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The presented system was tested on five challenging detection setups.
The NEA was tested on five real-world classification datasets and three well-known datasets for time series forecasting (TSF).
The questionnaire was tested on five separate occasions to determine efficacy before use.
In particular, the detection method was tested on two indoor sequences provided by [9] and three outdoor sequences, while the tracking and overall performance of Auto GMM-SAMT was tested on five outdoor sequences.
Since Auto GMM-SAMT is developed for video surveillance of traffic scenarios, which are recorded diagonally from above such that the homography leads to reasonable results, the tracking performance was tested on five outdoor sequences containing mainly three-dimensional rigid objects.
Each food combination was tested on five to eight daily sessions, with all nine animals completing a minimum of 30 trials (mean = 36.7, SD = 3.5) per combination.
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The approach was tested on four open source Java programs and one industrial Java project.
The SALL4-mediated transcriptional regulation was tested on two potential target genes: PTEN and SALL1.
Sporangial density was tested on four genotypes at 9 dpi (f).
The influence of two operational variables was tested on three levels in a 9 factorial design.
Its efficacy was tested on two children with facial burns resulting hypertrophic scars.
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