scholarly journals Use an Efficient Neural Network to Improve the Arabic Handwriting Recognition

2021 ◽  
Vol 8 ◽  
pp. 43-48
Author(s):  
Husam Ahmed Al Hamad
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Handwriting Recognition ◽  
Network Models ◽  
Neural Network Models ◽  
Segmentation Technique ◽  
Artificial Neural ◽  
Artificial Neural Network Models

Using an efficient neural network for recognition and segmentation will definitely improve the performance and accuracy of the results; in addition to reduce the efforts and costs. This paper investigates and compares between results of four different artificial neural network models. The same algorithm has been applied for all with applying two major techniques, first, neural-segmentation technique, second, apply a new fusion equation. The neural techniques calculate the confidence values for each Prospective Segmentation Points (PSP) using the proposed classifiers in order to recognize the better model, this will enhance the overall recognition results of the handwritten scripts. The fusion equation evaluates each PSP by obtaining a fused value from three neural confidence values. CPU times and accuracies are also reported. Experiments that were performed of classifiers will be compared with each other and with the literature.

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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