Anais do Encontro Nacional de Inteligência Artificial e Computacional (ENIAC)
type: channel
description: O ENIAC é um fórum com periodicidade anual para pesquisadores, educadores, alunos discutirem ideias e experiências relacionadas à inteligência artificial e computacional. Particularmente, o ENIAC é ideal para alunos de graduação e pós-graduação terem sua primeira experiência com publicação e apresentação em uma conferência grande.
publisher: Sociedade Brasileira de Computação - SBC
language: pt-BR
publication name: Anais do Encontro Nacional de Inteligência Artificial e Computacional (ENIAC)
issn: 2763-9061
items: Prefácio e Organização
items: Analysis of Socioeconomic and Anthropometric Data via Tree-based Models: Evidence for Policies Against Hunger
items: Short text classification of social groups focused on courses
items: Enhancing ARIMA with Residual Kalman Filtering for Robust Time Series Prediction
items: A heuristic for analyzing interactions between protectors and street animals
items: Artificial Intelligence and Special Education: A Systematic Review of Technologies with an Emphasis on Computer Vision
items: A Comparative Analysis of Combination Operators in Heterogeneous Ensembles for One-Step-Ahead Time Series Forecasting
items: An empirical investigation into predicting the volume of water accumulation in dams that supply the Metropolitan Region of Recife
items: OPAL: An Ontology-Based Framework with LSTM and PPO for Return Forecasting and Portfolio Optimization
items: Quality Assessment of Photoplethysmography Signals For Cardiovascular Biomarkers Monitoring Using Wearable Devices
items: Sensitive Data Protection in Police Reports: De-identification Techniques and Applications in Machine Learning
items: Can Deep Learning Models Differentiate Atrial Fibrillation from Atrial Flutter?
items: Enhancing E-Commerce with a RAG-Powered Conversational Recommender System
items: Evaluating Machine Learning Algorithms for Software Effort Estimation from Textual Descriptions of Requirements
items: Estimating the Relevance of Judicial Precedents Through Jurimetrics and Complex Network Analysis
items: LSTM-Powered Failure Forecasting: Boosting Battery Production Efficiency with Machine Learning
items: Mapping Artificial Intelligence Legislation in Latin America
items: Comparative Analysis of Text Classification Algorithms
items: A Weight Monitoring Platform for Small Ruminants Using U-Net and EfficientNetB1
items: A system for monitoring depression symptoms from posts on social networks
items: Integrating Deep Learning: Humans and Machines Managing Financial Risk in Oil & Gas Industry
items: Accelerating RAG Systems: A Performance-Oriented Systematic Mapping
items: Reasoning with LLMs aided by Knowledge Bases and by Context
items: Towards statistical-based prediction models for seasonal precipitation in the southeast of Brazil
items: Classification of SARS-CoV-2 Variants from Amino Acid Substitution Embeddings Using Supervised Learning
items: Sonora: An Autonomous Analyst for Anti-Money Laundering Based on Explainable Artificial Intelligence
items: Enhancing Crime Forecasting with Hybrid Approaches: Integrating Machine Learning and Statistical Models
items: Parallel-Hierarchical Multi-Task Learning for Skin Lesion Classification with Multimodal Data
items: LLM-Based Intelligent Agents for Music Recommendation: A Comparison with Classical Content-Based Filtering
items: Evolutionary Control of Industrial Processes Using Digital Twin
items: LLM-Driven Intrinsic Motivation for Sparse Reward Reinforcement Learning
items: Exploring Multi-Task Learning for Fairness in Machine Learning Regression
items: Assessing the Role of Sensitive Attributes in Adversarial Debiasing
items: Evaluation of LLMs for Effective Recommendation of Poker Strategies
items: Comparing Structural Quality of Code Generated by LLMs: A Static Analysis of Code Smells
items: YOLO-Based Detection of Buy and Sell Signals in Candlestick Charts with Moving Averages
items: Investigating the Emergent Reasoning Abilities of Large Language Models in Sentiment Analysis of Codified Music
items: BERT vs. LLM2Vec: A Comparative Study of Embedding Models for Semantic Information Retrieval
items: Which Pretext Tasks Matter? A Comparative Study of Self-Supervised ECG-Based Emotion Recognition
items: A DBSCAN-Based Approach for Evaluating Protein Clusters Using Embeddings Generated from k-mer Images with Vision Transformer
items: Analysis of Visual Explainers Using Clustering Techniques
items: Unveiling Algorithmic Biases Through Personas: A Comparative Analysis of ENEM’s Essays on Gemini
items: Exploring Bias in Pre-Trained CNNs: Fairness Assessment in Sjögren’s Disease Detection
items: Beyond Random Sampling: Instance Quality-Based Data Partitioning via Item Response Theory
items: Trend-based Dynamic Selection of Machine Learning Models for Pandemic Time Series Forecasting
items: Enhancing Legal Question Answering in Brazilian Portuguese through Domain-Specific Embedding Models
items: SHAP-Driven Explicability of CNN-Based Computer-Aided Diagnosis for Malaria
items: Structuring Information from Initial Petitions Using LLMs: A Study in Brazilian Courts of Justice
items: Predictive Modeling of Real Estate Prices Using Machine Learning
items: Semantic Clustering in the Context of Legislative Amendments
items: Agents4Gov: Privacy-Preserving Web Agents Using Open LLMs for Public Sector Tasks
items: Do SLM agents screen papers as well as LLMs?
items: Effectiveness of different backbones for the DeepLabV3+ network applied to semantic segmentation of agricultural crops with limited image data
items: Towards a better classification of deepfake videos
items: Detection of texts generated by LLMs in Portuguese
items: Sampling battleships boards for hit estimation using Monte Carlo Tree Search
items: Scientific Event Recommendation through Semantic Clustering and Embedding Analysis
items: Reinforcement Learning on Mobile Devices: Context-Aware Configuration Control
items: Vision-Language Models for Automated Property Feature Extraction in Tax Assessment: A Case Study
items: Integrating Generative AI into Honeypots for Capturing Botnets
items: Semantic Clustering of Civic Proposals: A Case Study on Brazil’s National Participation Platform
items: Classifying BDD Software Tests Using Machine Learning and NLP Techniques
items: A Non-Dominated Sorting Evolutionary Algorithm Updating When Required
items: CordelSextilha.BR: A Benchmark for Poetic Form in Brazilian Cordel Verse Generation
items: Comparative Analysis of Deep Learning Architectures for Classifying Soy Seeds
items: Trends and Challenges of AI Integration in Basic Education: Insights from a Bibliometric Study
items: Modeling Uncertainty in Crime Underreporting: An Indicator Kriging Approach
items: Question Answering Techniques for Portuguese Legal Documents: A Systematic Literature Review
items: Joint Learning of Sparse Gaussian Processes and Gaussian Process Latent Variable Models for Semi-supervised Tasks
items: NFL Play Classification Using GNN and Player Tracking Data
items: Comparing Knowledge Injection Methods for LLMs in a Low-Resource Regime
items: An Approach to HLA Allele Imputation in Bone Marrow Donor Registries
items: Evaluation of a Hybrid Approach to Legal Entity Recognition

Prefácio e Organização
type: item
description: Este documento traz o Prefácio e Organização dos Anais do ENIAC 2025.
creator: Hélida Salles Santos
creator: Mariana Recamonde-Mendoza
rights: ##submission.copyrightStatement##
date: 2025-09-29
publication date: 2025-09-29
starting page: i
ending page: xiii

Analysis of Socioeconomic and Anthropometric Data via Tree-based Models: Evidence for Policies Against Hunger
type: item
description: A fome e a insegurança alimentar persistem como desafios tanto em escala regional quanto global, sendo agravados pela limitada disponibilidade e acessibilidade a dados sobre essas questões. No entanto, dados socioeconômicos a nível individual (ou de agregado familiar) estão disponíveis, e são coletados, em todo o Brasil. Nesse sentido, este trabalho propõe usar modelos de aprendizado de máquina capazes de predizer indicadores antropométricos ligados à fome, a partir de dados socioeconômicos. Os dados em questão foram extraídos automaticamente da plataforma CECAD (Consulta, Seleção e Extração de Informações do CadÚnico). Os indicadores antropométricos (baixo peso para altura, baixo peso para idade e baixa altura para idade) foram coletados no SISVAN (Sistema de Vigilância Alimentar e Nutricional). Os experimentos focaram-se em modelos baseados em árvore de decisão (Random Forest, Gradient Boosting, XGBoost, LightGBM e CatBoost). Na avaliação dos modelos, todos os municípios brasileiros foram usados para o treinamento, com a exceção daqueles do estado do Ceará, que foram separados para teste. Os melhores modelos obtiveram resultados promissores na tarefa de predição, especialmente para o indicador de baixa altura para a idade, em que o erro percentual chegou a 22%.
creator: João Gabriel Soares Ferreira
creator: César Lincoln Cavalcante Mattos
creator: Antonio Rafael Braga
creator: Danielo G. Gomes
rights: ##submission.copyrightStatement##
date: 2025-09-29
publication date: 2025-09-29
starting page: 1
ending page: 12
doi: 10.5753/eniac.2025.10488

Short text classification of social groups focused on courses
type: item
description: The ubiquitous possession of communication devices enhances a Social IoT whose most visible face is Social Networks. Social networks, also called relationship networks, are an important source for collecting unbiased feelings or mental states of groups of students in relation to a course in which they participate. These groups in social networks, often isolated and without the knowledge of the school administration, function as a discussion forum and the analysis of the texts posted there can enhance educational management with preventive measures to mitigate factors of course failure. This work develops the classification of sentiment in texts of groups of students with a view to detecting the need for intervention by educational management. The accuracy of the method is reinforced by a domain adaptation technique to take advantage of labeled data from other related domains. The results obtained are compatible with the state of the art of sentiment classification in other application domains.
creator: Antonio Leandro Martins Candido
creator: Jose Everardo Bessa Maia
rights: ##submission.copyrightStatement##
date: 2025-09-29
publication date: 2025-09-29
starting page: 13
ending page: 24
doi: 10.5753/eniac.2025.10530

Enhancing ARIMA with Residual Kalman Filtering for Robust Time Series Prediction
type: item
description: Este trabalho propõe o ARIMA+KF Residual, um modelo híbrido que preserva a transparência do ARIMA e corrige seu resíduo por meio de um Filtro de Kalman treinado por gradiente. Após a seleção automática de ordem pela metodologia Box–Jenkins, o erro é representado como estado latente; e o resíduo filtrado somado à previsão linear. Avaliado em quatro séries-teste (Airline, Colorado River, Sunspot e Lynx) com procedimento rolling one-step, o método reduz o MSE em até 84% em comparação com SVR, MLP, LSTM e modelos híbridos ARIMA–RNA/SVM, oferecendo alta precisão com baixo esforço de ajuste.
creator: Mouglas Eugênio Nasário Gomes
creator: Bárbara Silva Morais
creator: Paulo Salgado Gomes de-Mattos-Neto
rights: ##submission.copyrightStatement##
date: 2025-09-29
publication date: 2025-09-29
starting page: 25
ending page: 36
doi: 10.5753/eniac.2025.11774

A heuristic for analyzing interactions between protectors and street animals
type: item
description: This work proposes the construction of a cooperation network among animal welfare advocates and establishments involved in animal protection efforts. The network was modeled as a multi-layered architecture, considering interactions between individuals, establishments, and the animals assisted. The goal is to investigate the presence of communities and identify the most influential advocates in the provision of aid, using metrics such as modularity and degree centrality through the Louvain algorithm. Identifying these patterns aims to optimize the logistics and efficiency of support interactions, as well as to provide insights for planning preventive actions in public policies related to animal protection.
creator: Marcus Vinício de Oliveira Braga
creator: Lilian Berton
rights: ##submission.copyrightStatement##
date: 2025-09-29
publication date: 2025-09-29
starting page: 37
ending page: 48
doi: 10.5753/eniac.2025.11782

Artificial Intelligence and Special Education: A Systematic Review of Technologies with an Emphasis on Computer Vision
type: item
description: Este trabalho apresenta uma Revisão Sistemática da Literatura (RSL) sobre o uso de tecnologias assistivas baseadas em visão computacional com potencial de adaptação para a educação inclusiva. A investigação contempla publicações entre 2020 e 2025 e tem como objetivo identificar soluções voltadas ao público com deficiência e analisar suas possibilidades de integração em contextos educacionais. Foram analisados oito estudos que abordam sistemas de auxílio à mobilidade, reconhecimento de sinais visuais, descrição de imagens e comunicação assistiva. Os principais desafios envolvem limitações técnicas dos dispositivos, escassez de adaptações educacionais e ausência de formação docente específica. Apesar disso, os trabalhos revisados evidenciam o potencial da visão computacional para promover autonomia e acessibilidade, reforçando a necessidade de pesquisas futuras que explorem sua aplicação direta na prática pedagógica inclusiva.
creator: Lucas Taylor de Sousa Aires
creator: Jânio Cayo Borges Lima
creator: Rubens Fernandes Nunes
rights: ##submission.copyrightStatement##
date: 2025-09-29
publication date: 2025-09-29
starting page: 49
ending page: 60
doi: 10.5753/eniac.2025.11784

A Comparative Analysis of Combination Operators in Heterogeneous Ensembles for One-Step-Ahead Time Series Forecasting
type: item
description: This study investigates the efficacy of four combination operators in heterogeneous ensembles for one-step-ahead time series forecasting tasks: simple mean, median, stacking with Support Vector Regression (SVR) as a metamodel, and weighted average. Nine machine learning and statistical models were trained and subsequently their outcomes were averaged. The results show that the simple mean operator outperforms the median operator in terms of RMSE and SMAPE, yielding an average error of 15.888% and 17.791%, respectively, when compared to individual models and previous iterations of themselves. In contrast, the novel weighting average operator yielded the lowest average error values for both metrics, indicating potential for further enhancements and research. In addition to the increase in accuracy, the data analysis reveals that for monthly time series and within a forecasting framework, there is a tendency for the standard deviation to decrease as more models are incorporated.
creator: Rodolfo Viegas de Albuquerque
creator: João Fausto Lorenzato de Oliveira
rights: ##submission.copyrightStatement##
date: 2025-09-29
publication date: 2025-09-29
starting page: 61
ending page: 72
doi: 10.5753/eniac.2025.11788

An empirical investigation into predicting the volume of water accumulation in dams that supply the Metropolitan Region of Recife
type: item
description: This article reports an empirical investigation on the prediction of the volume of water accumulated in dams, comparing the performance of traditional statistical methods, machine learning algorithms, and dynamic and static ensemble approaches, applied to volume time series, seeking to understand if there is any model that stands out from the others. The study showed that LSTM networks, isolated or used in ensemble, seem to be a sufficiently robust model to reproduce time series with a hydrological context.
creator: Rodolfo Amorim C. da Silva
creator: Rodolfo Viegas de Albuquerque
creator: Milton Tavares de Melo Neto
creator: João Fausto Lorenzato de Oliveira
rights: ##submission.copyrightStatement##
date: 2025-09-29
publication date: 2025-09-29
starting page: 73
ending page: 84
doi: 10.5753/eniac.2025.11789

OPAL: An Ontology-Based Framework with LSTM and PPO for Return Forecasting and Portfolio Optimization
type: item
description: Traditional portfolio optimization struggles with market volatility and nonlinear dynamics. We propose OPAL, a hybrid framework integrating semantic reasoning and machine learning for financial decision-making. OPAL combines ontology-based asset selection, Markowitz diversification, LSTM return forecasting, and PPO-based allocation. Using S&P 500 data (2015–2023), OPAL achieves a 118.05% cumulative return, 2.58 Sharpe ratio, and 4.99 Sortino ratio, surpassing baselines. Its modular design ensures transparency and adaptability.
creator: Igor Felipe Carboni Battazza
creator: Cleyton Mário de Oliveira Rodrigues
creator: João Fausto Lorenzato de Oliveira
rights: ##submission.copyrightStatement##
date: 2025-09-29
publication date: 2025-09-29
starting page: 85
ending page: 93
doi: 10.5753/eniac.2025.11791

Quality Assessment of Photoplethysmography Signals For Cardiovascular Biomarkers Monitoring Using Wearable Devices
type: item
description: Photoplethysmography (PPG) is a non-invasive technique widely used to monitor cardiovascular parameters such as heart rate. However, its reliability can be compromised by factors like motion artifacts. In this study, we extracted 27 statistical features from PPG signals and trained multiple machine learning models (XGBoost, CatBoost, Random Forest) to assess signal quality. Using a publicly available dataset of PPG time series, we evaluated model performance using sensitivity, positive predictive value, and F1-score. Our best model (CatBoost) achieved 94.7%, 95.9%, and 95.3% for these metrics, respectively. These results are comparable to state-of-the-art approaches but relying on relatively simple models.
creator: Felipe M. Dias
creator: Marcelo A. F. Toledo
creator: Diego A. C. Cardenas
creator: Douglas A. Almeida
creator: Filipe A. C. Oliveira
creator: Estela Ribeiro
creator: Jose E. Krieger
creator: Marco Antonio Gutierrez
rights: ##submission.copyrightStatement##
date: 2025-09-29
publication date: 2025-09-29
starting page: 94
ending page: 104
doi: 10.5753/eniac.2025.11804

Sensitive Data Protection in Police Reports: De-identification Techniques and Applications in Machine Learning
type: item
description: Este trabalho propõe uma metodologia para identificação e desidentificação de dados sensíveis em boletins de ocorrência por meio de técnicas de reconhecimento de entidades nomeadas (NER). São comparados dois modelos: o BERTimbau, baseado em transformers e treinado em português brasileiro, e o BiLSTM, com arquitetura recorrente tradicional. Os resultados indicaram que o BERTimbau obteve desempenho superior em F1-score macro e maior eficácia na desidentificação, especialmente em entidades minoritárias. O estudo reforça a necessidade de modelos contextuais e métricas robustas para garantir a privacidade em dados de segurança pública.
creator: Victor Souza
creator: Adam Santos
creator: Reginaldo Filho
creator: Anderson Soares
rights: ##submission.copyrightStatement##
date: 2025-09-29
publication date: 2025-09-29
starting page: 105
ending page: 116
doi: 10.5753/eniac.2025.11805

Can Deep Learning Models Differentiate Atrial Fibrillation from Atrial Flutter?
type: item
description: Atrial Fibrillation (AFib) and Atrial Flutter (AFlut) are prevalent arrhythmias that present similar clinical features, challenging automated ECG differentiation. This study investigates the classification of AFib and AFlut using 12-lead ECGs from the CinC2021 dataset and a private dataset. We evaluated both 1D and 2D deep learning models. For 1D models, LiteVGG-11 demonstrated the highest performance, achieving an Acc of 77.91, AUROC of 87.17, and F1 score of 76.59. For 2D models, the EfficientNet-B2 outperformed other architectures, with an Acc of 75.20, AUROC of 85.50, and F1 of 71.59. Our results show that 1D models outperform 2D ones and that performance varies significantly across datasets, highlighting the difficulty in distinguishing AFib from AFlut.
creator: Estela Ribeiro
creator: Quenaz Bezerra Soares
creator: Felipe Meneguitti Dias
creator: Jose E. Krieger
creator: Marco Antonio Gutierrez
rights: ##submission.copyrightStatement##
date: 2025-09-29
publication date: 2025-09-29
starting page: 117
ending page: 128
doi: 10.5753/eniac.2025.11814

Enhancing E-Commerce with a RAG-Powered Conversational Recommender System
type: item
description: Conversational recommender systems have emerged as a promising approach to enhancing user experience in e-commerce by enabling interactive and personalized product discovery. This paper proposes a conversational recommender system for e-commerce that employs retrieval-augmented generation (RAG) to improve product recommendations based on natural language queries. Experiments were conducted using the ESCI-S dataset, an enriched version of the Amazon ESCI shopping queries dataset, to evaluate embedding models and large language models. The goal of this study is to assess the effectiveness of an RAG-based conversational recommender system and to identify optimal configurations for enhanced performance in e-commerce applications.
creator: Danilo Xavier Valle
creator: Hilário Tomaz Alves de Oliveira
rights: ##submission.copyrightStatement##
date: 2025-09-29
publication date: 2025-09-29
starting page: 129
ending page: 140
doi: 10.5753/eniac.2025.11835

Evaluating Machine Learning Algorithms for Software Effort Estimation from Textual Descriptions of Requirements
type: item
description: A estimativa de esforço no desenvolvimento de software é uma tarefa desafiadora, subjetiva, que exige considerável empenho, impactando diretamente o planejamento e a alocação de recursos. Este trabalho investigou a eficácia de diferentes algoritmos de Aprendizado de Máquina (AM) e arquiteturas de redes neurais profundas na estimativa automática do esforço de desenvolvimento a partir de descrições textuais de requisitos. Foram avaliados algoritmos tradicionais de AM usando TF-IDF, bem como modelos baseados no BERT, em seis conjuntos de dados públicos. Os experimentos analisaram o desempenho dos modelos em dois cenários: intra e inter conjuntos de dados, utilizando a métrica de erro absoluto médio. Os resultados experimentais indicaram que os modelos baseados no BERT obtiveram o melhor desempenho no cenário intra bases de dados, mas apresentaram um declínio mais acentuado no cenário inter bases de dados em comparação com os algoritmos tradicionais.
creator: Rodrigo De Nadai Grigoleto
creator: Hilário Tomaz Alves de Oliveira
rights: ##submission.copyrightStatement##
date: 2025-09-29
publication date: 2025-09-29
starting page: 141
ending page: 152
doi: 10.5753/eniac.2025.11851

Estimating the Relevance of Judicial Precedents Through Jurimetrics and Complex Network Analysis
type: item
description: Este artigo apresenta uma análise jurimétrica do sistema de precedentes no ordenamento jurídico brasileiro, utilizando métodos estatísticos e computacionais para compreender a dinâmica decisória dos tribunais superiores. A abordagem baseia-se na Jurimetria, campo interdisciplinar que quantifica dados jurídicos para identificar padrões e tendências decisórias. O estudo propõe a construção de uma rede de precedentes, onde cada decisão judicial é representada como um nó e as citações entre elas formam as arestas. Métricas de centralidade, detecção de comunidades e modelagem temporal são aplicadas para identificar precedentes relevantes, padrões de citação e mudanças na interpretação do Direito ao longo do tempo. Os resultados demonstram que a rede jurídica exibe características de redes scale-free, com poucos hubs altamente conectados que exercem influência desproporcional. A análise temporal revela a evolução da jurisprudência e a robustez da rede frente a perturbações. Conclui-se que a abordagem proposta oferece subsídios empíricos para a atuação de operadores do Direito e para o aprimoramento da gestão judicial.
creator: Juvenal Domingos Júnior
creator: João Roberto Bertini Junior
rights: ##submission.copyrightStatement##
date: 2025-09-29
publication date: 2025-09-29
starting page: 153
ending page: 164
doi: 10.5753/eniac.2025.11853

LSTM-Powered Failure Forecasting: Boosting Battery Production Efficiency with Machine Learning
type: item
description: The growing adoption of Artificial Intelligence and machine learning in the industry aims to optimize processes and support decisions. In the electronics sector, electrical tests on components such as battery boards generate vast volumes of data that are often underutilized, while recurrent failures increase costs and affect productivity. This work proposes a predictive model based on recurrent neural networks to predict failures in battery boards from the analysis of historical test data. In our methodology, we first removed outliers and missing values. Then, we developed two recurrent neural network models, LSTM and GRU, to evaluate which would perform better as a prediction model. The results demonstrated that the LSTM 1 and GRU 1 models effectively generated predictions. Failure predictions, such as the 11 identified in the subsequent five days for the Operating Current variable, provide valuable strategic insights. This approach enables managers to make more assertive decisions and implement preventive actions, such as the early replacement of consumables, in order to reduce input costs and minimize unplanned downtime, optimizing efficiency in manufacturing systems.
creator: Shinichi Ishii
creator: Ana Cláudia de Araújo Moxotó
creator: Edjard Mota
rights: ##submission.copyrightStatement##
date: 2025-09-29
publication date: 2025-09-29
starting page: 165
ending page: 176
doi: 10.5753/eniac.2025.11873

Mapping Artificial Intelligence Legislation in Latin America
type: item
description: O estudo mapeou propostas legislativas voltadas à regulamentação do desenvolvimento e uso da Inteligência Artificial (IA) na América Latina. Foram analisados 124 projetos de lei de dez países, classificados segundo sua natureza jurídica, grupos sociais protegidos e alinhamento com marcos internacionais. Os resultados apontam predominância de proteção a crianças e mulheres, presença expressiva de normas criminais e distribuição relativamente uniforme das regulatórias, embora com disparidades regionais. Em números absolutos, normas penais superam as regulatórias, seguidas pelas cíveis e governamentais, evidenciando a coexistência entre políticas punitivas e regulatórias. O Brasil lidera em incentivos fiscais, enquanto a proteção a idosos e trabalhadores permanece incipiente. Observou-se convergência terminológica com padrões internacionais, demandando estudos adicionais para verificar se tal alinhamento resulta de evolução interna ou do chamado “Efeito Bruxelas”.
creator: Frederico Thiers Dutra de Oliveira da Silva
creator: Ana Cristina Bicharra Garcia
rights: ##submission.copyrightStatement##
date: 2025-09-29
publication date: 2025-09-29
starting page: 177
ending page: 188
doi: 10.5753/eniac.2025.11878

Comparative Analysis of Text Classification Algorithms
type: item
description: This study presents a comparative analysis of the performance of the text classification task with Transformer-based models (BERT/BERTimbau) in contrast to traditional machine learning algorithms (Decision Tree, XGBoost, Naive Bayes, SVM, MLP) using two textual representations: dense embeddings and TF-IDF. The evaluation was conducted on 5 datasets, 3 binary and 2 multiclass, with texts in Portuguese and English. While Transformers consistently delivered the best overall performance, TF-IDF proved highly competitive–outperforming embeddings and even matching or surpassing BERT in specific cases.
creator: Beatriz Ribeiro Borges
creator: Elaine Ribeiro Faria
creator: Paulo H. R. Gabriel
rights: ##submission.copyrightStatement##
date: 2025-09-29
publication date: 2025-09-29
starting page: 189
ending page: 200
doi: 10.5753/eniac.2025.12276

