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We performed a set of numerical experiments to evaluate the relative quality of the different solution approaches, as presented in Sect. 4.
Each BS learns its action selection strategy based on the MAB or satisfaction-based learning approaches as presented in Sections 3 and 3, respectively.
It is however unknown how it affects the heteroscedasticity approaches as presented above.
Obviously, existing phantoms and experimental approaches as presented before are far from allowing direct physical measurement of sensitivity distributions [ 30, 33– 33].
Therefore, the development of highly reliable and high-resolution genetic analysis approaches as presented by Hannemann et al. [ 10], is of high therapeutical relevance.
Integrated modeling approaches as presented here will foster initiatives for evolutionary enlightened fishery management and conservation of species with complex life cycle and long migration ranges.
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Hence, an efficient integrated approach, as presented in this paper, is necessary.
The Naïve Bayes classifier approach as presented by Hong, Margines, and Dey [56], clearly underperformed compared to the other approaches.
Therefore, an integrated scheduling approach as presented in this paper is required to obtain a global optimised solution.
The service guideline for the pattern-based approach is similar to that of the quasi-manual approach, as presented in the 'Outcomes: service guideline and service code' subsection.
We utilize here the same approach as presented in [23], modifying some of the parameters and thresholds for the EDCA based QoS architecture.
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Justyna Jupowicz-Kozak
CEO of Professional Science Editing for Scientists @ prosciediting.com