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A selection of cluster features are shown ranked by the information gain of the corresponding single-attribute rule in Table 2.
Information on the number of records to which each rule applies – that is, for which the antecedents are true (Instances) – and the proportion of those records for which the entire rule is true (Confidence) is given in parenthesis for each rule in Table 2.
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We summarize all the rules in Table 1.
In each time step, all sites are updated according to the transition rules in Table 2.
The compensator factor λ is calculated using fuzzy rules in Table 2.
By induction on the structure of the sequential process s and then by induction on the rules in Table 9.
With this knowledge, the fuzzy rules in Table 2 are initially derived by trial and error method.
According to the fuzzy logic rules in Table 1, the priority of each node is obtained by layer.
Let (t = m_1 {{{,mathop {longrightarrow }limits ^{sigma }}}}m_2) be a transition derivable by the rules in Table 9.
Here, we give, for completeness, the CA updating rules in Table 2, which follow those in [2] and [3], but with different probability expressions and different parameter values.
Transition (t = (m, sigma, m')) is derivable by the rules in Table 9 if and only if transition (t' = (m{{a'/a}}, sigma {{a'/a}},) (m'{{a'/a}})) is derivable by the rules.
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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