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The architecture of decision support system consists of three phases: Alert Preprocessing Phase, Model Constructing Phase and Rule Refining Phase.
Besides, the Model Constructing Phase is used to construct three kinds of rule classes: normal rule classes, intrusion rule classes and suspicious rule classes, to filter false alert patterns and analyze each existing or unknown alert patterns; each rule class represents a set of classification rules.
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However, a potential pitfall is that the model constructed during the training phase could also be negatively affected by unreliable or misleading user feedback.
Classification of RSS values according to position, however, has been shown to be an interesting alternative; here, the mobile terminal's position is estimated by classifying sets of measured RSS values, called fingerprints, obtained from distinct spatial regions, making use of a model constructed in a previous "training" phase.
For model validation four new fermentations were done showing that the model constructed describes reasonably well the dynamic of the three phases.
To ensure the correctness of the software system developed, all models constructed in each phase are verifiable.
The utilization of models constructed in the building design phases – for the reason of estimating energy performance – properly adapted for the task at hand can be a viable methodology to overcome this problem.
These techniques are practised in a model-driven development process in which models are constructed in each phase of the development.
An isotopomer model was constructed in two phases.
The analysis presented here paves the way for constructing quantitative phase field models for the irradiation-driven nucleation and growth of voids in crystalline solids by matching these models to a recently developed sharp interface theory.
The model was constructed using data from phase I studies of healthy volunteers and phase II studies of infected patients (Table 3).
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