Search alternatives:
dose optimization » based optimization (Expand Search), model optimization (Expand Search), wolf optimization (Expand Search)
yet optimization » art optimization (Expand Search), lead optimization (Expand Search), path optimization (Expand Search)
primary data » primary care (Expand Search)
image dose » image dipole (Expand Search)
Showing 1 - 11 results of 11 for search '(( binary image dose optimization algorithm ) OR ( primary data yet optimization algorithm ))*', query time: 0.48s Refine Results
  1. 1
    The relationship between the prevalence of PE and failure rate of each diagnostic strategy.

    The relationship between the prevalence of PE and failure rate of each diagnostic strategy. by Geert-Jan Geersing (331049)

    Published 2022
    Subjects:
    Save to List
    Saved in:
  2. 2
    Forest plot of failure rate and efficiency of the diagnostic strategies across healthcare settings.

    Forest plot of failure rate and efficiency of the diagnostic strategies across healthcare settings. by Geert-Jan Geersing (331049)

    Published 2022
    Subjects:
    Save to List
    Saved in:
  3. 3
    The relationship between the prevalence of PE and efficiency of each diagnostic strategy.

    The relationship between the prevalence of PE and efficiency of each diagnostic strategy. by Geert-Jan Geersing (331049)

    Published 2022
    Subjects:
    Save to List
    Saved in:
  4. 4
    Patient characteristics across different healthcare settings.

    Patient characteristics across different healthcare settings. by Geert-Jan Geersing (331049)

    Published 2022
    Subjects:
    Save to List
    Saved in:
  5. 5
    Sensitivity and specificity of diagnostic strategies across healthcare settings.

    Sensitivity and specificity of diagnostic strategies across healthcare settings. by Geert-Jan Geersing (331049)

    Published 2022
    Subjects:
    Save to List
    Saved in:
  6. 6
    Data_Sheet_2_Computational Neural Modeling of Auditory Cortical Receptive Fields.pdf

    Data_Sheet_2_Computational Neural Modeling of Auditory Cortical Receptive Fields.pdf by Jordan D. Chambers (6732224)

    Published 2019
    Save to List
    Saved in:
  7. 7
    Data_Sheet_1_Computational Neural Modeling of Auditory Cortical Receptive Fields.PDF

    Data_Sheet_1_Computational Neural Modeling of Auditory Cortical Receptive Fields.PDF by Jordan D. Chambers (6732224)

    Published 2019
    Save to List
    Saved in:
  8. 8
    Data_Sheet_3_Computational Neural Modeling of Auditory Cortical Receptive Fields.PDF

    Data_Sheet_3_Computational Neural Modeling of Auditory Cortical Receptive Fields.PDF by Jordan D. Chambers (6732224)

    Published 2019
    Save to List
    Saved in:
  9. 9
    Machine Learning-Driven Kinetic Elucidation for Sustainable Solvent-Free Continuous ε‑Caprolactone Production via Propionaldehyde-Mediated Nanocarbon Catalysis

    Machine Learning-Driven Kinetic Elucidation for Sustainable Solvent-Free Continuous ε‑Caprolactone Production via Propionaldehyde-Mediated Nanocarbon Catalysis by Qing Hu (152962)

    Published 2025
    “…A kinetic model was established by focusing on two primary reactions: Cy = O oxidation (Reaction I) and PRA auto-oxidation (Reaction II), from which the reliable kinetic parameters were obtained via genetic algorithm-based optimization. …”
    Save to List
    Saved in:
  10. 10
    Machine Learning-Driven Kinetic Elucidation for Sustainable Solvent-Free Continuous ε‑Caprolactone Production via Propionaldehyde-Mediated Nanocarbon Catalysis

    Machine Learning-Driven Kinetic Elucidation for Sustainable Solvent-Free Continuous ε‑Caprolactone Production via Propionaldehyde-Mediated Nanocarbon Catalysis by Qing Hu (152962)

    Published 2025
    “…A kinetic model was established by focusing on two primary reactions: Cy = O oxidation (Reaction I) and PRA auto-oxidation (Reaction II), from which the reliable kinetic parameters were obtained via genetic algorithm-based optimization. …”
    Save to List
    Saved in:
  11. 11
    Table_1_Screening of Long Non-coding RNAs Biomarkers for the Diagnosis of Tuberculosis and Preliminary Construction of a Clinical Diagnosis Model.docx

    Table_1_Screening of Long Non-coding RNAs Biomarkers for the Diagnosis of Tuberculosis and Preliminary Construction of a Clinical Diagnosis Model.docx by Juli Chen (12187358)

    Published 2022
    “…Background<p>Pathogenic testing for tuberculosis (TB) is not yet sufficient for early and differential clinical diagnosis; thus, we investigated the potential of screening long non-coding RNAs (lncRNAs) from human hosts and using machine learning (ML) algorithms combined with electronic health record (EHR) metrics to construct a diagnostic model.…”
    Save to List
    Saved in:

Search Tools: