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Record Nr. |
UNISA996464529303316 |
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Titolo |
Intelligent systems : 10th Brazilian conference, BRACIS 2021, virtual event, November 29-December 3, 2021 : proceedings, Part II / / edited by André Britto and Karina Valdivia Delgado |
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Pubbl/distr/stampa |
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Cham, Switzerland : , : Springer, , [2022] |
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©2022 |
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ISBN |
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Descrizione fisica |
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1 online resource (649 pages) |
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Collana |
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Lecture Notes in Computer Science ; ; v.13074 |
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Disciplina |
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Soggetti |
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Lingua di pubblicazione |
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Formato |
Materiale a stampa |
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Livello bibliografico |
Monografia |
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Nota di bibliografia |
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Includes bibliographical references and index. |
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Nota di contenuto |
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Intro -- Preface -- Organization -- Contents - Part II -- Contents - Part I -- Multidisciplinary Artificial and Computational Intelligence and Applications -- A Heterogeneous Network-Based Positive and Unlabeled Learning Approach to Detect Fake News -- 1 Introduction -- 2 Related Work -- 3 Positive and Unlabeled Learning Algorithms -- 4 Proposed Approach: PU-LP for Fake News Detection -- 4.1 News Collection and Representation Model -- 4.2 k-NN Matrix, Katz Index and Sets Extraction -- 4.3 Adding Features in the News Network -- 4.4 Label Propagation -- 5 Experimental Evaluation -- 5.1 News Datasets -- 5.2 Experimental Setup and Evaluation Criteria -- 5.3 Results and Discussions -- 6 Conclusion and Future Work -- References -- Anomaly Detection in Brazilian Federal Government Purchase Cards Through Unsupervised Learning Techniques -- 1 Introduction -- 2 Materials and Methods -- 2.1 K-Means Method -- 2.2 Agglomerative Clustering Method -- 2.3 Network-Based Approach -- 2.4 Hybrid Approach -- 3 Experimental Results -- 4 Final Remarks -- References -- De-Identification of Clinical Notes Using Contextualized Language Models and a Token Classifier -- 1 Introduction -- 2 Related Work -- 3 Materials and Methods -- 3.1 Data Source -- 3.2 Neural Network -- 3.3 Language Models -- 3.4 Design of the Experiments -- 4 Results -- 5 Conclusion -- References -- Detecting Early Signs of Insufficiency in COVID-19 Patients from CBC Tests Through a Supervised Learning |
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Approach -- 1 Introduction -- 2 The Proposed MNBHL Technique -- 2.1 Description of the Training Phase -- 2.2 Description of the Testing Phase -- 3 Materials and Methods -- 4 Tests Performed on Benchmark Datasets -- 5 Experimental Results -- 6 Final Remarks -- References -- Encoding Physical Conditioning from Inertial Sensors for Multi-step Heart Rate Estimation -- 1 Introduction -- 2 Methodology. |
2.1 Datasets -- 2.2 Pre-processing -- 3 The Physical Conditional Embedding LSTM Model -- 3.1 Adaptations for PPG-Based HR Estimation -- 4 Reference Methods -- 5 Empirical Evaluation -- 5.1 Experimental Setup -- 5.2 IMU-Based Multi-step HR Estimation -- 5.3 Performance Impact of PCE-LSTM's Hidden State Initialization -- 5.4 PPG-Based HR Estimation -- 6 Related Work -- 7 Conclusions -- References -- Ensemble of Protein Stability upon Point Mutation Predictors -- 1 Introduction -- 2 Background -- 2.1 Point Mutations and Their Effects on Protein Structures -- 2.2 Gibbs Free Energy (G) -- 2.3 Supervised Machine Learning -- 2.4 Ensemble Learning -- 3 Individual Tools for Predicting the Effects of Point Mutations in Protein Stability -- 3.1 CUPSAT -- 3.2 SDM -- 3.3 mCSM -- 3.4 DUET -- 3.5 MAESTRO -- 3.6 PoPMuSic -- 4 Proposed Methodology -- 4.1 Input Data -- 4.2 Meta Data -- 4.3 Ensemble Learning - Stacking -- 4.4 Ensemble Learning - Bagging/Boosting -- 5 Results and Discussion -- 5.1 Experiment 1: Balanced Training Dataset -- 5.2 Experiment 2: Unbalanced Training Set -- 6 Conclusion -- References -- Ethics of AI: Do the Face Detection Models Act with Prejudice? -- 1 Introduction -- 2 Background -- 3 Methodology -- 3.1 Dataset -- 3.2 Face Detection Models -- 3.3 Validation Metrics -- 3.4 Hypothesis Test -- 4 Experimental Results -- 5 Conclusion -- References -- Evaluating Topic Models in Portuguese Political Comments About Bills from Brazil's Chamber of Deputies -- 1 Introduction -- 2 Related Work -- 3 Portuguese Political Comments -- 3.1 Corpora -- 4 Methodology -- 4.1 Sentence Embeddings -- 4.2 Topic Models -- 5 Experimental Evaluation -- 5.1 Quantity and Quality Evaluation -- 5.2 Setup -- 5.3 Results -- 6 Discussion -- References -- Evaluation of Convolutional Neural Networks for COVID-19 Classification on Chest X-Rays -- 1 Introduction -- 2 Related Work. |
3 Materials and Methods -- 3.1 Dataset -- 3.2 Preprocessing -- 3.3 Data Augmentation -- 3.4 Convolutional Architectures -- 4 Results and Discussion -- 4.1 Models Training and Validation -- 4.2 Comparison with Related Work -- 5 Conclusion -- References -- Experiments on Portuguese Clinical Question Answering -- 1 Introduction -- 2 Related Work -- 2.1 Corpora -- 2.2 Transfer Learning -- 2.3 Biomedical and Clinical QA -- 3 Materials and Methods -- 3.1 BioBERTpt-squad-v1.1-portuguese: A Biomedical QA Model for Portuguese -- 3.2 Evaluation Setup -- 4 Results -- 5 Discussion -- 6 Conclusion -- References -- Long-Term Map Maintenance in Complex Environments -- 1 Introduction -- 2 Related Work -- 3 Large-Scale Mapping System -- 3.1 Pre-processing -- 3.2 Hypergraph Building -- 3.3 Hypergraph Optimization -- 3.4 Large-Scale Environment Mapping -- 3.5 Large-Scale Map Merging -- 4 Experimental Methodology -- 4.1 Experiments -- 5 Results and Discussions -- 5.1 Odometry Bias Calibration -- 5.2 Map Building and Merging -- 6 Conclusions and Future Work -- References -- Supervised Training of a Simple Digital Assistant for a Free Crop Clinic -- 1 Introduction -- 2 Related Works -- 3 Proposed Approach -- 3.1 System Overview -- 3.2 Crop Clinic Digital Assistant -- 4 Hyperparameter Tuning and Training -- 4.1 Dataset Collection -- 4.2 Dataset Division -- 4.3 Model Training -- 4.4 Prediction -- 4.5 Performance Optimization -- 5 Case Study and Discussion -- 6 Conclusion -- References -- The Future of AI: Neat or Scruffy? -- 1 |
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Introduction -- 2 The ``neats'' Vs. ``scruffies'' Debate in the History of AI -- 2.1 Marvin Minsky (1985-1995) -- 2.2 Nils Nilsson (2009) -- 2.3 Herbert Simon (1972) -- 2.4 John McCarthy (1958) -- 2.5 Russell and Norvig (1995-2020) -- 2.6 Yann LeCun (2018) -- 3 Is AI a Science of Intelligence or a Branch of Engineering?. |
