Machine Learning, Optimization, and Data Science: 8th International Conference, LOD 2022, Certosa Di Pontignano, Italy, September 18-22, 2022, Revised Selected Papers, Part I.
Gespeichert in:
| 1. Verfasser: | |
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| Format: | Elektronisch E-Book |
| Sprache: | English |
| Veröffentlicht: |
Cham
Springer
2023
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| Ausgabe: | 1st ed |
| Schriftenreihe: | Lecture Notes in Computer Science Series
v.13810 |
| Schlagworte: | |
| Online-Zugang: | DE-2070s |
| Beschreibung: | Description based on publisher supplied metadata and other sources |
| Beschreibung: | 1 Online-Ressource (639 Seiten) |
| ISBN: | 9783031255991 |
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| 490 | 0 | |a Lecture Notes in Computer Science Series |v v.13810 | |
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| 505 | 8 | |a Intro -- Preface -- Organization -- Contents - Part I -- Contents - Part II -- Detection of Morality in Tweets Based on the Moral Foundation Theory -- 1 Introduction -- 2 Theoretical Framework -- 3 Related Works -- 4 Methodology -- 4.1 The Moral Foundation Twitter Corpus -- 4.2 A BERT-Based Method for Detecting Moral Values -- 5 Results and Evaluation -- 5.1 Classification of Tweets Based on the MFT Dimentions -- 5.2 Detection of Moral Values with Polarity -- 6 Discussion -- 7 Conclusion and Future Work -- References -- Matrix Completion for the Prediction of Yearly Country and Industry-Level CO2 Emissions -- 1 Introduction -- 2 Description of the Dataset -- 3 Method -- 4 Results -- 5 Possible Developments -- References -- A Benchmark for Real-Time Anomaly Detection Algorithms Applied in Industry 4.0 -- 1 Introduction -- 2 Algorithm Selection and Anomaly Detection -- 3 Methodology -- 4 Design Principles as Solution Objectives -- 5 Formalization and Prototypical Implementation -- 6 Demonstration and Evaluation -- 7 Limitations and Future Research -- 8 Conclusion -- References -- A Matrix Factorization-Based Drug-Virus Link Prediction Method for SARS-CoV-2 Drug Prioritization -- 1 Introduction -- 2 Materials -- 2.1 DVA Dataset -- 2.2 Similarity Measurement for Drugs and Viruses -- 3 Methodology -- 3.1 Problem Description -- 3.2 Similarity Fusion -- 3.3 GRMF Model for Novel Drug-Virus Link Prediction -- 3.4 Experimental Setting and Evaluation Metrics -- 4 Results -- 4.1 Model Tuning -- 4.2 Effectiveness of Similarity Fusion -- 4.3 Performance Comparison -- 4.4 COVID Drug Prioritization -- 5 Conclusion -- References -- Hyperbolic Graph Codebooks -- 1 Introduction -- 2 Related Work -- 2.1 Graph Networks -- 2.2 Codebook Encodings -- 3 Background -- 3.1 Graph Representation Layers -- 3.2 Mapping to and Form the Tangent Space | |
| 505 | 8 | |a 4 Encoding Hyperbolic Graph Networks -- 4.1 Zeroth-Order Graph Encoding -- 4.2 First-Order Graph Encoding -- 4.3 Second-Order Graph Encoding -- 5 Experimental Setup -- 5.1 Datasets -- 5.2 Implementation Details -- 6 Experimental Results -- 6.1 Ablation Studies -- 6.2 Comparative Evaluation -- 7 Conclusions -- References -- A Kernel-Based Multilayer Perceptron Framework to Identify Pathways Related to Cancer Stages -- 1 Introduction -- 2 Materials -- 2.1 Constructing Datasets Using TCGA Data -- 2.2 Pathway Databases -- 3 Method -- 3.1 Problem Formulation -- 3.2 Structure of the Proposed MLP -- 4 Numerical Study -- 4.1 Experimental Settings -- 4.2 MLP Identifies Meaningful Biological Mechanisms -- 4.3 Predictive Performance Comparison -- 5 Conclusions -- References -- Loss Function with Memory for Trustworthiness Threshold Learning: Case of Face and Facial Expression Recognition -- 1 Introduction -- 2 Existing Work -- 2.1 Meta-learning -- 2.2 Uncertainty -- 2.3 Face and Facial Expression Recognition in Out-of-Distribution Settings -- 3 Proposed Solution -- 4 Data Set -- 5 Experiments -- 5.1 Trusted Accuracy Metrics -- 6 Results -- 7 Discussion, Conclusions, and Future Work -- References -- Machine Learning Approaches for Predicting Crystal Systems: A Brief Review and a Case Study -- 1 Introduction -- 2 Methods for Crystal System Prediction -- 2.1 ML Methods Using XRPD Patterns -- 2.2 ML Methods Using Features Derived from XRPD Patterns -- 2.3 Methods Using Other Features -- 2.4 Other Approaches -- 3 A Case Study: ML Approach Using Lattice Features -- 3.1 Data Preparation -- 3.2 Learning Models Using Lattice Values -- 4 Discussions -- References -- LS-PON: A Prediction-Based Local Search for Neural Architecture Search -- 1 Introduction -- 2 Related Works -- 3 Background -- 3.1 Neural Architecture Search -- 3.2 Local Search -- 4 Proposed Approach | |
| 505 | 8 | |a 4.1 Solution Encoding -- 4.2 Neighborhood Function -- 4.3 Solution Evaluation -- 4.4 Performance Prediction -- 4.5 LS-PON Process -- 5 Experiments -- 5.1 Benchmark Details -- 5.2 Experimentation Protocol -- 5.3 Results -- 6 Conclusion -- References -- Local Optimisation of Nyström Samples Through Stochastic Gradient Descent -- 1 Introduction -- 1.1 Kernel Matrix Approximation -- 1.2 Assessing the Accuracy of Nyström Approximations -- 1.3 Radial Squared-Kernel Discrepancy -- 2 A Convergence Result -- 3 Stochastic Approximation of the Radial-SKD Gradient -- 4 Numerical Experiments -- 4.1 Bi-Gaussian Example -- 4.2 Abalone Data Set -- 4.3 MAGIC Data Set -- 4.4 MiniBooNE Data Set -- 5 Conclusion -- References -- Explainable Machine Learning for Drug Shortage Prediction in a Pandemic Setting -- 1 Introduction -- 2 Related Work -- 3 Data Description -- 4 Data Preprocessing -- 4.1 What is Considered as Backorder -- 4.2 Features -- 5 Experiments -- 5.1 Experimental Design -- 5.2 Comparison Results -- 5.3 Results Discussions for Backorder Class -- 5.4 Decision Tree Visualization -- 5.5 Permutation Feature Importance -- 5.6 Comparison Between Accuracy and Velocity -- 6 Conclusion -- References -- Intelligent Robotic Process Automation for Supplier Document Management on E-Procurement Platforms -- 1 Introduction -- 2 Related Works -- 3 Document Management Process -- 3.1 Intelligent Document Management RPA -- 4 Experiments -- 4.1 Accuracy of Document-Agnostic Models -- 4.2 Accuracy of Document-Specific Models -- 5 Conclusions -- References -- Batch Bayesian Quadrature with Batch Updating Using Future Uncertainty Sampling -- 1 Introduction -- 2 Background -- 2.1 Bayesian Quadrature -- 2.2 Warped Bayesian Quadrature -- 2.3 Active Sampling -- 2.4 Batch Bayesian Quadrature -- 3 Method -- 4 Experiment -- 4.1 Test Functions -- 4.2 Dynamic Domain Decomposition | |
