Note: Intro -- Foreword -- Preface -- Acknowledgments -- Contents -- 1 Introduction -- 1.1 Introduction to Recommender Systems -- 1.2 Formulation of the Recommendation Problem -- 1.2.1 The Input to a Recommender System -- 1.2.2 The Output of a Recommender System -- 1.3 Methods of Collecting Knowledge About User Preferences -- 1.3.1 The Implicit Approach -- 1.3.2 The Explicit Approach -- 1.3.3 The Mixing Approach -- 1.4 Motivation of the Book -- 1.5 Contribution of the Book -- 1.6 Outline of the Book -- References -- 2 Review of Previous Work Related to Recommender Systems -- 2.1 Content-Based Methods -- 2.2 Collaborative Methods -- 2.2.1 User-Based Collaborative Filtering Systems -- 2.2.2 Item-Based Collaborative Filtering Systems -- 2.2.3 Personality Diagnosis -- 2.3 Hybrid Methods -- 2.3.1 Adding Content-Based Characteristics to Collaborative Models -- 2.3.2 Adding Collaborative Characteristics to Content-Based Models -- 2.3.3 A Single Unifying Recommendation Model -- 2.3.4 Other Types of Recommender Systems -- 2.4 Fundamental Problems of Recommender Systems -- References -- 3 The Learning Problem -- 3.1 Introduction -- 3.2 Types of Learning -- 3.3 Statistical Learning -- 3.3.1 Classical Parametric Paradigm -- 3.3.2 General Nonparametric---Predictive Paradigm -- 3.3.3 Transductive Inference Paradigm -- 3.4 Formulation of the Learning Problem -- 3.5 The Problem of Classification -- 3.5.1 Empirical Risk Minimization -- 3.5.2 Structural Risk Minimization -- 3.6 Support Vector Machines -- 3.6.1 Basics of Support Vector Machines -- 3.6.2 Multi-class Classification Based on SVM -- 3.7 One-Class Classification -- 3.7.1 One-Class SVM Classification -- 3.7.2 Recommendation as a One-Class Classification Problem -- References -- 4 Content Description of Multimedia Data -- 4.1 Introduction -- 4.2 MPEG-7 -- 4.2.1 Visual Content Descriptors. , 4.2.2 Audio Content Descriptors -- 4.3 MARSYAS: Audio Content Features -- 4.3.1 Music Surface Features -- 4.3.2 Rhythm Features and Tempo -- 4.3.3 Pitch Features -- References -- 5 Similarity Measures for Recommendations Based on Objective Feature Subset Selection -- 5.1 Introduction -- 5.2 Objective Feature-Based Similarity Measures -- 5.3 Architecture of MUSIPER -- 5.4 Incremental Learning -- 5.5 Realization of MUSIPER -- 5.5.1 Computational Realization of Incremental Learning -- 5.6 MUSIPER Operation Demonstration -- 5.7 MUSIPER Evaluation Process -- 5.8 System Evaluation Results -- References -- 6 Cascade Recommendation Methods -- 6.1 Introduction -- 6.2 Cascade Content-Based Recommendation -- 6.3 Cascade Hybrid Recommendation -- 6.4 Measuring the Efficiency of the Cascade Classification Scheme -- References -- 7 Evaluation of Cascade Recommendation Methods -- 7.1 Introduction -- 7.2 Comparative Study of Recommendation Methods -- 7.3 One-Class SVM---Fraction: Analysis -- 8 Conclusions and Future Work -- 8.1 Summary and Conclusions -- 8.2 Current and Future Work.

Note: Intro -- Foreword -- Preface -- Contents -- 1 Introduction to Handbook on Artificial Intelligence-Empowered Applied Software Engineering-VOL.1: Novel Methodologies to Engineering Smart Software Systems -- 1.1 Editorial Note -- 1.2 Book Summary and Future Volumes -- Bibliography for Further Reading -- Part I Survey of Recent Relevant Literature -- 2 Synergies Between Artificial Intelligence and Software Engineering: Evolution and Trends -- 2.1 Introduction -- 2.2 Methodology -- 2.3 The Evolution of AI in Software Engineering -- 2.4 Top Authors and Topics -- 2.5 Trends in AI Applications to Software Engineering -- 2.5.1 Machine Learning and Data Mining -- 2.5.2 Knowledge Representation and Reasoning -- 2.5.3 Search and Optimisation -- 2.5.4 Communication and Perception -- 2.5.5 Cross-Disciplinary Topics -- 2.6 AI-Based Tools -- 2.7 Conclusion -- References -- Part II Artificial Intelligence-Assisted Software Development -- 3 Towards Software Co-Engineering by AI and Developers -- 3.1 Introduction -- 3.2 Software Development Support and Automation Level by Machine Learning -- 3.2.1 Project Planning: Team Composition -- 3.2.2 Requirements Engineering: Data-Driven Persona -- 3.2.3 Design: Detection of Design Patterns -- 3.2.4 Categorization of Initiative and Level of Automation -- 3.3 Quality of AI Application Systems and Software -- 3.3.1 Metamorphic Testing -- 3.3.2 Improving Explainability -- 3.3.3 Systems and Software Architecture -- 3.3.4 Integration of Goals, Strategies, and Data -- 3.4 Towards Software Co-Engineering by AI and Developers -- 3.5 Conclusion -- References -- 4 Generalizing Software Defect Estimation Using Size and Two Interaction Variables -- 4.1 Introduction -- 4.2 Background -- 4.3 A Proposed Approach -- 4.3.1 Selection of Sample Projects -- 4.3.2 Data Collection -- 4.3.3 The Scope and Decision to Go with 'Interaction' Variables. , 4.3.4 Data Analysis and Results Discussion -- 4.3.5 The Turning Point -- 4.3.6 Models Performance-Outside Sample -- 4.4 Conclusion and Limitations -- 4.5 Future Research Directions -- 4.6 Annexure-Model Work/Details -- References -- 5 Building of an Application Reviews Classifier by BERT and Its Evaluation -- 5.1 Background -- 5.2 The Process of Building a Machine Learning Model -- 5.3 Dataset -- 5.4 Preprocessing -- 5.5 Feature Engineering -- 5.5.1 Bag of Words (BoW) [4] -- 5.5.2 FastText [5, 6] -- 5.5.3 Bidirectional Encoder Representations from Transformers (BERT) Embedding [7] -- 5.6 Machine-Learning Algorithms -- 5.6.1 Naive Bayes -- 5.6.2 Logistic Regression -- 5.6.3 BERT -- 5.7 Training and Evaluation Methods -- 5.8 Results -- 5.9 Discussion -- 5.9.1 Comparison of Classifier Performances -- 5.9.2 Performance of the Naive Bayes Classifiers -- 5.9.3 Performance of the Logistic Regression Classifiers -- 5.9.4 Visualization of Classifier Attention Using the BERT -- 5.10 Threats to Validity -- 5.10.1 Labeling Dataset -- 5.10.2 Parameter