Multi-disciplinary trends in artificial intelligence : 15th International Conference, MIWAI 2022, virtual event, November 17-19, 2022, proceedings / / Olarik Surinta and Kevin Kam Fung Yuen (editors)

• Pubblicazione: Cham, Switzerland : , : Springer, , [2022]; ©2022
• Descrizione fisica: 1 online resource (238 pages)
• Disciplina: 006.3
• Soggetto topico: Artificial intelligence
• Persona (resp. second.): SurintaOlarik; Kam Fung YuenKevin
• Nota di bibliografia: Includes bibliographical references and index.
• Nota di contenuto: Intro -- Preface -- Organization -- Contents -- Computing Nash Equilibrium of Crops in Real World Agriculture Domain -- 1 Introduction -- 2 Related Works -- 3 Non-cooperative Game -- 3.1 Strategic Form Game and Nash Equilibrium -- 3.2 Prisoner Dilemma -- 3.3 Cardinal vs Ordinal Utility -- 4 Complexity of the Problem -- 4.1 Typical Cases -- 4.2 Relation of Agent Payoffs -- 4.3 Case of 3 Agents and 2 Strategies -- 4.4 Case of 3 Agents and 3 Strategies -- 5 Searching for Nash Equilibrium -- 5.1 Control Loops -- 5.2 Algorithm for Examining Nash Equilibrium -- 5.3 Supporting Algorithms -- 6 Experiments and Results -- 6.1 Overview Result -- 6.2 Detailed Results -- 7 Conclusion -- References -- Evolutionary Feature Weighting Optimization and Majority Voting Ensemble Learning for Curriculum Recommendation in the Higher Education -- 1 Introduction -- 2 Material and Methods -- 2.1 Research Definition -- 2.2 Data Collection and Word Segmentation -- 2.3 Research Tools -- 3 Research Results -- 3.1 Model Performance Classified by Technique -- 3.2 Majority Voting Prototype Model -- 4 Research Discussion -- 5 Conclusion -- References -- Fuzzy Soft Relations-Based Rough Soft Sets Classified by Overlaps of Successor Classes with Measurement Issues -- 1 Introduction -- 2 Preliminaries -- 2.1 Some Basic Notions of Fuzzy Sets -- 2.2 Some Basic Notions of Soft Sets and Fuzzy Soft Relations -- 3 Main Results -- 3.1 Overlaps of Successor Classes via Fuzzy Soft Relations -- 3.2 Rough Soft Sets Based on Overlaps of Successor Classes -- 3.3 Measurement Issues -- 4 Conclusions -- References -- Helmet Detection System for Motorcycle Riders with Explainable Artificial Intelligence Using Convolutional Neural Network and Grad-CAM -- 1 Introduction -- 2 Related Works -- 2.1 Helmet Detection -- 2.2 Deep Learning and Convolution Neural Network. ; 2.3 Histograms of Oriented Gradient (HOG) -- 2.4 Object Detection -- 2.5 Convolutional Neural Network -- 2.6 Explainable AI -- 2.7 Grad-CAM -- 3 Methodology -- 3.1 Data Collection and Preprocessing -- 3.2 Deep Convolution Neural Network -- 4 Experiment Setup and Results -- 4.1 Visualization and Explainable AI -- 5 Conclusion -- References -- Hierarchical Human Activity Recognition Based on Smartwatch Sensors Using Branch Convolutional Neural Networks -- 1 Introduction -- 2 Related Works -- 3 The Sensor-Based HAR Framework -- 3.1 WISDM-HARB Dataset -- 3.2 Data Pre-processing -- 3.3 Branch Convolutional Neural Network -- 3.4 Performance Measurement Criteria -- 4 Experiments and Results -- 4.1 Experiments -- 4.2 Experimental Results -- 5 Conclusions -- References -- Improving Predictive Model to Prevent Students' Dropout in Higher Education Using Majority