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Gustavo Almeida
Gustavo Almeida
Dept of Energy Technology, Lappeenranta-Lahti University of Technology (LUT)
Verified email at lut.fi - Homepage
Title
Cited by
Cited by
Year
Learning from imbalanced data sets with weighted cross-entropy function
YS Aurelio, GM De Almeida, CL de Castro, AP Braga
Neural processing letters 50, 1937-1949, 2019
2192019
Hydrothermal carbonization of lignocellulosic agro-forest based biomass residues
CLM Martinez, E Sermyagina, J Saari, MS de Jesus, M Cardoso, ...
Biomass and Bioenergy 147, 106004, 2021
852021
Evaluation of thermochemical routes for the valorization of solid coffee residues to produce biofuels: A Brazilian case
CLM Martinez, J Saari, Y Melo, M Cardoso, GM de Almeida, ...
Renewable and Sustainable Energy Reviews 137, 110585, 2021
692021
Development of intelligent robotic process automation: A utility case study in Brazil
B Vajgel, PLP Corrêa, TT De Sousa, RVE Quille, JAR Bedoya, ...
Ieee Access 9, 71222-71235, 2021
332021
Fault detection and diagnosis using support vector machines—A SVC and SVR comparison
DL de Souza, MH Granzotto, GM de Almeida, LC Oliveira-Lopes
Journal of Safety Engineering 3 (1), 18-29, 2014
332014
Trend modelling with artificial neural networks. Case study: Operating zones identification for higher SO3 incorporation in cement clinker
RN Lima, GM de Almeida, AP Braga, M Cardoso
Engineering Applications of Artificial Intelligence 54, 17-25, 2016
222016
Predicting kappa number in a kraft pulp continuous digester: A comparison of forecasting methods
FM Correia, JVH d'Angelo, GM Almeida, SA Mingoti
Brazilian Journal of Chemical Engineering 35, 1081-1094, 2018
202018
Fault detection and diagnosis in the DAMADICS benchmark actuator system–A hidden Markov model approach
GM de Almeida, SW Park
IFAC Proceedings Volumes 41 (2), 12419-12424, 2008
202008
Prediction of mechanical properties of steel tubes using a machine learning approach
MV Carneiro, TT Salis, GM Almeida, AP Braga
Journal of Materials Engineering and Performance 30 (1), 434-443, 2021
142021
Three-layer approach to detect anomalies in industrial environments based on machine learning
D Gutierrez-Rojas, M Ullah, IT Christou, G Almeida, P Nardelli, D Carrillo, ...
2020 IEEE Conference on Industrial Cyberphysical Systems (ICPS) 1, 250-256, 2020
132020
MILKDE: A new approach for multiple instance learning based on positive instance selection and kernel density estimation
AWC Faria, FGF Coelho, AM Silva, HP Rocha, GM Almeida, AP Lemos, ...
Engineering Applications of Artificial Intelligence 59, 196-204, 2017
112017
Relatos de experiência de inserção de tecnologias digitais no ensino de engenharia
AB Belisário, DG Faria, DH de Souza Chaves, GM de Almeida, ...
Revista Docência do Ensino Superior 10, 1-18, 2020
82020
Process monitoring in chemical industries–A hidden Markov model approach
GM de Almeida, SW Park
Proceedings of the 18th European Symposium on Computer Aided Process …, 2008
72008
Fault detection in a sugar evaporation process using hidden Markov models
GM Almeida, SW Park
ADCONIP'05: proceedings, 2005
72005
Heat-loss cycle prediction in steelmaking plants through artificial neural network
ICD Duarte, GM Almeida, M Cardoso
Journal of the Operational Research Society 73 (2), 326-337, 2022
62022
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
GM de Almeida, SW Park
Intelligent Data Engineering and Automated Learning-IDEAL 2012: 13th …, 2012
62012
Detecção de situações anormais em caldeiras de recuperação química.
GM Almeida
Universidade de São Paulo, 2006
62006
Variables selection for neural networks identification for Kraft recovery boilers
GM Almeida, SW Park, M Cardoso
IFAC Proceedings Volumes 37 (16), 91-96, 2004
62004
Automatic update strategy for real-time discovery of hidden customer intents in chatbot systems
HD Rebelo, LAF de Oliveira, GM Almeida, CAM Sotomayor, ...
Knowledge-Based Systems 243, 108529, 2022
52022
Improving knowledge about permeability in membrane bioreactors through sensitivity analysis using artificial neural networks
AR Alkmim, GM de Almeida, DM de Carvalho, MCS Amaral, ...
Environmental Technology 41 (19), 2424-2438, 2020
52020
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