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Evaluation and Optimization of the Effectiveness of Science Popularization Resources Using a Multi-Model Machine Learning Approach Combining Entropy Weight, XGBoost, and SHAP

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Title: Evaluation and Optimization of the Effectiveness of Science Popularization Resources Using a Multi-Model Machine Learning Approach Combining Entropy Weight, XGBoost, and SHAP
Authors: Wu, Yuju, Cai, Xiaoying, Zhang, Zhe
Source: 2026 International Conference on Computational Intelligence and Machine Learning (CIML) Computational Intelligence and Machine Learning (CIML), 2026 International Conference on. :596-607 Mar, 2026
Relation: 2026 International Conference on Computational Intelligence and Machine Learning (CIML)
Database: IEEE Xplore Digital Library
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PubType: Conference
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  Data: Evaluation and Optimization of the Effectiveness of Science Popularization Resources Using a Multi-Model Machine Learning Approach Combining Entropy Weight, XGBoost, and SHAP
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RecordInfo BibRecord:
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        Value: 10.1109/CIML69155.2026.11692634
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        PageCount: 12
        StartPage: 596
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      – TitleFull: Evaluation and Optimization of the Effectiveness of Science Popularization Resources Using a Multi-Model Machine Learning Approach Combining Entropy Weight, XGBoost, and SHAP
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            NameFull: Wu, Yuju
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            NameFull: Cai, Xiaoying
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            NameFull: Zhang, Zhe
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              Type: published
              Y: 2026
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