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Showing 61 - 80 results of 416 for search '(((( element learning algorithm ) OR ( complement cc3d algorithm ))) OR ( level coding algorithm ))', query time: 0.36s Refine Results
  1. 61
    RealBench: A Repo-Level Code Generation Benchmark Aligned with Real-World Software Development Practices

    RealBench: A Repo-Level Code Generation Benchmark Aligned with Real-World Software Development Practices by RealBench RealBench (22275393)

    Published 2025
    “…<br>│ └── level4/ # Level 4 projects (expert complexity).<br>├── src/ # Main source code for the RealBench framework.…”
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  2. 62
    Comparison of Q-learning and DQN.

    Comparison of Q-learning and DQN. by Jiandong Qiu (20389944)

    Published 2025
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  3. 63
    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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  4. 64
    Mean values of different benchmark functions.

    Mean values of different benchmark functions. by Jalal- ud-Din (21474307)

    Published 2025
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  5. 65
    Detail of benchmark functions comparison.

    Detail of benchmark functions comparison. by Jalal- ud-Din (21474307)

    Published 2025
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  6. 66
    Average execution time (in seconds) of different benchmark functions.

    Average execution time (in seconds) of different benchmark functions. by Jalal- ud-Din (21474307)

    Published 2025
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  7. 67
    Stander deviation values of different benchmark functions.

    Stander deviation values of different benchmark functions. by Jalal- ud-Din (21474307)

    Published 2025
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  8. 68
    Comparison of different crossover operators.

    Comparison of different crossover operators. by Jalal- ud-Din (21474307)

    Published 2025
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  9. 69
    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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  10. 70
    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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  11. 71
    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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  12. 72
    Mitochondrial toxic prediction of marine alga toxins using a predictive model based on feature coupling and ensemble learning algorithms

    Mitochondrial toxic prediction of marine alga toxins using a predictive model based on feature coupling and ensemble learning algorithms by Guangyin Jia (21075537)

    Published 2025
    “…By comparing 8 machine learning algorithms and using a weighted soft voting method to integrate the two optimal algorithms, we established 108 prediction models and identified the best ensemble learning model MACCS_LK for screening and defining its application domain. …”
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  13. 73
    Determination of cyclic behavior of various cross-section composite column-beam connections reinforced with FRP composite using FEM analysis and ML models

    Determination of cyclic behavior of various cross-section composite column-beam connections reinforced with FRP composite using FEM analysis and ML models by Yasemin Simsek Turker (22340676)

    Published 2025
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  14. 74
    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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  15. 75
    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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  16. 76
    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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  17. 77
    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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  18. 78
    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. 79
    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. …”
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  20. 80
    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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