A Weight Monitoring Platform for Small Ruminants Using U-Net and EfficientNetB1
type: item
description: A criação de caprinos e ovinos em pequenas fazendas no Brasil enfrenta desafios no monitoramento do peso dos animais, devido à ausência de balanças precisas, essenciais para o manejo nutricional e sanitário. Este estudo propõe um sistema web para estimativa automática e não invasiva do peso corporal de pequenos ruminantes. Desenvolvido com tecnologias de código aberto, como Django e SQLite, o sistema utiliza deep learning com U-Net para segmentação dos animais, EfficientNetB1 para extração de características e regressores para a predição de peso. Foram avaliadas 203 imagens de 42 animais, com resultados promissores: Dice de 97,7 na segmentação, MAE de 2,65, RMSE de 4,20 e R² de 0,81 na regressão. A solução apresenta desempenho competitivo e baixo custo, viabilizado pelo uso de câmera comum e software livre.
creator: Rafael L. Araújo
creator: Júlio V. M. Marques
creator: Anthony I. M. Luz
creator: Marina C. M. Santos
creator: João dos Santos Neto
creator: Alcilene D. de Sousa
creator: Frank C. L. Veras
creator: Antônio O. de Carvalho Filho
creator: Romuere R. V. e Silva
rights: ##submission.copyrightStatement##
date: 2025-09-29
publication date: 2025-09-29
starting page: 201
ending page: 212
doi: 10.5753/eniac.2025.12277

A system for monitoring depression symptoms from posts on social networks
type: item
description: Este trabalho apresenta o desenvolvimento de um sistema para monitoramento de sintomas depressivos a partir de publicações em redes sociais, com foco na plataforma Reddit. Por meio da combinação de técnicas de Processamento de Linguagem Natural e modelos de aprendizado de máquina, as publicações foram classificadas quanto ao potencial indício de depressão, sendo complementadas por análises léxicas e temáticas. A aplicação web desenvolvida permite visualização estatística e monitoramento em tempo real, com funcionalidades voltadas ao apoio de profissionais de saúde mental. Os resultados obtidos evidenciam uma correlação significativa entre o conteúdo das comunidades analisadas e a presença de sinais depressivos, sugerindo o potencial do sistema como ferramenta auxiliar na triagem precoce de sintomas. Questões éticas relacionadas à privacidade e à acurácia das classificações também são discutidas.
creator: Rodrigo Carvalho
creator: Matheus Yasuo Ribeiro Utino
creator: Paulo Mann
creator: Rodrigo Salvador Monteiro
rights: ##submission.copyrightStatement##
date: 2025-09-29
publication date: 2025-09-29
starting page: 213
ending page: 224
doi: 10.5753/eniac.2025.12291

Integrating Deep Learning: Humans and Machines Managing Financial Risk in Oil & Gas Industry
type: item
description: Financial decision-making in large industries is increasingly complex due to market volatility and high capital exposure, requiring intelligent tools for strategic planning. This study proposes a Deep Learning framework integrated with Human-Computer Interaction (HCI) techniques to forecast oil prices and mitigate financial risk. Four models were evaluated with hyperparameter optimization on real-world datasets. A key contribution is the incorporation of graphical result visualization, enhancing user interpretation and decision-making. The findings underscore the value of AI-driven analytics and interactive interfaces in developing transparent financial decision support systems.
creator: Aguinaldo Júnio Flor
creator: Adiel T. de Almeida Filho
creator: Luiz Antônio de Almeida Neto
rights: ##submission.copyrightStatement##
date: 2025-09-29
publication date: 2025-09-29
starting page: 225
ending page: 236
doi: 10.5753/eniac.2025.12299

Accelerating RAG Systems: A Performance-Oriented Systematic Mapping
type: item
description: Optimizing latency and inference efficiency has become critical for the deployment of Retrieval-Augmented Generation (RAG) systems in production environments. While recent methods have explored GPU acceleration, keyvalue (KV) caching, and hierarchical indexing, their impact remains fragmented across studies. This paper presents a performance-oriented systematic mapping of optimization techniques targeting time-to-first-token (TTFT), latency, and caching efficiency metrics in RAG pipelines. Following the Kitchenham protocol and operationalized through the Parsifal platform, 34 studies were selected from an initial pool of 147. The analysis reveals growing research focus on prefix-aware KV reuse, asynchronous retrieval, and GPU-accelerated decoding, but also highlights gaps in unified evaluation and multilingual scalability. The findings provide a consolidated view of current strategies and identify research directions for scalable, latency-sensitive RAG systems.
creator: João Gabriel J. da Silva
creator: Sávio S. T. de Oliveira
creator: Arlindo R. Galvão Filho
rights: ##submission.copyrightStatement##
date: 2025-09-29
publication date: 2025-09-29
starting page: 237
ending page: 248
doi: 10.5753/eniac.2025.12304

Reasoning with LLMs aided by Knowledge Bases and by Context
type: item
description: Grandes Modelos de Linguagem (GML) podem exibir habilidades de raciocínio surpreendentes através da Chain-of-Thought e técnicas similares. Entretanto, sua dificuldade com alucinações, e a natureza opaca das suas operações internas, são desvantagens importantes. Este trabalho apresenta uma proposta para o raciocínio que emprega um solver lógico auxiliado por GMLs. Nós misturamos raciocínio simbólico com GMLs como apresentado em trabalhos anteriores, e melhoramos as habilidades de raciocínio com a busca por predicados lógicos com o contexto do fluxo lógico de uma base de conhecimento.
creator: Leonardo Riccioppo Garcez
creator: Fabio Gagliardi Cozman
rights: ##submission.copyrightStatement##
date: 2025-09-29
publication date: 2025-09-29
starting page: 249
ending page: 260
doi: 10.5753/eniac.2025.12362

Towards statistical-based prediction models for seasonal precipitation in the southeast of Brazil
type: item
description: Este estudo avalia a previsão de precipitação em 12 pontos de monitoramento no sudeste do Brasil, utilizando cinco modelos estatísticos (MLR, ARIMA, SARIMA, SARIMAX e VARMAX). A acurácia das previsões foi avaliada em dois momentos (M1 e M2), utilizando o RMSE. O modelo MLR apresentou o melhor desempenho no curto prazo (M1) em metade das localidades (50%), enquanto o SARIMAX foi superior em quatro delas (33%). No longo prazo (M2), o VARMAX superou os demais modelos em sete localidades (58%), evidenciando sua capacidade de capturar dinâmicas multivariadas. Os resultados destacam a importância dos modelos estatísticos para a previsão meteorológica local e o planejamento de infraestrutura.
creator: Helder Arruda
creator: Anita Drumond
creator: Ewerton Oliveira
creator: Julio Freitas
creator: Rafael Rocha
creator: Nikolas Carneiro
creator: Renata Tedeschi
creator: Ronnie Alves
creator: Sergio Viademonte
creator: Eduardo Carvalho
rights: ##submission.copyrightStatement##
date: 2025-09-29
publication date: 2025-09-29
starting page: 261
ending page: 272
doi: 10.5753/eniac.2025.12385

Classification of SARS-CoV-2 Variants from Amino Acid Substitution Embeddings Using Supervised Learning
type: item
description: Este trabalho propõe uma abordagem supervisionada para a classificação de variantes do SARS-CoV-2 (VOCs) a partir de embeddings vetoriais derivados de substituições de aminoácidos. Utilizando o modelo pré-treinado all-MiniLM-L6-v2, foram gerados vetores de alta dimensionalidade que codificam mutações sem necessidade de alinhamento genômico. Esses embeddings alimentaram classificadores como SVM, Random Forest, XGBoost e k-NN, avaliados por validação cruzada e em um teste externo com quase 288 mil amostras. O XGBoost obteve os melhores resultados, com acurácia de 99,83%, F1 macro de 99,83% e logLoss de 0,0068, mantendo desempenho elevado mesmo em dados não vistos. Os resultados evidenciam que modelos baseados em árvores superam alternativas como SVM, especialmente na variante Gamma. A proposta se mostra robusta, precisa e escalável para aplicação em sistemas automatizados de vigilância genômica, fornecendo uma segunda perspectiva de análise complementar à abordagem clínica e laboratorial, e superando métodos tradicionais e híbridas da literatura recente.
creator: Alessandro S. Silva
creator: Karolayne S. Azevedo
creator: Marcelo A. C. Fernandes
rights: ##submission.copyrightStatement##
date: 2025-09-29
publication date: 2025-09-29
starting page: 273
ending page: 284
doi: 10.5753/eniac.2025.12390

Sonora: An Autonomous Analyst for Anti-Money Laundering Based on Explainable Artificial Intelligence
type: item
description: This paper presents the development and evaluation of Sonora, an autonomous analyst based on explainable artificial intelligence (XAI) designed to support Anti-Money Laundering (AML) monitoring in financial institutions. Using real-world, imbalanced data from a Brazilian financial institution, we developed a predictive model to identify suspicious cases. A multi-stage evaluation process began with the selection of Gradient Boosting as a robust baseline model over other standard classifiers. This model was then benchmarked against an alternative model, XGBoost, selected from a pool of state-of-the-art boosting algorithms. After hyperparameter optimization for both finalists, a threshold-based analysis confirmed that Gradient Boosting delivered the best performance for a recall-focused strategy, achieving a recall of approximately 96%. We deliberately focus on recall rather than accuracy to minimize regulatory risk and ensure that critical cases are surfaced for human review. To foster institutional trust, Sonora integrates instance-level SHAP explanations, highlighting the most influential features in each classification, and it acts as a decision-support tool, not a replacement for human analysts.
creator: Vlademir Lenin Donato Batista
creator: Paulo André Lima de Castro
rights: ##submission.copyrightStatement##
date: 2025-09-29
publication date: 2025-09-29
starting page: 285
ending page: 296
doi: 10.5753/eniac.2025.12402

Enhancing Crime Forecasting with Hybrid Approaches: Integrating Machine Learning and Statistical Models
type: item
description: A previsão de crimes representa um desafio significativo para a segurança pública. Este artigo investiga o uso de sistemas híbridos para previsão de séries temporais de crimes, utilizando dados de diferentes estados brasileiros. A proposta consiste em combinar modelos de Aprendizado de Máquina (AM) para capturar padrões não lineares das séries temporais, seguidos pela aplicação do modelo estatístico Autoregressive Integrated Moving Average (ARIMA) sobre os resíduos de previsão, visando modelar padrões lineares remanescentes. Para avaliação, as versões híbridas propostas utilizaram os modelos de AM: Long Short-Term Memory (LSTM), Multilayer Perceptron (MLP) e Support Vector Regression (SVR). As versões da abordagem proposta (LSTM+ARIMA, MLP+ARIMA e SVR+ARIMA) foram comparadas com modelos individuais e híbridos tradicionais da literatura. Os resultados demonstram que as abordagens híbridas propostas apresentam desempenho superior, especialmente em cenários com alta complexidade e predominância de padrões não lineares, reforçando a importância de estratégias que integrem diferentes paradigmas de modelagem. O estudo avança o campo da previsão de crimes ao demonstrar a eficácia da abordagem híbrida proposta. Os resultados reforçam o potencial dos sistemas híbridos como uma base robusta e inovadora para sistemas de apoio à decisão em segurança pública, demonstrando que a integração de diferentes paradigmas de modelagem pode oferecer ganhos substanciais em precisão e utilidade prática.
creator: Paulo Cauas
creator: Ruam Pastor
creator: Paulo S. G. de Mattos Neto
creator: Filipe C. de L. Duarte
rights: ##submission.copyrightStatement##
date: 2025-09-29
publication date: 2025-09-29
starting page: 297
ending page: 308
doi: 10.5753/eniac.2025.12415

Parallel-Hierarchical Multi-Task Learning for Skin Lesion Classification with Multimodal Data
type: item
description: This paper introduces a novel approach for skin cancer detection that leverages both smartphone-captured images and clinical data. The proposed method utilizes a parallel-hierarchical multi-task learning framework to classify skin lesion types and assess malignancy simultaneously. Compared to existing methods reporting false positive rates around 3.6% in the Melanoma Skin Cancer dataset, our approach achieves a false positive rate of 8.2% while maintaining a high true positive rate of 94.03% for malignant lesion detection. These results demonstrate the potential of the approach to enhance diagnostic accuracy and support more effective treatment decisions in teledermatology settings.
creator: Camila Alves Dias
creator: Thiago Sotoriva Lermen
creator: Rômulo Marconato Stringhini
creator: Cristiano Andre da Costa
creator: Cláudio Rosito Jung
creator: Manuela M. Costa
creator: Dimitris V. Rados
creator: Fabiana Carvalho
creator: Marcelo R. Gonçalves
rights: ##submission.copyrightStatement##
date: 2025-09-29
publication date: 2025-09-29
starting page: 309
ending page: 320
doi: 10.5753/eniac.2025.12418

LLM-Based Intelligent Agents for Music Recommendation: A Comparison with Classical Content-Based Filtering
type: item
description: A crescente oferta de músicas nas plataformas de streaming tem causado sobrecarga de informação aos usuários. Para mitigar esse problema e aprimorar a experiência, sistemas de recomendação mais sofisticados têm sido propostos. Este trabalho investiga o uso de Modelos de Linguagem de Grande Escala (LLMs) das famílias Gemini e LLaMA, aliados a agentes inteligentes, em um sistema de recomendação musical personalizado multiagente. Os resultados são comparados a um modelo tradicional baseado em conteúdo, considerando satisfação do usuário, novidade e eficiência computacional. Os LLMs atingiram até 89,32% de satisfação, indicando seu potencial promissor nos sistemas de recomendação de músicas.
creator: Ronald Carvalho Boadana
creator: Ademir Guimarães da Costa Junior
creator: Ricardo Rios
creator: Fábio Santos da Silva
rights: ##submission.copyrightStatement##
date: 2025-09-29
publication date: 2025-09-29
starting page: 321
ending page: 332
doi: 10.5753/eniac.2025.12422

Evolutionary Control of Industrial Processes Using Digital Twin
type: item
description: A crescente variabilidade nos processos industriais modernos exige sistemas de controle mais flexíveis, capazes de se adaptar sem reprogramações manuais tradicionalmente empregadas nesses casos. Este trabalho objetiva mostrar que é possível complementar o controle industrial com uma camada adaptativa que responda dinamicamente a novos cenários, sem necessidade de reconfiguração. Para isso, investiga a aplicação do algoritmo NEAT para treinar um modelo de inteligência artificial capaz de aprender e ajustar-se autonomamente a mudanças no ambiente produtivo, aqui modelado por meio de um gêmeo digital. Os resultados obtidos indicam que tal abordagem pode alcançar desempenho robusto frente a diferentes configurações de processo, abrindo caminho para sistemas de automação mais resilientes e autônomos.
creator: Renato Bruno Silva
creator: Daniela Resende Silva Orbolato
rights: ##submission.copyrightStatement##
date: 2025-09-29
publication date: 2025-09-29
starting page: 333
ending page: 344
doi: 10.5753/eniac.2025.12423

LLM-Driven Intrinsic Motivation for Sparse Reward Reinforcement Learning
type: item
description: This paper explores the combination of two intrinsic motivation strategies to improve the efficiency of reinforcement learning (RL) agents in environments with extreme sparse rewards, where traditional learning struggles due to infrequent positive feedback. We propose integrating Variational State as Intrinsic Reward (VSIMR), which uses Variational AutoEncoders (VAEs) to reward state novelty, with an intrinsic reward approach derived from Large Language Models (LLMs). The LLMs leverage their pre-trained knowledge to generate reward signals based on environment and goal descriptions, guiding the agent. We implemented this combined approach with an Actor-Critic (A2C) agent in the MiniGrid DoorKey environment, a benchmark for sparse rewards. Our empirical results show that this combined strategy significantly increases agent performance and sampling efficiency compared to using each strategy individually or a standard A2C agent, which failed to learn. Analysis of learning curves indicates that the combination effectively complements different aspects of the environment and task: VSIMR drives exploration of new states, while the LLM-derived rewards facilitate progressive exploitation towards goals.
creator: André Quadros
creator: Cassio Silva
creator: Ronnie Alves
rights: ##submission.copyrightStatement##
date: 2025-09-29
publication date: 2025-09-29
starting page: 345
ending page: 355
doi: 10.5753/eniac.2025.12425

Exploring Multi-Task Learning for Fairness in Machine Learning Regression
type: item
description: Ensuring fairness in machine learning is a critical concern for high-stakes domains, yet most fairness-aware Multi-Task Learning (MTL) frameworks overlook regression problems in favor of classification. This work extends these techniques to regression, proposing a novel MTL framework that optimizes for equitable continuous outcomes across demographic subgroups. Our method dynamically reweights task-specific gradients during training to reduce disparities without compromising predictive accuracy. Evaluated on two real-world datasets, our approach, in the best scenarios, reduces subgroup disparity by up to 94.9% while also improving overall regression performance by up to 32.6%. These findings highlight the significant potential of fairness-aware MTL for creating more inclusive and responsible machine learning applications in sensitive domains.
creator: Bruno Pires
creator: Luiz Leduino
creator: Lilian Berton
rights: ##submission.copyrightStatement##
date: 2025-09-29
publication date: 2025-09-29
starting page: 356
ending page: 367
doi: 10.5753/eniac.2025.12440

Assessing the Role of Sensitive Attributes in Adversarial Debiasing
type: item
description: Fairness in machine learning refers to the development of models that do not systematically disadvantage individuals or groups based on sensitive attributes. One commonly adopted principle is fairness through unawareness, which holds that models should not explicitly incorporate sensitive attributes into the decision-making process. However, in some scenarios, such as healthcare, variables like sex and age are essential for accurate diagnoses and prognoses. Previous studies have assessed the influence of including or excluding such information during model training. Nonetheless, they have not considered Adversarial Debiasing, a classification algorithm specifically designed to promote equitable results. To address this gap, we propose a comprehensive empirical analysis to investigate the role of sensitive attributes in this algorithm. We experimentally evaluated Adversarial Debiasing across 20 settings using 23 datasets from varying domains, one predictive performance metric, three group fairness metrics, and a non-parametric statistical test. Our findings indicate that classifiers trained without including sensitive information in the input feature set produce more precise and fairer outcomes.
creator: Diego Minatel
creator: Antonio R. S. Parmezan
creator: Vinicius M. A. Souza
creator: Solange O. Rezende
rights: ##submission.copyrightStatement##
date: 2025-09-29
publication date: 2025-09-29
starting page: 368
ending page: 378
doi: 10.5753/eniac.2025.12452

Evaluation of LLMs for Effective Recommendation of Poker Strategies
type: item
description: Este trabalho avalia LLMs na recomendação de ações no pré-flop do pôquer NLHE 6-max. Usamos 1000 mãos rotuladas por estratégias GTO para testar os modelos abertos LLaMA 3.2-3B e Qwen 3-4B em modo zero-shot. Ambos alcançaram 67,7% de acurácia sem fine-tuning, revelando que LLMs generalistas já capturam padrões táticos relevantes. Os resultados destacam seu potencial como ferramentas assistivas. Como trabalhos futuros, propõe-se avaliar fases pós-flop, aplicar fine-tuning supervisionado e explorar usos didáticos interativos.
creator: Lucas de Oliveira Diniz
creator: Murillo Guimarães Carneiro
rights: ##submission.copyrightStatement##
date: 2025-09-29
publication date: 2025-09-29
starting page: 379
ending page: 390
doi: 10.5753/eniac.2025.12461

Comparing Structural Quality of Code Generated by LLMs: A Static Analysis of Code Smells
type: item
description: The automation of code generation through Large Language Models (LLMs) has emerged as a promising approach to support software development. However, concerns remain regarding the structural quality of the code produced, particularly the presence of code smells that affect maintainability. This paper presents an empirical study comparing code smells in outputs from four major LLMs: ChatGPT, DeepSeek, Amazon CodeWhisperer, and GitHub Copilot. Our analysis of 64 code units, generated from open-source projects, revealed that 60.9% contained at least one code smell. The results show significant variation, with DeepSeek having the lowest incidence of smells (43.8%) and Amazon CodeWhisperer the highest (68.8%). Long Method was the most frequent smell, constituting 40% of all occurrences. These findings provide empirical evidence of the structural quality differences in LLM-generated code and highlight the need for rigorous, automated quality assurance.
creator: Eduardo Sousa
creator: Erlene Santos
creator: Alberto Sampaio
creator: Carla Bezerra
rights: ##submission.copyrightStatement##
date: 2025-09-29
publication date: 2025-09-29
starting page: 391
ending page: 402
doi: 10.5753/eniac.2025.12470

YOLO-Based Detection of Buy and Sell Signals in Candlestick Charts with Moving Averages
type: item
description: In stock trading, identifying trends is essential. Candlestick charts show price variations while moving averages help smooth these fluctuations and highlight trends. Some works focus on event detection in candlestick charts but without using moving averages. In this study, we investigate whether incorporating semantic information from moving averages into candlestick chart analysis can enhance the detection of buy and sell signals. The proposed approach includes image generation, data augmentation, labeling, and signal identification in short-term trading scenarios. We conducted experiments using four versions of YOLO (v3, v8, v9, and v11). Compared to previous work that did not use moving averages, our results were significantly better regarding F1-Score (up to 0.06 higher) and recall (up to 0.18 higher).
creator: Marco Antonio Sousa Santos
creator: Adriano Rivolli da Silva
creator: André Roberto Ortoncelli
rights: ##submission.copyrightStatement##
date: 2025-09-29
publication date: 2025-09-29
starting page: 403
ending page: 414
doi: 10.5753/eniac.2025.12471

Investigating the Emergent Reasoning Abilities of Large Language Models in Sentiment Analysis of Codified Music
type: item
description: The perception of positive or negative sentiment in a piece of music is influenced by musical features such as tempo, notes, chords, and rhythms, collectively shaping its emotional character. This paper examines the ability of Large Language Models (LLMs) to account for these features and infer sentiment from symbolically encoded music, exploring both zero-shot and fine-tuned approaches. Our results show that while LLMs exhibit some capability in processing symbolic representation of musical elements, their ability to associate music with sentiment reliably remains limited. The models struggle to align their predictions with human-assigned labels, with accuracy hovering around 0.6. These results suggest that current text-based approaches may not fully capture the complex interplay between musical structure and emotional expression.
creator: Gabriel Assis
creator: Laura Alvarenga
creator: João Vitor de Moraes
creator: Lívia de Azevedo
creator: Aline Paes
rights: ##submission.copyrightStatement##
date: 2025-09-29
publication date: 2025-09-29
starting page: 415
ending page: 426
doi: 10.5753/eniac.2025.12548

BERT vs. LLM2Vec: A Comparative Study of Embedding Models for Semantic Information Retrieval
type: item
description: Semantic-based Information Retrieval (IR) has significantly benefited from advances in language models and embedding techniques. This work investigates the impact of different embedding strategies on the effectiveness of semantic retrieval, using 1-NN classification and F1-score as the evaluation metric. We evaluate two model families: BERT variants and the novel LLM2Vec approach. Experiments conducted on six diverse datasets show that LLM2Vec models consistently outperform BERT-based ones across all metrics, with the Mistral-7B-Instruct-v2 model in its unsupervised configuration achieving the highest scores. Additionally, we demonstrate that LLM2Vec performance is robust to prompt variations, highlighting its practical applicability in IR systems.
creator: Matheus Yasuo Ribeiro Utino
creator: Ricardo Marcondes Marcacini
rights: ##submission.copyrightStatement##
date: 2025-09-29
publication date: 2025-09-29
starting page: 427
ending page: 438
doi: 10.5753/eniac.2025.13224

Which Pretext Tasks Matter? A Comparative Study of Self-Supervised ECG-Based Emotion Recognition
type: item
description: A necessidade de grandes volumes de dados rotulados limita a escalabilidade de sistemas automáticos de reconhecimento de emoções baseados em biossinais. Este estudo propõe uma abordagem multitarefa de aprendizado auto-supervisionado (SSL), aplicada a sinais de eletrocardiograma, visando reduzir a dependência de anotações manuais extensivas. Uma rede convolucional foi pré-treinada para discriminar simultaneamente seis transformações sintéticas, sendo posteriormente ajustada via fine-tuning supervisionado com frações reduzidas (5%, 25%, 50%) de rótulos em quatro bases de dados públicas. Os resultados mostram que a combinação de quatro ou cinco tarefas auxiliares é suficiente para gerar representações eficazes, com ganhos de desempenho de até 19 pontos percentuais na classificação de valência, excitação e estresse. Com apenas 25 % de rótulos, o modelo atinge desempenho próximo ao máximo, evidenciando a viabilidade da proposta em cenários com rotulagem limitada.
creator: Kevin Gustavo Montero Quispe
creator: Daniel Mitsuaki da Silva Utyiama
creator: Eduardo James Pereira Souto
rights: ##submission.copyrightStatement##
date: 2025-09-29
publication date: 2025-09-29
starting page: 439
ending page: 450
doi: 10.5753/eniac.2025.13623

A DBSCAN-Based Approach for Evaluating Protein Clusters Using Embeddings Generated from k-mer Images with Vision Transformer
type: item
description: Este trabalho apresenta uma abordagem para análise de agrupamentos de proteínas a partir de embeddings vetoriais gerados por imagens de kmers processadas por um modelo Vision Transformer (ViT). A abordagem, independente de alinhamento, permite o uso de métodos de agrupamento baseados em densidade, como o DBSCAN, aplicado aos conjuntos UniRef100 e UniRef90. Foram propostas duas métricas: contaminação, que mede a pureza dos clusters em relação aos rótulos originais, e espalhamento, que quantifica a fragmentação de um rótulo em múltiplos grupos. Os resultados mostram que o UniRef100 apresentou baixa contaminação, enquanto o UniRef90 apresentou maior dispersão. A metodologia possibilita identificar subestruturas e divisões internas em rótulos e fornecer medidas para curadoria, refinamento e anotação funcional de bases biológicas.
creator: Giovanna A. P. Soares
creator: Hannah I. S. Marques
creator: Raquel de M. Barbosa
creator: Marcelo A. C. Fernandes
rights: ##submission.copyrightStatement##
date: 2025-09-29
publication date: 2025-09-29
starting page: 451
ending page: 462
doi: 10.5753/eniac.2025.13638

Analysis of Visual Explainers Using Clustering Techniques
type: item
description: A Inteligência Artificial (IA) tem se tornado cada vez mais presente no cotidiano, ampliando a demanda por métodos que expliquem as decisões tomadas por seus modelos — o que se convencionou chamar de Explainable Artificial Intelligence (XAI). No campo da explicabilidade visual, diversas técnicas e métricas vêm sendo propostas, no entanto, ainda não há consenso sobre como avaliá-las de forma comparativa e confiável. Este trabalho propõe uma nova metodologia para análise de comportamento de explicadores, com base em técnicas de agrupamento (clustering), permitindo avaliar múltiplos explicadores e métricas simultaneamente. Os resultados indicam que o comportamento entre explicadores varia conforme o conjunto de dados e o modelo de explicação utilizado.
creator: Lázaro Raimundo de Oliveira
creator: João Carlos Xavier Júnior
creator: Anne M. P. Canuto
rights: ##submission.copyrightStatement##
date: 2025-09-29
publication date: 2025-09-29
starting page: 463
ending page: 474
doi: 10.5753/eniac.2025.13657