4 Types of Neat and Scruffy's Attitudes in AI -- 4.1 Scruffy Type I: The Empirical Scientists -- 4.2 Scruffy Type II: The System Builders -- 4.3 Neats: The Computer Epistemologists -- 5 Implications for the Future of AI -- 6 Conclusion -- References -- Weapon Engagement Zone Maximum Launch Range Estimation Using a Deep Neural Network -- 1 Introduction -- 2 Background -- 2.1 Weapon Engagement Zone -- 2.2 Missile Model -- 2.3 Experimental Design -- 3 Methodology -- 3.1 Simulation -- 3.2 Preprocessing -- 3.3 Model Training -- 3.4 Model Evaluation -- 4 Results and Analysis -- 4.1 Exploratory Data Analysis -- 4.2 Model Predictions -- 4.3 Model Representation -- 5 Conclusions and Future Work -- References -- Neural Networks, Deep Learning and Computer Vision -- Code Autocomplete Using Transformers -- 1 Introduction -- 2 Related Work -- 3 Approach -- 3.1 Corpus -- 3.2 Model -- 4 Evaluation -- 4.1 DG Evaluation Metric -- 4.2 Methodology -- 4.3 Baseline -- 5 Results -- 6 Conclusion -- References -- Deep Convolutional Features for Fingerprint Indexing -- 1 Introduction -- 2 FVC Databases -- 3 Indexing, Evaluation, and Performance Metrics -- 4 Related Works -- 5 Proposed Indexing Method -- 5.1 Architecture -- 5.2 Deep Metric Learning -- 5.3 Approximate Nearest Neighbors -- 6 Experimental Evaluation -- 7 Results -- 8 Conclusion -- References -- How to Generate Synthetic Paintings to Improve Art Style Classification -- 1 Introduction -- 2 Related Work -- 2.1 Artwork Classification -- 2.2 Generative Adversarial Networks -- 3 Our Proposal -- 3.1 Image Augmentation -- 3.2 Generative Adversarial Network -- 3.3 Adversarial Loss Function -- 3.4 EfficientNet -- 4 Experimental Results -- 4.1 The Wikiart Dataset -- 4.2 GAN Training Configuration -- 4.3 EfficientNet B0 Training Configuration -- 4.4 Baseline Results -- 4.5 Sampling Low Quantity Classes. |
4.6 Sampling High Quantity Classes -- 4.7 Summary of Results -- 4.8 Generated Images -- 5 Conclusion -- References -- Iris-CV: Classifying Iris Flowers Is Not as Easy as You Thought -- 1 Introduction -- 2 Related Works -- 3 A New Iris Dataset -- 4 Benchmark Results -- 4.1 Deep Neural Net Results -- 5 Conclusion -- References -- Performance Analysis of YOLOv3 for Real-Time Detection of Pests in Soybeans -- 1 Introduction -- 2 Related Work -- 3 Materials and Methods -- 3.1 Image Acquisition -- 3.2 You only Look once (YOLOv3) -- 3.3 Experimental Design -- 3.4 Evaluation Metrics -- 4 Results and Discussion -- 5 Conclusion -- References -- Quaternion-Valued Convolutional Neural Network Applied for Acute Lymphoblastic Leukemia Diagnosis -- 1 Introduction -- 1.1 Complex and Quaternion-Valued Neural Networks -- 1.2 Contributions and the Organization of the Paper -- 2 Acute Lymphoblastic Leukemia (ALL) -- 2.1 Computer-Aided Diagnosis of Leukemia: Literature Review -- 3 Convolutional Neural Networks -- 3.1 Quaternion-Valued Convolutional Neural Networks -- 4 Computational Experiments -- 5 Concluding Remarks and Future Works -- References -- Sea State Estimation with Neural Networks Based on the Motion of a Moored FPSO Subjected to Campos Basin Metocean Conditions -- 1 Introduction -- 2 Background: the Description of Ocean Waves -- 3 Background: Neural Networks -- 4 An Estimation Procedure for Sea Parameters -- 4.1 Metocean Data -- 4.2 Data Processing -- 4.3 Network Architecture -- 5 Motion-Based Estimation Results and Discussion -- 6 Conclusions -- References -- Time-Dependent Item Embeddings for Collaborative Filtering -- 1 |
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Introduction -- 2 Related Work -- 3 The Proposed Method -- 3.1 Item2Vec -- 3.2 Sequential Item2Vec -- 4 Experiments and Results -- 4.1 Datasets -- 4.2 Experimental Protocol -- 4.3 Results -- 5 Conclusion -- References. |
Transfer Learning of Shapelets for Time Series Classification Using Convolutional Neural Network. |
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