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This work introduces a methodology to estimate coffee prices based on the use of Extreme Learning Machines. The process is initiated by identifying the presence of nonstationary components, like seasonality and trend. These components are withdrawn if they are found. Next, the temporal lags are selected based on the response of the Partial Autocorrelation Function filter. As predictors, we address the following models: Exponential Smoothing (ES), Autoregressive (AR) and Autoregressive Integrated and Moving Average (ARIMA) models, Multilayer Perceptron (MLP) and Extreme Learning Machines (ELMs) neural networks. The computational results based on three error metrics and two coffee types (Arabica and Robusta) showed that the neural networks, especially the ELM, can reach higher performance levels than the other models. The methodology, which presents preprocessing stages, lag selection, and use of ELM, is a novelty that contributes to the coffee prices forecasting field.
Carolina Deina; Matheus Henrique Do Amaral Prates; Carlos Henrique Rodrigues Alves; Marcella Scoczynski Ribeiro Martins; Flavio Trojan; Sergio Luiz Stevan; Hugo Valadares Siqueira. A Methodology for Coffee Price Forecasting Based on Extreme Learning Machines. Information Processing in Agriculture 2021, 1 .
AMA StyleCarolina Deina, Matheus Henrique Do Amaral Prates, Carlos Henrique Rodrigues Alves, Marcella Scoczynski Ribeiro Martins, Flavio Trojan, Sergio Luiz Stevan, Hugo Valadares Siqueira. A Methodology for Coffee Price Forecasting Based on Extreme Learning Machines. Information Processing in Agriculture. 2021; ():1.
Chicago/Turabian StyleCarolina Deina; Matheus Henrique Do Amaral Prates; Carlos Henrique Rodrigues Alves; Marcella Scoczynski Ribeiro Martins; Flavio Trojan; Sergio Luiz Stevan; Hugo Valadares Siqueira. 2021. "A Methodology for Coffee Price Forecasting Based on Extreme Learning Machines." Information Processing in Agriculture , no. : 1.
This work deals with metaheuristic optimization algorithms to derive the best parameters for the Gaussian Adaptive PID controller. This controller represents a multimodal problem, where several distinct solutions can achieve similar best performances, and metaheuristics optimization algorithms can behave differently during the optimization process. Finding the correct proportionality between the parameters is an arduous task that often does not have an algebraic solution. The Gaussian functions of each control action have three parameters, resulting in a total of nine parameters to be defined. In this work, we investigate three bio-inspired optimization methods dealing with this problem: Particle Swarm Optimization (PSO), the Artificial Bee Colony (ABC) algorithm, and the Whale Optimization Algorithm (WOA). The computational results considering the Buck converter with a resistive and a nonlinear load as a case study demonstrated that the methods were capable of solving the task. The results are presented and compared, and PSO achieved the best results.
Erickson Puchta; Priscilla Bassetto; Lucas Biuk; Marco Itaborahy Filho; Attilio Converti; Mauricio Kaster; Hugo Siqueira. Swarm-Inspired Algorithms to Optimize a Nonlinear Gaussian Adaptive PID Controller. Energies 2021, 14, 3385 .
AMA StyleErickson Puchta, Priscilla Bassetto, Lucas Biuk, Marco Itaborahy Filho, Attilio Converti, Mauricio Kaster, Hugo Siqueira. Swarm-Inspired Algorithms to Optimize a Nonlinear Gaussian Adaptive PID Controller. Energies. 2021; 14 (12):3385.
Chicago/Turabian StyleErickson Puchta; Priscilla Bassetto; Lucas Biuk; Marco Itaborahy Filho; Attilio Converti; Mauricio Kaster; Hugo Siqueira. 2021. "Swarm-Inspired Algorithms to Optimize a Nonlinear Gaussian Adaptive PID Controller." Energies 14, no. 12: 3385.
Several activities regarding water resources management are dependent on accurate monthly streamflow forecasting, such as flood control, reservoir operation, water supply planning, hydropower generation, energy matrix planning, among others. Most of the literature is focused on propose, compare, and evaluate the forecasting models. However, the decision on forecasting approaches plays a significant role in such models’ performance. In this paper, we are focused on investigating and confront the following forecasting approaches: i) use of a single model for the whole series (annual approach) versus using 12 models, each one responsible for predicting each month (monthly approach); ii) for multistep forecasting, the use of direct and recursive methods. The forecasting models addressed are the linear Autoregressive (AR) and Periodic Autoregressive (PAR) models, from the Box & Jenkins family, and the Extreme Learning Machines (ELM), an artificial neural network architecture. The computational analysis involves 20 time series associated with hydroelectric plants indicated that the monthly approach with the direct multistep method achieved the best overall performances, except for the cases in which the coefficient of variation is higher than two. In this case, the recursive approach stood out. Also, the ELM overcame the linear models in most cases.
Jonatas Belotti; José Jair Mendes; Murilo Leme; Flavio Trojan; Sergio L. Stevan; Hugo Siqueira. Comparative study of forecasting approaches in monthly streamflow series from Brazilian hydroelectric plants using Extreme Learning Machines and Box & Jenkins models. Journal of Hydrology and Hydromechanics 2021, 69, 180 -195.
