Search alternatives:
binary based » library based (Expand Search), linac based (Expand Search), binary mask (Expand Search)
binary based » library based (Expand Search), linac based (Expand Search), binary mask (Expand Search)
-
1
-
2
-
3
-
4
-
5
Proposed Algorithm.
Published 2025“…The objective is to optimize binary offloading decisions under dynamic wireless channel conditions and energy harvesting constraints. …”
-
6
Comparisons between ADAM and NADAM optimizers.
Published 2025“…The objective is to optimize binary offloading decisions under dynamic wireless channel conditions and energy harvesting constraints. …”
-
7
the functioning of BRPSO.
Published 2025“…A sensitivity analysis of key RFD parameters, including frictional moment and rigid beam length, highlights their influence on seismic performance. The optimization problem is formulated based on the seismic energy dissipation concept, employing a modified binary and real-coded particle swarm optimization (BRPSO) algorithm. …”
-
8
Characteristic of 6- and 10-story SMRF [99,98].
Published 2025“…A sensitivity analysis of key RFD parameters, including frictional moment and rigid beam length, highlights their influence on seismic performance. The optimization problem is formulated based on the seismic energy dissipation concept, employing a modified binary and real-coded particle swarm optimization (BRPSO) algorithm. …”
-
9
The RFD’s behavior mechanism (2002).
Published 2025“…A sensitivity analysis of key RFD parameters, including frictional moment and rigid beam length, highlights their influence on seismic performance. The optimization problem is formulated based on the seismic energy dissipation concept, employing a modified binary and real-coded particle swarm optimization (BRPSO) algorithm. …”
-
10
SHAP bar plot.
Published 2025“…<div><p>Background</p><p>The high prevalence of cognitive impairment (CI) in Chronic kidney disease (CKD) patients impacts their quality of life and prognosis, yet risk prediction models for CI in this population remain underexplored.</p><p>Objective</p><p>This study aimed to develop a risk prediction model for CI in CKD patients using machine learning algorithms, with the objective of enhancing risk prediction accuracy and facilitating early intervention.…”
-
11
Sample screening flowchart.
Published 2025“…<div><p>Background</p><p>The high prevalence of cognitive impairment (CI) in Chronic kidney disease (CKD) patients impacts their quality of life and prognosis, yet risk prediction models for CI in this population remain underexplored.</p><p>Objective</p><p>This study aimed to develop a risk prediction model for CI in CKD patients using machine learning algorithms, with the objective of enhancing risk prediction accuracy and facilitating early intervention.…”
-
12
Descriptive statistics for variables.
Published 2025“…<div><p>Background</p><p>The high prevalence of cognitive impairment (CI) in Chronic kidney disease (CKD) patients impacts their quality of life and prognosis, yet risk prediction models for CI in this population remain underexplored.</p><p>Objective</p><p>This study aimed to develop a risk prediction model for CI in CKD patients using machine learning algorithms, with the objective of enhancing risk prediction accuracy and facilitating early intervention.…”
-
13
SHAP summary plot.
Published 2025“…<div><p>Background</p><p>The high prevalence of cognitive impairment (CI) in Chronic kidney disease (CKD) patients impacts their quality of life and prognosis, yet risk prediction models for CI in this population remain underexplored.</p><p>Objective</p><p>This study aimed to develop a risk prediction model for CI in CKD patients using machine learning algorithms, with the objective of enhancing risk prediction accuracy and facilitating early intervention.…”
-
14
ROC curves for the test set of four models.
Published 2025“…<div><p>Background</p><p>The high prevalence of cognitive impairment (CI) in Chronic kidney disease (CKD) patients impacts their quality of life and prognosis, yet risk prediction models for CI in this population remain underexplored.</p><p>Objective</p><p>This study aimed to develop a risk prediction model for CI in CKD patients using machine learning algorithms, with the objective of enhancing risk prediction accuracy and facilitating early intervention.…”
-
15
Display of the web prediction interface.
Published 2025“…<div><p>Background</p><p>The high prevalence of cognitive impairment (CI) in Chronic kidney disease (CKD) patients impacts their quality of life and prognosis, yet risk prediction models for CI in this population remain underexplored.</p><p>Objective</p><p>This study aimed to develop a risk prediction model for CI in CKD patients using machine learning algorithms, with the objective of enhancing risk prediction accuracy and facilitating early intervention.…”
-
16
An Example of a WPT-MEC Network.
Published 2025“…The objective is to optimize binary offloading decisions under dynamic wireless channel conditions and energy harvesting constraints. …”
-
17
Related Work Summary.
Published 2025“…The objective is to optimize binary offloading decisions under dynamic wireless channel conditions and energy harvesting constraints. …”
-
18
Simulation parameters.
Published 2025“…The objective is to optimize binary offloading decisions under dynamic wireless channel conditions and energy harvesting constraints. …”
-
19
Training losses for N = 10.
Published 2025“…The objective is to optimize binary offloading decisions under dynamic wireless channel conditions and energy harvesting constraints. …”
-
20
Normalized computation rate for N = 10.
Published 2025“…The objective is to optimize binary offloading decisions under dynamic wireless channel conditions and energy harvesting constraints. …”