Search alternatives:
objects optimization » objectives optimization (Expand Search), objective optimization (Expand Search), robust optimization (Expand Search)
based objects » based object (Expand Search)
binary based » library based (Expand Search), linac based (Expand Search), binary mask (Expand Search)
based swarm » based sars (Expand Search), based smart (Expand Search), based arm (Expand Search)
objects optimization » objectives optimization (Expand Search), objective optimization (Expand Search), robust optimization (Expand Search)
based objects » based object (Expand Search)
binary based » library based (Expand Search), linac based (Expand Search), binary mask (Expand Search)
based swarm » based sars (Expand Search), based smart (Expand Search), based arm (Expand Search)
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Proposed Algorithm.
Published 2025“…The objective is to optimize binary offloading decisions under dynamic wireless channel conditions and energy harvesting constraints. …”
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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. …”
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Table1_Study of PARP inhibitors for breast cancer based on enhanced multiple kernel function SVR with PSO.docx
Published 2024“…The problem of multi-parameter optimization introduced in the support vector regression model was solved by the particle swarm optimization algorithm. …”
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DataSheet1_Study of PARP inhibitors for breast cancer based on enhanced multiple kernel function SVR with PSO.ZIP
Published 2024“…The problem of multi-parameter optimization introduced in the support vector regression model was solved by the particle swarm optimization algorithm. …”
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GSE96058 information.
Published 2024“…Subsequently, feature selection was conducted using ANOVA and binary Particle Swarm Optimization (PSO). During the analysis phase, the discriminative power of the selected features was evaluated using machine learning classification algorithms. …”
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The performance of classifiers.
Published 2024“…Subsequently, feature selection was conducted using ANOVA and binary Particle Swarm Optimization (PSO). During the analysis phase, the discriminative power of the selected features was evaluated using machine learning classification algorithms. …”
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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.…”
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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.…”
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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.…”
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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.…”
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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.…”
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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.…”
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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. …”
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Related Work Summary.
Published 2025“…The objective is to optimize binary offloading decisions under dynamic wireless channel conditions and energy harvesting constraints. …”
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Simulation parameters.
Published 2025“…The objective is to optimize binary offloading decisions under dynamic wireless channel conditions and energy harvesting constraints. …”
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Training losses for N = 10.
Published 2025“…The objective is to optimize binary offloading decisions under dynamic wireless channel conditions and energy harvesting constraints. …”
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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. …”