Mo-frequency cepstrum coefficients extraction from infant cry for classification of normal and pathological cry with feed-forward neural networks

2004 ◽  
Author(s):  
J.O. Garcia ◽  
C.A. Reyes Garcia
Keyword(s):  
Neural Networks ◽  
Feed Forward ◽  
Infant Cry ◽  
Feed Forward Neural Networks

JET CLASSIFICATION AT CDF

1992 ◽  
Vol 03 (supp01) ◽  
pp. 315-320
Author(s):  
S. Bianchin ◽  
A. Domini ◽  
M. Dall’Agata ◽  
M. De Nardi ◽  
F. Zara ◽  
...  
Keyword(s):  
Neural Networks ◽  
Feed Forward ◽  
Model Dependence ◽  
Fisher Discriminant ◽  
First Results ◽  
Feed Forward Neural Networks

Results for the classification of jets obtained with the use feed-forward neural networks are reviewed with particular attention to model-dependence, flavor sensitivity and Et-dependence. First results obtained with a Kohonen-type network are also presented and both are compared with those obtained with a Fisher discriminant.

An Evolutionary Approach to the Use of Neural Networks in the Segmentation of Handwritten Numerals

1997 ◽  
Vol 11 (05) ◽  
pp. 717-734 ◽  
Author(s):  
J. M. Westall ◽  
M. S. Narasimha
Keyword(s):  
Neural Networks ◽  
Production System ◽  
Background Noise ◽  
Evolutionary Approach ◽  
Feed Forward ◽  
Feed Forward Neural Networks ◽  
Decision Functions

Neural networks are now widely and successfully used in the recognition of handwritten numerals. Despite their wide use in recognition, neural networks have not seen widespread use in segmentation. Segmentation can be extremely difficult in the presence of connected numerals, fragmented numerals, and background noise, and its failure is a principal cause of rejected and incorrectly read documents. Therefore, strategies leading to the successful application of neural technologies to segmentation are likely to yield important performance benefits. In this paper we identify problems that have impeded the use of neural networks in segmentation and describe an evolutionary approach to applying neural networks in segmentation. Our approach, based upon the use of monotonic fuzzy valued decision functions computed by feed-forward neural networks, has been successfully employed in a production system.

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