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where β0,…1,…,β L denotes the weights which are used to compensate the RTF and L denotes the number of past frames in the segment.
where γ K,…,γ-1,…0,γ1,…,γ L denotes the weights which are used to compensate the RTF, L denotes the number of past frames in the segment, and R denotes the number of future frames in the segment.
Then, we use the adjacent matrices A = (a uv n*n to represent interactome network among n nodes where a uv denotes the weights of interactions between nodes u and v.
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This fuzzy soft set gives a relation matrix (weighted matrix) R, called symptom-disease matrix, where each element denotes the weight of the symptoms for a certain disease.
where denotes the weight of edge.
where α denotes the weighting factor.
is a network parameter which denotes the weight-adjustment period.
w ij denotes the weight between these two units.
Also, w z denotes the weight of the syndrome vector at the considered iteration.
where denotes the weight, which is set to 0.5 in our implementation.
denotes the weight vector steering toward direction, ideally, written as (4).
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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