| 505 | 8 | |a 4.3 Cessation Criteria -- 5 Results -- 6 Discussion and Conclusion -- References -- Sensitivity Analysis of Engineering Structures Utilizing Artificial Neural Networks and Polynomial Chaos Expansion -- 1 Introduction -- 2 Theoretical Background of PCE and ANN -- 2.1 Polynomial Chaos Expansion -- 2.2 Artificial Neural Network -- 3 Sensitivity Analysis -- 4 Applications -- 4.1 Fixed Beam -- 4.2 Post-tensioned Concrete Bridge -- 5 Conclusions -- References -- Transformers for COVID-19 Misinformation Detection on Twitter: A South African Case Study -- 1 Introduction -- 2 Background: The South African Context -- 3 Related Work -- 4 Method -- 4.1 Data Understanding -- 4.2 Data Preparation -- 4.3 Modeling -- 5 Results -- 6 Conclusion -- References -- MI2AMI: Missing Data Imputation Using Mixed Deep Gaussian Mixture Models -- 1 Introduction -- 2 Background on MDGMM -- 3 MI2AMI Description -- 3.1 General Overview -- 3.2 The Imputation Step -- 4 Numerical Illustration -- 4.1 Framework -- 4.2 Results -- 5 Discussion and Perspective -- References -- On the Utility and Protection of Optimization with Differential Privacy and Classic Regularization Techniques -- 1 Introduction -- 2 Privacy Threats -- 2.1 Membership Inference Attacks -- 2.2 Model Inversion Attacks -- 3 Privacy Defences -- 4 Method -- 5 Experiments and Results -- 6 Conclusion -- References -- MicroRacer: A Didactic Environment for Deep Reinforcement Learning -- 1 Introduction -- 1.1 Structure of the Article -- 2 Related Software -- 2.1 AWS Deep Racer -- 2.2 Torcs -- 2.3 Learn-to-race -- 2.4 CarRacing-v0 -- 3 MicroRacer -- 3.1 State and Actions -- 3.2 Rewards -- 3.3 Environment Interface -- 3.4 Competitive Race -- 3.5 Dependencies -- 4 Learning Models -- 4.1 Deep Deterministic Policy Gradient (DDPG) -- 4.2 Twin Delayed DDPG (TD3) -- 4.3 Proximal Policy Optimization (PPO) | |
| 505 | 8 | |a 4.4 Soft Actor-Critic (SAC) -- 4.5 DSAC -- 5 Baselines Benchmarks -- 5.1 Results -- 6 Conclusions -- References -- A Practical Approach for Vehicle Speed Estimation in Smart Cities -- 1 Introduction -- 2 Related Works -- 3 Materials and Methods -- 3.1 Vehicle Speed Estimation System -- 3.2 The BrnoCompSpeed Dataset -- 4 Experimental Analysis -- 4.1 Preliminary Quality Test -- 4.2 Impact of the Observation Angle -- 4.3 Impact of the Detection Area Size -- 4.4 Entry/Exit Point Improved Estimation -- 4.5 Comparison with the State-of-the-Art -- 5 Conclusions -- References -- Corporate Network Analysis Based on Graph Learning -- 1 Introduction -- 1.1 Related Work -- 1.2 Our Contributions -- 2 Data Sources -- 3 Customer Acquisition Application -- 4 Credit Risk Modeling Application for Manufacturing Industry -- 5 Discussion -- 6 Conclusion -- References -- Source Attribution and Emissions Quantification for Methane Leak Detection: A Non-linear Bayesian Regression Approach -- 1 Introduction -- 2 Methods -- 2.1 Bayesian Approach -- 2.2 Non-linear Bayesian Regression: Stationary Model -- 2.3 Ranking Model -- 3 Case Study -- 3.1 Data Collection and Processing -- 3.2 Scenario Simulation -- 3.3 Results -- 4 Conclusions -- 5 Future Work -- References -- Analysis of Heavy Vehicles Rollover with Artificial Intelligence Techniques -- 1 Introduction -- 2 System Overview -- 3 Methodology -- 3.1 Simscape: Data Generation and Elaboration -- 3.2 AI: Data Analysis and Results -- 4 Conclusions and Possible Future Developments -- References -- Hyperparameter Tuning of Random Forests Using Radial Basis Function Models -- 1 Introduction -- 2 The B-CONDOR Algorithm for Hyperparameter Tuning of Random Forests -- 2.1 Algorithm Description -- 2.2 Radial Basis Function Interpolation -- 3 Computational Experiments -- 3.1 Random Forest Hyperparameter Tuning Problems | |
| 505 | 8 | |a 3.2 Experimental Setup | |
| 650 | 4 | |a Machine learning-Congresses | |
| 650 | 4 | |a Mathematical optimization-Congresses | |
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| contents | Intro -- Preface -- Organization -- Contents - Part I -- Contents - Part II -- Detection of Morality in Tweets Based on the Moral Foundation Theory -- 1 Introduction -- 2 Theoretical Framework -- 3 Related Works -- 4 Methodology -- 4.1 The Moral Foundation Twitter Corpus -- 4.2 A BERT-Based Method for Detecting Moral Values -- 5 Results and Evaluation -- 5.1 Classification of Tweets Based on the MFT Dimentions -- 5.2 Detection of Moral Values with Polarity -- 6 Discussion -- 7 Conclusion and Future Work -- References -- Matrix Completion for the Prediction of Yearly Country and Industry-Level CO2 Emissions -- 1 Introduction -- 2 Description of the Dataset -- 3 Method -- 4 Results -- 5 Possible Developments -- References -- A Benchmark for Real-Time Anomaly Detection Algorithms Applied in Industry 4.0 -- 1 Introduction -- 2 Algorithm Selection and Anomaly Detection -- 3 Methodology -- 4 Design Principles as Solution Objectives -- 5 Formalization and Prototypical Implementation -- 6 Demonstration and Evaluation -- 7 Limitations and Future Research -- 8 Conclusion -- References -- A Matrix Factorization-Based Drug-Virus Link Prediction Method for SARS-CoV-2 Drug Prioritization -- 1 Introduction -- 2 Materials -- 2.1 DVA Dataset -- 2.2 Similarity Measurement for Drugs and Viruses -- 3 Methodology -- 3.1 Problem Description -- 3.2 Similarity Fusion -- 3.3 GRMF Model for Novel Drug-Virus Link Prediction -- 3.4 Experimental Setting and Evaluation Metrics -- 4 Results -- 4.1 Model Tuning -- 4.2 Effectiveness of Similarity Fusion -- 4.3 Performance Comparison -- 4.4 COVID Drug Prioritization -- 5 Conclusion -- References -- Hyperbolic Graph Codebooks -- 1 Introduction -- 2 Related Work -- 2.1 Graph Networks -- 2.2 Codebook Encodings -- 3 Background -- 3.1 Graph Representation Layers -- 3.2 Mapping to and Form the Tangent Space 4 Encoding Hyperbolic Graph Networks -- 4.1 Zeroth-Order Graph Encoding -- 4.2 First-Order Graph Encoding -- 4.3 Second-Order Graph Encoding -- 5 Experimental Setup -- 5.1 Datasets -- 5.2 Implementation Details -- 6 Experimental Results -- 6.1 Ablation Studies -- 6.2 Comparative Evaluation -- 7 Conclusions -- References -- A Kernel-Based Multilayer Perceptron Framework to Identify Pathways Related to Cancer Stages -- 1 Introduction -- 2 Materials -- 2.1 Constructing Datasets Using TCGA Data -- 2.2 Pathway Databases -- 3 Method -- 3.1 Problem Formulation -- 3.2 Structure of the