Tuning -- 5.11 Summary -- References -- 6 Harmony Search-Enhanced Software Architecture Reconstruction -- 6.1 Introduction -- 6.2 Related Work -- 6.3 HS Enhanced SAR -- 6.3.1 SAR Problem -- 6.3.2 HS Algorithm -- 6.3.3 Proposed Approach -- 6.4 Experimentation -- 6.4.1 Test Problems -- 6.4.2 Competitor approaches -- 6.5 Results and Discussion -- 6.6 Conclusion and Future Work -- References -- 7 Enterprise Architecture-Based Project Model for AI Service System Development -- 7.1 Introduction -- 7.2 Related Work -- 7.3 AI Servie System and Enterprise Architecture -- 7.3.1 AI Service System -- 7.3.2 Enterprise Architecture and AI Service System -- 7.4 Modeling Business IT Alignment for AI Service System -- 7.4.1 Generic Business-AI Alignment Model -- 7.4.2 Comparison with Project Canvas Model. , 7.5 Business Analysis Method for Constructing Domain Specific Business-AI Alignment Model -- 7.5.1 Business Analysis Tables -- 7.5.2 Model Construction Method -- 7.6 Practice -- 7.6.1 Subject Project -- 7.6.2 Result -- 7.7 Discussion -- 7.8 Conclusion -- References -- Part III Software Engineering Tools to Develop Artificial Intelligence Applications -- 8 Requirements Engineering Processes for Multi-agent Systems -- 8.1 Introduction -- 8.2 Background -- 8.2.1 Agents, Multiagent Systems, and the BDI Model -- 8.2.2 Requirements Engineering -- 8.3 Techniques and Process of Requirements Engineering for Multiagent Systems -- 8.3.1 Elicitation Requirements Techniques for Multiagent Systems -- 8.3.2 Requirements Engineering Processes for Multiagent Systems -- 8.3.3 Requirements Validation -- 8.4 Conclusion -- References -- 9 Specific UML-Derived Languages for Modeling Multi-agent Systems -- 9.1 Introduction -- 9.2 Backgroud -- 9.2.1 UML -- 9.2.2 Agents, Multiagent Systems, and the BDI Model -- 9.2.3 BDI Models -- 9.3 AUML-Agent UML -- 9.4 AORML-Agent-Object-Relationship Modeling Language -- 9.4.1 Considerations About AORML -- 9.5 AML-Agent Modeling Language -- 9.5.1 Considerations About AML -- 9.6 MAS-ML-Multiagent System Modeling Language -- 9.6.1 Considerations About MAS-ML -- 9.7 SEA-ML-Semantic Web Enabled Agent Modeling Language -- 9.7.1 Considerations -- 9.8 MASRML-A Domain-Specific Modeling Language for Multi-agent Systems Requirements -- 9.8.1 Considerations -- References -- 10 Methods for Ensuring the Overall Safety of Machine Learning Systems -- 10.1 Introduction -- 10.2 Related Work -- 10.2.1 Safety of Machine Learning Systems -- 10.2.2 Conventional Safety Model -- 10.2.3 STAMP and Its Related Methods -- 10.2.4 Standards for Software Lifecycle Processes and System Lifecycle Processes -- 10.2.5 Social Technology Systems and Software Engineering. , 10.2.6 Software Layer Architecture -- 10.2.7 Assurance Case -- 10.2.8 Autonomous Driving -- 10.3 Safety Issues in Machine Learning Systems -- 10.3.1 Eleven Reasons Why We Cannot Release Autonomous Driving Cars -- 10.3.2 Elicitation Method -- 10.3.3 Eleven Problems on Safety Assessment for Autonomous Driving Car Products -- 10.3.4 Validity to Threats -- 10.3.5 Safety Issues of Automatic Operation -- 10.3.6 Task Classification -- 10.3.7 Unclear Assurance Scope -- 10.3.8 Safety Assurance of the Entire System -- 10.3.9 Machine Learning and Systems -- 10.4 STAMP S& -- S Method -- 10.4.1 Significance of Layered Modeling of Complex Systems -- 10.4.2 STAMP S& -- S and Five Layers -- 10.4.3 Scenario -- 10.4.4 Specification and Standard -- 10.5 CC-Case -- 10.5.1 Definition of CC-Case -- 10.5.2 Technical Elements of CC-Case -- 10.6 Measures for Autonomous Driving -- 10.6.1 Relationship Between Issues and Measures Shown in This Section -- 10.6.2 Measure 1: Analyze Various Quality Attributes in Control Action Units -- 10.6.3 Measure 2: Modeling the Entire System -- 10.6.4 Measure 3: Scenario Analysis and Specification -- 10.6.5 Measure 4: Socio-Technical System -- 10.7 Considerations in Level 3 Autonomous Driving -- 10.7.1 Example of Autonomous Driving with the 5-layered Model of STAMP S& -- S -- 10.8 Conclusion -- References -- 11 MEAU: A Method for the Evaluation of the Artificial Unintelligence -- 11.1 Introduction -- 11.2 Machine Learning and Online Unintelligence: Improvisation or Programming? -- 11.3 The New Paradigm of Information from Digital Media and Social Networks -- 11.4 Numbers, Images and Texts: Sources of Errors, Misinformation and Unintelligence -- 11.5 MEAU: A Method for the Evaluation of the Artificial Unintelligence -- 11.6 Results -- 11.7 Lessons Learned -- 11.8 Conclusions -- Appendix 1 -- Appendix 2 -- Appendix 3 -- Appendix 4. , References -- 12 Quantum Computing Meets Artificial Intelligence: Innovations and Challenges -- 12.1 Introduction -- 12.1.1 Benefits of Quantum Computing for AI -- 12.2 Quantum Computing Motivations -- 12.2.1 What Does ``Quantum'' Mean? -- 12.2.2 The Wave-Particle Duality -- 12.2.3 Qubit Definition -- 12.2.4 The Schrödinger Equation -- 12.2.5 Superposition -- 12.2.6 Interference -- 12.2.7 Entanglement -- 12.2.8 Gate-Based Quantum Computing -- 12.3 Quantum Machine Learning -- 12.3.1 Variational Quantum Algorithms -- 12.3.2 Data Encoding -- 12.3.3 Quantum Neural Networks -- 12.3.4 Quantum Support Vector Machine -- 12.3.5 Variational Quantum Generator -- 12.4 Quantum Computing Limitations and Challenges -- 12.4.1 Scalability and Connectivity -- 12.4.2 Decoherence -- 12.4.3 Error Correction -- 12.4.4 Qubit Control -- 12.5 Quantum AI Software Engineering -- 12.5.1 Hybrid Quantum-Classical Frameworks -- 12.5.2 Friction-Less Development Environment -- 12.5.3 Quantum AI Software Life Cycle -- 12.6 A new Problem Solving Approach -- 12.6.1 Use Case 1: Automation and Transportation Sector -- 12.6.2 Use Case 2: Food for the Future World -- 12.6.3 Use Case 3: Cheaper Reliable Batteries -- 12.6.4 Use Case 4: Cleaner Air to Breathe -- 12.6.5 Use Case 5: AI-Driven Financial Solutions -- 12.7 Summary and Conclusion -- References.

