Intelligent Systems : 34th Brazilian Conference, BRACIS 2024, Belém do Pará, Brazil, November 17-21, 2024, Proceedings, Part I (Lecture Notes in Computer Science)

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Intelligent Systems : 34th Brazilian Conference, BRACIS 2024, Belém do Pará, Brazil, November 17-21, 2024, Proceedings, Part I (Lecture Notes in Computer Science)

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  • 製本 Paperback:紙装版/ペーパーバック版/ページ数 490 p.
  • 言語 ENG
  • 商品コード 9783031790287

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 18-21, 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

.- Main Track.

.- A Contrastive Objective for Training Continuous Generative Flow Networks.

.- A Data Distribution-based Ensemble Generation Applied to Wind Speed Forecasting.

.- A Large Dataset of Spontaneous Speech with the Accent Spoken in Sao Paulo for Automatic Speech Recognition Evaluation.

.- A Multi-Level Semantics Formalism for Multi-Agent Microservices.

.- A Novel Genetic Algorithm Approach for Discriminative Subspace Optimization.

.- A Performance Increment Strategy for Semantic Segmentation of Low-Resolution Images from Damaged Roads.

.- A Unified Framework for Average Reward Criterion and Risk.

.- Adaptive Client-Dropping in Federated Learning: Preserving Data Integrity in Medical Domains.

.- An Ensemble of LLMs finetuned with LoRA for NER in Portuguese legal documents.

.- An instance level analysis of classification difficulty for unlabeled data.

.- Analyzing the Impact of Coarsening on k-Partite Network Classification.

.- Applying Transformers for Anomaly Detection in Bus Trajectories.

.- Aroeira: A Curated Corpus for the Portuguese Language with a Large Number of Tokens.

.- Assessing Adversarial Effects of Noise in Missing Data Imputation.

.- Assessing European and Brazilian Portuguese LLMs for NER in specialized domains.

.- BASWE: Balanced Accuracy-based Sliding Window Ensemble for Classification in Imbalanced Data Streams with Concept Drift.

.- Beyond Audio Signals: Generative Model-Based Speaker Diarization in Portuguese.

.- Classification of Non-Alcoholic Hepatic Steatosis in Liver Thermal Imaging Using Siamese Neural Network.

.- Classifying graphs of elementary mathematical functions using Convolutional Neural Networks.

.- Comparing Neural Network Encodings for Logic-based Explainability.

.- Deep learning approach to temporal dimensionality reduction of volumetric computed tomography.

.- Deployment of IBM Federated Learning Platform and Aggregation Algorithm Comparison: A Case Study Using the MNIST Dataset.

.- Detection of Pathological Regions of the Gastrointestinal Tract in Capsule Images Using EfficientNetV2 and YOLOv8.

.- Dual-Bandwidth Spectrogram Analysis for Speaker Verification.

.- Dynamicity Analysis in the Selection of Classifier Ensembles Parameters.

.- Embedding Representations for AutoML Pipelines.

.- Enhancing Graph Data Quality by Leveraging Heterogeneous Node Features and Embeddings.

.- Ensemble of CNNs for Enhanced Leukocyte Classification in Acute Myeloid Leukemia Diagnosis.

.- ERASMO: Leveraging Large Language Models for Enhanced Clustering Segmentation.

.- Euclidean Alignment for Transfer Learning in Multi-band Common Spatial Pattern.

.- Evaluating CNN-Based Classification Models Combined with the Smoothed Pseudo Wigner-Ville Distribution to Identify Low Probability of Interception Radar Signals.

.- Evaluating Large Language Models for Tax Law Reasoning.

.- Explaining Biomarker Response to Anticoagulant Therapy in Atrial Fibrillation: A Study of Warfarin and Rivaroxaban with Machine Learning Models.

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