Let E prediction denote the energy consumption of mobility prediction.
For example, future research may examine the predictive validity of the SBSA (e.g., prediction of severity of social anxiety over time).
This could either be classification (e.g., discrimination between binder and non-binder) or regression (e.g., prediction of peptide binding affinity).
e) The prediction of pre-drill overpressure is required for the monitoring of hydrocarbon production in the boreholes (Sayers et al., 2002).
All of these results prove that artificial neural networks can be a successful tool to represent complex nonlinear systems effectively (e.g., prediction of thermophysical properties), if developed efficiently.
First, using the training set with each model, inferences were made regarding the predicted transmitting ability for E- BLUP, prediction of SNPs coefficients for Bayes A, and prediction of non-parametric coefficients for RKHS regression.
However, most of existing feature selection methods focus on the predictability (e.g., prediction accuracy) of selected results yet neglect stability.
However, before reaching this conclusion it is important to first consider carefully the predictions of E-I models.
In addition, features of each drug (e.g. prediction of drug target) were predicted by those of interaction-sharing drug (ISD) based on our hypothesis that an enriched common feature of ISDs is also likely to be a feature of the query drug.
As the result of the e-inclusion model is prediction of e-inclusion degree of person as well as e-inclusion risk factors for person, for instance inappropriate e-learning materials or no interest to learn, or dissatisfaction with e-learning environment, or others factors.
