Search alternatives:
bayesian optimization » based optimization (Expand Search)
from optimization » fox optimization (Expand Search), swarm optimization (Expand Search), codon optimization (Expand Search)
binary health » primary health (Expand Search)
bayesian optimization » based optimization (Expand Search)
from optimization » fox optimization (Expand Search), swarm optimization (Expand Search), codon optimization (Expand Search)
binary health » primary health (Expand Search)
-
1
Optimized Bayesian regularization-back propagation neural network using data-driven intrusion detection system in Internet of Things
Published 2025“…Hence, Binary Black Widow Optimization Algorithm (BBWOA) is proposed in this manuscript to improve the BRBPNN classifier that detects intrusion precisely. …”
-
2
-
3
-
4
Bayesian sequential design for sensitivity experiments with hybrid responses
Published 2023“…To deal with the problem of complex computation involved in searching for optimal designs, fast algorithms are presented using two strategies to approximate the optimal criterion, denoted as SI-optimal design and Bayesian D-optimal design, respectively. …”
-
5
-
6
SHAP bar plot.
Published 2025“…</p><p>Methods</p><p>A total of 415 CKD patients from the 2015 China Health and Retirement Longitudinal Survey (CHARLS) dataset were included in this study. …”
-
7
Sample screening flowchart.
Published 2025“…</p><p>Methods</p><p>A total of 415 CKD patients from the 2015 China Health and Retirement Longitudinal Survey (CHARLS) dataset were included in this study. …”
-
8
Descriptive statistics for variables.
Published 2025“…</p><p>Methods</p><p>A total of 415 CKD patients from the 2015 China Health and Retirement Longitudinal Survey (CHARLS) dataset were included in this study. …”
-
9
SHAP summary plot.
Published 2025“…</p><p>Methods</p><p>A total of 415 CKD patients from the 2015 China Health and Retirement Longitudinal Survey (CHARLS) dataset were included in this study. …”
-
10
ROC curves for the test set of four models.
Published 2025“…</p><p>Methods</p><p>A total of 415 CKD patients from the 2015 China Health and Retirement Longitudinal Survey (CHARLS) dataset were included in this study. …”
-
11
Display of the web prediction interface.
Published 2025“…</p><p>Methods</p><p>A total of 415 CKD patients from the 2015 China Health and Retirement Longitudinal Survey (CHARLS) dataset were included in this study. …”
-
12
-
13
-
14
Identification and quantitation of clinically relevant microbes in patient samples: Comparison of three k-mer based classifiers for speed, accuracy, and sensitivity
Published 2019“…We tested the accuracy, sensitivity, and resource requirements of three top metagenomic taxonomic classifiers that use fast k-mer based algorithms: Centrifuge, CLARK, and KrakenUniq. Binary mixtures of bacteria showed all three reliably identified organisms down to 1% relative abundance, while only the relative abundance estimates of Centrifuge and CLARK were accurate. …”
-
15
Natural language processing for automated quantification of bone metastases reported in free-text bone scintigraphy reports
Published 2020“…<p> The widespread use of electronic patient-generated health data has led to unprecedented opportunities for automated extraction of clinical features from free-text medical notes. …”
-
16
-
17
Table 1_Heavy metal biomarkers and their impact on hearing loss risk: a machine learning framework analysis.docx
Published 2025“…Demographic, clinical, and heavy metal biomarker data (e.g., blood lead and cadmium levels) were analyzed as features, with hearing loss status—defined as a pure-tone average threshold exceeding 25 dB HL across 500, 1,000, 2000, and 4,000 Hz in the better ear—serving as the binary outcome. Multiple machine learning algorithms, including Random Forest, XGBoost, Gradient Boosting, Logistic Regression, CatBoost, and MLP, were optimized and evaluated. …”
-
18
-
19
An intelligent decision-making system for embryo transfer in reproductive technology: a machine learning-based approach
Published 2025“…The aim of this study is to build Machine learning (ML) decision-support models to predict the optimal range of embryo numbers to transfer, using data from infertile couples identified through literature reviews. …”