Note: Intro -- Foreword -- Preface -- Contents -- 1 Multimedia Services in Intelligent Environments: Advances in Recommender Systems -- Abstract -- 1…Introduction -- 2…Recommender Systems -- 3…Conclusions -- References -- 2 A Survey of Approaches to Designing Recommender Systems -- Abstract -- 1…Introduction to Recommender Systems -- 1.1 Formulation of the Recommendation Problem -- 1.1.1 The Input to a Recommender System -- 1.1.2 The Output of a Recommender System -- 1.2 Methods of Collecting Knowledge About User Preferences -- 1.2.1 The Implicit Approach -- 1.2.2 The Explicit Approach -- 1.2.3 The Mixing Approach -- 2…Summarization of Approaches to Recommendation -- 2.1 Content-Based Methods -- 2.2 Collaborative Methods -- 2.2.1 User-Based Collaborative Filtering Systems -- 2.2.2 Item-Based Collaborative Filtering Systems -- 2.2.3 Personality Diagnosis -- 2.3 Hybrid Methods -- 2.3.1 Adding Content-Based Characteristics to Collaborative Models -- 2.3.2 Adding Collaborative Characteristics to Content-Based Models -- 2.3.3 A Single Unifying Recommendation Model -- 2.3.4 Other Types of Recommender Systems -- 2.4 Fundamental Problems of Recommender Systems -- References -- 3 Hybrid User Model for Capturing a User's Information Seeking Intent -- Abstract -- 1…Introduction -- 2…Related Work -- 2.1 Methodologies for Building a User Model for Information Retrieval -- 2.2 Decision Theory for Information Retrieval -- 3…Capturing a User's Intent in an Information Seeking Task -- 3.1 Overview -- 3.2 Interest Set -- 3.3 Context Network -- 3.4 Preference Network -- 4…Hybrid User Model -- 4.1 Overview -- 4.2 Sub-Value Function Over Query -- 4.3 Sub-Value Function for Threshold -- 4.4 Complexity of Hybrid User Model -- 4.4.1 Implementation -- 5…Evaluation -- 5.1 Objectives -- 5.2 Testbeds -- 5.3 Vector Space Model and Ide dec-hi -- 5.4 Procedures. , 5.5 Traditional Procedure -- 5.6 Procedure to Assess Long-Term Effect -- 6…Results and Discussion -- 6.1 Results of Traditional Procedure -- 6.2 Results of New Procedure to Assess Long-Term Effect -- 7…Discussion -- 8…Application of Hybrid User Model -- 9…Conclusions and Future Work -- References -- 4 Recommender Systems: Network Approaches -- Abstract -- 1…Introduction -- 2…Recommender Systems Review -- 3…Background: Graphs and NoSQL -- 3.1 Current NoSQL Implementations -- 3.2 The Algebraic Connectivity Metric -- 3.3 Recommendation Comparison and Propagation -- 4…The Effect of Algebraic Connectivity on Recommendations -- 4.1 Application to Improve Recommendations -- 5…Recommendations Experiment and Results -- 6…Conclusion -- References -- Resource List -- 5 Toward the Next Generation of Recommender Systems: Applications and Research Challenges -- Abstract -- 1…Introduction -- 2…Recommender Systems in Software Engineering -- 3…Recommender Systems in Data and Knowledge Engineering -- 4…Recommender Systems for Configurable Items -- 5…Recommender Systems for Persuasive Technologies -- 6…Further Applications -- 7…Issues for Future Research -- 8…Conclusions -- References -- 6 Content-Based Recommendation for Stacked-Graph Navigation -- Abstract -- 1…Introduction -- 2…Related Work -- 3…Stacked Graphs -- 3.1 Views and View Properties -- 4…Content-Based Recommendation -- 4.1 View Data Set -- 4.2 User Profile -- 4.2.1 Inferring Preferences for Seen Views -- 4.2.2 Inferring Preferences for Attributes of Seen Views -- 4.3 Content-Based Recommendation -- 4.4 Usage Scenario -- 5…User Study -- 6…Results and Discussions -- 7…Conclusion and Future Work -- References -- 7 Pattern Extraction from Graphs and Beyond -- Abstract -- 1…Introduction -- 2…Foundations -- 2.1 Graphs -- 2.2 Graph Representations -- 2.3 Basic Notions of Graph Components -- 3…Explicit Models. , 3.1 Tree -- 3.2 Cohesive Subgraphs -- 3.3 Cliques -- 4…Implicit Models -- 4.1 Modularity and Its Approximation -- 4.2 Network Flow -- 5…Beyond Static Patterns -- 5.1 Sequential Pattern Mining in Data Stream -- 5.2 Explicit Approaches for Tracing Communities -- 5.3 Implicit Approaches for Tracing Communities -- 6…Conclusion -- References -- Source List -- 8 Dominant AHP as Measuring Method of Service Values -- Abstract -- 1…Introduction -- 2…Necessity of Measuring Service Values -- 2.1 Significance of Service Science -- 2.2 Scientific Approach to Service Science -- 3…AHP as a Measuring Method of Service Values -- 3.1 Saaty's AHP -- 3.2 Dominant AHP -- 4…AHP and Dominant AHP from a Perspective of Utility Function -- 4.1 Expressive form of Multi-Attribute Utility Function -- 4.2 Saaty's AHP from a perspective of utility function -- 4.3 Dominant AHP from a viewpoint of utility function -- 5…Conclusion -- 9 Applications of a Stochastic Model in Supporting Intelligent Multimedia Systems and Educational Processes -- Abstract -- 1…Introduction -- 2…Formulating a Minimum of a Random Number of Nonnegative Random Variables -- 3…Distribution Function of the Formulated Minimum -- 4…Applications in Systems and Processes -- 5…Conclusions -- References.

Note: Intro -- Foreword -- Preface -- Contents -- 1 Machine Learning Paradigms -- References -- Learning Analytics with the Purpose to Measure Student Engagement, to Quantify the Learning Experience and to Facilitate Self-Regulation -- 2 Using a Multi Module Model for Learning Analytics to Predict Learners' Cognitive States and Provide Tailored Learning Pathways and Assessment -- 2.1 Introduction -- 2.2 Related Work -- 2.3 Multi Module Model and Logical Architecture of the System -- 2.4 Learners Clustering, Using the K-Means Algorithm, Supporting System's Modules -- 2.5 Evaluation and Discussion of Experimental Results -- 2.6 Ethics and Privacy for Learning Analytics -- 2.7 Conclusions and Future Work -- References -- 3 Analytics for Student Engagement -- 3.1 Effects of Student Engagement -- 3.2 Conceptualizing Student Engagement -- 3.3 Measuring Student Engagement -- 3.4 Analytics for Student Engagement -- 3.4.1 Early Alert Analytics -- 3.4.2 Dashboard Visualization Analytics -- 3.5 Dashboard Visualizations of Student Engagement -- 3.6 Comparative Reference Frame -- 3.7 Challenges and Potential Solutions for Analytics of Student Engagement: -- 3.7.1 Challenge 1: Connecting Engagement Analytics to Recommendations for Improvement -- 3.7.2 Potential Solutions: Using Diverse Metrics of Engagement to Improve Feedback Provided -- 3.7.3 Challenge 2: Quantifying Meaningful Engagement -- 3.7.4 Potential Solutions: Analytics Reflecting Quantity and Quality of Student Engagement -- 3.7.5 Challenge 3: Purposeful Engagement Reflection -- 3.7.6 Potential Solutions: Options for Purposeful Engagement Reflection -- 3.7.7 Challenge 4: Finding an Appropriate Reference Norm -- 3.7.8 Potential Solutions: Alternative Reference Frames -- 3.8 Conclusion -- References -- 4 Assessing Self-regulation, a New Topic in Learning Analytics: Process of Information Objectification. , 4.1 Introduction -- 4.2 Math Learning Process -- 4.3 Analyzing Empirical Evidence -- 4.3.1 Observations on a Learning Episode -- 4.3.2 Setting the Task -- 4.3.3 Students and Knowing Math -- 4.4 Math Meaningfulness and Three Modes of Manipulating the Blue Graph -- 4.4.1 The Adaptation Process: Dragging Points and Using Sliders -- 4.4.2 Typing the Parameters Values -- 4.4.3 Perceiving the 'a' Parameter and Its Properties -- 4.4.4 Typing Values Without Immediate Feedback -- 4.5 Discussion -- 4.5.1 Metacognitive Enactivism -- 4.6 As a Conclusion -- 4.6.1 Objectification as a Condition for Academic Knowing -- References -- Learning Analytics to Predict Student Performance -- 5 Learning Feedback Based on Dispositional Learning Analytics -- 5.1 Introduction -- 5.2 Related Work -- 5.2.1 Educational Context -- 5.2.2 The Crucial Predictive Power of Cognitive Data -- 5.2.3 An Unexpected Source of Variation: National Cultural Values -- 5.2.4 LA, Formative Assessment, Assessment of Learning and Feedback Preferences -- 5.2.5 LA and Learning Emotions -- 5.3 The Current Study -- 5.3.1 Participants -- 5.3.2 E-tutorial Trace Data -- 5.3.3 Performance Data -- 5.3.4 Disposition Data -- 5.3.5 Analyses -- 5.4 Results -- 5.4.1 Performance -- 5.4.2 National Cultural Values -- 5.4.3 Cognitive Learning Processing Strategies -- 5.4.4 Metacognitive Learning Regulation Strategies -- 5.4.5 Attitudes and Beliefs Towards Learning Quantitative Methods -- 5.4.6 Epistemic Learning