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12981
Incorporating Transformers and Attention Networks for Stock Movement Prediction
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Predicting Critical Bicycle-Vehicle Conflicts at Signalized Intersections
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Explainable artificial intelligence for targeted protein degradation predictions
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Nonlinear Reduced-Order Observer-Based Predictive Control for Diving of an Autonomous Underwater Vehicle
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Prediction of Mold Spoilage for Soy/Polyethylene Composite Fibers
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12988
The Comprehensive Contributions of Endpoint Degree and Coreness in Link Prediction
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Predictive Functional Control-Based Zenith Pass Controller Design for Roll-Pitch Seeker
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12991
Research of worm-propagation prediction based on stochastic experiment
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12992
Machine learning for active sites prediction of quinoline derivatives
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Scoring system development for prediction of extravesical bladder cancer
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12996
IGBT Module Lifetime Prediction Technology for Rail Transit
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A comprehensive diagnostic framework for hepatitis C using structured data and predictive analytics
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12998
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12999
Satellite Image Price Prediction Based on Machine Learning
Published 2025-06-01“…For optical imagery, the Bayesian-optimized XGBoost model achieves the best performance (<inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mrow><mi>R</mi><mo>=</mo><mn>0.9870</mn></mrow></semantics></math></inline-formula>, <inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mrow><mi>RMSE</mi><mo>=</mo><mi>$</mi><mn>3.44</mn><mo>/</mo><msup><mi>km</mi><mn>2</mn></msup></mrow></semantics></math></inline-formula>, <inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mrow><mi>NSE</mi><mo>=</mo><mn>0.9651</mn></mrow></semantics></math></inline-formula>, <inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mrow><mi>KGE</mi><mo>=</mo><mn>0.8950</mn></mrow></semantics></math></inline-formula>), followed closely by CatBoost (<inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mrow><mi>R</mi><mo>=</mo><mn>0.9826</mn></mrow></semantics></math></inline-formula>, <inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mrow><mi>RMSE</mi><mo>=</mo><mi>$</mi><mn>3.83</mn><mo>/</mo><msup><mi>km</mi><mn>2</mn></msup></mrow></semantics></math></inline-formula>). …”
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13000
Prediction of the corrosion rates of subsea pipelines via KPCA
Published 2025-07-01Get full text
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