scholarly journals Levenberg-Marquardt Neural Network Algorithm for Degree of Arteriovenous Fistula Stenosis Classification Using a Dual Optical Photoplethysmography Sensor

Sensors ◽  
2018 ◽  
Vol 18 (7) ◽  
pp. 2322 ◽  
Author(s):  
Yi-Chun Du ◽  
Alphin Stephanus
Keyword(s):  
Arteriovenous Fistula ◽  
Back Propagation ◽  
Potential Contribution ◽  
Training Algorithms ◽  
Marquardt Algorithm ◽  
Significant Difference ◽  
Levenberg Marquardt ◽  
Before And After ◽  
Medical Diagnostic ◽  
Arteriovenous Fistula Stenosis

This paper proposes a noninvasive dual optical photoplethysmography (PPG) sensor to classify the degree of arteriovenous fistula (AVF) stenosis in hemodialysis (HD) patients. Dual PPG measurement node (DPMN) becomes the primary tool in this work for detecting abnormal narrowing vessel simultaneously in multi-beds monitoring patients. The mean and variance of Rising Slope (RS) and Falling Slope (FS) values between before and after HD treatment was used as the major features to classify AVF stenosis. Multilayer perceptron neural networks (MLPN) training algorithms are implemented for this analysis, which are the Levenberg-Marquardt, Scaled Conjugate Gradient, and Resilient Back-propagation, to identify the degree of HD patient stenosis. Eleven patients were recruited with mean age of 77 ± 10.8 years for analysis. The experimental results indicated that the variance of RS in the HD hand between before and after treatment was significant difference statistically to stenosis (p < 0.05). Levenberg-Marquardt algorithm (LMA) was significantly outperforms the other training algorithm. The classification accuracy and precision reached 94.82% and 92.22% respectively, thus this technique has a potential contribution to the early identification of stenosis for a medical diagnostic support system.

Comparison of an Artificial Neural Network and a Multiple Linear Regression in Predicting the Heat of Combustion of Diesel Fuel Based on Hydrocarbon Groups

2020 ◽  
Vol 71 (6) ◽  
pp. 66-74
Author(s):  
Younis M. Younis ◽  
Salman H. Abbas ◽  
Farqad T. Najim ◽  
Firas Hashim Kamar ◽  
Gheorghe Nechifor
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Diesel Fuel ◽  
Back Propagation ◽  
Second Step ◽  
Heating Value ◽  
Network Training ◽  
Marquardt Algorithm ◽  
Levenberg Marquardt ◽  
Propagation Network

A comparison between artificial neural network (ANN) and multiple linear regression (MLR) models was employed to predict the heat of combustion, and the gross and net heat values, of a diesel fuel engine, based on the chemical composition of the diesel fuel. One hundred and fifty samples of Iraqi diesel provided data from chromatographic analysis. Eight parameters were applied as inputs in order to predict the gross and net heat combustion of the diesel fuel. A trial-and-error method was used to determine the shape of the individual ANN. The results showed that the prediction accuracy of the ANN model was greater than that of the MLR model in predicting the gross heat value. The best neural network for predicting the gross heating value was a back-propagation network (8-8-1), using the Levenberg�Marquardt algorithm for the second step of network training. R = 0.98502 for the test data. In the same way, the best neural network for predicting the net heating value was a back-propagation network (8-5-1), using the Levenberg�Marquardt algorithm for the second step of network training. R = 0.95112 for the test data.

scholarly journals Efficacy of high-pressure balloon for the treatment of arteriovenous fistula stenosis: a meta-analysis

2021 ◽  
Author(s):  
Yu Li ◽  
Wenhao Cui ◽  
Jukun Wang ◽  
Chao Zhang ◽  
Tao Luo
Keyword(s):  
High Pressure ◽  
Arteriovenous Fistula ◽  
Meta Analysis ◽  
Cochrane Library ◽  
Technical Success Rate ◽  
Stenosis Rate ◽  
Significant Difference ◽  
Arteriovenous Fistula Stenosis ◽  
Using Data ◽  
The Cochrane Library

