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Found 2 papers, 1 papers with code
Predicting rice blast disease: machine learning versus process based models
In this study, we compared four models for predicting rice blast disease, two operational process-based models (Yoshino and WARM) and two approaches based on machine learning algorithms (M5Rules and RNN), the former inducing a rule-based model and the latter building a neural network.
Fast Distance-based Anomaly Detection in Images Using an Inception-like Autoencoder
First, using normal examples, a convolutional autoencoder (CAE) is trained to extract a low-dimensional representation of the images.
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