2 citations · 6 across the 6 of their papers we have counts for
7 papers
Creating Ensembles of Classifiers through UMDA for Aerial Scene Classification
Fabio A. Faria, Luiz H. Buris, Luis A. M. Pereira +1
Aerial scene classification, which aims to semantically label remote sensing images in a set of predefined classes (e.g., agricultural, beach, and harbor), is a very challenging ta…
ForestEyes Project: Conception, Enhancements, and Challenges
Fernanda B. J. R. Dallaqua, Álvaro Luiz Fazenda, Fabio A. Faria
Rainforests play an important role in the global ecosystem. However, significant regions of them are facing deforestation and degradation due to several reasons. Diverse government…
Neuroevolution-based Classifiers for Deforestation Detection in Tropical Forests
Guilherme A. Pimenta, Fernanda B. J. R. Dallaqua, Alvaro Fazenda +1
Tropical forests represent the home of many species on the planet for flora and fauna, retaining billions of tons of carbon footprint, promoting clouds and rain formation, implying…
An Evolutionary Approach for Creating of Diverse Classifier Ensembles
Alvaro R. Ferreira, Fabio A. Faria, Gustavo Carneiro +1
Classification is one of the most studied tasks in data mining and machine learning areas and many works in the literature have been presented to solve classification problems for…
The Brazilian Data at Risk in the Age of AI?
Raoni F. da S. Teixeira, Rafael B. Januzi, Fabio A. Faria
Advances in image processing and analysis as well as machine learning techniques have contributed to the use of biometric recognition systems in daily people tasks. These tasks ran…
Mixup-based Deep Metric Learning Approaches for Incomplete Supervision
Luiz H. Buris, Daniel C. G. Pedronette, Joao P. Papa +3
Deep learning architectures have achieved promising results in different areas (e.g., medicine, agriculture, and security). However, using those powerful techniques in many real ap…