Emotions -- 5.4.7 Activity Learning Emotions -- 5.4.8 Adaptive Motivation and Engagement -- 5.4.9 Maladaptive Motivation and Engagement -- 5.5 Discussion and Conclusion -- References -- 6 The Variability of the Reasons for Student Dropout in Distance Learning and the Prediction of Dropout-Prone Students -- 6.1 Introduction -- 6.2 Literature Review -- 6.3 HOU Distance Learning Methodology and Data Description. , 6.4 Interview Based Survey Results -- 6.5 Machine Learning Techniques, Experiments and Results -- 6.5.1 Machine Learning Techniques, Experiments and Results -- 6.5.2 The Experiments -- 6.5.3 Results -- 6.5.4 Student Behavior Tool -- 6.6 Discussion -- 6.7 Conclusion -- Appendix -- References -- Learning Analytics Incorporated in Tools for Building Learning Materials and Educational Courses -- 7 An Architectural Perspective of Learning Analytics -- 7.1 Introduction -- 7.2 What is an Architectural Perspective? -- 7.3 Functional Viewpoints -- 7.3.1 Knowledge Discovery Functions -- 7.3.2 Analytical Functions -- 7.3.3 Predictive Functions -- 7.3.4 Generative Functions -- 7.4 Quality Attributes -- 7.5 Information Viewpoint -- 7.6 Architectural Patterns and Styles -- 7.6.1 Model-View-Control (MVC) -- 7.6.2 Publisher-Subscriber -- 7.6.3 Microservices -- 7.6.4 An Architecture for Learning Analytics -- 7.7 Discussion -- References -- 8 Multimodal Learning Analytics in a Laboratory Classroom -- 8.1 Introduction -- 8.2 Classroom Research -- 8.3 The Science of Learning Research Classroom -- 8.4 The Social Unit of Learning Project -- 8.5 Conceptualization(s) of Engagement -- 8.6 Multimodal Learning Analytics of Engagement in Classrooms -- 8.7 Observation Data -- 8.8 Features Selection, Extraction and Evaluation -- 8.8.1 Multimodal Behavioral Features -- 8.8.2 Feature Visualization -- 8.8.3 Feature Extraction Conclusions -- 8.9 Illustration of High Level Construct Based on Features Extracted -- 8.9.1 Attention to Teacher Speech -- 8.9.2 Teacher Attention -- 8.9.3 Student Concentration During Individual Task -- 8.9.4 Engagement During Pair and Group Work -- 8.10 Implications -- 8.11 Conclusion -- References -- 9 Dashboards for Computer-Supported Collaborative Learning -- 9.1 The Emergence of Learning Analytics and Dashboards -- 9.2 Collaborative Learning Theories. , 9.2.1 Group Cognition (GC) -- 9.2.2 Shared Mental Models (SMMs) -- 9.2.3 Situational Awareness (SA) -- 9.2.4 Socially Shared Regulation of Learning (SSRL) -- 9.3 Tools for CSCL -- 9.3.1 Group Awareness Tools (GATs) -- 9.3.2 Shared Mirroring Systems -- 9.3.3 Ambient Displays -- 9.4 Learning Dashboards for CSCL -- 9.5 How Can Collaborative Learning Dashboards Be Improved? -- 9.5.1 Principle 1: Adopt Iterative, User-Centred Design -- 9.5.2 Principle 2: Navigate the Theoretical Space -- 9.5.3 Principle 3: Visualize to Support Decision-Making -- References -- Learning Analytics as Tools to Support Learners and Educators in Synchronous and Asynchronous e-Learning -- 10 Learning Analytics in Distance and Mobile Learning for Designing Personalised Software -- 10.1 Introduction -- 10.2 Distance Learning -- 10.3 Mobile Learning and Mobile Learning Analytics -- 10.4 Personalised Learning Software -- 10.5 Data Collection -- 10.5.1 Modalities of Interaction in PCs -- 10.5.2 Modalities of Interaction in Smartphones -- 10.6 Multi-criteria Analysis -- 10.6.1 Combining Modalities of Interaction in HCI -- 10.6.2 Combining Modalities of Interaction in Smartphones -- 10.7 Conclusions -- References -- 11 Optimizing Programming Language Learning Through Student Modeling in an Adaptive Web-Based Educational Environment -- 11.1 Introduction -- 11.2 Related Work -- 11.3 Description of the Student Model -- 11.3.1 Analyzing Data That Have Been Gathered by the Implementation of ELaC -- 11.3.2 The Improved Student Model of ELaCv2 -- 11.4 Description of the Operation of the Student Model -- 11.4.1 Examples of Operation -- 11.5 Evaluation-Results -- 11.6 Conclusion -- References.

Note: Intro -- Foreword -- Preface -- Acknowledgments -- Contents -- Part I Machine Learning Fundamentals -- 1 Introduction -- References -- 2 Machine Learning -- 2.1 Introduction -- 2.2 Machine Learning Categorization According to the Type of Inference -- 2.2.1 Model Identification -- 2.2.2 Shortcoming of the Model Identification Approach -- 2.2.3 Model Prediction -- 2.3 Machine Learning Categorization According to the Amount of Inference -- 2.3.1 Rote Learning -- 2.3.2 Learning from Instruction -- 2.3.3 Learning by Analogy -- 2.4 Learning from Examples -- 2.4.1 The Problem of Minimizing the Risk Functional from Empirical Data -- 2.4.2 Induction Principles for Minimizing the Risk Functional on Empirical Data -- 2.4.3 Supervised Learning -- 2.4.4 Unsupervised Learning -- 2.4.5 Reinforcement Learning -- 2.5 Theoretical Justifications of Statistical Learning Theory -- 2.5.1 Generalization and Consistency -- 2.5.2 Bias-Variance and Estimation-Approximation Trade-Off -- 2.5.3 Consistency of Empirical Minimization Process -- 2.5.4 Uniform Convergence -- 2.5.5 Capacity Concepts and Generalization Bounds -- 2.5.6 Generalization Bounds -- References -- 3 The Class Imbalance Problem -- 3.1 Nature of the Class Imbalance Problem -- 3.2 The Effect of Class Imbalance on Standard Classifiers -- 3.2.1 Cost Insensitive Bayes Classifier -- 3.2.2 Bayes Classifier Versus Majority Classifier -- 3.2.3 Cost Sensitive Bayes Classifier -- 3.2.4 Nearest Neighbor Classifier -- 3.2.5 Decision Trees -- 3.2.6 Neural Networks -- 3.2.7 Support Vector Machines -- References -- 4 Addressing the Class Imbalance Problem -- 4.1 Resampling Techniques -- 4.1.1 Natural Resampling -- 4.1.2 Random Over-Sampling and Random Under-Sampling -- 4.1.3 Under-Sampling Methods -- 4.1.4 Over-Sampling Methods -- 4.1.5 Combination Methods -- 4.2 Cost Sensitive Learning -- 4.2.1 The MetaCost Algorithm. , 4.3 One Class Learning -- 4.3.1 One Class Classifiers -- 4.3.2 Density Models -- 4.3.3 Boundary Methods -- 4.3.4 Reconstruction Methods -- 4.3.5 Principal Components Analysis -- 4.3.6 Auto-Encoders and Diabolo Networks -- References -- 5 Machine Learning Paradigms -- 5.1 Support Vector Machines -- 5.1.1 Hard Margin Support Vector Machines -- 5.1.2 Soft Margin Support Vector Machines -- 5.2 One-Class Support Vector Machines -- 5.2.1 Spherical Data Description -- 5.2.2 Flexible Descriptors -- 5.2.3 v - SVC -- References -- Part II Artificial Immune Systems -- 6 Immune System Fundamentals -- 6.1 Introduction -- 6.2 Brief History and Perspectives on Immunology -- 6.3 Fundamentals and Main Components -- 6.4 Adaptive Immune System -- 6.5 Computational Aspects of Adaptive Immune System -- 6.5.1 Pattern Recognition -- 6.5.2 Immune Network Theory -- 6.5.3 The Clonal Selection Principle -- 6.5.4 Immune Learning and Memory -- 6.5.5 Immunological Memory as a Sparse Distributed Memory -- 6.5.6 Affinity Maturation -- 6.5.7 Self/Non-self Discrimination -- References -- 7 Artificial Immune Systems -- 7.1 Definitions -- 7.2 Scope of AIS -- 7.3 A Framework for Engineering AIS -- 7.3.1 Shape-Spaces -- 7.3.2 Affinity Measures -- 7.3.3 Immune Algorithms -- 7.4 Theoretical Justification of the Machine Learning -- 7.5 AIS-Based Clustering -- 7.5.1 Background Immunological Concepts -- 7.5.2 The Artificial Immune Network (AIN) Learning Algorithm -- 7.5.3 AiNet Characterization and Complexity Analysis -- 7.6 AIS-Based Classification -- 7.6.1 Background Immunological Concepts -- 7.6.2 The Artificial Immune Recognition System (AIRS) Learning Algorithm -- 7.6.3 Source Power of AIRS Learning Algorithm and Complexity Analysis -- 7.7 AIS-Based Negative Selection -- 7.7.1 Background Immunological Concepts -- 7.7.2 Theoretical Justification of the Negative Selection Algorithm. , 7.7.3 Real-Valued Negative Selection with Variable-Sized Detectors -- 7.7.4 AIS-Based One-Class Classification -- 7.7.5 V-Detector Algorithm -- References -- 8 Experimental Evaluation of Artificial Immune System-Based Learning Algorithms -- 8.1 Experimentation -- 8.1.1 The Test Data Set -- 8.1.2 Artificial Immune System-Based Music Piece Clustering and Database Organization -- 8.1.3 Artificial Immune System-Based Customer Data Clustering in an e-Shopping Application -- 8.1.4 AIS-Based Music Genre Classification -- 8.1.5 Music Recommendation Based on Artificial Immune Systems -- References -- 9 Conclusions and Future Work.

