Sentence examples for predictions were explored from inspiring English sources

Model predictions were explored for a range of HFM lengths and larger HFM ID to compare with those from rectangular modules with shorter HFMs.

A review of empirical correlations and ML techniques that have been developed for viscosity predictions is explored in this section.

The independence between the effect of miR-30e* on disease-specific survival and the classical clinico-pathological risk factors was investigated, and the advantages deriving from the use of a combined gene- and miRNA-based outcome prediction were explored.

Instead of using this approach, application of some artificial intelligence techniques for production prediction is explored in this paper.

The role of temporal aspects in prediction is explored by Tylenda et al. [43], who exploit the information of recent interaction between individuals to improve the prediction accuracy.

The effect of non-linear mappings during image registration to a standard stereotactic space on the prediction is explored with reference to the amount of local deformation.

In addition, gene models that had alternative model predictions which were longer were explored using InterProscan5 and manually corrected where applicable.

The predictions of the chosen models were explored using a set of heat-treated steels.

Two machine learning methods, support vector machine (SVM) and random forest (RF), were explored to develop prediction models for diversely structural selective inhibitors of MMP-3 over MMP-1 and MMP-9 versus MMP-1 in this work, individually.

Potential outliers were explored based on a 95% prediction eclipse.

These predictions will be explored elsewhere.