Voting and Data Mining Techniques -- 1 Introduction -- 2 Materials and Methods -- 2.1 Population and Sample -- 2.2 Data Acquisition Procedure -- 2.3 Model Construction Tools -- 2.4 Model Performance Evaluation Tools -- 3 Research Results -- 3.1 Generated Model Results -- 3.2 Majority Voting Prototype Model -- 4 Research Discussion -- 5 Conclusion -- 6 Limitation -- References -- LCIM: Mining Low Cost High Utility Itemsets -- 1 Introduction -- 2 Related Work -- 3 Problem Definition -- 4 The LCIM Algorithm -- 4.1 Search Space Exploration and Pruning Properties -- 4.2 The Cost-List Data Structure -- 4.3 The Algorithm -- 5 Experimental Evaluation -- 6 Conclusion -- References -- MaxFEM: Mining Maximal Frequent Episodes in Complex Event Sequences -- 1 Introduction -- 2 Problem Definition -- 3 The MaxFEM Algorithm -- 4 Experimental Evaluation -- 5 Conclusion -- References. ; Method for Image-Based Preliminary Assessment of Car Park for the Disabled and the Elderly Using Convolutional Neural Networks and Transfer Learning -- 1 Introduction -- 2 Related Work -- 2.1 Manual Assessment of the Disabled Facilities -- 2.2 Computer Vision Techniques for Assessing Disabled Facilities or Accessibility -- 3 Research Methods -- 3.1 Data Collection and Labeling -- 3.2 Preliminary Assessment Method -- 3.3 Evaluating the Performance of the Method -- 4 Results and Discussion -- 5 Conclusion and Future Work -- References -- Multi-resolution CNN for Lower Limb Movement Recognition Based on Wearable Sensors -- 1 Introduction -- 2 Related Works -- 2.1 Types of Sensor Modalities -- 2.2 Deep Learning Approaches -- 3 The Sensor-Based HAR Framework -- 3.1 HARTH Dataset -- 3.2 Data Pre-processing -- 3.3 The Proposed Multi-resolution CNN Model -- 3.4 Performance Measurement Criteria -- 4 Experiments and Results -- 4.1 Experiments -- 4.2 Experimental Results -- 5 Conclusions -- References -- News Feed: A Multiagent-Based Push Notification System -- 1 Introduction -- 2 Review -- 3 Architecture, Internal and External Data Sources -- 4 Informative Multiagent-Based Personalized Data System -- 4.1 Components of Agents -- 4.2 Collective Agent -- 4.3 Analytic Agent -- 4.4 Dispense Agent -- 4.5 Farmer Agent -- 4.6 Scalable Design -- 4.7 Algorithm for Collecting Data -- 5 Results -- 5.1 Collecting Delay Time -- 5.2 Distributing Delay Time -- 5.3 Final Results -- 6 Conclusion -- References -- Optimizing the Social Force Model Using New Hybrid WOABAT-IFDO in Crowd Evacuation in Panic Situation -- 1 Introduction -- 2 Related Works -- 3 The Hybrid of WOABAT-IFDO and SFM Optimization Design Framework -- 3.1 Evacuation Time Validation -- 4 Result of Evacuation Time Hybrid WOABAT-IFDO in SFM vs Single SFM. ; 4.1 Analysis of the Hypothesis for Evacuation Time Validation -- 5 Conclusions -- References -- Recognizing Driver Activities Using Deep Learning Approaches Based on Smartphone Sensors -- 1 Introduction -- 2 Related Works -- 3 Sensor-Based HAR Methodology -- 3.1 Driver Activity Dataset -- 3.2 Data Pre-processing -- 3.3 The Proposed DriveNeXt Architecture -- 4 Experiments and Research Findings -- 4.1 Research Setting -- 4.2 Research Findings -- 5 Conclusion and Future Works -- References -- Sentence-Level Sentiment Analysis