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

Abstract Raman spectroscopy (RS) of nasopharyngeal carcinoma (NPC) tissue provides substantial biomolecular information and various biomedicine features for tissue at different stages of cancer development. This study suggested an automatic and quick method for the classification of Raman spectra at different stages of NPC by multivariate statistical analysis. During RS measurement, Raman spectra were acquired from all NPC tissues in two groups of samples: 30 early-stage NPC patients (stages I and II) and 46 advanced-stage NPC patients (stages III and IV). In addition, a tentative diagnostic algorithm comprising principal components analysis and support vector machine was used to effectively classify multivariate data from the Raman spectra to yield sensitivities (70%; 21 of 30 samples) and specificities (91%; 42 of 46 samples) by the leave-one-out cross-validation method. Meaningful chemical compositions in the classification process were then deduced by analyzing the classified mathematical model. This beneficial work provides a great potential clinical method for the automatic classification of NPC stages and the speculation of the chemical compositions for NPC staging.

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Short-Term Load Forecasting Based on Wavelet Transform and Least Squares Support Vector Machine Optimized by Improved Cuckoo Search

Energies ◽

10.3390/en9100827 ◽

2016 ◽

Vol 9 (10) ◽

pp. 827 ◽

Cited By ~ 21

Author(s):

Yi Liang ◽

Dongxiao Niu ◽

Minquan Ye ◽

Wei-Chiang Hong

Keyword(s):

Support Vector Machine ◽

Wavelet Transform ◽

Least Squares ◽

Load Forecasting ◽

Cuckoo Search ◽

Support Vector ◽

Short Term ◽

Short Term Load Forecasting

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Incipient Fault Diagnosis for the Fastening Bolt Loosening of Limit Switch Based on Wavelet Transform and Support Vector Machine

Journal of Failure Analysis and Prevention ◽

10.1007/s11668-021-01227-2 ◽

2021 ◽

Author(s):

Ling Wang ◽

Jianqiu Gao ◽

Jing Pan ◽

Yanfeng Gao ◽

Binrui Wang

Keyword(s):

Support Vector Machine ◽

Wavelet Transform ◽

Fault Diagnosis ◽

Support Vector ◽

Incipient Fault ◽

Bolt Loosening ◽

Incipient Fault Diagnosis ◽

Limit Switch

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

Jurnal Teknologi ◽

10.11113/jt.v79.5693 ◽

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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