AMA StyleJonatas Belotti, José Jair Mendes, Murilo Leme, Flavio Trojan, Sergio L. Stevan, Hugo Siqueira. Comparative study of forecasting approaches in monthly streamflow series from Brazilian hydroelectric plants using Extreme Learning Machines and Box & Jenkins models. Journal of Hydrology and Hydromechanics. 2021; 69 (2):180-195.
Chicago/Turabian StyleJonatas Belotti; José Jair Mendes; Murilo Leme; Flavio Trojan; Sergio L. Stevan; Hugo Siqueira. 2021. "Comparative study of forecasting approaches in monthly streamflow series from Brazilian hydroelectric plants using Extreme Learning Machines and Box & Jenkins models." Journal of Hydrology and Hydromechanics 69, no. 2: 180-195.
This work investigates different Bayesian network structure learning techniques by thoroughly studying several variants of Hybrid Multi-objective Bayesian Estimation of Distribution Algorithm (HMOBEDA), applied to the MNK Landscape combinatorial problem. In the experiments, we evaluate the performance considering three different aspects: optimization abilities, robustness and learning efficiency. Results for instances of multi- and many-objective MNK-landscape show that, score-based structure learning algorithms appear to be the best choice. In particular, HMOBEDA\(_{k2}\) was capable of producing results comparable with the other variants in terms of the runtime of convergence and the coverage of the final Pareto front, with the additional advantage of providing solutions that are less sensible to noise while the variability of the corresponding Bayesian network models is reduced.
Marcella S. R. Martins; Mohamed El Yafrani; Myriam Delgado; Ricardo Lüders; Roberto Santana; Hugo V. Siqueira; Huseyin G. Akcay; Belaïd Ahiod. Analysis of Bayesian Network Learning Techniques for a Hybrid Multi-objective Bayesian Estimation of Distribution Algorithm: a case study on MNK Landscape. Journal of Heuristics 2021, 27, 549 -573.
AMA StyleMarcella S. R. Martins, Mohamed El Yafrani, Myriam Delgado, Ricardo Lüders, Roberto Santana, Hugo V. Siqueira, Huseyin G. Akcay, Belaïd Ahiod. Analysis of Bayesian Network Learning Techniques for a Hybrid Multi-objective Bayesian Estimation of Distribution Algorithm: a case study on MNK Landscape. Journal of Heuristics. 2021; 27 (4):549-573.
Chicago/Turabian StyleMarcella S. R. Martins; Mohamed El Yafrani; Myriam Delgado; Ricardo Lüders; Roberto Santana; Hugo V. Siqueira; Huseyin G. Akcay; Belaïd Ahiod. 2021. "Analysis of Bayesian Network Learning Techniques for a Hybrid Multi-objective Bayesian Estimation of Distribution Algorithm: a case study on MNK Landscape." Journal of Heuristics 27, no. 4: 549-573.
A adolescência é um período de transformações neurobiológicas e comportamentais. O desenvolvimento moral (DM), ainda em amadurecimento nesta fase, é importante na tomada de decisão, como o uso ou não de SPA. Objetiva-se aplicar algoritmos de clusterização para agrupamento de perfil de adolescentes escolares com tendência ao uso de SPAs, baseado nos fatores socioenonômicos e no nível de DM. Trata-se de um estudo transversal com amostra por conveniência. Participaram do estudo 100 adolescentes, entre 12 a 18 anos, de ambos os sexos e regularmente matriculados no ensino médio. Foram utilizados o questionário socioeconômico, CRAFFT/CESARE (acrônimo de Car; Relax; Alone; Forget; Family/Friends; Trouble) e o teste de competência moral. Foi utilizado o método de clusterização para análise de dados. Quanto aos resultados encontrados, foi verificado que onze (11.0%) apresentam risco para o uso, abuso ou dependência de SPAs, 6.0%, suspeita de dependência de SPAs e 2.0%, risco de dependência de SPAs. No dilema do médico, houve predomínio dos estágio 1 e 2, do juiz, 5 e 6 e operário, 4 e 5. O método Fuzzy C-Means mostrou-se mais adequado, com alta correlação com as respostas fornecidas pelo teste CRAFFT/CESARE. Portanto, os métodos de clusterização apresentam resultados com valores próximos aos obtidos pelo teste CRAFFT/ CESARE quanto ao perfil de adolescentes escolares com predisposição ao uso de SPAs, contribuindo na relação dos fatores determinantes na tomada de decisão nessa etapa da vida.
Dayane Diniz Martins; Laís Balla Lucena; Athos Ricardo Moraes Bastos Damasceno; Hugo Siqueira; Yara De Souza Tadano; Ivete Furtado Ribeiro Caldas. Clusterização do perfil de adolescentes escolares com predisposição ao uso de substância psicoativas. Research, Society and Development 2021, 10, 1 .
AMA StyleDayane Diniz Martins, Laís Balla Lucena, Athos Ricardo Moraes Bastos Damasceno, Hugo Siqueira, Yara De Souza Tadano, Ivete Furtado Ribeiro Caldas. Clusterização do perfil de adolescentes escolares com predisposição ao uso de substância psicoativas. Research, Society and Development. 2021; 10 (2):1.
Chicago/Turabian StyleDayane Diniz Martins; Laís Balla Lucena; Athos Ricardo Moraes Bastos Damasceno; Hugo Siqueira; Yara De Souza Tadano; Ivete Furtado Ribeiro Caldas. 2021. "Clusterização do perfil de adolescentes escolares com predisposição ao uso de substância psicoativas." Research, Society and Development 10, no. 2: 1.
