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An aerosol sampler equipped with a polytetrafluoroethylene filter with 1 μm pore size was used for personal samplings in order to measure inhalable dust and superabsorbent polymer.
The Institute of Occupational Medicine, IOM sampler equipped with a polytetrafluoroethylene filter with 1 μm pore size was used for personal samplings in order to measure inhalable dust and super absorbent polymer [ 13].
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Samples were normalized for unique read number via down-sampling, in order to eliminate bias from sequencing depth variation [ 18].
For validation of classification performance, we used the 10-fold cross validation procedure, repeated 100 times with different sub-samplings in order to estimate the mean performance of the signature and the confidence interval (results are expressed as mean±95% confidence interval, approximately corresponding to 2 standard deviations).
In addition, the SMOTE (Synthetic Minority Over-sampling Technique) algorithm is adopted to resample fault samples in order to solve the serious imbalance problem of samples.
Future work will focus on the improvement of the proposed sub-sampling technique in order to use fewer samples for the information recovery and achieve a lower error.
Furthermore, our sample selection procedure included over-sampling cases in order to improve power to discriminate between patients that would go on to develop breast cancer from those that would not.
In our previous work, we used PSO as an evolutionary under-sampling technique, in order to select the best possible sample subset from the majority class.
For the interpolation filter, we need more sampling points in order to achieve ideal precision.
Otherwise, we reduce the sampling frequency in order to save energy.
In this work, we investigate an effective and efficient data sampling scheme in order to achieve timely monitoring of collective attention.
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