Passive sensing data. حسب Thierry Jean (20691795)

"…Results also showed that metrics that do not account for imbalance (mean absolute error, accuracy) systematically overestimated performance, XGBoost models performed on par with or better than LSTM models, and a significant yet very small decrease in performance was observed as the forecast horizon expanded. …"

Surveys. حسب Thierry Jean (20691795)

"…Results also showed that metrics that do not account for imbalance (mean absolute error, accuracy) systematically overestimated performance, XGBoost models performed on par with or better than LSTM models, and a significant yet very small decrease in performance was observed as the forecast horizon expanded. …"

Variables of trees located at coordinates . حسب Alexandra M. Campbell (3284094)

Global parameters and variables for the model. حسب Alexandra M. Campbell (3284094)

Variables of patch located at coordinates . حسب Alexandra M. Campbell (3284094)

MRSP Data collected in March 2018. حسب Alexandra M. Campbell (3284094)

Model prediction error analysis. حسب Hongqi Wang (2208238)

"…Comparative analysis highlights the significant enhancement in prediction accuracy achieved by the proposed ensemble model over single machine learning models, with root mean square error (RMSE) values below 0.05 and mean absolute percentage error (MAPE) values remaining under 2.5% in both frozen and unfrozen states. …"

Empirical model prediction error analysis. حسب Hongqi Wang (2208238)

"…Comparative analysis highlights the significant enhancement in prediction accuracy achieved by the proposed ensemble model over single machine learning models, with root mean square error (RMSE) values below 0.05 and mean absolute percentage error (MAPE) values remaining under 2.5% in both frozen and unfrozen states. …"

Model prediction error trend chart. حسب Hongqi Wang (2208238)

"…Comparative analysis highlights the significant enhancement in prediction accuracy achieved by the proposed ensemble model over single machine learning models, with root mean square error (RMSE) values below 0.05 and mean absolute percentage error (MAPE) values remaining under 2.5% in both frozen and unfrozen states. …"

Model prediction error analysis index. حسب Hongqi Wang (2208238)

"…Comparative analysis highlights the significant enhancement in prediction accuracy achieved by the proposed ensemble model over single machine learning models, with root mean square error (RMSE) values below 0.05 and mean absolute percentage error (MAPE) values remaining under 2.5% in both frozen and unfrozen states. …"

Mean parameter values for the selected crops. حسب Gourab Saha (8987405)

"…Multi-spectral band images from Landsat-8 satellite images of a chosen land are employed from USGS Earth Resources Observation and Science (EROS) Center for extracting indices that are used for agricultural analysis, determining the vegetation index, water index, and salinity index of that land using K-means. Furthermore, crop yield is predicted using Linear Regression and Random Forest, achieving accuracies of 93.49% and 95.87%, respectively, while using RMSE (Root Mean Squared Error) as the loss function. …"

Characteristics of the included studies. حسب Si He (3236646)

"…The overall results revealed that NPWT significantly decreased the sternal wound reinfection (SWRI) rate (RR [95% CI] = 0.179 [0.099 to 0.323], 95% prediction interval [PI]: 0.082 to 0.442), in-hospital mortality (RR [95% CI] = 0.242 [0.149 to 0.394], 95% PI: 0.144 to 0.461), and shortened the length of intensive care unit (ICU) stay (SMD [95% CI] = −0.601 [−0.820 to −0.382], 95% PI: −1.317 to 0.128) compared with conventional wound care. …"

Extracted data and used for analysis. حسب Si He (3236646)

"…The overall results revealed that NPWT significantly decreased the sternal wound reinfection (SWRI) rate (RR [95% CI] = 0.179 [0.099 to 0.323], 95% prediction interval [PI]: 0.082 to 0.442), in-hospital mortality (RR [95% CI] = 0.242 [0.149 to 0.394], 95% PI: 0.144 to 0.461), and shortened the length of intensive care unit (ICU) stay (SMD [95% CI] = −0.601 [−0.820 to −0.382], 95% PI: −1.317 to 0.128) compared with conventional wound care. …"

Excluded studies with reasons for exclusion. حسب Si He (3236646)

"…The overall results revealed that NPWT significantly decreased the sternal wound reinfection (SWRI) rate (RR [95% CI] = 0.179 [0.099 to 0.323], 95% prediction interval [PI]: 0.082 to 0.442), in-hospital mortality (RR [95% CI] = 0.242 [0.149 to 0.394], 95% PI: 0.144 to 0.461), and shortened the length of intensive care unit (ICU) stay (SMD [95% CI] = −0.601 [−0.820 to −0.382], 95% PI: −1.317 to 0.128) compared with conventional wound care. …"

Structure diagram of ensemble model. حسب Hongqi Wang (2208238)

"…Comparative analysis highlights the significant enhancement in prediction accuracy achieved by the proposed ensemble model over single machine learning models, with root mean square error (RMSE) values below 0.05 and mean absolute percentage error (MAPE) values remaining under 2.5% in both frozen and unfrozen states. …"

Fitting formula parameter table. حسب Hongqi Wang (2208238)

"…Comparative analysis highlights the significant enhancement in prediction accuracy achieved by the proposed ensemble model over single machine learning models, with root mean square error (RMSE) values below 0.05 and mean absolute percentage error (MAPE) values remaining under 2.5% in both frozen and unfrozen states. …"

Test plan. حسب Hongqi Wang (2208238)

"…Comparative analysis highlights the significant enhancement in prediction accuracy achieved by the proposed ensemble model over single machine learning models, with root mean square error (RMSE) values below 0.05 and mean absolute percentage error (MAPE) values remaining under 2.5% in both frozen and unfrozen states. …"

Fitting surface parameters. حسب Hongqi Wang (2208238)

"…Comparative analysis highlights the significant enhancement in prediction accuracy achieved by the proposed ensemble model over single machine learning models, with root mean square error (RMSE) values below 0.05 and mean absolute percentage error (MAPE) values remaining under 2.5% in both frozen and unfrozen states. …"

Model generalisation validation error analysis. حسب Hongqi Wang (2208238)

"…Comparative analysis highlights the significant enhancement in prediction accuracy achieved by the proposed ensemble model over single machine learning models, with root mean square error (RMSE) values below 0.05 and mean absolute percentage error (MAPE) values remaining under 2.5% in both frozen and unfrozen states. …"

Fitting curve parameters. حسب Hongqi Wang (2208238)

"…Comparative analysis highlights the significant enhancement in prediction accuracy achieved by the proposed ensemble model over single machine learning models, with root mean square error (RMSE) values below 0.05 and mean absolute percentage error (MAPE) values remaining under 2.5% in both frozen and unfrozen states. …"