Unveiling Algorithmic Biases Through Personas: A Comparative Analysis of ENEM’s Essays on Gemini
type: item
description: The advancement of generative Artificial Intelligence (AI) models raises questions about their objectivity in evaluation tasks. This study investigates whether AI can assess essays in Portuguese impartially and how assigning author personas affects their evaluations. Using a comparative methodology, 801 essays were evaluated by a human and by the Gemini AI using anonymously and with five different personas. The AI demonstrated bias, assigning higher scores to personas perceived as more experienced and lower scores to less experienced ones. The research concludes that AI systems can amplify demographic biases, highlighting the risks of their application in educational contexts and the need to develop audit mechanisms to ensure equity.
creator: Luana B. Mendes
creator: Keslley W. C. Guimarães
creator: Igor C. Valenciano
creator: Bianca Cristina O. do E. S. Silva
creator: Patricia de Souza
creator: Thiago M. Ventura
creator: Allan G. de Oliveira
rights: ##submission.copyrightStatement##
date: 2025-09-29
publication date: 2025-09-29
starting page: 475
ending page: 486
doi: 10.5753/eniac.2025.13714

Exploring Bias in Pre-Trained CNNs: Fairness Assessment in Sjögren’s Disease Detection
type: item
description: Sjögren’s disease is an autoimmune disorder that primarily affects moisture-producing glands, leading to symptoms such as dry eyes, dry mouth, and systemic complications. Accurate diagnosis remains a challenge, particularly in medical imaging applications. In this study, we employ various Convolutional Neural Networks (CNNs) for Sjögren’s disease detection, using a dataset with inherent biases. The goal is to analyze the impact of transfer learning on fairness, investigating whether pre-trained models amplify or mitigate biases in medical image classification. We compare different CNN architectures and evaluate fairness metrics to assess performance disparities across demographic subgroups. The findings contribute to the development of bias-aware AI models, ensuring more equitable and reliable deep learning applications in health.
creator: Bruna Ferreira dos Santos
creator: Lilian Berton
rights: ##submission.copyrightStatement##
date: 2025-09-29
publication date: 2025-09-29
starting page: 487
ending page: 498
doi: 10.5753/eniac.2025.13767

Beyond Random Sampling: Instance Quality-Based Data Partitioning via Item Response Theory
type: item
description: Robust validation of Machine Learning (ML) models is essential, but traditional data partitioning approaches often ignore the intrinsic quality of each instance. This study proposes the use of Item Response Theory (IRT) parameters to characterize and guide the partitioning of datasets in the model validation stage. The impact of IRT-informed partitioning strategies on the performance of several ML models in four tabular datasets was evaluated. The results obtained demonstrate that IRT reveals an inherent heterogeneity of the instances and highlights the existence of informative subgroups of instances within the same dataset. Based on IRT, balanced partitions were created that consistently help to better understand the tradeoff between bias and variance of the models. In addition, the guessing parameter proved to be a determining factor: training with high-guessing instances can significantly impair model performance and resulted in cases with accuracy below 50%, while other partitions reached more than 70% in the same dataset.
creator: Lucas Cardoso
creator: Vitor Santos
creator: Ricardo Prudêncio
creator: José Ribeiro
creator: Regiane Kawasaki
creator: Ronnie Alves
rights: ##submission.copyrightStatement##
date: 2025-09-29
publication date: 2025-09-29
starting page: 499
ending page: 510
doi: 10.5753/eniac.2025.13812

Trend-based Dynamic Selection of Machine Learning Models for Pandemic Time Series Forecasting
type: item
description: Pandemic time series forecasting is challenging due to rapidly changing patterns, which general-purpose predictors often fail to capture. Recent studies suggest that specialized models are better suited for such complexity, especially when trend changes are key. This work proposes a forecasting approach that dynamically selects specialized Support Vector Regression (SVR) models based on trend classification (SVRTC). SVRTC maintains a pool of models, each tailored to exponential growth, plateau, or decline patterns. Using COVID-19 case series from eight countries, SVRTC was compared to traditional forecasting and existing dynamic selection methods. It achieved remarkable performance among traditional Machine Learning models and competitive results against state-of-the-art dynamic selection, with low computational cost, indicating strong potential for broader application.
creator: Eduardo H. X. de M. e Menezes
creator: Jair P. de Sales
creator: Paulo S. G. de Mattos Neto
rights: ##submission.copyrightStatement##
date: 2025-09-29
publication date: 2025-09-29
starting page: 511
ending page: 522
doi: 10.5753/eniac.2025.13836

Enhancing Legal Question Answering in Brazilian Portuguese through Domain-Specific Embedding Models
type: item
description: The increasing digitization of legal documents presents significant challenges for information retrieval. Traditional keyword-based search methods often fail to capture the semantic nuances of complex legal queries. RetrievalAugmented Generation (RAG) has emerged as a powerful paradigm for building Question Answering (Q&A) systems, but its effectiveness is highly dependent on the quality of its retrieval component. This paper addresses the problem of improving semantic search over legal texts from the Court of Accounts of the State of Goiás (TCE-GO). We introduce two specialized embedding models created by fine-tuning the state-of-the-art BGE-M3 model on domain-specific corpora of jurisprudence and legislation, respectively. Our experimental results demonstrate that these specialized models significantly outperform general-purpose multilingual and Portuguese models in retrieval tasks, as measured by MRR@10 and Recall@10. Notably, our fine-tuned models, despite their moderate size, surpass much larger models, highlighting that domain specialization is a more parameter-efficient strategy than simply scaling model size for niche domains.
creator: Artur M. A. Novais
creator: David O. C. Ferreira
creator: Josiel P. C. Silva
creator: Matheus F. C. Brakes
creator: João P. C. Presa
creator: Sávio S. T. de Oliveira
rights: ##submission.copyrightStatement##
date: 2025-09-29
publication date: 2025-09-29
starting page: 523
ending page: 533
doi: 10.5753/eniac.2025.13841

SHAP-Driven Explicability of CNN-Based Computer-Aided Diagnosis for Malaria
type: item
description: Redes Neurais Convolucionais (CNNs) têm grande potencial para classificação de imagens médicas, mas sua adoção clínica depende da confiabilidade dos modelos. Neste estudo, utilizamos Shapley Additive Explanations (SHAP) para explicar um sistema de CAD (Computer-Aided Diagnosis) baseado em CNN para diagnóstico de malária em imagens de microscopia. Aplicamos o Gradient Explainer para destacar as regiões de pixel mais relevantes para as previsões do CAD. Ao oferecer explicações transparentes em nível de pixel, esta abordagem capacita profissionais de saúde a compreenderem as decisões do sistema e fortalece a confiança no diagnóstico automatizado de malária.
creator: Matheus Silva dos Santos
creator: Fagner Cunha
creator: Rafael Giusti
creator: Juan Gabriel Colonna
rights: ##submission.copyrightStatement##
date: 2025-09-29
publication date: 2025-09-29
starting page: 534
ending page: 545
doi: 10.5753/eniac.2025.13859

Structuring Information from Initial Petitions Using LLMs: A Study in Brazilian Courts of Justice
type: item
description: This study proposes the use of Large Language Models to extract structured information from Brazilian legal petitions. We guided four models (from the Gemini and Gemma3 families) to generate structured JSON outputs, providing a comparative performance benchmark for this novel task. The main contribution is a validated workflow, where results show Gemini models significantly outperform Gemma in capturing complex semantic data. This work establishes a robust evaluation and an important methodological baseline for a critical task in Brazilian legal Natural Language Processing (NLP).
creator: Rhedson Esashika
creator: Carlos M. S. Figueiredo
creator: Tiago de Melo
rights: ##submission.copyrightStatement##
date: 2025-09-29
publication date: 2025-09-29
starting page: 546
ending page: 557
doi: 10.5753/eniac.2025.13876

Predictive Modeling of Real Estate Prices Using Machine Learning
type: item
description: Determinar o valor de um imóvel é uma tarefa complexa devido à ampla variedade de fatores que influenciam essa precificação, como localização, tamanho, número de compartimentos e demais atributos relevantes. Com o objetivo de simplificar o processo de precificação, este artigo propõe o uso de Algoritmos de Aprendizado de Máquina capazes de estimar o valor de um imóvel com base em suas características físicas e de localidade. Os algoritmos foram treinados em uma base de dados contendo anúncios online de venda de imóveis na cidade de Belém do Pará, entre dezembro de 2023 a janeiro de 2025. Os resultados desta prova de conceito indicam que o algoritmo Random Forest Regressor obteve o melhor desempenho, com um R² de 0.902 e um MAPE de 26%.
creator: Atílio Cardoso Azevedo
creator: Reginaldo Cordeiro dos Santos Filho
rights: ##submission.copyrightStatement##
date: 2025-09-29
publication date: 2025-09-29
starting page: 558
ending page: 568
doi: 10.5753/eniac.2025.13885

Semantic Clustering in the Context of Legislative Amendments
type: item
description: This study explores the semantic clustering of Brazilian legislative amendments through Large Language Models. Given the high number of amendments drafted annually in the Brazilian Federal Chamber of Deputies and the Federal Senate, and the consequent extensive hours spent by teams responsible for grouping these documents, the use of automated techniques for efficient analysis and organization presents itself as a beneficial alternative for legislative bodies. The study compared approaches with and without text preprocessing, varying the model’s temperature parameter to assess its impact on result quality. To validate the effectiveness of the formed clusters, precision, recall, and F1 metrics were applied.
creator: Pedro L. C. de Andrade
creator: Ellen P. R. Souza
creator: Nádia F. F. da Silva
creator: André C. P. L. F. de Carvalho
rights: ##submission.copyrightStatement##
date: 2025-09-29
publication date: 2025-09-29
starting page: 569
ending page: 579
doi: 10.5753/eniac.2025.13910

Agents4Gov: Privacy-Preserving Web Agents Using Open LLMs for Public Sector Tasks
type: item
description: Effective public governance requires an organizational culture that values transparency, technical competence, and ethical behavior, while also addressing persistent challenges related to efficiency and compliance in public sector institutions. The digital transformation of the public sector, particularly through the integration of Large Language Models (LLMs), represents a strategic shift toward data-driven governance, process automation, and scalable innovation. These emerging technologies offer unprecedented opportunities to enhance inclusiveness, transparency, security, efficiency, accessibility, and citizen-responsive public services. However, while closed-source LLMs can automate digital tasks, their adoption is limited by concerns over privacy and cost. We present Agents4Gov, a privacy-focused and cost-effective framework for automating browser tasks using open-source LLMs. Built on Browser-Use, it combines a powerful model for planning with a lightweight model for execution, supports asynchronous task requests via email, and can run on scheduled intervals to avoid the need for always-on infrastructure. Evaluated on MiniWoB++, Agents4Gov with LLaMA 4 models achieves competitive results compared to traditional Browser-Use agents powered by GPT-4o-based LLMs.
creator: João Victor de Castro Oliveira
creator: Nathan Rufino
creator: Silvio Levcovitz
creator: Matheus Yasuo Ribeiro Utino
creator: Fábio Lobato
creator: Marcelo Turine
creator: Solange Rezende
creator: Antonio Fernando Lavareda Jacob Junior
creator: Ricardo Marcondes Marcacini
rights: ##submission.copyrightStatement##
date: 2025-09-29
publication date: 2025-09-29
starting page: 580
ending page: 591
doi: 10.5753/eniac.2025.13911

Do SLM agents screen papers as well as LLMs?
type: item
description: Sumarizar o conhecimento científico é crucial, e a Revisão Sistemática da Literatura é um dos principais métodos utilizados, sobretudo na pesquisa baseada em evidências, sendo um processo complexo e demorado. Este trabalho foca na etapa triagem de artigos, sendo uma das mais cruciais, pois as demais dependem da qualidade desta. A literatura aponta bons resultados com LLMs comerciais como ChatGPT e Gemini para esta tarefa, mas faltam estudos sobre o uso de modelos menores (SLMs) executados localmente. Analisamos três abordagens com SLMs em comparação a um LLM comercial, usado como baseline. Os resultados mostram bom desempenho dos SLMs quando estratificamos e simplificamos em subtarefas. O Qwen 3 - 8B alcançou acurácia de até 94,35%. Com abordagem multiagente, o Phi 4 - 14B obteve 79,5% e o Qwen 3 - 4B, 78,8%, em relação ao LLM comercial.
creator: João Goulart Mendes de Freitas Filho
creator: Sílvio R. Fernandes
rights: ##submission.copyrightStatement##
date: 2025-09-29
publication date: 2025-09-29
starting page: 592
ending page: 603
doi: 10.5753/eniac.2025.13929

Effectiveness of different backbones for the DeepLabV3+ network applied to semantic segmentation of agricultural crops with limited image data
type: item
description: Este estudo investigou o desempenho da ResNet-50, ConvNeXt e Vision Transformer (ViT) para a rede DeepLabV3+, aplicados à segmentação semântica de imagens de plantações agrícolas de milho com ervas daninhas utilizando um conjunto reduzido de 58 imagens. Para mitigar a escassez de dados, empregou-se técnicas como aumento de dados e a combinação de duas funções de perda. Os modelos foram avaliados em termos de acurácia e mIoU. A ResNet-50 obteve o maior mIoU geral no conjunto de teste (0,7255), mas o ViT mostrou-se superior na identificação da erva daninha, destacando seu potencial para capturar detalhes minuciosos. Este artigo contribui para otimizar a identificação de ervas daninhas e reduzir o uso de agrotóxicos.
creator: Gabriel F. D. Pereira
creator: Luiz F. S. Coletta
creator: André L. D. Rossi
rights: ##submission.copyrightStatement##
date: 2025-09-29
publication date: 2025-09-29
starting page: 604
ending page: 615
doi: 10.5753/eniac.2025.13943

Towards a better classification of deepfake videos
type: item
description: The increasing accessibility and realism of deepfake technology pose serious threats to information integrity, privacy, and public trust. In this work, we propose a deep learning model for detecting deepfake videos based on short video segments. The architecture combines a TimeDistributed wrapper over an Xception backbone with global average pooling and a dense classification layer. To enhance performance, a Bayesian hyperparameter optimization strategy was employed, tuning both architectural and training parameters. The proposed model was evaluated on the Celeb-DF-V2 dataset, using only 24 frames per video, approximately one second of content. Despite this constraint, the model achieved a competitive accuracy of 97.30% and an AUC of 0.9957, outperforming several existing approaches that rely on longer video sequences. These results demonstrate the feasibility of detecting deepfakes efficiently using shorter clips, suggesting a viable direction for real-time and resource-constrained applications.
creator: Matheus P. R. Vieira
creator: Arthur Negrão de F. M. C.
creator: Guilherme A. L. Silva
creator: Eduardo Luz
creator: Pedro Silva
rights: ##submission.copyrightStatement##
date: 2025-09-29
publication date: 2025-09-29
starting page: 616
ending page: 627
doi: 10.5753/eniac.2025.13951

Detection of texts generated by LLMs in Portuguese
type: item
description: Com o crescente acesso e uso de modelos de Inteligência Artificial (IA) generativa, intensificam-se as preocupações quanto ao uso indevido dessas tecnologias. Embora tenham sido desenvolvidos com o objetivo de auxiliar tarefas cotidianas, seu uso mal-intencionado pode favorecer a ocorrência de plágio e a disseminação de desinformação. Devido à sua recente introdução e elevada capacidade, os textos produzidos por Large Language Models (LLMs) ainda apresentam desafios consideráveis em sua detecção. Diante desse cenário, este trabalho propõe a construção de um conjunto de dados em língua portuguesa contendo exemplos de textos de autoria humana, textos gerados por IA e textos humanos reescritos por LLMs. Foram também desenvolvidos cinco modelos classificadores baseados em arquiteturas da família LLaMA e BERT, além de uma rede neural recorrente com camadas LSTM bidirecionais. Os classificadores propostos apresentaram desempenho expressivo, alcançando acurácias de até 98,18% na classificação binária (texto escrito ou não por LLM) e 97,7% na classificação de três classes (humano, gerado por IA e reescrito por IA), utilizando o conjunto de teste definido.
creator: Guilherme S. M. de C. Paes
creator: Arthur Negrão de F. M. C.
creator: Guilherme Silva
creator: Ederson Júnior
creator: Eduardo Luz
creator: Pedro Silva
rights: ##submission.copyrightStatement##
date: 2025-09-29
publication date: 2025-09-29
starting page: 628
ending page: 639
doi: 10.5753/eniac.2025.13952

Sampling battleships boards for hit estimation using Monte Carlo Tree Search
type: item
description: Batalha Naval é um jogo clássico de tabuleiro com duas pessoas. Um dos problemas fundamentais do jogo é determinar a próxima posição do tabuleiro a ser revelada pelo adversário. Intuitivamente, a posição escolhida deveria corresponder a um barco (acerto), tendo em vista o objetivo do jogo de afundar todos os barcos do adversário. Este trabalho utiliza o algoritmo de Monte Carlo Tree Search para gerar amostras de tabuleiros válidos com todos os barcos a partir de um tabuleiro inicial (configuração atual do jogo). A probabilidade de acerto de cada posição oculta do tabuleiro é calculada utilizando as amostras geradas. Para avaliar a qualidade deste algoritmo, partidas foram simuladas onde cada jogada utiliza a posição de maior probabilidade de acerto do turno anterior. Resultados indicam que a acurácia (top-1) depois de uma jogada é cerca de 65% e cresce monotonicamente, chegando a 80% depois de 10 jogadas.
creator: Eduardo Naslausky
creator: Daniel Ratton Figueiredo
rights: ##submission.copyrightStatement##
date: 2025-09-29
publication date: 2025-09-29
starting page: 640
ending page: 651
doi: 10.5753/eniac.2025.13956

Scientific Event Recommendation through Semantic Clustering and Embedding Analysis
type: item
description: This work presents a series of experiments aimed at recommending scientific events within the Brazilian Computer Society (SBC), leveraging semantic embeddings generated by Large Language Models (LLMs) and unsupervised clustering techniques. Using BERTopic for topic modeling and multilingual representations, the experimental approach processes over 12,000 articles from 30 events to align submissions with relevant themes. The best configuration reached an accuracy of 0.91 and demonstrates the potential of LLM-based embeddings to support decision-making in scientific dissemination.
creator: Karla S. Silva
creator: Rebeca L. Rezende
creator: João V. D. Silva
creator: Gabriel Cardoso
creator: Thiago M. Ventura
creator: Allan G. de Oliveira
rights: ##submission.copyrightStatement##
date: 2025-09-29
publication date: 2025-09-29
starting page: 652
ending page: 663
doi: 10.5753/eniac.2025.13973

Reinforcement Learning on Mobile Devices: Context-Aware Configuration Control
type: item
description: A configuração automática em dispositivos móveis enfrenta dificuldades para se adaptar às preferências reais dos usuários, frequentemente causando frustração com ajustes genéricos de brilho, volume e notificações. Este artigo propõe uma solução embarcada baseada em aprendizado por reforço, capaz de ajustar dinamicamente o brilho da tela e o volume de mídia de acordo com o contexto. O sistema coleta dados de sensores, como luz ambiente, localização e status de reunião, discretizando essas variáveis para alimentar um agente Q-Learning. As ações são refinadas por meio de feedback manual interpretado como reforço supervisionado. Para estados não vistos, as preferências são inferidas usando similaridade contextual. Todos os módulos operam offline, sem dependência de serviços em nuvem ou conectividade externa. Os resultados mostram que o agente aprendeu políticas coerentes com o comportamento do usuário, manteve estabilidade em contextos recorrentes, respondeu corretamente a novos cenários e reduziu a necessidade de intervenção manual. A arquitetura proposta demonstra a viabilidade de agentes autônomos embarcados, oferecendo personalização inteligente diretamente em dispositivos Android.
creator: Alcilene Batista
creator: Elian Souza
creator: Raimundo Barreto
rights: ##submission.copyrightStatement##
date: 2025-09-29
publication date: 2025-09-29
starting page: 664
ending page: 675
doi: 10.5753/eniac.2025.13983

Vision-Language Models for Automated Property Feature Extraction in Tax Assessment: A Case Study
type: item
description: The calculation of property taxes, such as the Urban Building and Land Tax (IPTU) in Brazil, is a critical function for municipal revenue. This process traditionally relies on manual, on-site inspections to assess property characteristics, a task that is costly, time-consuming, and prone to subjectivity. This paper explores the potential of automating this process through the application of state-of-the-art Vision-Language Models (VLMs). We present a novel benchmark to evaluate the capabilities of twelve different VLMs in identifying and classifying specific building features as defined by the municipal legislation of Goiânia, Brazil. Using a dataset of images from public real estate listings and a zero-shot prompting strategy, we tasked the models with extracting 11 distinct construction categories, such as flooring, structure, and finishes. Our results indicate that proprietary models, particularly Google’s Gemini 1.5 Pro and Gemini 1.5 Flash, achieve the highest performance, with macro F1-scores of 0.77 and 0.76, respectively. We provide a detailed analysis of model performance across different categories, revealing that while some features like ’Structure’ and ’Electrical Installation’ are identified with high accuracy, others like ’Sanitary Installation’ and ’External Finishes’ remain challenging due to their visual subtlety or absence in typical photographs. Our findings demonstrate the significant potential of VLMs to streamline public administration tasks, while also highlighting current limitations and avenues for future research.
creator: Gustavo R. Ribeiro
creator: Pedro A. M. Saraiva
creator: Luis H. A. Rosa
creator: Enzo L. Marques
creator: Gustavo L. B. Pereira
creator: Pedro M. L. Campos
creator: Luiz M. L. Pascoal
creator: Sávio S. T. de Oliveira
rights: ##submission.copyrightStatement##
date: 2025-09-29
publication date: 2025-09-29
starting page: 676
ending page: 687
doi: 10.5753/eniac.2025.13998

Integrating Generative AI into Honeypots for Capturing Botnets
type: item
description: Honeypot é uma técnica que complementa os sistemas de defesa em cibersegurança. Um honeypot consiste em um sistema "isca" que simula alvos atraentes e vulneráveis para potenciais atacantes. Neste artigo, abordamos a falta de capacidade de honeypots baseados em scripts para simular um dispositivo real para atacantes botnet. Além disso, outro problema abordado é a falta de funcionalidades automatizadas de geração de dados relacionados ao contexto, análise de dados de log para criação de relatórios e emissão de alertas e ações de mitigação. Para atacar estes problemas, propomos um honeypot que integra IA generativa, um modelo grande de linguagem capaz de melhorar a interação com o atacante e prover ações automatizadas de: (i) adaptação de respostas de comando; (ii) geração de informação de contexto; (iii) geração de relatórios, execução de ações de defesa e emissão de alertas. Além disso, este artigo apresenta e avalia uma prova de conceito da funcionalidade responsável pelas respostas de linha de comando, usando o honeypot Cowrie como referência. Foram feitos testes para medir a precisão das respostas de linha de comandos produzidos pelo honeypot proposto, o tempo necessário para geração das respostas e o número de Tokens. Os resultados obtidos revelam a viabilidade da integração proposta, mostrando que modelo grande de linguagem é uma alternativa promissora para que honeypots atinjam um maior nível no engano de atacantes, em especial botnets.
creator: Cleber Reis
creator: Eduardo Lourenço
creator: Reginaldo Santos
creator: André Riker
rights: ##submission.copyrightStatement##
date: 2025-09-29
publication date: 2025-09-29
starting page: 688
ending page: 699
doi: 10.5753/eniac.2025.14010

Semantic Clustering of Civic Proposals: A Case Study on Brazil’s National Participation Platform
type: item
description: Promover a participação em plataformas digitais, como o Brasil Participativo, tornou-se uma prioridade para governos em todo o mundo. No entanto, devido ao enorme volume de contribuições, grande parte desse engajamento fica subutilizada, pois sua organização apresenta desafios significativos: (1) a classificação manual é inviável em escala; (2) é necessária a participação de especialistas; e (3) é preciso o alinhamento com taxonomias oficiais. Neste artigo, apresentamos uma abordagem que combina BERTopic com palavras-semente e validação automática realizada por grandes modelos de linguagem. Resultados iniciais indicam que os tópicos gerados são coerentes e alinhados institucionalmente, com esforço humano mínimo. Essa metodologia permite aos governos transformar grandes volumes de contribuições dos cidadãos em dados acionáveis para a formulação de políticas públicas.
creator: Ronivaldo Ferreira
creator: Guilherme da Silva
creator: Carla Rocha
creator: Gustavo Pinto
rights: ##submission.copyrightStatement##
date: 2025-09-29
publication date: 2025-09-29
starting page: 700
ending page: 711
doi: 10.5753/eniac.2025.14018

Classifying BDD Software Tests Using Machine Learning and NLP Techniques
type: item
description: The increasing complexity and volume of Behavior-Driven Development (BDD) test scripts have made manual validation time-consuming, error-prone, and difficult to standardize. To address these challenges, this study proposes an automated approach for analyzing BDD scenarios through a hybrid solution that combines Natural Language Processing (NLP) and Machine Learning (ML) techniques. The solution consists of two components: an NLP-based validator that detects structural and linguistic inconsistencies in Gherkin steps, and a supervised ML classifier that assigns each step to one of three functional roles: Precondition, Action, or Expected Result independent of the original Gherkin keywords. The methodology includes the development and evaluation of a classification model trained on a labeled dataset of 1,500 synthetic BDD steps. Performance was validated using accuracy, precision, recall, and F1-score metrics, and further confirmed with real-world test data. The proposed system provides near-instant feedback per step, enabling efficient integration into real-time development workflows. This research demonstrates the feasibility of combining rule-based validation and machine learning classification to improve the quality, consistency, and maintainability of BDD test artifacts.
creator: David Ferreira Brandão
creator: Cleyton Mário de Oliveira Rodrigues
creator: Wylliams Barbosa Santos
rights: ##submission.copyrightStatement##
date: 2025-09-29
publication date: 2025-09-29
starting page: 712
ending page: 723
doi: 10.5753/eniac.2025.14033

