Explanipedia

Machine Learning in Health: A Systematic Review Open

Alessandra Rodrigues Cardoso Padovam, Danilo Rodrigues Pereira, Romis Attux · 2025

The aim of this study is to construct a taxonomy of Machine Learning (ML) techniques most commonly employed in health applications. The systematic review was performed between October 2023 and November 2024, and comprised articles publishe…

Machine Learning in Health: A Systematic Review Open

Alessandra Rodrigues Cardoso Padovam, Danilo Rodrigues Pereira, Romis Attux · 2025

Comparison of Neural Models for X-ray Image Classification in COVID-19 Detection Open

Jimi Togni, Romis Attux · 2025

This study presents a comparative analysis of methods for detecting COVID-19 infection in radiographic images. The images, sourced from publicly available datasets, were categorized into three classes: 'normal,' 'pneumonia,' and 'COVID.' F…

Comparative Analysis of Deseasonalization and Detrending Methods in Energy Consumption Forecasting Open

Tainá Coimbra, Levy Boccato, Hugo Valadares Siqueira, Romis Attux · 2024

Energy consumption forecasting is a valuable tool for management decision-making that can lower expenses and enhance efficiency in a power system. Several time series prediction models can address this issue, ranging from statistical model…

Leveraging data from plant monitoring into crop models Open

Monique Pires Gravina de Oliveira, T.Q. Zorzeto, Romis Attux, Luiz Henrique Rodrigues · 2023

Researchers using crop models have been devising new roles for data and crop modeling based on the former’s increased availability and the new techniques developed for the latter. From the various available techniques, modeling may be tack…

Contributions to Social Media Analysis Based on Topic Modelling Open

Giordanno Brunno Bergamini Gomes, Romis Attux · 2023

This work proposes a computational approach to a task deeply related to human sciences, that of employing natural language processing in text analysis. Researchers working in that field are often faced with the need for extracting informat…

A Frequency-Domain Approach with Learnable Filters for Image Classification Open

José Augusto Stuchi, Natália Gil Canto, Levy Boccato, Romis Attux · 2023

Covid-19 detection using chest X-rays: is lung segmentation important for generalization? Open

Pedro R. A. S. Bassi, Romis Attux · 2022

Purpose We evaluated the generalization capability of deep neural networks (DNNs) in the task of classifying chest X-rays as Covid-19, normal or pneumonia, when trained in a relatively small and mixed datasets. Methods We proposed a DNN to…

Analysis of Trade-offs in Fair Principal Component Analysis Based on Multi-objective Optimization Open

Guilherme Dean Pelegrina, Renan Brotto, Leonardo Tomazeli Duarte, Romis Attux, João Marcos Travassos Romano · 2022

In dimensionality reduction problems, the adopted technique may produce\ndisparities between the representation errors of different groups. For\ninstance, in the projected space, a specific class can be better represented in\ncomparison wi…

FBDNN: filter banks and deep neural networks for portable and fast brain-computer interfaces Open

Pedro R. A. S. Bassi, Romis Attux · 2022

Objective. To propose novel SSVEP classification methodologies using deep neural networks (DNNs) and improve performances in single-channel and user-independent brain-computer interfaces (BCIs) with small data lengths. Approach. We propose…

FBCNN: A Deep Neural Network Architecture for Portable and Fast Brain-Computer Interfaces. Open

Pedro R. A. S. Bassi, Romis Attux · 2021

Objective: To propose a novel deep neural network (DNN) architecture -- the filter bank convolutional neural network (FBCNN) -- to improve SSVEP classification in single-channel BCIs with small data lengths. Methods: We propose two model…

COVID-19 detection using chest X-rays: is lung segmentation important for generalization? Open

Pedro R. A. S. Bassi, Romis Attux · 2021

Purpose: we evaluated the generalization capability of deep neural networks (DNNs), trained to classify chest X-rays as Covid-19, normal or pneumonia, using a relatively small and mixed dataset. Methods: we proposed a DNN to perform lung s…

Transfer learning and SpecAugment applied to SSVEP based BCI classification Open

Pedro R. A. S. Bassi, Willian Rampazzo, Romis Attux · 2021

Reorganization of Resting-State EEG Functional Connectivity Patterns in Children with Cerebral Palsy Following a Motor Imagery Virtual-Reality Intervention Open

Carlos Alberto Stefano Filho, J. Ignacio Serrano, Romis Attux, Gabriela Castellano, Eduardo Rocón , et al. · 2021

Motor imagery (MI) has been suggested to provide additional benefits when included in traditional approaches of physical therapy for children with cerebral palsy (CP). Regardless, little is understood about the underlying neurological subs…

Tensor-Train Networks for Learning Predictive Modeling of Multidimensional Data Open

Michele Nazareth da Costa, Romis Attux, Andrzej Cichocki, João Marcos Travassos Romano · 2021

