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Two reproducible sample generation approaches are proposed in this research.
This method relies on a technique for random sample generation in a given domain.
The proposed approach firstly applies reproducible sample generation to convert the observational data to experimental data.
The sample generation is then based on the conditional densities method.
The 1/f ambient noise sample generation is based on transformation functions performed on uniform random sequences.
This way, we avoid the step of sample generation and evaluation, and thus the processing load is dramatically decreased.
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The three-part sequential processing routine requires: 1) a regressive data-compression preprocessing, 2) a smart-sample generation using general-regression neural networks (GRNN), and 3) a screening power prediction using 'reverse' swarm intelligence (SI).
The samples generation procedure is in the following steps.
In a previous study, these transformation functions were successfully evaluated for a low-frequency optical noise samples generation [18].
MrBayes recorded the trees for both partitions during each sampled generation.
Sampling, generation of spiked samples, extraction and UHPLC TOF-MS analysis were performed at the National Food Administration, Sweden.
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