Multi-disciplinary trends in artificial intelligence : 14th International Conference, MIWAI 2021, virtual event, July 2-3, 2021 : proceedings / / Phatthanaphong Chomphuwiset, Junmo Kim, Pornntiwa Pawara (editors)

• Pubblicazione: Cham, Switzerland : , : Springer, , [2021]; ©2021
• Descrizione fisica: 1 online resource (202 pages)
• Disciplina: 006.3
• Soggetto topico: Artificial intelligence
• Persona (resp. second.): Phatthanaphong Chomphuwiset; KimJunmo; PawaraPornntiwa
• Nota di contenuto: Intro -- Preface -- Organization -- Bias, Trust, and Doing Good: The Impacts of Digital Technology on Human Ethics, and Vice Versa (Abstract of Keynote Speaker) -- Contents -- 3D Point Cloud Upsampling and Colorization Using GAN -- 1 Introduction -- 2 Related Work -- 3 Method -- 3.1 Data Pre-processing -- 3.2 Network Architecture -- 3.3 Objective -- 3.4 Post-processing -- 4 Experiments -- 4.1 Dataset -- 4.2 Implementation Details -- 4.3 Upsampling and Colorization Results -- 4.4 Only Colorization Results -- 4.5 Ablation Study -- 5 Evaluation -- 5.1 Evaluation Metrics -- 5.2 Evaluation Results -- 6 Conclusion and Discussion -- References -- Learning Behavioral Rules from Multi-Agent Simulations for Optimizing Hospital Processes -- 1 Introduction -- 1.1 Motivation -- 1.2 Related Work -- 2 Mutli-agent Simulation Setting -- 2.1 Scenario Description -- 2.2 Modeling of the Scenario -- 2.3 Simulation Results as Gantt Charts -- 3 Alternative Solution: Learning Behavioral Rules -- 3.1 Preliminaries -- 3.2 An Advanced Algorithm for Learning HKBs from Data -- 3.3 Application in the Hospital Process Multi-agent Simulation Setting -- 4 Demonstration and Basic Evaluation -- 5 Conclusion and Future Work -- References -- An Open-World Novelty Generator for Authoring Reinforcement Learning Environment of Standardized Toolkits -- 1 Introduction -- 2 System Architecture -- 3 Experimental Results -- 3.1 Domain Editor Results -- 3.2 Environment Editor Results -- 4 Discussion and Future Work -- References -- Book Cover and Content Similarity Retrieval Using Computer Vision and NLP Techniques -- 1 Introduction -- 2 Literature Review -- 3 Proposed Method -- 3.1 Image Preprocessing -- 3.2 Feature Extraction Using Speed-Up Robust Features (SURF Descriptor) -- 3.3 Word Segmentation -- 3.4 Stop Word Removal -- 3.5 Feature Generation -- 3.6 Similarity Measurements. ; 3.7 Performance Evaluation -- 4 Experiment and Result -- 5 Conclusion -- References -- Fast Classification Learning with Neural Networks and Conceptors for Speech Recognition and Car Driving Maneuvers -- 1 Introduction -- 2 Background and Related Works -- 2.1 Recurrent Neural Networks -- 2.2 Echo State Networks and Conceptors -- 3 Classification with Conceptors -- 3.1 Conceptor Algebra -- 3.2 Classification -- 4 Case Studies -- 4.1 Speech Recognition -- 4.2 Car Driving Maneuvers -- 5 Evaluation -- 5.1 Speech Recognition -- 5.2 Car Driving Maneuvers -- 5.3 Identifying Essential Factors -- 6 Conclusions -- References -- Feature Group Importance for Automated Essay Scoring -- 1 Introduction -- 2 Related Work -- 3 Evaluation Methodology -- 3.1 Data Preprocessing -- 3.2 Learning Algorithms -- 3.3 Evaluation Metric for Learning Algorithm -- 3.4 Experimental Setup -- 3.5 Feature Influence -- 3.6 Feature Selection -- 4 Results and Discussion -- 4.1 QWK Scores Result for Comparison -- 4.2 Feature Selection Results -- 5 Conclusion -- References -- Feature Extraction Efficient for Face Verification Based on Residual Network Architecture -- 1 Introduction -- 2 Related Work -- 3 The Proposed Face Verification System -- 3.1 Ace Detection Using MMOD + CNN -- 3.2 Deep Feature Extraction Using ResNet-50 Architecture -- 4 Experiments -- 4.1 Face Databases -- 4.2 Evaluation Metrics -- 4.3 Evaluation -- 4.4 Discussion -- 5 Conclusion -- References -- Acquiring Input Features from Stock Market Summaries: A NLG Perspective -- 1 Introduction -- 2 Related Work -- 3 Dataset and Problem Formulation -- 3.1 Preliminary -- 3.2 Market Summaries Preprocessing -- 3.3 Dataset Statistics -- 3.4 Generating Input Features -- 3.5 Linearization -- 4 Experimental Setup -- 