Proposed MLP -- 4 Numerical Study -- 4.1 Experimental Settings -- 4.2 MLP Identifies Meaningful Biological Mechanisms -- 4.3 Predictive Performance Comparison -- 5 Conclusions -- References -- Loss Function with Memory for Trustworthiness Threshold Learning: Case of Face and Facial Expression Recognition -- 1 Introduction -- 2 Existing Work -- 2.1 Meta-learning -- 2.2 Uncertainty -- 2.3 Face and Facial Expression Recognition in Out-of-Distribution Settings -- 3 Proposed Solution -- 4 Data Set -- 5 Experiments -- 5.1 Trusted Accuracy Metrics -- 6 Results -- 7 Discussion, Conclusions, and Future Work -- References -- Machine Learning Approaches for Predicting Crystal Systems: A Brief Review and a Case Study -- 1 Introduction -- 2 Methods for Crystal System Prediction -- 2.1 ML Methods Using XRPD Patterns -- 2.2 ML Methods Using Features Derived from XRPD Patterns -- 2.3 Methods Using Other Features -- 2.4 Other Approaches -- 3 A Case Study: ML Approach Using Lattice Features -- 3.1 Data Preparation -- 3.2 Learning Models Using Lattice Values -- 4 Discussions -- References -- LS-PON: A Prediction-Based Local Search for Neural Architecture Search -- 1 Introduction -- 2 Related Works -- 3 Background -- 3.1 Neural Architecture Search -- 3.2 Local Search -- 4 Proposed Approach 4.1 Solution Encoding -- 4.2 Neighborhood Function -- 4.3 Solution Evaluation -- 4.4 Performance Prediction -- 4.5 LS-PON Process -- 5 Experiments -- 5.1 Benchmark Details -- 5.2 Experimentation Protocol -- 5.3 Results -- 6 Conclusion -- References -- Local Optimisation of Nyström Samples Through Stochastic Gradient Descent -- 1 Introduction -- 1.1 Kernel Matrix Approximation -- 1.2 Assessing the Accuracy of Nyström Approximations -- 1.3 Radial Squared-Kernel Discrepancy -- 2 A Convergence Result -- 3 Stochastic Approximation of the Radial-SKD Gradient -- 4 Numerical Experiments -- 4.1 Bi-Gaussian Example -- 4.2 Abalone Data Set -- 4.3 MAGIC Data Set -- 4.4 MiniBooNE Data Set -- 5 Conclusion -- References -- Explainable Machine Learning for Drug Shortage Prediction in a Pandemic Setting -- 1 Introduction -- 2 Related Work -- 3 Data Description -- 4 Data Preprocessing -- 4.1 What is Considered as Backorder -- 4.2 Features -- 5 Experiments -- 5.1 Experimental Design -- 5.2 Comparison Results -- 5.3 Results Discussions for Backorder Class -- 5.4 Decision Tree Visualization -- 5.5 Permutation Feature Importance -- 5.6 Comparison Between Accuracy and Velocity -- 6 Conclusion -- References -- Intelligent Robotic Process Automation for Supplier Document Management on E-Procurement Platforms -- 1 Introduction -- 2 Related Works -- 3 Document Management Process -- 3.1 Intelligent Document Management RPA -- 4 Experiments -- 4.1 Accuracy of Document-Agnostic Models -- 4.2 Accuracy of Document-Specific Models -- 5 Conclusions -- References -- Batch Bayesian Quadrature with Batch Updating Using Future Uncertainty Sampling -- 1 Introduction -- 2 Background -- 2.1 Bayesian Quadrature -- 2.2 Warped Bayesian Quadrature -- 2.3 Active Sampling -- 2.4 Batch Bayesian Quadrature -- 3 Method -- 4 Experiment -- 4.1 Test Functions -- 4.2 Dynamic Domain Decomposition 4.3 Cessation Criteria -- 5 Results -- 6 Discussion and Conclusion -- References -- Sensitivity Analysis of Engineering Structures Utilizing Artificial Neural Networks and Polynomial Chaos Expansion -- 1 Introduction -- 2 Theoretical Background of PCE and ANN -- 2.1 Polynomial Chaos Expansion -- 2.2 Artificial Neural Network -- 3 Sensitivity Analysis -- 4 Applications -- 4.1 Fixed Beam -- 4.2 Post-tensioned Concrete Bridge -- 5 Conclusions -- References -- Transformers for COVID-19 Misinformation Detection on Twitter: A South African Case Study -- 1 Introduction -- 2 Background: The South African Context -- 3 Related Work -- 4 Method -- 4.1 Data Understanding -- 4.2 Data Preparation -- 4.3 Modeling -- 5 Results -- 6 Conclusion -- References -- MI2AMI: Missing Data Imputation Using Mixed Deep Gaussian Mixture Models -- 1 Introduction -- 2 Background on MDGMM -- 3 MI2AMI Description -- 3.1 General Overview -- 3.2 The Imputation Step -- 4 Numerical Illustration -- 4.1 Framework -- 4.2 Results -- 5 Discussion and Perspective -- References -- On the Utility and Protection of Optimization with Differential Privacy and Classic Regularization Techniques -- 1 Introduction -- 2 Privacy Threats -- 2.1 Membership Inference Attacks -- 2.2 Model Inversion Attacks -- 3 Privacy Defences -- 4 Method -- 5 Experiments and Results -- 6 Conclusion -- References -- MicroRacer: A Didactic Environment for Deep Reinforcement Learning -- 1 Introduction -- 1.1 Structure of the Article -- 2 Related Software -- 2.1 AWS Deep Racer -- 2.2 Torcs -- 2.3 Learn-to-race -- 2.4 CarRacing-v0 -- 3 MicroRacer -- 3.1 State and Actions -- 3.2 Rewards -- 3.3 Environment Interface -- 3.4 Competitive Race -- 3.5 Dependencies -- 4 Learning Models -- 4.1 Deep Deterministic Policy Gradient (DDPG) -- 4.2 Twin Delayed DDPG (TD3) -- 4.3 Proximal Policy Optimization (PPO) 4.4 Soft Actor-Critic (SAC) -- 4.5 DSAC -- 5 Baselines Benchmarks -- 5.1 Results -- 6 Conclusions -- References -- A Practical Approach for Vehicle Speed Estimation in Smart Cities -- 1 Introduction -- 2 Related Works -- 3 Materials and Methods -- 3.1 Vehicle Speed Estimation System -- 3.2 The BrnoCompSpeed Dataset -- 4 Experimental Analysis -- 4.1 Preliminary Quality Test -- 4.2 Impact of the Observation Angle -- 4.3 Impact of the Detection Area Size -- 4.4 Entry/Exit Point Improved Estimation -- 4.5 Comparison with the State-of-the-Art -- 5 Conclusions -- References -- Corporate Network Analysis Based on Graph Learning -- 1 Introduction -- 1.1 Related Work -- 1.2 Our Contributions -- 2 Data Sources -- 3 Customer Acquisition Application -- 4 Credit Risk Modeling Application for Manufacturing Industry -- 5 Discussion -- 6 Conclusion -- References -- Source Attribution and Emissions Quantification for Methane Leak Detection: A Non-linear Bayesian Regression Approach -- 1 Introduction -- 2 Methods -- 2.1 Bayesian Approach -- 2.2 Non-linear Bayesian Regression: Stationary Model -- 2.3 Ranking Model -- 3 Case Study -- 3.1 Data Collection and Processing -- 3.2 Scenario Simulation -- 3.3 Results -- 4 Conclusions -- 5 Future Work -- References -- Analysis of Heavy Vehicles Rollover with Artificial Intelligence Techniques -- 1 Introduction -- 2 System Overview -- 3 Methodology -- 3.1 Simscape: Data Generation and Elaboration -- 3.2 AI: Data Analysis and Results -- 4 Conclusions and Possible Future Developments -- References -- Hyperparameter Tuning of Random Forests Using Radial Basis Function Models -- 1 Introduction -- 2 The B-CONDOR Algorithm for Hyperparameter Tuning of Random Forests -- 2.1 Algorithm Description -- 2.2 Radial Basis Function Interpolation -- 3 Computational Experiments -- 3.1 Random Forest Hyperparameter Tuning Problems 3.2 Experimental Setup |