AbstractObjectiveThe objective of the present study was to compare the effectiveness of high-pressure balloon (HPB) versus conventional balloon angioplasty (BA) in treating arteriovenous fistula (AVF) stenosis.Materials and MethodsA meta-analysis was conducted using data acquired from PubMed, EMBASE, the Cochrane Library, SinoMed, CNKI, WanFang and VIP databases from the time the databases were established to November 2020. All analyses included in the studies comprised the subgroups of HPB and BA. The patency of AVF was compared between the two groups at 3 months, 6 months and 12 months after operation.ResultsNine studies comprising 475 patients were included in the meta-analysis. The pooled results revealed that stenosis rate of AVFs treated with HPB was significantly lower than that of AVFs treated with conventional balloon at 3 months (OR= 0.37, 95% CI 0.21 to 0.67, p<0.001) and 6 months after operation (OR= 0.33, 95% CI 0.15 to 0.75, p=0.008). In addition, the technical success rate of HPB groups was high (OR= 0.14, 95% CI 0.05 to 0.35, p<0.001). However, no significant difference was observed between the experimental and control groups at 12 months after operation (OR= 0.61, 95% CI 0.29 to 1.25, p=0.18). No significant publication bias was observed in the analyses.ConclusionHPB is a potential primary option for the treatment of AVF stenosis, with a lower 3- and 6-month stenosis rate than BA. However, the long-term effect of HPB was not satisfactory; therefore, further research should be conducted to elucidate the relationship between the two groups.

scholarly journals Efficacy of High-Pressure Balloon for the Treatment of Arteriovenous Fistula Stenosis: A Meta-Analysis

2021 ◽  
pp. 152660282110586
Author(s):  
Yu Li ◽  
Wenhao Cui ◽  
Jukun Wang ◽  
Chao Zhang ◽  
Tao Luo
Keyword(s):  
High Pressure ◽  
Arteriovenous Fistula ◽  
Meta Analysis ◽  
Cochrane Library ◽  
Restenosis Rate ◽  
Technical Success Rate ◽  
Significant Difference ◽  
Arteriovenous Fistula Stenosis ◽  
Using Data ◽  
Meta Analyses

Objective: The objective of the present study was to compare the effectiveness of high-pressure balloon (HPB) versus conventional balloon (CB) angioplasty in treating arteriovenous fistula (AVF) stenosis. Materials and Methods: A meta-analysis was conducted using data acquired from PubMed, EMBASE, the Cochrane Library, SinoMed, CNKI, WanFang, and VIP databases from the time the databases were established to December 2020. All analyses included in the studies comprised the subgroups of HPB and CB. The patency rates of AVF were compared between 2 groups at 3, 6, and 12 months after operation. Results: Seven studies comprising 364 patients were included in the meta-analyses. The pooled results revealed that restenosis rate of AVFs treated with HPB was significantly lower than that of AVFs treated with CB at 3 months (odds ratio [OR] = 0.32, 95% confidence interval [CI] = 0.16 to 0.61, p<0.001) and 6 months after operation (OR= 0.29, 95% CI = 0.11 to 0.79, p = 0.01). In addition, the technical success rate of HPB groups was higher (OR = 0.13, 95% CI = 0.05 to 0.36, p<0.001). However, no significant difference was observed between HPB and CB groups at 12 months after operation (OR = 0.68, 95% CI = 0.30 to 1.52, p = 0.35). No significant publication bias was observed in the analyses. Conclusion: High-pressure balloon is a potential option for the treatment of AVF stenosis, with a lower 3- and 6-month restenosis rate than CB. However, 12-month patency rate of HPB was not superior to CB. Therefore, further studies should be conducted to investigate the mechanisms of restenosis after angioplasty.

scholarly journals Using Feed Forward Neural Network to Solve Eigenvalue Problems

2014 ◽  
Vol 2014 ◽  
pp. 1-8
Author(s):  
Luma N. M. Tawfiq ◽  
Othman M. Salih
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Artificial Neural Networks ◽  
Eigenvalue Problems ◽  
Back Propagation ◽  
Training Algorithms ◽  
Parallel Processor ◽  
Feed Forward Neural Network ◽  
Levenberg Marquardt ◽  
Quasi Newton

The aim of this paper is to presents a parallel processor technique for solving eigenvalue problem for ordinary differential equations using artificial neural networks. The proposed network is trained by back propagation with different training algorithms quasi-Newton, Levenberg-Marquardt, and Bayesian Regulation. The next objective of this paper was to compare the performance of aforementioned algorithms with regard to predicting ability.

