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Therefore, in our case study, we aim to classify driver behavior based on outlier detection.
The aim of this case study is to evaluate the designed and implemented algorithms, as shown in Section 4, to identify drivers' driving behavior based on outlier detection.
This result is the same as that of studies based on outlier detection: the thresholds are usually set to two or three standard deviations from the mean [16].
Identification of SFPs was based on outlier detection using regression analysis of normalized hybridization data within a probe set for each gene.
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Based on outlier-detection as described above, we previously proposed motif-regions for which the relative frequencies of outliers falling into one of them yields a network specific fingerprint [41].
In addition to the full data set, SNPs were categorized either as outliers or as neutral, based on outlier analyses (see Outlier Detection and Environmental Association section).
Based on outlier diagnostics seven samples were categorized as outliers and removed from the data set prior to further analysis.
So, in BDA analyzing the dataset based on univariate outlier detection method leads to epistemological pitfall.
(ii) A prototype to identify driver behavior based on online outlier detection.
Based on online outlier detection, this section shows and compares the drivers' driving profiles.
Next, we implemented a novel scoring mechanism based on online outlier detection to quantitatively evaluate drivers' maneuvers as either cautious or reckless.
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