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Methodology -- 4 Design Principles as Solution Objectives -- 5 Formalization and Prototypical Implementation -- 6 Demonstration and Evaluation -- 7 Limitations and Future Research -- 8 Conclusion -- References -- A Matrix Factorization-Based Drug-Virus Link Prediction Method for SARS-CoV-2 Drug Prioritization -- 1 Introduction -- 2 Materials -- 2.1 DVA Dataset -- 2.2 Similarity Measurement for Drugs and Viruses -- 3 Methodology -- 3.1 Problem Description -- 3.2 Similarity Fusion -- 3.3 GRMF Model for Novel Drug-Virus Link Prediction -- 3.4 Experimental Setting and Evaluation Metrics -- 4 Results -- 4.1 Model Tuning -- 4.2 Effectiveness of Similarity Fusion -- 4.3 Performance Comparison -- 4.4 COVID Drug Prioritization -- 5 Conclusion -- References -- Hyperbolic Graph Codebooks -- 1 Introduction -- 2 Related Work -- 2.1 Graph Networks -- 2.2 Codebook Encodings -- 3 Background -- 3.1 Graph Representation Layers -- 3.2 Mapping to and Form the Tangent Space</subfield></datafield><datafield tag="505" ind1="8" ind2=" "><subfield code="a">4 Encoding Hyperbolic Graph Networks -- 4.1 Zeroth-Order Graph Encoding -- 4.2 First-Order Graph Encoding -- 4.3 Second-Order Graph Encoding -- 5 Experimental Setup -- 5.1 Datasets -- 5.2 Implementation Details -- 6 Experimental Results -- 6.1 Ablation Studies -- 6.2 Comparative Evaluation -- 7 Conclusions -- References -- A Kernel-Based Multilayer Perceptron Framework to Identify Pathways Related to Cancer Stages -- 1 Introduction -- 2 Materials -- 2.1 Constructing Datasets Using TCGA Data -- 2.2 Pathway Databases -- 3 Method -- 3.1 Problem Formulation -- 3.2 Structure of the Proposed MLP -- 4 Numerical Study -- 4.1 Experimental Settings -- 4.2 MLP Identifies Meaningful Biological Mechanisms -- 4.3 Predictive Performance Comparison -- 5 Conclusions -- References -- Loss Function with Memory for Trustworthiness Threshold Learning: Case of Face and Facial Expression Recognition -- 1 Introduction -- 2 Existing Work -- 2.1 Meta-learning -- 2.2 Uncertainty -- 2.3 Face and Facial Expression Recognition in Out-of-Distribution Settings -- 3 Proposed Solution -- 4 Data Set -- 5 Experiments -- 5.1 Trusted Accuracy Metrics -- 6 Results -- 7 Discussion, Conclusions, and Future Work -- References -- Machine Learning Approaches for Predicting Crystal Systems: A Brief Review and a Case Study -- 1 Introduction -- 2 Methods for Crystal System Prediction -- 2.1 ML Methods Using XRPD Patterns -- 2.2 ML Methods Using Features Derived from XRPD Patterns -- 2.3 Methods Using Other Features -- 2.4 Other Approaches -- 3 A Case Study: ML Approach Using Lattice Features -- 3.1 Data Preparation -- 3.2 Learning Models Using Lattice Values -- 4 Discussions -- References -- LS-PON: A Prediction-Based Local Search for Neural Architecture Search -- 1 Introduction -- 2 Related Works -- 3 Background -- 3.1 Neural Architecture Search -- 3.2 Local Search -- 4 Proposed Approach</subfield></datafield><datafield tag="505" ind1="8" ind2=" "><subfield code="a">4.1 Solution Encoding -- 4.2 Neighborhood Function -- 4.3 Solution Evaluation -- 4.4 Performance Prediction -- 4.5 LS-PON Process -- 5 Experiments -- 5.1 Benchmark Details -- 5.2 Experimentation Protocol -- 5.3 Results -- 6 Conclusion -- References -- Local Optimisation of Nyström Samples Through Stochastic Gradient Descent -- 1 Introduction -- 1.1 Kernel Matrix Approximation -- 1.2 Assessing the Accuracy of Nyström Approximations -- 1.3 Radial Squared-Kernel Discrepancy -- 2 A Convergence Result -- 3 Stochastic Approximation of the Radial-SKD Gradient -- 4 Numerical Experiments -- 4.1 Bi-Gaussian Example -- 4.2 Abalone Data Set -- 4.3 MAGIC Data Set -- 4.4 MiniBooNE Data Set -- 5 Conclusion -- References -- Explainable Machine Learning for Drug Shortage Prediction in a Pandemic Setting -- 1 Introduction -- 2 Related Work -- 3 Data Description -- 4 Data Preprocessing -- 4.1 What is Considered as Backorder -- 4.2 Features -- 5 Experiments -- 5.1 Experimental Design -- 5.2 Comparison Results -- 5.3 Results Discussions for Backorder Class -- 5.4 Decision Tree Visualization -- 5.5 Permutation Feature Importance -- 5.6 Comparison Between Accuracy and Velocity -- 6 Conclusion -- References -- Intelligent Robotic Process Automation for Supplier Document Management on E-Procurement Platforms -- 1 Introduction -- 2 Related Works -- 3 Document Management Process -- 3.1 Intelligent Document Management RPA -- 4 Experiments -- 4.1 Accuracy of Document-Agnostic Models -- 4.2 Accuracy of Document-Specific Models -- 5 Conclusions -- References -- Batch Bayesian Quadrature with Batch Updating Using Future Uncertainty Sampling -- 1 Introduction -- 2 Background -- 2.1 Bayesian Quadrature -- 2.2 Warped Bayesian Quadrature -- 2.3 Active Sampling -- 2.4 Batch Bayesian Quadrature -- 3 Method -- 4 Experiment -- 4.1 Test Functions -- 4.2 Dynamic Domain Decomposition</subfield></datafield><datafield tag="505" ind1="8" ind2=" "><subfield code="a">4.3 Cessation Criteria -- 5 Results -- 6 Discussion and Conclusion -- References -- Sensitivity Analysis of Engineering Structures Utilizing Artificial Neural Networks and Polynomial Chaos Expansion -- 1 Introduction -- 2 Theoretical Background of PCE and ANN -- 2.1 Polynomial Chaos Expansion -- 2.2 Artificial Neural Network -- 3 Sensitivity Analysis -- 4 Applications -- 4.1 Fixed Beam -- 4.2 Post-tensioned Concrete Bridge -- 5 Conclusions -- References -- Transformers for COVID-19 Misinformation Detection on Twitter: A South African Case Study -- 1 Introduction -- 2 Background: The South African Context -- 3 Related Work -- 4 Method -- 4.1 Data Understanding -- 4.2 Data Preparation -- 4.3 Modeling -- 5 Results -- 6 Conclusion -- References -- MI2AMI: Missing Data Imputation Using Mixed Deep Gaussian Mixture Models -- 1 Introduction -- 2 Background on MDGMM -- 3 MI2AMI Description -- 3.1 General Overview -- 3.2 The Imputation Step -- 4 Numerical Illustration -- 4.1 Framework -- 4.2 Results -- 5 Discussion and Perspective -- References -- On the Utility and Protection of Optimization with Differential Privacy and Classic Regularization Techniques -- 1 Introduction -- 2 Privacy Threats -- 2.1 Membership Inference Attacks -- 2.2 Model Inversion Attacks -- 3 Privacy Defences -- 4 Method -- 5 Experiments and Results -- 6 Conclusion -- References -- MicroRacer: A Didactic Environment for Deep Reinforcement Learning -- 1 Introduction -- 1.1 Structure of the Article -- 2 Related Software -- 2.1 AWS Deep Racer -- 2.2 Torcs -- 2.3 Learn-to-race -- 2.4 CarRacing-v0 -- 3 MicroRacer -- 3.1 State and Actions -- 3.2 Rewards -- 3.3 Environment Interface -- 3.4 Competitive Race -- 3.5 Dependencies -- 4 Learning Models -- 4.1 Deep Deterministic Policy Gradient (DDPG) -- 4.2 Twin Delayed DDPG (TD3) -- 4.3 Proximal Policy Optimization (PPO)</subfield></datafield><datafield tag="505" ind1="8" ind2=" "><subfield code="a">4.4 Soft Actor-Critic (SAC) -- 4.5 DSAC -- 5 Baselines Benchmarks -- 5.1 Results -- 6 Conclusions -- References -- A Practical Approach for Vehicle Speed Estimation in Smart Cities -- 1 Introduction -- 2 Related Works -- 3 Materials and Methods -- 3.1 Vehicle Speed Estimation System -- 3.2 The BrnoCompSpeed Dataset -- 4 Experimental Analysis -- 4.1 Preliminary Quality Test -- 4.2 Impact of the Observation Angle -- 4.3 Impact of the Detection Area Size -- 4.4 Entry/Exit Point Improved Estimation -- 4.5 Comparison with the State-of-the-Art -- 5 Conclusions -- References -- Corporate Network Analysis Based on Graph Learning -- 1 Introduction -- 1.1 Related Work -- 1.2 Our Contributions -- 2 Data Sources -- 3 Customer Acquisition Application -- 4 Credit Risk Modeling Application for Manufacturing Industry -- 5 Discussion -- 6 Conclusion -- References -- Source Attribution and Emissions Quantification for Methane Leak Detection: A Non-linear Bayesian Regression Approach -- 1 Introduction -- 2 Methods -- 2.1 Bayesian Approach -- 2.2 Non-linear Bayesian Regression: Stationary Model -- 2.3 Ranking Model -- 3 Case Study -- 3.1 Data Collection and Processing -- 3.2 Scenario Simulation -- 3.3 Results -- 4 Conclusions -- 5 Future Work -- References -- Analysis of Heavy Vehicles Rollover with Artificial Intelligence Techniques -- 1 Introduction -- 2 System Overview -- 3 Methodology -- 3.1 Simscape: Data Generation and Elaboration -- 3.2 AI: Data Analysis and Results -- 4 Conclusions and Possible Future Developments -- References -- Hyperparameter Tuning of Random Forests Using Radial Basis Function Models -- 1 Introduction -- 2 The B-CONDOR Algorithm for Hyperparameter Tuning of Random Forests -- 2.1 Algorithm Description -- 2.2 Radial Basis Function Interpolation -- 3 Computational Experiments -- 3.1 Random Forest Hyperparameter Tuning Problems</subfield></datafield><datafield tag="505" ind1="8" ind2=" "><subfield code="a">3.2 Experimental Setup</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Machine 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| id | DE-604.BV049872920 |
| illustrated | Not Illustrated |
| indexdate | 2024-11-05T17:02:42Z |
| institution | BVB |
| isbn | 9783031255991 |
| language | English |
| oai_aleph_id | oai:aleph.bib-bvb.de:BVB01-035212378 |
| oclc_num | 1372631889 |
| open_access_boolean | |
| owner | DE-2070s |
| owner_facet | DE-2070s |
| physical | 1 Online-Ressource (639 Seiten) |
| psigel | ZDB-30-PQE ZDB-30-PQE HWR_PDA_PQE |
| publishDate | 2023 |
| publishDateSearch | 2023 |
| publishDateSort | 2023 |
| publisher | Springer |
| record_format | marc |
| series2 | Lecture Notes in Computer Science Series |
| spelling | Nicosia, Giuseppe Verfasser aut Machine Learning, Optimization, and Data Science 8th International Conference, LOD 2022, Certosa Di Pontignano, Italy, September 18-22, 2022, Revised Selected Papers, Part I. 1st ed Cham Springer 2023 ©2023 1 Online-Ressource (639 Seiten) txt rdacontent c rdamedia cr rdacarrier Lecture Notes in Computer Science Series v.13810 Description based on publisher supplied metadata and other sources Intro -- Preface -- Organization -- Contents - Part I -- Contents - Part II -- Detection of Morality in Tweets Based on the Moral Foundation Theory -- 1 Introduction -- 2 Theoretical Framework -- 3 Related Works -- 4 Methodology -- 4.1 The Moral Foundation Twitter Corpus -- 4.2 A BERT-Based Method for Detecting Moral Values -- 5 Results and Evaluation -- 5.1 Classification of Tweets Based on the MFT Dimentions -- 5.2 Detection of Moral Values with Polarity -- 6 Discussion -- 7 Conclusion and Future Work -- References -- Matrix Completion for the Prediction of Yearly Country and Industry-Level CO2 Emissions -- 1 Introduction -- 2 Description of the Dataset -- 3 Method -- 4 Results -- 5 Possible Developments -- References -- A Benchmark for Real-Time Anomaly Detection Algorithms Applied in Industry 4.0 -- 1 Introduction -- 2 Algorithm Selection and Anomaly Detection -- 3 Methodology -- 4 Design Principles as Solution Objectives -- 5 Formalization and Prototypical Implementation -- 6 Demonstration and Evaluation -- 7 Limitations and Future Research -- 8 Conclusion -- References -- A Matrix Factorization-Based Drug-Virus Link Prediction Method for SARS-CoV-2 Drug Prioritization -- 1 Introduction -- 2 Materials -- 2.1 DVA Dataset -- 2.2 Similarity Measurement for Drugs and Viruses -- 3 Methodology -- 3.1 Problem Description -- 3.2 Similarity Fusion -- 3.3 GRMF Model for Novel Drug-Virus Link Prediction -- 3.4 Experimental Setting and Evaluation Metrics -- 4 Results -- 4.1 Model Tuning -- 4.2 Effectiveness of Similarity Fusion -- 4.3 Performance Comparison -- 4.4 COVID Drug Prioritization -- 5 Conclusion -- References -- Hyperbolic Graph Codebooks -- 1 Introduction -- 2 Related Work -- 2.1 Graph Networks -- 2.2 Codebook Encodings -- 3 Background -- 3.1 Graph Representation Layers -- 3.2 Mapping to and Form the Tangent Space 4 Encoding Hyperbolic Graph Networks -- 4.1 Zeroth-Order Graph Encoding -- 4.2 First-Order Graph Encoding -- 4.3 Second-Order Graph Encoding -- 5 Experimental Setup -- 5.1 Datasets -- 5.2 Implementation Details -- 6 Experimental Results -- 6.1 Ablation Studies -- 6.2 Comparative Evaluation -- 7 Conclusions -- References -- A Kernel-Based Multilayer Perceptron Framework to Identify Pathways Related to Cancer Stages -- 1 Introduction -- 2 Materials -- 2.1 Constructing Datasets Using TCGA Data -- 2.2 Pathway Databases -- 3 Method -- 3.1 Problem Formulation -- 3.2 Structure of the Proposed MLP -- 4 