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After feature extraction, the affective response is analyzed individually by the classification module of each category.
Finally, the evaluation of new data by trained ANNs is achieved by the Classification module with a complete documentation via printout and log-files.
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Some sentences provide details on previously mentioned information, but by themselves do not contain any language clues for the classification module to recognize their relevance to a given information element.
The Classification module and the TemplateBase module are used by the padding document to extend its specification language to support templates.
The classification module can be implemented using ontology as the basis information (Sect. 6.1).
The classification module maps the reported symptoms to the CDC provided clinical symptoms of Ebola.
The classification module outputs five top-scored sentences in decreasing order of their confidence scores.
For a supervised analysis of the data, the classification module contains six diverse feature selection methods and six prediction algorithms, which can be combined freely by the user [see Glaab et al. (2009) and Supplementary Material].
In addition, the fault type as well as the faulty phase selection was determined by the fault classification module of ANN.
Each entry is then processed by the three classification modules: homology (HOM), structure (STR), and target site duplication (TSD).
Finally, the fourth module, called the MCCs Classification Module, classifies the detected clustered microcalcifications into five categories according to BI-RADS (Breast Imaging Reporting and Data System) format recommended by the American College of Radiology.
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