| Research on health inequalities: a bibliometric analysis (1966–2014) L Bouchard, M Albertini, R Batista, J De Montigny Social Science & Medicine 141, 100-108, 2015 | 128 | 2015 |
| Estimating photovoltaic power generation: Performance analysis of artificial neural networks, Support Vector Machine and Kalman filter RVA Monteiro, GC Guimaraes, FAM Moura, MRMC Albertini, MK Albertini Electric Power Systems Research 143, 643-656, 2017 | 63 | 2017 |
| A self-organizing neural network for detecting novelties MK Albertini, RF de Mello Proceedings of the 2007 ACM symposium on Applied computing, 462-466, 2007 | 51 | 2007 |
| Evaluation of transfer learning of pre-trained CNNs applied to breast cancer detection on infrared images E Chaves, CB Gonçalves, MK Albertini, S Lee, G Jeon, HC Fernandes Applied optics 59 (17), E23-E28, 2020 | 34 | 2020 |
| Entropy-based image fusion with joint sparse representation and rolling guidance filter Y Liu, X Yang, R Zhang, MK Albertini, T Celik, G Jeon Entropy 22 (1), 118, 2020 | 21 | 2020 |
| VisGraphNet: A complex network interpretation of convolutional neural features JB Florindo, YS Lee, K Jun, G Jeon, MK Albertini Information Sciences 543, 296-308, 2021 | 18 | 2021 |
| A self-organizing neural network to approach novelty detection MK Albertini, RF de Mello Machine Learning: Concepts, Methodologies, Tools and Applications, 262-282, 2012 | 18 | 2012 |
| Multifocus image fusion using convolutional neural network Y Wen, X Yang, T Celik, O Sushkova, MK Albertini Multimedia Tools and Applications 79, 34531-34543, 2020 | 13 | 2020 |
| Aerial image super-resolution based on deep recursive dense network for disaster area surveillance F Liu, Q Yu, L Chen, G Jeon, MK Albertini, X Yang Personal and Ubiquitous Computing, 1-10, 2022 | 12 | 2022 |
| Mp-draughts: Unsupervised learning multi-agent system based on mlp and adaptive neural networks VAR Duarte, RMS Julia, MK Albertini, HC Neto 2015 IEEE 27th international conference on tools with artificial …, 2015 | 11 | 2015 |
| Image super-resolution via enhanced multi-scale residual network MJ Wang, X Yang, M Anisetti, R Zhang, MK Albertini, K Liu Journal of Parallel and Distributed Computing 152, 57-66, 2021 | 8 | 2021 |
| Energy-based function to evaluate data stream clustering MK Albertini, RF de Mello Advances in Data Analysis and Classification 7, 435-464, 2013 | 8 | 2013 |
| Entropy-based approach to analyze and classify mineral aggregates L Tais de Gouveia, LF Costa, LJ Senger, MK Albertini, ... Journal of computing in civil engineering 25 (1), 75-84, 2011 | 8 | 2011 |
| Lightweight network with one-shot aggregation for image super-resolution R Tang, L Chen, Y Zou, Z Lai, MK Albertini, X Yang Journal of Real-Time Image Processing 18 (4), 1275-1284, 2021 | 7 | 2021 |
| Data mining of meteorological-related attributes from smartphone data LFA Brito, MK Albertini INFOCOMP Journal of Computer Science 15 (2), 1-9, 2016 | 7 | 2016 |
| A dynamic data structure for temporal reachability with unsorted contact insertions LFA Brito, MK Albertini, A Casteigts, BAN Travençolo Social Network Analysis and Mining 12 (1), 22, 2022 | 6 | 2022 |
| Medical image super-resolution with laplacian dense network R Tang, L Chen, R Zhang, A Ahmad, MK Albertini, X Yang Multimedia Tools and Applications, 1-14, 2022 | 6 | 2022 |
| Wild boar recognition using convolutional neural networks LC Silva, MBS Pádua, LM Ogusuku, M Keese Albertini, R Pimentel, ... Concurrency and Computation: Practice and Experience 33 (22), e6010, 2021 | 6 | 2021 |
| LMSN: a lightweight multi-scale network for single image super-resolution Y Zou, X Yang, MK Albertini, F Hussain Multimedia Systems 27, 845-856, 2021 | 6 | 2021 |
| Using multiple clustering algorithms to generate constraint rules and create consensus clusters GR da Silva, MK Albertini 2017 Brazilian Conference on Intelligent Systems (BRACIS), 312-317, 2017 | 6 | 2017 |
Rodrigo Fernandes de MelloItaú Unibanco SA在 itau-unibanco.com.br 的电子邮件经过验证
