AUTOMATIC RECOGNITION OF PHYTOPLANKTON’S BY COMBINING FLOW CYTOMETRY AND DECISION TREEDIAF YOUSSOUF, KADDECHE MOHAMED AND ELAKREMI SOUMEYA
In this paper, we present a classification method of Multi-parametric flow cytometry (FC) data for phytoplankton species discrimination. The flow cytometry data is an invaluable mine of quantitative and qualitative information to conduct single cell analysis for biological cells. However, the new generation of FC allows us to measure more number of parameters and cells, the FC data analysis has become more complex and labor-intensive than it previously. The data were treated by a proposed method: the decision tree. This method is developed to create a real-time processing system that can detect and count the cells of phytoplankton and accelerate the task of analyses and identification, this will help biologist to analyze simples in situ.
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