Time series forecasting problems are often addressed using linear techniques, especially the autoregressive (AR) models, due to their simplicity combined with good performances. It is possible to generalize a linear predictor by allowing infinite impulse response (IIR) through the addition of feedback loops, as occurs in the autoregressive and moving average (ARMA) models and IIR filters. However, the calculation of the free coefficients of these structures is more complex, as the optimization problem has no closed‐form solution. This work conducts an extensive investigation on the use of linear models to predict monthly seasonal streamflow series associated with Brazilian hydroelectric plants. The main goal is to reach the best achievable performance with linear approaches. We propose the application of recursive models, estimating their parameters with the aid of bioinspired metaheuristics: particle swarm optimization (PSO), genetic algorithm (GA), differential evolution (DE), and two immune‐inspired algorithms, the CLONALG, and the artificial immune network for optimization (Opt‐aiNet). The AR model is also considered. The results to multistep ahead forecasting indicated that the insertion of feedback loops increased the performances, with ARMA being the best predictor. The DE, PSO, and GA led to the minimum values of mean‐squared error during the tests, while DE yielded the smallest dispersion.
Hugo Siqueira; Jonatas Trabuco Belotti; Levy Boccato; Ivette Luna; Romis Attux; Christiano Lyra. Recursive linear models optimized by bioinspired metaheuristics to streamflow time series prediction. International Transactions in Operational Research 2020, 1 .
AMA StyleHugo Siqueira, Jonatas Trabuco Belotti, Levy Boccato, Ivette Luna, Romis Attux, Christiano Lyra. Recursive linear models optimized by bioinspired metaheuristics to streamflow time series prediction. International Transactions in Operational Research. 2020; ():1.
Chicago/Turabian StyleHugo Siqueira; Jonatas Trabuco Belotti; Levy Boccato; Ivette Luna; Romis Attux; Christiano Lyra. 2020. "Recursive linear models optimized by bioinspired metaheuristics to streamflow time series prediction." International Transactions in Operational Research , no. : 1.
In obstetrics, ultrasound is used for assessment of fetal development during pregnancy. The images generated by ultrasound are used to obtain measurements of fetal head length, body size, and the analysis of fetal movements, to identify and prevent the onset of congenital disease. This work presents the development of a new method for the segmentation of two-dimensional ultrasound images of fetal skulls based on a V-Net architecture called Fully Convolutional Neural Network - Combination (VNetc). We created a new combination of strategies using a 3D V-Net as base, such as pre-processing, use of Batch Normalization and Dropout, and evaluation of distinct activation layers, activation function, data augmentation, loss function, and network depth. The computational results reveal the feasibility of the proposal in the correct segmentation of fetal skulls and head circumference measurements, reaching up to 97.91% of correctness, overcoming states-of-the-art methods.
Everton Leonardo Skeika; Mathias Rodrigues Da Luz; Bruno Jose Torres Fernandes; Hugo Valadares Siqueira; Mauren Louise Sguario Coelho De Andrade. Convolutional Neural Network to Detect and Measure Fetal Skull Circumference in Ultrasound Imaging. IEEE Access 2020, 8, 191519 -191529.
AMA StyleEverton Leonardo Skeika, Mathias Rodrigues Da Luz, Bruno Jose Torres Fernandes, Hugo Valadares Siqueira, Mauren Louise Sguario Coelho De Andrade. Convolutional Neural Network to Detect and Measure Fetal Skull Circumference in Ultrasound Imaging. IEEE Access. 2020; 8 (99):191519-191529.
Chicago/Turabian StyleEverton Leonardo Skeika; Mathias Rodrigues Da Luz; Bruno Jose Torres Fernandes; Hugo Valadares Siqueira; Mauren Louise Sguario Coelho De Andrade. 2020. "Convolutional Neural Network to Detect and Measure Fetal Skull Circumference in Ultrasound Imaging." IEEE Access 8, no. 99: 191519-191529.
Wind speed is one of the primary renewable sources for clean power. However, it is intermittent, presents nonlinear patterns, and has nonstationary behavior. Thus, the development of accurate approaches for its forecasting is a challenge in wind power generation engineering. Hybrid systems that combine linear statistical and Artificial Intelligence (AI) forecasters have been highlighted in the literature due to their accuracy. Those systems aim to overcome the limitations of the single linear and AI models. In the literature about wind speed, these hybrid systems combine linear and nonlinear forecasts using a simple sum. However, the most suitable function for combining linear and nonlinear forecasts is unknown and the linear relationship assumption can degenerate or underestimate the performance of the whole system. Thus, properly combining the forecasts of linear and nonlinear models is an open question and its determination is a challenge. This article proposes a hybrid system for monthly wind speed forecasting that uses a nonlinear combination of the linear and nonlinear models. A data-driven intelligent model is used to search for the most suitable combination, aiming to maximize the performance of the system. An evaluation has been carried out using the monthly data from three wind speed stations in northeast Brazil, evaluated with two traditional metrics. The assessment is performed for two scenarios: with and without exogenous variables. The results show that the proposed hybrid system attains an accuracy superior to other hybrid systems and single linear and AI models.