Note: Intro -- Preface -- Contents -- 1 Intelligent Interactive Multimedia Systems in Practice: An Introduction -- Abstract -- 1.1 Introduction -- 1.2 Chapters Included in the Book -- 1.3 Conclusion -- References -- 2 On the Use of Multi-attribute Decision Making for Combining Audio-Lingual and Visual-Facial Modalities in Emotion Recognition -- Abstract -- 2.1 Introduction -- 2.2 Related Work -- 2.2.1 Multi-attribute Decision Making -- 2.3 Aims and Settings of the Empirical Studies -- 2.3.1 Elicitation of Emotions and Creation of Databases -- 2.3.2 Creation of Databases of Known Expressions of Emotions -- 2.3.3 Analysis of Recognisability of Emotions by Human Observers -- 2.4 Empirical Study for Audio-Lingual Emotion Recognition -- 2.4.1 The Experimental Educational Application for Elicitation of Emotions -- 2.4.2 Audio-Lingual Modality Analysis -- 2.5 Empirical Study for Visual-Facial Emotion Recognition -- 2.5.1 Visual-Facial Empirical Study on Subjects -- 2.5.2 Visual-Facial Empirical Study by Human Observers -- 2.6 Discussion and Comparison of the Results from the Empirical Studies -- 2.7 Combining the Results from the Empirical Studies Through MADM -- 2.8 Discussion and Conclusions -- References -- 3 Cooperative Learning Assisted by Automatic Classification Within Social Networking Services -- Abstract -- 3.1 Introduction -- 3.2 Related Work -- 3.2.1 Social Networking Services -- 3.2.2 Intelligent Computer-Assisted Language Learning -- 3.3 Algorithm of the System Functioning -- 3.3.1 Description of Automatic Classification -- 3.3.2 Optimization Objective and Its Definition -- 3.3.3 Initialization of Centroids -- 3.3.4 Incorporation of Automatic Classification -- 3.4 General Overview of the System -- 3.5 Evaluation of the System -- 3.6 Conclusions and Future Work -- References. , 4 Improving Peer-to-Peer Communication in e-Learning by Development of an Advanced Messaging System -- Abstract -- 4.1 Introduction -- 4.2 Related Work -- 4.3 Data Analysis System Design -- 4.4 Experimental Results -- 4.5 Conclusions and Future Work -- References -- 5 Fuzzy-Based Digital Video Stabilization in Static Scenes -- Abstract -- 5.1 Introduction -- 5.2 Related Work -- 5.3 Method of Frame Deblurring -- 5.4 Fuzzy-Based Video Stabilization Method -- 5.4.1 Estimation of Local Motion Vectors -- 5.4.2 Smoothness of GMVs Building -- 5.4.3 Static Scene Alignment -- 5.5 Experimental Results -- 5.6 Conclusion -- References -- 6 Development of Architecture, Information Archive and Multimedia Formats for Digital e-Libraries -- Abstract -- 6.1 Introduction -- 6.2 Related Work -- 6.3 Overview of Standards and Document Formats -- 6.4 Requirements and Objectives -- 6.5 Proposed Architecture of Digital e-Library Warehouse -- 6.6 Proposed EPUB Format Extensions -- 6.7 Client Software Design and Researches of Vulnerability -- 6.8 Conclusion -- References -- 7 Layered Ontological Image for Intelligent Interaction to Extend User Capabilities on Multimedia Systems in a Folksonomy Driven Environment -- Abstract -- 7.1 Introduction -- 7.2 Human Based Computation -- 7.2.1 Motivation of Human Contribution -- 7.3 Background of Related Work -- 7.3.1 Object Tracking -- 7.4 Dynamic Learning Ontology Structure -- 7.4.1 Richer Semantics of Attributes -- 7.4.2 Object on Layered Representation -- 7.4.3 Semantic Attributes -- 7.4.4 Attribute Bounding Box Position -- 7.4.5 Attributes Extraction and Sentiment Analysis -- 7.4.6 Folksodriven Bounding Box Notation -- 7.5 Image Analysis and Feature Selection -- 7.5.1 Object Position Detection -- 7.6 Previsions on Ontology Structure -- 7.7 A Case Study: In-Video Advertisement -- 7.7.1 In-Video Advertisement Functionality. , 7.7.2 Web GRP -- 7.7.3 Folksodriven Ontology Prediction for Advertisement -- 7.7.4 In-Video Advertisement Validation -- 7.8 Relevant Resources -- 7.9 Conclusion -- References.

