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Showing 1 - 20 results of 53 for search '(( binary complex process classification algorithm ) OR ( binary based co optimization algorithm ))', query time: 0.54s Refine Results
  1. 1
    Feature selection convergence time comparison of various binary metaheuristic algorithms.

    Feature selection convergence time comparison of various binary metaheuristic algorithms. by Amal H. Alharbi (21755906)

    Published 2025
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  2. 2
    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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  3. 3
    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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  4. 4
    The results of 10 performance metrics from the miRNA biomarker study (M0 vs M1), comparing NQE+SVC and NQE+QSVC in the 5 PCs case.

    The results of 10 performance metrics from the miRNA biomarker study (M0 vs M1), comparing NQE+SVC and NQE+QSVC in the 5 PCs case. by Aninda Astuti (21736334)

    Published 2025
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  5. 5
    The PCA plots of the data distribution (mRNA) projected onto two principal components selected from PC1 through PC12.

    The PCA plots of the data distribution (mRNA) projected onto two principal components selected from PC1 through PC12. by Aninda Astuti (21736334)

    Published 2025
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  6. 6
    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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  7. 7
    The workflow diagram for the gene expression biomarker study which involve using principal component analysis (PCA) to reduce the number of features (principal components), thus requiring fewer qubits.

    The workflow diagram for the gene expression biomarker study which involve using principal component analysis (PCA) to reduce the number of features (principal components), thus re... by Aninda Astuti (21736334)

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  8. 8
    Classification performance results using 5-fold cross-validation for the training datasets after applying the data imbalance tool, SMOTE and NQE for data embedding. The values without the ‘±’ signs represent the results for the ‘test’ set.

    Classification performance results using 5-fold cross-validation for the training datasets after applying the data imbalance tool, SMOTE and NQE for data embedding. The values with... by Aninda Astuti (21736334)

    Published 2025
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  9. 9
    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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  10. 10
    The PCA plots of the data distribution (mRNA) projected onto two principal components selected from PC1 through PC11.

    The PCA plots of the data distribution (mRNA) projected onto two principal components selected from PC1 through PC11. by Aninda Astuti (21736334)

    Published 2025
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  11. 11
    The results of 10 performance metrics from the miRNA biomarker study (M0 vs M1), comparing NQE+NN and NQE+QNN in the 6 PCs case.

    The results of 10 performance metrics from the miRNA biomarker study (M0 vs M1), comparing NQE+NN and NQE+QNN in the 6 PCs case. by Aninda Astuti (21736334)

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  12. 12
    The results of 10 performance metrics from the miRNA biomarker study (M0 vs M1), comparing NQE+NN and NQE+QNN in the 5 PCs case.

    The results of 10 performance metrics from the miRNA biomarker study (M0 vs M1), comparing NQE+NN and NQE+QNN in the 5 PCs case. by Aninda Astuti (21736334)

    Published 2025
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  13. 13
    The results of 10 performance metrics from the miRNA biomarker study (M0 vs M1), comparing NQE+NN and NQE+QNN in the 4 PCs case.

    The results of 10 performance metrics from the miRNA biomarker study (M0 vs M1), comparing NQE+NN and NQE+QNN in the 4 PCs case. by Aninda Astuti (21736334)

    Published 2025
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  14. 14
    The PCA plots of the data distribution (mRNA) projected onto two principal components selected from PC1 through PC10.

    The PCA plots of the data distribution (mRNA) projected onto two principal components selected from PC1 through PC10. by Aninda Astuti (21736334)

    Published 2025
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  15. 15
    The results of 10 performance metrics from the miRNA biomarker study (M0 vs M1), comparing NQE+SVC and NQE+QSVC in the 4 PCs case.

    The results of 10 performance metrics from the miRNA biomarker study (M0 vs M1), comparing NQE+SVC and NQE+QSVC in the 4 PCs case. by Aninda Astuti (21736334)

    Published 2025
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  16. 16
    Classification performance results using 5-fold cross-validation for the training datasets after applying the data imbalance tool, SMOTE and NQE for data embedding. The values without the ‘±’ signs represent the results for the ‘test’ set.

    Classification performance results using 5-fold cross-validation for the training datasets after applying the data imbalance tool, SMOTE and NQE for data embedding. The values with... by Aninda Astuti (21736334)

    Published 2025
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  17. 17
    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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  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
    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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  20. 20
    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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