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Different secondary metabolites, minerals and vitamins have been extracted from food waste, using various extraction approaches.
Similar solid-phase extraction approaches are used for sampling and extracting wet precipitation [ 38, 39].
The global feature extraction approaches are based on the statistical methods, wherein features are extracted from the whole face image.
Existing advertisement extraction approaches can be classified into two categories.
Automatic feature extraction approaches refer to those acquiring image features through a D-CNN.
The evaluation of motion pattern extraction approaches is difficult and time consuming for a human operator.
However, all these feature extraction approaches present the problem of data association in SLAM [21].
But these mechanisms of extraction seem to be lower compared with other extraction approaches.
In literature, the approaches used to represent the face images are classified broadly into two categories, namely, the global feature extraction approaches and the local feature extraction approaches [2].
Firstly we have rule extraction approaches directly from data which are utilised to produce a rule base predictive system.
To this aim, a micro-extraction procedure has been selected by comparing different previously published extraction approaches [25],[25].
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