scholarly journals Feed Forward Artificial Neural Network Model to Estimate the TPH Removal Efficiency in Soil Washing Process

2017 ◽  
Vol 6 (1) ◽  
pp. 96-104
Author(s):  
Hossein Jafari Mansoorian ◽  
Mostafa Karimaee ◽  
Mahdi Hadi ◽  
Elaheh Jame Porazmey ◽  
Farzan Barati ◽  
...  
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Removal Efficiency ◽  
Network Model ◽  
Neural Network Model ◽  
Artificial Neural Network Model ◽  
Soil Washing ◽  
Feed Forward ◽  
Washing Process ◽  
Artificial Neural

Health Insurance Claim Prediction Using Artificial Neural Networks

2020 ◽  
Vol 9 (3) ◽  
pp. 40-57
Author(s):  
Sam Goundar ◽  
Suneet Prakash ◽  
Pranil Sadal ◽  
Akashdeep Bhardwaj
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Artificial Neural Network ◽  
Network Model ◽  
Neural Network Model ◽  
Artificial Neural Network Model ◽  
Insurance Company ◽  
Feed Forward ◽  
Medical Claims ◽  
Artificial Neural

A number of numerical practices exist that actuaries use to predict annual medical claim expense in an insurance company. This amount needs to be included in the yearly financial budgets. Inappropriate estimating generally has negative effects on the overall performance of the business. This study presents the development of artificial neural network model that is appropriate for predicting the anticipated annual medical claims. Once the implementation of the neural network models was finished, the focus was to decrease the mean absolute percentage error by adjusting the parameters, such as epoch, learning rate, and neurons in different layers. Both feed forward and recurrent neural networks were implemented to forecast the yearly claims amount. In conclusion, the artificial neural network model that was implemented proved to be an effective tool for forecasting the anticipated annual medical claims for BSP Life. Recurrent neural network outperformed the feed forward neural network in terms of accuracy and computation power required to carry out the forecasting.

Health Insurance Claim Prediction Using Artificial Neural Networks

2022 ◽  
pp. 1174-1193
Author(s):  
Sam Goundar ◽  
Suneet Prakash ◽  
Pranil Sadal ◽  
Akashdeep Bhardwaj
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Artificial Neural Network ◽  
Network Model ◽  
Neural Network Model ◽  
Artificial Neural Network Model ◽  
Feed Forward Neural Network ◽  
Feed Forward ◽  
Medical Claims ◽  
Artificial Neural

A number of numerical practices exist that actuaries use to predict annual medical claim expense in an insurance company. This amount needs to be included in the yearly financial budgets. Inappropriate estimating generally has negative effects on the overall performance of the business. This study presents the development of artificial neural network model that is appropriate for predicting the anticipated annual medical claims. Once the implementation of the neural network models was finished, the focus was to decrease the mean absolute percentage error by adjusting the parameters, such as epoch, learning rate, and neurons in different layers. Both feed forward and recurrent neural networks were implemented to forecast the yearly claims amount. In conclusion, the artificial neural network model that was implemented proved to be an effective tool for forecasting the anticipated annual medical claims for BSP Life. Recurrent neural network outperformed the feed forward neural network in terms of accuracy and computation power required to carry out the forecasting.

scholarly journals Feed-Forward Artificial Neural Network Model for Air Pollutant Index Prediction in the Southern Region of Peninsular Malaysia

2013 ◽  
Vol 04 (12) ◽  
pp. 1-10 ◽  
Author(s):  
Azman Azid ◽  
Hafizan Juahir ◽  
Mohd Talib Latif ◽  
Sharifuddin Mohd Zain ◽  
Mohamad Romizan Osman
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Network Model ◽  
Neural Network Model ◽  
Artificial Neural Network Model ◽  
Peninsular Malaysia ◽  
Air Pollutant ◽  
Southern Region ◽  
Feed Forward ◽  
Artificial Neural

Feed-forward artificial neural network model for forecasting rainfall run-off

1998 ◽  
Vol 9 (4) ◽  
pp. 419-432 ◽  
Author(s):  
Roger D. Braddock ◽  
Michael L. Kremmer ◽  
Louis Sanzogni
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Network Model ◽  
Neural Network Model ◽  
Artificial Neural Network Model ◽  
Feed Forward ◽  
Run Off ◽  
Artificial Neural

Prediction of removal efficiency of Lanaset Red G on walnut husk using artificial neural network model

2012 ◽  
Vol 103 (1) ◽  
pp. 64-70 ◽  
Author(s):  
Abuzer Çelekli ◽  
Sevil Sungur Birecikligil ◽  
Faruk Geyik ◽  
Hüseyin Bozkurt
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Removal Efficiency ◽  
Network Model ◽  
Neural Network Model ◽  
Artificial Neural Network Model ◽  
Artificial Neural ◽  
Walnut Husk

Artificial Neural Network Model for Prediction of Latent Heat Flux over Bay of Bengal

2019 ◽  
Vol 7 (5) ◽  
pp. 901-905
Author(s):  
Kanchan. V. Shende ◽  
Vishal S. Shirsat ◽  
M. R. Ramesh Kumar ◽  
K.V. Kale
Keyword(s):  
Neural Network ◽  
Heat Flux ◽  
Artificial Neural Network ◽  
Latent Heat ◽  
Latent Heat Flux ◽  
Network Model ◽  
Neural Network Model ◽  
Bay Of Bengal ◽  
Artificial Neural Network Model ◽  
Artificial Neural
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