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Showing 1 - 5 results of 5 for search '(( binary pairs processes classification algorithm ) OR ( binary b codon optimization algorithm ))', query time: 0.49s Refine Results
  1. 1
    Receiver operating curves for NLP classification.

    Receiver operating curves for NLP classification. by Charlene Jennifer Ong (8997278)

    Published 2020
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  2. 2
    Data_Sheet_1_Multiclass Classification Based on Combined Motor Imageries.pdf

    Data_Sheet_1_Multiclass Classification Based on Combined Motor Imageries.pdf by Cecilia Lindig-León (7889777)

    Published 2020
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  3. 3
    <b>BRISC: Annotated Dataset for Brain Tumor Segmentation and Classification</b>

    <b>BRISC: Annotated Dataset for Brain Tumor Segmentation and Classification</b> by BRISC Dataset (22559540)

    Published 2025
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  4. 4
    DataSheet_2_MRI-Based Radiomics to Differentiate between Benign and Malignant Parotid Tumors With External Validation.pdf

    DataSheet_2_MRI-Based Radiomics to Differentiate between Benign and Malignant Parotid Tumors With External Validation.pdf by Francesca Piludu (10706391)

    Published 2021
    “…The model with the final feature set was achieved using the support vector machine binary classification algorithm.</p>Results<p>Models for discriminating between Warthin’s and malignant tumors, benign and Warthin’s tumors and benign and malignant tumors had an accuracy of 86.7%, 91.9% and 80.4%, respectively. …”
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  5. 5
    DataSheet_1_MRI-Based Radiomics to Differentiate between Benign and Malignant Parotid Tumors With External Validation.xlsx

    DataSheet_1_MRI-Based Radiomics to Differentiate between Benign and Malignant Parotid Tumors With External Validation.xlsx by Francesca Piludu (10706391)

    Published 2021
    “…The model with the final feature set was achieved using the support vector machine binary classification algorithm.</p>Results<p>Models for discriminating between Warthin’s and malignant tumors, benign and Warthin’s tumors and benign and malignant tumors had an accuracy of 86.7%, 91.9% and 80.4%, respectively. …”
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