Gustavo Matheus de Almeida

According to our database, Gustavo Matheus de Almeida authored at least 17 papers between 2008 and 2026.

Collaborative distances:

• Dijkstra number of four.
• Erdős number of four.

2026

A Deep Reinforcement Learning Approach for Fault Detection in Continuous Chemical Processes.

Rodrigo F. Mantovani, Roberto da Costa Quinino, Éder D. Oliveira, Antônio P. Braga, Gustavo Matheus de Almeida

IEEE Access, 2026

2024

Unified Framework to Select an IoT Platform for PtX Cogeneration Plants.

Mehar Ullah, Daniel Gutierrez-Rojas, Gustavo Matheus de Almeida, Tero Tynjälä

Proceedings of the 47th MIPRO ICT and Electronics Convention, 2024

2023

Dynamic mathematical modelling and simulation of a primary oil separation unit.

María E. G. Condori, Lucio F. D. Passos, Hugo C. C. Michel, Gustavo Matheus de Almeida, Marcelo Cardoso

Int. J. Simul. Process. Model., 2023

2022

Cost-Sensitive Learning based on Performance Metric for Imbalanced Data.

Yuri Sousa Aurelio, Gustavo Matheus de Almeida, Cristiano Leite de Castro, Antônio de Pádua Braga

Neural Process. Lett., 2022

Automatic update strategy for real-time discovery of hidden customer intents in chatbot systems.

Hugo D. Rebelo, Lucas A. F. de Oliveira, Gustavo Matheus de Almeida, Cesar Augusto Medina Sotomayor, Vivian S. N. Magalhães, Geraldo L. Rochocz

Knowl. Based Syst., 2022

Heat-loss cycle prediction in steelmaking plants through artificial neural network.

Iara Campolina Dias Duarte, Gustavo Matheus de Almeida, Marcelo Cardoso

J. Oper. Res. Soc., 2022

Multi-objective neural network model selection with a graph-based large margin approach.

Luiz C. B. Torres, Cristiano Leite Castro, Honovan P. Rocha, Gustavo Matheus de Almeida, Antônio P. Braga

Inf. Sci., 2022

Improving Fault Detection in Industrial Processes by Event-Driven Data Acquisition.

Daniel Gutierrez-Rojas, Pekka Räsänen, Ana Brandão Belisário, Merim Dzaferagic, Gustavo Matheus de Almeida, Pedro H. J. Nardelli

IEEE Access, 2022

2021

Intent Identification in Unattended Customer Queries Using an Unsupervised Approach.

Hugo D. Rebelo, Lucas A. F. de Oliveira, Gustavo Matheus de Almeida, Cesar Augusto Medina Sotomayor, Geraldo L. Rochocz, Willian E. D. Melo

J. Inf. Knowl. Manag., 2021

Development of Intelligent Robotic Process Automation: A Utility Case Study in Brazil.

Bruna Vajgel, Pedro Luiz Pizzigatti Corrêa, Thais Tóssoli De Sousa, Rosa Virginia Encinas Quille, John A. R. Bedoya, Gustavo Matheus de Almeida, Lucia Vilela Leite Filgueiras, Vanessa Rafaela de Souza Demuner, Denis Mollica

IEEE Access, 2021

2020

Prediction of Mechanical Properties of Seamless Steel Tubes Using Artificial Neural Networks.

Ramon Santos Correa, Patricia Teixeira Sampaio, Rafael Utsch Braga, Victor Alberto Lambertucci, Gustavo Matheus de Almeida, Antônio de Pádua Braga

Int. J. Comput. Intell. Appl., 2020

Three-layer Approach to Detect Anomalies in Industrial Environments based on Machine Learning.

Daniel Gutierrez-Rojas, Mehar Ullah, Ioannis T. Christou, Gustavo Matheus de Almeida, Pedro H. J. Nardelli, Dick Carrillo, Jean Michel de Souza Sant'Ana, Hirley Alves, Merim Dzaferagic, Alessandro Chiumento, Charalampos Kalalas

Proceedings of the IEEE Conference on Industrial Cyberphysical Systems, 2020

2019

Learning from Imbalanced Data Sets with Weighted Cross-Entropy Function.

Yuri Sousa Aurelio, Gustavo Matheus de Almeida, Cristiano Leite Castro, Antônio de Pádua Braga

Neural Process. Lett., 2019

2017

MILKDE: A new approach for multiple instance learning based on positive instance selection and kernel density estimation.

Alexandre W. C. Faria, Frederico Gualberto Ferreira Coelho, Alisson Marques da Silva, Honovan P. Rocha, Gustavo Matheus de Almeida, André P. Lemos, Antônio P. Braga

Eng. Appl. Artif. Intell., 2017

2016

Trend modelling with artificial neural networks. Case study: Operating zones identification for higher SO₃ incorporation in cement clinker.

R. N. Lima, Gustavo Matheus de Almeida, Antônio P. Braga, Marcelo Cardoso

Eng. Appl. Artif. Intell., 2016

2012

Fault Detection in Continuous Industrial Chemical Processes: A New Approach Using the Hidden Markov Modeling. Case Study: A Boiler from a Brazilian Cellulose Pulp Mill.

Gustavo Matheus de Almeida, Song Won Park

Proceedings of the Intelligent Data Engineering and Automated Learning - IDEAL 2012, 2012

2008

Graphical representation of cause-effect relationships among chemical process variables using a neural network approach.

Gustavo Matheus de Almeida, Marcelo Cardoso, Danilo C. Rena, Song Won Park

Proceedings of the International Joint Conference on Neural Networks, 2008