TreeMap 2022 CONUS: A tree-level model of the forests of the conterminous United States circa 2022 حسب Rachel M. Houtman (19658365)

"…The raster of plot identifiers can be linked to the FIA databases available through the FIA DataMart to map hundreds of attributes available there, or to the comma-separated file included in this data publication to access a more limited set of tree-level attributes. The data files included in this publication also contain attributes for each tree in the plots that were assigned, including the FIA plot PLT_CN for the plot on which the tree was measured (or control number, a unique identifier for each time a plot is measured), the subplot number, the tree record number, the corresponding number of trees per acre it represents due to the study design, the status (live or dead), species, diameter, height, actual height (where broken), crown ratio and a code for cause of death where applicable. …"

Experimental setup. حسب Hui Yang (91136)

Depth division situation. حسب Hui Yang (91136)

The division of CTU in polar regions. حسب Hui Yang (91136)

Minimal data set definition. حسب Hui Yang (91136)

CU segmentation process. حسب Hui Yang (91136)

Division accuracy. حسب Hui Yang (91136)

Selection strategy process. حسب Hui Yang (91136)

Division accuracy. حسب Hui Yang (91136)

The division of CTU in the equatorial region. حسب Hui Yang (91136)

Adjusting the process. حسب Hui Yang (91136)

Indicator analysis of integrated models. حسب Hui Yang (91136)

UC division situation. حسب Hui Yang (91136)

Comparison of deep partitioning models. حسب Hui Yang (91136)

Projection weight distribution. حسب Hui Yang (91136)

Horizontal comparison of models. حسب Hui Yang (91136)

Adaptive model BD rate analysis. حسب Hui Yang (91136)

Index analysis of deep partitioning strategy. حسب Hui Yang (91136)

Example of time-frequency representation of sEMG signal. حسب Francesco Di Nardo (734450)

Table 1_Rapid forensic ancestry inference in selected Northeast Asian populations: a Y-STR based attention-based ensemble framework for initial investigation guidance.xlsx حسب Kyo-Chan Koo (22263367)

"…We developed a machine learning architecture centered on an attention-based ensemble mechanism that incorporates three complementary algorithms: a One-vs-Rest Random Forest, XGBoost, and Logistic Regression, each configured to effectively manage imbalanced datasets.…"