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Showing 1 to 4 of 4 for “"Ensemble Learning Models"”.

  1. Real-Time Thermography for In Operando Additive Manufacturing Defect Mitigation using a Machine Learning Approach

    … were utilized to train a supervised machine learning model (Multi-Layer Perceptron) for defect prediction, while benchmarking against two ensemble learning models—Random Forest and XGBoost—for comparative accuracy evaluation. Additional experiments on stainless steel (316L) investigated the …

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  2. Optimization and Machine Learning Applied to Inverse Problems in Partial Differential Equations

    … for this system including a few based on machine learning algorithms. Extending the work, instead of relying on traveling wave solutions, we applied a network-based model to solve the PDEs as well. We designed a PDE discovery model with the help of a resampling method. These algorithms contain …

    claremont Repository record for Optimization and Machine Learning Applied to Inverse Problems in Partial Differential Equations (opens in a new tab)

  3. Cluster-enhanced Ensemble Learning for Mapping Surface Ozone in China

    … This research employs cluster-enhanced ensemble learning methodologies. First, the K-means clustering algorithm categorizes ozone-related data, providing a basis for further analysis. The optimal cluster number is determined using the elbow method. Next, various ensemble learning models, …

    helsinki Repository record for Cluster-enhanced Ensemble Learning for Mapping Surface Ozone in China (opens in a new tab)

  4. A Machine Learning Model for Octane Number Prediction

    … al. 2018). Previous research has used empirical models in the form of phenomeno-logical and machine learning models (Gonz´alez 2019). Phenomeno-logical models have been used in the past as a way of programming an engineer's thought process in the form of differential equations put together. …

    cape-town Repository record for A Machine Learning Model for Octane Number Prediction (opens in a new tab)