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property optimization » process optimization (Expand Search), policy optimization (Expand Search), robust optimization (Expand Search)
model optimization » codon optimization (Expand Search), global optimization (Expand Search), based optimization (Expand Search)
binary based » library based (Expand Search), linac based (Expand Search), binary mask (Expand Search)
binary data » primary data (Expand Search), dietary data (Expand Search)
data model » data models (Expand Search)
property optimization » process optimization (Expand Search), policy optimization (Expand Search), robust optimization (Expand Search)
model optimization » codon optimization (Expand Search), global optimization (Expand Search), based optimization (Expand Search)
binary based » library based (Expand Search), linac based (Expand Search), binary mask (Expand Search)
binary data » primary data (Expand Search), dietary data (Expand Search)
data model » data models (Expand Search)
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Seed mix selection model
Published 2022“…</p> <p> </p> <p>We applied the seed mix selection model using a binary genetic algorithm to select seed mixes (R package ‘GA’; Scrucca 2013; Scrucca 2017). …”
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DataSheet_1_Near infrared spectroscopy for cooking time classification of cassava genotypes.docx
Published 2024“…Two NIRs devices, the portable QualitySpec® Trek (QST) and the benchtop NIRFlex N-500 were used to collect spectral data. Classification of genotypes was carried out using the K-nearest neighbor algorithm (KNN) and partial least squares (PLS) models. …”
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145
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. …”
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146
Data_Sheet_1_A Data-Driven Framework for Identifying Intensive Care Unit Admissions Colonized With Multidrug-Resistant Organisms.docx
Published 2022“…</p>Materials and Methods<p>Leveraging data from electronic healthcare records and a unique MDRO universal screening program, we developed a data-driven modeling framework to predict MRSA, VRE, and CRE colonization upon intensive care unit (ICU) admission, and identified the associated socio-demographic and clinical factors using logistic regression (LR), random forest (RF), and XGBoost algorithms. …”
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147
Supplementary Material 8
Published 2025“…</p><p dir="ltr">When applied to AMR prediction, SMOTE enhances the ability of classification models to accurately identify resistant <i>Escherichia coli</i> strains by balancing the dataset, ensuring that machine learning algorithms do not overlook rare resistance patterns. …”
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Table_1_Near infrared spectroscopy for cooking time classification of cassava genotypes.docx
Published 2024“…Two NIRs devices, the portable QualitySpec® Trek (QST) and the benchtop NIRFlex N-500 were used to collect spectral data. Classification of genotypes was carried out using the K-nearest neighbor algorithm (KNN) and partial least squares (PLS) models. …”
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152
Flowchart of the entire pipeline.
Published 2024“…Then, the protein feature generation algorithms described in our previous study [<a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0315330#pone.0315330.ref022" target="_blank">22</a>] are applied to the data, and pairwise ML models are trained and evaluated (see Section Evaluation of pairwise machine learning models). …”
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153
Machine Learning-Ready Dataset for Cytotoxicity Prediction of Metal Oxide Nanoparticles
Published 2025“…Details on the data sourcing process, prompt engineering strategies for large language model (LLM)-based extraction, and validation protocols are provided in the Supplementary Information section.…”
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154
Table_1_Machine Learning Techniques in Blood Pressure Management During the Acute Phase of Ischemic Stroke.DOCX
Published 2022“…</p>Methods<p>This diagnostic accuracy study used retrospective data from MIMIC-III and eICU databases. Decision trees were constructed by a hierarchical binary recursive partitioning algorithm to predict the BP-lowering of 10–30% off the maximal value when antihypertensive treatment was given in patients with an extremely high BP (above 220/110 or 180/105 mmHg for patients receiving thrombolysis), according to the American Heart Association/American Stroke Association (AHA/ASA), the European Society of Cardiology, and the European Society of Hypertension (ESC/ESH) guidelines. …”
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Active Learning Accelerated Discovery of Stable Iridium Oxide Polymorphs for the Oxygen Evolution Reaction
Published 2020“…We emphasize that the proposed AL algorithm can be easily generalized to search for any binary metal oxide structure with a defined stoichiometry.…”
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156
Image 1_A multimodal AI-driven framework for cardiovascular screening and risk assessment in diverse athletic populations: innovations in sports cardiology.png
Published 2025“…RSEE projects heterogeneous input data into an exertion-conditioned latent space, aligning model predictions with observed physiological variance and mitigating false positives by explicitly modeling the overlap between athletic remodeling and subclinical pathology.…”
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157
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. …”
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158
Table 1_Heavy metal biomarkers and their impact on hearing loss risk: a machine learning framework analysis.docx
Published 2025“…</p>Results<p>The CatBoost model demonstrated the strongest performance, achieving an accuracy of 74.9% and an AUC of 0.792 on test data. …”