بدائل البحث:
optimisation algorithm » optimization algorithms (توسيع البحث), maximization algorithm (توسيع البحث), identification algorithm (توسيع البحث)
process optimisation » process optimization (توسيع البحث), robust optimisation (توسيع البحث), process simulation (توسيع البحث)
yet optimization » art optimization (توسيع البحث), lead optimization (توسيع البحث), path optimization (توسيع البحث)
based process » based processes (توسيع البحث), based probes (توسيع البحث), based proteins (توسيع البحث)
binary based » library based (توسيع البحث), linac based (توسيع البحث), binary mask (توسيع البحث)
based yet » based cft (توسيع البحث)
optimisation algorithm » optimization algorithms (توسيع البحث), maximization algorithm (توسيع البحث), identification algorithm (توسيع البحث)
process optimisation » process optimization (توسيع البحث), robust optimisation (توسيع البحث), process simulation (توسيع البحث)
yet optimization » art optimization (توسيع البحث), lead optimization (توسيع البحث), path optimization (توسيع البحث)
based process » based processes (توسيع البحث), based probes (توسيع البحث), based proteins (توسيع البحث)
binary based » library based (توسيع البحث), linac based (توسيع البحث), binary mask (توسيع البحث)
based yet » based cft (توسيع البحث)
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<i>hi</i>PRS algorithm process flow.
منشور في 2023"…From this dataset we can compute the MI between each interaction and the outcome and <b>(D)</b> obtain a ranked list (<i>I</i><sub><i>δ</i></sub>) based on this metric. <b>(E)</b> Starting from the interaction at the top of <i>I</i><sub><i>δ</i></sub>, <i>hi</i>PRS constructs <i>I</i><sub><i>K</i></sub>, selecting <i>K</i> (where <i>K</i> is user-specified) terms through the greedy optimization of the ratio between MI (<i>relevance</i>) and a suitable measure of similarity for interactions (<i>redundancy)</i> (cf. …"
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SHAP bar plot.
منشور في 2025"…</p><p>Results</p><p>Logistic regression analysis identified age, hemoglobin concentration, education level, and social participation as significant factors influencing CI. Models based on NNET, RF, LR, and SVM algorithms were developed, achieving AUC of 0.918, 0.889, 0.872, and 0.760, respectively, on the test set. …"
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Sample screening flowchart.
منشور في 2025"…</p><p>Results</p><p>Logistic regression analysis identified age, hemoglobin concentration, education level, and social participation as significant factors influencing CI. Models based on NNET, RF, LR, and SVM algorithms were developed, achieving AUC of 0.918, 0.889, 0.872, and 0.760, respectively, on the test set. …"
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Descriptive statistics for variables.
منشور في 2025"…</p><p>Results</p><p>Logistic regression analysis identified age, hemoglobin concentration, education level, and social participation as significant factors influencing CI. Models based on NNET, RF, LR, and SVM algorithms were developed, achieving AUC of 0.918, 0.889, 0.872, and 0.760, respectively, on the test set. …"
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SHAP summary plot.
منشور في 2025"…</p><p>Results</p><p>Logistic regression analysis identified age, hemoglobin concentration, education level, and social participation as significant factors influencing CI. Models based on NNET, RF, LR, and SVM algorithms were developed, achieving AUC of 0.918, 0.889, 0.872, and 0.760, respectively, on the test set. …"
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ROC curves for the test set of four models.
منشور في 2025"…</p><p>Results</p><p>Logistic regression analysis identified age, hemoglobin concentration, education level, and social participation as significant factors influencing CI. Models based on NNET, RF, LR, and SVM algorithms were developed, achieving AUC of 0.918, 0.889, 0.872, and 0.760, respectively, on the test set. …"
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Display of the web prediction interface.
منشور في 2025"…</p><p>Results</p><p>Logistic regression analysis identified age, hemoglobin concentration, education level, and social participation as significant factors influencing CI. Models based on NNET, RF, LR, and SVM algorithms were developed, achieving AUC of 0.918, 0.889, 0.872, and 0.760, respectively, on the test set. …"
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Construction of reduced chemical mechanisms orientated toward specific applications: a case study of primary reference fuel
منشور في 2022"…And then, the genetic algorithm with binary variables is applied to further reduce the pathways of the small-molecule reactions targeted at ignition delay times in shock tubes, major species (fuel, O<sub>2</sub>, CO, and CO<sub>2</sub>) profiles in jet-stirred reactors, and laminar flame speeds, respectively. …"
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Supplementary Material 8
منشور في 2025"…</li><li><b>XGboost: </b>An optimized gradient boosting algorithm that efficiently handles large genomic datasets, commonly used for high-accuracy predictions in <i>E. coli</i> classification.…"