Paulo S. G. De Mattos Neto; Joao Fausto Lorenzato de Oliveira; Domingos Savio De Oliveira Santos Junior; Hugo Valadares Siqueira; Manoel Henrique Da Nobrega Marinho; Francisco Madeiro. A Hybrid Nonlinear Combination System for Monthly Wind Speed Forecasting. IEEE Access 2020, 8, 191365 -191377.
AMA StylePaulo S. G. De Mattos Neto, Joao Fausto Lorenzato de Oliveira, Domingos Savio De Oliveira Santos Junior, Hugo Valadares Siqueira, Manoel Henrique Da Nobrega Marinho, Francisco Madeiro. A Hybrid Nonlinear Combination System for Monthly Wind Speed Forecasting. IEEE Access. 2020; 8 ():191365-191377.
Chicago/Turabian StylePaulo S. G. De Mattos Neto; Joao Fausto Lorenzato de Oliveira; Domingos Savio De Oliveira Santos Junior; Hugo Valadares Siqueira; Manoel Henrique Da Nobrega Marinho; Francisco Madeiro. 2020. "A Hybrid Nonlinear Combination System for Monthly Wind Speed Forecasting." IEEE Access 8, no. : 191365-191377.
This work presents an investigation on the application of three deseasonalization models to monthly seasonal streamflow series forecasting: seasonal difference, moving average, and padronization. The deseasonalization is a mandatory preprocessing step for predicting series that present seasonal behavior. The predictors addressed are the linear periodic autoregressive model and an artificial neural network architecture, the extreme learning machines. The computational results showed that the padronization is the most adequate to deal with this problem.
Hugo Siqueira; Yara De Souza Tadano; Thiago Antonini Alves; Romis Attux; Christiano Lyra Filho. Deseasonalization Methods in Seasonal Streamflow Series Forecasting. New Trends in Computational Vision and Bio-inspired Computing 2020, 1551 -1560.
AMA StyleHugo Siqueira, Yara De Souza Tadano, Thiago Antonini Alves, Romis Attux, Christiano Lyra Filho. Deseasonalization Methods in Seasonal Streamflow Series Forecasting. New Trends in Computational Vision and Bio-inspired Computing. 2020; ():1551-1560.
Chicago/Turabian StyleHugo Siqueira; Yara De Souza Tadano; Thiago Antonini Alves; Romis Attux; Christiano Lyra Filho. 2020. "Deseasonalization Methods in Seasonal Streamflow Series Forecasting." New Trends in Computational Vision and Bio-inspired Computing , no. : 1551-1560.
Estimating future streamflows is a key step in producing electricity for countries with hydroelectric plants. Accurate predictions are particularly important due to environmental and economic impact they lead. In order to analyze the forecasting capability of models regarding monthly seasonal streamflow series, we realized an extensive investigation considering: six versions of unorganized machines—extreme learning machines (ELM) with and without regularization coefficient (RC), and echo state network (ESN) using the reservoirs from Jaeger’s and Ozturk et al., with and without RC. Additionally, we addressed the ELM as the combiner of a neural-based ensemble, an investigation not yet accomplished in such context. A comparative analysis was performed utilizing two linear approaches (autoregressive model (AR) and autoregressive and moving average model (ARMA)), four artificial neural networks (multilayer perceptron, radial basis function, Elman network, and Jordan network), and four ensembles. The tests were conducted at five hydroelectric plants, using horizons of 1, 3, 6, and 12 steps ahead. The results indicated that the unorganized machines and the ELM ensembles performed better than the linear models in all simulations. Moreover, the errors showed that the unorganized machines and the ELM-based ensembles reached the best general performances.
Jônatas Belotti; Hugo Siqueira; Lilian Araujo; Jr. Sérgio L. Stevan; Paulo S.G. De Mattos Neto; Manoel Henrique Nóbrega Marinho; João Fausto L. De Oliveira; Fábio Usberti; Marcos De Almeida Leone Filho; Attilio Converti; Leonie Asfora Sarubbo. Neural-Based Ensembles and Unorganized Machines to Predict Streamflow Series from Hydroelectric Plants. Energies 2020, 13, 4769 .
AMA StyleJônatas Belotti, Hugo Siqueira, Lilian Araujo, Jr. Sérgio L. Stevan, Paulo S.G. De Mattos Neto, Manoel Henrique Nóbrega Marinho, João Fausto L. De Oliveira, Fábio Usberti, Marcos De Almeida Leone Filho, Attilio Converti, Leonie Asfora Sarubbo. Neural-Based Ensembles and Unorganized Machines to Predict Streamflow Series from Hydroelectric Plants. Energies. 2020; 13 (18):4769.
Chicago/Turabian StyleJônatas Belotti; Hugo Siqueira; Lilian Araujo; Jr. Sérgio L. Stevan; Paulo S.G. De Mattos Neto; Manoel Henrique Nóbrega Marinho; João Fausto L. De Oliveira; Fábio Usberti; Marcos De Almeida Leone Filho; Attilio Converti; Leonie Asfora Sarubbo. 2020. "Neural-Based Ensembles and Unorganized Machines to Predict Streamflow Series from Hydroelectric Plants." Energies 13, no. 18: 4769.
