Estimating the diffuse solar radiation using a coupled support vector machine–wavelet transform model

2016 ◽  
Vol 56 ◽  
pp. 428-435 ◽  
Author(s):  
Shahaboddin Shamshirband ◽  
Kasra Mohammadi ◽  
Hossein Khorasanizadeh ◽  
Por Lip Yee ◽  
Malrey Lee ◽  
...  
Keyword(s):  
Support Vector Machine ◽  
Wavelet Transform ◽  
Solar Radiation ◽  
Support Vector ◽  
Diffuse Solar Radiation

A new hybrid support vector machine–wavelet transform approach for estimation of horizontal global solar radiation

2015 ◽  
Vol 92 ◽  
pp. 162-171 ◽  
Author(s):  
Kasra Mohammadi ◽  
Shahaboddin Shamshirband ◽  
Chong Wen Tong ◽  
Muhammad Arif ◽  
Dalibor Petković ◽  
...  
Keyword(s):  
Support Vector Machine ◽  
Wavelet Transform ◽  
Solar Radiation ◽  
Global Solar Radiation ◽  
Support Vector

Raman spectroscopy-based label-free cell identification using wavelet transform and support vector machine

RSC Advances ◽  
2016 ◽  
Vol 6 (55) ◽  
pp. 50027-50033 ◽  
Author(s):  
S. Bakhtiaridoost ◽  
H. Habibiyan ◽  
S. Muhammadnejad ◽  
M. Haddadi ◽  
H. Ghafoorifard ◽  
...  
Keyword(s):  
Support Vector Machine ◽  
Raman Spectroscopy ◽  
Wavelet Transform ◽  
Raman Spectra ◽  
Free Cell ◽  
Support Vector ◽  
Label Free ◽  
Cell Identification ◽  
Rare Cells

Wavelet transform and SVM applied to Raman spectra makes a powerful and accurate tool for identification of rare cells such as CTCs.

AUTOMATED RECOGNITION OF SINGLE & HYBRID POWER QUALITY DISTURBANCES USING WAVELET TRANSFORM BASED SUPPORT VECTOR MACHINE

2016 ◽  
Vol 79 (1) ◽  
Author(s):  
Suhail Khokhar ◽  
A. A. Mohd Zin ◽  
M. A. Bhayo ◽  
A. S. Mokhtar
Keyword(s):  
Neural Network ◽  
Support Vector Machine ◽  
Wavelet Transform ◽  
Power Quality ◽  
Probabilistic Neural Network ◽  
Recognition Rate ◽  
Classification Performance ◽  
Support Vector ◽  
Discrete Wavelet ◽  
Feed Forward Neural Network

The monitoring of power quality (PQ) disturbances in a systematic and automated way is an important issue to prevent detrimental effects on power system. The development of new methods for the automatic recognition of single and hybrid PQ disturbances is at present a major concern. This paper presents a combined approach of wavelet transform based support vector machine (WT-SVM) for the automatic classification of single and hybrid PQ disturbances. The proposed approach is applied by using synthetic models of various single and hybrid PQ signals. The suitable features of the PQ waveforms were first extracted by using discrete wavelet transform. Then SVM classifies the type of PQ disturbances based on these features. The classification performance of the proposed algorithm is also compared with wavelet based radial basis function neural network, probabilistic neural network and feed-forward neural network. The experimental results show that the recognition rate of the proposed WT-SVM based classification system is more accurate and much better than the other classifiers. 

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