has been cited by the following article(s):
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[1]
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Predicting Student Performance and Academic Success in Higher Education using a Hybrid XGBoost-LSTM Model
2025 8th International Conference on Computing Methodologies and Communication (ICCMC),
2025
DOI:10.1109/ICCMC65190.2025.11140772
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[2]
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A Medal Prediction Model Based on XGBoost-Logistic Regression and Quantification of the “Great Coach” Effect
Highlights in Science, Engineering and Technology,
2025
DOI:10.54097/t2cfqx21
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[3]
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A Novel Decision Tree and LSTM Powered Intelligent Agent System for Early Detection of Vegetable Plant Diseases
2025 6th International Conference on Electronics and Sustainable Communication Systems (ICESC),
2025
DOI:10.1109/ICESC65114.2025.11212443
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[4]
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Next-Generation MCDM: How AI is Revolutionizing the Decision Support Process
2025 Twelfth International Conference on Intelligent Computing and Information Systems (ICICIS),
2025
DOI:10.1109/ICICIS66182.2025.11313193
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[5]
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Machine learning and artificial intelligence in type 2 diabetes prediction: a comprehensive 33-year bibliometric and literature analysis
Frontiers in Digital Health,
2025
DOI:10.3389/fdgth.2025.1557467
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[6]
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Hybrid Deep LSTM-XGBoost Based Fault Line Identification and Distance Estimation in Grids with Distributed Generators
2024 IEEE International Conference on Power Electronics, Drives and Energy Systems (PEDES),
2024
DOI:10.1109/PEDES61459.2024.10961680
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