Artificial neural network metamodel for sensitivity analysis in a total hip replacement health economic model

2019 ◽  
Vol 20 (6) ◽  
pp. 629-640 ◽  
Author(s):  
M Fasihul Alam ◽  
Andrew Briggs
Keyword(s):  
Neural Network ◽  
Health Economic ◽  
Sensitivity Analysis ◽  
Artificial Neural Network ◽  
Total Hip Replacement ◽  
Economic Model ◽  
Hip Replacement ◽  
Total Hip ◽  
Health Economic Model ◽  
Artificial Neural

scholarly journals Detecting total hip replacement prosthesis design on plain radiographs using deep convolutional neural network

2020 ◽  
Vol 38 (7) ◽  
pp. 1465-1471 ◽  
Author(s):  
Alireza Borjali ◽  
Antonia F. Chen ◽  
Orhun K. Muratoglu ◽  
Mohammad A. Morid ◽  
Kartik M. Varadarajan
Keyword(s):  
Neural Network ◽  
Convolutional Neural Network ◽  
Total Hip Replacement ◽  
Hip Replacement ◽  
Deep Convolutional Neural Network ◽  
Prosthesis Design ◽  
Plain Radiographs ◽  
Total Hip ◽  
Replacement Prosthesis

scholarly journals Ten-year estimation of Oriental beech (Fagus orientalis Lipsky) volume increment in natural forests: a comparison of an artificial neural networks model, multiple linear regression and actual increment

2021 ◽  
Author(s):  
Mahmoud Bayat ◽  
Pete Bettinger ◽  
Majid Hassani ◽  
Sahar Heidari
Keyword(s):  
Neural Network ◽  
Sensitivity Analysis ◽  
Artificial Neural Network ◽  
Productive Capacity ◽  
Natural Forests ◽  
Neural Network Method ◽  
Volume Increment ◽  
Network Method ◽  
Artificial Neural Network Method ◽  
Artificial Neural

Abstract Determining forest volume increment, the potential of wood production in natural forests, is a complex issue but is of fundamental importance to sustainable forest management. Determining potential volume increment through growth and yield models is necessary for proper management and future prediction of forest characteristics (diameter, height, volume, etc.). Various methods have been used to determine the productive capacity and amount of acceptable harvest in a forest, and each has advantages and disadvantages. One of these methods involves the artificial neural network techniques, which can be effective in natural resource management due to its flexibility and potentially high accuracy in prediction. This research was conducted in the Ramsar forests of the Mazandaran Province of Iran. Volume increment was estimated using both an artificial neural network and regression methods, and these were directly compared with the actual increment of 20 one-hectare permanent sample plots. A sensitivity analysis for inputs was employed to determine which had the most effect in predicting increment. The actual average annual volume increment of beech was 4.52 m3ha−1 yr−1, the increment was predicted to be 4.35 and 4.02 m3ha−1 yr−1 through the best models developed using an artificial neural network and using regression, respectively. The results showed that an estimate of increment can be predicted relatively well using the artificial neural network method, and that the artificial neural network method is able to estimate the increment with higher accuracy than traditional regression models. The sensitivity analysis showed that the standing volume at the beginning of the measurement period and the diameter of trees had the greatest impact on the variation of volume increment.

scholarly journals Development of Effective Artificial Neural Network Model using Sequential Sensitivity Analysis and Randomized Training

2021 ◽  
Vol 10 (6) ◽  
pp. 12-20
Author(s):  
Akshay Daydar ◽  
Keyword(s):  
Neural Network ◽  
Sensitivity Analysis ◽  
Artificial Neural Network ◽  
Research Work ◽  
Large Data ◽  
Qualitative Reasoning ◽  
Machine Learning Algorithms ◽  
Finance Industry ◽  
Training Approach ◽  
Artificial Neural

As the machine learning algorithms evolve, there is a growing need of how to train the algorithm effectively for the large data with available resources in practically less time. The paper presents an idea of developing an effective model that focuses on the implementation of sequential sensitivity analysis and randomized training approach which can be one solution to this growing need. Many researchers focused on the implementation of sensitivity analysis to eliminate the insignificant features ands reduce the complexity in data selection. These sensitivity analysis methods relatively take a large time for validation through modeling and hence found impractical for large data. On the other hand, the randomized training approach was found to be the most popular approach for training the data but there is a very brief explanation available in research articles on how this training method is meaningful in getting higher accuracy. The current work focuses on the use of sequential sensitivity analysis and randomized training in an artificial neural network (ANN) for high dimensionality thermal power plant data. The sequential sensitivity analysis (SSA) technique includes the use of correlation analysis (CA), Analysis of variance (ANOVA), Akaike information criterion (AIC) in a sequential manner to reduce the validation time for all possible feature combinations. Only selected combinations are then tested against different training methods such as downward extrapolation, upward extrapolation, interpolation and randomized training in ANN. The paper also focuses on suggesting the significance of training with randomized training with comparison-based qualitative reasoning. The statistical parameters, mean square error (RMSE), Mean absolute relative difference (MARD) and R Square (R^2)were accessed for validation purposes. The research work mainly useful in the field of Ecommerce, Finance, industry and in facilities where large data is generated.

scholarly journals Artificial Neural Network Modeling and Sensitivity Analysis of Performance and Emissions in a Compression Ignition Engine Using Biodiesel Fuel

Energies ◽  
2018 ◽  
Vol 11 (9) ◽  
pp. 2410 ◽  
Author(s):  
Farzad Jaliliantabar ◽  
Barat Ghobadian ◽  
Gholamhassan Najafi ◽  
Talal Yusaf
Keyword(s):  
Neural Network ◽  
Sensitivity Analysis ◽  
Artificial Neural Network ◽  
Research Work ◽  
Neural Network Modeling ◽  
Biodiesel Fuel ◽  
Compression Ignition ◽  
Compression Ignition Engine ◽  
Performance And Emissions ◽  
Artificial Neural

In the present research work, a neural network model has been developed to predict the exhaust emissions and performance of a compression ignition engine. The significance and novelty of the work, with respect to existing literature, is the application of sensitivity analysis and an artificial neural network (ANN) simultaneously in order to predict the engine parameters. The inputs of the model were engine load (0, 25, 50, 75 and 100%), engine speed (1700, 2100, 2500 and 2900 rpm) and the percent of biodiesel fuel derived from waste cooking oil in diesel fuel (B0, B5, B10, B15 and B20). The relationship between the input parameters and engine cylinder performance and emissions can be determined by the network. The global sensitivity analysis results show that all the investigated factors are effective on the created model and cannot be ignored. In addition, it is found that the most emissions decreased while using biodiesel fuel in the compression ignition engine.

Sign in / Sign up

Export Citation Format

Share Document