Journal papers

Cooperative ensemble learning model improves electric short-term load forecasting
Efficient models for short-term load forecasting (STLF) plays a crucial role in establishing the companies’ energetic planning due to …
Cite DOI
Cooperative ensemble learning model improves electric short-term load forecasting
Discrete differential evolution metaheuristics for permutation flow shop scheduling problems
Scheduling problems (SP) in the permutation flow shop (PFS) environment are present in many intermittent production industries, …
Cite DOI
Discrete differential evolution metaheuristics for permutation flow shop scheduling problems
Efficient bootstrap stacking ensemble learning model applied to wind power generation forecasting
The use of wind energy plays a vital role in society owing to its economic and environmental importance. Knowing the wind power …
Cite DOI
Efficient bootstrap stacking ensemble learning model applied to wind power generation forecasting
Inteligência artificial como suporte a tomada de decisão para previsão de novos casos do coronavírus (COVID-19)
PDF Cite
Inteligência artificial como suporte a tomada de decisão para previsão de novos casos do coronavírus (COVID-19)
Multi-step wind speed forecasting based on hybrid multi-stage decomposition model and long short-term memory neural network
The intermittent nature of wind can represent an obstacle to get reliable wind speed forecasting, thus many methods were developed to …
Cite DOI
Multi-step wind speed forecasting based on hybrid multi-stage decomposition model and long short-term memory neural network
Short-term forecasting COVID-19 cumulative confirmed cases: Perspectives for Brazil
The new Coronavirus (COVID-19) is an emerging disease responsible for infecting millions of people since the first notification until …
Cite DOI
Short-term forecasting COVID-19 cumulative confirmed cases: Perspectives for Brazil

Book chapters

Beta-hCG test demand forecasting using stacking ensemble-learning and machine learning approaches
Demand forecasting is essential for decision-making, since these forecasts are important inputs for strategic management decisions. In …
Cite Slides DOI Award
Beta-hCG test demand forecasting using stacking ensemble-learning and machine learning approaches
Classification of Leukocytes: Comparison of different feature extraction and machine learning approaches
Blood cells can be separated into three types: erythrocytes, leukocytes and platelets, and to evaluate the health of a patient, a …
Cite Slides DOI
Classification of Leukocytes: Comparison of different feature extraction and machine learning approaches
Ensemble learning models coupled with urban mobility information applied to predict COVID-19 incidence cases
The coronavirus disease (COVID-19), according to the World Health Organization, by September 4th, 2020, has infected more than 26 …
Cite DOI
Ensemble learning models coupled with urban mobility information applied to predict COVID-19 incidence cases

Conference papers

Matheus Henrique Dal Molin Ribeiro, Sinvaldo Rodrigues Moreno, Ramon Gomes da Silva, José Henrique Kleinübing Larcher, Cristiane Canton, Viviana Cocco Mariani, Leandro dos Santos Coelho (2022). Wind power forecasting based on bagging extreme learning machine ensemble model. ESANN 2022 proceedings.

Cite Poster Slides DOI

Ramon Gomes da Silva, Matheus Henrique Dal Molin Ribeiro, José Henrique Kleinübing Larcher, Viviana Cocco Mariani, Leandro Santos Coelho (2021). Artificial intelligence and signal decomposition approach applied to retail sales forecasting. Anais do 15 Congresso Brasileiro de Inteligência Computacional.

Cite Slides DOI

Matheus Henrique Dal Molin Ribeiro, Ramon Gomes da Silva, Viviana Cocco Mariani, Leandro Santos Coelho (2021). Dengue cases forecasting based on extreme gradient boosting ensemble with coyote optimization. Anais do 15 Congresso Brasileiro de Inteligência Computacional.

Cite Slides DOI

José Henrique Kleinübing Larcher, Ramon Gomes da Silva, Matheus Henrique Dal Molin Ribeiro, Leandro Santos Coelho, Viviana Cocco Mariani (2021). Forecasting COVID-19 pandemic using an echo state neural network-based framework. International Joint Conference on Neural Networks (IJCNN).

