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Among these, gradient [10, 30 32] and local binary pattern (LBP) [5, 7, 33] based methods are widely explored and proved to be successful in different applications.
Deep learning [33] based methods are widely used in machine learning and make machine learning approach its original goal: artificial intelligence (AI).
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Currently, vision-based methods are widely used in this regard.
Rule-based methods are widely used in the field of agrochemicals to identify chemicals with desirable properties.
For approximating an arbitrary distribution by a Gaussian, the transformation-based methods are widely used, see[22, 23].
Although ligand-based methods are widely used for target identification and have achieved a great amount of success, they become utterly useless for the remaining "unknown space" (i.e., dissimilar ligands).
This is only feasible when the LPF bandwidth is set to B 1. Considering that such maxima-searching spectral-based methods are widely used in parameter estimation when the conventional SC is implemented, its performance is taken as a reference of comparison for the parametric estimation algorithm proposed here.
Although energy-based methods are widely used to tackle this problem, they are currently limited by their accuracy.
Toxicogenomics-based methods are widely used in laboratory settings to develop biomarkers of exposure, early biologic response, and susceptibility.
Thus, graph-theory-based methods are widely used in the comparative study of the molecular interaction networks (MINs).
Fluorescence-based methods are widely used for biomarker identification and determination of relative expression levels; however, photobleaching of organic fluorophores severely limits quantitative analysis.
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