Note: Intro -- Preface -- Organization -- Keynote Address -- Industry 4.0 Meets Data Science: The Pathway for Society 5.0 -- Continual Learning for Intelligent Systems in Changing Environments -- Where is the Research on Evolutionary Multi-objective Optimization Heading to? -- Designing a Software Framework Based on an Object Detection Model and a Fuzzy Logic System for Weed Detection and Pasture Assessment -- An Overview of Machine Learning Based Intelligent Computing and Applications -- Semisupervised Learning with Spatial Information and Granular Neural Networks -- IoT Based General Purpose Sensing Application for Smart Home Environment -- Emerging Topics in Wireless and Network Communications - A Standards Perspective -- Emerging Topics in Wireless and Network Communications - A Standards Perspective -- Contents -- Intelligent Computing -- Ensemble Learning Model for EEG Based Emotion Classification -- 1 Introduction -- 2 Related Works -- 3 System Model and Methodology -- 3.1 Feature Extraction -- 3.2 Deep Learning Model Implementation -- 4 Dataset Description -- 5 Experimental Setup and Results -- 6 Conclusion -- References -- Foundation for the Future of Higher Education or 'Misplaced Optimism'? Being Human in the Age of Artificial Intelligence -- 1 Introduction -- 2 Education Using Artificial Intelligence (AIEd) -- 3 Methods -- 3.1 Search Strategy -- 3.2 Reliability of Agreement Amongst Raters -- 3.3 Collection, Codification, and Analysis of Data -- 3.4 Limitations -- 4 Results -- 4.1 Forecasting and Characterising -- 4.2 Curriculum Technology that Uses Artificial Intelligence -- 4.3 Constant Re-Evaluation -- 4.4 A System that May Change to Fit the USER'S Needs -- 5 Conclusion and Way Forward -- References -- AI Enabled Internet of Medical Things Framework for Smart Healthcare -- 1 Introduction -- 2 AI Based IoMT Health Domains. , 3 AI Enabled IoMT Architectures for Smart Healthcare Systems -- 4 Research Challenges of AI Enabled Smart Healthcare Systems -- 4.1 Data Accuracy -- 4.2 Data Security -- 4.3 System Efficiency -- 4.4 Quality of Service -- 5 Conclusion -- References -- Metaverse and Posthuman Animated Avatars for Teaching-Learning Process: Interperception in Virtual Universe for Educational Transformation -- 1 Introduction -- 2 Objectives of the Research and Knowledge Gap -- 3 Methods and Methodology -- 4 Results and Discussion -- 4.1 Educational Metaverse: A Categorical Analysis -- 4.2 A Wide Variety of Virtual Worlds for Use in Education -- 4.3 Situations for Learning, Tiers of Education, and VR Learning Environments -- 4.4 Students' Avatars (Digital Personas) in the Metaverse -- 4.5 Alterations in Educational Multiverse -- 5 Conclusion and Way Forward -- References -- Tuning Functional Link Artificial Neural Network for Software Development Effort Estimation -- 1 Introduction -- 2 Functional Link ANN-based SDEE -- 2.1 Justification of the Use of Chebyshev Polynomial as the Orthogonal Basis Function -- 3 Swarm Intelligence-Based Learning Algorithms for the CFLANN -- 3.1 Classical PSO -- 3.2 Improved PSO Technique -- 3.3 Adaptive PSO -- 3.4 GA -- 3.5 BP -- 4 Performance Evaluation Metrics -- 5 Description of the Dataset -- 6 Experiments and Results -- 7 Conclusion and Future Work -- References -- METBAG - A Web Based Business Application -- 1 Introduction -- 2 Literature Review -- 2.1 Gaps and Solutions -- 2.2 Deep Neural Networks (DNN) and LSTM -- 3 Architecture of the System -- 3.1 Architecture of the User Side of the System -- 3.2 Architecture of the Admin Side of the System -- 4 Workflow Diagrams -- 5 Procedures -- 5.1 Procedure: Price Prediction -- 5.2 Procedure: Password Security -- 5.3 Procedure: Dashboard -- 6 Result Analysis -- 6.1 Price Prediction. , 6.2 Password Security -- 7 Conclusions and Future Work -- References -- Designing Smart Voice Command Interface for Geographic Information System -- 1 Introduction -- 1.1 Review of Literature on Voice Command Interface -- 2 Design and Implementation -- 2.1 Review of Literature on Voice Command Interface -- 2.2 Modules Used -- 2.3 Methodology -- 2.4 Implementation -- 3 Results and Discussion -- 3.1 Testing Model by Creating a War Zone like Environment -- 3.2 Spectrogram and Waveform Samples for Spoken Voice -- 3.3 Comparative Analysis Based on Word Error Rate -- 4 Conclusion -- References -- Smart Garbage Classification -- 1 Introduction -- 2 Literature Review -- 2.1 Gaps in Literature -- 3 System Details -- 3.1 Waste Scanning Through Camera -- 3.2 Waste is Segregated and the Lid Opens -- 3.3 Moving of Hands and Trash Being Put into Respective Compartment -- 4 Component Modules and Description -- 5 Algorithmic Steps -- 6 Result and Analysis -- 7 Conclusions -- References -- Optical Sensor Based on MicroSphere Coated with Agarose for Heavy Metal Ion Detection -- 1 Introduction -- 2 Sensing Principle -- 3 Sensor Design -- 4 Results and Discussions -- 5 Conclusion -- References -- Influential Factor Finding for Engineering Student Motivation -- 1 Introduction -- 2 Related Studies -- 3 Experiment -- 3.1 Logistic Regression -- 4 Discussion -- 5 Conclusion -- References -- Prediction of Software Reliability Using Particle Swarm Optimization -- 1 Introduction -- 2 Related Work -- 3 Reliability Prediction Algorithm Using PSO -- 4 Experimental Result and Comparison -- 5 Conclusions -- References -- An Effective Optimization of EMG Based Artificial Prosthetic Limbs -- 1 Introduction -- 2 Literature Review -- 3 Design and Manufacturing -- 3.1 Cad Model -- 3.2 Manufacturing and Assembly -- 4 Electrical Components and Design -- 4.1 Electromyography Sensing. , 5 Artificial Intelligence -- 5.1 Gesture Recognition -- 5.2 Grasping Capacity (According to Size) -- 5.3 Analysis of EMG Signals -- 6 Conclusion -- References -- Communications -- Performance Analysis of Fading Channels in a Wireless Communication -- 1 Introduction -- 2 Fading Channels -- 2.1 Performance Analysis of Rayleigh Fading -- 2.2 Description of the Performance of Rician Fading Channel -- 2.3 Performance Analysis of NAKAGAMI-M Fading Channel. -- 3 Experimentation and Result Analysis -- 3.1 Simulation and Discussion of Rayleigh Fading Channel -- 3.2 Simulation Results of Rician Fading Channel -- 3.3 Simulation Results of Nakagami-M Fading Channel -- 4 Conclusion -- References -- Power Conscious Clustering Algorithm Using Fuzzy Logic in Wireless Sensor Networks -- 1 Introduction -- 2 Related Works -- 3 Proposed Model -- 4 Simulation Setup and Evaluation -- 5 Conclusions -- References -- Cryptanalysis on ``An Improved RFID-based Authentication Protocol for Rail Transit'' -- 1 Introduction -- 1.1 Motivation and Contribution -- 1.2 Organization of the Paper -- 2 Preliminary -- 2.1 Secure Requirements -- 2.2 Threat Model -- 3 Review of Zhu et al.'s Protocol -- 3.1 Set up Phase -- 3.2 Authentication Phase -- 4 Weakness of Zhu et al.'s Protocol -- 4.1 Known Session-Specific Temporary Information Attack -- 4.2 Lack of Scalability -- 5 Conclusion -- References -- A Novel Approach to Detect Rank Attack in IoT Ecosystem -- 1 Introduction -- 2 Background -- 2.1 Generic IoT Network Architecture -- 2.2 RPL Protocol -- 2.3 Rank Attack in IoT -- 2.4 IDS for IoT Ecosystem -- 3 Related Work -- 4 Proposed Security Approach -- 4.1 Proposed Approach Assumption -- 4.2 Security Model -- 4.3 Proposed Rank Attack Detection Solution -- 5 Experiments and Results Analysis -- 5.1 Setup and Execution of Experiments. , 5.2 After Proposed Security Solution Implementation Performance Analysis -- 5.3 Comparison of the Suggested Security Solution to Similar Works -- 6 Conclusion -- References -- Energy Efficient Adaptive Mobile Wireless Sensor Network in Smart Monitoring Applications -- 1 Introduction -- 2 Related Works -- 3 Proposed Method -- 3.1 Mobile Sensor Node Architecture -- 4 Simulation and Result Analysis -- 4.1 Simulation Set Up -- 4.2 Experimental Analysis -- 5 Conclusion -- References -- Orthogonal Chirp Division Multiplexing: An Emerging Multi Carrier Modulation Scheme -- 1 Introduction -- 2 Compatibility with OFDM -- 3 Computational Complexity -- 3.1 Computational Complexity of OCDM -- 3.2 Computational Complexity of OFDM -- 4 Applications of OCDM -- 4.1 OCDM for Wireless Communication -- 4.2 OCDM for Optical Fiber Communication -- 4.3 OCDM for IM/DD Based Short Reach Systems -- 4.4 OCDM for Underwater Acoustic Communication -- 4.5 OCDM for Baseband Data Communication -- 4.6 OCDM for MIMO Communication -- 5 Simulation Results -- 6 Conclusion -- References -- Machine Learning and Data Analytics -- COVID-19 Outbreak Estimation Approach Using Hybrid Time Series Modelling -- 1 Introduction -- 2 Background -- 2.1 LSTM Network for Modelling Time Series -- 2.2 ARIMA Model -- 2.3 Seasonal ARIMA Model -- 3 Proposed Model -- 4 Implementation and Results Discussion -- 4.1 Prediction Using LSTM Model -- 4.2 Prediction Using ARIMA Model -- 4.3 Prediction Using Hybrid Model -- 5 Conclusion -- References -- Analysis of Depression, Anxiety, and Stress Chaos Among Children and Adolescents Using Machine Learning Algorithms -- 1 Introduction -- 1.1 Background -- 1.2 Motivation and Objective of the Work -- 2 Literature Review -- 3 Methodology -- 3.1 Data Set Description -- 3.2 Implementation -- 4 Results and Discussion -- 4.1 Classification Results for Depression. , 4.2 Classification Results for Anxiety.

