For the farmers, the mean proportion of faecal pools assessed as diarrhoeic was 0.48, the mean proportion of correctly classified faecal pools was 0.84, the mean diagnostic sensitivity was 0.83 and the mean diagnostic specificity was 0.86.
Mosquitoes were classified by species; pooled by date, trap location, species, and trap type; and tested for WNV infection [average number of mosquitoes tested in each group was 13.4 (range, 1 25); see Supplemental Material (doi 10.1289/ehp.1001939) for details on the WNV testing procedures].
The diagnostic ability of each participant was evaluated by calculating the assessed diarrhoea prevalence, the proportion of correctly classified faecal pools and diagnostic sensitivity and specificity.
The true diarrhoea status of the faecal pools (defined by the faecal dry matter content) was used to calculate the diagnostic sensitivity, specificity and proportion of correctly classified faecal pools for each participant.
Fractions 4 12, representing well-translated polysomes were pooled and classified as light (poorly translated) polysome fractions (2 3 ribosomes) and well-translated heavy polysome fractions (≥4 ribosomes).
These data were pooled and classified in a summarizing table.
Where the trees species had less than 10 individuals, their numbers were pooled and classified as primary and secondary species for the chi-square test.
In the current study, multiple supervised machine learning approaches are used to classify melt pools to predict porosity in a part.
Two different accuracy measures are used and numerical experiments show that among the classification approaches used (i.e., Decision Tree (DT), K-Nearest Neighbor (KNN), Support Vector Machine (SVM), Linear Discriminant Analysis (LDA), and Quadratic Discriminant Analysis (QDA)), KNN results in the highest rate of accurately classifying melt pools (98.44%).
