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objects optimization » objectives optimization (Expand Search), objective optimization (Expand Search), robust optimization (Expand Search)
cell optimization » field optimization (Expand Search), wolf optimization (Expand Search), lead optimization (Expand Search)
based objects » based object (Expand Search)
binary based » library based (Expand Search), linac based (Expand Search), binary mask (Expand Search)
genes based » gene based (Expand Search), lens based (Expand Search)
Showing 1 - 20 results of 115 for search '(( genes based objects optimization algorithm ) OR ( binary based cell optimization algorithm ))', query time: 0.71s Refine Results
  1. 1
    Optimal image settings for object detection of Iba1 immunolabelled cells in DAB.

    Optimal image settings for object detection of Iba1 immunolabelled cells in DAB. by Danish M. Anwer (15383441)

    Published 2023
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  2. 2
    Table_1_A scheduling route planning algorithm based on the dynamic genetic algorithm with ant colony binary iterative optimization for unmanned aerial vehicle spraying in multiple tea fields.XLSX

    Table_1_A scheduling route planning algorithm based on the dynamic genetic algorithm with ant colony binary iterative optimization for unmanned aerial vehicle spraying in multiple... by Yangyang Liu (807797)

    Published 2022
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  3. 3
    Data_Sheet_3_Strategies to Assure Optimal Trade-Offs Among Competing Objectives for the Genetic Improvement of Soybean.docx

    Data_Sheet_3_Strategies to Assure Optimal Trade-Offs Among Competing Objectives for the Genetic Improvement of Soybean.docx by Vishnu Ramasubramanian (4951354)

    Published 2021
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  4. 4
    Data_Sheet_7_Strategies to Assure Optimal Trade-Offs Among Competing Objectives for the Genetic Improvement of Soybean.docx

    Data_Sheet_7_Strategies to Assure Optimal Trade-Offs Among Competing Objectives for the Genetic Improvement of Soybean.docx by Vishnu Ramasubramanian (4951354)

    Published 2021
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  5. 5
    Data_Sheet_2_Strategies to Assure Optimal Trade-Offs Among Competing Objectives for the Genetic Improvement of Soybean.ZIP

    Data_Sheet_2_Strategies to Assure Optimal Trade-Offs Among Competing Objectives for the Genetic Improvement of Soybean.ZIP by Vishnu Ramasubramanian (4951354)

    Published 2021
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  6. 6
    Data_Sheet_4_Strategies to Assure Optimal Trade-Offs Among Competing Objectives for the Genetic Improvement of Soybean.xlsx

    Data_Sheet_4_Strategies to Assure Optimal Trade-Offs Among Competing Objectives for the Genetic Improvement of Soybean.xlsx by Vishnu Ramasubramanian (4951354)

    Published 2021
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  7. 7
    Data_Sheet_6_Strategies to Assure Optimal Trade-Offs Among Competing Objectives for the Genetic Improvement of Soybean.docx

    Data_Sheet_6_Strategies to Assure Optimal Trade-Offs Among Competing Objectives for the Genetic Improvement of Soybean.docx by Vishnu Ramasubramanian (4951354)

    Published 2021
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  8. 8
    Data_Sheet_1_Strategies to Assure Optimal Trade-Offs Among Competing Objectives for the Genetic Improvement of Soybean.PDF

    Data_Sheet_1_Strategies to Assure Optimal Trade-Offs Among Competing Objectives for the Genetic Improvement of Soybean.PDF by Vishnu Ramasubramanian (4951354)

    Published 2021
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  9. 9
    Data_Sheet_5_Strategies to Assure Optimal Trade-Offs Among Competing Objectives for the Genetic Improvement of Soybean.xlsx

    Data_Sheet_5_Strategies to Assure Optimal Trade-Offs Among Competing Objectives for the Genetic Improvement of Soybean.xlsx by Vishnu Ramasubramanian (4951354)

    Published 2021
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  10. 10
    Reproducing kernel-based semiparametric functional smoothed score estimation with binary responses

    Reproducing kernel-based semiparametric functional smoothed score estimation with binary responses by Meichen Liu (8168148)

    Published 2025
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  11. 11
    A* Path-Finding Algorithm to Determine Cell Connections

    A* Path-Finding Algorithm to Determine Cell Connections by Max Weng (22327159)

    Published 2025
    “…Pixel paths were classified using a z-score brightness threshold of 1.21, optimized for noise reduction and accuracy. The A* algorithm then evaluated connectivity by minimizing Euclidean distance and heuristic cost between cells. …”
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  12. 12
    GeneScan profiles of human peripheral blood samples.

    GeneScan profiles of human peripheral blood samples. by Nika Abdollahi (4966120)

    Published 2022
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  13. 13
    IGHV/J gene usage distribution of the AMR1 repertoire compared to the polyclonal background.

    IGHV/J gene usage distribution of the AMR1 repertoire compared to the polyclonal background. by Nika Abdollahi (4966120)

    Published 2022
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  14. 14
    IGHV/J gene usage distribution of the AMR2 repertoire compared to the polyclonal background.

    IGHV/J gene usage distribution of the AMR2 repertoire compared to the polyclonal background. by Nika Abdollahi (4966120)

    Published 2022
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  15. 15
    IGHV/J gene usage distribution of the AMR3 repertoire compared to the polyclonal background.

    IGHV/J gene usage distribution of the AMR3 repertoire compared to the polyclonal background. by Nika Abdollahi (4966120)

    Published 2022
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  16. 16
    Illustration of the BRIDES algorithm and the selection process.

    Illustration of the BRIDES algorithm and the selection process. by Cindy Bouchard (6844979)

    Published 2022
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  17. 17
    Distributions of model parameters mirror differentially expressed ion channel genes.

    Distributions of model parameters mirror differentially expressed ion channel genes. by Daniele Linaro (227790)

    Published 2022
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  18. 18
    <i>OptRAM</i>: <i>In-silico</i> strain design via integrative regulatory-metabolic network modeling

    <i>OptRAM</i>: <i>In-silico</i> strain design via integrative regulatory-metabolic network modeling by Fangzhou Shen (5140994)

    Published 2019
    “…To address challenges in metabolic engineering, computational strain optimization algorithms based on genome-scale metabolic models have increasingly been used to aid in overproducing products of interest. …”
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  19. 19
    The illustration depicts our CQ-CNN architecture for binary image classification.

    The illustration depicts our CQ-CNN architecture for binary image classification. by Mominul Islam (3513758)

    Published 2025
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  20. 20
    ROC curve for binary classification.

    ROC curve for binary classification. by Nicodemus Songose Awarayi (18414494)

    Published 2024
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