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model optimization » codon optimization (Expand Search), global optimization (Expand Search), based optimization (Expand Search)
mask process » based process (Expand Search), basic process (Expand Search), a process (Expand Search)
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a model » _ model (Expand Search)
model optimization » codon optimization (Expand Search), global optimization (Expand Search), based optimization (Expand Search)
mask process » based process (Expand Search), basic process (Expand Search), a process (Expand Search)
binary mask » binary image (Expand Search)
binary a » binary _ (Expand Search), binary b (Expand Search), hilary a (Expand Search)
a model » _ model (Expand Search)
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181
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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182
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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183
Data_Sheet_1_Prediction of Mental Health in Medical Workers During COVID-19 Based on Machine Learning.ZIP
Published 2021“…In this study, we propose a novel prediction model based on optimization algorithm and neural network, which can select and rank the most important factors that affect mental health of medical workers. …”
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184
Table_1_iRNA5hmC: The First Predictor to Identify RNA 5-Hydroxymethylcytosine Modifications Using Machine Learning.docx
Published 2020“…Afterward, we utilized a two-stage feature space optimization strategy to improve the feature representation ability, and trained a predictive model using support vector machine (SVM). …”
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185
Predictive Analysis of Mushroom Toxicity Based Exclusively on Their Natural Habitat.
Published 2025“…Optimization with GridSearchCV corroborated this stagnation, identifying a simple linear model (C=0.05, gamma='scale') as the optimal configuration, indicating that the additional complexity of nonlinear kernels did not confer predictive gains. …”
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186
DataSheet_1_Exploring deep learning radiomics for classifying osteoporotic vertebral fractures in X-ray images.docx
Published 2024“…Logistic regression emerged as the optimal machine learning algorithm for both DLR models. …”
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187
Table_1_Machine Learning Techniques in Blood Pressure Management During the Acute Phase of Ischemic Stroke.DOCX
Published 2022“…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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188
Table 1_Creating an interactive database for nasopharyngeal carcinoma management: applying machine learning to evaluate metastasis and survival.docx
Published 2024“…Similarly, for cancer-specific survival (CSS) prediction, the RSF model demonstrated a mean C-index of 0.822, a 5-year AUC of 0.884, and a Brier score of 0.165. …”
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189
Data_Sheet_1_The impact of family urban integration on migrant worker mental health in China.docx
Published 2024“…</p>Methods<p>This paper uses multi-dimensional indexes to measure family urban integration, covering economic, social and psychological dimensions, which may consider the complexity of integration. Utilizing a machine learning clustering algorithm, the research endeavors to assess the level of urban integration experienced by migrant workers and their respective families. …”
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190
Machine Learning-Ready Dataset for Cytotoxicity Prediction of Metal Oxide Nanoparticles
Published 2025“…</p><p dir="ltr">Encoding: Categorical variables such as surface coating and cell type were grouped into logical classes and label-encoded to enable model compatibility.</p><p dir="ltr"><b>Applications and Model Compatibility:</b></p><p dir="ltr">The dataset is optimized for use in supervised learning workflows and has been tested with algorithms such as:</p><p dir="ltr">Gradient Boosting Machines (GBM),</p><p dir="ltr">Support Vector Machines (SVM-RBF),</p><p dir="ltr">Random Forests, and</p><p dir="ltr">Principal Component Analysis (PCA) for feature reduction.…”