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Detalhes

Detalhes

  • Nome

    Bruno Fonseca Coelho
  • Cargo

    Assistente de Investigação
  • Desde

    15 maio 2023
Publicações

2025

Information bottleneck with input sampling for attribution

Autores
Oliveira Coelho, BF; Cardoso, JS;

Publicação
Neurocomputing

Abstract
In order to facilitate the adoption of deep learning in areas where decisions are of critical importance, understanding the model's internal workings is paramount. Nevertheless, since most models are considered black boxes, this task is usually not trivial, especially when the user does not have access to the network's intermediate outputs. In this paper, we propose IBISA, a model-agnostic attribution method that reaches state-of-the-art performance by optimizing sampling masks using the Information Bottleneck Principle. Our method improves on the previously known RISE and IBA techniques by placing the bottleneck right after the image input without complex formulations to estimate the mutual information. The method also requires only twenty forward passes and ten backward passes through the network, which is significantly faster than RISE, which needs at least 4000 forward passes. We evaluated IBISA using a VGG-16 and a ResNET-50 model, showing that our method produces explanations comparable or superior to IBA, RISE, and Grad-CAM but much more efficiently. © 2025 The Authors

2025

Evaluation of cortical lateralization for identifying Parkinson’s disease patients using electroencephalographic signals and machine learning

Autores
Massaranduba, ABR; Coelho, BFO; Santos Souza, CAd; Viana, GG; Brys, I; Ramos, RP;

Publicação
Current Psychology

Abstract

2024

On the feasibility of Vis–NIR spectroscopy and machine learning for real time SARS-CoV-2 detection

Autores
Coelho, BFO; Nunes, SLP; de França, CA; Costa, DdS; do Carmo, RF; Prates, RM; Filho, EFS; Ramos, RP;

Publicação
Spectrochimica Acta Part A: Molecular and Biomolecular Spectroscopy

Abstract

2024

Feature Extraction from EEG signals for detection of Parkinsons Disease

Autores
Souza, C; Viana, G; Coelho, B; Massaranduba, AB; Ramos, R;

Publicação
Anais do XVI Congresso Brasileiro de Inteligência Computacional

Abstract
The Electroencephalogram (EEG) is a medical tool that captures, in a non-invasive way, electrical signals from the brain activities performed by neurons. EEG signals have been the target of study as a biomarker of Parkinsons disease (PD), where several methods of analysis are applied. The present work aims to evaluate features extracted from EEG signals, through methodologies such as HOS, Haralick descriptors, and Fractal Features, as new biomarkers for PD identification. Data from 50 individuals, available at the Open Neuro repository, who underwent an attentional cognitive task were analyzed. RF and SVM algorithms were employed for the classification of the extracted features. The best accuracy achieved was 79.49% in differentiating between Parkinsons subjects and control subjects using Haralick descriptors and RF classifier, suggesting that these features can identify activations in brain areas caused by dopaminergic medication.

2023

Discrete Representation of Photovoltaic Modules

Autores
Massaranduba, AB; Coelho, B; Machado, E; Silva, E; Pinto, A;

Publicação
IEEE Latin America Transactions

Abstract