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Showing 1 - 20 results of 154 for search '(( binary b binary classification algorithm ) OR ( binary task based optimization algorithm ))*', query time: 1.14s Refine Results
  1. 1
    Diagnostic performance for determining malignancy; binary classification.

    Diagnostic performance for determining malignancy; binary classification. by Seung Seog Han (4782486)

    Published 2020
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  2. 2
    Urgent versus nonurgent binary classification performance.

    Urgent versus nonurgent binary classification performance. by Kyung Won Kwon (18150416)

    Published 2024
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  3. 3
    An example of 2-dimensional non-linearly separable and non-separable data for binary classification.

    An example of 2-dimensional non-linearly separable and non-separable data for binary classification. by Yuji Oyamada (11714754)

    Published 2021
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  4. 4
    (a) Accuracy and (b) loss curves of training and validation during binary class classification for 512 features extracted by the CNN.

    (a) Accuracy and (b) loss curves of training and validation during binary class classification for 512 features extracted by the CNN. by Md. Sabbir Hossain (9958939)

    Published 2025
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  5. 5
    The performance of the machine learning algorithms to differentiate between W and other sleep stages during three and five class binary classification tasks on the test set sleep stages during three and five class binary classification tasks on the test set.

    The performance of the machine learning algorithms to differentiate between W and other sleep stages during three and five class binary classification tasks on the test set sleep s... by Sowmya M. Ramaswamy (18954755)

    Published 2024
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  6. 6
    Random forest binary classification of the colorized Corona image for the RSK region, Morocco (1972).

    Random forest binary classification of the colorized Corona image for the RSK region, Morocco (1972). by Safia Loulad (16913691)

    Published 2023
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  7. 7
    Training loss results plotted against the number of iterations for (a) 9, (b) 10, (c) 11, and (d) 12 principal components.

    Training loss results plotted against the number of iterations for (a) 9, (b) 10, (c) 11, and (d) 12 principal components. by Aninda Astuti (21736334)

    Published 2025
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  8. 8
    The results of the ten performance metrics for NQE+SVC vs NQE+QSVC using (a) 9, (b) 10, (c) 11, and (d) 12 principle components, respectively.

    The results of the ten performance metrics for NQE+SVC vs NQE+QSVC using (a) 9, (b) 10, (c) 11, and (d) 12 principle components, respectively. by Aninda Astuti (21736334)

    Published 2025
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  9. 9
    Several loop patterns found in binary code.

    Several loop patterns found in binary code. by Alba Pedro-Zapater (8600382)

    Published 2020
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  10. 10
    Correlation heatmap of performance scores among different feature selection algorithms: bOcOA, bPSO, bGWO, bFA, bGA, and bWAO.

    Correlation heatmap of performance scores among different feature selection algorithms: bOcOA, bPSO, bGWO, bFA, bGA, and bWAO. by Doaa Sami Khafaga (21463870)

    Published 2025
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  11. 11
    Density distribution plot of performance metrics for various feature selection algorithms: bOcOA, bPSO, bGWO, bFA, bGA, and bWAO.

    Density distribution plot of performance metrics for various feature selection algorithms: bOcOA, bPSO, bGWO, bFA, bGA, and bWAO. by Doaa Sami Khafaga (21463870)

    Published 2025
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  12. 12
    ROC curves of the proposed method for binary class classification: (A) GNB, (B) GBM, (C) KNN, (D) SVM, (E) RF, and (F) Ensemble learning.

    ROC curves of the proposed method for binary class classification: (A) GNB, (B) GBM, (C) KNN, (D) SVM, (E) RF, and (F) Ensemble learning. by Md. Sabbir Hossain (9958939)

    Published 2025
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  13. 13
    The train and test kernel matrix matrices.

    The train and test kernel matrix matrices. by Aninda Astuti (21736334)

    Published 2025
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  14. 14
    Data embedding by using Neural Quantum Embedding (NQE).

    Data embedding by using Neural Quantum Embedding (NQE). by Aninda Astuti (21736334)

    Published 2025
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  15. 15
    The workflow of integrating NQE with QSVC and QNN classifiers.

    The workflow of integrating NQE with QSVC and QNN classifiers. by Aninda Astuti (21736334)

    Published 2025
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  16. 16
    Training loss results plotted against the number of iterations.

    Training loss results plotted against the number of iterations. by Aninda Astuti (21736334)

    Published 2025
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  17. 17
    The quantum embedding circuit designed for the miRNA dataset, featuring three layers of repeated Hadamard (H), RZ, and control-NOT unitary gates.

    The quantum embedding circuit designed for the miRNA dataset, featuring three layers of repeated Hadamard (H), RZ, and control-NOT unitary gates. by Aninda Astuti (21736334)

    Published 2025
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  18. 18
    The results of the 4 differentially expressed miRNAs obtained by analysing miRNAs expression profiles using the R ‘limma’ package.

    The results of the 4 differentially expressed miRNAs obtained by analysing miRNAs expression profiles using the R ‘limma’ package. by Aninda Astuti (21736334)

    Published 2025
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  19. 19
    The results of the 48 differentially expressed genes obtained by analysing gene expression profiles using the DEseq2 package.

    The results of the 48 differentially expressed genes obtained by analysing gene expression profiles using the DEseq2 package. by Aninda Astuti (21736334)

    Published 2025
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  20. 20
    The results of the ten performance metrics for NQE+SVC vs NQE+QSVC based on classifying miRNA expression profiles.

    The results of the ten performance metrics for NQE+SVC vs NQE+QSVC based on classifying miRNA expression profiles. by Aninda Astuti (21736334)

    Published 2025
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