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The feasibility of automated identification of six algae types using neural networks and fluorescence-based spectral-morphological features
A number of morphological and spectral fluorescence features are then extracted from the isolated micro-organism imaging data, and used to train neural network classification models designed for the purpose of identification of the six algae types given an isolated micro-organism.
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