L
Leonardo F. S. Scabini
Researcher at University of São Paulo
Publications - 29
Citations - 204
Leonardo F. S. Scabini is an academic researcher from University of São Paulo. The author has contributed to research in topics: Computer science & Complex network. The author has an hindex of 7, co-authored 21 publications receiving 113 citations. Previous affiliations of Leonardo F. S. Scabini include Federal University of Mato Grosso do Sul.
Papers
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Journal ArticleDOI
Social interaction layers in complex networks for the dynamical epidemic modeling of COVID-19 in Brazil.
Leonardo F. S. Scabini,Lucas Correia Ribas,Mariane Barros Neiva,Altamir Gomes Bispo Junior,Alex J. F. Farfán,Odemir Martinez Bruno +5 more
TL;DR: The increase of isolation shows to be the best option to keep the situation under the healthcare system capacity, aside from ensuring a faster decrease of new case occurrences, and a significantly smaller death toll.
Journal ArticleDOI
Electrochemical and optical detection and machine learning applied to images of genosensors for diagnosis of prostate cancer with the biomarker PCA3.
Valquiria da Cruz Rodrigues,Juliana C. Soares,Andrey Soares,Daniel Cesar Braz,Matias Eliseo Melendez,Lucas Correia Ribas,Leonardo F. S. Scabini,Odemir Martinez Bruno,André Lopes Carvalho,Rui Manuel Reis,Rafaela C. Sanfelice,Osvaldo N. Oliveira +11 more
TL;DR: In this paper, the authors report on genosensors with different detection principles for a prostate cancer specific DNA sequence (PCA3), which are made with carbon printed electrodes or quartz coated with layer-by-layer (LbL) films containing gold nanoparticles and chondroitin sulfate and a layer of complementary DNA sequence.
Journal ArticleDOI
Fusion of complex networks and randomized neural networks for texture analysis
Lucas Correia Ribas,Jarbas Joaci de Mesquita Sá Junior,Leonardo F. S. Scabini,Odemir Martinez Bruno +3 more
TL;DR: In this article, a high discriminative texture analysis method based on the fusion of complex networks and randomized neural networks is presented, where the input image is modeled as a complex network and its topological properties as well as the image pixels are used to train randomized neural network to create a signature that represents the deep characteristics of the texture.
Journal ArticleDOI
Detection of a SARS-CoV-2 sequence with genosensors using data analysis based on information visualization and machine learning techniques
Josélia Costa Soares,Andrey Soares,Valquiria da Cruz Rodrigues,P. R. A. Oiticica,Paulo A. Raymundo-Pereira,José L. Bott-Neto,Lorenzo A. Buscaglia,L. D. C. de Castro,Lucas Correia Ribas,Leonardo F. S. Scabini,Laís Canniatti Brazaca,Laís Canniatti Brazaca,Daniel S. Correa,Luiz H. C. Mattoso,M. C. de Oliveira,de Carvalho, Acplf, Carrilho, E.,Odemir Martinez Bruno,Matias Eliseo Melendez,Osvaldo N. Oliveira +18 more
TL;DR: First genosensor for SARS-CoV-2 where multimodal detection principles can be employed, including image analysis based on machine learning.
Journal ArticleDOI
Multilayer complex network descriptors for color–texture characterization
Leonardo F. S. Scabini,Rayner H. Montes Condori,Wesley Nunes Gonçalves,Odemir Martinez Bruno +3 more
TL;DR: In this article, a new method based on complex networks is proposed for color-texture analysis, which consists of modeling the image as a multilayer complex network where each color channel is a layer, and each pixel (in each colour channel) is represented as a network vertex.