Numerical Study -- 4.1 Experimental Settings -- 4.2 MLP Identifies Meaningful Biological Mechanisms -- 4.3 Predictive Performance Comparison -- 5 Conclusions -- References -- Loss Function with Memory for Trustworthiness Threshold Learning: Case of Face and Facial Expression Recognition -- 1 Introduction -- 2 Existing Work -- 2.1 Meta-learning -- 2.2 Uncertainty -- 2.3 Face and Facial Expression Recognition in Out-of-Distribution Settings -- 3 Proposed Solution -- 4 Data Set -- 5 Experiments -- 5.1 Trusted Accuracy Metrics -- 6 Results -- 7 Discussion, Conclusions, and Future Work -- References -- Machine Learning Approaches for Predicting Crystal Systems: A Brief Review and a Case Study -- 1 Introduction -- 2 Methods for Crystal System Prediction -- 2.1 ML Methods Using XRPD Patterns -- 2.2 ML Methods Using Features Derived from XRPD Patterns -- 2.3 Methods Using Other Features -- 2.4 Other Approaches -- 3 A Case Study: ML Approach Using Lattice Features -- 3.1 Data Preparation -- 3.2 Learning Models Using Lattice Values -- 4 Discussions -- References -- LS-PON: A Prediction-Based Local Search for Neural Architecture Search -- 1 Introduction -- 2 Related Works -- 3 Background -- 3.1 Neural Architecture Search -- 3.2 Local Search -- 4 Proposed Approach 4.1 Solution Encoding -- 4.2 Neighborhood Function -- 4.3 Solution Evaluation -- 4.4 Performance Prediction -- 4.5 LS-PON Process -- 5 Experiments -- 5.1 Benchmark Details -- 5.2 Experimentation Protocol -- 5.3 Results -- 6 Conclusion -- References -- Local Optimisation of Nyström Samples Through Stochastic Gradient Descent -- 1 Introduction -- 1.1 Kernel Matrix Approximation -- 1.2 Assessing the Accuracy of Nyström Approximations -- 1.3 Radial Squared-Kernel Discrepancy -- 2 A Convergence Result -- 3 Stochastic Approximation of the Radial-SKD Gradient -- 4 Numerical Experiments -- 4.1 Bi-Gaussian Example -- 4.2 Abalone Data Set -- 4.3 MAGIC Data Set -- 4.4 MiniBooNE Data Set -- 5 Conclusion -- References -- Explainable Machine Learning for Drug Shortage Prediction in a Pandemic Setting -- 1 Introduction -- 2 Related Work -- 3 Data Description -- 4 Data Preprocessing -- 4.1 What is Considered as Backorder -- 4.2 Features -- 5 Experiments -- 5.1 Experimental Design -- 5.2 Comparison Results -- 5.3 Results Discussions for Backorder Class -- 5.4 Decision Tree Visualization -- 5.5 Permutation Feature Importance -- 5.6 Comparison Between Accuracy and Velocity -- 6 Conclusion -- References -- Intelligent Robotic Process Automation for Supplier Document Management on E-Procurement Platforms -- 1 Introduction -- 2 Related Works -- 3 Document Management Process -- 3.1 Intelligent Document Management RPA -- 4 Experiments -- 4.1 Accuracy of Document-Agnostic Models -- 4.2 Accuracy of Document-Specific Models -- 5 Conclusions -- References -- Batch Bayesian Quadrature with Batch Updating Using Future Uncertainty Sampling -- 1 Introduction -- 2 Background -- 2.1 Bayesian Quadrature -- 2.2 Warped Bayesian Quadrature -- 2.3 Active Sampling -- 2.4 Batch Bayesian Quadrature -- 3 Method -- 4 Experiment -- 4.1 Test Functions -- 4.2 Dynamic Domain Decomposition 4.3 Cessation Criteria -- 5 Results -- 6 Discussion and Conclusion -- References -- Sensitivity Analysis of Engineering Structures Utilizing Artificial Neural Networks and Polynomial Chaos Expansion -- 1 Introduction -- 2 Theoretical Background of PCE and ANN -- 2.1 Polynomial Chaos Expansion -- 2.2 Artificial Neural Network -- 3 Sensitivity Analysis -- 4 Applications -- 4.1 Fixed Beam -- 4.2 Post-tensioned Concrete Bridge -- 5 Conclusions -- References -- Transformers for COVID-19 Misinformation Detection on Twitter: A South African Case Study -- 1 Introduction -- 2 Background: The South African Context -- 3 Related Work -- 4 Method -- 4.1 Data Understanding -- 4.2 Data Preparation -- 4.3 Modeling -- 5 Results -- 6 Conclusion -- References -- MI2AMI: Missing Data Imputation Using Mixed Deep Gaussian Mixture Models -- 1 Introduction -- 2 Background on MDGMM -- 3 MI2AMI Description -- 3.1 General Overview -- 3.2 The Imputation Step -- 4 Numerical Illustration -- 4.1 Framework -- 4.2 Results -- 5 Discussion and Perspective -- References -- On the Utility and Protection of Optimization with Differential Privacy and Classic Regularization Techniques -- 1 Introduction -- 2 Privacy Threats -- 2.1 Membership Inference Attacks -- 2.2 Model Inversion Attacks -- 3 Privacy Defences -- 4 Method -- 5 Experiments and Results -- 6 Conclusion -- References -- MicroRacer: A Didactic Environment for Deep Reinforcement Learning -- 1 Introduction -- 1.1 Structure of the Article -- 2 Related Software -- 2.1 AWS Deep Racer -- 2.2 Torcs -- 2.3 Learn-to-race -- 2.4 CarRacing-v0 -- 3 MicroRacer -- 3.1 State and Actions -- 3.2 Rewards -- 3.3 Environment Interface -- 3.4 Competitive Race -- 3.5 Dependencies -- 4 Learning Models -- 4.1 Deep Deterministic Policy Gradient (DDPG) -- 4.2 Twin Delayed DDPG (TD3) -- 4.3 Proximal Policy Optimization (PPO) 4.4 Soft Actor-Critic (SAC) -- 4.5 DSAC -- 5 Baselines Benchmarks -- 5.1 Results -- 6 Conclusions -- References -- A Practical Approach for Vehicle Speed Estimation in Smart Cities -- 1 Introduction -- 2 Related Works -- 3 Materials and Methods -- 3.1 Vehicle Speed Estimation System -- 3.2 The BrnoCompSpeed Dataset -- 4 Experimental Analysis -- 4.1 Preliminary Quality Test -- 4.2 Impact of the Observation Angle -- 4.3 Impact of the Detection Area Size -- 4.4 Entry/Exit Point Improved Estimation -- 4.5 Comparison with the State-of-the-Art -- 5 Conclusions -- References -- Corporate Network Analysis Based on Graph Learning -- 1 Introduction -- 1.1 Related Work -- 1.2 Our Contributions -- 2 Data Sources -- 3 Customer Acquisition Application -- 4 Credit Risk Modeling Application for Manufacturing Industry -- 5 Discussion -- 6 Conclusion -- References -- Source Attribution and Emissions Quantification for Methane Leak Detection: A Non-linear Bayesian Regression Approach -- 1 Introduction -- 2 Methods -- 2.1 Bayesian Approach -- 2.2 Non-linear Bayesian Regression: Stationary Model -- 2.3 Ranking Model -- 3 Case Study -- 3.1 Data Collection and Processing -- 3.2 Scenario Simulation -- 3.3 Results -- 4 Conclusions -- 5 Future Work -- References -- Analysis of Heavy Vehicles Rollover with Artificial Intelligence Techniques -- 1 Introduction -- 2 System Overview -- 3 Methodology -- 3.1 Simscape: Data Generation and Elaboration -- 3.2 AI: Data Analysis and Results -- 4 Conclusions and Possible Future Developments -- References -- Hyperparameter Tuning of Random Forests Using Radial Basis Function Models -- 1 Introduction -- 2 The B-CONDOR Algorithm for Hyperparameter Tuning of Random Forests -- 2.1 Algorithm Description -- 2.2 Radial Basis Function Interpolation -- 3 Computational Experiments -- 3.1 Random Forest Hyperparameter Tuning Problems 3.2 Experimental Setup Machine learning-Congresses Mathematical optimization-Congresses Big Data (DE-588)4802620-7 gnd rswk-swf Informatik (DE-588)4026894-9 gnd rswk-swf Optimierung (DE-588)4043664-0 gnd rswk-swf Maschinelles Lernen (DE-588)4193754-5 gnd rswk-swf Informatik (DE-588)4026894-9 s Maschinelles Lernen (DE-588)4193754-5 s Optimierung (DE-588)4043664-0 s Big Data (DE-588)4802620-7 s DE-604 Ojha, Varun Sonstige oth La Malfa, Emanuele Sonstige oth La Malfa, Gabriele Sonstige oth Pardalos, Panos Sonstige oth Di Fatta, Giuseppe Sonstige oth Giuffrida, Giovanni Sonstige oth Umeton, Renato Sonstige oth Erscheint auch als Druck-Ausgabe Nicosia, Giuseppe Machine Learning, Optimization, and Data Science Cham : Springer,c2023 9783031255984 |
| spellingShingle | Nicosia, Giuseppe Machine Learning, Optimization, and Data Science 8th International Conference, LOD 2022, Certosa Di Pontignano, Italy, September 18-22, 2022, Revised Selected Papers, Part I. Intro -- Preface -- Organization -- Contents - Part I -- Contents - Part II -- Detection of Morality in Tweets Based on the Moral Foundation Theory -- 1 Introduction -- 2 Theoretical Framework -- 3 Related Works -- 4 Methodology -- 4.1 The Moral Foundation Twitter Corpus -- 4.2 A BERT-Based Method for Detecting Moral Values -- 5 Results and Evaluation -- 5.1 Classification of Tweets Based on the MFT Dimentions -- 5.2 Detection of Moral Values with Polarity -- 6 Discussion -- 7 Conclusion and Future Work -- References -- Matrix Completion for the Prediction of Yearly Country and Industry-Level CO2 Emissions -- 1 Introduction -- 2 Description of the Dataset -- 3 Method -- 4 Results -- 5 Possible Developments -- References -- A Benchmark for Real-Time Anomaly Detection Algorithms Applied in Industry 4.0 -- 1 Introduction -- 2 Algorithm Selection and Anomaly Detection -- 3 Methodology -- 4 Design Principles as Solution Objectives -- 5 Formalization and Prototypical Implementation -- 6 Demonstration and Evaluation -- 7 Limitations and Future Research -- 8 Conclusion -- References -- A Matrix Factorization-Based Drug-Virus Link Prediction Method for SARS-CoV-2 Drug Prioritization -- 1 Introduction -- 2 Materials -- 2.1 DVA Dataset -- 2.2 Similarity Measurement for Drugs and Viruses -- 3 Methodology -- 3.1 Problem Description -- 3.2 Similarity Fusion -- 3.3 GRMF Model for Novel Drug-Virus Link Prediction -- 3.4 Experimental Setting and Evaluation Metrics -- 4 Results -- 4.1 Model Tuning -- 4.2 Effectiveness of Similarity Fusion -- 4.3 Performance Comparison -- 4.4 COVID Drug Prioritization -- 5 Conclusion -- References -- Hyperbolic Graph Codebooks -- 1 Introduction -- 2 Related Work -- 2.1 Graph Networks -- 2.2 Codebook Encodings -- 3 Background -- 3.1 Graph Representation Layers -- 3.2 Mapping to and Form the Tangent Space 4 Encoding Hyperbolic Graph Networks -- 4.1 Zeroth-Order Graph Encoding -- 4.2 First-Order Graph Encoding -- 4.3 Second-Order Graph Encoding -- 5 Experimental Setup -- 5.1 Datasets -- 5.2 Implementation Details -- 6 Experimental Results -- 6.1 Ablation Studies -- 6.2 Comparative Evaluation -- 7 Conclusions -- References -- A Kernel-Based Multilayer Perceptron Framework to Identify Pathways Related to Cancer Stages -- 1 Introduction -- 2 Materials -- 2.1 Constructing Datasets Using TCGA Data -- 2.2 Pathway Databases -- 3 Method -- 3.1 Problem Formulation -- 3.2 Structure of the Proposed MLP -- 4 Numerical Study -- 4.1 Experimental Settings -- 4.2 MLP Identifies Meaningful Biological Mechanisms -- 4.3 Predictive Performance Comparison -- 5 Conclusions -- References -- Loss Function with Memory for Trustworthiness Threshold Learning: Case of Face and Facial Expression Recognition -- 1 Introduction -- 2 Existing Work -- 2.1 Meta-learning -- 2.2 Uncertainty -- 2.3 Face and Facial Expression Recognition in Out-of-Distribution Settings -- 3 Proposed Solution -- 4 Data Set -- 5 Experiments -- 5.1 Trusted Accuracy Metrics -- 6 Results -- 7 Discussion, Conclusions, and Future Work -- References -- Machine Learning Approaches for Predicting Crystal Systems: A Brief Review and a Case Study -- 1 Introduction -- 2 Methods for Crystal System Prediction -- 2.1 ML Methods Using XRPD Patterns -- 2.2 ML Methods Using Features Derived from XRPD Patterns -- 2.3 Methods Using Other Features -- 2.4 Other Approaches -- 3 A Case Study: ML Approach Using Lattice Features -- 3.1 Data Preparation -- 3.2 Learning Models Using Lattice Values -- 4 Discussions -- References -- LS-PON: A Prediction-Based Local Search for Neural Architecture Search -- 1 Introduction -- 2 Related Works -- 3 Background -- 3.1 Neural Architecture Search -- 3.2 Local Search -- 4 Proposed Approach 4.1 Solution Encoding -- 4.2 Neighborhood Function -- 4.3 Solution Evaluation -- 4.4 Performance Prediction -- 4.5 LS-PON Process -- 5 Experiments -- 5.1 Benchmark Details -- 5.2 Experimentation Protocol -- 5.3 Results -- 6 Conclusion -- References -- Local Optimisation of Nyström Samples Through Stochastic Gradient Descent -- 1 Introduction -- 1.1 Kernel Matrix Approximation -- 1.2 Assessing the Accuracy of Nyström Approximations -- 1.3 Radial Squared-Kernel Discrepancy -- 2 A Convergence Result -- 3 Stochastic Approximation of the Radial-SKD Gradient -- 4 Numerical Experiments -- 4.1 Bi-Gaussian Example -- 4.2 Abalone Data Set -- 4.3 MAGIC Data Set -- 4.4 MiniBooNE Data Set -- 5 Conclusion -- References -- Explainable Machine Learning for Drug Shortage Prediction in a Pandemic Setting -- 1 Introduction -- 2 Related Work -- 3 Data Description -- 4 Data Preprocessing -- 4.1 What is Considered as Backorder -- 4.2 Features -- 5 Experiments -- 5.1 Experimental Design -- 5.2 Comparison Results -- 5.3 Results Discussions for Backorder Class -- 5.4 Decision Tree Visualization -- 5.5 Permutation Feature Importance -- 5.6 Comparison Between Accuracy and Velocity -- 6 Conclusion -- References -- Intelligent Robotic Process Automation for Supplier Document Management on E-Procurement Platforms -- 1 Introduction -- 2 Related Works -- 3 Document Management Process -- 3.1 Intelligent Document