scholarly journals Artificial Neural Network Models for Material Classification by Photon Scattering Analysis

2020 ◽  
Vol 9 (03) ◽  
pp. 01-04
Author(s):  
Dr. Naveen Jain
Keyword(s):  
Neural Network ◽  
Risk Factors ◽  
Artificial Neural Network ◽  
Network Models ◽  
Photon Scattering ◽  
Neural Network Models ◽  
Material Classification ◽  
Artificial Neural ◽  
Artificial Neural Network Models ◽  
The Impact

This article explains the risk factors involved in a business. In each type of business, there are certain risk factors for the implementation of anything in the business. The type of risks involved can depend upon many factors. It also depends on the type of business an organisation is doing. But it is very important that the risk analyst does all the analysis of the risks that might arise in future and must take necessary actions in order to avoid those risks. The risk analyst can also try to reduce the impact of the risks on the business. Therefore, it is very important that the risk analyst should have the knowledge of how to analyse risk and then can act upon them.

scholarly journals Analysis of Photon Scattering Trends for Material Classification Using Artificial Neural Network Models

2013 ◽  
Vol 60 (2) ◽  
pp. 515-519 ◽  
Author(s):  
M. Iqbal Saripan ◽  
Wira Hidayat Mohd Saad ◽  
Suhairul Hashim ◽  
Ahmad Taufek Abdul Rahman ◽  
Kevin Wells ◽  
...  
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Network Models ◽  
Photon Scattering ◽  
Neural Network Models ◽  
Material Classification ◽  
Artificial Neural ◽  
Artificial Neural Network Models

Automatic Electrocardiographic Analysis Using Artificial Neural Network Models

2011 ◽  
Vol 403-408 ◽  
pp. 3587-3593
Author(s):  
T.V.K. Hanumantha Rao ◽  
Saurabh Mishra ◽  
Sudhir Kumar Singh
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Radial Basis Function ◽  
Basis Function ◽  
Network Models ◽  
Neural Network Models ◽  
Radial Basis ◽  
Artificial Neural ◽  
Artificial Neural Network Models ◽  
Self Organizing

In this paper, the artificial neural network method was used for Electrocardiogram (ECG) pattern analysis. The analysis of the ECG can benefit from the wide availability of computing technology as far as features and performances as well. This paper presents some results achieved by carrying out the classification tasks by integrating the most common features of ECG analysis. Four types of ECG patterns were chosen from the MIT-BIH database to be recognized, including normal sinus rhythm, long term atrial fibrillation, sudden cardiac death and congestive heart failure. The R-R interval features were performed as the characteristic representation of the original ECG signals to be fed into the neural network models. Two types of artificial neural network models, SOM (Self- Organizing maps) and RBF (Radial Basis Function) networks were separately trained and tested for ECG pattern recognition and experimental results of the different models have been compared. The trade-off between the time consuming training of artificial neural networks and their performance is also explored. The Radial Basis Function network exhibited the best performance and reached an overall accuracy of 93% and the Kohonen Self- Organizing map network reached an overall accuracy of 87.5%.

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