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Quality control of herbal medicines by using spectroscopic techniques and multivariate statistical analysis
The present study proposes Fourier transform infrared spectroscopy along with the statistical method principal component analysis (PCA) to identify and discriminate herbal medicines for quality control.
Denoising by Singular Value Decomposition and Its Application to Electronic Nose Data Processing
This paper analyzes the role of singular value decomposition (SVD) in denoising sensor array data of electronic nose systems. It is argued that the SVD decomposition of raw data matrix distributes
Remote sensing and machine learning for crop water stress determination in various crops: a critical review
This study aims to present an overall review of the widely used methods for crop water stress monitoring using remote sensing and machine learning and focuses on future directions for researchers.
A tumour perception system based on a multi-layer mass-spring model
Experiments indicate that the proposed model faithfully mimics actual clinical tumour, which is more accurate compared with models simulated by other systems.
Estimation of realistic renewable and non-renewable energy use targets for livestock production systems utilising an artificial neural network method: A step towards livestock sustainability
Abstract This study estimated energy use flow of buffalo farms, energy use indices, production efficiency, energy use targets, impact of energy inputs on energy output, and sensitivity analysis of
Human health damages related to air pollution in China
Investigating the relationship between air pollution and residents’ health by nesting the household registration data of the China Migrant Dynamic Survey in 2014 with city characteristic data and pollution data indicated that an increase in the concentration of air pollution significantly reduced residents' health levels.
A gradient boosting machine learning approach in modeling the impact of temperature and humidity on the transmission rate of COVID-19 in India
The gradient boosting model (GBM) has been implemented to explore the effect of the minimum temperature, maximum temperature, minimum humidity, and maximum humidity on the infection count of COVID-19 and results in the best accuracy.
Statistical pattern analysis assisted selection of polymers for odor sensor array
  • S. K. Jha, R. Yadava
  • Materials Science
    International Conference on Signal Processing…
  • 21 July 2011
This paper demonstrates application and usefulness of multivariate statistical methods like principal component analysis (PCA) and hierarchical cluster analysis (HCA) for design and development of
Optimized KPCA method for chemical vapor class recognition by SAW sensor array response analysis
The suitability of kernel principal component analysis (KPCA) as a robust feature extraction and denoising method in sensor array based vapor detection system (E-nose) is confirmed and polynomial kernel achieves persistently maximum class recognition rate of VOCs even in presence of high level of additive Gaussian noise and outlier.
Power Scaling of Chemiresistive Sensor Array Data for Odor Classification
The steady state responses of chemical sensors like the tin-oxide and composite conducting polymer sensors exhibit power law dependency on the vapor concentration. In this research, it is shown that