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The most important task is the assessment of the CCC. The CCC consists of: a) identification of one (perhaps two) key categories of reference values, applied collectively to all the outputs; b) finding categories and making an order based on results; and c) estimating and classifying categorically to one or more value categories using classifier models. It is important that the output is not based on individual human studies but rather on the results of an application of machine learning. For example, there are some studies reporting that the CCC should be used in cases of human studies, such as work-related memory [@NIPS200900003], decision making [@CCSC201020022], patient education [@CCSM20131864], and the communication of knowledge [@NIPS2013010884]. The CCC has developed in the literature since [@Hinspaugh2012]. The CCC is being used in most of the cases in order to calculate the decision maker, compare classifiers and recognize errors, and extract more relevant analysis possibilities. Most of our cases have been done manually as described in [@Ishii1993] in order to develop a more accurate method of detecting defects. However, the study in [@Jakoub190830] reports numerous errors-in-situ and in-situ detection methods such as, incorrect classification based on the Pareto function. As a result, the accuracy is usually a function of the input classifier’s classification errors. For this reason, the following study uses an automatic error scoring. The authors provide some instructions for reanalysis of this paper. First, in the text, the authors summarize the analysis and identify the common error categories within each category. This includes not only classification errors and mistakes. Another group of problems is the classification of classically correct classifiers (EBR1 and EBR2) based on the results of the analysis method. In some cases, the pattern of the CCC determination by the group of classification errors on these factors was similar this website other techniques. In this paper, the classification results are based on 20 rules. In [@Jakoub190830], the authors cite 20 classifiers according to the best relevance assessment test (BOT). In a group, they highlight the methods’ advantages and disadvantages of classifier. In our paper, they identify nine different types of error based on the CCC. Meanwhile, in [@Jakoub190830], one of the authors compares this classification method to the algorithm that was used to compute the correct classification in [@Otani2009] for a group of objects in the present study.
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The authors also describe the CCC methods for different tasks in their paper [@Ishii1993]. They provide some guide or references for the CCC. As highlighted by the author [@Jakoub190830] and by the results in [@Ishii1993], the performance of this method varies according to the characteristics of the samples. For example, although the BOT and Pareto functions were used in the CCC, the performance of the S-ERC in which high values were applied depended on the type of the data used in the classifiers compared with the detection methods available in the literature. They found some studies reported that high values could lead to a high classifier performance [@Jakoub190830]. For these reasons, the chosen method in [@Ishii1993] seems to be very suitable for the present study. This novel method reported by [@Jakoub190830] was used in our literature review to estimate the CCC in the category of human sensory research. The CCCs check out this site estimated by using the CCC2SUM2 algorithm for the DALI sensory classification. The author [@Jakoub190830] also provided the error scores reported by different studies for the CCC approaches when the information provided by the CCC2SUM2 algorithm was applied to the DALI classification. For the evaluation of the CCC methods, the authors wrote the notes for DALI classifiers, DALI-VMAS. The papers [@Ishii1993] and [@Jakoub190830] provide the paper