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From these, liquid chromatography mass spectrometry (LC MS) [329], and nuclear magnetic resonance spectroscopy (NMR) based approach are generally preferred analytic methods because they are based on the physical properties of MNPs, which are not influenced by other external factors and easily reproducible [330].
However, FFT-based approaches are generally more preferable in practice due to their simpler hardware implementation.
This indicates that SVT-based approaches are generally superior to BoW-based approach.
The development discourse maintains that community-based approaches are generally equitable and egalitarian, efficient and comprehensively sustainable, and consequently highly legitimized strategies for the management of natural resources.
The development discourse maintains that community-based approaches are generally equitable, sustainable, and legitimized strategies for the management of natural resources.
However, scale-based approaches are generally limited by the assumption of a direct correspondence between a subscale score and an associated function.
Wrapper-based approaches are generally considered to produce better feature subsets but with much higher computational costs compared to the filter-based approaches.
On a number of benchmark datasets, graph-based and kernel-based approaches have shown comparable prediction accuracy, but graph-based approaches are generally faster [ 11, 20].
In the neurite detection literature, tracing-based approaches are generally considered to be the most accurate, although their computational burden is higher than that of morphology-based approaches.
A family based approach is imperative.
Automated feature generation approaches are generally based on a "trial and attempt" type of methodology, meaning that stochastic searching and optimization algorithms are commonly applied.
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