An Improvised Machine Learning Model Based on Mutual Information Feature Selection Approach for Microbes Classification.

ABSTRACT: The accurate classification of microbes is critical in today's context for monitoring the ecological balance of a habitat. Hence, in this research work, a novel method to automate the process of identifying microorganisms has been implemented. To extract the bodies of microorganisms accurately, a generalized segmentation mechanism which consists of a combination of convolution filter (Kirsch) and a variance-based pixel clustering algorithm (Otsu) is proposed. With exhaustive corroboration, a set of twenty-five features were identified to map the characteristics and morphology for all kinds of microbes. Multiple techniques for feature selection were tested and it was found that mutual information (MI)-based models gave the best performance. Exhaustive hyperparameter tuning of multilayer lay

SUBMITTER: Dhindsa A 

PROVIDER: S-EPMC7927045 | biostudies-literature | 2021 Feb

REPOSITORIES: biostudies-literature