Hearing Loss Identification via Fractional Fourier Entropy and Direct Acyclic Graph Support Vector Machine

2020 ◽  
pp. 277-289
Author(s):  
Liying Wang ◽  
Zhiqiang Xu
Keyword(s):  
Support Vector Machine ◽  
Hearing Loss ◽  
Support Vector ◽  
Acyclic Graph ◽  
Direct Acyclic Graph ◽  
Fractional Fourier Entropy

Leaf Recognition for Plant Classification Using Direct Acyclic Graph Based Multi-Class Least Squares Twin Support Vector Machine

2016 ◽  
Vol 16 (03) ◽  
pp. 1650012 ◽  
Author(s):  
Divya Tomar ◽  
Sonali Agarwal
Keyword(s):  
Support Vector Machine ◽  
Least Squares ◽  
Plant Species ◽  
Recognition System ◽  
Twin Support Vector Machine ◽  
Support Vector ◽  
Individual Plant ◽  
Species Classification ◽  
Acyclic Graph ◽  
Direct Acyclic Graph

As most of the plant species are at the risk of extinction, the task of plant identification has become a challenging process and an active area of research. In this paper, we propose a leaf recognition system for plant species classification using leaf image data through a novel direct acyclic graph based multi-class least squares twin support vector machine (DAG-MLSTSVM) classifier. Hybrid feature selection (HFS) approach is used to obtain the best discriminant features for the recognition of individual plant species. Leaves are recognized on the basis of shape and texture features. The experimental results indicate that the proposed DAG-MLSTSVM based plant leaf recognition system is highly accurate and having faster processing speed as compared to artificial neural network and direct acyclic graph based support vector machine.

Model for noise‐induced hearing loss using support vector machine

2005 ◽  
Vol 118 (3) ◽  
pp. 1896-1896
Author(s):  
Wei Qiu ◽  
Jun Ye ◽  
Xiaohong Liu‐White ◽  
Roger P. Hamernik
Keyword(s):  
Support Vector Machine ◽  
Hearing Loss ◽  
Support Vector ◽  
Noise Induced Hearing Loss
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