On Simulation Performance of Feedforward and NARX Networks Under Different Numerical Training Algorithms

2016 ◽  
pp. 171-183 ◽  
Author(s):  
Salim Lahmiri
Keyword(s):  
Conjugate Gradient ◽  
Stock Price ◽  
Moving Average ◽  
Autoregressive Moving Average ◽  
Training Algorithms ◽  
Conjugate Gradient Algorithms ◽  
Simulation Performance ◽  
Gradient Algorithms ◽  
Marquardt Algorithm ◽  
Levenberg Marquardt

This chapter focuses on comparing the forecasting ability of the backpropagation neural network (BPNN) and the nonlinear autoregressive moving average with exogenous inputs (NARX) network trained with different algorithms; namely the quasi-Newton (Broyden-Fletcher-Goldfarb-Shanno, BFGS), conjugate gradient (Fletcher-Reeves update, Polak-Ribiére update, Powell-Beale restart), and Levenberg-Marquardt algorithm. Three synthetic signals are generated to conduct experiments. The simulation results showed that in general the NARX which is a dynamic system outperforms the popular BPNN. In addition, conjugate gradient algorithms provide better prediction accuracy than the Levenberg-Marquardt algorithm widely used in the literature in modeling exponential signal. However, the LM performed the best when used for forecasting the Moroccan and South African stock price indices under both the BPNN and NARX systems.

Artificial Neural Network Training Algorithms in Modeling of Radial Overcut in EDM

2018 ◽  
pp. 140-150
Author(s):  
Raja Das ◽  
Mohan Kumar Pradhan
Keyword(s):  
Complex Geometry ◽  
Electrical Discharge Machining ◽  
Cutting Tools ◽  
Back Propagation ◽  
Training Algorithms ◽  
Radial Overcut ◽  
Levenberg Marquardt ◽  
Resilient Back Propagation ◽  
Improved Performance ◽  
Traditional Manufacturing

This chapter describes with the comparison of the most used back propagations training algorithms neural networks, mainly Levenberg-Marquardt, conjugate gradient and Resilient back propagation are discussed. In the present study, using radial overcut prediction as illustrations, comparisons are made based on the effectiveness and efficiency of three training algorithms on the networks. Electrical Discharge Machining (EDM), the most traditional non-traditional manufacturing procedures, is growing attraction, due to its not requiring cutting tools and permits machining of hard, brittle, thin and complex geometry. Hence it is very popular in the field of modern manufacturing industries such as aerospace, surgical components, nuclear industries. But, these industries surface finish has the almost importance. Based on the study and test results, although the Levenberg-Marquardt has been found to be faster and having improved performance than other algorithms in training, the Resilient back propagation algorithm has the best accuracy in testing period.

Artificial Neural Network Training Algorithms in Modeling of Radial Overcut in EDM

2022 ◽  
pp. 329-339
Author(s):  
Raja Das ◽  
Mohan Kumar Pradhan
Keyword(s):  
Complex Geometry ◽  
Electrical Discharge Machining ◽  
Cutting Tools ◽  
Back Propagation ◽  
Training Algorithms ◽  
Radial Overcut ◽  
Levenberg Marquardt ◽  
Resilient Back Propagation ◽  
Improved Performance ◽  
Traditional Manufacturing

This chapter describes with the comparison of the most used back propagations training algorithms neural networks, mainly Levenberg-Marquardt, conjugate gradient and Resilient back propagation are discussed. In the present study, using radial overcut prediction as illustrations, comparisons are made based on the effectiveness and efficiency of three training algorithms on the networks. Electrical Discharge Machining (EDM), the most traditional non-traditional manufacturing procedures, is growing attraction, due to its not requiring cutting tools and permits machining of hard, brittle, thin and complex geometry. Hence it is very popular in the field of modern manufacturing industries such as aerospace, surgical components, nuclear industries. But, these industries surface finish has the almost importance. Based on the study and test results, although the Levenberg-Marquardt has been found to be faster and having improved performance than other algorithms in training, the Resilient back propagation algorithm has the best accuracy in testing period.

Sign in / Sign up

Export Citation Format

Share Document