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Showing 61 - 80 results of 169 for search '(( binary data model optimization algorithm ) OR ( binary data field optimization algorithm ))', query time: 0.32s Refine Results
  1. 61
    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)

    Published 2025
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  2. 62
    Comparison in terms of the sensitivity.

    Comparison in terms of the sensitivity. by Ying Li (38224)

    Published 2024
    “…In this paper, we present an improved mountain gazelle optimizer (IMGO) based on the newly proposed mountain gazelle optimizer (MGO) and design a binary version of IMGO (BIMGO) to solve the feature selection problem for medical data. …”
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  3. 63
    Parameter sensitivity of BIMGO.

    Parameter sensitivity of BIMGO. by Ying Li (38224)

    Published 2024
    “…In this paper, we present an improved mountain gazelle optimizer (IMGO) based on the newly proposed mountain gazelle optimizer (MGO) and design a binary version of IMGO (BIMGO) to solve the feature selection problem for medical data. …”
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  4. 64
    Details of the medical datasets.

    Details of the medical datasets. by Ying Li (38224)

    Published 2024
    “…In this paper, we present an improved mountain gazelle optimizer (IMGO) based on the newly proposed mountain gazelle optimizer (MGO) and design a binary version of IMGO (BIMGO) to solve the feature selection problem for medical data. …”
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  5. 65
    The flowchart of IMGO.

    The flowchart of IMGO. by Ying Li (38224)

    Published 2024
    “…In this paper, we present an improved mountain gazelle optimizer (IMGO) based on the newly proposed mountain gazelle optimizer (MGO) and design a binary version of IMGO (BIMGO) to solve the feature selection problem for medical data. …”
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  6. 66
    Comparison in terms of the selected features.

    Comparison in terms of the selected features. by Ying Li (38224)

    Published 2024
    “…In this paper, we present an improved mountain gazelle optimizer (IMGO) based on the newly proposed mountain gazelle optimizer (MGO) and design a binary version of IMGO (BIMGO) to solve the feature selection problem for medical data. …”
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  7. 67
    Iterative chart of control factor.

    Iterative chart of control factor. by Ying Li (38224)

    Published 2024
    “…In this paper, we present an improved mountain gazelle optimizer (IMGO) based on the newly proposed mountain gazelle optimizer (MGO) and design a binary version of IMGO (BIMGO) to solve the feature selection problem for medical data. …”
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  8. 68
    Details of 23 basic benchmark functions.

    Details of 23 basic benchmark functions. by Ying Li (38224)

    Published 2024
    “…In this paper, we present an improved mountain gazelle optimizer (IMGO) based on the newly proposed mountain gazelle optimizer (MGO) and design a binary version of IMGO (BIMGO) to solve the feature selection problem for medical data. …”
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  9. 69
    Related researches.

    Related researches. by Ying Li (38224)

    Published 2024
    “…In this paper, we present an improved mountain gazelle optimizer (IMGO) based on the newly proposed mountain gazelle optimizer (MGO) and design a binary version of IMGO (BIMGO) to solve the feature selection problem for medical data. …”
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  10. 70
    S1 Dataset -

    S1 Dataset - by Ying Li (38224)

    Published 2024
    “…In this paper, we present an improved mountain gazelle optimizer (IMGO) based on the newly proposed mountain gazelle optimizer (MGO) and design a binary version of IMGO (BIMGO) to solve the feature selection problem for medical data. …”
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  11. 71
    Analysis and design of algorithms for the manufacturing process of integrated circuits

    Analysis and design of algorithms for the manufacturing process of integrated circuits by Sonia Fleytas (16856403)

    Published 2023
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  12. 72
    Random forest model performs better than support vector machine algorithms and when it primarily uses spontaneous photopic ERG of 60-s duration in humans.

    Random forest model performs better than support vector machine algorithms and when it primarily uses spontaneous photopic ERG of 60-s duration in humans. by Ramsés Noguez Imm (6658637)

    Published 2023
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  13. 73
    Generalized Tensor Decomposition With Features on Multiple Modes

    Generalized Tensor Decomposition With Features on Multiple Modes by Jiaxin Hu (1327875)

    Published 2021
    “…Our proposal handles a broad range of data types, including continuous, count, and binary observations. …”
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  14. 74
    LITNET-2020 data splitting approach.

    LITNET-2020 data splitting approach. by Asmaa Ahmed Awad (16726315)

    Published 2023
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  15. 75
    Latent state estimations for simulated data.

    Latent state estimations for simulated data. by Samiul Alam (18563323)

    Published 2024
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  16. 76
    Latent state estimations for experimental data with no stressors.

    Latent state estimations for experimental data with no stressors. by Samiul Alam (18563323)

    Published 2024
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  17. 77
    Latent state estimations for experimental data with interruptions.

    Latent state estimations for experimental data with interruptions. by Samiul Alam (18563323)

    Published 2024
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  18. 78
    Latent state estimations for experimental data with time limit.

    Latent state estimations for experimental data with time limit. by Samiul Alam (18563323)

    Published 2024
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  19. 79
    Solubility Prediction of Different Forms of Pharmaceuticals in Single and Mixed Solvents Using Symmetric Electrolyte Nonrandom Two-Liquid Segment Activity Coefficient Model

    Solubility Prediction of Different Forms of Pharmaceuticals in Single and Mixed Solvents Using Symmetric Electrolyte Nonrandom Two-Liquid Segment Activity Coefficient Model by Getachew S. Molla (6416744)

    Published 2019
    “…Moreover, a design of experiments is included in the methodology to generate and use experimental data appropriately for model parameter regression and model validation. …”
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  20. 80
    Fig 1 -

    Fig 1 - by Jakub Stoklosa (3315042)

    Published 2023
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