Machine Learning

Hybrid machine learning models applied to daily urban water consumption prediction

This study proposes hybrid machine learning (ML) models to predict the daily urban water consumption scenario in a neighborhood Brazilian city. The framework evaluates various signal decomposition modes, including empirical wavelet transform (EWT), …

Decoding Electroencephalography Signal Response by Stacking Ensemble Learning and Adaptive Differential Evolution

Electroencephalography (EEG) is an exam widely adopted to monitor cerebral activities regarding external stimuli, and its signals compose a nonlinear dynamical system. There are many difficulties associated with EEG analysis. For example, noise can …

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 this context, the contribution of this study is to propose a hybrid forecasting framework that combines machine …

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 Complete Blood Count (CBC) is necessary. CBC is amongst the most performed tests worldwide, and when evaluated manually …

Signal decomposition and stacking-ensemble learning approaches applied to time series forecasting

Time series forecasting is an essential approach for businesses and researchers to make informed decisions by predicting future trends and patterns in a given time series data. Nevertheless, forecasting time series accurately can be challenging due …

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 their importance in electric power distribution and generation systems. An ensemble learning model based on dual …

A novel decomposition-ensemble learning framework for multi-step ahead wind energy forecasting

Wind energy is one of the sources which is still in development in Brazil. However, it already represents 17% of the National Interconnected System. Due to the high level of uncertainty and fluctuations in wind speed, predicting wind energy with high …

An improved ensemble learning model for multi-step ahead wind power generation forecasting

The development and expansion of clean energy, such as wind energy, are important in the preservation of the environment and development of local economies and an alternative to hydroelectric and thermal energies. In this respect, the development of …

Multi-step wind speed forecasting based on multi-stage decomposition approach

Wind energy is one of the sources which is still in development in Brazil, however, it already represents 17% of the National Interconnected System. Due to the high level of uncertainty and fluctuations in wind speed, prediction of wind speed with …

Forecasting the cumulative cases of COVID-19 in four large Brazilian cities using machine learning approaches

The Coronavirus disease 2019 (COVID-19) is a disease responsible for infecting millions of people since the first notification until nowadays. Developing efficient short-term forecasting models allow knowing the number of future COVID-19 cases. In …