Showing 181 - 190 results of 190 for search '(( binary mask process optimization algorithm ) OR ( binary a model optimization algorithm ))', query time: 0.26s Refine Results
  1. 181

    Active Learning Accelerated Discovery of Stable Iridium Oxide Polymorphs for the Oxygen Evolution Reaction by Raul A. Flores (2910539)

    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.…”
  2. 182

    Flowchart of the entire pipeline. by Andreas Denger (12111159)

    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). …”
  3. 183

    Data_Sheet_1_Prediction of Mental Health in Medical Workers During COVID-19 Based on Machine Learning.ZIP by Xiaofeng Wang (119575)

    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. …”
  4. 184

    Table_1_iRNA5hmC: The First Predictor to Identify RNA 5-Hydroxymethylcytosine Modifications Using Machine Learning.docx by Yuan Liu (88411)

    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). …”
  5. 185

    Predictive Analysis of Mushroom Toxicity Based Exclusively on Their Natural Habitat. by Enrico Bertozzi (22461709)

    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. …”
  6. 186

    DataSheet_1_Exploring deep learning radiomics for classifying osteoporotic vertebral fractures in X-ray images.docx by Jun Zhang (48506)

    Published 2024
    “…Logistic regression emerged as the optimal machine learning algorithm for both DLR models. …”
  7. 187

    Table_1_Machine Learning Techniques in Blood Pressure Management During the Acute Phase of Ischemic Stroke.DOCX by Orit Mazza (12081914)

    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. …”
  8. 188

    Table 1_Creating an interactive database for nasopharyngeal carcinoma management: applying machine learning to evaluate metastasis and survival.docx by Yanbo Sun (2202439)

    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. …”
  9. 189

    Data_Sheet_1_The impact of family urban integration on migrant worker mental health in China.docx by Xiaotong Sun (6535064)

    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. …”
  10. 190

    Machine Learning-Ready Dataset for Cytotoxicity Prediction of Metal Oxide Nanoparticles by Soham Savarkar (21811825)

    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.…”