Management RPA -- 4 Experiments -- 4.1 Accuracy of Document-Agnostic Models -- 4.2 Accuracy of Document-Specific Models -- 5 Conclusions -- References -- Batch Bayesian Quadrature with Batch Updating Using Future Uncertainty Sampling -- 1 Introduction -- 2 Background -- 2.1 Bayesian Quadrature -- 2.2 Warped Bayesian Quadrature -- 2.3 Active Sampling -- 2.4 Batch Bayesian Quadrature -- 3 Method -- 4 Experiment -- 4.1 Test Functions -- 4.2 Dynamic Domain Decomposition 4.3 Cessation Criteria -- 5 Results -- 6 Discussion and Conclusion -- References -- Sensitivity Analysis of Engineering Structures Utilizing Artificial Neural Networks and Polynomial Chaos Expansion -- 1 Introduction -- 2 Theoretical Background of PCE and ANN -- 2.1 Polynomial Chaos Expansion -- 2.2 Artificial Neural Network -- 3 Sensitivity Analysis -- 4 Applications -- 4.1 Fixed Beam -- 4.2 Post-tensioned Concrete Bridge -- 5 Conclusions -- References -- Transformers for COVID-19 Misinformation Detection on Twitter: A South African Case Study -- 1 Introduction -- 2 Background: The South African Context -- 3 Related Work -- 4 Method -- 4.1 Data Understanding -- 4.2 Data Preparation -- 4.3 Modeling -- 5 Results -- 6 Conclusion -- References -- MI2AMI: Missing Data Imputation Using Mixed Deep Gaussian Mixture Models -- 1 Introduction -- 2 Background on MDGMM -- 3 MI2AMI Description -- 3.1 General Overview -- 3.2 The Imputation Step -- 4 Numerical Illustration -- 4.1 Framework -- 4.2 Results -- 5 Discussion and Perspective -- References -- On the Utility and Protection of Optimization with Differential Privacy and Classic Regularization Techniques -- 1 Introduction -- 2 Privacy Threats -- 2.1 Membership Inference Attacks -- 2.2 Model Inversion Attacks -- 3 Privacy Defences -- 4 Method -- 5 Experiments and Results -- 6 Conclusion -- References -- MicroRacer: A Didactic Environment for Deep Reinforcement Learning -- 1 Introduction -- 1.1 Structure of the Article -- 2 Related Software -- 2.1 AWS Deep Racer -- 2.2 Torcs -- 2.3 Learn-to-race -- 2.4 CarRacing-v0 -- 3 MicroRacer -- 3.1 State and Actions -- 3.2 Rewards -- 3.3 Environment Interface -- 3.4 Competitive Race -- 3.5 Dependencies -- 4 Learning Models -- 4.1 Deep Deterministic Policy Gradient (DDPG) -- 4.2 Twin Delayed DDPG (TD3) -- 4.3 Proximal Policy Optimization (PPO) 4.4 Soft Actor-Critic (SAC) -- 4.5 DSAC -- 5 Baselines Benchmarks -- 5.1 Results -- 6 Conclusions -- References -- A Practical Approach for Vehicle Speed Estimation in Smart Cities -- 1 Introduction -- 2 Related Works -- 3 Materials and Methods -- 3.1 Vehicle Speed Estimation System -- 3.2 The BrnoCompSpeed Dataset -- 4 Experimental Analysis -- 4.1 Preliminary Quality Test -- 4.2 Impact of the Observation Angle -- 4.3 Impact of the Detection Area Size -- 4.4 Entry/Exit Point Improved Estimation -- 4.5 Comparison with the State-of-the-Art -- 5 Conclusions -- References -- Corporate Network Analysis Based on Graph Learning -- 1 Introduction -- 1.1 Related Work -- 1.2 Our Contributions -- 2 Data Sources -- 3 Customer Acquisition Application -- 4 Credit Risk Modeling Application for Manufacturing Industry -- 5 Discussion -- 6 Conclusion -- References -- Source Attribution and Emissions Quantification for Methane Leak Detection: A Non-linear Bayesian Regression Approach -- 1 Introduction -- 2 Methods -- 2.1 Bayesian Approach -- 2.2 Non-linear Bayesian Regression: Stationary Model -- 2.3 Ranking Model -- 3 Case Study -- 3.1 Data Collection and Processing -- 3.2 Scenario Simulation -- 3.3 Results -- 4 Conclusions -- 5 Future Work -- References -- Analysis of Heavy Vehicles Rollover with Artificial Intelligence Techniques -- 1 Introduction -- 2 System Overview -- 3 Methodology -- 3.1 Simscape: Data Generation and Elaboration -- 3.2 AI: Data Analysis and Results -- 4 Conclusions and Possible Future Developments -- References -- Hyperparameter Tuning of Random Forests Using Radial Basis Function Models -- 1 Introduction -- 2 The B-CONDOR Algorithm for Hyperparameter Tuning of Random Forests -- 2.1 Algorithm Description -- 2.2 Radial Basis Function Interpolation -- 3 Computational Experiments -- 3.1 Random Forest Hyperparameter Tuning Problems 3.2 Experimental Setup Machine learning-Congresses Mathematical optimization-Congresses Big Data (DE-588)4802620-7 gnd Informatik (DE-588)4026894-9 gnd Optimierung (DE-588)4043664-0 gnd Maschinelles Lernen (DE-588)4193754-5 gnd |
| subject_GND | (DE-588)4802620-7 (DE-588)4026894-9 (DE-588)4043664-0 (DE-588)4193754-5 |
| title | Machine Learning, Optimization, and Data Science 8th International Conference, LOD 2022, Certosa Di Pontignano, Italy, September 18-22, 2022, Revised Selected Papers, Part I. |
| title_auth | Machine Learning, Optimization, and Data Science 8th International Conference, LOD 2022, Certosa Di Pontignano, Italy, September 18-22, 2022, Revised Selected Papers, Part I. |
| title_exact_search | Machine Learning, Optimization, and Data Science 8th International Conference, LOD 2022, Certosa Di Pontignano, Italy, September 18-22, 2022, Revised Selected Papers, Part I. |
| title_full | Machine Learning, Optimization, and Data Science 8th International Conference, LOD 2022, Certosa Di Pontignano, Italy, September 18-22, 2022, Revised Selected Papers, Part I. |
| title_fullStr | Machine Learning, Optimization, and Data Science 8th International Conference, LOD 2022, Certosa Di Pontignano, Italy, September 18-22, 2022, Revised Selected Papers, Part I. |
| title_full_unstemmed | Machine Learning, Optimization, and Data Science 8th International Conference, LOD 2022, Certosa Di Pontignano, Italy, September 18-22, 2022, Revised Selected Papers, Part I. |
| title_short | Machine Learning, Optimization, and Data Science |
| title_sort | machine learning optimization and data science 8th international conference lod 2022 certosa di pontignano italy september 18 22 2022 revised selected papers part i |
| title_sub | 8th International Conference, LOD 2022, Certosa Di Pontignano, Italy, September 18-22, 2022, Revised Selected Papers, Part I. |
| topic | Machine learning-Congresses Mathematical optimization-Congresses Big Data (DE-588)4802620-7 gnd Informatik (DE-588)4026894-9 gnd Optimierung (DE-588)4043664-0 gnd Maschinelles Lernen (DE-588)4193754-5 gnd |
| topic_facet | Machine learning-Congresses Mathematical optimization-Congresses Big Data Informatik Optimierung Maschinelles Lernen |
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