UID:
almahu_9949407266602882
Format:
XXIII, 660 p. 174 illus., 137 illus. in color.
,
online resource.
Edition:
1st ed. 2022.
ISBN:
9783031216862
Series Statement:
Lecture Notes in Artificial Intelligence, 13653
Content:
The two-volume set LNAI 13653 and 13654 constitutes the refereed proceedings of the 11th Brazilian Conference on Intelligent Systems, BRACIS 2022, which took place in Campinas, Brazil, in November/December 2022. The 89 papers presented in the proceedings were carefully reviewed and selected from 225 submissions. The conference deals with theoretical aspects and applications of artificial and computational intelligence.
Note:
Mortality Risk Evaluation: a Proposal for Intensive Care Units Patients Exploring Machine Learning Methods -- Requirements Elicitation Techniques and Tools in the Context of Artificial Intelligence -- An Efficient Drift Detection Module for Semi-supervised Data Classification in Non-Stationary Environments -- The impact of state representation on approximate Q-learning for a selection hyper-heuristic -- A network-based visual analytics approach for performance evaluation of swarms of robots in the surveillance task -- Ulysses-RFSQ: a novel method to improve Legal Information Retrieval based on Relevance Feedback -- Adaptive Fast XGBoost for Regression -- The Effects of Under and Over Sampling in Exoplanet Transit Identification with Low Signal-to-Noise Ratio Data -- Estimating Bone Mineral Density Based on Age -- Feature Extraction for a Genetic Programming-Based Brain-Computer Interface -- Selecting Optimal Trace Clustering Pipelines with Meta-Learning -- Sequential Short-Text Classification from Multiple Textual Representations with Weak Supervision -- Towards a better understanding of heuristic approaches applied to the biological motif discovery -- Mutation Rate Analysis Using a Self-Adaptive Genetic Algorithm on the OneMax Problem -- Application of the Sugeno Integral in Fuzzy Rule-Based Classification -- Improving the FQF Distributional Reinforcement Learning Algorithm in MinAtar Enviroment -- Glomerulosclerosis identification using a modified Dense Convolutional Network -- Diffusion-based Approach to Style Modeling in Expressive TTS -- Automatic Rule Generation for Cellular Automata using Fuzzy Times Series Methods -- Explanation-by-Example Based on Item Response Theory -- Short-and-Long-Term Impact of Initialization Functions in NeuroEvolution -- Analysis of the influence of the MVDR filter parameters on the performance of SSVEP-based BCI -- A Novel Multi-objective Decomposition Formulation for Per-Instance Configuration -- Improving Group Search Optimization for Automatic Data Clustering Using Merge and Split Operators -- Leveraging Textual Descriptions for House Price Valuation -- Measuring Ethics in AI with AI: A Methodology and Dataset Construction -- Time Robust Trees: Using Temporal Invariance to Improve Generalization -- Generating Diverse Clustering Datasets with Targeted Characteristics -- On AGM Belief Revision for Computational Tree Logic -- Hyperintensional Models and Belief Change -- A Multi-Population Schema designed for Biased Random-Key Genetic Algorithms on Continuous Optimisation Problems -- Answering Questions about COVID-19 Vaccines using ChatBot Technologies -- Analysis of the neutrality of AutoML Search Spaces with Local Optima Networks -- Dealing with inconsistencies in ASPIC+ -- A Grammar-based Genetic Programming Hyper-Heuristic for Corridor Allocation Problem -- Generalising Semantics to Weighted Bipolar Argumentation Frameworks -- The use of multiple criteria decision aiding methods in recommender systems: a literature -- Explaining Learning Performance with Local Performance Regions and Maximally Relevant Meta-Rules review -- Artificial Intelligence: Algorithmic Transparency and Public Policies: The Case of Facial Recognition Technologies in the Public Transportation System of Large Brazilian Municipalities -- Resource Allocation Optimization in Business Processes Supported by Reinforcement Learning and Process Mining -- Exploitability Assessment with Genetically Tuned Interconnected Neural Networks -- Predicting Compatibility of Cultivars in Grafting Processes using Kernel Methods and Collaborative Filtering -- Cross-validation Strategies for Balanced and Imbalanced Datasets -- Geographic Context-Based Stacking Learning for Election Prediction from Socio-Economic Data.
In:
Springer Nature eBook
Additional Edition:
Printed edition: ISBN 9783031216855
Additional Edition:
Printed edition: ISBN 9783031216879
Language:
English
DOI:
10.1007/978-3-031-21686-2
URL:
https://doi.org/10.1007/978-3-031-21686-2
