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Application prediction mechanism.
In-line application prediction mechanism.
The accuracy of application prediction increases with the amount of collected data.
A generalization of the several stages of the proposed approach Application prediction mechanism On-line device's context identification.
In this paper, we focus mainly on the data collecting mechanism, the application classification, the application prediction, and the optimization mechanism as follows.
The first aspect concerns the significance of the feature space itself, as one requires that the space retains the relevant information to the application prediction task.
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Uploading the collected data to the back-end component in order to be processed via mechanisms like application classification, applications prediction, and user behavior profiling.
For object-oriented applications, prediction models using design metrics can be used to identify faulty classes early on.
This step is implemented in two phases: (a) Uploading the collected data to the back-end component in order to be processed via mechanisms like application classification, applications prediction, and user behavior profiling.
Most often, a zero-mean uncorrelated Gaussian prediction error is assumed, although in many engineering applications prediction errors will show non-negligible spatial and/or temporal correlation (e.g. when densely populated sensor grids are used).
Thus it is clear the great need to foster the application of prediction methods with a good predictive performance when experimental values are not available.
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