Development of advanced data mining techniques for microbiological control

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The proposed research will solve the data analysis problem related to microbiological data for LuminUltra Technologies Ltd, and will be integrated into their data analysis system LumiCalc. The microbiological data in LumiCalc is collected through a variety of tests on different sites. The ultimate goal of data analysis is to predict the amount of biomass measured by ATP (Adenosine Triphosphate). ATP can be affected by many different variables, such as food, nutrients, temperature, pH, etc. Therefore, causal relations correlations) play an important role. With a large amount of historical data, users need a powerful tool that allows them to analyze data and discover similarities, patterns, trends, and especially causal relations. Data mining techniques have been applied to dealing with the flood of biological data. In this research project, we will investigate various data mining algorithms and models for LuminUltra data analysis, select a few suitable models, and implement and integrate them into the LumiCalc software.