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IEEE7y
Stacked Sparse Autoencoder Modeling Using the Synergy of Airborne LiDAR and Satellite Optical and SAR Data to Map Forest Above-Ground Biomass
The LiDAR plots-stratified random samples of forest biomass extracted from the LiDAR simulated strips in the reference map-were adopted as a surrogate for traditional field plots. In addition to the ...
Frontiers1y
Prediction of miRNAs and diseases association based on sparse autoencoder and MLP
Methods: Therefore, we propose a new sparse autoencoder and MLP method (SPALP ... including K-Nearest Neighbors (KNN), Decision Tree, Random Forest, Logistic Regression, XGBoost. ROC and PR curves are ...
Frontiers1y
Prediction of microbe–drug associations based on a modified graph attention variational autoencoder and random forest
Methods: In this study, we developed a computational framework based on a modified graph attention variational autoencoder (MGAVAEMDA ... and graphical adjacency matrix were input into the random ...
GitHub1y
j-csc/ids_dl
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 ...
IEEE7y
Stacked Sparse Autoencoder Modeling Using the Synergy of Airborne LiDAR and Satellite Optical and SAR Data to Map Forest Above-Ground Biomass
The LiDAR plots-stratified random samples of forest biomass extracted from the LiDAR simulated strips in the reference map-were adopted as a surrogate for traditional field plots. In addition to the ...