This paper investigates the performance of two neural network (NN) methods viz. a radial basis function network (RBFN) and a multilayer feed forward network (MFFN) to predict the radioactivity levels at a given test site. A comparative evaluation of the two networks is done using Root mean square error (RMSE), Pearsons r, Mean error (ME) and Mean Absolute error (MAE). It was found that the RBFN performed marginally better compared to the other metho
This thesis describes the implementation of a Radial Basis Function (RBF) network to be used in pred...
We compared linear and neural network models for estimating human signal detection performance from ...
The objective of this study is twofold: (1) to investigate the factors that affect the success of un...
Existing applications of artificial neural networks in physics research and development have been an...
The development of nuclear technologies has directed environmental radioactivity research toward con...
In this paper, three individual models and one generalized radial basis function neural network (RBF...
We propose to use a Radial Basis Function (RBF) network for source localization in the brain, and sy...
The aim of this study was to determine spatial risk dispersion of ambient gamma dose rate (AGDR) by ...
This paper studies the application of radial basis functions to predict nitrogen oxides 24 hours in ...
An artificial neural network (ANN) model for the prediction of measuring uncertainties in gamma-ray ...
At first, simulated ?γ-ray spectra for a set of 25 radionuclides, have been produced using the Gamm...
A three-layer feed-forward artificial neural network with six different algorithms applied on differ...
An artificial neural network (ANN) model was used for the prediction of peak-to-background ratio (PB...
Regional ionosphere mapping is necessary to understand the underlying characteristics of ionosphere....
Characterization and modeling of ionospheric variability in space and time is very important for com...
This thesis describes the implementation of a Radial Basis Function (RBF) network to be used in pred...
We compared linear and neural network models for estimating human signal detection performance from ...
The objective of this study is twofold: (1) to investigate the factors that affect the success of un...
Existing applications of artificial neural networks in physics research and development have been an...
The development of nuclear technologies has directed environmental radioactivity research toward con...
In this paper, three individual models and one generalized radial basis function neural network (RBF...
We propose to use a Radial Basis Function (RBF) network for source localization in the brain, and sy...
The aim of this study was to determine spatial risk dispersion of ambient gamma dose rate (AGDR) by ...
This paper studies the application of radial basis functions to predict nitrogen oxides 24 hours in ...
An artificial neural network (ANN) model for the prediction of measuring uncertainties in gamma-ray ...
At first, simulated ?γ-ray spectra for a set of 25 radionuclides, have been produced using the Gamm...
A three-layer feed-forward artificial neural network with six different algorithms applied on differ...
An artificial neural network (ANN) model was used for the prediction of peak-to-background ratio (PB...
Regional ionosphere mapping is necessary to understand the underlying characteristics of ionosphere....
Characterization and modeling of ionospheric variability in space and time is very important for com...
This thesis describes the implementation of a Radial Basis Function (RBF) network to be used in pred...
We compared linear and neural network models for estimating human signal detection performance from ...
The objective of this study is twofold: (1) to investigate the factors that affect the success of un...