Intelligent systems : 11th Brazilian conference, BRACIS 2022, Campinas, Brazil, November 28-December 1, 2022, proceedings. Part I / / Alhamzah Alnoor, Khaw Khai Wah, Azizul Hassan, editors

• Pubblicazione: Cham, Switzerland : , : Springer, , [2022]; ©2022
• Descrizione fisica: 1 online resource (682 pages)
• Disciplina: 006.3
• Soggetto topico: Artificial intelligence.<U+0009>
• Persona (resp. second.): AlnoorAlhamzah; WahKhaw Khai; HassanAzizul
• Nota di bibliografia: Includes bibliographical references.
• Nota di contenuto: Intro -- Preface -- Organization -- Contents - Part I -- Contents - Part II -- Mortality Risk Evaluation: A Proposal for Intensive Care Units Patients Exploring Machine Learning Methods -- 1 Introduction -- 2 Related Works Exploring Machine Learning to Predict Mortality Risk in ICUs -- 3 Prediction of Mortality Risk in ICUs: Approach Design -- 3.1 Discussion of the Research Problem -- 3.2 Database and Study Population -- 3.3 Exploratory Data Analysis and Variable Selection -- 3.4 Data Preprocessing -- 3.5 Feature Construction and Data Normalization -- 4 Prediction of Mortality Risk in ICUs: Approach Evaluation -- 4.1 Tuning of the Best Performing Machine Learning Method -- 4.2 Performance Analysis of the Best Performance Method -- 5 Conclusions -- References -- Requirements Elicitation Techniques and Tools in the Context of Artificial Intelligence -- 1 Introduction -- 2 Background -- 2.1 Software Requirements -- 2.2 Ethical Requirements for AI -- 2.3 Related Works -- 3 Research Methodology -- 4 Survey Results and Discussion -- 4.1 Threats to Validity -- 5 Conclusions -- References -- An Efficient Drift Detection Module for Semi-supervised Data Classification in Non-stationary Environments -- 1 Introduction -- 2 Background -- 2.1 Flexible Confidence of a Classifier Semi-supervised Technique -- 2.2 Classifier Ensemble -- 2.3 Data Stream Classification -- 3 Related Work -- 4 The Proposed Approach -- 5 Experimental Methodology -- 6 Experimental Results -- 6.1 Batch Size Analysis -- 6.2 The Proposed Methods versus DyDaSL - FT -- 6.3 DyDaSL versus State-of-Art -- 7 Final Remarks -- References -- The Impact of State Representation on Approximate Q-Learning for a Selection Hyper-heuristic -- 1 Introduction -- 2 Reinforcement Learning -- 3 Selection Hyper-heuristic -- 4 Proposed Approach -- 4.1 State Module -- 4.2 Reward Module -- 4.3 Agent Module. ; 5 Experimental Setup -- 6 Results and Discussion -- 6.1 Bin Packing -- 6.2 Flow Shop -- 6.3 MAX-SAT -- 6.4 Personnel Scheduling -- 6.5 Traveling Salesman Problem -- 6.6 Vehicle Routing Problem -- 6.7 Overall Comparison -- 7 Conclusion -- References -- A Network-Based Visual Analytics Approach for Performance Evaluation of Swarms of Robots in the Surveillance Task -- 1 Introduction -- 2 Related Work -- 2.1 Visualisation in Robotics -- 2.2 Visualisation of Temporal Networks -- 2.3 PheroCom Model -- 3 Visualisation Proposal -- 4 Case Study -- 4.1 Surveillance Network -- 4.2 Experiments -- 5 Limitations -- 6 Conclusion and Future Work -- References -- Ulysses-RFSQ: A Novel Method to Improve Legal Information Retrieval Based on Relevance Feedback -- 1 Introduction -- 2 Literature Review -- 2.1 Legal Information Retrieval -- 2.2 Relevance Feedback and Its Use for Similar Queries -- 3 Ulysses-RFSQ: Improving LIR With Relevance Feedback -- 3.1 Step 1: Ranking The Documents -- 3.2 Step 