Full Description
The four-volume set LNAI 15412-15415 constitutes the refereed proceedings of the 34th Brazilian Conference on Intelligent Systems, BRACIS 2024, held in Belém do Pará, Brazil, during November 17-21, 2024.
The 116 full papers presented here were carefully reviewed and selected from 285 submissions. They were organized in three key tracks: 70 articles in the main track, showcasing cutting-edge AI methods and solid results; 10 articles in the AI for Social Good track, featuring innovative applications of AI for societal benefit using established methodologies; and 36 articles in other AI applications, presenting novel applications using established AI methods, naturally considering the ethical aspects of the application.
Contents
.- Best Papers.
.- A Topology-inspired approach to AGM Belief Change.
.- A Transformer-based Tabular Approach to Detect Toxic Comments.
.- Automatic Text Simplification for the Legal Domain in Brazilian Portuguese.
.- Developing Resource-Efficient Clinical LLMs for Brazilian Portuguese.
.- One-Class Learning for Data Stream through Graph Neural Networks.
.- Semi-Periodic Activation for Time Series Classification.
.- AI Applications for Social Good.
.- Automated and Intelligent Vocational Guidance System for Classifying Specialties Based on POSCOMP Microdata.
.- Classifying potentially non-compliant Portuguese language sentences concerning privacy policies.
.- Diversity in Data for Speech Processing in Brazilian Portuguese.
.- Elevating Healthcare AI: Achieving Efficiency and Accuracy in Medical Applications with Surrogate-Based Multiobjective Compression of ResNet50 CNNs.
.- Explainability of Machine Learning Models with XGBoost and SHAP values in the Context of Coping with Disasters.
.- Exploring Climatic Shifts in Brazilian Climates: Insights from ARMAX and Decision Trees and and Artificial Neural Networks.
.- Gender-Neutral English to Portuguese Machine Translator: Promoting Inclusive Language.
.- Hybrid Artificial Intelligence Model for Detecting Signs of Delayed Child Development.
.- Low birth weight in Brazil vulnerable groups: an analysis based on data mining and big data.
.- Modeling EEG data into graphs for the Prognostic of Patients in Coma using Graph Neural Networks.
.- Tackling Low-Resource ECG Classification with Self-Supervised Learning.
.- Tuning Hypothesis Creation: Combining Discrete and Continuous Spaces for Zero-Shot Hate Speech Detection.
