Showing 141 - 146 results of 146 for search '(( binary data driven optimization algorithm ) OR ( binary based model optimization algorithm ))*', query time: 0.53s Refine Results
  1. 141

    DataSheet_1_Near infrared spectroscopy for cooking time classification of cassava genotypes.docx by Massaine Bandeira e Sousa (7866242)

    Published 2024
    “…Classification of genotypes was carried out using the K-nearest neighbor algorithm (KNN) and partial least squares (PLS) models. …”
  2. 142

    Table_1_Near infrared spectroscopy for cooking time classification of cassava genotypes.docx by Massaine Bandeira e Sousa (7866242)

    Published 2024
    “…Classification of genotypes was carried out using the K-nearest neighbor algorithm (KNN) and partial least squares (PLS) models. …”
  3. 143

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

    Published 2020
    “…In this predictor, we introduced a sequence-based feature algorithm consisting of two feature representations, (1) k-mer spectrum and (2) positional nucleotide binary vector, to capture the sequential characteristics of 5hmC sites. …”
  4. 144

    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. …”
  5. 145

    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.…”
  6. 146

    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.…”