The resulting three-volume work, known as ICD-10 (International Statistical Classification of Diseases and Related Health Problems), was published in 1992; it eventually replaced the two-volume ICD-9 in countries worldwide that used the classification.
For Concurrency bug classification, we used the classification scheme for concurrency bugs described in Section 3.1.
For radiological evaluation, we used the classification system of Cappana et al. [11].
We used the classification accuracy to measure the quality of a solution called also fitness.
In order to assess the magnitude on effect size, we used the classification presented byRomano et al. (2006).
In our study, we used the classification results of SVMs that use the MFCC features to benchmark our CNN algorithm.
We used the classification models developed from the Train Corpus on the Test Corpus, resulting in an F1of 0.54 for "Occurrences only".
In this study, we used the classification classified by Fu (2001): micropores <10 nm, transition pore being 10 100 nm, mesopores being 100 1000 nm, macropores are larger than 1000 nm.
For this classification, inductive criteria (Mejía Navarrete 2011) and open coding (Strauss 1987; Strauss and Corbin 1998) were used: the classification categories were not the result of a restrictive a priori theoretical framework but rather several readings of the data.
However, since the information given there is not always the most recent, in case of all stars used for this work we searched the available literature and in dubious cases we used the classification given in the most recent publications.
To assign different types of sources to different areas of diagrams, we used the classification algorithm from Scikit-learn (Pedregosa et al., 2011) based on a Support Vector Machine (SVM) (Chang and Lin, 2011).
