Microcalcification detection using a fuzzy inference system and support vector machines

2012 ◽  
Author(s):  
Younes Kabbadj ◽  
Fakhita Regragui ◽  
Mohammed Majid Himmi
Keyword(s):  
Support Vector Machines ◽  
Fuzzy Inference System ◽  
Fuzzy Inference ◽  
Support Vector ◽  
Inference System ◽  
Vector Machines ◽  
Microcalcification Detection

Hybrid Approach for Analyzing Acute Spots of Clinical Speech Data Using Fuzzy Inference System

2015 ◽  
pp. 93-137
Author(s):  
C. R. Bharathi ◽  
V. Shanthi
Keyword(s):  
Fuzzy Inference System ◽  
Speech Therapy ◽  
Fuzzy Inference ◽  
Hybrid Approach ◽  
Principal Component ◽  
Support Vector ◽  
Inference System ◽  
Vector Machines ◽  
Speech Data ◽  
Speech Training

Acoustical measures of vocal functions are used in the assessments of voice disorders and monitoring the subject's improvement with speech therapy. In this chapter, a hybrid approach is proposed to identify the acute spots in pathological speech signals. These spots represents where the speech disorder occurs. The speech training for that specific portion of speech in particular could be given for enhancing the speeches. Dimensionality reduction is done using Principal Component Analysis (PCA) on Mel Frequency Cepstrum Coefficients (MFCC) extracted. By statistical method it is proved that overall 91.60% of the words were classified correctly. The features were trained using Support Vector Machines (SVM) for categorizing normally and abnormally pronounced words. The peaks found by Fast Fourier Transform (FFT) in abnormal words is made use of in the Fuzzy Inference System (FIS) for finding the acute spots in which the aberration has occurred in the word. This hybrid approach was found to have around 98% accuracy.

scholarly journals Driver Drowsiness Detection Based on Steering Wheel Data Applying Adaptive Neuro-Fuzzy Feature Selection

Sensors ◽  
2019 ◽  
Vol 19 (4) ◽  
pp. 943 ◽  
Author(s):  
Sadegh Arefnezhad ◽  
Sajjad Samiee ◽  
Arno Eichberger ◽  
Ali Nahvi
Keyword(s):  
Feature Selection ◽  
Fuzzy Inference System ◽  
Fuzzy Inference ◽  
Detection System ◽  
Steering Wheel ◽  
Support Vector ◽  
Inference System ◽  
Drowsiness Detection ◽  
Driver Drowsiness ◽  
Neuro Fuzzy

This paper presents a novel feature selection method to design a non-invasive driver drowsiness detection system based on steering wheel data. The proposed feature selector can select the most related features to the drowsiness level to improve the classification accuracy. This method is based on the combination of the filter and wrapper feature selection algorithms using adaptive neuro-fuzzy inference system (ANFIS). In this method firstly, four different filter indexes are applied on extracted features from steering wheel data. After that, output values of each filter index are imported as inputs to a fuzzy inference system to determine the importance degree of each feature and select the most important features. Then, the selected features are imported to a support vector machine (SVM) for binary classification to classify the driving conditions in two classes of drowsy and awake. Finally, the classifier accuracy is exploited to adjust parameters of an adaptive fuzzy system using a particle swarm optimization (PSO) algorithm. The experimental data were collected from about 20.5 h of driving in the simulator. The results show that the drowsiness detection system is working with a high accuracy and also confirm that this method is more accurate than the recent available algorithms.

Sign in / Sign up

Export Citation Format

Share Document