Biodiversity of Algae from the Tajan River Basin (Mazandaran-Iran)

Ebadi, Abdol Ghaffar; Hisoriev, Hikmat; Ebadi, Abdol Ghaffar

doi:10.21608/ejabf.2018.5034

Cited by 2 publications

(1 citation statement)

References 3 publications

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“…There is evidence that probability classifiers are also in use for identifications of microbial species and the outcome in terms of average accuracy is above 90%. Studies specific to the particular zone (river basin [ 36 ], littoral, benthic [ 7 , 37 , 38 ]) of the water bodies have also been found in the current literature.…”

Section: Introductionmentioning

confidence: 98%

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

Dhindsa

Bhatia

Agrawal

et al. 2021

Entropy

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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 layer perceptron (MLP), k-nearest neighbors (KNN), quadratic discriminant analysis (QDA), logistic regression (LR), and support vector machine (SVM) was done. It was found that SVM radial required further improvisation to attain a maximum possible level of accuracy. Comparative analysis between SVM and improvised SVM (ISVM) through a 10-fold cross validation method ultimately showed that ISVM resulted in a 2% higher performance in terms of accuracy (98.2%), precision (98.2%), recall (98.1%), and F1 score (98.1%).

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