This paper explores the issue of choosing the best data to use when running a scheduling model to select a permanent workforce for a service facility.
In a classification task, recall is a criterion of the classification ability of a prediction model to select labeled instances from training and testing datasets.
In a recent work (Messih et al., 2014), we used a Random Forest model to select templates for the third hypervarable loop of immunoglobulins, a rather elusive prolem so far.
They also developed a probabilistic model to select significant features from the 10-fold results.
We combined the terrain, watershed size, geology, and deposition variables into a GIS model to select pairs that minimized differences in these factors.
Finally, we used a statistical model to select only those modules where all targets within the module were differentially expressed between two those groups of samples.
Following the association analyses, Q-Q (quantile-quantile) plots were constructed for each of the three models and compared to the results of a naïve model to select the most appropriate model for analysis.
This paper extends an existing model to select task periods in real-time for CPS with fixed priority controller tasks scheduled by rate-monotonic algorithm.
In the final stage, all articles were discussed using a group consensus model to select the final articles included in this review.
Therefore, based on an analysis of the mode distribution of these two standards, this study develops a conditional probability model to select candidate modes.