Cite Slides DOI

Matheus Henrique Dal Molin Ribeiro, Ramon Gomes da Silva, José Henrique Kleinübing Larcher, Viviana Cocco Mariani, Leandro Santos Coelho (2021). Seasonal-trend and multiobjective ensemble learning model for water consumption forecasting. International Joint Conference on Neural Networks (IJCNN).

Cite Slides DOI

Matheus Henrique Dal Molin Ribeiro, Ramon Gomes da Silva, Sinvaldo Rodrigues Moreno, Viviana Mariani, Leandro Coelho (2021). An improved ensemble learning model for multi-step ahead wind power generation forecasting. Proceedings of the 26th International Congress of Mechanical Engineering.

Cite Slides DOI

Ramon Gomes da Silva, Sinvaldo Rodrigues Moreno, Matheus Henrique Dal Molin Ribeiro, Viviana Mariani, Leandro Coelho (2021). Multi-step wind speed forecasting based on multi-stage decomposition approach. Proceedings of the 26th International Congress of Mechanical Engineering.

Cite Slides DOI

Ramon Gomes da Silva, Matheus Henrique Dal Molin Ribeiro, José Henrique Kleinübing Larcher, Viviana Cocco Mariani, Leandro Santos Coelho (2020). Forecasting the cumulative cases of COVID-19 in four large Brazilian cities using machine learning approaches. INnovation for Systems Information and Decision meeting (INSID).

PDF Cite Slides

Matheus Henrique Dal Molin Ribeiro, Ramon Gomes da Silva, Viviana Cocco Mariani, Leandro Santos Coelho (2020). Prediction of Residential Buildings Efficiency based on Differential Evolution Optimization and Random Forest Model. Proceedings of the 18th Brazilian Congress of Thermal Sciences and Engineering.

Cite Slides DOI

Ramon Gomes da Silva, Matheus Henrique Dal Molin Ribeiro, Sinvaldo Rodrigues Moreno, Viviana Cocco Mariani, Leandro Santos Coelho (2020). Wind Energy Multi-Step Ahead Forecasting Based on Variational Mode Decomposition. Proceedings of the 18th Brazilian Congress of Thermal Sciences and Engineering.

Cite Slides DOI

Matheus Henrique Dal Molin Ribeiro, Ramon Gomes da Silva, Cristiane Canton, Naylene Fraccanabbia, Viviana Cocco Mariani, Leandro Santos Coelho (2020). Electricity energy price forecasting based on hybrid multi-stage heterogeneous ensemble: Brazilian commercial and residential cases. International Joint Conference on Neural Networks (IJCNN).

Cite Slides DOI

Ramon Gomes da Silva, Matheus Henrique Dal Molin Ribeiro, Naylene Fraccanabbia, Viviana Cocco Mariani, Leandro Santos Coelho (2020). Multi-step ahead Bitcoin Price Forecasting Based on VMD and Ensemble Learning Methods. International Joint Conference on Neural Networks (IJCNN).

Cite Slides DOI

Naylene Fraccanabbia, Ramon Gomes da Silva, Matheus Henrique Dal Molin Ribeiro, Sinvaldo Rodrigues Moreno, Leandro Santos Coelho, Viviana Cocco Mariani (2020). Solar Power Forecasting Based on Ensemble Learning Methods. International Joint Conference on Neural Networks (IJCNN).

Cite Slides DOI

Matheus Henrique Dal Molin Ribeiro, Ramon Gomes da Silva, Naylene Fraccanabbia, Viviana Cocco Mariani, Leandro dos Santos Coelho (2019). Forecasting epidemiological time series based on decomposition and optimization approaches. 14th Brazilian Computational Intelligence Meeting.

Cite Slides DOI

Sinvaldo Rodrigues Moreno, Ramon Gomes da Silva, Matheus Henrique Dal Molin Ribeiro, Naylene Fraccanabbia, Viviana Cocco Mariani, Leandro dos Santos Coelho (2019). Very short-term wind energy forecasting based on stacking ensemble. 14th Brazilian Computational Intelligence Meeting.

Cite Slides DOI