A Non-Dominated Sorting Evolutionary Algorithm Updating When Required
type: item
description: The NSGA-III algorithm relies on uniformly distributed reference points to promote diversity in many-objective optimization problems. However, this strategy may underperform when facing irregular Pareto fronts, where certain vectors remain unassociated with any optimal solutions. While adaptive schemes such as A-NSGA-III address this issue by dynamically modifying reference points, they may introduce unnecessary complexity in regular scenarios. This paper proposes NSGA-III with Update when Required (NSGA-III-UR), a hybrid algorithm that selectively activates reference vector adaptation based on the estimated regularity of the Pareto front. Experimental results on benchmark suites (DTLZ1–7, IDTLZ1–2), and real-world problems demonstrate that NSGA-III-UR consistently outperforms NSGA-III and A-NSGA-III across diverse problem landscapes.
creator: Lucas R. C. Farias
creator: Abimael J. F. Santos
creator: Matheus R. B. Nobre
rights: ##submission.copyrightStatement##
date: 2025-09-29
publication date: 2025-09-29
starting page: 724
ending page: 735
doi: 10.5753/eniac.2025.14037

CordelSextilha.BR: A Benchmark for Poetic Form in Brazilian Cordel Verse Generation
type: item
description: We introduce CordelSextilha.BR, the first benchmark for automatic generation of Brazilian cordel sextilhas. We compile 1,519 public-domain stanzas, remove the final line, and gather expert ratings for rhyme, meter, coherence, and “cordelisticity”. Rule-based metrics (RhythmAcc, RhymeAcc) align with human judgements; GPT-4o and LLaMA-3.2 (8B) reach about 80% rhyme and about 75% rhythm accuracy. The corpus, metrics and baselines enable RLHF research in low-resource poetry.
creator: Bryan K. S. Barbosa
creator: Marcela Y. A. Barbosa
rights: ##submission.copyrightStatement##
date: 2025-09-29
publication date: 2025-09-29
starting page: 736
ending page: 747
doi: 10.5753/eniac.2025.14067

Comparative Analysis of Deep Learning Architectures for Classifying Soy Seeds
type: item
description: A qualidade das sementes de soja desempenha um papel crucial na produtividade agrícola. Estudos têm aplicado técnicas de aprendizado profundo para automatizar a classificação de sementes. No entanto, essas abordagens focam em classificações binárias ou não comparam múltiplas arquiteturas para a classificação multiclasse. Este trabalho propõe uma comparação de modelos para classificar sementes de soja em cinco categorias. A metodologia envolve a avaliação de diferentes modelos utilizando o conjunto de dados Soybean Seeds, seguido do ajuste fino do modelo com melhor desempenho. Os resultados mostram que o modelo InceptionV3 alcançou a maior acurácia, melhorando de 76,68% com aprendizado por transferência para 86% após ajuste fino.
creator: Vinicius Godoy Marques
creator: Lucas Correia Ribas
creator: Caetano Mazzoni Ranieri
rights: ##submission.copyrightStatement##
date: 2025-09-29
publication date: 2025-09-29
starting page: 748
ending page: 759
doi: 10.5753/eniac.2025.14087

Trends and Challenges of AI Integration in Basic Education: Insights from a Bibliometric Study
type: item
description: This article reviews 43 publications (2021-2024) on Artificial Intelligence (AI) in Basic Education, using Guedes’ (2018) methodology. A global increase in interest is noted, with China and the USA leading contributions. Prominent research areas include ”Education,” ”Technology,” ”Teacher Perceptions,” and ”Ethics,” categorized into: AI perceptions, factors in teacher adoption, and professional development. While AI has the potential to personalize learning, teacher concern and uncertainty require continuous training in technical and pedagogical skills, focusing on critical thinking and curriculum integration. The study emphasizes educational leadership and suggests future research on student-centered and adaptive learning models.
creator: Lucas da S. Schwarzbach
creator: Rosa Maria Vicari
creator: Luis Otoni M. Ribeiro
creator: Regina Barwaldt
rights: ##submission.copyrightStatement##
date: 2025-09-29
publication date: 2025-09-29
starting page: 760
ending page: 771
doi: 10.5753/eniac.2025.14169

Modeling Uncertainty in Crime Underreporting: An Indicator Kriging Approach
type: item
description: This study confronts crime underreporting in São Paulo, a critical challenge to public policy in emerging nations. We introduce a suitable geostatistical methodology to estimate crime probability from official police reports. The framework employs Indicator Kriging, a non-parametric method that excels with incomplete and missing data. It transforms crime counts into binary indicators based on hotspots, enabling robust probability estimation even in areas with sparse or no records. This approach effectively overcomes critical data gaps, generating detailed and accurate spatial probability maps of known criminal patterns. The resulting model offers a powerful analytical tool for targeted public security, revealing latent crime dynamics.
creator: Thiago Corrêa de Oliveira Jessé
creator: Caeatano Mazzoni Ranieri
rights: ##submission.copyrightStatement##
date: 2025-09-29
publication date: 2025-09-29
starting page: 772
ending page: 782
doi: 10.5753/eniac.2025.14189

Question Answering Techniques for Portuguese Legal Documents: A Systematic Literature Review
type: item
description: The exponential growth of Portuguese-language legal documents has renewed interest in Question Answering (QA) systems capable of returning concise, legally sound answers to natural-language queries. This study presents a systematic literature review, conducted according to PRISMA 2020 guidelines, that synthesises current evidence on QA techniques applied to Lusophone legal texts. Searches, without temporal restrictions, were executed in nine databases (ACM, El Compendex, ISI Web of Science, Periódico Capes, Scielo, Science@Direct, Scopus, Sol SBC and Springer Link) using a string that combine jurisprudential, linguistic and methodological terms. After duplicate removal, independent screening and quality appraisal, ten primary studies met the inclusion criteria (peer-reviewed publications developing or evaluating QA pipelines over Brazilian or Portuguese legislation). Publication activity is recent: more than 70% of the papers appeared between 2023 and 2025 and focus on Brazilian statutes and court decisions. Most pipelines adopt hybrid retrieval—BM25 or symbolic regex filters coupled with BERT-family dense encoders fine-tuned on legal corpora, while Retrieval-Augmented Generation with GPT-class models emerges in the latest research. Reported exact-match scores range from 0.60 to 0.83 and F1 from 0.75 to 0.87; however, only a quarter of the studies release code or data, hindering reproducibility. Common gaps include limited handling of the temporal validity of norms, scarce evaluation by legal specialists, and the absence of benchmark datasets for Portuguese. Overall, QA research for Lusophone law is accelerating yet remains fragmented; future work should prioritize shared resources, temporally aware models, and metrics that capture legal soundness beyond lexical overlap.
creator: Maurício Rodrigues Lima
creator: Vinícius Teles
creator: Sávio Teles
creator: Elisângela Silva Dias
rights: ##submission.copyrightStatement##
date: 2025-09-29
publication date: 2025-09-29
starting page: 783
ending page: 794
doi: 10.5753/eniac.2025.14205

Joint Learning of Sparse Gaussian Processes and Gaussian Process Latent Variable Models for Semi-supervised Tasks
type: item
description: The speed and variety of collected data have increased in a surprising way, corroborating with the creation of large and diverse datasets. When using such data for training supervised machine learning models, it is necessary to annotate the available samples. However, labeling instances can be challenging, expensive, and time-consuming. In this context, semi-supervised learning models have been extensively researched over the past decades. Among the supervised learning methods, models based on Gaussian Processes (GPs) offer the advantage of quantifying uncertainties and providing significant modeling flexibility. Nevertheless, like several learning strategies, they cannot be directly applied to semi-supervised scenarios. To overcome this issue, the current work proposes a GP-based approach to perform semi-supervised learning. The proposal consists in simultaneously training an unsupervised GP latent variable model (GPLVM) and a supervised sparse GP model. The approach leverages both labeled and unlabeled data to create a more effective final classifier. Additionally, a neural network is included to reproduce the latent variables learned by the GPLVM, which enables scaling and eases its use with unseen data. The introduced solution is evaluated on public datasets and compared with standard semi-supervised approaches from the literature, in both inductive and transductive settings. The experiments indicate that the proposed technique, despite some mixing results, is competitive, especially in transductive learning problems.
creator: Ana Alice Ximenes Mota Peres
creator: César Lincoln Cavalcante Mattos
rights: ##submission.copyrightStatement##
date: 2025-09-29
publication date: 2025-09-29
starting page: 795
ending page: 806
doi: 10.5753/eniac.2025.14206

NFL Play Classification Using GNN and Player Tracking Data
type: item
description: This study addresses the classification of offensive plays in the National Football League (NFL) as either pass or rush, utilizing official NFL datasets from 2022-2023 season. The central hypothesis posits that the relative spatial positioning of players on the field, along with their interactions, directly influences the offensive strategy. To investigate this, we generate graph-based representations connecting players, which serve as input for a Graph Convolutional Network (GCN) for the subsequent classification of these graph structures. The empirical results demonstrate that the GCN-based framework outperforms conventional machine learning architectures, namely Random Forest and Multilayer Perceptron.
creator: Carlos E. G. R. Souza
creator: Eduardo J. S. Luz
creator: Vander L. S. Freitas
rights: ##submission.copyrightStatement##
date: 2025-09-29
publication date: 2025-09-29
starting page: 807
ending page: 818
doi: 10.5753/eniac.2025.14217

Comparing Knowledge Injection Methods for LLMs in a Low-Resource Regime
type: item
description: Large language models (LLMs) often require massive corpora to effectively acquire new knowledge, yet updating a model with only thousands – or even a few million – tokens remains difficult. In this work, we study how to inject such small, unstructured knowledge and how this affects catastrophic forgetting. Using a recent-news corpus, we probe models with question-answer pairs to assess knowledge acquisition. Alongside a continued-pre-training baseline, we evaluate data augmentation strategies that create synthetic variations. Our results show that continued pre-training alone yields modest gains, whereas diverse rephrasings significantly boost learning – especially when prompts maximize variability. We also evaluate forgetting in this low-data regime and find that retrieval-augmented generation (RAG) updates, though effective, degrade out-of-domain performance more than parametric approaches. Finally, we show that the model can produce high-quality synthetic data itself, pointing toward self-improving updates. Code and data are available at https://github.com/hugoabonizio/knowledge-injection-methods/.
creator: Hugo Abonizio
creator: Thales Almeida
creator: Roberto Lotufo
creator: Rodrigo Nogueira
rights: ##submission.copyrightStatement##
date: 2025-09-29
publication date: 2025-09-29
starting page: 819
ending page: 830
doi: 10.5753/eniac.2025.14220

An Approach to HLA Allele Imputation in Bone Marrow Donor Registries
type: item
description: As principais informações dos registros de doadores de medula óssea são os alelos dos genes HLA. Em função dos custos e tipos dos exames necessários para se obter essas informações, muitos desses alelos não se encontram nos banco de dados. Assim, o objetivo deste trabalho é, de forma inédita, avaliar a possibilidade de imputar os alelos dos genes não informados nesses bancos de dados. Para mitigar essas lacunas, foram investigados algoritmos baseados em Rede Neural Recorrente do tipo Long-Short Time Memory (LSTM). A acurácia de 76% mostra a viabilidade de imputar os alelos faltantes, apesar do forte desbalanceamento das classes e por se tratar de uma das regiões mais polimórificas do DNA humano (i.e. muitas opções de alelos distintos).
creator: Felipe S. C. Eduardo
creator: Nathalia de Azevedo
creator: Luís Cristóvão M. S. Pôrto
creator: Karla Figueiredo
creator: Alexandre C. Sena
rights: ##submission.copyrightStatement##
date: 2025-09-29
publication date: 2025-09-29
starting page: 831
ending page: 842
doi: 10.5753/eniac.2025.14236

Evaluation of a Hybrid Approach to Legal Entity Recognition
type: item
description: The Brazilian judiciary system’s extensive volume of documents and technical language necessitates efficient methods for automating legal text analysis, where Legal Entity Recognition (LER) presents a significant challenge. This study evaluated the performance of LER models within the Brazilian legal domain through a comprehensive assessment across all publicly available Portuguese legal datasets: LeNER-BR, CDJur-BR, and UlyssesNER-BR. Eleven models were evaluated, encompassing classical-based, transformer-based, and hybrid approaches. Using Precision, Recall, and F1-Score metrics, the evaluation indicated that hybrid approaches consistently outperform both classicalbased and standalone transformer-based approaches in legal entity extraction tasks.
creator: Fernando Hurias Lopes
creator: Luís Paulo Faina Garcia
rights: ##submission.copyrightStatement##
date: 2025-09-29
publication date: 2025-09-29
starting page: 843
ending page: 854
doi: 10.5753/eniac.2025.14243

An Initial Study of Bird’s-Eye View Generation for Autonomous Vehicles using Cross-View Transformers
type: item
description: Bird’s-Eye View (BEV) maps provide a structured, top-down abstraction that is crucial for autonomous-driving perception. In this work, we employ Cross-View Transformers (CVT) for learning to map camera images to three BEV’s channels - road, lane markings, and planned trajectory - using a realistic simulator for urban driving. Our study examines generalization to unseen towns, the effect of different camera layouts, and two loss formulations (focal and L1). Using training data from only a town, a four-camera CVT trained with the L1 loss delivers the most robust test performance, evaluated in a new town. Overall, our results underscore CVT’s promise for mapping camera inputs to reasonably accurate BEV maps.
creator: Felipe Carlos dos Santos
creator: Eric Aislan Antonelo
creator: Gustavo Claudio Karl Couto
rights: ##submission.copyrightStatement##
date: 2025-09-29
publication date: 2025-09-29
starting page: 855
ending page: 866
doi: 10.5753/eniac.2025.14251

BLUEX Revisited: Enhancing Benchmark Coverage with Automatic Captioning
type: item
description: With the growing capabilities of Large Language Models (LLMs), there is an increasing need for robust evaluation methods, especially in multilingual and non-English contexts. We present an updated version of the BLUEX dataset, now including 2024-2025 exams and automatically generated image captions using state-of-the-art models, enhancing its relevance for data contamination studies in LLM pretraining. Captioning strategies increase accessibility to text-only models by more than 40%, producing 1,422 usable questions, more than doubling the number in the original BLUEX. We evaluated commercial and open-source LLMs and their ability to leverage visual context through captions.
creator: João Guilherme Alves Santos
creator: Giovana Kerche Bonás
creator: Thales Sales Almeida
rights: ##submission.copyrightStatement##
date: 2025-09-29
publication date: 2025-09-29
starting page: 867
ending page: 878
doi: 10.5753/eniac.2025.14256

Somatic Q-Learning
type: item
description: O Aprendizado por Reforço é uma área do aprendizado de máquina com algoritmos inspirados em conceitos biológicos, onde um agente aprende a partir de uma ação e a observação do estado e da recompensa, resultantes desta ação, obtidos do ambiente. Nesta área, um dos algoritmos mais utilizados para ambientes não modelados é o Q-Learning. Este algoritmo possui algumas limitações quando ao número de ações possíveis e tamanho do espaço de estados, além de um aumento exponencial do tempo de treinamento em relação à essas duas variáveis, tornando algumas aplicações deste inviáveis. Esta pesquisa apresenta uma adaptação deste algoritmo, utilizando um mecanismo bio-inspirado no funcionamento dos marcadores somáticos, proposto por António Damásio, com o objetivo de viabilizar o uso do Q-Learning para ambientes onde este mostra-se inviável.
creator: Artur P. Carneiro
creator: Danilo H. Perico
creator: Reinaldo A. C. Bianchi
rights: ##submission.copyrightStatement##
date: 2025-09-29
publication date: 2025-09-29
starting page: 879
ending page: 890
doi: 10.5753/eniac.2025.14262

Optimizing Complex Neural Networks with Population-Based Genetic Algorithms
type: item
description: O projeto de arquiteturas de Redes Neurais Convolucionais (CNNs) é uma tarefa complexa e dispendiosa, exigindo considerável experiência e experimentação. O Neural Architecture Search (NAS) visa automatizar este processo. Este trabalho apresenta um sistema de NAS baseado em Algoritmos Genéticos (AG) que utiliza uma representação em grafo para as arquiteturas. O sistema emprega operadores genéticos customizados para a manipulação direta da topologia da rede, abrangendo camadas convolucionais e densas. A função de aptidão considera tanto a métrica de desempenho quanto o número de parâmetros, buscando um equilíbrio entre eficácia e eficiência. Para reduzir o tempo de treinamento de arquiteturas pouco promissoras, foi desenvolvido um mecanismo de early stopping baseado em lotes. Mecanismos como elitismo e correção automática de invalidez de grafos (e.g., ciclos, dimensões incompatíveis) também são integrados ao processo evolutivo. Experimentos conduzidos nos conjuntos de dados CIFAR-10 e SVHN demonstram a capacidade do sistema em evoluir arquiteturas competitivas. A principal contribuição é um framework robusto e adaptável para a exploração do espaço de arquiteturas neurais.
creator: Mateus de Freitas Rosa
creator: Murillo Guimarães Carneiro
rights: ##submission.copyrightStatement##
date: 2025-09-29
publication date: 2025-09-29
starting page: 891
ending page: 902
doi: 10.5753/eniac.2025.14265

Automatic label error detection on text datasets labeled with data programming
type: item
description: Supervised machine learning relies on the availability of large volumes of accurately annotated data, a requirement that data programming (DP) alleviates by aggregating weak supervision sources into probabilistic labels. However, DP generated labels remain susceptible to noise, compromising downstream model performance. In this work, we integrate and evaluate four Automatic Error Detection (AED) techniques: Retag, Confident Learning, Source-aware Influence Functions, and Unsupervised Label Functions Correction (ULF); within standard two-stage DP pipelines. Using the WRENCH benchmark on two text-classification tasks (YouTube and SMS spam detection), we optimize each pipeline via Bayesian optimization and collect the metrics Matthews correlation coefficient, accuracy, F1 score, and computational cost. Our experiments show that Influence Functions combined with the Hyper Label Model achieve the most positive trade-off between accuracy improvement and runtime on balanced data, while simpler DP baselines outperform all analyzed AED methods under class imbalance. These findings underscore both the promise and practical limitations of AED in refining weakly supervised workflows, guiding future development of cost-effective label-noise mitigation strategies.
creator: Nalbert G. M. Leal
creator: Daniel S. A. Araújo
creator: Elias J. Menezes Neto
rights: ##submission.copyrightStatement##
date: 2025-09-29
publication date: 2025-09-29
starting page: 903
ending page: 914
doi: 10.5753/eniac.2025.14268

Pig Detection Using Computer Vision Models Trained on Synthetic Datasets
type: item
description: Pork is a major commodity in the global food industry, yet monitoring pigs in their living spaces remains a challenge due to the scarcity of annotated datasets for AI models. In this study, an existing method for generating training datasets by utilizing a limited number of annotated images is adapted for pig detection, with the goal of enabling accurate pig counting in future applications. Using the COCO dataset and the Multi-Camera Pig Dataset, we applied Digital Image Processing and Meta’s Segment Anything Model 2 (SAM2) to augment data. A YOLO model trained on the generated datasets demonstrated that manual annotation can be reduced by 40% with minimal performance loss, significantly decreasing dataset production effort.
creator: Alvaro V. Martins
creator: Thiago M. Paixão
creator: Francisco A. Boldt
creator: Isabella C. Condotta
rights: ##submission.copyrightStatement##
date: 2025-09-29
publication date: 2025-09-29
starting page: 915
ending page: 926
doi: 10.5753/eniac.2025.14272

Language-Driven Graphs for Short Video Similarity
type: item
description: Comparing videos by similarity is a central task in modern video analysis, but its effectiveness depends critically on the chosen representation. While visual embeddings effectively capture appearance, they often lack semantic abstraction. Recently, Large Language Models (LLMs) have emerged as a promising way to generate rich textual descriptions, yet the structural properties they induce in video similarity spaces remain underexplored. We introduce a graph-based methodology to investigate these properties, systematically comparing the structure of similarity graphs derived from visual features, human-written text, and LLM-generated text. Our framework evaluates how well each graph preserves semantic consistency, both in immediate neighborhoods (local cohesion) and across longer paths (global organization). Our analysis reveals a fundamental trade-off: visual graphs exhibit high local purity but decay rapidly, whereas LLM-based graphs preserve superior global semantic coherence. We demonstrate that LLMs build an abstract space that prioritizes deep thematic links—such as grouping videos by concepts like ’stage performance’ across formal categories—over superficial purity. This structure offers a powerful, semantically organized alternative to the local cohesion of visual models.
creator: Juliano Koji Yugoshi
creator: Ricardo Marcondes Marcacini
rights: ##submission.copyrightStatement##
date: 2025-09-29
publication date: 2025-09-29
starting page: 927
ending page: 937
doi: 10.5753/eniac.2025.14276

Growing Self-Organizing Maps for Firewall Log Classification in Data Streams
type: item
description: The growing volume of data and dynamic nature of computational networks present significant challenges to information security, particularly in data stream classification. This work investigates multiclass classification of real firewall logs from an academic environment. To address class imbalance, binary classification (allowed and denied actions) was performed. We propose an adaptation of the Growing Self-Organizing Map (GSOM) for streaming classification. Experiments were conducted in offline and online phases, evaluating three update strategies. The results demonstrate GSOM’s streaming adaptability and potential for classifying firewall logs in data streams.
creator: Wagner Rafael Giarini
creator: Herbert Gonçalves Dias
creator: Ricardo Cerri
rights: ##submission.copyrightStatement##
date: 2025-09-29
publication date: 2025-09-29
starting page: 938
ending page: 949
doi: 10.5753/eniac.2025.14281

Analyzing Data Bias through Data Augmentation: A Case Study in Financial Data
type: item
description: This study examines the impact of introducing group unbalances through oversampling strategies on model fairness and feature importance, especially concerning sensitive attributes like sex, marital status, and education in a financial dataset. We hypothesize that linear models are more vulnerable to fairness distortions introduced by oversampling generated by synthetic data generation than more complex models, such as gradient-boosted decision trees and support vector machines with an RBF kernel. To test this, we evaluate oversampling approaches like SMOTE and RandomOverSampler within a consistent framework, comparing linear classifiers against XGBoost and SVM (RBF). Our assessment includes predictive performance, the stability of fairness metrics, and changes in feature importance rankings before and after oversampling.
creator: Julia dos Santos Porphirio
creator: Diogo José dos Santos
creator: Sérgio Azevedo
creator: Lilian Berton
rights: ##submission.copyrightStatement##
date: 2025-09-29
publication date: 2025-09-29
starting page: 950
ending page: 961
doi: 10.5753/eniac.2025.14285

Exploring multimodal implicit behavior learning for vehicle navigation in simulated cities
type: item
description: Standard Behavior Cloning (BC) fails to learn multimodal driving decisions, where multiple valid actions exist for the same scenario. We explore Implicit Behavioral Cloning (IBC) with Energy-Based Models (EBMs) to better capture this multimodality. We propose Data-Augmented IBC (DA-IBC), which improves learning by perturbing expert actions to form the counterexamples of IBC training and using better initialization for derivative-free inference. Experiments in the CARLA simulator with Bird’s-Eye View inputs demonstrate that DA-IBC outperforms standard IBC in urban driving tasks designed to evaluate multimodal behavior learning in a test environment. The learned energy landscapes are able to represent multimodal action distributions, which BC fails to achieve.
creator: Eric Aislan Antonelo
creator: Gustavo Claudio Karl Couto
creator: Christian Möller
rights: ##submission.copyrightStatement##
date: 2025-09-29
publication date: 2025-09-29
starting page: 962
ending page: 973
doi: 10.5753/eniac.2025.14289

A Comparative Analysis of Graph Construction Methods for Fairness in Graph-Based Semi-Supervised Learning
type: item
description: Graph-based semi-supervised learning (GSSL) relies on the quality of the input graph, often constructed from tabular data. We compare KNN, Mutual KNN (M-KNN), and Sequential KNN (S-KNN) on fairness benchmark datasets. While KNN achieves the highest AUC-ROC, M-KNN and S-KNN, which promote more balanced degree distributions, yield fairer outcomes as measured by Disparate Impact and Average Absolute Odds Difference, with only a small loss in AUC-ROC. Additionally, our analysis reveals that degree disparities between protected groups are linked to unfair outcomes, and that more balanced connectivity can mitigate these effects, highlighting the importance of fairness-aware graph construction in GSSL.
creator: Willian Dihanster Gomes de Oliveira
creator: Lilian Berton
rights: ##submission.copyrightStatement##
date: 2025-09-29
publication date: 2025-09-29
starting page: 974
ending page: 985
doi: 10.5753/eniac.2025.14296

Evaluating Teacher Forcing and Curriculum Learning in Recurrent Models for Oceanographic Time Series
type: item
description: Sea level prediction is vital for port and coastal operations, where short-term forecasting accuracy is critical for navigation and planning. Autoregressive neural models are powerful tools for this task, especially when combined with training strategies like Teacher Forcing and Curriculum Learning. This study analyzes their impact on RNN performance for short-term sea level forecasting using high-frequency data from the Port of Santos, Brazil. We compare standard autoregressive training, Teacher Forcing, and Curriculum Learning across MAE, RMSE, and R² metrics, focusing on long prediction windows. Results show that low levels of teacher forcing improve convergence and reduce error over long horizons, highlighting its value for precision and long-term stability.
creator: Tiago H. Marum
creator: Ronney Agra
creator: Marcel Rodrigues de Barros
creator: Anna Helena Reali Costa
creator: Fábio Gagliardi Cozman
creator: Fábio Cunha Lofrano
creator: Fernando Akira Kurokawa
rights: ##submission.copyrightStatement##
date: 2025-09-29
publication date: 2025-09-29
starting page: 986
ending page: 996
doi: 10.5753/eniac.2025.14299

Intelligent Agent for Legal Health Case Automation: A Case Study in the Brazilian Public Sector
type: item
description: This work presents the design, implementation, and evaluation of an intelligent agent developed to support the State Health Attorney’s Office of Ceará (PROSAÚDE) in managing over 3,000 monthly legal cases related to public health. The agent automates key decisions on legal contestation by interpreting court documents and applying internal legal norms. Built under the Judiciary vertical of Ceará’s Chief Scientist Program, the system combines large language models (GPT-4o), regulatory rule sets, and robotic process automation to extract medical/legal data, retrieve external sources (e.g., ANVISA), and generate official dispatches. It ensures consistent application of PROSAÚDE’s Internal Ordinance, which defines when appeals are required. Results show 98.4% precision in rule application and 93.8% recall in medication detection. In human-in-the-loop validation, 98.7% of dispatches required no revision. The solution highlights the potential of AI-driven legal automation to improve efficiency and accuracy in high-volume public workflows.
creator: L. Rocha
creator: B. Amaro
creator: V. Furtado
creator: N. Duque
creator: V. Menezes
creator: J. Cláudio
rights: ##submission.copyrightStatement##
date: 2025-09-29
publication date: 2025-09-29
starting page: 997
ending page: 1008
doi: 10.5753/eniac.2025.14301

