A Self-Organizing Deep Neuro-Fuzzy System Approach for Classification of Kidney Cancer Subtypes Using miRNA Genomics Data

2021 ◽  
pp. 106132
Author(s):  
Saeed Pirmoradi ◽  
Mohammad Teshnehlab ◽  
Nosratollah Zarghami ◽  
Arash Sharifi
Keyword(s):  
Kidney Cancer ◽  
Fuzzy System ◽  
System Approach ◽  
Cancer Subtypes ◽  
Neuro Fuzzy ◽  
Self Organizing

Accurate Molecular Classification of Kidney Cancer Subtypes Using MicroRNA Signature

2011 ◽  
Vol 59 (5) ◽  
pp. 721-730 ◽  
Author(s):  
Youssef M. Youssef ◽  
Nicole M.A. White ◽  
Jörg Grigull ◽  
Adriana Krizova ◽  
Christina Samy ◽  
...  
Keyword(s):  
Kidney Cancer ◽  
Molecular Classification ◽  
Cancer Subtypes

scholarly journals Classification Of Wheat Grains Using Image Processing Techniques Based Neuro-Fuzzy System Model

2018 ◽  
pp. 57-61
Author(s):  
Ahmet Kayabasi ◽  
Kadir Sabanci ◽  
Abdurrahim Toktas
Keyword(s):  
Image Processing ◽  
Fuzzy System ◽  
Absolute Error ◽  
Data Set ◽  
Wheat Grains ◽  
Image Processing Techniques ◽  
Neuro Fuzzy ◽  
Length Width ◽  
Processing Techniques

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.

RFCMAC: A novel reduced localized neuro-fuzzy system approach to knowledge extraction

2011 ◽  
Vol 38 (10) ◽  
pp. 12066-12084 ◽  
Author(s):  
Richard J. Oentaryo ◽  
Michel Pasquier ◽  
Chai Quek
Keyword(s):  
Fuzzy System ◽  
System Approach ◽  
Knowledge Extraction ◽  
Neuro Fuzzy

A hybrid neuro-fuzzy system for ECG classification of myocardial infarction

2002 ◽  
Author(s):  
P. Bozzola ◽  
G. Bortolan ◽  
C. Combi ◽  
F. Pinciroli ◽  
C. BroHet
Keyword(s):  
Myocardial Infarction ◽  
Fuzzy System ◽  
Neuro Fuzzy
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