Sentence examples for rough precision from inspiring English sources

He suggested this just by arranging his pencils and notepad and repositioning his chair with rough precision.

This paper first describes the content and methods for the evaluation of urban underground space resources, and introduces the digitalization technologies to handle the problems of strong subjectivity, low efficiency and rough precision encountered when using traditional evaluation tools.

In this paper, we present the applicability of variable precision rough set model for clustering student suffering studies anxiety.

In this paper, an adaptive algorithm is designed for dynamic risk management in petroleum project investment based on a variable precision rough set (VPRS) model.

Secondly, we define the rough degree and precision for the purpose of characterizing the uncertainty of these summarized covering approximation operators under a unified framework.

At this stage of the research, we show how variable precision rough set model can be used to groups student in each study's anxiety.

This paper put forward an optimizing model based on variable precision rough sets (VPRS) for the land cover discrimination in wetland inventory.

Then, based on the defined conditional probability in this paper, we design a multi-granulation variable precision rough set and consider the relationship with MGRS.

Attribute reduction with variable precision rough sets (VPRS) attempts to select the most information-rich attributes from a dataset by incorporating a controlled degree of misclassification into approximations of rough sets.

Furthermore, we present two types of local MGRS frameworks under PRSs and variable precision rough sets, where the relationships between them and the LMG-DTRS model are also discussed.

The proposed MIO classification method designated as the FVM-index method integrates fuzzy set theory (FST), variable precision rough set (VPRS) theory, and a modified cluster validity index (MCVI) function, and is designed specifically to filter out the uncertainty and inaccuracy inherent in the surveyed MIO real-valued dataset; thereby improving the classification performance.