Particle Tracking Classification in the CONNIE Experiment
type: item
description: The Coherent Neutrino-Nucleus Interaction Experiment (CONNIE) is a particle physics experiment located at the Angra 2 Nuclear Reactor. Using silicon Skipper-CCDs, CONNIE aims to identify and investigate coherent elastic neutrino-nucleus scattering (CEνNS). However, background particles from various sources, combined with the large volume of unlabeled data, present significant challenges, making statistical analysis difficult. To address these challenges, this work investigates and implements machine learning and image processing methodologies to classify distinct CONNIE events. A labeled dataset of real experimental events will also be created using the Annotation Redundancy with Targeted Quality Assurance method. This approach involves multiple annotators labeling the same data, enabling comparison to identify discrepancies, refine labeling accuracy, and enhance dataset reliability.
creator: Sara Mirthis Dantas dos Santos
creator: Irina Nasteva
creator: Paula Dornhofer Paro Costa
rights: ##submission.copyrightStatement##
date: 2025-09-29
publication date: 2025-09-29
starting page: 1009
ending page: 1020
doi: 10.5753/eniac.2025.14311

Missing Data Under Green AI Umbrella
type: item
description: Missing data is a common issue that can undermine machine learning performance, and imputation methods have emerged as state-of-the-art solutions. However, training these methods can be costly and environmentally impactful. In this work, we investigate the missing data problem under Green AI constraints using a Data-Centric AI approach. We evaluate three missingness mechanisms, four missing rates, and ten datasets to assess both data quality and downstream performance. We also propose an optimization model to select the best-performing imputation method while considering sustainability constraints, offering a path toward more responsible and effective data imputation.
creator: Arthur Dantas Mangussi
creator: Ricardo Cardoso Pereira
creator: Pedro Henriques Abreu
creator: Ana Carolina Lorena
rights: ##submission.copyrightStatement##
date: 2025-09-29
publication date: 2025-09-29
starting page: 1021
ending page: 1032
doi: 10.5753/eniac.2025.14312

Local explainability of fuzzy and classic models focusing on disaster management in Brazilian municipalities
type: item
description: Disasters are socio-environmental processes of losses and damages related to severe or extreme events. The lower the socio-spatial self-protection to deal with such events, the greater the chances of a disaster occurring. In Brazil, when the local damages and losses incurred exceed the own resources from the municipal administration to assist the affected people, the triggering of declaration of emergency issued by Brazilian municipalities is required as a legal instrument to obtain appropriate external support. In this article, we develop a study that applies machine learning and explainable AI techniques to generate fuzzy and classic classification models and interpret predictions with a data set that relates the declaration of emergencies and indicators corresponding to Sustainable Development Goals 1, 3, 6 and 10, from 2016 to 2022. A qualitative analysis was performed on the results provided by the explainable AI techniques that led to the identification of indicators that have the greatest influence on predictions and provided additional support to field researchers and decision makers in the context of disaster response.
creator: Renata Ribeiro
creator: Norma Valencio
creator: Heloisa Camargo
rights: ##submission.copyrightStatement##
date: 2025-09-29
publication date: 2025-09-29
starting page: 1033
ending page: 1044
doi: 10.5753/eniac.2025.14320

Evaluating BERT Models for Semantic Retrieval in Long Portuguese Legal Documents
type: item
description: O crescente número de documentos digitais no Judiciário brasileiro cria novos desafios para a eficiência processual. Este estudo avaliou cinco modelos BERT na recuperação de informações densas para documentos judiciais longos, utilizando a segmentação e a recuperação de vetores com o Elastic-search. Modelos de uso geral, específicos de domínio e específicos de tarefa foram testados para medir a coerência intra-cluster. O BumbaBERT (específico de domínio) teve o melhor desempenho, confirmando que a especialização de domínio é crucial para a recuperação semântica eficaz em cenários de “zero-shot” no contexto jurídico brasileiro.
creator: Adrielson Ferreira Justino
creator: Antônio Fernando Lavareda Jacob Junior
creator: Ricardo Marcondes Marcacini
creator: Fábio Manoel França Lobato
rights: ##submission.copyrightStatement##
date: 2025-09-29
publication date: 2025-09-29
starting page: 1045
ending page: 1056
doi: 10.5753/eniac.2025.14328

Applying Diffusion Models for Classification of Central Segregation in Steel Plates
type: item
description: A classificação automática dos níveis de segregação em placas de lingotamento contínuo é crucial para garantir a qualidade do aço. A macrossegregação, caracterizada por variações químicas localizadas, representa um desafio significativo, especialmente na região central das placas. Este trabalho propõe uma abordagem baseada em redes neurais convolucionais, especificamente a EfficientNet-B0, com transferência de aprendizado, aliada à geração de dados sintéticos por meio de modelos de difusão (Stable Diffusion). Os resultados experimentais obtidos em um conjunto de treino de 150 imagens para treino e 30 para teste indicaram acurácia inicial de 80% sem nenhum aumento de dados, com uma melhora superior a 13% após a inclusão de dados sintéticos, além de ganhos em precisão e revocação. A proposta contribui para a automação da inspeção de falhas e se alinha aos princípios da Indústria 4.0.
creator: Luciano M. Oliveira
creator: Guilherme Silva
creator: Arthur Negrão
creator: Andre L. Ottoni
creator: Pedro Silva
rights: ##submission.copyrightStatement##
date: 2025-09-29
publication date: 2025-09-29
starting page: 1057
ending page: 1068
doi: 10.5753/eniac.2025.14329

Evaluating the Ability of ChatGPT and DeepSeek to Solve Propositional Logic Proofs Using the Analytic Tableau Deductive System
type: item
description: Os Modelos de Linguagem de Grande Escala (LLMs) têm sido amplamente aplicados em contextos educacionais, mas enfrentam desafios em tarefas que exigem raciocínio lógico rigoroso. Este artigo avalia o desempenho dos modelos ChatGPT-4o e DeepSeek R1 (DeepThink) na resolução de exercícios de Lógica Proposicional, utilizando o sistema dedutivo Tableau Analítico. As respostas foram analisadas com base na aplicação correta das regras do sistema. Os resultados mostram que, embora o DeepSeek tenha superado o ChatGPT em número de acertos, ambos os modelos ainda demonstram limitações significativas, especialmente em provas com múltiplas aplicações de regra.
creator: Taís Rodrigues Sandes
creator: Davi Romero de Vasconcelos
creator: Maria Viviane de Menezes
creator: Victória de Oliveira Lima
rights: ##submission.copyrightStatement##
date: 2025-09-29
publication date: 2025-09-29
starting page: 1069
ending page: 1080
doi: 10.5753/eniac.2025.14333

Integration of Subjective and Emotional Physiological Data for Classifying Sleep Quality with Machine Learning
type: item
description: A qualidade do sono é um fator crítico para a saúde física e mental, associando-se diretamente a condições como ansiedade, doenças cardiovasculares e distúrbios metabólicos. Este trabalho propõe um pipeline de aprendizado de máquina para classificar a qualidade do sono em “boa” ou “ruim”, integrando dados fisiológicos (actigrafia), subjetivos (PSQI) e emocionais (STAI-Y2). Os experimentos foram realizados sobre o conjunto MMASH com validação intersujeitos (LOSO), reforçando a generalização dos resultados. A metodologia alcançou acurácia de 0.98 ± 0.01 com uma rede neural multicamadas, superando estudos relacionados que empregaram validação menos rigorosa. Esses achados demonstram a viabilidade da combinação de múltiplas dimensões de dados para monitoramento não invasivo, com potencial aplicação em dispositivos vestíveis e intervenções personalizadas de saúde do sono.
creator: Nicolly Alves da Silva
creator: Hygo Sousa de Oliveira
creator: Eduardo James Pereira Souto
creator: Rafael Giusti
rights: ##submission.copyrightStatement##
date: 2025-09-29
publication date: 2025-09-29
starting page: 1081
ending page: 1092
doi: 10.5753/eniac.2025.14363

Fair-LS: A Group-Specific Clamping Factor for Fair Label Spreading in Graph-Based Semi-Supervised Learning
type: item
description: Graph-based semi-supervised learning methods, such as Label Spreading, can propagate bias when the graph structure reflects group disparities. We propose Fair-LS, an extension of Label Spreading that introduces group-specific clamping factors to control label diffusion differently for privileged and unprivileged groups. Experiments on benchmark datasets, Adult Census Income, Compas Recidivism, and German Credit, show that Fair-LS could improve fairness metrics (Disparate Impact and Average Absolute Odds Difference) with minimal reduction in AUC-ROC. The results suggest that adapting propagation dynamics by group is an effective strategy to reduce bias in semi-supervised learning.
creator: Willian Dihanster Gomes de Oliveira
creator: Lilian Berton
rights: ##submission.copyrightStatement##
date: 2025-09-29
publication date: 2025-09-29
starting page: 1093
ending page: 1103
doi: 10.5753/eniac.2025.14368

UTMap: Triplet Neural Network for Uncertainty Medical Image Analysis
type: item
description: This paper proposes a framework to enhance the reliability of deep classifiers in medical imaging by combining visualization and uncertainty quantification through meta-learning. The methodology employs a Triplet Neural Network (UTMap) to project instances into the Instance Uncertainty Space (IUS), which highlights patterns of confidence and uncertainty. Additionally, the Neighborhood Reliability Score (NRS) metric is introduced to estimate uncertainty based on the spatial relationships within the IUS. Experimental results show that the IUS effectively represents classifier behavior and that the NRS achieves competitive performance compared to traditional uncertainty estimation algorithms in distinguishing between correct and incorrect predictions.
creator: Rafael Souza e Silva
creator: André C. P. L. F. de Carvalho
rights: ##submission.copyrightStatement##
date: 2025-09-29
publication date: 2025-09-29
starting page: 1104
ending page: 1115
doi: 10.5753/eniac.2025.14372

Deep Learning Ensemble for Multiclass Recognition of Mature Leukocytes in Acute Myeloid Leukemia (AML)
type: item
description: A classificação manual de leucócitos em esfregaços sanguíneos é subjetiva, demorada e propensa a erros, sobretudo em contextos clínicos de alta demanda. Diante disso, este estudo propõe uma abordagem com Redes Neurais Convolucionais (CNNs) para a classificação multiclasse de leucócitos maduros, visando apoiar o diagnóstico da Leucemia Mieloide Aguda (LMA). Oito CNNs pré-treinadas foram avaliadas num conjunto de 30.929 imagens de seis subtipos celulares. Um comitê com voto majoritário (MobileNetV2, ResNet101 e MobileNet) obteve acurácia de 93,18%. Os resultados destacam o potencial das CNNs e da estratégia de comitês na identificação automatizada de leucócitos em exames hematológicos voltados à LMA.
creator: Nicole E. M. Silvestre
creator: Leonardo P. Sousa
creator: Ana V. S. Coelho
creator: Maíla L. Claro
creator: André M. Santana
creator: Rodrigo M. S. Veras
rights: ##submission.copyrightStatement##
date: 2025-09-29
publication date: 2025-09-29
starting page: 1116
ending page: 1127
doi: 10.5753/eniac.2025.14377

Optimizing Resume Data Extraction with Small Language Models: A Comparative Study on Efficiency and Privacy
type: item
description: Resume data extraction remains a critical challenge in automated recruitment systems, where traditional approaches struggle with unstructured document formats while facing significant computational and privacy constraints. Large Language Models (LLMs) have shown promise for information extraction tasks but present substantial barriers including high computational costs and data privacy concerns when processing sensitive personal information. This study investigates the fine-tuning of Small Language Models (SLMs) as a viable alternative, specifically examining Phi-3 mini for extracting structured data from resumes. We employ advanced fine-tuning techniques including Parameter Efficient Fine-Tuning (PEFT) and Reparameterized Fine-Tuning with LoRA Quantization Adaptation (QLoRA) on a synthetic dataset of 2,100 resumes generated using privacy-preserving methods. Our comparative evaluation against GPT-4o demonstrates that the fine-tuned SLM achieves superior performance with 87.9% accuracy and 91.5 BLEU score, compared to GPT-4o’s 85.5% accuracy and 83.9 BLEU score, while significantly reducing computational costs. These findings establish SLMs as scalable and privacy-conscious alternatives for applications requiring nuanced information processing, offering comparable accuracy to LLMs with enhanced efficiency and data protection.
creator: Wagner Costa Santos
creator: Gustavo Tomaello Juiz
creator: Arnaldo Candido Junior
rights: ##submission.copyrightStatement##
date: 2025-09-29
publication date: 2025-09-29
starting page: 1128
ending page: 1137
doi: 10.5753/eniac.2025.14382

Comparing Prompt-based LLMs, Fine-Tuning, and Classical Models for Legal Text Classification in Portuguese
type: item
description: This study compares fine-tuned Transformer models, LLMs with prompting, and traditional models in Portuguese legal text classification. A new Portuguese dataset was introduced for empirical evaluation. Four input formats were tested in prompting, complete text, summaries, centroids, and descriptions. Summaries achieved effective performance with reduced token usage. Similarly, KNN proved competitive in resource-limited scenarios. Input format and model capacity affected performance. The study discusses trade-offs between efficiency and interpretability. Guidelines are proposed for choosing effective strategies in legal NLP tasks.
creator: Willgnner Ferreira Santos
creator: Arlindo Rodrigues Galvão Filho
creator: Sávio Salvarino Teles de Oliveira
creator: João Paulo Cavalcante Presa
rights: ##submission.copyrightStatement##
date: 2025-09-29
publication date: 2025-09-29
starting page: 1138
ending page: 1149
doi: 10.5753/eniac.2025.14393

Knowledge Transfer in Agent Organizations: A Systematic Review
type: item
description: Este artigo apresenta uma revisão sistemática da literatura que tem como objetivo analisar a transferência de conhecimento em sistemas multiagente, com foco particular no nível organizacional. Seguindo o protocolo PRISMA, a revisão examinou publicações de 2019 a 2025 em quatro bases de dados principais: IEEE Xplore, Scopus, Web of Science e ACM Digital Library. Por meio de um processo de seleção estruturado, foram identificados três estudos relevantes, cada um abordando diferentes modelos organizacionais, tipos de conhecimento e estratégias. Os resultados revelam que a literatura científica sobre esse tema ainda é limitada, com a maioria das contribuições centradas em estruturas teóricas e simulações. Foi identificada uma lacuna significativa na forma de ausência de validação empírica. A análise destaca a necessidade de pesquisas futuras que não apenas ampliem o panorama teórico, mas também integrem dimensões sociais, tecnológicas e organizacionais, a fim de promover o desenvolvimento de sistemas multiagente mais eficientes e colaborativos.
creator: Marcone de Freitas Marques
creator: Giovani Parente Farias
creator: Eder Mateus Nunes Gonçalves
creator: Diana Francisca Adamatti
rights: ##submission.copyrightStatement##
date: 2025-09-29
publication date: 2025-09-29
starting page: 1150
ending page: 1160
doi: 10.5753/eniac.2025.14399

Combined Classifiers for Detection of Breast Cancer Metastasis in Histopathological Images
type: item
description: O câncer de mama é uma doença responsável pela maioria das mortes no Brasil e afeta principalmente mulheres. Entre as consequências de sua ocorrência estão a predisposição genética, o sedentarismo e a menopausa tardia. É uma doença que requer diagnóstico o mais rápido possível para que o paciente possa iniciar o tratamento. Além disso, estudos sugerem que patologistas podem atingir uma precisão de cerca de 72% ao analisar um exame composto por milhares de imagens histopatológicas de cortes de linfonodos. Nesse contexto, este trabalho descreve um classificador que combina ResNet50, Random Forest (RF) e Support Vector Machine (SVM) treinados pelo conjunto de dados PatchCamelyon. Os resultados indicam que o método proposto atingiu uma precisão e F1-score de 81%.
creator: Luis Jhonne Carvalhal de Melo
creator: Omar Andres Carmona Cortes
creator: Antônio Fernando Lavareda Jacob Júnior
rights: ##submission.copyrightStatement##
date: 2025-09-29
publication date: 2025-09-29
starting page: 1161
ending page: 1172
doi: 10.5753/eniac.2025.14404

Multi-Class Classification of Immature Leukocytes in AML Blood Smears Using an Ensemble of CNNs
type: item
description: A Leucemia Mieloide Aguda (LMA) é uma neoplasia caracterizada pela proliferação anormal de leucócitos imaturos, cuja identificação é essencial para o diagnóstico. Este estudo avaliou o uso de Redes Neurais Convolucionais (CNNs) na classificação automática de cinco subtipos celulares em esfregaços sanguíneos. Oito arquiteturas pré-treinadas foram testadas com aumento de dados, validação cruzada e aprendizado por transferência. A partir das quatro melhores redes foram formados quatro comitês por votação majoritária, sendo que o mais eficaz alcançou acurácia de 88,49% e superou os modelos individuais. Os resultados indicam o potencial da abordagem para auxiliar o diagnóstico da LMA.
creator: Ana V. S. Coelho
creator: Leonardo P. Sousa
creator: Nicole E. M. Silvestre
creator: Maíla L. Claro
creator: André M. Santana
creator: Rodrigo M. S. Veras
rights: ##submission.copyrightStatement##
date: 2025-09-29
publication date: 2025-09-29
starting page: 1173
ending page: 1184
doi: 10.5753/eniac.2025.14441

Segmentation of the Glomerular Region in Pathological Kidney Slides
type: item
description: Este estudo propõe um método para segmentação da região glomerular em imagens histológicas renais, utilizando redes neurais convolucionais (CNNs) baseadas nas arquiteturas U-Net e Sharp U-Net com backbones pré-treinados. Foram avaliadas 643 imagens coradas por hematoxilina-eosina (HE), ácido periódico de Schiff (PAS) e ácido periódico-metenamina prata (PAMS), aplicando validação cruzada estratificada em 5 folds. A U-Net com VGG-19 mostrou o maior Dice médio (95,45%), seguida pela Sharp U-Net com DenseNet201. Os resultados foram consistentes entre as colorações, com leve vantagem para PAMS e PAS. O método revelou precisão e robustez, destacando seu potencial como ferramenta de apoio ao diagnóstico em nefropatologia.
creator: Débora B. Nascimento
creator: Luciano Oliveira
creator: Washington Santos
creator: Angelo Duarte
creator: Kelson R. T. Aires
creator: Rodrigo M. S. Veras
rights: ##submission.copyrightStatement##
date: 2025-09-29
publication date: 2025-09-29
starting page: 1185
ending page: 1196
doi: 10.5753/eniac.2025.14444

Numerical information extraction in legal texts using open and closed Large Language Models
type: item
description: This paper investigates numerical named-entity recognition in Portuguese legal texts using both closed and open-source decoder-only Large Language Models (LLMs). We conduct a quantitative and qualitative evaluation of two paradigms: (1) fine-tuning a modified version of LLaMA 2 via LoRA on over 600 new, manually annotated, judicial rulings, and (2) prompt engineering with closed models (OpenAI’s GPT and Google’s Gemini). We compare the performance of instruction tuning and prompt construction with closed models, with a parameter-efficient fine-tuning approach that bridges decoder-only LLMs with traditional encoder-only architectures. The results reveal that the modified, LoRA-tuned, LLaMA 2 achieves competitive entity-recognition performance while offering greater transparency and parameter-efficiency, whereas prompt-engineered closed models simplify deployment but incur limitations in consistency and fine-grained control.
creator: Bruno V. Bitelli
creator: Marcelo Finger
rights: ##submission.copyrightStatement##
date: 2025-09-29
publication date: 2025-09-29
starting page: 1197
ending page: 1208
doi: 10.5753/eniac.2025.14453

Multimodal Fusion Strategies for Multivariate Time Series Classification
type: item
description: Time series classification (TSC) is relevant in various domains, including finance, healthcare, and human activity recognition. While deep learning methods have achieved state-of-the-art performance in TSC, most advancements focus on univariate or straightforward adaptations for multivariate time series, neglecting different inter-variable dependencies. This problem increases in scenarios where the data comes from multiple sensors. In this case, the classifier does not consider the particularities of each source. For instance, consider a motion recognition dataset containing data from accelerometers and gyroscopes. A simple architecture for multivariate TSC applies the same transformations, e.g., kernel convolutions, to both sensors, even if they naturally present different characteristics. This paper argues that treating the variables of distinct sources as separate modalities, akin to sensor data, and employing specialized models for each source can enhance the classification performance. To evaluate this claim, we propose treating sensor inputs as multimodal data, applying fusion techniques to integrate multi-source multivariate time series classifiers. Experimental analysis shows that our approach outperforms traditional singlemodel architectures, particularly in complex tasks. In these worst scenarios for the single-modality models, our proposal achieves accuracy up to twelve percentage points higher, achieving the best results in the literature. It highlights the importance of considering multi-source dependencies in TSC, highlighting the potential of fusion-based strategies to advance the multivariate time series classification field.
creator: Samuel Thomaz Bastos
creator: Rafael da Costa Silva
creator: Diego Furtado Silva
rights: ##submission.copyrightStatement##
date: 2025-09-29
publication date: 2025-09-29
starting page: 1209
ending page: 1220
doi: 10.5753/eniac.2025.14459

LLM Agents for Search via Reinforcement Learning with Trajectory-Level Self-Evaluation
type: item
description: Agentic search enables language models to iteratively query and reason over retrieved information to answer complex questions. While reinforcement learning is used to train such agents, most approaches rely solely on final-answer accuracy as the reward signal, which can limit learning. We analyze a reward strategy combining final-answer correctness with trajectory-level feedback, guiding the agent to improve its search behavior. The agent is trained with QLoRA for efficient fine-tuning and performs self-evaluation by comparing answers to references and scoring search trajectories using predefined rubrics, without external supervision. Experiments on multi-hop QA tasks show gains of up to 5.7 points over standard reward setup.
creator: Leandro Yamachita da Costa
creator: João Baptista de Oliveira e Souza Filho
rights: ##submission.copyrightStatement##
date: 2025-09-29
publication date: 2025-09-29
starting page: 1221
ending page: 1232
doi: 10.5753/eniac.2025.14460

FairGNN-Bagging: Balancing model performance and algorithmic fairness in graph neural networks using ensemble learning
type: item
description: Graph Neural Networks (GNNs) are powerful tools for learning from graph-structured data, with applications in social networks, bioinformatics, and fraud detection. However, GNNs can inherit and amplify biases present in data, leading to unfair predictions regarding sensitive attributes such as gender or race. To mitigate these issues, fairness-aware frameworks like NIFTY and its variant with Biased Edge Dropout have been proposed. In this work, we introduce FairGNN-Bagging, an ensemble framework that combines NIFTY with Bagging to improve fairness in node classification tasks. Our method uses graph perturbations to generate an augmented graph dataset to train an ensemble of GNNs in parallel. These models are then aggregated via majority voting or fairness-aware selective voting. We evaluate our approach on three real-world datasets, showing that it achieves better fairness metrics while preserving or improving predictive performance.
creator: Juan Carlos Elias Obando Valdivia
creator: Marcel Rodrigues de Barros
creator: Artur Jordão Lima Correia
creator: Anna Helena Reali Costa
rights: ##submission.copyrightStatement##
date: 2025-09-29
publication date: 2025-09-29
starting page: 1233
ending page: 1244
doi: 10.5753/eniac.2025.14477

A Convolutional Neural Network Integrating PPG Signal and Extracted Features for Dehydration Classification
type: item
description: A desidratação é um sério problema de saúde que pode levar a consequências graves, tornando sua detecção precisa crucial para manter a função corporal adequada. Neste trabalho, nós propomos um modelo de aprendizado de máquina híbrido que pode classificar indivíduos em estados hidratados ou desidratados. Nossa abordagem combina uma rede neural convolucional rasa que extrai recursos locais não supervisionados com características estatísticas de dados de séries temporais obtidos de sensores como Fotopletismografia (PPG) e Atividade Eletrodérmica (EDA). Os resultados mostram que o modelo de classificação proposto alcança uma precisão de 73%, sendo superior à maioria dos trabalhos existentes na literatura que utiliza dados extraídos dos sinais PPG e/ou EDA para classificação de hidratação.
creator: José Mateus Cordova Rodrigues
creator: Ayrton Finicelli Lemes
creator: Daniel Mitsuaki da Silva Utyiama
creator: Pedro Daniel da Silva Gohl
creator: Eduardo James Pereira Souto
creator: Rafael Giusti
rights: ##submission.copyrightStatement##
date: 2025-09-29
publication date: 2025-09-29
starting page: 1245
ending page: 1256
doi: 10.5753/eniac.2025.14491

L(3,2,1)-Labeling on Graphs: Comparison between Greedy Heuristics and Genetic Algorithm
type: item
description: Diversos problemas de rotulação em grafos encontram aplicação na atribuição de canais de frequência a transmissores. Um deles é o Problema da Rotulação-L(3, 2, 1), no qual rótulos do conjunto {0, 1, . . . , k} são atribuídos aos vértices de um grafo de forma a satisfazer a restrição de que, para quaisquer dois vértices u, v ∈ V (G), deve-se ter que |f(u) − f(v)| ≥ 4 − dist(u, v), onde dist(u, v) é a distância entre u e v em G. Como esse problema é NP-completo, este trabalho propõe abordagens heurísticas e meta-heurísticas para obter soluções aproximadas. Foram implementados dois algoritmos gulosos e um algoritmo genético. Os resultados mostram que a abordagem gulosa é mais eficiente em tempo de execução, enquanto o algoritmo genético gera soluções de melhor qualidade.
creator: Jhon Kennedy C. de L. Queiroz
creator: Atílio G. Luiz
rights: ##submission.copyrightStatement##
date: 2025-09-29
publication date: 2025-09-29
starting page: 1257
ending page: 1268
doi: 10.5753/eniac.2025.14495

