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Microalgae are unicellular organisms that have different shapes, sizes and structures. Classifying these microalgae manually can be an expensive task, because thousands of microalgae can be found in even a small sample of water. This paper presents an approach for an automatic/semi-automatic classification of microalgae based on semi-supervised and active learning algorithms, using Gaussian mixture models. The results show that the approach has an excellent cost-benefit relation, classifying more than 90 % of microalgae in a well distributed way, overcoming the supervised algorithm SVM.
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... Regression algorithms are further divided into two simple linear regression and multi-linear regression. LR, Ridge Regression (RR), Support Vector SVM), LR, Polynomial Regression (PR), Bayesian Linear Regression (BLR), and Lasso Regression (LR) are called regression algorithms [50]. ...
... For example, self-trained classifiers and Generative adversarial networks are used for this type of learning. [50] [52]. ...
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... These interpretations reveal the advantages of image processing that employs deep learning for classification of microalgae. Gaussian mixed semisupervised classification model was employed by Drews-et al. (2013) for the microalgae classification with the output achieved via FlowCAM. The authors employed features of microalgae such as length, width, diameter, aspect ratio etc., as input variables. ...
... The flow of pure semi-supervised and transductive learning is exhibited in Fig. 7. The commonly used semi-supervised learning models are self-training [100,101], Gaussian mixture model (GMM) [102,103], and semi-supervised CNN [99,104], etc. ...
... The findings show the benefits of image analysis employing deep learning for microalgal culture classification. Drews-et al. (2013) have used the Gaussian mixed semisupervised classification model and active learning to classify the microalgae using the data obtained from the FlowCAM. They have used microalgal features such as diameter, length, width, aspect ratio, etc., as the input data. ...
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Microalgae are unicellular organisms that have different shapes, sizes and structures. Classifying these microalgae manually can be a expensive task, because in a small sample of water, thousands of microalgae can be found. This paper presents an approach for an automatic/semi-automatic classification of microalgae based on semi-supervised and active learning algorithms, using Gaussians Mixtures Models. The results show that the approach has an excellent cost-benefit, correctly classifying approximately 94% microalgae in a well distributed way, overcoming the supervised algorithm SVM.
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