^{[8]}A back propagation artificial neural network (BPANN) model was constructed for predicting the sensory quality of garlic from four different areas in China.

^{[9]}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.

^{[10]}In order to obtain friction and wear performance of different brakes in different conditions with less test data, back propagation artificial neural network model has been established by some physical parameters and working conditions to train and predict friction and wear performance of carbon brake disk.

^{[11]}In addition, DeepCID showed better sensitivity when compared with the logistic regression (LR) with L1-regularization, k-nearest neighbor (kNN), random forest (RF) and back propagation artificial neural network (BP-ANN) models for ternary mixture spectral datasets.

^{[12]}Passey method and back propagation artificial neural network (BPANN) based on well logs have been extensively employed to determine organic richness.

^{[13]}In this study, hybrid signal processing combining a two-point normalization (TPN) method for the effective compensation of the drifts and a back propagation artificial neural network (ANN) algorithm for the interpretation of the interferences was developed.

^{[14]}Here, we present, to the best of our knowledge, a new method, using a back propagation artificial neural network (BPNN) to predict, rather than search, the optimized parameters.

^{[15]}Back propagation artificial neural network (BP-ANN) was adopted as the first classifier to divide yoga postures into different categories, and fuzzy C-means (FCM) was utilized as the second classifier to classify the postures in a category.

^{[16]}Three layers of back propagation artificial neural network and support vector machine (SVM) methods were applied to patients’ data to predict whether they are infected with VAP with Pseudomonas aeruginosa infection.

^{[17]}A multiple nonlinear regression model and a back propagation artificial neural network (BP) prediction model, based on adaptive genetic algorithm (AGA), were established.

^{[18]}We built up an element to ceramic system framework by back propagation artificial neural network (BPANN) with the accuracy approaching to 90% and the correlation coefficients approaching to 0.

^{[19]}Finally, an intelligent classifier was capable of recognizing the medium of the discharge source using the feed forward with back propagation artificial neural networks (ANN).

^{[20]}According to the MLR results, we developed a back propagation artificial neural network (BP-ANN) model by selecting tan-sigmoid as the transfer function of the hidden layers nodes, and pure-line of the output layer nodes, with training goal of 0.

^{[21]}Hyperspectral images analyzing coupled with back propagation artificial neural network (BPANN) accurately recorded how SAS impacts the insect cuticle via the effective wavelengths.

^{[22]}Then, three common machine learning algorithms (random forest [RF], support vector machine [SVM], and back propagation artificial neural network [BP-ANN]) were used to simulate and predict blood glucose status.

^{[23]}Some speech recognition research have been proposed in disseminating male and female voices, this paper performs male and female voice extraction by using Mel Frequency Cestrum Coefficients (MFCC) as the characteristic vector in back propagation artificial neural network (ANN).

^{[24]}Back Propagation Artificial Neural Network (BP-ANN) was proved to be effective for quantification analysis.

^{[25]}In this paper, intelligent technology of combined low field NMR (LF-NMR) and back propagation artificial neural network (BP-ANN) was used to monitor moisture content in carrot during microwave vacuum drying.

^{[26]}Optimization is carried out with an integral hybrid genetic algorithm (GA) with back propagation artificial neural network (BPANN) and response surface methodology (RSM) based on rotatable central composite design (RCCD).

^{[27]}To effectively utilize the available resources, we have proposed Dynamic Agent-Based’Load Balancing (DA-LB) on Software-Defined Networking (SDN) using Back Propagation Artificial Neural Network (BPANN).

^{[28]}The current study reports on the use of the Back Propagation Artificial Neural Network (BPANN).

^{[29]}An back propagation artificial neural network (BP-ANN) model was established with the input values of LF-NMR parameters and the output values of sensors for different flavor substances obtained from electronic nose.

^{[30]}Subsequently, the results obtained from the experimental tests of Nusselt number, overall heat transfer coefficient and pressure drop using a modified twisted tape in a double pipe heat exchanger and a back propagation artificial neural network with a multilayered perceptron structure show the existence of a relationship between heat transfer parameters and input data such as cold and hot fluid temperatures, volume fractions of nanofluid, twisted tapes of different twist ratios, the rotation velocity of twisted tapes, Reynolds number of hot and cold fluids, viscosity and thermal conductivity of the nanofluid.

^{[31]}According to the results of the correlation, the input parameters are selected and two models of the CPCP based on the multivariate regression analysis and Back Propagation Artificial Neural Network (BP ANN) algorithm are proposed respectively.

^{[32]}Back propagation artificial neural networks was used to develop the prediction model by using the risk management and population health framework.

