Showing 141 - 147 results of 147 for search '(( final phase process optimization algorithm ) OR ( binary data based optimization algorithm ))', query time: 0.41s Refine Results
  1. 141

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

    Published 2024
    “…Cooking data were classified into binary and multiclass variables (CT4C and CT6C). …”
  2. 142

    Supplementary Material 8 by Nishitha R Kumar (19750617)

    Published 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.…”
  3. 143

    Seed mix selection model by Bethanne Bruninga-Socolar (10923639)

    Published 2022
    “…The genetic algorithm then operated over 1000 iterations, applying crossover and mutation processes to optimize bee richness. …”
  4. 144

    Flow diagram of the automatic animal detection and background reconstruction. by David Tadres (9120564)

    Published 2020
    “…(E) The threshold value is calculated based on the histogram: it is the mean of the image subtracted by 4 (optimal value defined by trial and error). …”
  5. 145

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

    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.…”
  6. 146

    DataSheet_1_A Novel Radiogenomics Biomarker Based on Hypoxic-Gene Subset: Accurate Survival and Prognostic Prediction of Renal Clear Cell Carcinoma.doc by Jiahao Gao (7799222)

    Published 2021
    “…Building on this, a hypoxia-gene related radiogenomics biomarker (prediction of hypoxia-genes signature by contrast-enhanced CT radiomics) was constructed in the TCIA-KIRC database by extracting features in the venous phase of contrast-enhanced CT images, selecting features using the mRMR and LASSO algorithms, and building logistic regression models. …”
  7. 147

    <b>AI for imaging plant stress in invasive species </b>(dataset from the article https://doi.org/10.1093/aob/mcaf043) by Erola Fenollosa (20977421)

    Published 2025
    “…</li><li>The dataframe of extracted colour features from all leaf images and lab variables (ecophysiological predictors and variables to be predicted)</li><li>Set of scripts used for image pre-processing, features extraction, data analytsis, visualization and Machine learning algorithms training, using ImageJ, R and Python.…”