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ORCID12th BRACIS 2023: Belo Horizonte, Brazil, - Part II
- Murilo Coelho Naldi
, Reinaldo A. C. Bianchi:
Intelligent Systems - 12th Brazilian Conference, BRACIS 2023, Belo Horizonte, Brazil, September 25-29, 2023, Proceedings, Part II. Lecture Notes in Computer Science 14196, Springer 2023, ISBN 978-3-031-45388-5
Transformer Applications
- Leticia da Silva Bomfim, Oton Cunha, Michelle Kuroda, Alexandre Campane Vidal, Hélio Pedrini:
Transformer Model for Fault Detection from Brazilian Pre-salt Seismic Data. 3-17 - Alexandre Gomes de Lima, José G. Moreno, Taoufiq Dkaki, Eduardo Henrique da S. Aranha, Mohand Boughanem:
Evaluating Recent Legal Rhetorical Role Labeling Approaches Supported by Transformer Encoders. 18-32 - Victor Hugo Braguim Canto, João Renato Ribeiro Manesco, Gustavo Botelho de Souza, Aparecido Nilceu Marana:
Dog Face Recognition Using Vision Transformer. 33-47
Convolutional Neural Networks
- Anisio P. Santos, Anage C. Mundim Filho, Robinson Sabino-Silva, Murillo G. Carneiro:
Convolutional Neural Networks for the Molecular Detection of COVID-19. 51-62 - João Pedro Oliveira Batisteli, Silvio Jamil Ferzoli Guimarães, Zenilton Kleber Gonçalves do Patrocínio Jr.:
Hierarchical Graph Convolutional Networks for Image Classification. 63-76 - Dieine Estela Bernieri Schiavon, Carla Diniz Lopes Becker, Viviane Rodrigues Botelho, Thatiane Alves Pianoski:
Interpreting Convolutional Neural Networks for Brain Tumor Classification: An Explainable Artificial Intelligence Approach. 77-91 - Filipe Ramos, Guilherme Silva, Eduardo Luz, Pedro Silva:
Enhancing Stock Market Predictions Through the Integration of Convolutional and Recursive LSTM Blocks: A Cross-market Analysis. 92-106 - Cicero L. Costa, Danielli A. Lima, Célia A. Zorzo Barcelos, Bruno A. N. Travençolo:
Ensemble Architectures and Efficient Fusion Techniques for Convolutional Neural Networks: An Analysis on Resource Optimization Strategies. 107-121
Deep Learning Applications
- João P. B. Andrade, Leonardo Ferreira da Costa, Lucas S. Fernandes, Paulo A. L. Rego, José G. R. Maia:
Dog Face Recognition Using Deep Features Embeddings. 125-139 - Diego Pinheiro da Silva, William da Rosa Fröhlich, Marco Antônio Schwertner, Sandro José Rigo:
Clinical Oncology Textual Notes Analysis Using Machine Learning and Deep Learning. 140-153 - Arthur Guilherme Santos Fernandes, Geraldo Braz Júnior, João Otávio Bandeira Diniz, Aristófanes Corrêa Silva, Caio Eduardo Falcõa Matos:
EfficientDeepLab for Automated Trachea Segmentation on Medical Images. 154-166 - Alison Corrêa Mendes, Alexandre César Pinto Pessoa, Anselmo Cardoso de Paiva:
Multi-label Classification of Pathologies in Chest Radiograph Images Using DenseNet. 167-180 - Bruno Machado Pacheco, Victor Hugo Rocha de Oliveira, Augusto Braga Fernandes Antunes, Saulo Domingos de Souza Pedro, Danilo Silva:
Does Pre-training on Brain-Related Tasks Results in Better Deep-Learning-Based Brain Age Biomarkers? 181-194
Reinforcement Learning and GAN
- André A. V. Escorel Ribeiro, Rodrigo Cesar Lira, Mariana Macedo, Hugo Valadares Siqueira, Carmelo J. A. Bastos Filho:
Applying Reinforcement Learning for Multiple Functions in Swarm Intelligence. 197-212 - Mauricio W. Barg, Barbara S. Rodrigues, Gabriela Teixeira Justino, Kleyton Pontes Cotta, Hugo R. V. Portuita, Flávio L. Loução Jr., Iran Pereira Abreu, Antonio C. P. Brasil Jr.:
Deep Reinforcement Learning for Voltage Control in Power Systems. 213-227 - Bruno Kemmer, Rodolfo Simões, Victor Ivamoto, Clodoaldo Ap. M. Lima:
Performance Analysis of Generative Adversarial Networks and Diffusion Models for Face Aging. 228-242 - Victor Ivamoto, Rodolfo Simões, Bruno Kemmer, Clodoaldo Ap. M. Lima:
Occluded Face In-painting Using Generative Adversarial Networks - A Review. 243-258
Classification
- Gabriel C. Michelassi, Henrique S. Bortoletti, Tuany D. Pinheiro, Thiago Nobayashi, Fabio R. D. de Barros, Rafael Luiz Testa, Andréia F. Silva, Mirian C. Revers, Joana Portolese, Hélio Pedrini, Helena Paula Brentani, Fátima L. S. Nunes, Ariane Machado-Lima:
Classification of Facial Images to Assist in the Diagnosis of Autism Spectrum Disorder: A Study on the Effect of Face Detection and Landmark Identification Algorithms. 261-275 - Rodney Renato de Souza Silva, Ricardo Cerri:
Constructive Machine Learning and Hierarchical Multi-label Classification for Molecules Design. 276-290 - Aline Marques Del Valle, Rafael Gomes Mantovani, Ricardo Cerri:
AutoMMLC: An Automated and Multi-objective Method for Multi-label Classification. 291-306 - Pedro Henrique Nascimento Castro, Gabriel Cássia Fortuna, Pedro Silva, Andrea G. C. Bianchi, Gladston Moreira, Eduardo José da S. Luz:
Merging Traditional Feature Extraction and Deep Learning for Enhanced Hop Variety Classification: A Comparative Study Using the UFOP-HVD Dataset. 307-322 - Mateus Roder, Leandro Aparecido Passos, João Paulo Papa, André Luis Debiaso Rossi:
Feature Selection and Hyperparameter Fine-Tuning in Artificial Neural Networks for Wood Quality Classification. 323-337 - Thiago M. Carvalho, Marley M. B. R. Vellasco, José F. M. do Amaral, Karla Figueiredo:
A Feature-Based Out-of-Distribution Detection Approach in Skin Lesion Classification. 338-352 - Maria Gabriela Valeriano, Pedro Yuri Arbs Paiva, Carlos Roberto Veiga Kiffer, Ana Carolina Lorena:
A Framework for Characterizing What Makes an Instance Hard to Classify. 353-367 - Lauro Moraes, Eduardo José da S. Luz, Gladston Moreira:
Physicochemical Properties for Promoter Classification. 368-382
Machine Learning Analysis
- Renata Pelissari, Betania S. C. Campello, Guilherme Dean Pelegrina, Ricardo Suyama, Leonardo Tomazeli Duarte:
Critical Analysis of AI Indicators in Terms of Weighting and Aggregation Approaches. 385-399 - Ricardo J. Pfitscher, Gabriel B. Rodenbusch, Anderson Dias, Paulo Vieira, Nuno M. M. D. Fouto:
Estimating Code Running Time Complexity with Machine Learning. 400-414 - Marcel Chacon Gonçalves, Rodrigo Silva:
The Effect of Statistical Hypothesis Testing on Machine Learning Model Selection. 415-427
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