Matheus Henrique Dal Molin Ribeiro

Orcid: 0000-0001-7387-9077

According to our database1, Matheus Henrique Dal Molin Ribeiro authored at least 18 papers between 2019 and 2024.

Collaborative distances:
  • Dijkstra number2 of five.
  • Erdős number3 of five.

Timeline

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Bibliography

2024
Variational mode decomposition and bagging extreme learning machine with multi-objective optimization for wind power forecasting.
Appl. Intell., 2024

2023
Decoding Electroencephalography Signal Response by Stacking Ensemble Learning and Adaptive Differential Evolution.
Sensors, August, 2023

2022
Extreme gradient boosting model based on improved Jaya optimizer applied to forecasting energy consumption in residential buildings.
Evol. Syst., 2022

Discrete differential evolution metaheuristics for permutation flow shop scheduling problems.
Comput. Ind. Eng., 2022

Forecasting the COVID-19 Space-Time Dynamics in Brazil With Convolutional Graph Neural Networks and Transport Modals.
IEEE Access, 2022

Wind power forecasting based on bagging extreme learning machine ensemble model.
Proceedings of the 30th European Symposium on Artificial Neural Networks, 2022

2021
Hybrid Wavelet Stacking Ensemble Model for Insulators Contamination Forecasting.
IEEE Access, 2021

Seasonal-trend and multiobjective ensemble learning model for water consumption forecasting.
Proceedings of the International Joint Conference on Neural Networks, 2021

Forecasting COVID-19 pandemic using an echo state neural network-based framework.
Proceedings of the International Joint Conference on Neural Networks, 2021

2020
Multi-step ahead meningitis case forecasting based on decomposition and multi-objective optimization methods.
J. Biomed. Informatics, 2020

Short-term forecasting COVID-19 cumulative confirmed cases: Perspectives for Brazil.
CoRR, 2020

Forecasting Brazilian and American COVID-19 cases based on artificial intelligence coupled with climatic exogenous variables.
CoRR, 2020

Short-term forecasting of Amazon rainforest fires based on ensemble decomposition model.
CoRR, 2020

Ensemble approach based on bagging, boosting and stacking for short-term prediction in agribusiness time series.
Appl. Soft Comput., 2020

Multi-step ahead Bitcoin Price Forecasting Based on VMD and Ensemble Learning Methods.
Proceedings of the 2020 International Joint Conference on Neural Networks, 2020

Electricity energy price forecasting based on hybrid multi-stage heterogeneous ensemble: Brazilian commercial and residential cases.
Proceedings of the 2020 International Joint Conference on Neural Networks, 2020

Solar Power Forecasting Based on Ensemble Learning Methods.
Proceedings of the 2020 International Joint Conference on Neural Networks, 2020

2019
Multi-Objective Ensemble Model for Short-Term Price Forecasting in Corn Price Time Series.
Proceedings of the International Joint Conference on Neural Networks, 2019


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