Particulate matter (PM) is one of the most harmful air pollutants to human health studied worldwide. In this scenario, it is of paramount importance to monitor and predict PM concentration. Artificial neural networks (ANN) are commonly used to forecast air pollution levels due to their accuracy. The use of partition on prediction problems is well known because decomposition of time series allows the latent components of the original series to be revealed. It is a matter of extracting the “deterministic” component, which is easy to predict the random components. However, there is no evidence of its use in air pollution forecasting. In this work, we introduce a different approach consisting of the decomposition of the time series in contiguous monthly partitions, aiming to develop specialized predictors to solve the problem because air pollutant concentration has seasonal behavior. The goal is to reach prediction accuracy higher than those obtained by using the entire series. Experiments were performed for seven time series of daily particulate matter concentrations (PM2.5 and PM10–particles with diameter less than 2.5 and 10 micrometers, respectively) in Finland and Brazil, using four ANNs: multilayer perceptron, radial basis function, extreme learning machines, and echo state networks. The experimental results using three evaluation measures showed that the proposed methodology increased all models’ prediction capability, leading to higher accuracy compared to the traditional approach, even for extremely high air pollution events. Our study has an important contribution to air quality prediction studies. It can help governments take measures aiming air pollution reduction and preparing hospitals during extreme air pollution events, which is related to the following United Nations sustainable developments goals: SDG 3—good health and well-being and SDG 11—sustainable cities and communities.
Paulo De Mattos Neto; Manoel Henrique Nóbrega Marinho; Hugo Siqueira; Yara De Souza Tadano; Vivian Machado; Thiago Antonini Alves; João De Oliveira; Francisco Madeiro. A Methodology to Increase the Accuracy of Particulate Matter Predictors Based on Time Decomposition. Sustainability 2020, 12, 7310 .
AMA StylePaulo De Mattos Neto, Manoel Henrique Nóbrega Marinho, Hugo Siqueira, Yara De Souza Tadano, Vivian Machado, Thiago Antonini Alves, João De Oliveira, Francisco Madeiro. A Methodology to Increase the Accuracy of Particulate Matter Predictors Based on Time Decomposition. Sustainability. 2020; 12 (18):7310.
Chicago/Turabian StylePaulo De Mattos Neto; Manoel Henrique Nóbrega Marinho; Hugo Siqueira; Yara De Souza Tadano; Vivian Machado; Thiago Antonini Alves; João De Oliveira; Francisco Madeiro. 2020. "A Methodology to Increase the Accuracy of Particulate Matter Predictors Based on Time Decomposition." Sustainability 12, no. 18: 7310.
Jônatas T. Belotti; Diego S. Castanho; Lilian N. Araujo; Lucas V. da Silva; Thiago Antonini Alves; Yara S. Tadano; Sergio L. Stevan; Fernanda C. Corrêa; Hugo V. Siqueira. Air pollution epidemiology: A simplified Generalized Linear Model approach optimized by bio-inspired metaheuristics. Environmental Research 2020, 191, 1 .
AMA StyleJônatas T. Belotti, Diego S. Castanho, Lilian N. Araujo, Lucas V. da Silva, Thiago Antonini Alves, Yara S. Tadano, Sergio L. Stevan, Fernanda C. Corrêa, Hugo V. Siqueira. Air pollution epidemiology: A simplified Generalized Linear Model approach optimized by bio-inspired metaheuristics. Environmental Research. 2020; 191 ():1.
Chicago/Turabian StyleJônatas T. Belotti; Diego S. Castanho; Lilian N. Araujo; Lucas V. da Silva; Thiago Antonini Alves; Yara S. Tadano; Sergio L. Stevan; Fernanda C. Corrêa; Hugo V. Siqueira. 2020. "Air pollution epidemiology: A simplified Generalized Linear Model approach optimized by bio-inspired metaheuristics." Environmental Research 191, no. : 1.
Streamflow series forecasting composes a fundamental step in planning electric energy production for hydroelectric plants. In Brazil, such plants produce almost 70% of the total energy. Therefore, it is of great importance to improve the quality of streamflow series forecasting by investigating state-of-the-art time series forecasting algorithms. To this end, this work proposes the development of ensembles of unorganized machines, namely Extreme Learning Machines (ELMs) and Echo State Networks (ESNs). Two primary contributions are proposed: (1) a new training logic for ESNs that enables the application of bootstrap aggregation (bagging); and (2) the employment of multi-objective optimization to select and adjust the weights of the ensemble’s base models, taking into account the trade-off between bias and variance. Experiments are conducted on streamflow series data from five real-world Brazilian hydroelectric plants, namely those in Sobradinho, Serra da Mesa, Jiraú, Furnas and Água Vermelha. The statistical results for four different prediction horizons (1, 3, 6, and 12 months ahead) indicate that the ensembles of unorganized machines achieve better results than autoregressive (AR) models in terms of the Nash–Sutcliffe model efficiency coefficient (NSE), root mean squared error (RMSE), coefficient of determination (R2), and RMSE-observations standard deviation ratio (RSR). In such results, the ensembles with ESNs and the multi-objective optimization design procedure achieve the best scores.
Victor Henrique Alves Ribeiro; Gilberto Reynoso-Meza; Hugo Valadares Siqueira. Multi-objective ensembles of echo state networks and extreme learning machines for streamflow series forecasting. Engineering Applications of Artificial Intelligence 2020, 95, 103910 .