CRF2: Context Reasoning Faithful Framework
type: item
description: Large Language Models (LLMs) excel in tasks like text generation and question answering, but often rely on parametric knowledge, leading to hallucinations when required to reason strictly within a given context. In this paper, we propose a conceptual framework for context-faithful reasoning, ensuring responses are grounded solely in the provided information. We evaluate our approach using the RealTime QA dataset, which features open-domain, time-sensitive questions that require grounding in a specific document. Our experiments demonstrate that the proposed method outperforms existing prompt engineering techniques on abstention tasks, achieving an accuracy of 0.95 compared to baseline models.
creator: Hugo Firmino Damasceno
creator: Leonardo Sampaio Rocha
rights: ##submission.copyrightStatement##
date: 2025-09-29
publication date: 2025-09-29
starting page: 1269
ending page: 1280
doi: 10.5753/eniac.2025.14515

AquaPanema: Development of a Species Recognition Application in the Paranapanema Basin to Aid Environmental Education
type: item
description: A integração entre ensino de Ciências, educação ambiental e as tecnologias digitais é essencial para promover a conscientização ecológica. Considerando a expressiva biodiversidade de peixes da bacia do rio Paranapanema e das dificuldades envolvidas na identificação taxonômica, este estudo apresenta o desenvolvimento do AquaPanema, um aplicativo móvel que utiliza visão computacional para reconhecer espécies nativas. A solução emprega redes neurais convolucionais e aprendizado por transferência, alcançando acurácia superior a 95% na classificação. Com interface intuitiva, o aplicativo oferece funcionalidades como login, histórico de imagens e exibição de informações taxonômicas. A proposta contribui para a educação ambiental ao tornar o conhecimento científico mais acessível e ao estimular o interesse público pela conservação da biodiversidade por meio de experiências de aprendizagem interativas.
creator: Fábio Ribeiro Barbosa
creator: Carolinne Roque e Faria
rights: ##submission.copyrightStatement##
date: 2025-09-29
publication date: 2025-09-29
starting page: 1281
ending page: 1292
doi: 10.5753/eniac.2025.11686

A Multimodal Approach for Music Genre Classification Using Audio and Lyrics Embeddings
type: item
description: A classificação de gêneros musicais é uma tarefa importante para sistemas de recomendação e organização. Este trabalho investiga métodos de aprendizado profundo para classificação automática, combinando redes neurais convolucionais (CNNs) para análise espectral de áudio e embeddings de grandes modelos de linguagem (LLMs) extraídos das letras. Os experimentos no conjunto GTZAN mostram que os métodos apenas de áudio alcançaram maior acurácia geral, enquanto a abordagem multimodal redistribuiu o desempenho entre as classes, melhorando alguns gêneros (ex.: Country, Pop).
creator: J. M. L. Silva
creator: C. M. S. Figueiredo
creator: E. B. Guedes
creator: T. De Melo
rights: ##submission.copyrightStatement##
date: 2025-09-29
publication date: 2025-09-29
starting page: 1293
ending page: 1304
doi: 10.5753/eniac.2025.11742

Evaluating the explainability of BrainGNN on prediction of ASD diagnosis
type: item
description: Due to the absence of a biological marker for Autism Spectrum Disorder (ASD), most of recent research attempts to uncover the neurological patterns of ASD using Deep Learning, and these patterns, hidden in the latent feature space of neural networks, have to be interpreted with the use of Explainable AI. However, although many of the models proposed for the problem report results of explainability, they are not evaluated with any metric, so their reliability is unknown. The objective of this paper is to propose an evaluation framework to fill this gap, and here, we focus on a detailed analysis of a well-known model from the literature, BrainGNN. We trained BrainGNN in varying hyperparameter settings that influence explainability and analyzed our findings for each case.
creator: Matheo Angelo Pereira Dantas
creator: André Carlos Ponce de Leon Ferreira de Carvalho
rights: ##submission.copyrightStatement##
date: 2025-09-29
publication date: 2025-09-29
starting page: 1305
ending page: 1315
doi: 10.5753/eniac.2025.11771

Clustering-Based Analysis of Green Area Reduction and Thermal Impact in Marabá-PA
type: item
description: This study investigates the thermal impact related with the reduction of green areas in the municipality of Marabá-PA through the analysis of satellite imagery and the application of Machine Learning (ML) clustering techniques. The temporal evaluation reveals a significant expansion of urban areas accompanied by a marked decline in vegetated regions. The results demonstrate a strong correlation between decreased green cover and increased land surface temperatures, exacerbating the effects of the local heat island. These findings underscore the urgent need for integrated environmental preservation strategies and the implementation of public policies that promote sustainable urban growth. Recommendations include prioritizing the restoration of green infrastructure, adopting integrated urban planning that respects historical land use patterns, and balancing economic development with the conservation of natural resources to mitigate thermal impacts and improve regional quality of life.
creator: Mayara Ferreira
creator: Vitor Castro
creator: Marcela Alves
creator: Hugo Kuribayashi
rights: ##submission.copyrightStatement##
date: 2025-09-29
publication date: 2025-09-29
starting page: 1316
ending page: 1327
doi: 10.5753/eniac.2025.11778

Evaluation of the Detection Capabilities of Ovarian Structures in Ultrasound Images of Cows Using Convolutional Neural Networks
type: item
description: Ultrasound plays a crucial role in veterinary medicine, being widely used for the detection of diseases and the identification of reproductive conditions in animals in a less invasive and more cost-effective manner. However, the effectiveness of this method is highly dependent on the experience and training of the professional conducting the analysis, as the relatively low quality of the generated images can hinder accurate interpretation of the results. This study evaluates the ability to detect ovarian structures in a cow ultrasound image dataset using convolutional neural networks with the YoloV8 model. The results demonstrated high accuracy in the task, achieving approximately 90% accuracy in detecting objects of interest.
creator: João Moura
creator: Marcela Alves
creator: Hugo Kuribayashi
creator: André Cascalho
creator: Adam Santos
rights: ##submission.copyrightStatement##
date: 2025-09-29
publication date: 2025-09-29
starting page: 1328
ending page: 1339
doi: 10.5753/eniac.2025.11790

Evaluating the Effects of Feature set and Hyperparameter Optimization on sEMG-Based Gesture Recognition
type: item
description: Gesture recognition using myoelectric signals (sEMG) is a powerful tool for Human-Machine Interfaces (HMIs). This study investigates the classification of sEMG signals with varying sets of gestures and predictive features using Bayesian Optimization for hyperparameter tuning of Machine Learning (ML) algorithms employed for this task. Experiments were conducted on the Ninapro DB2 and DB3 datasets, covering data from both intact and amputated individuals and exploring different ML algorithms. Even though Support Vector Machines (SVMs) showed the most notable improvement through hyperparameter tuning, the best experimental results were achieved by the Random Forest (RF) classifier on both datasets, with an average F-Score of 0.853 in DB2 and 0.720 in DB3, using only five gestures and nine features. Overall, a larger feature set enhanced signal representation, particularly in reduced gesture sets, while data from amputated individuals posed greater classification challenges.
creator: Gabriel Molina de Lima
creator: André L. D. Rossi
creator: José Jair Alves Mendes Junior
creator: Daniel Prado Campos
creator: Rafael Gomes Mantovani
rights: ##submission.copyrightStatement##
date: 2025-09-29
publication date: 2025-09-29
starting page: 1340
ending page: 1351
doi: 10.5753/eniac.2025.11792

Rating Prediction in Brazilian Portuguese Reviews: An Approach Based on Textual Features
type: item
description: Este trabalho explora a predição de ratings em avaliações de usuários da Amazon escritas em português brasileiro, utilizando features textuais e modelos de aprendizado de máquina. Diferentes grupos de características textuais foram propostos e analisados, e os experimentos indicam que informações léxicas desempenham um papel fundamental na tarefa. A eficácia dos modelos varia entre categorias de produtos, sendo maior em domínios com vocabulário mais homogêneo. Como contribuição, este estudo reforça a importância de representações textuais na análise automatizada de avaliações e amplia o conhecimento sobre predição de ratings no contexto da língua portuguesa.
creator: Emanuelle Marreira
creator: Miguel de Oliveira
creator: Tiago de Melo
rights: ##submission.copyrightStatement##
date: 2025-09-29
publication date: 2025-09-29
starting page: 1352
ending page: 1363
doi: 10.5753/eniac.2025.11794

Enhancing Epidemiological Insights with RAG for SIREVA-SUS Reports
type: item
description: This study introduces a Retrieval-Augmented Generation (RAG) framework designed to extract and generate epidemiological insights from SIREVA-SUS reports, Brazil’s national surveillance system for bacterial pathogens. By integrating dense information retrieval with generative language models, this approach facilitates explainable question answering over extensive, unstructured epidemiological data, aiding healthcare professionals and researchers in detecting trends, outbreaks, and antimicrobial resistance patterns. The evaluation includes a comparison of various large language models (LLMs), such as the multilingual qwen and Portuguese fine-tuned models Sabia, port5, and Bertimbau. Additionally, the architecture examines multiple retrieval strategies, including vector, dense, sparse, hybrid, and reranker methods. Preliminary findings suggest that the system effectively retrieves relevant information with reasonable BERTScore. This work reinforces the importance of language-specific evaluation and opens new directions for deploying RAG systems in public health decision support.
creator: Christian Freitas
creator: Ricardo Trainotti Rabonato
creator: Lilian Berton
rights: ##submission.copyrightStatement##
date: 2025-09-29
publication date: 2025-09-29
starting page: 1364
ending page: 1375
doi: 10.5753/eniac.2025.11796

OASIS: An Online Adaptive System with Incremental Selection for Concept Drift in Financial Time Series Forecasting
type: item
description: A previsão de séries temporais financeiras é necessária para decisões econômicas, mas a instabilidade causada pela mudança de conceito (concept drift) tende a comprometer a acurácia dos modelos ao alterar a distribuição dos dados ao longo do tempo. Embora alguns estudos abordem a mudança de conceito, muitos ainda falham em lidar adequadamente com suas variações — a real e a virtual — o que limita a eficácia dos modelos nesses cenários não estacionários. Por isso, este trabalho propõe o OASIS (On-line Adaptive System with Incremental Selection). Resultados em sete séries financeiras demonstraram que o OASIS supera ou se iguala aos métodos da literatura que também se propõem a lidar com a mudança de conceito.
creator: Liedson D. O. Leite
creator: Gabriel F. Sarmento
creator: Gustavo H. F. de M. Oliveira
rights: ##submission.copyrightStatement##
date: 2025-09-29
publication date: 2025-09-29
starting page: 1376
ending page: 1387
doi: 10.5753/eniac.2025.11799

AutoIPA: development of an online platform to facilitate the use of automated phonetic transcription using Artificial Intelligence
type: item
description: A transcrição fonética é uma ferramenta útil para descrever a variedade inerente à linguagem. Contudo, essa técnica ainda é pouco acessível para o público geral. A partir desse paradigma, desenvolveu-se o AutoIPA (autoipa.org): uma plataforma web baseada em IA para facilitar o acesso e a utilização da transcrição fonética para o Alfabeto Fonético Internacional (IPA). Nessa toada, foram estudados e avaliados diferentes modelos pré-existentes para viabilizar o desenvolvimento de uma aplicação que automatiza a transcrição fonética. Preliminarmente, notou-se a escassez de bases de dados rotuladas, o que restringe a performance dos modelos existentes. Ademais, observou-se que as ferramentas ainda são pouco acessíveis.
creator: Guilherme Brizzi
creator: Ana Lilian Alfonso Toledo
creator: Felipe Crivellaro Minuzzi
rights: ##submission.copyrightStatement##
date: 2025-09-29
publication date: 2025-09-29
starting page: 1388
ending page: 1397
doi: 10.5753/eniac.2025.11801

Object Recognition of School Supplies via Convolutional Neural Networks for Accessibility in the Educational Environment
type: item
description: Este trabalho apresenta uma prova de conceito baseada em Redes Neurais Convolucionais (RNCs), com o objetivo de promover acessibilidade no ambiente educacional para pessoas com deficiência visual. O sistema realiza identificação em tempo real de objetos escolares por meio da arquitetura YOLOv5 e fornece feedback sonoro ao usuário. A solução utiliza uma rede pré-treinada para detectar itens relevantes, integrando bibliotecas como PyTorch, OpenCV e pyttsx3 para conversão de texto em fala. Além disso, o modelo conta com uma interface gráfica que facilita a usabilidade, tornando-se uma ferramenta inclusiva no contexto educacional.
creator: Giulia C. Bezerra
creator: João Victor M. Vantil
creator: Letícia T. M. Zoby
rights: ##submission.copyrightStatement##
date: 2025-09-29
publication date: 2025-09-29
starting page: 1398
ending page: 1406
doi: 10.5753/eniac.2025.11839

Ontology for Educational Recommendation System in an Immersive Environment
type: item
description: Educational Recommendation Systems (ERS) face challenges in evaluating student behavior and performance, aiming to generate personalized learning suggestions. This paper proposes an ontology designed to support ERS in immersive learning environments. The ontology models key elements such as student interactions, learning materials, and competencies. As part of the validation process, an automatic script was developed to generate ontology instances, and SPARQL queries were executed to simulate interaction scenarios. The results include the defined ontology taxonomy and a demonstration of how its structure can represent adaptive learning contexts.
creator: Yuri O. Magalhães
creator: Ana Luiza B. P. Barros
creator: Robson G. F. Feitosa
creator: Thiago L. Matos
creator: Claudio Fortier
creator: Antônio B. Serra
creator: Gustavo A. L. Campos
rights: ##submission.copyrightStatement##
date: 2025-09-29
publication date: 2025-09-29
starting page: 1407
ending page: 1418
doi: 10.5753/eniac.2025.11842

A Study on Convolutional Vector-Valued Neural Networks for Color Image Classification
type: item
description: Neural networks have gained significant attention in recent years since the development of deep learning. Vector-valued neural networks (V-nets), including hypercomplex-valued neural networks, are appropriate for processing multidimensional data, such as images or hyperspectral imaging. In this article, we present the mathematical foundation for the development of V-nets. We implement and apply these models for a color image classification task, specifically for detecting acute lymphoblastic leukemia in blood smear images.
creator: Carolina Rodrigues
creator: Marcos Eduardo Valle
rights: ##submission.copyrightStatement##
date: 2025-09-29
publication date: 2025-09-29
starting page: 1419
ending page: 1430
doi: 10.5753/eniac.2025.11952

Improving Fuzzy Rule-Based Classifier Systems Considering Data Streams and Concept Drift
type: item
description: Neste trabalho é avaliado o desempenho de um Sistema Classificador Baseado em Regras Fuzzy considerando fluxo de dados e concept drift. O métod Hoeffding Drift Detection Method é aplicado para detectar as mudanças de conceito no fluxo de dados, as quais podem ser de dois tipos, gradual e abrupto. Após a detecção do concept drift, três cenários foram avaliados para o ajuste das regras do sistema. No primeiro, uma nova base de regras é gerada. No segundo, um valor de utilidade é calculado para as regras, e as que tiverem utilidade abaixo dessa média são descartadas. Após isso, novas regras são criadas utilizando os dados atuais. Por fim, no terceiro cenário, novas regras são geradas, e em seguida, a utilidade é calculada. As regras, novas e antigas, que tenham utilidade menor do que a média são descartadas. Experimentos foram executados nos três cenários considerando datasets artificiais e reais, variando o tamanho da janela de dados entre os conceitos em 100 e 1000 exemplos. Os resultados mostram que os cenários 1 e 2 ajustaram melhor o sistema durante a mudança dos conceitos, tanto para os dados artificiais quanto para os reais. Por fim, o teste estatístico de Friedman foi aplicado e constatou que os resultados são significativos, com exceção de apenas dois casos.
creator: Vanderleicio Carvalho Leite Junior
creator: Matheus Giovanni Pires
rights: ##submission.copyrightStatement##
date: 2025-09-29
publication date: 2025-09-29
starting page: 1431
ending page: 1442
doi: 10.5753/eniac.2025.12289

Efficient Ensemble of CNN and Transformer Models for Cassava Leaf Disease Classification
type: item
description: Este trabalho propõe uma abordagem de ensemble para a classificação multiclasse de doenças em folhas de mandioca, integrando duas arquiteturas com características complementares em capacidade representacional e eficiência computacional: CropNet, baseada na MobileNetV3, e Swin Transformer. As imagens utilizadas foram coletadas em campo, refletindo variações reais de iluminação, fundo e posicionamento. Os modelos foram avaliados por meio de validação cruzada estratificada com K = 5 folds, utilizando o F1-score macro como principal métrica, dada a distribuição desbalanceada entre classes. Técnicas de aumento de dados foram aplicadas dinamicamente durante o treinamento para melhorar a capacidade de generalização. O ensemble superou os modelos individuais, alcançando F1-score macro médio de 0,8329 e acurácia de 0,9087. Esses resultados demonstram robustez preditiva e indicam potencial para diagnósticos visuais precisos em contextos agrícolas com restrições computacionais.
creator: Kaique O. A. dos Santos
creator: Raimundo Correa de Oliveira
rights: ##submission.copyrightStatement##
date: 2025-09-29
publication date: 2025-09-29
starting page: 1443
ending page: 1454
doi: 10.5753/eniac.2025.12386

Incremental Learning Approaches for Flood Detection in Dynamic River Environments
type: item
description: Identifying urban floods promptly can mitigate risks to public safety. Traditional models for this task rely on static datasets and are ill-suited to handle continuous environmental variation. This work presents an image-based flood monitoring system that integrates an incremental learning pipeline capable of adapting in real-time to distributional shifts. The model is periodically updated through cloud-based retraining informed by expert feedback. We evaluate different replay buffer strategies and demonstrate that the buffer replacement policy has a greater influence on performance than the buffer size. Our results show that incremental learning with selective replay significantly improves model robustness in dynamic flood monitoring scenarios.
creator: Otávio Ferracioli Coletti
creator: Saulo Matos Neves
creator: Caetano Mazzoni Ranieri
creator: Gowri Sankar Ramachandran
creator: Raja Jurdak
creator: Jó Ueyama
rights: ##submission.copyrightStatement##
date: 2025-09-29
publication date: 2025-09-29
starting page: 1455
ending page: 1466
doi: 10.5753/eniac.2025.12389

From Text to Barcode: Inferring Product Identifiers in Electronic Invoices with Missing Information
type: item
description: Notas fiscais eletrônicas são fontes valiosas de informação para a administração pública, permitindo o monitoramento de preços, detecção de fraudes e maior transparência. Um desafio recorrente, porém, é a ausência ou inconsistência de identificadores estruturados, como códigos de barras (GTINs), essenciais para comparar produtos entre transações. Frequentemente, apenas descrições textuais curtas, ruidosas e despadronizadas estão disponíveis. Este trabalho propõe uma estratégia híbrida para inferência de identificadores, combinando string-matching de alta precisão com modelos interpretáveis de aprendizado de máquina baseados em representações vetoriais. Os resultados mostram que o string-matching é preciso, mas limitado em cobertura, enquanto classificadores supervisionados ampliam a abrangência, especialmente com n-gramas de caracteres. A abordagem foi incorporada a uma ferramenta open-source para mineração fiscal, adotando métodos simples e eficientes, adequados ao processamento de dados em larga escala.
creator: Carlos Filipe de Castro Lemos
creator: Brucce Neves dos Santos
creator: Ricardo Marcondes Marcacini
rights: ##submission.copyrightStatement##
date: 2025-09-29
publication date: 2025-09-29
starting page: 1467
ending page: 1478
doi: 10.5753/eniac.2025.12392

Fooling the Model, Failing the Road: Benchmarking Inertial Sensor Fidelity in Driving Simulators
type: item
description: CARLA and SUMO are widely adopted open-source simulators in autonomous driving and intelligent transportation systems. While neither was initially designed to generate high-fidelity inertial data such as that from an Inertial Measurement Unit (IMU), both have been employed in contexts where such data is critical. In this study, we evaluate the quality of inertial data produced by CARLA and SUMO through direct comparison with the naturalistic UAH-DriveSet dataset. Our analysis reveals that SUMO performs better in qualitative and quantitative assessments. To our knowledge, this is the first systematic comparison of its kind, highlighting limitations in current simulation tools and offering insights for autonomy-related studies using simulated inertial data.
creator: Renan Florencio
creator: Silvio Fernandes
creator: Paula D. Paro Costa
rights: ##submission.copyrightStatement##
date: 2025-09-29
publication date: 2025-09-29
starting page: 1479
ending page: 1490
doi: 10.5753/eniac.2025.12416

Assessing the Potential of Deep Learning in the Acoustic Identification of Threatened Birds in the Pantanal
type: item
description: Este trabalho investiga a viabilidade do uso de técnicas de machine learning aplicadas à bioacústica para identificação automatizada de aves ameaçadas no bioma Pantanal. Dados bioacústicos foram coletados da plataforma Xeno-canto e utilizados juntamente com redes neurais convolucionais e mel spectrogram. Os resultados demonstraram desempenho satisfatório, com valores de recall que variaram entre as espécies selecionadas: Anodorhynchus hyacinthinus (0,94), Calidris canutus (0,93), Calidris pusilla (0,80), Crax fasciolata (0,70) e Harpia harpyja (0,89). Concluiu-se que a aplicação de machine learning para detecção de aves ameaçadas é tecnicamente viável, mesmo para espécies em extinção.
creator: Gabriel A. Correia
creator: Thiago M. Ventura
rights: ##submission.copyrightStatement##
date: 2025-09-29
publication date: 2025-09-29
starting page: 1491
ending page: 1502
doi: 10.5753/eniac.2025.12424

Analyzing Political Discourse on Instagram during the 2024 Municipal Elections in Northeastern Brazilian Capitals
type: item
description: Este estudo investiga as publicações realizadas no Instagram por candidatos às prefeituras das capitais da região Nordeste do Brasil durante as eleições municipais de 2024. Por meio da aplicação de técnicas de Processamento de Linguagem Natural e Mineração de Subgrupos, foram analisados os textos de candidatos que ocupavam as primeiras e segundas posições nas pesquisas eleitorais, com o objetivo de identificar padrões e comportamentos linguísticos ao longo da campanha. Os resultados revelam diferenças discursivas marcantes entre candidatos oponentes em cada cidade, bem como em relação ao espectro político (direita e esquerda).
creator: Giovanna Valentina Esteves Xavier Teles de Andrade
creator: Tarcísio Daniel Pontes Lucas
rights: ##submission.copyrightStatement##
date: 2025-09-29
publication date: 2025-09-29
starting page: 1503
ending page: 1514
doi: 10.5753/eniac.2025.12444

Sentiment Analysis for Monitoring Public Opinion on Social Media about Public Services
type: item
description: Este trabalho apresenta um sistema para monitoramento da perspectiva pública sobre serviços públicos no Brasil, a partir da análise de sentimentos de postagens da rede social Twitter. A metodologia envolve quatro etapas: coleta, pré-processamento, análise e visualização dos dados. A coleta é feita por um scraper em Python, capaz de contornar limitações das APIs oficiais e extrair tweets públicos relacionados a temas como educação, saúde e transporte. Os dados são estruturados em JSON e armazenados no banco NoSQL MongoDB. O pré-processamento aplica métodos de Processamento de Linguagem Natural para limpeza e normalização textual. A análise de sentimentos é elaborada com o modelo pré-treinado lipaoMai/BERT, que classifica os textos como positivos, negativos ou neutros. Os resultados são exibidos em um dashboard interativo desenvolvido com Angular e PrimeNG, alimentado por uma API RESTful em Java Spring Boot. A plataforma evidencia o potencial da utilização de Inteligência Artificial e técnicas de mineração de texto na gestão pública, ao oferecer apoio estratégico à formulação de decisões fundamentadas em dados atualizados e representativos da opinião da sociedade.
creator: Giulia C. Bezerra
creator: João Victor M. Vantil
creator: Letícia T. M. Zoby
rights: ##submission.copyrightStatement##
date: 2025-09-29
publication date: 2025-09-29
starting page: 1515
ending page: 1526
doi: 10.5753/eniac.2025.12445

A Hybrid Approach Combining CNN and Ensemble Algorithms for Dermoscopic Image Classification
type: item
description: Este trabalho apresenta uma abordagem híbrida para a classificação de lesões cutâneas pigmentadas, incluindo tipos de câncer de pele, em imagens dermatoscópicas. A técnica combina redes neurais convolucionais (CNNs) como extratoras de características e algoritmos ensemble como classificadores, além da introdução de duas etapas distintas de pré-processamento para aumento de dados. A primeira etapa ocorre antes do treinamento das CNNs, enquanto a segunda é aplicada antes do treinamento dos classificadores. Os experimentos realizados com a base HAM10000 evidenciam a eficácia do método, alcançando F1-Scores gerais superiores a 80%. Além disso, o estudo aponta direções para trabalhos futuros, visando o aprimoramento do método.
creator: Pedro A. A. Soares
creator: Leandro A. Ensina
creator: Juliano H. Foleis
rights: ##submission.copyrightStatement##
date: 2025-09-29
publication date: 2025-09-29
starting page: 1527
ending page: 1538
doi: 10.5753/eniac.2025.12460

Vector-Based Approach to the Max-Cut Problem: Conversion to Clustering and Validation on Structured Graphs
type: item
description: Este trabalho propõe uma abordagem não supervisionada para o Problema do Corte Máximo (Max-Cut) via conversão em tarefa de agrupamento. Combina três representações de vértices (Node2Vec, Spectral e atributos locais) com algoritmos KMeans e hierárquico. Validação em 57 instâncias da Big Mac Library (coeficiente de aglomeração > 0.2) mostra desempenho superior da representação por atributos locais com KMeans (91.4% de similaridade, 41 instâncias > 90%), seguida por Spectral (82%) e Node2Vec (72%). Resultados indicam eficácia em grafos com alta aglomeração, com limitações em estruturas esparsas (coeficiente < 0.2). A abordagem substitui métodos combinatórios por análise vetorial direta, oferecendo solução escalável para Max-Cut em média escala.
creator: Jailon W. B. Oliveira da Silva
creator: Carlos V. Dantas Araújo
creator: Pablo L. Braga Soares
rights: ##submission.copyrightStatement##
date: 2025-09-29
publication date: 2025-09-29
starting page: 1539
ending page: 1550
doi: 10.5753/eniac.2025.13098