for Student Feedback Relevant to Teaching Process Assessment -- 1 Introduction -- 2 Datasets -- 3 Preliminaries -- 3.1 Aspect-Based Keyword Corpus Development -- 3.2 Development of the Aspect Analyzer and the Sentiment Analyzer Using the Text Classification Technique -- 4 The Proposed Method -- 4.1 Pre-processing Student Comments and Text Representation -- 4.2 Identifying Aspect Class for Each Sentence Using the Aspect Analyzer -- 4.3 Assigning Sentence Polarity for Each Sentence Using the Sentiment Analyzer -- 4.4 Summarizing the Overall Sentiment Polarity of a Student Comment -- 5 Results -- 5.1 Evaluation of the Aspect Analyzer and the Sentiment Analyzer -- 5.2 Comparison of Proposed and Baseline Methods: In the Case of Modeling the Aspect Analyzer -- 6 Conclusion -- References -- Sentiment Analysis of Local Tourism in Thailand from YouTube Comments Using BiLSTM -- 1 Introduction -- 2 Related Work -- 2.1 Big Data Analytics -- 2.2 Deep Learning -- 2.3 Social Media Analytics (SMA) -- 2.4 Sentiment Analysis -- 3 Methodology -- 3.1 Data Collection -- 3.2 Sentiment Analysis -- 3.3 Summarizing Results -- 4 Evaluation -- 5 Conclusion -- References -- Stable Coalitions of Buyers in Real World Agriculture Domain -- 1 Introduction -- 2 Related Works -- 3 Real World Domain -- 3.1 Computing Values for a Group of Farmers. ; 3.2 Computing Payoffs for Farmers -- 4 Stability in Coalition of Farmers -- 4.1 Coalition Formation -- 4.2 Kernel Solution Concept -- 5 Algorithms -- 5.1 Overview -- 5.2 Algorithm to Generate Coalitions -- 5.3 Algorithm to Verify Kernel -- 6 Experiments and Results -- 7 Conclusion -- References -- The Analysis of Explainable AI via Notion of Congruence -- 1 Introduction -- 2 Background -- 2.1 Abstract Argumentation -- 2.2 Assumption-Based Argumentation -- 2.3 Probabilistic Argumentation -- 3 Procedure -- 3.1 Translating BN Model to PABA Framework -- 3.2 Translating Argumentation Tree to PABA Framework -- 3.3 Establishing Notion of Congruence -- 4 Conclusion -- References -- Using Ensemble Machine Learning Methods to Forecast Particulate Matter (PM2.5) in Bangkok, Thailand -- 1 Introduction -- 2 Literature Review -- 3 Dataset Overview and Preparation -- 4 Research Methods -- 4.1 Seasonal ARIMA with Exogenous Covariates -- 4.2 Prophet Model -- 4.3 Regression Tree -- 4.4 Support Vector Regression -- 4.5 Artificial Neural Network -- 4.6 K-Nearest Neighbors (KNN) Regression -- 5 Results and Conclusions -- References -- Wearable Fall Detection Based on Motion Signals Using Hybrid Deep Residual Neural Network -- 1 Introduction -- 2 Related Works -- 2.1 Fall Detection System -- 2.2 Automatic Fall Detection by Using DL -- 3 Fall Detection Approach -- 3.1 FallAllD Dataset -- 3.2 Pre-processing of Data -- 3.3 Hybrid Deep Residual Neural Network -- 3.4 Interpretation Measurements -- 4 Experimental Results -- 5 Conclusion and Future Studies -- References -- Author Index.
• Titolo autorizzato: Multi-Disciplinary Trends in Artificial Intelligence
• ISBN: 3-031-20992-3
• Formato: Materiale a stampa
• Livello bibliografico: Monografia
• Lingua di pubblicazione: Inglese
• Record Nr.: 996500065203316
• Lo trovi qui: Univ. di Salerno
• Serie: Lecture notes in computer science ; ; Volume 13651.