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Three metabolites, in three samples from each sample group that were statistically significant in differentiating between sample classes using pattern recognition modelling, were selected for confirmation using unprocessed chromatographic data.
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Researchers from different application domains within the field of context-aware computing have investigated context recognition for the past 2 decade or so by developing a diversity of sensing devices (in addition to methods and techniques for signal and data processing, pattern recognition, modeling, and reasoning tasks).
Therefore, 695 unique metabolite peaks (105 samples), were retained for initial pattern recognition modeling.
These 11 samples were excluded from further analysis leaving a total of 94 samples for pattern recognition modeling.
Regression modeling, time-series modeling, pattern recognition modeling and stochastic process modeling are tested to model the expected occupancy status and their performances are compared in terms of the degree of statistical approximation to actual occupancy.
Finally, the SVM-based pattern recognition model is used to classify different ECG patterns.
In this paper, a novel pattern recognition model for kick diagnosis is proposed.
The results indicate the developed Apta-LoopEnc is able to significantly improve the performance of different pattern recognition models.
The concentration of adulterants is an important factor of influencing milk discrimination results of the NIR pattern recognition models.
The results show that the personalized occupancy profiles acquired through time-series modeling, pattern recognition modeling and stochastic process modeling outperform the fixed design profiles and observation-based profiles currently used in building energy simulations.
Our goal was to automate completely the process of producing pattern recognition models, consequently, we felt it was important to include pre-processing options, the number of principal components and wavelength selection in the chromosomes.
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