Spatial Decomposition and Hybrid Optimization for the Online Order Batching Problem under Logistic Constraints
type: item
description: Este trabalho aborda o Problema de Loteamento de Pedidos Online, desafio central na otimização de centros de distribuição. A abordagem híbrida proposta combina aprendizado de máquina não supervisionado (clusterização baseada em densidade espacial) com Têmpera Simulada Multi-Start para convergência rápida a soluções de alta qualidade. Validada em 144 instâncias de benchmark dos conjuntos Albareda-Sambola e Henn, o método alcança soluções com gap médio de 7.9% em relação ao estado da arte, enquanto reduz o tempo computacional em 93.2% em comparação ao mesmo. A técnica resolve instâncias grandes (n=250) em menos de 60 segundos, demonstrando viabilidade para operações logísticas em tempo real.
creator: Carlos V. Dantas Araújo
creator: Jailon W. B. Oliveira da Silva
creator: Pablo L. Braga Soares
rights: ##submission.copyrightStatement##
date: 2025-09-29
publication date: 2025-09-29
starting page: 1551
ending page: 1562
doi: 10.5753/eniac.2025.13614

Clustering-LNS: Hybrid Methodology for the Single Row Facility Layout Problem
type: item
description: Este trabalho introduz uma metodologia híbrida para o Problema de Arranjo Físico em Linha Única (SRFLP), combinando aprendizado não supervisionado com a meta-heurística de Busca em Vizinhança Ampla (LNS). A abordagem proposta utiliza algoritmos de clusterização para analisar a matriz de custos do problema, gerando uma solução inicial de alta qualidade que guia a LNS. O método foi testado em 128 instâncias de benchmark, demonstrando alto desempenho. Para instâncias clássicas (n ≤ 110), o método proposto encontrou consistentemente soluções de qualidade igual ao estado da arte. Em instâncias de grande escala (n ≤ 500), alcançou as melhores soluções conhecidas em aproximadamente 40% dos casos, com um gap médio inferior a 1% nos demais.
creator: Carlos V. Dantas Araújo
creator: Jailon W. B. Oliveira da Silva
creator: Pablo L. Braga Soares
rights: ##submission.copyrightStatement##
date: 2025-09-29
publication date: 2025-09-29
starting page: 1563
ending page: 1574
doi: 10.5753/eniac.2025.13642

BichoBot – A Chatbot to Assist in the Reception of Incoming University Students
type: item
description: The beginning of the journey in a higher education course is often surrounded by doubts from incoming students about various topics such as scholarship policies, course characteristics, library use, university restaurant, among other aspects of university life. Information about these topics is often found on different websites and documents, which makes it difficult for incoming students to find. Given this difficulty, actions that centralize this information to help students identify it become necessary. Thus, in this context, this article presents BichoBot, a chatbot developed to support freshmen in accessing institutional information at the Federal University of Ceará (UFC) - Quixadá Campus. BichoBot involves the use of the Rasa and RAG frameworks, with a LLaMA 3 language model. BichoBot offers contextualized and reliable answers via Telegram. The proposed solution was evaluated by UFC freshmen, demonstrating good performance in addressing the most common questions from students.
creator: Sheiely do Ó Nascimento
creator: Enyo Gonçalves
creator: Marcos Oliveira
creator: Erlânio Freire Barros
rights: ##submission.copyrightStatement##
date: 2025-09-29
publication date: 2025-09-29
starting page: 1575
ending page: 1586
doi: 10.5753/eniac.2025.13665

Automated Vin Identification Approach With Computer Vision For Vehicle Recognition
type: item
description: Impulsionado pela necessidade de combater fraudes veiculares, que ultrapassaram R$ 504 milhões no primeiro semestre de 2024 no Brasil, este trabalho apresenta um sistema automatizado de visão computacional para a detecção e o reconhecimento de números de chassi. A solução é estruturada em uma pipeline de cinco etapas, que abrange desde a localização da região de interesse com Caixas Delimitadoras Orientadas (OBB) e a correção de sua orientação, até o reconhecimento óptico de caracteres (OCR) e um refinamento final via pós-processamento com regras de negócios. A principal contribuição reside na demonstração de que o enriquecimento do treinamento com dados sintéticos é crucial para a robustez do sistema em cenários reais, permitindo que a pipeline alcance 96% de acurácia na leitura completa do chassi. Futuras melhorias se concentrarão na otimização da acurácia para uma leitura perfeita e na exploração de arquiteturas end-to-end para simplificar a solução e analisar seu desempenho computacional em tempo real.
creator: Lucas Buligon Antunes
creator: Guilherme Loan Schneider
creator: Igor Wiese
creator: Thiago França Naves
creator: Anderson Soares
rights: ##submission.copyrightStatement##
date: 2025-09-29
publication date: 2025-09-29
starting page: 1587
ending page: 1598
doi: 10.5753/eniac.2025.13666

Exploring Emerging Topics on Antibiotic Use in Brazilian Tweets via Unsupervised Learning
type: item
description: Antibiotic misuse plays a critical role in the global spread of antimicrobial resistance, underscoring the need to understand how the public discusses and perceives antibiotic use. This study investigates emerging topics related to antibiotic use among Brazilian Twitter users by applying unsupervised learning techniques. We collected a corpus of Portuguese-language tweets and modeled the textual data using BERTimbau, a language representation model pretrained specifically for Brazilian Portuguese. To uncover latent structures in the data, we applied K-Means clustering on the sentence embeddings, enabling us to identify and interpret thematic groupings within the public discourse. The analysis revealed recurring topics such as respiratory infections, public figures, and self-care dominate discussions. These insights demonstrate the value of combining social media data with unsupervised learning to support public health communication and surveillance strategies in Brazil.
creator: Hudson Mazza
creator: Lilian Berton
rights: ##submission.copyrightStatement##
date: 2025-09-29
publication date: 2025-09-29
starting page: 1599
ending page: 1609
doi: 10.5753/eniac.2025.13765

Reinforcement Learning for Automated Investment in the Brazilian Stock Market: A Comparative Study of DQN, PPO, and Their Recurrent Versions
type: item
description: Este trabalho investiga a aplicação de Aprendizado por Reforço no desenvolvimento de agentes de investimento automatizado no mercado de ações brasileiro (B3), com foco em estratégias de curto prazo (swing trading). Foram comparados os algoritmos DQN, PPO e suas versões recorrentes (R-DQN e R-PPO). O ambiente de simulação utilizou dados históricos de cinco empresas brasileiras, com indicadores técnicos como atributos de entrada. Para garantir comparação justa, controlou-se a exposição aos dados e adotou-se uma função recompensa padronizada. Os resultados mostraram que algoritmos baseados em DQN superaram os de PPO, e que as versões recorrentes tiveram desempenho superior às tradicionais.
creator: Paulo R. Sturion
creator: André Carlos P. de L. F. de Carvalho
rights: ##submission.copyrightStatement##
date: 2025-09-29
publication date: 2025-09-29
starting page: 1610
ending page: 1621
doi: 10.5753/eniac.2025.13791

Comparative Study of Methods of Feature Extraction from Keypoints for the Classification of General Movements in Newborns
type: item
description: A avaliação de movimentos gerais (GMA) é um método muito usado para prever disfunção motora, principalmente paralisia cerebral. Nos últimos anos, vários trabalhos têm feito progresso na automatização da GMA utilizando características extraídas manualmente de pontos-chaves identificados em vídeos, combinadas com modelos de aprendizado de máquina rasos ou profundos. Entretanto, não existe um consenso na literatura quanto ao melhor método de extração de características e há uma falta de trabalhos comparando métodos diferentes, especialmente para recém-nascidos. Nesse contexto, este trabalho visa comparar três métodos encontrados na literatura de extração de características a partir de pontos-chaves para a classificação de movimentos gerais de recém-nascidos.
creator: André Valente de Cristo
creator: Eulanda Miranda dos Santos
creator: Rafael Giusti
creator: Ayrles Silva Gonçalves Barbosa Mendonça
rights: ##submission.copyrightStatement##
date: 2025-09-29
publication date: 2025-09-29
starting page: 1622
ending page: 1633
doi: 10.5753/eniac.2025.13795

A Method to Predict Specific Types of Violence Against Women in Pernambuco
type: item
description: This study presents the main observations on the use of Machine Learning algorithms to predict specific types of violence against women in the state of Pernambuco. Only in 2024, more than 30.000 cases of gender-based violence were reported, with more than 100 resulting in homicide, in addition to serious harm to women’s health. Therefore, the objective of the study is to find the best models for predicting specific types of violence to mitigate future cases. To this end, data from the Ministry of Women from 2015 to 2021 were used, in addition to tests with different Machine Learning models, resulting in models with accuracy and precision above 70% and 75%, respectively. In addition, an interpretability analysis was performed to identify bias in the best model.
creator: Arthur Carvalho da S. Xavier
creator: Cleyton M. de Oliveira Rodrigues
rights: ##submission.copyrightStatement##
date: 2025-09-29
publication date: 2025-09-29
starting page: 1634
ending page: 1645
doi: 10.5753/eniac.2025.13843

Uncovering Algorithmic Fairness in Deep Learning–Based Imputation of Multivariate Clinical Time Series in Heart Failure Patients
type: item
description: Deep learning for missing data imputation (MDI) in healthcare time series has advanced, but fairness concerns remain underexplored. Traditional evaluations focus on global error metrics (e.g., MAE), overlooking disparities across variables and protected subgroups. We analyze five state-of-the-art MDI models (BRITS, SAITS, USGAN, GPVAE, MRNN) on a heart failure dataset (PhysioNet), assessing fairness via Lorenz curves and the Gini coefficient. Results reveal that low MAE does not imply fairness. SAITS was most efficient (MAE = 0.241) but least fair (Gini = 0.615), while MRNN was less efficient (MAE = 0.672) yet fairer (Gini = 0.439). We highlight the need to balance accuracy and fairness in MDI for clinical applications.
creator: Rayssa Muniz
creator: Victor Lima
creator: Paloma Saldanha
creator: Andrea Ribeiro
creator: Rodrigo de Paula
creator: Martin Cadeiras
creator: Carlo da Silva
creator: Hítalo Silva
creator: Paulo Rocha
creator: Diego Pinheiro
rights: ##submission.copyrightStatement##
date: 2025-09-29
publication date: 2025-09-29
starting page: 1646
ending page: 1657
doi: 10.5753/eniac.2025.13855

Analyzing datasets under Instance Space Analysis framework: extension and exploration of hardness embeddings
type: item
description: In Machine Learning (ML), no single algorithm consistently outperforms others across all datasets. Meta-Learning aims to address the challenge of choosing suitable ML algorithms for new tasks by leveraging relationships between dataset characteristics and algorithm performance. One framework that supports this process is Instance Space Analysis (ISA), which enables the visualization of algorithm performance and instance hardness in a two-dimensional space. This study expands the ISA framework for analyzing classification datasets and ML algorithms, making it more complete and enabling meaningful insights into the relationships between dataset characteristics and ML classification performance.
creator: Diogo B. Rodrigues
creator: Lucas R. R. Barros
creator: Alfredo A. A. E. de Queiroz
creator: Ana C. Lorena
rights: ##submission.copyrightStatement##
date: 2025-09-29
publication date: 2025-09-29
starting page: 1658
ending page: 1669
doi: 10.5753/eniac.2025.13857

CANDAS Dataset: A Cooling Fan Sound Dataset with Modeled Disturbances and Controlled Experimental Conditions
type: item
description: Predictive maintenance cuts economic and safety risks in rotating machinery by leveraging vibration and acoustic data, which machine-learning models convert into intelligent fault detectors. Acoustic signals are especially powerful for early fault detection and cooling fans, with simple rotational dynamics, are convenient proxies for complex rotors. Yet existing fan datasets lack disturbance models and controlled conditions. We present CANDAS, a controlled sound dataset featuring 28 h of recordings from two cooling fans under five modeled disturbance conditions. Baseline experiments with three anomalydetection models validate its value, advancing reproducible research on acoustic fault detection in rotating machinery.
creator: Henrique Lima
creator: Cristofer Silva
creator: Ricardo Nery
creator: Wilmer Córdoba Camacho
creator: Ricardo Brazileiro
creator: Rodrigo de Paula Monteiro
creator: Andrea Maria Nogueira Cavalcanti Ribeiro
creator: Carmelo José Albanez Bastos-Filho
creator: Mariela Cerrada
creator: Diego Pinheiro
rights: ##submission.copyrightStatement##
date: 2025-09-29
publication date: 2025-09-29
starting page: 1670
ending page: 1681
doi: 10.5753/eniac.2025.13864

Comparative Evaluation of Class Balancing Strategies for Depression Detection in Reddit Posts
type: item
description: Conjuntos de dados reais em saúde geralmente são desbalanceados, o que dificulta a construção de modelos preditivos eficazes. Este trabalho avalia o impacto de técnicas de balanceamento na detecção de sinais de depressão em postagens do Reddit, utilizando o conjunto de dados do eRisk 2017. Foram testadas quatro estratégias de amostragem e uma abordagem sem reamostragem, aplicadas aos modelos Random Forest, XGBoost e GLM. As métricas analisadas foram sensibilidade, especificidade e AUC-ROC. A subamostragem parcial atingiu AUC-ROC de 0,72 com GLM (lambda = 1), enquanto a combinação com superamostragem obteve 0,68 com RF (mtry = 126). Os resultados reforçam a importância de técnicas de balanceamento para predição em saúde mental.
creator: Thallyson G. M. C. Fontenele
creator: Bruno Feres de Souza
rights: ##submission.copyrightStatement##
date: 2025-09-29
publication date: 2025-09-29
starting page: 1682
ending page: 1693
doi: 10.5753/eniac.2025.13879

Comparison of Machine Learning Techniques for Biomechanical Movement Analysis in Physical Exercises
type: item
description: A execução incorreta de exercícios pode comprometer a reabilitação e gerar novas lesões, tornando essencial a análise biomecânica precisa. Este estudo investiga métodos alternativos para essa análise usando visão computacional e aprendizado de máquina, focando em três exercícios: agachamento, flexão de joelhos e extensão de quadril. Foram testados três modelos com diferentes estratégias de pré-processamento e aprendizado, destacando-se a combinação MediaPipe e LSTM (Long Short-Term Memory) pelas características complementares. Todos apresentaram resultados satisfatórios em acurácia e viabilidade, permitindo comparação entre abordagens e o desenvolvimento de um protótipo funcional.
creator: Patrick Burin Rodriguez
creator: Luis Ricardo Fiera
creator: Luciano Antunes
creator: Marlon Oliveira
rights: ##submission.copyrightStatement##
date: 2025-09-29
publication date: 2025-09-29
starting page: 1694
ending page: 1703
doi: 10.5753/eniac.2025.13881

FCKAN: Evaluating KAN for Time Series Classification and Extrinsic Regression
type: item
description: Time Series Classification (TSC) and Time Series Extrinsic Regression (TSER) are critical tasks across diverse fields. While Fully Convolutional Networks (FCNs) effectively capture temporal dependencies, Kolmogorov–Arnold Networks (KANs) offer greater flexibility and interpretability. However, integrating KANs with temporal encoders and their application to regression tasks remain largely unexplored. This paper introduces FCKAN and Hybrid FCN-KAN, two novel architectures that combine FCNs and KANs for TSC and TSER. The first is an end-to-end model, while the second is a hybrid approach that leverages a pre-trained FCN as a feature extractor followed by a KAN. We conduct experiments on 147 benchmark datasets. For TSC, both architectures outperform non-temporal baselines and achieve competitive performance with FCNs. In TSER, although all models are statistically equivalent, temporal models consistently outperform non-temporal baselines.
creator: Gabriel da Costa Merlin
creator: Adilson Junior Alves Medronha
creator: Diego Furtado Silva
rights: ##submission.copyrightStatement##
date: 2025-09-29
publication date: 2025-09-29
starting page: 1704
ending page: 1715
doi: 10.5753/eniac.2025.13884

Negotiating LLMs for Enhanced Hate Speech Classification and Interpretability
type: item
description: Traditional hate speech classification frameworks rely on a single Large Language Model (LLM) operating in an isolated, single-turn decision-making process. However, this approach can suffer from limitations in handling nuanced linguistic phenomena such as sarcasm, ambiguity, and contextual shifts. To address these challenges, we introduce a multi-LLM negotiation framework, where two specialized models engage in iterative exchanges to refine the classification decision. The generator proposes a classification alongside a rationale, while the discriminator evaluates its credibility and requests adjustments until a consensus is reached. Experiments conducted on Twitter in Portuguese hate speech detection dataset, demonstrate that the negotiation-based approach using Sabizinho 3 and GPT 4.1 Nano with zero-shot, achieve competitive precision and recall. Furthermore, results indicate that this framework allow interpretability of the final classification.
creator: João Assaoka
creator: Lilian Berton
rights: ##submission.copyrightStatement##
date: 2025-09-29
publication date: 2025-09-29
starting page: 1716
ending page: 1727
doi: 10.5753/eniac.2025.13886

Comparative Analysis of Classical and Deep Algorithms for Text Clustering in Brazilian Portuguese
type: item
description: This work compares different combinations of text embedding models and clustering algorithms applied to Portuguese-language texts. Three datasets were used (poems, Reddit, and product reviews), evaluating models such as BERTimbau and ST5, combined with classic algorithms and the deep method DEC. Using accuracy, V-Measure, and ARI as metrics, results show that BERTimbau performs better on formal texts, while ST5 excels in informal content. DEC outperformed others only on the largest dataset (product reviews), highlighting the potential of deep clustering approaches for Portuguese text analysis.
creator: Paulo V. Mourão
creator: Marcela P. Pessoa
creator: Oswald M. Ekwoge
creator: Marcelo E. Anjos
rights: ##submission.copyrightStatement##
date: 2025-09-29
publication date: 2025-09-29
starting page: 1728
ending page: 1738
doi: 10.5753/eniac.2025.13904

A Robust Pseudo-label Reevaluation Strategy for the Self-training Algorithm
type: item
description: Este trabalho propõe uma extensão do algoritmo Self-Training com reavaliação iterativa de pseudo-rótulos, utilizando a métrica de silhueta para identificar e remover instâncias ruidosas, e um comitê de classificadores com votação ponderada para reforçar decisões em casos de baixa confiança. A abordagem visa mitigar a propagação de erros e aumentar a robustez do aprendizado semissupervisionado. Avaliações realizadas em 18 bases de dados demonstraram desempenho superior ao self-training original em termos de acurácia, F1-score e estabilidade, especialmente em cenários com poucos dados rotulados.
creator: Luiz M. S. Silva
creator: Renan M. R. A. Costa
creator: José A. A. Paiva
creator: Arthur C. Gorgônio
creator: Karliane M. O. Vale
creator: Flavius L. Gorgônio
rights: ##submission.copyrightStatement##
date: 2025-09-29
publication date: 2025-09-29
starting page: 1739
ending page: 1750
doi: 10.5753/eniac.2025.13905

Use of Machine Learning Techniques for Organizing Digital Documents in PDF Format
type: item
description: Este trabalho apresenta um sistema automatizado para organização de documentos PDF utilizando técnicas de Machine Learning, comparando Regressão Logística e BERT para tarefas de classificação. Utilizando dois conjuntos de dados públicos, que foram combinados para totalizar 4.901 documentos distribuídos em até cinco categorias, o estudo envolveu pré-processamento, extração de texto e codificação de categorias. Os modelos foram avaliados por acurácia, precisão, recall e F1-score, e integrados a uma API REST desenvolvida com Django. O BERT alcançou até 0,97 de acurácia e F1-score acima de 0,95, superando a Regressão Logística. Os resultados destacam a eficácia da IA avançada na melhoria da organização de documentos digitais.
creator: Thiago Serafina Possamai
creator: Luis Ricardo Fiera
creator: Luciano Antunes
creator: Marlon Oliveira
rights: ##submission.copyrightStatement##
date: 2025-09-29
publication date: 2025-09-29
starting page: 1751
ending page: 1760
doi: 10.5753/eniac.2025.13906

ErrorSim: a deep learning error simulator in left ventricle segmentations
type: item
description: A segmentação do ventrículo esquerdo em exames de Ressonância Magnética Cardíaca é importante para o diagnóstico médico. Métodos de aprendizado profundo têm se destacado ao obter segmentações semelhantes à de especialistas. Entretanto, uma das limitações atuais é a produção arbitrária de erros anatômicos que podem comprometer o diagnóstico. Dado esse problema, esse trabalho propõe um arcabouço para categorização e simulação controlada e automática de diferentes erros anatômicos. O arcabouço favorece o desenvolvimento de métodos voltados à detecção e à correção desses erros. Resultados indicam que o arcabouço proposto é capaz de gerar erros de diversas categorias e replicar os mesmos erros produzidos por redes (Dice > 0.8).
creator: Bruno F. Raquel
creator: Matheus A. O. Ribeiro
creator: Fátima L. S. Nunes
rights: ##submission.copyrightStatement##
date: 2025-09-29
publication date: 2025-09-29
starting page: 1761
ending page: 1772
doi: 10.5753/eniac.2025.13909

Dynamic Sign Recognition in Brazilian Sign Language Through Convolutional Neural Networks
type: item
description: O reconhecimento automático da Língua Brasileira de Sinais (LIBRAS) é fundamental para promover a inclusão e a acessibilidade de pessoas surdas. Este estudo foca no reconhecimento de letras representadas por sinais dinâmicos, que envolvem movimentos temporais e ainda representam um desafio para os modelos computacionais. Foram avaliadas cinco arquiteturas de Redes Neurais Convolucionais (CNNs) — AlexNet, DenseNet121, InceptionV3, ResNet18 e VGG16Net — sendo que a ResNet18 obteve a maior acurácia (74,29%), destacando-se na captura de características complexas. Apesar do uso de modelos mais simples em comparação a outros presentes na literatura, os resultados foram relevantes, considerando as limitações do conjunto de dados.
creator: João Paulo Moreira Rodrigues
creator: Ana Claudia Barbosa
creator: Luis Ricardo Fiera
creator: Marlon Oliveira
rights: ##submission.copyrightStatement##
date: 2025-09-29
publication date: 2025-09-29
starting page: 1773
ending page: 1784
doi: 10.5753/eniac.2025.13920

bni: A PDDL to C compiler with integrated REPL for interactive testing
type: item
description: This paper presents the implementation of a modular parser and an interactive REPL PDDL, entirely developed in the C programming language. The proposed solution enables the translation of PDDL domain and problem specifications into efficient, manipulable C data structures, fostering transparency, performance, and portability. Complementing the parser, the integrated REPL facilitates incremental testing and interactive debugging of planning domains and problem instances, supporting a cycle of real-time action validation and state exploration. The system also incorporates an integrated validation functionality. This feature enables both incremental and post hoc verification of action sequences and complete plans, ensuring that they adhere to the defined domain and problem specifications. The validate capability of the tool allows users to detect logical inconsistencies and confirm goal achievement efficiently, even in large-scale instances. Compared to established tools such as VAL, the proposed solution offers improved flexibility and performance for plan validation, particularly in scenarios involving extensive and complex plans. This work represents a novel contribution to the field of automated planning, bridging the gap between high-level modelling languages and low-level systems programming.
creator: Bruno Campos Ribeiro
creator: Igor e Silva Penha
creator: Bruno Cesar Ribas
rights: ##submission.copyrightStatement##
date: 2025-09-29
publication date: 2025-09-29
starting page: 1785
ending page: 1796
doi: 10.5753/eniac.2025.13960

Efficient Compression-Based Low-resource Text Classification
type: item
description: In recent years, the machine learning community has engaged on training increasingly powerful large language models (LLMs) for text classification tasks. These LLMs demand huge computational capabilities, requiring billions of parameters and enormous amounts of labeled data, which can make them expensive to use, optimize, and deploy in practice. However, recent studies on compressor-based distance metrics for text classification have been proposed, aiming to achieve competitive accuracy when compared with these LLM models. Compression-based models are usually based on k-nearest neighbors (KNN) and demand very few training parameters. In this paper, we propose a compression-based method that uses a Burkhard-Keller tree (BKTree) for similarity search, accelerating the KNN on compressed data. When compared with the state-of-the-art, the proposed method achieved speedups of 20x, 25x, 7x, 6.6x, 8x, 10x, 1.4x, 1.9x, 11x, and 12x for the Brotli, FSST, LZ4, LZAV, LZF, QuickLZ, Shoco, Smaz, Snappy, and ZLib compression algorithms, respectively. The proposed method is able to achieve similar prediction performance results while reducing the average runtime and computational resources, demonstrating its advantage in saving computational resources.
creator: Bruno Vargas de Souza
creator: Pedro Garcia Freitas
rights: ##submission.copyrightStatement##
date: 2025-09-29
publication date: 2025-09-29
starting page: 1797
ending page: 1808
doi: 10.5753/eniac.2025.13966

Convolutional Neural Networks for Sign Language Detection and Interpretation to Assist Individuals with Hearing Impairment
type: item
description: Este artigo apresenta um protótipo web para identificação e classificação de sinais do alfabeto manual da Língua Brasileira de Sinais (LIBRAS), utilizando redes neurais convolucionais treinadas com aprendizado supervisionado e otimizadas com Stochastic Gradient Descent. A solução emprega TensorFlow, Keras e MediaPipe para detecção em tempo real, com imagens RGB de 64x64 píxeis. O modelo alcançou 98,67% de acurácia na validação, com desempenho semelhante no teste. A análise por classe indicou 100% de precisão em 12 das 21 letras. Em testes com webcam, a acurácia variou entre 50% e 100% conforme o cenário. Os resultados demonstram o potencial do sistema em aplicações acessíveis no navegador.
creator: Gustavo Antonio dos Santos Fontana
creator: Marlon Oliveira
creator: Valter Blauth Junior
creator: Merisandra C. Mattos
creator: Sérgio Coral
rights: ##submission.copyrightStatement##
date: 2025-09-29
publication date: 2025-09-29
starting page: 1809
ending page: 1820
doi: 10.5753/eniac.2025.14000

Artificial Intelligence for Book Cover Recognition and Virtual Annotation on Physical Books: An Approach to Support Academic Study
type: item
description: Esta pesquisa apresenta um protótipo de inteligência artificial para reconhecimento automático de livros por imagens de capas, com funcionalidades de anotação virtual e catalogação digital para apoio aos estudos acadêmicos. A proposta supera limitações dos livros físicos, como a falta de anotações diretas e dificuldades na organização e recuperação de referências. A metodologia aplica visão computacional e aprendizado de máquina, utilizando redes neurais convolucionais para identificação precisa. Um sistema interativo permite criar anotações virtuais nas imagens, ampliando a interação com o conteúdo. O protótipo obteve resultados promissores, com acurácia superior a 99%, mesmo em imagens capturadas em condições não controladas.
creator: Vitor Jacinto Frassetto
creator: André Faria Ruaro
creator: Leila Laís Gonçalves
creator: Marlon Oliveira
rights: ##submission.copyrightStatement##
date: 2025-09-29
publication date: 2025-09-29
starting page: 1821
ending page: 1832
doi: 10.5753/eniac.2025.14002

