In addition to the deep learning model, i.e., stacked sparse autoencoder network (SSAE), five different prediction techniques including multiple stepwise linear regressions, K-nearest neighbor, ...

Random forest, an efficient multiclass classifier, is employed to classify the sparse vectors of images. The outcome of the experiments demonstrates that the proposed algorithm outperforms the ...

In this article, the authors discuss how to detect fraud in credit card transactions, using Random Forest, Logistic Regression, Isolation Forest and Neural Autoencoder. BT. Live Webinar and Q&A: ...

The model I implemented with pytorch can be found in the autoencoder.py file. Here's the structure of the neural network: The other models used contains a Random Forest model, a Logistic Regression ...

For anomaly detection, autoencoder is widely used. But using autoencoder, which have many variables with strong correlations, is said to cause a decline of detection power. To avoid the above problem, ...

These two feature matrices are input into a sparse autoencoder, from which we obtain the latent features of miRNAs (M) and diseases (D). The dimension of the M matrix is 495 × 128, and the dimension ...