^{[33]}A feed forward back propagation artificial neural network (ANN) system was used to predict the properties of flake Al-Cu-Mg alloy powders.

^{[34]}In this study, a feedforward back propagation artificial neural network that is connected to Gutenberg-Richter relationship and that bases on b value used in earthquake predictions was developed.

^{[35]}By combining back propagation artificial neural network (BPANN) and ε-support vector regression (ε-SVR), a novel hybrid model was constructed to impute the historical PM2.

^{[36]}DBSA-GMM was applied in the condition prediction of a fleet of commercial aero-engines and showed advantageous prediction precision over Auto-Regressive Moving Average (ARMA), Back Propagation Artificial Neural Network (BP-ANN), and former similarity based prediction (SBP) methods.

^{[37]}The HD model has been implemented using the back propagation Artificial Neural Network (ANN) Multilayer Perceptron algorithm and the other two models have been implemented using the Decision Tree J48 algorithm.

^{[38]}The score matrix was obtained by principal component analysis (PCA), and the back propagation artificial neural network (BP-ANN) model was established to identify the kind of the brands of the bottled water based on their spectral properties.

^{[39]}A finite element model which considers all construction phases of an earth dam has been generated and then orthogonal design, back propagation artificial neural network and Particle Swarm Optimization algorithm has been used simultaneously to perform inverse modeling.

^{[40]}Incorporating a multivariate approach combining principal component analysis (PCA) and back propagation artificial neural network (BP-ANN), we performed noninvasive blood glucose measurements on 12 randomly selected volunteers, respectively.

^{[41]}This article proposes a human emotion classification model using ridgelet transformation-based emotion image features and with back propagation artificial neural network.

^{[42]}

## support vector machine

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.^{[1]}Error back propagation artificial neural networks and support vector machine models were established based on corrected versus original spectra.

^{[2]}Three layers of back propagation artificial neural network and support vector machine (SVM) methods were applied to patients’ data to predict whether they are infected with VAP with Pseudomonas aeruginosa infection.

^{[3]}Then, three common machine learning algorithms (random forest [RF], support vector machine [SVM], and back propagation artificial neural network [BP-ANN]) were used to simulate and predict blood glucose status.

^{[4]}

## Error Back Propagation Artificial

To investigate the feasibility of using near-infrared (NIR) spectral technology to detect the soluble solids content (SSC) of^{[1]}Error back propagation artificial neural networks and support vector machine models were established based on corrected versus original spectra.

^{[2]}

## back propagation artificial neural

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).^{[1]}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.

^{[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]}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.

^{[4]}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.

^{[5]}To investigate the feasibility of using near-infrared (NIR) spectral technology to detect the soluble solids content (SSC) of

^{[6]}Error back propagation artificial neural networks and support vector machine models were established based on corrected versus original spectra.

^{[7]}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.

^{[8]}A back propagation artificial neural network (BPANN) model was constructed for predicting the sensory quality of garlic from four different areas in China.

^{[9]}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.

^{[10]}In order to obtain friction and wear performance of different brakes in different conditions with less test data, back propagation artificial neural network model has been established by some physical parameters and working conditions to train and predict friction and wear performance of carbon brake disk.

^{[11]}In addition, DeepCID showed better sensitivity when compared with the logistic regression (LR) with L1-regularization, k-nearest neighbor (kNN), random forest (RF) and back propagation artificial neural network (BP-ANN) models for ternary mixture spectral datasets.

^{[12]}Passey method and back propagation artificial neural network (BPANN) based on well logs have been extensively employed to determine organic richness.

^{[13]}In this study, hybrid signal processing combining a two-point normalization (TPN) method for the effective compensation of the drifts and a back propagation artificial neural network (ANN) algorithm for the interpretation of the interferences was developed.

^{[14]}Here, we present, to the best of our knowledge, a new method, using a back propagation artificial neural network (BPNN) to predict, rather than search, the optimized parameters.

^{[15]}Back propagation artificial neural network (BP-ANN) was adopted as the first classifier to divide yoga postures into different categories, and fuzzy C-means (FCM) was utilized as the second classifier to classify the postures in a category.

^{[16]}Three layers of back propagation artificial neural network and support vector machine (SVM) methods were applied to patients’ data to predict whether they are infected with VAP with Pseudomonas aeruginosa infection.

^{[17]}A multiple nonlinear regression model and a back propagation artificial neural network (BP) prediction model, based on adaptive genetic algorithm (AGA), were established.