2: Selecting Similar Queries -- 3.3 Step 3: Updating The Ranking -- 3.4 Step 4: Acquiring The Relevance Feedback Information -- 4 Experimental Setup -- 4.1 Corpora -- 4.2 Pre-processing -- 4.3 BM25 Algorithms -- 4.4 Cut-off Parameter -- 4.5 IR Evaluation Measure -- 5 Results and Discussion -- 5.1 BM25 Algorithms Comparison -- 6 Conclusion -- References -- Adaptive Fast XGBoost for Regression -- 1 Introduction -- 2 Related Works -- 3 Proposal: Adaptive Fast XGBoost Regressor -- 3.1 Implementation -- 4 Results Assessment -- 4.1 Testing Methodology -- 4.2 Analysis of the Results -- 5 Conclusion and Future Work -- References -- The Effects of Under and Over Sampling in Exoplanet Transit Identification with Low Signal-to-Noise Ratio Data -- 1 Introduction -- 2 Related Work -- 3 Folding the Light Curve -- 4 Materials and Methods -- 5 Results -- 6 Discussion -- 7 Conclusions. ; References -- Estimating Bone Mineral Density Based on Age, Sex, and Anthropometric Measurements -- 1 Introduction -- 2 Related Work -- 3 Methodology -- 3.1 Dataset -- 3.2 Learning Process -- 3.3 Evaluation -- 3.4 Interpretation -- 4 Results and Discussion -- 4.1 Cross-Validation Results -- 4.2 Interpretation -- 5 Conclusion -- References -- Feature Extraction for a Genetic Programming-Based Brain-Computer Interface -- 1 Introduction -- 2 Brain-Computer Interface and Post-stroke Motor Rehabilitation -- 3 Datasets -- 3.1 BCI Competition IV 2a -- 3.2 BCI Competition IV 2b -- 3.3 Our Dataset -- 4 Proposed Method -- 4.1 Initial Feature Extraction -- 4.2 Genetic Programming -- 5 Computational Experiments -- 5.1 Analysis of the Number of Electrodes -- 5.2 Analysis of Within and Cross-Session Training -- 5.3 Analysis Using Data Obtained with Low-Cost Equipment -- 6 Concluding Remarks and Future Work -- References -- Selecting Optimal Trace Clustering Pipelines with Meta-learning -- 1 Introduction -- 2 Related Work -- 3 Problem Statement -- 4 MtL-Based Solution for Trace Clustering -- 5 Experimental Setup -- 5.1 Event Logs and Featurization -- 5.2 Trace Encoding Techniques -- 5.3 Trace Clustering Algorithms -- 5.4 Ranking Metrics -- 5.5 Meta-model -- 6 Results and Discussion -- 6.1 Meta-learning Exploratory Analysis -- 6.2 Meta-model Performance -- 7 Conclusion -- 8 Limitations and Broader Impact Statement -- References -- Sequential Short-Text Classification from Multiple Textual Representations with Weak Supervision -- 1 Introduction -- 2 Related Work -- 3 Methods -- 3.1 Labeling Function -- 3.2 TD-BERT -- 4 Evaluation -- 4.1 Datasets -- 4.2 Pre-processing -- 4.3 Classification Models and Experimental Setup -- 4.4 Results and Discussion -- 5 Conclusion -- References. ; Towards a Better Understanding of Heuristic Approaches Applied to the Biological Motif Discovery -- 1 Introduction -- 2 Literature Review -- 3 Problem Definition and Algorithms -- 3.1 VNS -- 3.2 EM -- 3.3 ILS -- 3.4 Constructive Procedure -- 4 Experiments -- 4.1 Datasets -- 4.2 Results and Discussion -- 4.3 Statistical Analysis -- 5 Conclusion -- References -- Mutation Rate Analysis Using a Self-Adaptive Genetic Algorithm on the OneMax Problem -- 1 Introduction -- 2 Background -- 2.1 Genetic Algorithm -- 2.2 Fuzzy Logic -- 3 Methodology -- 3.1 Test Environment -- 3.2 Problem Description -- 3.3 Genetic Algorithm -- 3.4 Application of Fuzzy Logic -- 3.5 Evaluation Metrics -- 4 Results and Discussions -- 5 Conclusion -- References -- Application of the Sugeno Integral in Fuzzy Rule-Based Classification -- 1 Introduction -- 2 Preliminary Concepts and the Sugeno-like Generalization -- 3 Application of the Sugeno Integral to Classification in FRBCS -- 3.1 The New Fuzzy Reasoning Method -- 3.2 Experimental Framework -- 4 Experimental Results -- 4.1 Statistical Analysis -- 5 Conclusion -- References -- Improving the FQF Distributional Reinforcement Learning Algorithm in MinAtar Environment -- 1 Introduction -- 2 Reinforcement Learning -- 3 Related Work -- 3.1 DQN -- 3.2 Prioritized Experience Replay -- 3.3 Rainbow -- 3.4 Distributional Reinforcement Learning and FQF -- 3.5 Munchausen R.L. -- 3.6 MinAtar -- 4 Methodology -- 5 Experiments and Analysis of Results -- 5.1 Hyperparameter Tuning -- 5.2 Main Results -- 6 Conclusions and Future Works -- References -- Glomerulosclerosis Identification Using a Modified Dense Convolutional Network -- 1 Introduction -- 2 Related Works -- 3 Materials and Methods -- 3.1 Proposed Methodology -- 3.2 Image Dataset -- 3.3 Pre-processing and Data Augmentation -- 3.4 Evaluated Convolutional Neural Networks. ; 3.5 Transfer Learning and Fine-Tunning -- 3.6 Evaluation Metrics -- 4 Results and Discussion -- 5 Conclusion and Future Works -- References -- Diffusion-Based Approach to Style Modeling in Expressive TTS -- 1 Introduction -- 2 Related Works -- 3 Background -- 3.1 Denoising Diffusion Probabilistic Models -- 3.2 Shallow Diffusion Mechanism -- 4 Model -- 4.1 Model Architecture -- 4.2 Training and Inference -- 5 Experiments -- 5.1 Experimental Setup -- 5.2 Naturalness -- 5.3 Style Transfer -- 6 Discussion -- 7 Conclusion -- References -- Automatic Rule Generation for Cellular Automata Using Fuzzy Times Series Methods -- 1 Introduction -- 2 Background -- 2.1 Cellular Automata -- 2.2 Fuzzy Time Series -- 3 Related Work -- 4 Proposed Method -- 4.1 Training Procedure -- 4.2 Forecast Procedure -- 5 Computational Experiments -- 5.1 Dataset Description -- 5.2 CA-FTS Modeling -- 5.3 Discussion -- 6 Conclusions and Future Work -- References -- Explanation-by-Example Based on Item Response Theory -- 1 Introduction -- 2 Background -- 2.1 Explainable Artificial Intelligence - XAI -- 2.2 Item Response Theory - IRT -- 3 Methodology -- 3.1 ML and IRT -- 3.2 Evaluated Datasets -- 4 Results and Discussion -- 4.1 Datasets Through the Lens of IRT -- 4.2 Random Forest Through the Lens of IRT -- 5 Final Considerations -- References -- Short-and-Long-Term Impact of Initialization Functions in NeuroEvolution -- 1 Introduction -- 2 Related Work -- 3 Theoretical Foundation -- 3.1 CoDeepNEAT -- 3.2 Short, Medium and Long Term Analyses -- 3.3 Initialization and Activation Functions -- 4 Experiments and Discussion -- 5 Conclusion -- References -- Analysis of the Influence of the MVDR Filter Parameters on the Performance of SSVEP-Based BCI -- 1 Introduction -- 2 Methodology -- 2.1 Database Description -- 2.2 CAR -- 2.3 MVDR Filter -- 2.4 Feature Extraction -- 2.5 Linear Classifier. ; 3 Results and Discussion.
• Sommario/riassunto: 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.
• Titolo autorizzato: Intelligent Systems
• ISBN: 3-031-21686-5
• Formato: Materiale a stampa
• Livello bibliografico: Monografia
• Lingua di pubblicazione: Inglese
• Record Nr.: 996500063203316
• Lo trovi qui: Univ. di Salerno
• Serie: Lecture Notes in Computer Science