AMA StyleVictor Henrique Alves Ribeiro, Gilberto Reynoso-Meza, Hugo Valadares Siqueira. Multi-objective ensembles of echo state networks and extreme learning machines for streamflow series forecasting. Engineering Applications of Artificial Intelligence. 2020; 95 ():103910.
Chicago/Turabian StyleVictor Henrique Alves Ribeiro; Gilberto Reynoso-Meza; Hugo Valadares Siqueira. 2020. "Multi-objective ensembles of echo state networks and extreme learning machines for streamflow series forecasting." Engineering Applications of Artificial Intelligence 95, no. : 103910.
The forecasting of monthly seasonal streamflow time series is an important issue for countries where hydroelectric plants contribute significantly to electric power generation. The main step in the planning of the electric sector’s operation is to predict such series to anticipate behaviors and issues. In general, several proposals of the literature focus just on the determination of the best forecasting models. However, the correct selection of input variables is an essential step for the forecasting accuracy, which in a univariate model is given by the lags of the time series to forecast. This task can be solved by variable selection methods since the performance of the predictors is directly related to this stage. In the present study, we investigate the performances of linear and non-linear filters, wrappers, and bio-inspired metaheuristics, totaling ten approaches. The addressed predictors are the extreme learning machine neural networks, representing the non-linear approaches, and the autoregressive linear models, from the Box and Jenkins methodology. The computational results regarding five series from hydroelectric plants indicate that the wrapper methodology is adequate for the non-linear method, and the linear approaches are better adjusted using filters.
Hugo Siqueira; Mariana Macedo; Yara De Souza Tadano; Thiago Antonini Alves; Jr. Sergio L. Stevan; Jr. Domingos S. Oliveira; Manoel H.N. Marinho; Paulo S.G. De Mattos Neto; João F. L. De Oliveira; Ivette Luna; Marcos De Almeida Leone Filho; Leonie Asfora Sarubbo; Attilio Converti. Selection of Temporal Lags for Predicting Riverflow Series from Hydroelectric Plants Using Variable Selection Methods. Energies 2020, 13, 4236 .
AMA StyleHugo Siqueira, Mariana Macedo, Yara De Souza Tadano, Thiago Antonini Alves, Jr. Sergio L. Stevan, Jr. Domingos S. Oliveira, Manoel H.N. Marinho, Paulo S.G. De Mattos Neto, João F. L. De Oliveira, Ivette Luna, Marcos De Almeida Leone Filho, Leonie Asfora Sarubbo, Attilio Converti. Selection of Temporal Lags for Predicting Riverflow Series from Hydroelectric Plants Using Variable Selection Methods. Energies. 2020; 13 (16):4236.
Chicago/Turabian StyleHugo Siqueira; Mariana Macedo; Yara De Souza Tadano; Thiago Antonini Alves; Jr. Sergio L. Stevan; Jr. Domingos S. Oliveira; Manoel H.N. Marinho; Paulo S.G. De Mattos Neto; João F. L. De Oliveira; Ivette Luna; Marcos De Almeida Leone Filho; Leonie Asfora Sarubbo; Attilio Converti. 2020. "Selection of Temporal Lags for Predicting Riverflow Series from Hydroelectric Plants Using Variable Selection Methods." Energies 13, no. 16: 4236.
The use of wearable equipment and sensing devices to monitor physical activities, whether for well-being, sports monitoring, or medical rehabilitation, has expanded rapidly due to the evolution of sensing techniques, cheaper integrated circuits, and the development of connectivity technologies. In this scenario, this paper presents a state-of-the-art review of sensors and systems for rehabilitation and health monitoring. Although we know the increasing importance of data processing techniques, our focus was on analyzing the implementation of sensors and biomedical applications. Although many themes overlap, we organized this review based on three groups: Sensors in Healthcare, Home Medical Assistance, and Continuous Health Monitoring; Systems and Sensors in Physical Rehabilitation; and Assistive Systems.
Lucas Medeiros Souza Do Nascimento; Lucas Vacilotto Bonfati; Melissa La Banca Freitas; José Jair Alves Mendes Junior; Hugo Valadares Siqueira; Jr. Sergio Luiz Stevan. Sensors and Systems for Physical Rehabilitation and Health Monitoring—A Review. Sensors 2020, 20, 4063 .
AMA StyleLucas Medeiros Souza Do Nascimento, Lucas Vacilotto Bonfati, Melissa La Banca Freitas, José Jair Alves Mendes Junior, Hugo Valadares Siqueira, Jr. Sergio Luiz Stevan. Sensors and Systems for Physical Rehabilitation and Health Monitoring—A Review. Sensors. 2020; 20 (15):4063.
Chicago/Turabian StyleLucas Medeiros Souza Do Nascimento; Lucas Vacilotto Bonfati; Melissa La Banca Freitas; José Jair Alves Mendes Junior; Hugo Valadares Siqueira; Jr. Sergio Luiz Stevan. 2020. "Sensors and Systems for Physical Rehabilitation and Health Monitoring—A Review." Sensors 20, no. 15: 4063.
