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Intelligent Systems: 12th Brazilian Conference, BRACIS 2023, Belo Horizonte, Brazil, September 25–29, 2023, Proceedings, Part II de Murilo C. Naldi

Intelligent Systems: 12th Brazilian Conference, BRACIS 2023, Belo Horizonte, Brazil, September 25–29, 2023, Proceedings, Part II: Lecture Notes in Computer Science, cartea 14196

Editat de Murilo C. Naldi, Reinaldo A. C. Bianchi
en Limba Engleză Paperback – 12 oct 2023
The three-volume set LNAI 14195, 14196, and 14197 constitutes the refereed proceedings of the 12th Brazilian Conference on Intelligent Systems, BRACIS 2023, which took place in Belo Horizonte, Brazil, in September 2023.
The 90 full papers included in the proceedings were carefully reviewed and selected from 242 submissions. They have been organized in topical sections as follows:
Part I: Best papers; resource allocation and planning; rules and feature extraction; AI and education; agent systems; explainability; AI models; 
Part II: Transformer applications; convolutional neural networks; deep learning applications; reinforcement learning and GAN; classification; machine learning analysis;
Part III: Evolutionary algorithms; optimization strategies; computer vision; language and models; graph neural networks; pattern recognition; AI applications. 
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Specificații

ISBN-13: 9783031453885
ISBN-10: 3031453883
Ilustrații: XVIII, 433 p. 177 illus., 141 illus. in color.
Dimensiuni: 155 x 235 mm
Greutate: 0.63 kg
Ediția:1st ed. 2023
Editura: Springer Nature Switzerland
Colecția Springer
Seriile Lecture Notes in Computer Science, Lecture Notes in Artificial Intelligence

Locul publicării:Cham, Switzerland

Cuprins

Transformer Model for Fault Detection From Brazilian Pre-Salt Seismic Data.- Evaluating Recent Legal Rhetorical Role Labeling Approaches Supported by Transformer Encoders.- Dog Face Recognition Using Vision Transformer.- Convolutional neural networks for the molecular detection of Covid-19.- Hierarchical Graph Convolutional Networks for Image Classification.- Interpreting Convolutional Neural Networks for Brain Tumor Classification: An Explainable Artificial Intelligence Approach.- Enhancing Stock Market Predictions through the Integration of Convolutional and Recursive LSTM Blocks: A Cross-Market Analysis.- Ensemble architectures and efficient fusion techniques for Convolutional Neural Networks: an analysis on resource optimization strategies.- Dog Face Recognition using Deep Feature Embeddings.- Clinical oncology textual notes analysis using machine learning and deep learning.- EfficientDeepLab For Automated Trachea Segmentation On Medical Images.- Multi-Label Classification of Pathologies in Chest Radiograph Images Using DenseNet.- Does pre-training on brain-related tasks results in better deep-learning-based brain age biomarkers.- Applying Reinforcement Learning for Multiple Functions in Swarm Intelligence.- Deep Reinforcement Learning for Voltage Control in Power Systems.- Performance Analysis of Generative Adversarial Networks and Diffusion Models for Face Aging.- Occluded Face In-painting Using Generative Adversarial Networks - A Review
Classification of facial images to assist in the diagnosis of Autism Spectrum Disorder: a study on the effect of face detection and landmark identification algorithms.- Constructive Machine Learning and Hierarchical Multi-label Classification for Molecules Design.- AutoMMLC: An Automated and Multi-objective Method for Multi-label Classification.- Merging Traditional Feature Extraction and Deep Learning for Enhanced Hop Variety Classification: A Comparative Study Using the UFOP-HVD Dataset.- Feature Selection and Hyperparameter Fine-tuning in Artificial Neural Networks for Wood Quality Classification.- A Feature-based Out-of-Distribution Detection Approach in Skin Lesion Classification.- A framework for characterizing what makes an instance hard to classify.- Physicochemical Properties for Promoter Classification.- Critical analysis of AI indicators in terms of weighting and aggregation approaches.- Estimating Code Running Time Complexity with Machine Learning
The Effect of Statistical Hypothesis Testing on Machine Learning Model Selection.