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We show through simulations that the proposed system has a short convergence time and presents a high recall ratio.
To address this challenge, we propose a partial flow statistics collection (PFSC) problem, in which we need to inquire statistics of flows from a subset of switches such that the flow recall ratio on every switch is at least a given value β ∈ (0, 1] while minimizing the number of queried switches.
The precision ratio was 35.29% and recall ratio was 73.17% when identifying a negative emotional tendency.
The precision ratio was 97.36% and recall ratio was 88.04% when identifying a positive emotional tendency.
In addition, the recall ratio represents the percentage of the data correctly estimated for labels.
Regarding what these ratios express, a higher precise ratio indicates a lower number of erroneous relations, while a higher recall ratio indicates fewer omissions in the prepared materials.
Similar(47)
Despite the fact that both test subject B and C's precise and recall ratios were 100%% for relations at the attribute level, their recall ratios declined for relations at the attribute value level.
The subjects' knowledge descriptions were evaluated based on the precise and recall ratios of the attributes and attribute values.
Tables 3 and 4 show the precise and recall ratios for the cause/effect relations and generated problems, respectively.
The results revealed a significant difference of 0.1 % in the precise (df = 6, t = 6.16) and recall ratios (df = 6, t = 3.90) for the attributes.
The precise and recall ratios were calculated for the test and control groups, respectively, and a t test examining the differences in their mean values was performed.
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Since I tried Ludwig back in 2017, I have been constantly using it in both editing and translation. Ever since, I suggest it to my translators at ProSciEditing.

Justyna Jupowicz-Kozak
CEO of Professional Science Editing for Scientists @ prosciediting.com