Air pollution is a relevant issue studied worldwide, and its prediction is important for social and economic management. Linear multivariate regression models (LMR) and artificial neural networks (ANN) are widely applied to forecasting concentrations of pollutants. However, unorganized machines are scarcely used. The present investigation proposes the application of unorganized machines (echo state networks - ESN and extreme learning machines - ELM) to forecast hourly concentrations of particulate matter with the aerodynamic diameter up to 10 µm (PM10), carbon monoxide (CO), and ozone (O3) at the metropolitan region of Recife, Pernambuco, Brazil. The results were compared with multilayer perceptron neural network (MLP) and LMR. The prediction was made using or not meteorological variables (wind speed, temperature, and relative humidity) as input data. The results showed that the inclusion of these variables could increase the general performance of the models considering one step ahead forecasting horizons. Also, the ELM and the LMR achieved the best overall results.
Daniel Silva Campos; Yara De Souza Tadano; Thiago Antonini Alves; Hugo Valadares Siqueira; Manoel Henrique Nóbrega Marinho. Unorganized machines and linear multivariate regression model applied to atmospheric pollutant forecasting. Acta Scientiarum. Technology 2020, 42, e48203 -e48203.
AMA StyleDaniel Silva Campos, Yara De Souza Tadano, Thiago Antonini Alves, Hugo Valadares Siqueira, Manoel Henrique Nóbrega Marinho. Unorganized machines and linear multivariate regression model applied to atmospheric pollutant forecasting. Acta Scientiarum. Technology. 2020; 42 ():e48203-e48203.
Chicago/Turabian StyleDaniel Silva Campos; Yara De Souza Tadano; Thiago Antonini Alves; Hugo Valadares Siqueira; Manoel Henrique Nóbrega Marinho. 2020. "Unorganized machines and linear multivariate regression model applied to atmospheric pollutant forecasting." Acta Scientiarum. Technology 42, no. : e48203-e48203.
This work presents a comparative study between dimensionality reduction and feature selection to classification problem for six hand gestures by sEMG signal. The classified signals are wrist flexion, wrist extension, wrist flexion for the left, wrist extension to the right, forearm supination, and forearm pronation. An armband with eight channels was used to acquire the signals from 13 subjects (8 male and 5 female). Then, 29 features from time and frequency domain were extracted. Linear Discriminant Analysis (LDA), Quadratic Discriminant Analysis (QDA), and Support Vector Machine (SVM) were used as classifiers. Regarding the dimensionality reduction, Principal Component Analysis and LDA were applied in the signal; for feature selection, the feature combination for wrapper method step wise forward was used. The best scenario with dimensionality reduction was obtained with QDA classifier and 80 attributes from PCA, reaching accuracies of 84%. In the second scenario, with 112 attributes (8 features), a non-linear SVM (with Gaussian kernel) reached accuracies of 91%. Both methods presented similar performances among the accuracies for each class; however, dimensionality reduction approach presented less computational cost whilst has a lower accuracy compared with feature selection approach.
J. Mendes; Melissa La Banca Freitas; Hugo Valadares Siqueira; André Eugênio Lazzaretti; Sergio Luiz Stevan; Sérgio Francisco Pichorim. Comparative analysis among feature selection of sEMG signal for hand gesture classification by armband. IEEE Latin America Transactions 2020, 18, 1135 -1143.
AMA StyleJ. Mendes, Melissa La Banca Freitas, Hugo Valadares Siqueira, André Eugênio Lazzaretti, Sergio Luiz Stevan, Sérgio Francisco Pichorim. Comparative analysis among feature selection of sEMG signal for hand gesture classification by armband. IEEE Latin America Transactions. 2020; 18 (06):1135-1143.
Chicago/Turabian StyleJ. Mendes; Melissa La Banca Freitas; Hugo Valadares Siqueira; André Eugênio Lazzaretti; Sergio Luiz Stevan; Sérgio Francisco Pichorim. 2020. "Comparative analysis among feature selection of sEMG signal for hand gesture classification by armband." IEEE Latin America Transactions 18, no. 06: 1135-1143.
The emission of pollutants from vehicles is presented as a prime factor deteriorating air quality. Thus, seeking public policies encouraging the use and the development of more sustainable vehicles is paramount to preserve populations’ health. To better understand the health risks caused by air pollution and exclusively by mobile sources urges the question of which input variables should be considered. Therefore, this research aims to estimate the impacts on populations’ health related to road transport variables for São Paulo, Brazil, the largest metropolis in South America. We used three Artificial Neural Networks (ANN) (Multilayer Perceptron—MLP, Extreme Learning Machines—ELM, and Echo State Neural Networks—ESN) to estimate the impacts of carbon monoxide, nitrogen oxides, ozone, sulfur dioxide, and particulate matter on outcomes for respiratory diseases (morbidity—hospital admissions and mortality). We also used unusual inputs, such as road vehicles fleet, distributed and sold fuels amount, and vehicle average mileage. We also used deseasonalization and the Variable Selection Methods (VSM) (Mutual Information Filter and Wrapper). The results showed that the VSM excluded some variables, but the best performances were reached considering all of them. The ELM achieved the best overall results to morbidity, and the ESN to mortality, both using deseasonalization. Our study makes an important contribution to the following United Nations Sustainable Development Goals: 3—good health and well-being, 7—affordable and clean energy, and 11—sustainable cities and communities. These research findings will guide government about future legislations, public policies aiming to warranty and improve the health system.
Yslene Kachba; Daiane Maria De Genaro Chiroli; Jônatas T. Belotti; Thiago Antonini Alves; Yara De Souza Tadano; Hugo Siqueira. Artificial Neural Networks to Estimate the Influence of Vehicular Emission Variables on Morbidity and Mortality in the Largest Metropolis in South America. Sustainability 2020, 12, 2621 .