In this work, we firstly apply the Train-Tensor (TT) networks to construct a compact representation of the classical Multilayer Perceptron, representing a reduction of up to 95% of the coefficients. A comparative analysis between tensor mo…

Cascade of Linear Predictors for Deconvolution of Non-Stationary Channels in Sparse and Antisparse Scenarios Open

Renan Brotto, Kenji Nose-Filho, Romis Attux, João Marcos Travassos Romano · 2021

This work deals with adaptive predictive deconvolution of non-stationary channels. In particular, we investigate the use of a cascade of linear predictors in the recovering of sparse and antisparse original signals. To do so, we first disc…

Author Index Open

Abdali Mohammadi, Abe Hiroki, Abe Kuniya, Abeyrathna Dilanga, Oscar Acosta , et al. · 2020

Frequency learning for image classification Open

José Augusto Stuchi, Levy Boccato, Romis Attux · 2020

Machine learning applied to computer vision and signal processing is achieving results comparable to the human brain on specific tasks due to the great improvements brought by the deep neural networks (DNN). The majority of state-of-the-ar…

A multi-objective-based approach for Fair Principal Component Analysis. Open

Guilherme Dean Pelegrina, Renan Brotto, Leonardo Tomazeli Duarte, João Marcos Travassos Romano, Romis Attux · 2020

In dimension reduction problems, the adopted technique may produce disparities between the representation errors of two or more different groups. For instance, in the projected space, a specific class can be better represented in compariso…

A novel multi-objective-based approach to analyze trade-offs in Fair Principal Component Analysis Open

Guilherme Dean Pelegrina, Renan Brotto, Leonardo Tomazeli Duarte, Romis Attux, João Marcos Travassos Romano · 2020

In dimension reduction problems, the adopted technique may produce disparities between the representation errors of two or more different groups. For instance, in the projected space, a specific class can be better represented in compariso…

Automatic Generation of Error Correcting Systems Based on Convolutional Codes Open

Lucas Muniz, Daniel F. Silva, Romis Attux, Denis G. Fantinato, Carla Négri Lintzmayer · 2020

Analysis of an Adversarial Approach to Blind Source Separation Open

Juan Manuel Espinoza Bullón, Romis Attux, Levy Boccato · 2020

Channel Equalization Based on Decision Trees Open

David Baptista, Rafael Ferrari, Romis Attux · 2020

This paper analyzes the application of decision trees to the problem of communication channel equalization. Decision trees are interesting structures because they are nonlinear and relatively simple from a computational standpoint. They ar…

Redes neurais profundas: implementação no contexto de reconhecimento de imagens Open

Pedro R. A. S. Bassi, Romis Attux · 2019

Este trabalho pertence à temática de interfaces cérebro-computador. Nele, utilizamos redes neurais profundas do tipo Triplet para analisar sinais de eletroencefalografia (EEG), os quais haviam sido convertidos em imagens através de transfo…

Implementation of a brain-computer interface based on motor imagery Open

Lucas Iijima, Romis Attux · 2019

In this work, a Brain-Computer Interface based on motor imagery was implemented focusing on the signal processing stage. Methods based on machine learning were used along with feature selection techniques on the treatment and classificatio…

Fundamentals of Deep Neural Networks - application based on MLPS Open

José Barretto, Romis Attux · 2019

Deep Neural Networks have become one of the center pieces in the field of machine learning, mostly due to accuracy, practical applications and efficiency. These algorithms have proven to be the state of the art in solving tasks such as reg…

Fundamentals of deep neural networks - application based in convolutional networks Open

Rafael Gonçalves, Romis Attux · 2019

Deep neural networks are becoming the state-of-the-art in the field of machine learning, especially because of their notable performance in a variety of current problems. Among these algorithms we can cite convolutional neural networks as …

Application of multi-objective optimization to blind source separation Open

Guilherme Dean Pelegrina, Romis Attux, Leonardo Tomazeli Duarte · 2019

Aplicações da desconvolução preditiva no processamento de dados sísmicos Open

Patricia Santana de Sousa, Rafael Ferrari, Romis Attux · 2019

Para se obter uma imagem da subsuperfície através da sísmica de reflexão, é necessário filtrar uma série de distorções e ruídos que estão presentes nos dados sísmicos adquiridos em campo. A desconvolução preditiva é uma técnica de filtrage…

Interface cérebro-computador baseada em SSVEP: fundamentos e análise de efeitos do ambiente Open

Júlio Pasinatto, Romis Attux, Thiago Bulhões da Silva Costa · 2019

Neste trabalho, um filtro de Wiener foi implementado e teve sua performance avaliada, explorando o conceito de cancelamento de ruído na etapa de pré-processamento de uma Interface Cérebro-Computador baseada em SSVEP.

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