Prediction (a-d) and infusion deviation (e-f) results under different training sets and test sets. حسب Leijie Wang (21485157)

الموضوعات:

Accuracy and loss of LSTM model training and testing. حسب Leijie Wang (21485157)

الموضوعات:

Setting of experimental environment and model parameters. حسب Guozhu Sui (21672798)

Data set. حسب Belachew Tekleyohannes Wogayehu (19470257)

Comprehensive evaluation of machine-learning models in the training cohort. حسب Wentao Yang (205781)

الموضوعات:

LOSS curves for BWO-BiLSTM model training. حسب Xiangjuan Liu (618000)

Performance comparison of the machine learning models in the training cohort (n = 29,309). حسب Wentao Yang (205781)

الموضوعات:

Histogram of number of drugs with (a) increased and (b) decreased usage within 12 months. حسب Nguyen Thach (20347852)

Classification model parameter settings. حسب Chao Tang (10925)

Histograms of the extent of the (a) increase and (b) decrease by the difference in categorical usage within 12 months for all drugs. حسب Nguyen Thach (20347852)

Impact of red blood cell-platelet ratio trajectories on prognosis and clinical outcomes in the first four days before ICU admission in the training set. حسب Jie Zhao (49409)

PCA-CGAN model parameter settings. حسب Chao Tang (10925)

The speed of the virtual player’s approach influenced the variability of time to first avoidance, such that the variability in time to first avoidance behaviour decreased from the... حسب Brooke J. Thompson (19949761)

Parameter Settings. حسب Yutong Liu (4027994)

RMSE versus training parameters. حسب Zhe Hu (787283)

"…Then, the LSTM model is trained using this sliding window approach, achieving a root mean square error(RMSE) of 0.03 on the test set. …"

Example of short escape latency during training, i.e., animals that did escape well within 180 sec trial duration. حسب Ella R. Dockendorf (21533334)

The minimal anonymized data set. حسب Zufan Alamrie Asmare (16474982)

Mask R-CNN training loss graph. حسب Dennis Dennis (20490126)

Algorithm training accuracy experiments. حسب Yingying Liu (360782)

"…The research focuses on the advantages and problems of residual networks and depth-wise separable convolution modules, designs a new remote sensing image change detection model, and finally sets up experiments for verification. The average accuracy of the proposed detection model before and after training convergence was 0.54 and 0.97. …"

Time step optimization can greatly improve the efficiency of training and inference. حسب Weijia Zhu (65481)

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