A hybrid neuro-fuzzy system for the classification of normal, fusion and PVC cardiac beats in the MIT-BIH database

Author(s):  
C. Ramirez-Rodriguez
Keyword(s):  
Author(s):  
Ahmet Kayabasi ◽  
Kadir Sabanci ◽  
Abdurrahim Toktas

In this study, an image processing techniques (IPTs) and a Sugeno-typed neuro-fuzzy system (NFS) model is presented for classifying the wheat grains into bread and durum. Images of 200 wheat grains are taken by a high resolution camera in order to generate the data set for training and testing processes of the NFS model. The features of 5 dimensions which are length, width, area, perimeter and fullness are acquired through using IPT. Then NFS model input with the dimension parameters are trained through 180 wheat grain data and their accuracies are tested via 20 data. The proposed NFS model numerically calculate the outputs with mean absolute error (MAE) of 0.0312 and classify the grains with accuracy of 100% for the testing process. These results show that the IPT based NFS model can be successfully applied to classification of wheat grains.


Author(s):  
P. Bozzola ◽  
G. Bortolan ◽  
C. Combi ◽  
F. Pinciroli ◽  
C. BroHet

2005 ◽  
Vol 29 (2) ◽  
pp. 155-165 ◽  
Author(s):  
Necaattin Barışçı ◽  
Ergün Topal ◽  
Fırat Hardalaç ◽  
İnan Güler
Keyword(s):  

2018 ◽  
Vol 13 ◽  
pp. 81-91 ◽  
Author(s):  
Oscar Takam Nkamgang ◽  
Daniel Tchiotsop ◽  
Beaudelaire Saha Tchinda ◽  
Hilaire Bertrand Fotsin

Sign in / Sign up

Export Citation Format

Share Document