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A generalized radial basis function (GRBF) neural network architecture that generates a set of fuzzy classification rules was developed for protein sequences classification by Wang et al. [ 11].
We design an exact simultaneous column-and-row generation algorithm whose novel element is a row-generating subproblem that generates a set of columns and rows.
A bank of observers generates a set of residuals.
This generates a set of contact waves moving along the reactor height in opposite directions.
Given an object name, Ftree generates a set of keys using multiple hash functions.
The technique rapidly generates a set of designs that offer the best compromises between those two critical energy components.
Moreover, the analysis of composition results proves that the proposed approach generates a set of pleasant and diverse melodies.
An automation engine generates a set of flexure-based design topologies and also controllers of varying order in the optimization.
Computation results show that the proposed algorithm performs well, the algorithm generates a set of Pareto optimal solutions.
It first generates a set of latent treatment patterns from diagnosis labels, followed by sampling treatments from each pattern.
Given an aligned set of protein sequences, EMOTIF generates a set of motifs with a wide range of specificities and sensitivities.
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Justyna Jupowicz-Kozak
CEO of Professional Science Editing for Scientists @ prosciediting.com