Note: Intro -- Foreword -- References -- Preface -- Contents -- 1 Introduction to Fusion of Machine Learning Paradigms -- 1.1 Editorial -- References -- Part I Recent Application Areas of Fusion of Machine Learning Paradigms -- 2 Artificial Intelligence as Dual-Use Technology -- 2.1 Introduction -- 2.2 What Is DUT -- 2.3 AI: Concepts, Models and Technology -- 2.4 Agent-Based AI and Autonomous System -- 2.4.1 Basic Model of Agent-Based AI -- 2.4.2 Conceptual Model of Autonomous Weapon System -- 2.5 Dual-Use Technology and DARPA -- 2.5.1 Historical View and Role of DARPA -- 2.5.2 DARPA's Contribution to DUT R& -- D on AI -- 2.6 DARPA-Like Organizations in Major Countries -- 2.7 Dual-Use Dilemma -- 2.8 Concluding Remarks -- References -- 3 Diabetic Retinopathy Detection Using Transfer and Reinforcement Learning with Effective Image Preprocessing and Data Augmentation Techniques -- 3.1 Introduction -- 3.2 Background -- 3.2.1 Deep Learning for Diabetic Retinopathy -- 3.2.2 Image Preprocessing Techniques -- 3.2.3 Reinforcement Learning and Deep Learning -- 3.3 Data Augmentation Techniques -- 3.3.1 Traditional Data Augmentation -- 3.3.2 SMOTE-Based Data Augmentation -- 3.3.3 Data Augmentation Using Generative Adversarial Networks -- 3.4 Datasets of Eye Fundus Images -- 3.5 Transfer Learning Experiments -- 3.5.1 Dataset -- 3.5.2 Image Preprocessing -- 3.5.3 Image Augmentation -- 3.5.4 Deep Learning Experiments -- 3.5.5 Reinforcement Learning Experiments -- 3.6 Conclusion and Future Work -- References -- 4 A Novel Approach for Non-linear Deep Fuzzy Rule-Based Model and Its Applications in Biomedical Analyses -- 4.1 Introduction -- 4.2 Method -- 4.2.1 Preliminaries -- 4.2.2 Hierarchical Fuzzy Structure -- 4.2.3 Stacked Deep Fuzzy Rule-Based System (SD-FRBS) -- 4.2.4 Adaptation of the First-Order TSK Structure in SD-FRBS. , 4.2.5 Concatenated Deep Fuzzy Rule-Based System (CD-FRBS) -- 4.3 Data Description and Results -- 4.3.1 MIMIC-III Dataset -- 4.3.2 SD-FRBS as a Multivariate Regressor for Granger Causality Estimation-In EEG Connectivity Index Extraction -- 4.3.3 CD-FRBS in Staging Depression Severity -- 4.4 Discussion and Conclusion -- 4.4.1 Suggested Future Works -- References -- 5 Harmony Search-Based Approaches for Fine-Tuning Deep Belief Networks -- 5.1 Introduction -- 5.2 Theoretical Background -- 5.2.1 Deep Belief Networks -- 5.2.2 Harmony Search -- 5.3 Methodology -- 5.3.1 Datasets -- 5.3.2 Experimental Setup -- 5.4 Experimental Results -- 5.5 Conclusions -- References -- 6 Toward Smart Energy Systems: The Case of Relevance Vector Regression Models in Hourly Solar Power Forecasting -- 6.1 Introduction -- 6.2 Relevance Vector Regression -- 6.3 RVR Based Day Ahead Forecasting -- 6.4 Results -- 6.5 Conclusion -- References -- 7 Domain-Integrated Machine Learning for IC Image Analysis -- 7.1 Introduction -- 7.2 Hierarchical Multi-classifier System -- 7.2.1 Architecture of Hierarchical Multi-classifier System -- 7.2.2 Result and Discussion on Case Study -- 7.3 Deep Learning with Pseudo Labels -- 7.3.1 Methodology -- 7.3.2 Application to IC Image Analysis -- 7.4 Conclusions and Future Works -- References -- Part II Applications that Can Clearly Benefit from Fusion of Machine Learning Paradigms -- 8 Fleshing Out Learning Analytics and Educational Data Mining with Data and ML Pipelines -- 8.1 Introduction -- 8.2 Data and ML Pipelines -- 8.3 Related Work -- 8.4 An Automated EDM and LA Methodology -- 8.4.1 A Data Pipeline Scenario -- 8.4.2 An ML Pipeline Scenario -- 8.5 Experiments and Results -- 8.6 Conclusions and Future Work -- References -- 9 Neural Networks Based Throughput Estimation of Short Production Lines Without Intermediate Buffers -- 9.1 Introduction. , 9.2 Data Sets of i-Stage Production Line Problems -- 9.3 Deep Learning and Multilayer Perceptron -- 9.4 Experimental Process of Deep Learning Approach -- 9.5 Results of Deep Learning Approach -- 9.6 Conclusions -- References.

Note: Intro -- Foreword -- Preface -- Contents -- 1 Intelligent Computing Systems -- 2 Semantic Tools -- Their Use for Knowledge Management in the Public Sector -- Abstract -- 1 Outlines -- 2 Introduction---Presentation of the Field of Interest -- 2.1 E-Government---The Opportunities Through the Semantic Web -- 2.2 Public Open Data for the Transition to `Open Government' -- 3 Related Work -- 4 Semantic Representation of Knowledge -- 4.1 The RDF Data Model -- 4.2 The URI's Use -- 4.3 RDF Schema Specification Language -- 4.4 Web Ontology Language---OWL -- 5 Reasoning Tools -- 5.1 SWRL Rules -- 5.2 The Query Language SQWRL -- 6 Presentation of Our Ontology Through Prot00E9g00E9 -- 6.1 The Ontology Development in Prot00E9g00E9 4.3 -- 6.2 The E-Government Ontology -- 6.2.1 Defining Classes -- 6.2.2 Defining Properties -- 6.3 The Use of RDF, RDFS, OWL and SWRL Through a Case Study -- 7 Data Mining Technology from Ontologies -- 7.1 SPARQL -- 7.2 SPARQL-DL in OWL2 Query Tab of Prot00E9g00E9 -- 7.3 DL Query Tool of Prot00E9g00E9 -- 8 Evaluation of Ontology -- 8.1 Categorization of the Ontology -- 8.2 Basic Principles of Design -- 8.3 Methodology of the Ontology Development -- 9 Conclusions -- References -- 3 From Game Theory to Complexity, Emergence and Agent-Based Modeling in World Politics -- Abstract -- 1 Introduction -- 2 Game Theory in World Politics -- 2.1 A Game Theoretic Approach of Global Environmental Diplomacy -- 3 From Game Theory to Complexity -- 3.1 Emergence in World Politics -- 4 Simulating Complexity with Agent-Based Modeling -- 4.1 Agent-Based Modeling Research in World Politics -- 4.1.1 Political Applications of ABM -- 5 Conclusions -- Acknowledgments -- References -- List of Software Resources -- 4 A Semantic Approach for Representing and Querying Business Processes -- Abstract -- 1 Introduction. , 2 Semantic Web Techniques in Management Information Systems -- 2.1 What's Worth in Combining Management Information Systems with Semantic Web Technologies? -- 2.2 Process Models, Conceptual Models and Ontologies -- 2.3 Querying Business Process Models -- 2.4 Related Work -- 3 A BPMN Semantic Process Model -- 3.1 The Research Methodology -- 3.2 Developing Business Process Models -- 3.3 Developing the Ontology -- 3.3.1 The Scope of the BPMN Elements -- 3.3.2 The Scope of the Generic BPMN Alternative Models -- 3.3.3 The Scope of the Agent or Actor Participating in the Process -- 3.4 Validating the Ontology -- 4 Querying Conventional Databases and Semantic Models -- 5 Conclusions -- References -- 5 Using Conversational Knowledge Management as a Lens for Virtual Collaboration in the Course of Small Group Activities -- Abstract -- 1 Introduction -- 2 Related Work and Motivation -- 2.1 Conversational Patterns -- 2.2 Design Frames and Technologies for CK Management -- 2.3 Consolidation and Research Focus -- 3 Methodology -- 3.1 Data Samples and Analysis -- 3.2 Language-Action Models -- 4 Implementation -- 4.1 Transformable Document Templates -- 4.2 The Portlets -- 5 Concluding Remarks -- Acknowledgment -- References -- 6 Spatial Environments for m-Learning: Review and Potentials -- Abstract -- 1 Introduction -- 2 List of Resources -- 3 Classification Criteria -- 4 Exemplary Environments -- 5 Comparison -- 6 Results -- 7 Conclusions/Future Work -- References -- 7 Science Teachers' Metaphors of Digital Technologies and Social Media in Pedagogy in Finland and in Greece -- Abstract -- 1 Introduction -- 2 Theoretical Background -- 2.1 Approaching Science -- 2.2 The Relationship Between Science and Digital Technology -- 3 The Study -- 3.1 Aims & -- Methods -- 3.2 The Context and the Participants -- 4 Findings -- 4.1 Science as Way of Thinking. , 4.2 Science as Method -- 5 Conclusions -- References -- 8 Data Driven Monitoring of Energy Systems: Gaussian Process Kernel Machine for Fault Identification with Application to Boiling Water Reactors -- Abstract -- 1 Introduction -- 2 Gaussian Process Kernel Machines -- 3 Methodology -- 4 Application to Monitoring Complex Energy Systems: The Boiling Water Reactor (BWR) Case -- 4.1 Problem Statement -- 4.2 Testing Results -- 5 Conclusions -- References -- 9 A Framework to Assess the Behavior and Performance of a City Towards Energy Optimization -- Abstract -- 1 Introduction -- 2 Policy Context -- 3 Current Relevant Initiatives -- 4 Description of the Framework -- 5 Municipal Building Level SCEAF -- 6 Conclusions -- Acknowledgment -- References -- 10 An Energy Management Platform for Smart Microgrids -- Abstract -- 1 Introduction -- 2 The Smart Polygeneration Microgrid Pilot Plant -- 3 The Energy Management Platform -- 4 The Supervisory, Control and Data Acquisition (SCADA) System -- 5 Results and Discussion -- 6 Conclusions and Future Research Lines -- References -- List of Resources -- 11 Transit Journaling and Traffic Sensitive Routing for a Mixed Mode Public Transportation System -- Abstract -- 1 Introduction -- 1.1 Limited Scope of Data -- 1.2 Formal Route Names Versus Informal Headsigns -- 1.3 Insufficient Stop Descriptions -- 1.4 Traffic Sensitivity in Routing/Trip Planning -- 2 Related Work -- 2.1 Crowdsourced Mapping and Real-time Tracking -- 2.2 Activity Detection -- 2.3 Trip Planning/Routing -- 2.3.1 Dijkstra's Algorithm -- 2.3.2 A* Search -- 2.3.3 Raptor -- 2.4 Trip Planning with Real-time Data -- 3 Methodology/Design -- 3.1 The Server/Back-End -- 3.1.1 GTFS Data Pre-processing -- 3.1.2 Server Design -- 3.1.3 The Modified RAPTOR Search Algorithm -- 3.2 The Mobile App -- 3.2.1 Search -- 3.2.2 Results/Journey Displays -- 3.2.3 Recording. , 3.2.4 Traffic Report -- 3.2.5 Results Display -- 3.2.6 Journey Display -- 3.2.7 Journal -- 3.2.8 Stop Editor -- 3.2.9 Route Editor -- 4 Tests and Results -- 4.1 Basic Routing Capacity -- 4.1.1 Survey -- 4.1.2 Demographics -- 4.1.3 Algorithm Evaluation -- 4.2 Traffic Sensitivity -- 4.3 Journey Recorder -- 5 Future Work -- 5.1 Base Estimate Correction -- 5.2 Preference-Weighing System -- 5.3 Traffic Flow Prediction -- 5.4 Further Evaluation of Mapping Ability -- 6 Conclusion -- References -- 12 Adaptation of Automatic Information Extraction Method for Environmental Heatmaps to U-Matrices of Self Organising Maps -- Abstract -- 1 Introduction -- 2 Problem Formulation -- 3 HInEx---Heatmap Information Extraction -- 3.1 The Idea -- 3.2 Heatmap Area Isolation -- 3.3 Clustering Image Pixels Based on Colors -- 3.4 Generating Tree Description -- 3.5 The Key Search and Its Analysis -- 3.6 The Axis Search and Their Analysis -- 3.7 Complete Heatmap Description -- 4 SOM Cluster Number Extraction Based on U-Matrix -- 4.1 The Idea of HInEx Application to SOM U-Matrix -- 4.2 Clustering -- 4.3 Extracting a U-Matrix Cell Corresponding to a Single Distance Between Neurons -- 4.4 Searching a Color Representing the Minimal Neuron Distance in SOM -- 4.5 Threshold-like Operation -- 4.6 Dilatation and Erosion-like Operations -- 4.7 Searching for the Number of Groups in SOM -- 5 SOM Generator Description -- 6 Experimental Study -- 7 Conclusion -- Acknowledgements -- References -- 13 Evolutionary Computing and Genetic Algorithms: Paradigm Applications in 3D Printing Process Optimization -- Abstract -- 1 Introduction -- 2 Evolutionary Optimization -- 3 Determination of the Pareto-Optimal Build Orientations in Stereolithography -- 3.1 Orientation Selection in SL -- 3.2 Algorithm Configuration and Implementation -- 3.3 Build Orientation Case Study. , 4 Determination of the Optimum Packing Layout in Stereolithography Machine Workspace -- 4.1 Optimization Scheme -- 4.2 Packing Layout Construction Process -- 4.3 Packing Layout Case Studies -- 5 Concluding Remarks -- References -- 14 Car-Like Mobile Robot Navigation: A Survey -- Abstract -- 1 Introduction -- 2 RRT-Based Methods -- 2.1 Unsafe Path Planning -- 2.2 Safe Path Planning -- 2.3 Rapidly Exploring Random Tree Algorithm on Rough Terrains (RRT-RT) -- 2.4 RRT Motion Planning Subsystem -- 2.5 Partial Motion Planning -- 2.6 Sensor-Based Random Tree (SRT) -- 2.7 RRT* Algorithm -- 2.8 Voronoi Fast Marching (VFM) and Fast Marching (FM2) -- 2.9 SBL Algorithm -- 2.10 Single-Query Motion Planning -- 2.11 Dynamic-Domain RRT -- 2.12 Transition-Based RRT -- 2.13 Parallelizing Rapidly-Exploring Random Tree (RRT) Algorithm on Large-Scale Distributed-Memory Architectures -- 2.14 Obstacle Sensitive Cost Function for Navigating Car-Like Robots -- 3 Methods Based on Fuzzy Logic -- 3.1 Distributed Active-Vision Network-Space System -- 3.2 Internet-Based Smart Space Navigation Using Fuzzy-Neural Adaptive Control -- 4 Sensor-Based Methods -- 4.1 Dynamic Window Approach (DWA) -- 4.2 Generalized Voronoi Graph (GVG) Theory -- 4.3 Navigation in Dynamic Environments Using Trajectory Deformation -- 4.4 Probabilistic Velocity Obstacle (PVO) -- 5 SLAM-Based Methods -- 5.1 On-line Path Following -- 5.2 The CyCab: A Car-Like Robot Navigating Autonomously and Safely Among Pedestrians -- 5.3 V-Slam -- 5.4 SLAM-Based Turning Strategy in Restricted Environments -- 5.5 L-Slam -- 6 Conclusions and Future Work -- 6.1 Future Directions in Autonomous Robot Navigation and Obstacle Perception -- 6.2 Future Directions in Applications of Autonomously-Navigating Robots -- References -- 15 Computing a Similarity Coefficient for Mining Massive Data Sets -- Abstract -- 1 Introduction. , 2 Related Work.