Saulo Martiello Mastelini

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Sylvio Barbon JuniorUniversity of Trieste, Department of Engineering and ArchitectureAdresse e-mail validée de dia.units.it
Everton Jose SantanaVisiting Instructor, Stanford UniversityAdresse e-mail validée de stanford.edu
Gabriel F. C. CamposComputer Science, State University of LondrinaAdresse e-mail validée de uel.br
Edesio AlcobaçaPhD candidate, Universidade de São PauloAdresse e-mail validée de usp.br
Tiago BotariASMLAdresse e-mail validée de asml.com
Rafael Gomes MantovaniFederal Technology University - Paraná, campus of ApucaranaAdresse e-mail validée de utfpr.edu.br
Felipe Kenji NakanoPost doc Researcher KU Leuven KULAKAdresse e-mail validée de kuleuven.be
Ricardo CerriAssistant Professor, Institute of Mathematics and Computer Science, University of São PauloAdresse e-mail validée de icmc.usp.br
Gabriel Jonas AguiarPh.D. Candidate at Virginia Commonwealth UniversityAdresse e-mail validée de vcu.edu
Victor Guilherme Turrisi da CostaAppleAdresse e-mail validée de apple.com
Elza Iouko IdaProfessora da Área de Ciência de Alimentos da Universidade Estadual de LondrinaAdresse e-mail validée de uel.br
Alessandro UlriciProfessor of Analytical Chemistry and Chemometrics, University of Modena and Reggio EmiliaAdresse e-mail validée de unimore.it
Leonimer Flávio de MeloProfessor Dr. de Engenharia Elétrica, Universidade Estadual de LondrinaAdresse e-mail validée de uel.br
ROSALBA CALVINIUniversità di Modena e Reggio EmiliaAdresse e-mail validée de unimore.it
Jessica Fernandes LopesPhD Student, State University of Londrina (UEL)Adresse e-mail validée de uel.br
José Luis Seixas JuniorPhD student, Eötvös Loránd UniversityAdresse e-mail validée de inf.elte.hu
Rodrigo Capobianco GuidoSão Paulo State University (UNESP)Adresse e-mail validée de ieee.org
Fabio Luiz MelquiadesPhysics professor/researcher, UELAdresse e-mail validée de uel.br
Adriana Lourenço SoaresUniversidade Estadual de LondrinaAdresse e-mail validée de uel.br
Gabriel Marques TavaresPostdoctoral Researcher, LMU MunichAdresse e-mail validée de dbs.ifi.lmu.de

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Saulo Martiello Mastelini

Ph.D., Universidade de São Paulo

Adresse e-mail validée de alumni.usp.br - Page d'accueil

Multi-output prediction multi-target regression data streams


Titre Trier par citations Trier par année Trier par titre	Citée par Citée par	Année
River: machine learning for streaming data in python J Montiel, M Halford, SM Mastelini, G Bolmier, R Sourty, R Vaysse, ... Journal of Machine Learning Research 22 (110), 1-8, 2021	292	2021
Machine learning hyperparameter selection for contrast limited adaptive histogram equalization GFC Campos, SM Mastelini, GJ Aguiar, RG Mantovani, LF Melo, ... EURASIP Journal on Image and Video Processing 2019, 1-18, 2019	119	2019
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	91	2016
Explainable machine learning algorithms for predicting glass transition temperatures E Alcobaça, SM Mastelini, T Botari, BA Pimentel, DR Cassar, ... Acta materialia 188, 92-100, 2020	90	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	80	2019
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	57	2020
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	47	2019
Deep regressor stacking for air ticket prices prediction E Santana, S Mastelini Simpósio Brasileiro de Sistemas de Informação (SBSI), 25-31, 2017	42	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	40	2018
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	40	2017
Online extra trees regressor SM Mastelini, FK Nakano, C Vens, ACP de Leon Ferreira IEEE Transactions on Neural Networks and Learning Systems 34 (10), 6755-6767, 2022	36	2022
Predicting and interpreting oxide glass properties by machine learning using large datasets DR Cassar, SM Mastelini, T Botari, E Alcobaça, AC de Carvalho, ... Ceramics international 47 (17), 23958-23972, 2021	36	2021
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, 191-215, 2019	33	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	30	2020
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, 2018	26*	2018
Improved prediction of soil properties with multi-target stacked generalisation on EDXRF spectra EJ Santana, FR dos Santos, SM Mastelini, FL Melquiades, S Barbon Jr Chemometrics and Intelligent Laboratory Systems 209, 104231, 2021	25	2021
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	23	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	22	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	22	2017
Machine learning unveils composition-property relationships in chalcogenide glasses SM Mastelini, DR Cassar, E Alcobaça, T Botari, AC de Carvalho, ... Acta Materialia 240, 118302, 2022	21	2022

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