Search alternatives:
acid optimization » based optimization (Expand Search), lead optimization (Expand Search), art optimization (Expand Search)
based objective » based object (Expand Search), based selective (Expand Search), based objects (Expand Search)
binary based » library based (Expand Search), linac based (Expand Search), binary mask (Expand Search)
genes based » gene based (Expand Search), lens based (Expand Search)
based acid » based bci (Expand Search), based ai (Expand Search), based agi (Expand Search)
acid optimization » based optimization (Expand Search), lead optimization (Expand Search), art optimization (Expand Search)
based objective » based object (Expand Search), based selective (Expand Search), based objects (Expand Search)
binary based » library based (Expand Search), linac based (Expand Search), binary mask (Expand Search)
genes based » gene based (Expand Search), lens based (Expand Search)
based acid » based bci (Expand Search), based ai (Expand Search), based agi (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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Data_Sheet_1_Transcriptome-Based Selection and Validation of Reference Genes for Gene Expression Analysis of Alicyclobacillus acidoterrestris Under Acid Stress.PDF
Published 2021“…The expression stability of eight new RGs and commonly used RG 16s rRNA was assessed using geNorm, NormFinder, and BestKeeper algorithms. Moreover, the comprehensive analysis using the RefFinder program and the validation using target gene ctsR showed that dnaG and dnaN were the optimal multiple RGs for normalization at pH 4.0; ytvI, dnaG, and 16s rRNA at pH 3.5; icd and dnaG at pH 3.0; and ytvI, dnaG, and spoVE at pH 2.5. …”
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Data_Sheet_2_Transcriptome-Based Selection and Validation of Reference Genes for Gene Expression Analysis of Alicyclobacillus acidoterrestris Under Acid Stress.xls
Published 2021“…The expression stability of eight new RGs and commonly used RG 16s rRNA was assessed using geNorm, NormFinder, and BestKeeper algorithms. Moreover, the comprehensive analysis using the RefFinder program and the validation using target gene ctsR showed that dnaG and dnaN were the optimal multiple RGs for normalization at pH 4.0; ytvI, dnaG, and 16s rRNA at pH 3.5; icd and dnaG at pH 3.0; and ytvI, dnaG, and spoVE at pH 2.5. …”
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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. …”