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Prediction of copper contamination in soil across EU using spectroscopy and machine learning: Handling class imbalance problem
Published 2025-03-01“…To address this limitation, we conducted a comprehensive evaluation of three basic machine learning (ML) algorithms and four imbalanced ML algorithms. …”
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Learning Optimal Dynamic Treatment Regime from Observational Clinical Data through Reinforcement Learning
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Improving fluoroprobe sensor performance through machine learning
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Reinforcement learning-based assimilation of the WOFOST crop model
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Concrete Crack Detection and Segregation: A Feature Fusion, Crack Isolation, and Explainable AI-Based Approach
Published 2024-08-01“…To isolate and quantify the crack region, this research combines image thresholding, morphological operations, and contour detection with the convex hulls method and forms a novel algorithm. …”
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Development and Validation of DIANA (Diabetes Novel Subgroup Assessment tool): A web-based precision medicine tool to determine type 2 diabetes endotype membership and predict indi...
Published 2025-08-01“…This study employed local interpretable model-agnostic explanations (LIME) and SHapley Additive exPlanations (SHAP) to demystify the endotype prediction model. A random forest model was built to assess an individual's risk for nephropathy and retinopathy based on individual risk algorithms.…”
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Data Mining Classification Techniques for Diabetes Prediction
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Impact of climate change over distribution and potential range of chestnut in the Iberian Peninsula
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Fault Detection in Photovoltaic Systems Using a Machine Learning Approach
Published 2025-01-01“…The proposed fault detection solutions rely on analyzing different algorithms, including Support Vector Machine, Artificial Neural Network, Random Forest, Decision Tree, and Logistic Regression. …”
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Lightweight Deepfake Detection Based on Multi-Feature Fusion
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A framework of crop water productivity estimation from UAV observations: A case study of summer maize
Published 2025-08-01“…To address this challenge, our research develops an innovative UAV-based monitoring framework through systematic integration of long-term multispectral/thermal infrared observations with multi-model fusion: (1) Surface Energy Balance Algorithm for Land (SEBAL) and FAO-56 Penman-Monteith models for evapotranspiration (ET) estimation; (2) Random Forest algorithm incorporating four phenotypical growth indicators for yield estimation, ultimately enabling CWP quantification. …”
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A Meta-Learning-Based Ensemble Model for Explainable Alzheimer’s Disease Diagnosis
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Acoustic-based models to assess herd-level calves' emotional state: A machine learning approach
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