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significant trained » significant trend (Expand Search), significant strain (Expand Search), significant brain (Expand Search)
trained based » defined based (Expand Search), trait based (Expand Search), ranked based (Expand Search)
ns decrease » _ decrease (Expand Search), a decrease (Expand Search), we decrease (Expand Search)
nn decrease » _ decrease (Expand Search), a decrease (Expand Search), mean decrease (Expand Search)
Showing 61 - 80 results of 16,341 for search '(( significant trained based ) OR ( significant ((ns decrease) OR (nn decrease)) ))', query time: 0.66s Refine Results
  1. 61
    Number of images and training parameters used to train the model.

    Number of images and training parameters used to train the model. by Yuki Kurita (15627093)

    Published 2023
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  2. 62
    Plant growth, nodulation, and N content as affected by manipulating of GmNN1/FT2a.

    Plant growth, nodulation, and N content as affected by manipulating of GmNN1/FT2a. by Xinxin Li (281237)

    Published 2022
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  3. 63
    Expression of <i>GmNN1/FT2a</i> in various tissues of soybean plants.

    Expression of <i>GmNN1/FT2a</i> in various tissues of soybean plants. by Xinxin Li (281237)

    Published 2022
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  4. 64
    GmNN1/FT2a interacted with GmNYFA-C in the BiFC assay.

    GmNN1/FT2a interacted with GmNYFA-C in the BiFC assay. by Xinxin Li (281237)

    Published 2022
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  5. 65
    Training curves of improved VIT-B/16 on training and test sets.

    Training curves of improved VIT-B/16 on training and test sets. by Shuang Hong (17558808)

    Published 2024
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  6. 66
    Profile’s characteristics based on game category, sport experience and training hours.

    Profile’s characteristics based on game category, sport experience and training hours. by J. Arturo Abraldes (14227362)

    Published 2022
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  7. 67
    Cortical regions with significant component differences in cortical morphological features.

    Cortical regions with significant component differences in cortical morphological features. by Liang Wang (23021)

    Published 2025
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  8. 68
    Comparison of nodulation traits between ND and NS soybean materials.

    Comparison of nodulation traits between ND and NS soybean materials. by Xinxin Li (281237)

    Published 2022
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  9. 69
    Administration model of a blended basketball training program based on the ADDIE framework.

    Administration model of a blended basketball training program based on the ADDIE framework. by Jun Sun (48981)

    Published 2025
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  10. 70
    Gene specific primers used for <i>GmNN1/FT2a</i> fine mapping.

    Gene specific primers used for <i>GmNN1/FT2a</i> fine mapping. by Xinxin Li (281237)

    Published 2022
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  11. 71
    The proposed ReCNN-based TL scheme for model training.

    The proposed ReCNN-based TL scheme for model training. by Chaojin Qing (12225185)

    Published 2022
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  12. 72
    Network pharmacology analysis predicted the targets of NS on atherosclerosis.

    Network pharmacology analysis predicted the targets of NS on atherosclerosis. by Yihua Wang (491766)

    Published 2025
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  13. 73
    Forecast results. <i>p</i>-value is given in the last row, and the sign ‘*’ shows significant at <i>p</i> = 0.05 level.

    Forecast results. <i>p</i>-value is given in the last row, and the sign ‘*’ shows significant at <i>p</i> = 0.05 level. by Hongxing Peng (14293805)

    Published 2025
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  14. 74
    Evaluation of trained SAO-based LDA model (Perplexity, Coherence score) (AI).

    Evaluation of trained SAO-based LDA model (Perplexity, Coherence score) (AI). by Minyoung Park (393019)

    Published 2025
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  15. 75
    Evaluation of trained SAO-based LDA model (Perplexity, Coherence score) (Biotechnology).

    Evaluation of trained SAO-based LDA model (Perplexity, Coherence score) (Biotechnology). by Minyoung Park (393019)

    Published 2025
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  16. 76
    CNN segmentation validation loss throughout training.

    CNN segmentation validation loss throughout training. by Alex J. Allphin (17102503)

    Published 2023
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  17. 77
    Training/validation loss and training/validation accuracy of four ensemble models using Adam and Nadam optimizers.

    Training/validation loss and training/validation accuracy of four ensemble models using Adam and Nadam optimizers. by Abdullah Al Maruf (21446154)

    Published 2025
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  18. 78
    Temporal profiles of the key BO-NN features.

    Temporal profiles of the key BO-NN features. by Julia Berezutskaya (9080269)

    Published 2020
    “…The shifts associated with non-significant prediction accuracies are greyed-out. <b>(c)</b> The results of the MDS for the key BO-NN features. …”
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  19. 79
    The nsP4-Q192L and C483Y mutations decrease the 4′-FlU sensitivity of both replication and transcription.

    The nsP4-Q192L and C483Y mutations decrease the 4′-FlU sensitivity of both replication and transcription. by Peiqi Yin (6533747)

    Published 2025
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  20. 80
    Model training pipeline.

    Model training pipeline. by Tony C. Lee (21989390)

    Published 2025
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