AMA StyleYslene Kachba, Daiane Maria De Genaro Chiroli, Jônatas T. Belotti, Thiago Antonini Alves, Yara De Souza Tadano, Hugo Siqueira. Artificial Neural Networks to Estimate the Influence of Vehicular Emission Variables on Morbidity and Mortality in the Largest Metropolis in South America. Sustainability. 2020; 12 (7):2621.
Chicago/Turabian StyleYslene Kachba; Daiane Maria De Genaro Chiroli; Jônatas T. Belotti; Thiago Antonini Alves; Yara De Souza Tadano; Hugo Siqueira. 2020. "Artificial Neural Networks to Estimate the Influence of Vehicular Emission Variables on Morbidity and Mortality in the Largest Metropolis in South America." Sustainability 12, no. 7: 2621.
Gesture recognition by surface electromyography (sEMG) signals is used for several applications as prosthesis control and human-machines interfaces. One of trending approaches to sEMG acquisition is the multiple-channel armband with equidistant electrodes. Several efforts are made to improve the performance of these devices in gestures recognition applications, especially in feature selection. It is necessary choose one approach for feature selection due to the great number of electromyography features available to use. In this work, an extensive comparison of feature reduction techniques and their influences in the classification process is presented. Unlike other works, we presents the comparison between methods of feature selection and classification; this main contribution is show how the feature reduction process can aid and increase the performance of classification of sEMG in armband acquisition approach. Two general methods were employed, feature selection by wrapper forward stepwise and dimensionality reduction, resulting in eight different techniques. The following dimensionality reduction techniques were used: Principal Component Analysis, Linear Discriminant Analysis, Isomap, Manifold Charting, Autoencoder, t-distributed Stochastic Neighbor Embedding, and Large Margin Nearest Neighbor (LMNN). Seven classifiers were used, aiming at recognize six gestures acquired from an 8-channel armband of 13 subjects. An average accuracy of 89.4 % was obtained with 5 features and an Extreme Learning Machine classifier, in the feature selection approach. On other hand, considering 40 dimensions, an average accuracy of 94 % was obtained, regarding a combination between Support Vector Machine with Gaussian kernel and a LMNN technique. These results showed significant differences in statistical tests.
José Jair Alves Mendes Junior; Melissa L.B. Freitas; Hugo Valadares Siqueira; André E. Lazzaretti; Sergio F. Pichorim; Sergio L. Stevan. Feature selection and dimensionality reduction: An extensive comparison in hand gesture classification by sEMG in eight channels armband approach. Biomedical Signal Processing and Control 2020, 59, 101920 .
AMA StyleJosé Jair Alves Mendes Junior, Melissa L.B. Freitas, Hugo Valadares Siqueira, André E. Lazzaretti, Sergio F. Pichorim, Sergio L. Stevan. Feature selection and dimensionality reduction: An extensive comparison in hand gesture classification by sEMG in eight channels armband approach. Biomedical Signal Processing and Control. 2020; 59 ():101920.
Chicago/Turabian StyleJosé Jair Alves Mendes Junior; Melissa L.B. Freitas; Hugo Valadares Siqueira; André E. Lazzaretti; Sergio F. Pichorim; Sergio L. Stevan. 2020. "Feature selection and dimensionality reduction: An extensive comparison in hand gesture classification by sEMG in eight channels armband approach." Biomedical Signal Processing and Control 59, no. : 101920.
The study of solar activity is of great interest for the recognition of its influence on the earth. A great step in astronomy is the prediction of solar activity, allowing better preparation for study and recognition of future solar and terrestrial events. In our research we used Neural Networks models to predict sunspot numbers based on solar activity recorded between 1818 and 2019. Solar activity data were taken from the Solar Influences Data analysis Center (SIDC) website and Sunspot Index and Long-term Solar Observations (SILSO). Results show a high potential of this processing that become a competitive approach for the sunspots prediction.
Sthefanie Premebida; Denise Pechebovicz; Thiago Camargo; Henrique Nazario; Vinicios Soa; Virginia Baroncini; Erikson De Morais; Hugo Valadares Siqueira; Diego Oliva; Marcella Scoczynski Ribeiro Martins. Sunspot behavior forecast using neural networks approaches. 2020 IEEE International Conference on Industrial Technology (ICIT) 2020, 696 -700.
AMA StyleSthefanie Premebida, Denise Pechebovicz, Thiago Camargo, Henrique Nazario, Vinicios Soa, Virginia Baroncini, Erikson De Morais, Hugo Valadares Siqueira, Diego Oliva, Marcella Scoczynski Ribeiro Martins. Sunspot behavior forecast using neural networks approaches. 2020 IEEE International Conference on Industrial Technology (ICIT). 2020; ():696-700.
Chicago/Turabian StyleSthefanie Premebida; Denise Pechebovicz; Thiago Camargo; Henrique Nazario; Vinicios Soa; Virginia Baroncini; Erikson De Morais; Hugo Valadares Siqueira; Diego Oliva; Marcella Scoczynski Ribeiro Martins. 2020. "Sunspot behavior forecast using neural networks approaches." 2020 IEEE International Conference on Industrial Technology (ICIT) , no. : 696-700.