Comparative Analysis between Genetic Programming and Machine Learning Algorithms in Forecasting Financial Trends
type: item
description: Este estudo apresenta uma análise comparativa entre a Programação Genética (GP) e cinco algoritmos de aprendizado de máquina (SVM, AdaBoost, XGBoost, LSTM e DNN) aplicados à previsão de tendências financeiras. Os dados utilizados compreendem séries históricas diárias dos índices NASDAQ, S&P 500 e Nikkei 225, no período de janeiro de 2015 a janeiro de 2025. A avaliação do desempenho foi realizada com base nas métricas Sharpe Ratio e Sortino Ratio, considerando tanto o retorno quanto o risco associado. Os resultados indicam que a GP demonstrou maior estabilidade nos mercados asiáticos, enquanto os modelos LSTM e XGBoost se destacaram por seus desempenhos superiores em mercados norte-americanos.
creator: Marcos V. R. Pedroza
creator: Carlos A. Silva
rights: ##submission.copyrightStatement##
date: 2025-09-29
publication date: 2025-09-29
starting page: 1833
ending page: 1843
doi: 10.5753/eniac.2025.14096

Comparative Analysis of Implicit Sentiment Detection with Causal and Prompt-Based LLMs
type: item
description: Implicit Sentiment Analysis (ISA) remains a challenging NLP problem, as models frequently rely on superficial shortcuts rather than deep contextual cues. This paper directly contrasts two paradigms: a specialized causal model named CLEAN, designed for robustness against spurious correlations and built on a BERT backbone, and a suite of modern open-source large language models (LLMs) such as Llama-3, Gemma-3, Qwen-3, and DeepSeek-R1, executed locally via a streamlined deployment framework. Experiments using widely recognized benchmarks for sentiment analysis reveal that, although prompted LLMs markedly outperform traditional fine-tuning, the causal CLEAN model retains a robustness advantage on the most subtle implicit cases. Our analysis clarifies current trade-offs between the broad versatility of LLMs and the targeted precision of causal methods. As future work, we highlight three directions: (i) combining causal regularization techniques with parameter-efficient fine-tuning approaches like low-rank adaptation methods to fuse both strengths, (ii) extending evaluation to cross-domain and multilingual ISA scenarios, and (iii) integrating explanation-based feedback loops to further reduce shortcut learning observed in prior approaches to sentiment analysis.
creator: Marco Antônio Martins Ribeiro de Jesus
creator: Ahmed Esmin
rights: ##submission.copyrightStatement##
date: 2025-09-29
publication date: 2025-09-29
starting page: 1844
ending page: 1853
doi: 10.5753/eniac.2025.14104

Predicting Student Dropout Rates at Higher Degree Using Machine Learning and Dimensionality Reduction
type: item
description: This study proposes four indices for predicting student dropout in a computing course at a higher education institution, using machine learning (ML) and dimensionality reduction. Ten classification algorithms were applied to three datasets, including versions with and without the proposed indices. The best performance was achieved by QDA. SHAP analysis highlighted persistence and number of enrollments as the most relevant predictors.
creator: Wanessa S. Bezerra
creator: Karliane M. O. Vale
creator: Flavius L. Gorgônio
creator: Fabrício V. A. Guerra
creator: Arthur C. Gorgônio
creator: Anne M. P. Canuto
rights: ##submission.copyrightStatement##
date: 2025-09-29
publication date: 2025-09-29
starting page: 1854
ending page: 1865
doi: 10.5753/eniac.2025.14119

A Novel Approach for Unveiling Layered Geometric Patterns in Noisy Unsupervised Data: A Study on Drug-Like Molecules
type: item
description: Identifying meaningful examples in datasets where most or all data belong to a single category is a common challenge in Machine Learning (ML). In many real-world scenarios, such as in science, medicine, and industry, data for the target class is often abundant, while data from other classes is scarce or missing. This makes it difficult for ML models to differentiate between what belongs to the target class and what does not. This paper presents a novel approach to address this issue by combining clustering and geometric analysis techniques. We develop a one-class classification method capable of detecting when a sample belongs to the target class, even in the absence of labeled data from other classes. The proposed method is applied to a curated dataset containing the chemical properties of 1,615 drug molecules approved by U.S. Food and Drug Administration (FDA), offering a valuable resource for future research. Our findings indicate that integrating geometric and density-based insights improves generalization and risk estimation in one-class learning tasks, providing a robust solution for analyzing noisy, unlabeled data.
creator: Luiz C. D. Cavalcanti
creator: Ricardo A. Rios
creator: Tiago J. S. Lopes
creator: Tatiane N. Rios
rights: ##submission.copyrightStatement##
date: 2025-09-29
publication date: 2025-09-29
starting page: 1866
ending page: 1877
doi: 10.5753/eniac.2025.14128

GCS-H: A Geo-Structural Constructive Heuristic for the Uncapacitated r-Allocation p-Hub Median Problem
type: item
description: Este trabalho apresenta a GCS-H, uma heurística construtiva de passagem única para o Problema p-Hub Mediana com r-Alocação (UrApHMP). A abordagem utiliza Escalonamento Multidimensional (MDS) e K-Means para diversificação geográfica de candidatos e PageRank para seleção baseada em importância estrutural, seguida por uma construção gulosa. Validada em 231 instâncias do benchmark AP, a GCS-H supera a meta-heurística GRASP (redução de custo média de 2,47%) e alcança um gap médio de 2,35% em relação às soluções do estado da arte (Scatter Search). A heurística substitui buscas iterativas por análise estrutural, oferecendo uma solução escalável para problemas de localização de hubs.
creator: Carlos V. Dantas Araújo
creator: Jailon W. B. Oliveira da Silva
creator: Pablo L. Braga Soares
rights: ##submission.copyrightStatement##
date: 2025-09-29
publication date: 2025-09-29
starting page: 1878
ending page: 1889
doi: 10.5753/eniac.2025.14185

A Pipeline for Automated Supervised Tuning of LLMs
type: item
description: The increasing complexity of language models and the growing diversity of Natural Language Processing (NLP) tasks have emphasized the need for automated and efficient tuning processes. But, while advances in Automated Machine Learning (AutoML) and parameter-efficient fine-tuning (PEFT) techniques have been made, there is still a lack of structured workflows adapted to the specific challenges of Large Language Models (LLM). This work proposes a conceptual, modular Automated Large Language Model (AutoLLM) pipeline that integrates both full and lightweight supervised fine-tuning strategies under a unified optimization framework. The pipeline leverages simulation-based search methods, particularly bioinspired algorithms such as Genetic Algorithms (GA), to automate hyperparameter tuning in supervised LLM adaptation. Preliminary experiments were conducted using Quantized Low-Rank Adaptation (QLoRA) for lightweight fine-tuning on the summarization task. The results illustrate the adaptability and efficiency of the approach in optimizing LLMs for one downstream application while maintaining computational feasibility. The proposed pipeline offers a structured foundation for advancing AutoML in the context of language model tuning.
creator: Diego D. Fernandes
creator: Thalyson G. N. Silva
creator: Rafael S. Santos
creator: Felipe G. Marajo
creator: Cleilton L. Rocha
creator: Ana Luiza B. P. Barros
creator: Gustavo A. L. Campos
rights: ##submission.copyrightStatement##
date: 2025-09-29
publication date: 2025-09-29
starting page: 1890
ending page: 1901
doi: 10.5753/eniac.2025.14226

Obtaining Plan Traces from Natural Language Conversations in a Healthcare Chatbot
type: item
description: Automated Planning is a subarea of Artificial Intelligence (AI) that studies the deliberative process of choosing actions for an agent to achieve its goals. A planner is a problem-solving algorithm that takes as input a high-level description of the agent and its environment (planning domain) and produces a sequence of actions (plan) that moves the agent from an initial state to a goal state. Acquiring a planning domain can be performed using algorithms that learn the domain description from a kind of specific data (plan traces). Obtaining such plan traces is difficult and a viable strategy is to extract it from Natural Language information. This work aims to build a database of plan traces from Natural Language dialogue data. These data represent conversations between real users and a healthcare chatbot.
creator: Bianca Precebes da Silva
creator: Maria Viviane de Menezes
creator: Lívia Almada Cruz
creator: Bruno da Silva Pinho
creator: Luis Fernando Oliveira Sousa
rights: ##submission.copyrightStatement##
date: 2025-09-29
publication date: 2025-09-29
starting page: 1902
ending page: 1913
doi: 10.5753/eniac.2025.14239

Towards a Network Intrusion Classification System using Machine Learning
type: item
description: With the expansion of the Internet and digital transformation, cybersecurity threats have increased, including attacks such as Denial of Service (DoS), ransomware, phishing, and trojans. Intrusion Detection Systems (IDS) play a crucial role in mitigating these threats, often leveraging artificial intelligence for enhanced detection. This study proposes a machine learningbased approach for cyber-attack classification, designed for integration into IDS. Using a multilayer neural network, the model successfully classified nine attack types and normal network activities, achieving an accuracy of 90.49% through optimized intermediate layer configurations.
creator: Luna dos Santos Almeida
creator: Fabiola Spredemann
creator: Analucia Schiaffino Morales
creator: Roberto Rodrigues-Filho
creator: Alison R. Panisson
rights: ##submission.copyrightStatement##
date: 2025-09-29
publication date: 2025-09-29
starting page: 1914
ending page: 1925
doi: 10.5753/eniac.2025.14253

Parameter Estimation of a Wave Energy Converter Model using Physics Informed Neural Networks
type: item
description: Wave Energy Converters (WECs) play a vital role in the pursuit of sustainable marine energy, offering a promising solution to harness the vast potential of ocean waves to generate energy. However, the accurate identification of physical parameters governing WEC dynamics is a major challenge, especially when direct measurements of the system are limited or noisy. This paper addresses the inverse problem of estimating unknown parameters in a physical model for a WEC system using Physics-Informed Neural Networks (PINNs). The proposed approach incorporates the governing physical equations as soft constraints, giving rise to a physical loss function that is combined with a data loss function and optimized during training. By generating synthetic data that simulates the dynamic response of a WEC, we employ PINNs to infer three critical physical parameters of the model The approach demonstrates high accuracy, particularly when sufficient data is available for training, and remains robust under varying levels of noise. These findings highlight the suitability of PINNs for inverse problems in wave energy systems, supporting their use in system identification and control.
creator: João Pinheiro L. P.
creator: Rodrigo de S. Luna
creator: Daniel R. Figueiredo
rights: ##submission.copyrightStatement##
date: 2025-09-29
publication date: 2025-09-29
starting page: 1926
ending page: 1937
doi: 10.5753/eniac.2025.14260

HarpIA: a tool for comparative analysis of embedded AI models on Android
type: item
description: Este trabalho discute a fragmentação do ecossistema Android, que dificulta o benchmarking de frameworks de aprendizado de máquina, tornando-o um processo lento e dependente de recompilações. Para mitigar essa limitação, apresentamos a HarpIA, uma ferramenta de benchmarking cuja arquitetura dinâmica possibilita a avaliação de modelos de TensorFlow, PyTorch e MindSpore sem a necessidade de recompilar o APK a cada teste. O foco do estudo foi a validação da ferramenta, cuja confiabilidade foi verificada ao se comparar as métricas de tempo de inferência e consumo energético — coletadas durante testes com o dataset ImageNet-V2 em modelos convertidos via ONNX — com referências de desempenho da literatura. Como resultado, a HarpIA se apresenta como uma solução que apoia desenvolvedores na realização de comparações de desempenho de forma mais eficiente, auxiliando na escolha de frameworks para o ambiente Android.
creator: Ricardo Miranda Filho
creator: Pedro Matias
creator: Rosiane de Freitas
rights: ##submission.copyrightStatement##
date: 2025-09-29
publication date: 2025-09-29
starting page: 1938
ending page: 1949
doi: 10.5753/eniac.2025.14267

An Integrated Platform for Monitoring and Predictive Analysis to Support Decision-Making in Basic Sanitation
type: item
description: Este artigo apresenta o desenvolvimento de um sistema integrado de monitoramento e análise preditiva para o saneamento básico, com foco na ampliação da cobertura de esgoto e na redução de perdas de água. A solução foi validada com dados reais da Saneago (2019–2024) e demonstrou alto desempenho no modelo de regressão linear múltipla (R2 = 0,986, MAE = 0,48). A plataforma inclui dashboards, simulação de metas e projeções até 2030. Os resultados apontam avanços operacionais, como redução do índice de perdas em 1,9 ponto percentual e expansão da cobertura de esgoto em 7,92 p.p. O sistema mostrou-se promissor para apoiar decisões estratégicas, com potencial de adaptação a diferentes contextos operacionais.
creator: Daianne Valéria Silva
creator: Fagner Pinho Pereira Vieira dos Santos
creator: Willgnner Ferreira Santos
creator: Alisson Rodrigues Alves
rights: ##submission.copyrightStatement##
date: 2025-09-29
publication date: 2025-09-29
starting page: 1950
ending page: 1961
doi: 10.5753/eniac.2025.14338

Reinforcement Learning and Loss of Plasticity Phenomenon in Coverage Path Planning Environments: An Exploratory Study
type: item
description: This paper investigates the phenomenon of loss of plasticity in the context of Reinforcement Learning (RL) applied to Coverage Path Planning (CPP) environments, particularly with the use of curriculum learning. Unlike shortest-path problems, CPP focuses on systematically covering an area. Recent advances in deep RL often face challenges with continual learning, where the network’s ability to adapt diminishes with successive new tasks. We developed a reproducible Gym environment for single-agent CPP with varying difficulty levels and evaluated three RL models: Deep Q-Network (DQN), Proximal Policy Optimization (PPO) without L2 regularization, and PPO with L2 regularization. Our findings indicate that loss of plasticity is present in this CPP task, especially with the DQN model, as agents struggled to generalize and retain knowledge across different map layouts. Curriculum learning, in this context, did not improve the agent’s performance and sometimes appeared to hinder learning new levels. While PPO models showed slight improvements in learning later levels compared to DQN, none of the tested models fully mitigated the loss of plasticity.
creator: João Lucas Cadorniga
creator: Pedro Pertusi
creator: Fabrício J. Barth
rights: ##submission.copyrightStatement##
date: 2025-09-29
publication date: 2025-09-29
starting page: 1962
ending page: 1971
doi: 10.5753/eniac.2025.14347

Characterizing Monte Carlo Dropout to Quantify Uncertainty in Trained Neural Networks
type: item
description: Uncertainty quantification is crucial in deep learning applications where reliable decisions depend not only on accuracy but also on predictive confidence. This work investigates Monte Carlo Dropout (MC Dropout), an efficient method to estimate epistemic uncertainty in neural networks. In particular, the influence of its uncertainty parameter is characterized using the California Housing dataset in a regression task. Empirical results reveal that the estimated uncertainty is highly sensitive to this parameter, affecting both the sample average and standard deviation. Interestingly, the error (RMSE) of the model is not monotonic with the uncertainty parameter, indicating an optimal uncertainty value. These findings underscore the need for careful dropout selection during inference and motivate future research on model calibration.
creator: Rafael de S. Luna
creator: Rodrigo de S. Luna
creator: Daniel R. Figueiredo
rights: ##submission.copyrightStatement##
date: 2025-09-29
publication date: 2025-09-29
starting page: 1972
ending page: 1983
doi: 10.5753/eniac.2025.14366

An Intelligent System Based on LLMs for Automated Healthcare Data Collection
type: item
description: The Internet contains a large volume of important information for the health sector, spread across several websites. The excess of data and the variety of sources make it difficult to efficiently extract this information. This work proposes to automate this collection using Web Scraping, Natural Language Processing and Language Models. Techniques with BeautifulSoup (for static websites) and Selenium (for dynamic websites) were applied, with support from the GPT-4o-mini model. The tests showed a significant reduction in collection and structuring time. The methodology obtained 90% accuracy on static websites and 73% on dynamic websites. Despite limitations with extensive texts and Document Object Model (DOM) structure, the results show that the approach is viable.
creator: Nathália C. O. C. Guimarães
creator: Felipe C. B. Mello
creator: Talita J. Fernandes
creator: Luís F. H. Campelo
creator: João G. M. G. Teodoro
creator: Felipe A. L. Soares
rights: ##submission.copyrightStatement##
date: 2025-09-29
publication date: 2025-09-29
starting page: 1984
ending page: 1995
doi: 10.5753/eniac.2025.14369

Beyond Accuracy: A Comparative Study of Machine Learning Models for Extreme Weather Forecasting in Rio de Janeiro with a Green AI Perspective
type: item
description: In recent years, climate change has severely impacted the city of Rio de Janeiro. Some extreme events have occurred, such as temperatures above 40° C and heavy rains that have caused landslides, floods, and deaths. This study investigates the application of Machine Learning (ML) models (MLP, XGBoost, and LSTM) to predict extreme temperature and precipitation for the city of Rio de Janeiro, while critically evaluating their environmental impact through a Green AI perspective. Beyond traditional accuracy metrics like RMSE, our aim is to assess the trade-off between good prediction and computational efficiency, energy consumption, CO2 emissions, and water usage during model training.
creator: Gabriel B. Breder
creator: Gyslla Vasconcelos
creator: Mariza Ferro
rights: ##submission.copyrightStatement##
date: 2025-09-29
publication date: 2025-09-29
starting page: 1996
ending page: 2007
doi: 10.5753/eniac.2025.14418

An Agent-Based Model Integrating the Theory of Planned Behavior for Simulating the Effect of Public Policies on Agricultural Diversity
type: item
description: O estudo propõe um modelo baseado em agentes integrando a Teoria do Comportamento Planejado (TCP) para simular o impacto de políticas públicas na diversificação agrícola no município de Santana de São Francisco (SE). Implementado no NetLogo, o modelo simulou cenários com variações climáticas, preços de mercado e incentivos financeiros. Os resultados indicaram que incentivos econômicos isolados não aumentaram significativamente a diversificação, enquanto choques climáticos extremos (alta variabilidade de precipitação e umidade) tiveram maior influência nas decisões dos produtores. Entrevistas revelaram barreiras como falta de recursos financeiros e humanos, reduzindo a eficácia das políticas. Conclui-se que estratégias eficazes devem combinar incentivos, suporte técnico e adaptação climática, além de envolver os produtores no desenho das políticas.
creator: Pedro H. I. Soares
creator: Marcos A.S. da Silva
creator: Márcia H.G. Dompieri
creator: Fábio R. de Moura
creator: Neíza C. S. Batista
creator: Sonise dos S. Medeiros
rights: ##submission.copyrightStatement##
date: 2025-09-29
publication date: 2025-09-29
starting page: 2008
ending page: 2019
doi: 10.5753/eniac.2025.14420

Optimizing Energy Savings on University Campus Using IoT and Automated Planning
type: item
description: O crescimento populacional e o desenvolvimento econômico têm impulsionado o aumento do consumo de energia. Nos prédios públicos, os sistemas de climatização e iluminação correspondem a uma parcela expressiva desse consumo. A adoção de tecnologias de automação, com o uso de dispositivos baseados em Internet das Coisas (IoT), pode contribuir para a redução do desperdício de energia nesse tipo de ambiente. No entanto, soluções baseadas exclusivamente em IoT tendem a desconsiderar a dinamicidade dos espaços e o comportamento dos usuários. Para superar essa limitação, abordagens baseadas em Inteligência Artificial, como Planejamento Automatizado, têm sido utilizadas para gerenciar esses dispositivos e alcançar metas como a minimização do consumo de energia mantendo o conforto e a segurança dos usuários. Neste contexto, este trabalho propõe a integração de uma camada física composta por sensores e atuadores a um sistema de planejamento automatizado, possibilitando a auto-adaptação dos dispositivos às condições e necessidades específicas do ambiente em tempo real. Uma versão preliminar do sistema proposto foi implementada no Campus da Universidade Federal do Ceará em Quixadá (UFC-Quixadá), permitindo o monitoramento e o controle inteligente dos dispositivos em ambientes reais.
creator: Kassia C. S. Lopes
creator: Wellington Umbelino
creator: Ana Lívia Lopes
creator: Arthur R. da Silva
creator: Cecília M. C. Mesquita
creator: Marcus V. Nogueira
creator: Maria E. S. Marques
creator: Pedro N. Damacena
creator: William M. Brito
creator: Viviane Menezes
creator: Michel Sales
rights: ##submission.copyrightStatement##
date: 2025-09-29
publication date: 2025-09-29
starting page: 2020
ending page: 2031
doi: 10.5753/eniac.2025.14421

Analyzing Procedural Terrain Generation in Games from a Constraint Programming Perspective
type: item
description: A geração procedural de terrenos bidimensionais se apresenta como uma estratégia fundamental no desenvolvimento de muitos jogos digitais. Em 2016, o algortimo Wave Function Collapse (WFC) se destacou como uma técnica eficaz para a geração de terrenos de pequeno porte. Com o objetivo de abordar instâncias maiores, uma variante denominada Nested WFC (N-WFC) foi proposta em 2023. Este artigo apresenta uma análise do WFC e do N-WFC pela perspectiva do paradigma de Programação por Restrições, no qual as relações que definem a validade dos terrenos são modeladas por meio de restrições. Uma análise experimental, conduzida na Unreal Engine e em C++, com ambos algoritmos, indica que para conjuntos específicos de tiles a consistência de arco direcionada é suficiente para gerar o terreno sem efetuar backtracking.
creator: André Felipe de Almeida Pontes
creator: Gabriel Pimentel Dolzan
creator: Guilherme Alex Derenievicz
rights: ##submission.copyrightStatement##
date: 2025-09-29
publication date: 2025-09-29
starting page: 2032
ending page: 2043
doi: 10.5753/eniac.2025.14512

ModBERTBr: A ModernBERT-based Model for Brazilian Portuguese
type: item
description: A key model in the Large Language Model (LLM) field is the Bidirectional Encoder Representations from Transformers (BERT), known for its effectiveness and versatility. The current state-of-the-art variant of BERT is ModernBERT, and despite excelling in efficiency and performance, it is limited to English. This paper addresses this notable gap by introducing ModBERTBr, a novel pre-trained model based on the ModernBERT architecture, which is explicitly tailored for Brazilian Portuguese and incorporates cutting-edge research and technologies. Through both intrinsic and extrinsic evaluations, ModBERTBr was assessed against multiple baseline models, showing consistent improvements and competitive performance compared to its predecessors.
creator: Wallace Ben Teng Lin Wu
creator: Luis Paulo Faina Garcia
rights: ##submission.copyrightStatement##
date: 2025-09-29
publication date: 2025-09-29
starting page: 2044
ending page: 2055
doi: 10.5753/eniac.2025.14516

Modeling complex human activities with inertial units, ambient sensors, and machine learning
type: item
description: O reconhecimento de atividades humanas é essencial para aplicações em saúde, esportes e ambientes inteligentes. Trabalhos recentes utilizam desde modelos clássicos até redes neurais profundas. Contudo, poucos exploram a influência da similaridade entre atividades complexas na acurácia dos modelos. Este trabalho compara árvores de decisão, florestas aleatórias e uma rede neural convolucional unidimensional (CNN-1D) em dois conjuntos de dados públicos: PAMAP2 e HWU-USP. No PAMAP2, a CNN-1D mostrou-se mais robusta (acurácia de 62,48% ± 10,31%), enquanto no HWU-USP o modelo Floresta Aleatória foi mais estável (acurácia de 38,93% ± 3,94%), destacando a importância do ajuste de hiperparâmetros para tarefas complexas.
creator: Laura Sthefany Colombo
creator: André Luiz da Silva Conde
creator: Leonardo Tórtoro Pereira
creator: Caetano Mazzoni Ranieri
rights: ##submission.copyrightStatement##
date: 2025-09-29
publication date: 2025-09-29
starting page: 2056
ending page: 2067
doi: 10.5753/eniac.2025.14517

Sex Estimation from 3D Analysis of Paranasal Sinuses: A Multicenter Study Using Deep Learning and Machine Learning
type: item
description: Os seios paranasais são usados na identificação forense de sexo, geralmente via medidas manuais em imagens de tomografia (CT). Esse estudo propõe automatizar essa classificação por meio de redes neurais convolucionais 3D (ResNet-50, DenseNet-201) integradas a algoritmos supervisionados (SVM, Random Forest, MLP, KNN). Foram testadas oito combinações, das quais a ResNet-50 + RF obteve melhor desempenho com acurácia de 92,60%, precisão de 91,18%, recall de 94,55% e F1-score de 92,64%. Os resultados indicam alto potencial do uso conjunto de deep learning e aprendizado supervisionado para determinar o sexo a partir de máscaras segmentadas dos seios paranasais, com aplicação relevante na área forense.
creator: Maria Fernanda A. F. Scarcela
creator: Antonio Everton C. Teixeira
creator: Diego S. de Mendonça
creator: Saulo A. F. de Oliveira
creator: Bruno R. dos Santos Silva
creator: Carlos Caminha
creator: Fábio W. G. Costa
creator: Wellington Franco
rights: ##submission.copyrightStatement##
date: 2025-09-29
publication date: 2025-09-29
starting page: 2068
ending page: 2079
doi: 10.5753/eniac.2025.14530

Optimizing Reduced-Lead ECG Diagnosis: An Interpretable Pipeline for Lead Selection and Model Adaptation
type: item
description: The 12-lead electrocardiogram (ECG) is vital for heart diagnosis but mostly limited to clinics due to its complexity. Wearable devices use fewer electrical viewpoints (leads), democratizing cardiac care by enabling monitoring at home or in low-resource settings. This shift raises two key challenges: selecting the most informative leads and adapting AI models to keep accuracy. We tackle this with a data-driven pipeline that ranks leads by combining multiple model-interpretability methods. Our evaluation shows that a model architecturally adapted to use only the top two leads (V1, I) achieves a macro F1-score of 0.885, matching the full 12-lead baseline. This work provides a framework for efficient, powerful AI systems, advancing accessible cardiac diagnostics.
creator: Luisa G. Porfírio
creator: Guilherme H. G. Evangelista
creator: Pedro B. Rigueira
creator: Caio Souza Grossi
creator: Artur Xavier
creator: Victoria Andrade Flores de Mello
creator: Raquel Teodoro
creator: Pedro Dutenhefner
creator: Gabriela M. M. Paixão
creator: Gisele L. Pappa
creator: Antonio Ribeiro
creator: Wagner Meira Jr.
rights: ##submission.copyrightStatement##
date: 2025-09-29
publication date: 2025-09-29
starting page: 2080
ending page: 2091
doi: 10.5753/eniac.2025.14543