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So, among the test methods included in this study, the impact strength test is the most reproducible test; but the variability of test results for the other test methods is within acceptable limits for most engineering purposes.
Here we study this condition for the main machine learning methods included in this study: SVM, RF, and PS-IRV.
Due to the complexity of certain DIF methods included in this study, we chose the Rasch model to improve the efficiency of the simulation study because the Rasch model estimates fewer parameters than other models (e.g., 2-parameter IRT model).
The number of methods included in this study may be relatively small, but the remit of this work is well-defined and focused.
A detailed description of the sampling sites and collection methods included in this study is discussed in Rusch et al. (2007) [41].
Bioline Selecta is one of the quickest salmonella detection methods included in this study.
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The main motivation here was to assemble reasonably sized datasets for each activity class, suitable for comparing the performance of the machine learning methods included in the study while avoiding creating highly unbalanced datasets.
The Bioline Optima ELISA method showed the poorest performance of all methods included in the study, with an overall calculated sensitivity of 0.23 (see Table 1).
The various commercial ELISA methods and the NMKL method showed the poorest performance of the methods included in the study, and were closely dependent on the origin of the faeces used and on which salmonella strain was to be detected.
Nine segmentation methods were included in this study, with five being excluded from analysis after being reviewed by a radiologist.
Multiple types of surface cooling methods were included in this study, whereas a heat-exchange catheter with saline-circulating balloons was the only endovascular cooling method device.
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