Saulo Martiello Mastelini
Saulo Martiello Mastelini
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Cited by
Cited by
Digital image analyses as an alternative tool for chicken quality assessment
DF Barbin, SM Mastelini, S Barbon Jr, GFC Campos, APAC Barbon, ...
Biosystems Engineering 144, 85-93, 2016
Explainable machine learning algorithms for predicting glass transition temperatures
E Alcobaca, SM Mastelini, T Botari, BA Pimentel, DR Cassar, ...
Acta Materialia 188, 92-100, 2020
Computer vision system and near-infrared spectroscopy for identification and classification of chicken with wooden breast, and physicochemical and technological characterization
BC Geronimo, SM Mastelini, RH Carvalho, SB Júnior, DF Barbin, ...
Infrared Physics & Technology 96, 303-310, 2019
Deep regressor stacking for air ticket prices prediction
E Santana, S Mastelini
Anais do XIII simpósio brasileiro de sistemas de informação, 25-31, 2017
Predicting poultry meat characteristics using an enhanced multi-target regression method
EJ Santana, BC Geronimo, SM Mastelini, RH Carvalho, DF Barbin, EI Ida, ...
Biosystems Engineering 171, 193-204, 2018
Machine learning hyperparameter selection for contrast limited adaptive histogram equalization
GFC Campos, SM Mastelini, GJ Aguiar, RG Mantovani, LF de Melo, ...
EURASIP Journal on Image and Video Processing 2019 (1), 1-18, 2019
Multi-output tree chaining: An interpretative modelling and lightweight multi-target approach
SM Mastelini, VGT da Costa, EJ Santana, FK Nakano, RC Guido, R Cerri, ...
Journal of Signal Processing Systems 91 (2), 191-215, 2019
DSTARS: A multi-target deep structure for tracking asynchronous regressor stacking
SM Mastelini, EJ Santana, R Cerri, S Barbon Jr
Applied Soft Computing 91, 106215, 2020
Development of a flexible Computer Vision System for marbling classification
APA da Costa Barbon, S Barbon Jr, GFC Campos, JL Seixas Jr, LM Peres, ...
Computers and Electronics in Agriculture 142, 536-544, 2017
Multi-target prediction of wheat flour quality parameters with near infrared spectroscopy
SB Junior, SM Mastelini, APAC Barbon, DF Barbin, R Calvini, JF Lopes, ...
Information processing in agriculture 7 (2), 342-354, 2020
Improving hierarchical classification of transposable elements using deep neural networks
FK Nakano, SM Mastelini, S Barbon, R Cerri
2018 International Joint Conference on Neural Networks (IJCNN), 1-8, 2018
Stacking methods for hierarchical classification
F kenji Nakano, SM Mastelini, S Barbon, R Cerri
2017 16th IEEE international conference on machine learning and applications …, 2017
A meta-learning approach for selecting image segmentation algorithm
GJ Aguiar, RG Mantovani, SM Mastelini, AC de Carvalho, GFC Campos, ...
Pattern Recognition Letters 128, 480-487, 2019
User classification on online social networks by post frequency
G Tavares, S Mastelini
Anais do XIII Simpósio Brasileiro de Sistemas de Informação, 464-471, 2017
River: machine learning for streaming data in Python
J Montiel, M Halford, SM Mastelini, G Bolmier, R Sourty, R Vaysse, ...
White striping degree assessment using computer vision system and consumer acceptance test
T Kato, SM Mastelini, GFC Campos, APA da Costa Barbon, SH Prudencio, ...
Asian-Australasian journal of animal sciences 32 (7), 1015, 2019
Benchmarking multi-target regression methods
SM Mastelini, EJ Santana, VGT da Costa, S Barbon
2018 7th Brazilian Conference on Intelligent Systems (BRACIS), 396-401, 2018
On ensemble techniques for data stream regression
HM Gomes, J Montiel, SM Mastelini, B Pfahringer, A Bifet
2020 International Joint Conference on Neural Networks (IJCNN), 1-8, 2020
Making data stream classification tree-based ensembles lighter
VGT da Costa, SM Mastelini, ACPLF de Carvalho, S Barbon
2018 7th Brazilian Conference on Intelligent Systems (BRACIS), 480-485, 2018
Improved prediction of soil properties with multi-target stacked generalisation on EDXRF spectra
EJ Santana, FR Santos, SM Mastelini, FL Melquiades, S Barbon Jr
arXiv preprint arXiv:2002.04312, 2020
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