| Description |
1 online resource (xii, 225 pages) : illustrations (some color) |
| Series |
Lecture notes in computer science. Lecture notes in artificial intelligence ; 13651 |
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LNCS sublibrary: SL7 -Artificial intelligence |
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Lecture notes in computer science ; 13651.
|
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Lecture notes in computer science. Lecture notes in artificial intelligence.
|
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LNCS sublibrary. SL 7, Artificial intelligence.
|
| Contents |
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 |
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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 |
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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 |
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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 |
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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 |
| Summary |
This book constitutes the refereed proceedings of the 15th International Conference on Multi-disciplinary Trends in Artificial Intelligence, MIWAI 2022, held online on November 1719, 2022. The 14 full papers and 5 short papers presented were carefully reviewed and selected from 42 submissions. |
| Notes |
Selected conference papers |
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Includes author index |
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Online resource; title from PDF title page (SpringerLink, viewed November 16, 2022) |
| Subject |
Artificial intelligence -- Congresses
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Artificial intelligence
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| Genre/Form |
proceedings (reports)
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Conference papers and proceedings
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Conference papers and proceedings.
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Actes de congrès.
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| Form |
Electronic book
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| Author |
Surinta, Olarik, editor
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Yuen, Kevin Kam Fung, editor
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| ISBN |
9783031209925 |
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3031209923 |
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9788303120991 |
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8303120999 |
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