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The dataset was normalized and transformed using principal component analysis (PCA) in order to increase the efficiency of the modelling.
Fourier transform infrared (FTIR) spectroscopy was combined to Principal Component Analysis (PCA) in order to extract chemical information during oil oxidation process.
There is a need for sensitive imaging techniques to assess metastatic and locally recurrent PCa in order to improve the outcome for these patients [3].
The Bacchelli and Papi algorithm uses a linear transformation, "a wavelet transform," before computing the PCA in order to achieve better results.
The approach combines a modified GA with principal component analysis (PCA) in order to improve the estimation accuracy of production performance.
Many of these processes utilize principal component analysis (PCA) in order to carry out statistical process monitoring due to its computational simplicity.
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The maximum likelihood PCA (MLPCA) method has been devised in chemometrics as a generalization of the well-known PCA method in order to derive consistent estimators in the presence of errors with known error distribution.
Dynamic Principal Component Analysis (DPCA) is an extension of Principal Component Analysis (PCA), developed in order to add the ability to capture the autocorrelative behavior of processes, to the existent and well-known PCA capability for modeling cross-correlation between variables.
We applied the Principal Components Analysis (PCA) technique in order to find different functional networks corresponding to the different stimuli.
The percentage changed uptake, based on SUVmax and SUVmeanTH, of each tracer was evaluated in control and AD-treated androgen-sensitive (LAPC-4) and androgen-independent (22Rv1) PCa xenografts in order to assess the impact of androgen on tracer uptake.
The full data set of obtained results was analyzed using principal component analysis (PCA) models in order to describe the systematic variability.
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