Matheus Henrique Dal Molin Ribeiro

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• Variational mode decomposition and bagging extreme learning machine with multi-objective optimization for wind power forecasting
• Decoding Electroencephalography Signal Response by Stacking Ensemble Learning and Adaptive Differential Evolution
• Beta-hCG test demand forecasting using stacking ensemble-learning and machine learning approaches
• Cooperative ensemble learning model improves electric short-term load forecasting
• Multi-step short-term wind speed forecasting based on multi-stage decomposition coupled with stacking-ensemble learning approach
• Wind power forecasting based on bagging extreme learning machine ensemble model
• Discrete differential evolution metaheuristics for permutation flow shop scheduling problems
• Efficient bootstrap stacking ensemble learning model applied to wind power generation forecasting
• Ensemble learning models coupled with urban mobility information applied to predict COVID-19 incidence cases
• Artificial intelligence and signal decomposition approach applied to retail sales forecasting
• Dengue cases forecasting based on extreme gradient boosting ensemble with coyote optimization
• Forecasting COVID-19 pandemic using an echo state neural network-based framework
• Seasonal-trend and multiobjective ensemble learning model for water consumption forecasting
• A novel decomposition-ensemble learning framework for multi-step ahead wind energy forecasting
• An improved ensemble learning model for multi-step ahead wind power generation forecasting
• Multi-step wind speed forecasting based on multi-stage decomposition approach
• Forecasting the cumulative cases of COVID-19 in four large Brazilian cities using machine learning approaches
• Inteligência artificial como suporte a tomada de decisão para previsão de novos casos do coronavírus (COVID-19)
• Prediction of Residential Buildings Efficiency based on Differential Evolution Optimization and Random Forest Model
• Wind Energy Multi-Step Ahead Forecasting Based on Variational Mode Decomposition
• Forecasting Brazilian and American COVID-19 cases based on artificial intelligence coupled with climatic exogenous variables
• Electricity energy price forecasting based on hybrid multi-stage heterogeneous ensemble: Brazilian commercial and residential cases
• Multi-step ahead Bitcoin Price Forecasting Based on VMD and Ensemble Learning Methods
• Solar Power Forecasting Based on Ensemble Learning Methods
• Short-term forecasting COVID-19 cumulative confirmed cases: Perspectives for Brazil
• Forecasting epidemiological time series based on decomposition and optimization approaches
• Very short-term wind energy forecasting based on stacking ensemble