… Edge Organization C2 model—often … using the dynamic knowledge measurement system
articulated above, we find that measured values are highly consistent with theoretic predictions …

… the prediction performance of the XGBoost model is demonstrated by comparing it to four other
machine learning models. … An extreme gradient boosting method for identifying the factors …

… covers deep learning, structure design and optimization … Identification and prediction of
dynamic systems using an interactively … Deep neural networks: A new framework for modeling …

… In this study we model the physiological effects of AGEs by in-situ measurements of … a
doubling of diabetes associated deaths between 2005 and 2030 |7|, making diabetes a primary …

… sample data modeling and dioxin emission prediction of the … used for mapping model
hyper-parameter optimization and the … simulation model based on Bayesian theory with extreme …

… Identify outliers, which are extreme or erroneous data points that … build an ensemble of weak
prediction models (eg, decision … model interpretation techniques can be applied to optimize …

… inversion model constructed,the R2 and RMSE of the GWR model are 0. 48 … model are
0.54 and 4.35 g/kg,respectively. The prediction accuracy of the soil salinization inversion model …

… Machine learning for characterizing risk of type 2 diabetes … A unified approach to interpreting
model predictions[C]//… Explainable extreme gradient boosting tree-based prediction of …

… injury via excessive contraction. … to predict the possibility of future tissue toxicity using a
minimal number of animal experiments. For example, an in silico model was used to predict the …

… A neural network model was established and the accuracy of the neural network diagnostic
model was verified clinically. Results A total of 223 samples were collected in this study, 156 …