^{[18]}We built up an element to ceramic system framework by back propagation artificial neural network (BPANN) with the accuracy approaching to 90% and the correlation coefficients approaching to 0.

^{[19]}Finally, an intelligent classifier was capable of recognizing the medium of the discharge source using the feed forward with back propagation artificial neural networks (ANN).

^{[20]}According to the MLR results, we developed a back propagation artificial neural network (BP-ANN) model by selecting tan-sigmoid as the transfer function of the hidden layers nodes, and pure-line of the output layer nodes, with training goal of 0.

^{[21]}Hyperspectral images analyzing coupled with back propagation artificial neural network (BPANN) accurately recorded how SAS impacts the insect cuticle via the effective wavelengths.

^{[22]}Then, three common machine learning algorithms (random forest [RF], support vector machine [SVM], and back propagation artificial neural network [BP-ANN]) were used to simulate and predict blood glucose status.

^{[23]}Some speech recognition research have been proposed in disseminating male and female voices, this paper performs male and female voice extraction by using Mel Frequency Cestrum Coefficients (MFCC) as the characteristic vector in back propagation artificial neural network (ANN).

^{[24]}Back Propagation Artificial Neural Network (BP-ANN) was proved to be effective for quantification analysis.

^{[25]}In this paper, intelligent technology of combined low field NMR (LF-NMR) and back propagation artificial neural network (BP-ANN) was used to monitor moisture content in carrot during microwave vacuum drying.

^{[26]}Optimization is carried out with an integral hybrid genetic algorithm (GA) with back propagation artificial neural network (BPANN) and response surface methodology (RSM) based on rotatable central composite design (RCCD).

^{[27]}To effectively utilize the available resources, we have proposed Dynamic Agent-Based’Load Balancing (DA-LB) on Software-Defined Networking (SDN) using Back Propagation Artificial Neural Network (BPANN).

^{[28]}The current study reports on the use of the Back Propagation Artificial Neural Network (BPANN).

^{[29]}An back propagation artificial neural network (BP-ANN) model was established with the input values of LF-NMR parameters and the output values of sensors for different flavor substances obtained from electronic nose.

^{[30]}Subsequently, the results obtained from the experimental tests of Nusselt number, overall heat transfer coefficient and pressure drop using a modified twisted tape in a double pipe heat exchanger and a back propagation artificial neural network with a multilayered perceptron structure show the existence of a relationship between heat transfer parameters and input data such as cold and hot fluid temperatures, volume fractions of nanofluid, twisted tapes of different twist ratios, the rotation velocity of twisted tapes, Reynolds number of hot and cold fluids, viscosity and thermal conductivity of the nanofluid.

^{[31]}According to the results of the correlation, the input parameters are selected and two models of the CPCP based on the multivariate regression analysis and Back Propagation Artificial Neural Network (BP ANN) algorithm are proposed respectively.

^{[32]}Back propagation artificial neural networks was used to develop the prediction model by using the risk management and population health framework.

^{[33]}A feed forward back propagation artificial neural network (ANN) system was used to predict the properties of flake Al-Cu-Mg alloy powders.

^{[34]}In this study, a feedforward back propagation artificial neural network that is connected to Gutenberg-Richter relationship and that bases on b value used in earthquake predictions was developed.

^{[35]}By combining back propagation artificial neural network (BPANN) and ε-support vector regression (ε-SVR), a novel hybrid model was constructed to impute the historical PM2.

^{[36]}DBSA-GMM was applied in the condition prediction of a fleet of commercial aero-engines and showed advantageous prediction precision over Auto-Regressive Moving Average (ARMA), Back Propagation Artificial Neural Network (BP-ANN), and former similarity based prediction (SBP) methods.

^{[37]}The HD model has been implemented using the back propagation Artificial Neural Network (ANN) Multilayer Perceptron algorithm and the other two models have been implemented using the Decision Tree J48 algorithm.

^{[38]}The score matrix was obtained by principal component analysis (PCA), and the back propagation artificial neural network (BP-ANN) model was established to identify the kind of the brands of the bottled water based on their spectral properties.

^{[39]}A finite element model which considers all construction phases of an earth dam has been generated and then orthogonal design, back propagation artificial neural network and Particle Swarm Optimization algorithm has been used simultaneously to perform inverse modeling.

^{[40]}Incorporating a multivariate approach combining principal component analysis (PCA) and back propagation artificial neural network (BP-ANN), we performed noninvasive blood glucose measurements on 12 randomly selected volunteers, respectively.

^{[41]}This article proposes a human emotion classification model using ridgelet transformation-based emotion image features and with back propagation artificial neural network.

^{[42]}