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Showing 1 - 20 results of 654 for search '(( elements ii algorithm ) OR ((( elements learning algorithm ) OR ( level coding algorithm ))))', query time: 0.39s Refine Results
  1. 1
    Comparison of different algorithms.

    Comparison of different algorithms. by Jiandong Qiu (20389944)

    Published 2025
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  2. 2
    , MSE, and MAE of the optimised algorithms.

    , MSE, and MAE of the optimised algorithms. by Itxasne Antúnez Sáenz (22794696)

    Published 2025
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  3. 3
    Performance of machine learning algorithms.

    Performance of machine learning algorithms. by Wenxue Liu (6877754)

    Published 2025
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  4. 4
    Table 2_Predictive prioritization of genes significantly associated with biotic and abiotic stresses in maize using machine learning algorithms.xlsx

    Table 2_Predictive prioritization of genes significantly associated with biotic and abiotic stresses in maize using machine learning algorithms.xlsx by Anjan Kumar Pradhan (9386369)

    Published 2025
    “…These genes had an abundance of antioxidant responsive elements and abscisic acid responsive elements in their promoter region, suggesting their role in stress response. …”
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  5. 5
    Homophilic vs.

    Homophilic vs. by Caesar Tawfeeq (21087272)

    Published 2025
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  6. 6
    values scored by the different regression algorithms. Linear Regression (LR), Random Forest (RF), Decision Tree (DT), X Gradient Boosting(XGB), Support Vector Machine (SVM), Gradient Boosting (GB) and AdaBoost (AB).

    values scored by the different regression algorithms. Linear Regression (LR), Random Forest (RF), Decision Tree (DT), X Gradient Boosting(XGB), Support Vector Machine (SVM), Gradie... by Itxasne Antúnez Sáenz (22794696)

    Published 2025
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  7. 7
    Analysis of elements of emergency resource scheduling model in chemical industry park.

    Analysis of elements of emergency resource scheduling model in chemical industry park. by Yuhang Wang (332111)

    Published 2025
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  8. 8
    Main code.

    Main code. by Jingguo Bi (21439766)

    Published 2025
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  9. 9
    Risk element category diagram.

    Risk element category diagram. by Yao Hu (3479972)

    Published 2025
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  10. 10
    Ig domain extension and circular permutations A) C2A domain - This type II C2 domain has a G+G++ hairpin extension (shown in cyan) and no A.

    Ig domain extension and circular permutations A) C2A domain - This type II C2 domain has a G+G++ hairpin extension (shown in cyan) and no A. by Caesar Tawfeeq (21087272)

    Published 2025
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  11. 11
    Optimization results of MBB multi-material structure under different methods. (a) A level-set method for multi-material topology optimization [33]. (b) An alternating active-phase algorithm for multi-material topology optimization [34]. (c) A new mapping based interpolation function for multi-material topology optimization [35]. (d) A material-field series expansion (MFSE) model for multi-material topology optimization [36].

    Optimization results of MBB multi-material structure under different methods. (a) A level-set method for multi-material topology optimization [33]. (b) An alternating active-phase... by Zhao Li (300229)

    Published 2025
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  12. 12
    Machine Learning Models for Efficient Property Prediction of ABX<sub>3</sub> Materials: A High-Throughput Approach

    Machine Learning Models for Efficient Property Prediction of ABX<sub>3</sub> Materials: A High-Throughput Approach by Soundous Touati (20282599)

    Published 2024
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  13. 13
    Machine Learning Models for Efficient Property Prediction of ABX<sub>3</sub> Materials: A High-Throughput Approach

    Machine Learning Models for Efficient Property Prediction of ABX<sub>3</sub> Materials: A High-Throughput Approach by Soundous Touati (20282599)

    Published 2024
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  14. 14
    Machine Learning Models for Efficient Property Prediction of ABX<sub>3</sub> Materials: A High-Throughput Approach

    Machine Learning Models for Efficient Property Prediction of ABX<sub>3</sub> Materials: A High-Throughput Approach by Soundous Touati (20282599)

    Published 2024
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  15. 15
    Table 6_Predictive prioritization of genes significantly associated with biotic and abiotic stresses in maize using machine learning algorithms.xlsx

    Table 6_Predictive prioritization of genes significantly associated with biotic and abiotic stresses in maize using machine learning algorithms.xlsx by Anjan Kumar Pradhan (9386369)

    Published 2025
    “…These genes had an abundance of antioxidant responsive elements and abscisic acid responsive elements in their promoter region, suggesting their role in stress response. …”
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  16. 16
    Table 7_Predictive prioritization of genes significantly associated with biotic and abiotic stresses in maize using machine learning algorithms.xlsx

    Table 7_Predictive prioritization of genes significantly associated with biotic and abiotic stresses in maize using machine learning algorithms.xlsx by Anjan Kumar Pradhan (9386369)

    Published 2025
    “…These genes had an abundance of antioxidant responsive elements and abscisic acid responsive elements in their promoter region, suggesting their role in stress response. …”
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  17. 17
    Table 3_Predictive prioritization of genes significantly associated with biotic and abiotic stresses in maize using machine learning algorithms.xlsx

    Table 3_Predictive prioritization of genes significantly associated with biotic and abiotic stresses in maize using machine learning algorithms.xlsx by Anjan Kumar Pradhan (9386369)

    Published 2025
    “…These genes had an abundance of antioxidant responsive elements and abscisic acid responsive elements in their promoter region, suggesting their role in stress response. …”
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  18. 18
    Table 1_Predictive prioritization of genes significantly associated with biotic and abiotic stresses in maize using machine learning algorithms.xlsx

    Table 1_Predictive prioritization of genes significantly associated with biotic and abiotic stresses in maize using machine learning algorithms.xlsx by Anjan Kumar Pradhan (9386369)

    Published 2025
    “…These genes had an abundance of antioxidant responsive elements and abscisic acid responsive elements in their promoter region, suggesting their role in stress response. …”
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  19. 19
    Table 4_Predictive prioritization of genes significantly associated with biotic and abiotic stresses in maize using machine learning algorithms.xlsx

    Table 4_Predictive prioritization of genes significantly associated with biotic and abiotic stresses in maize using machine learning algorithms.xlsx by Anjan Kumar Pradhan (9386369)

    Published 2025
    “…These genes had an abundance of antioxidant responsive elements and abscisic acid responsive elements in their promoter region, suggesting their role in stress response. …”
    Save to List
    Saved in:
  20. 20
    Table 5_Predictive prioritization of genes significantly associated with biotic and abiotic stresses in maize using machine learning algorithms.xlsx

    Table 5_Predictive prioritization of genes significantly associated with biotic and abiotic stresses in maize using machine learning algorithms.xlsx by Anjan Kumar Pradhan (9386369)

    Published 2025
    “…These genes had an abundance of antioxidant responsive elements and abscisic acid responsive elements in their promoter region, suggesting their role in stress response. …”
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    Saved in:

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