scholarly journals FPGA Synthesis of SIRM Fuzzy System-Classification Of Diabetic Epilepsy Risk Level

2012 ◽  
Vol 38 ◽  
pp. 391-404
Author(s):  
N.B. Balamurugan ◽  
M. Jothi ◽  
R. Harikumar
Keyword(s):  
Fuzzy System ◽  
Risk Level

Classification of flight takeoff risk level based on selective ensemble clustering

2021 ◽  
Author(s):  
Zhenya Liu ◽  
Haiyan Chen ◽  
Xiaye Hou ◽  
Ligang Yuan
Keyword(s):  
Risk Level ◽  
Ensemble Clustering ◽  
Selective Ensemble

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.

Classification of Multi-Class Datasets Using 2D Membership Functions in TSK Fuzzy System

2010 ◽  
Vol 2 (1) ◽  
pp. 33-40
Author(s):  
Loghman Kaki ◽  
Mohammad Teshnelab ◽  
Mahdi Aliyari Shooredeli
Keyword(s):  
Fuzzy System ◽  
Membership Functions

Speech Recognition Using Combined Fuzzy and Ant Colony algorithm

2016 ◽  
Vol 6 (5) ◽  
pp. 2205
Author(s):  
Fooad Jalili ◽  
Milad Jafari Barani
Keyword(s):  
Speech Recognition ◽  
Dimensionality Reduction ◽  
Input Signal ◽  
Speech Signal ◽  
Fuzzy System ◽  
Ant Colony Algorithm ◽  
Noise Removal ◽  
Ant Colony ◽  
Speech Detection

<p><span>In recent years various methods has been proposed for speech recognition and removing noise from the speech signal became an important issue. In this paper a fuzzy system has been proposed for speech recognition that can obtain accurate results using classification of speech signals with “Ant Colony” algorithm.  First, speech samples are given to the fuzzy system to obtain a pattern for every set of signals that can be helpful for dimensionality reduction, easier checking of outcome and better recognition of signals.  Then, the “ACO” algorithm is used to cluster these signals and determine a cluster for each input signal. Also, with this method we will be able to recognize noise and consider it in a separate cluster and remove it from the input signal. Results show that the accuracy for speech detection and noise removal is desirable.</span></p>

w@reRISK method: Security risk level classification of stock keeping units in a warehouse

2015 ◽  
Vol 79 ◽  
pp. 358-368 ◽  
Author(s):  
Miguel Gastón Cedillo-Campos ◽  
Hermes Orestes Cedillo-Campos
Keyword(s):  
Risk Level ◽  
Security Risk

Intelligent classification of electrocardiogram (ECG) signal using extended Kalman Filter (EKF) based neuro fuzzy system

2006 ◽  
Vol 82 (2) ◽  
pp. 157-168 ◽  
Author(s):  
Yeong Pong Meau ◽  
Fatimah Ibrahim ◽  
Selvanathan A.L. Narainasamy ◽  
Razali Omar
Keyword(s):  
Kalman Filter ◽  
Extended Kalman Filter ◽  
Fuzzy System ◽  
Ecg Signal ◽  
Neuro Fuzzy ◽  
Electrocardiogram Ecg

scholarly journals Assessment of Food Security Risk Level Using Type 2 Fuzzy System

2016 ◽  
Vol 102 ◽  
pp. 547-554 ◽  
Author(s):  
Rahib H. Abiyev ◽  
Kaan Uyar ◽  
Umit Ilhan ◽  
Elbrus Imanov
Keyword(s):  
Food Security ◽  
Fuzzy System ◽  
Risk Level ◽  
Security Risk
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