4.1 Results -- 4.2 Human Evaluation -- 4.3 Discussion and and Error Analysis -- 5 Conclusion -- References. ; A Comparative of a New Hybrid Based on Neural Networks and SARIMA Models for Time Series Forecasting -- 1 Introduction -- 2 Methodology -- 2.1 Decomposition Method -- 2.2 Seasonal Autoregressive Integrated Moving Average (SARIMA) Model -- 2.3 Artificial Neural Network (ANN) -- 2.4 Radial Basis Function (RBF) -- 2.5 Proposed Method -- 3 Data Preparation and Model Evaluation Criteria -- 3.1 Data Descriptions and Data Preparation -- 3.2 Model Evaluation Criteria -- 4 Results and Discussion -- 5 Conclusion and Future Research -- 5.1 Conclusion -- 5.2 Future Research -- References -- Cartpole Problem with PDL and GP Using Multi-objective Fitness Functions Differing in a Priori Knowledge -- 1 Introduction -- 2 Cartpole Problem with Pdl -- 2.1 PDL and Genetic Programming -- 2.2 Experiment & -- Fitness Function Design -- 3 Results -- 4 Conclusions and Future Work -- References -- Learning Robot Arm Controls Using Augmented Random Search in Simulated Environments -- 1 Introduction -- 2 Estimating Policy Using Random Search -- 2.1 Policy Space Search Using Augmented Random Search -- 3 Empirical Set up Using Robot Arm Domain -- 3.1 Designing Robot Arm-Reaching Tasks -- 3.2 State Representations -- 3.3 Training a Robot Arm Using ARS -- 4 Empirical Results and Discussion -- 5 Conclusion -- References -- An Analytical Evaluation of a Deep Learning Model to Detect Network Intrusion -- 1 Introduction -- 2 Literature Review -- 3 Dataset Overview -- 4 Research Methodology -- 4.1 Preprocessing -- 4.2 Feature Selection -- 4.3 Class Imbalance Handling -- 4.4 Long Short Term Memory (LSTM) -- 4.5 Machine Learning Models -- 5 Results and Discussions -- 6 Conclusion -- References -- Application of Machine Learning Techniques to Predict Breast Cancer Survival -- 1 Introduction -- 2 Material and Method -- 2.1 Machine Learning Techniques -- 2.2 Methods. ; 3 Experiment Results -- 3.1 Insight Model Performance -- 3.2 Overall Model Performance -- 4 Discussion and Conclusion -- References -- Thai Handwritten Recognition on BEST2019 Datasets Using Deep Learning -- 1 Introduction -- 2 Related Works -- 2.1 Thai Language Property -- 2.2 Thai Handwritten Recognition -- 3 The Datasets -- 4 Methodology of Thai Handwritten Recognition -- 4.1 Text Localization -- 4.2 Model Generation -- 4.3 Connectionist Temporal Classification (CTC) -- 4.4 Character Error Rate (CER) -- 5 Experiment and Results -- 6 Conclusion -- References -- Comparing of Multi-class Text Classification Methods for Automatic Ratings of Consumer Reviews -- 1 Introduction -- 2 Related Work -- 3 Dataset -- 4 The Method of Multi-class Classifiers Modelling -- 4.1 Pre-processing of Movie Reviews -- 4.2 Feature Selection and Text Representation -- 4.3 Term Weighting -- 4.4 Multi-class Classifiers Modelling -- 5 Experimental Results -- 6 Conclusion -- References -- Improving Safety and Efficiency for Navigation in Multiagent Systems -- 1 Introduction -- 2 Fundamentals -- 2.1 Reciprocal Velocity Obstacles (RVO) -- 2.2 3D Reciprocal Velocity Obstacle (3DRVO) -- 2.3 Three Dimensional Collision Avoidance -- 3 Believe-Desire-Intention Architecture -- 3.1 Planning Strategy with Sub-goal -- 4 K-D Tree Algorithms -- 4.1 3D-Tree Algorithms -- 5 Experiment and Results -- 5.1 The Scenes -- 5.2 Results -- 6 Conclusion and Future Work -- References -- Correction to: Thai Handwritten Recognition on BEST2019 Datasets Using Deep Learning -- Correction to: Chapter "Thai Handwritten Recognition on BEST2019 Datasets Using Deep Learning" in: P. Chomphuwiset et al. (Eds.): Multi-disciplinary Trends in Artificial Intelligence, LNAI 12832, https://doi.org/10.1007/978-3-030-80253-0_14 -- Author Index.
• Titolo autorizzato: Multi-disciplinary Trends in Artificial Intelligence
• ISBN: 3-030-80253-1
• Formato: Materiale a stampa
• Livello bibliografico: Monografia
• Lingua di pubblicazione: Inglese
• Record Nr.: 996464509903316
• Lo trovi qui: Univ. di Salerno
• Serie: Lecture notes in computer science. . -Lecture notes in artificial intelligence ; ; 12832.