Note: Intro -- Foreword -- Further Reading -- Preface -- Contents -- 1 Introduction to Advances in Machine Learning/Deep Learning-Based Technologies -- 1.1 Editorial Note -- 1.2 Book Summary and Future Volumes -- References -- Part I Machine Learning/Deep Learning in Socializing and Entertainment -- 2 Semi-supervised Feature Selection Method for Fuzzy Clustering of Emotional States from Social Streams Messages -- 2.1 Introduction -- 2.2 The FS-EFCM Algorithm -- 2.2.1 EFCM Execution: Main Steps -- 2.2.2 Initial Parameter Setting -- 2.3 Experimental Results -- 2.3.1 Dataset -- 2.3.2 Feature Selection -- 2.3.3 FS-EFCM at Work -- 2.4 Conclusion -- References -- 3 AI in (and for) Games -- 3.1 Introduction -- 3.2 Game Content and Databases -- 3.3 Intelligent Game Content Generation and Selection -- 3.3.1 Generating Content for a Language Education Game -- 3.4 Conclusions -- References -- Part II Machine Learning/Deep Learning in Education -- 4 Computer-Human Mutual Training in a Virtual Laboratory Environment -- 4.1 Introduction -- 4.1.1 Purpose and Development of the Virtual Lab -- 4.1.2 Different Playing Modes -- 4.1.3 Evaluation -- 4.2 Background and Related Work -- 4.3 Architecture of the Virtual Laboratory -- 4.3.1 Conceptual Design -- 4.3.2 State-Transition Diagrams -- 4.3.3 High Level Design -- 4.3.4 State Machine -- 4.3.5 Individual Scores -- 4.3.6 Quantization -- 4.3.7 Normalization -- 4.3.8 Composite Evaluation -- 4.3.9 Success Rate -- 4.3.10 Weighted Average -- 4.3.11 Artificial Neural Network -- 4.3.12 Penalty Points -- 4.3.13 Aggregate Score -- 4.4 Machine Learning Algorithms -- 4.4.1 Genetic Algorithm for the Weighted Average -- 4.4.2 Training the Artificial Neural Network with Back-Propagation -- 4.5 Implementation -- 4.5.1 Instruction Mode -- 4.5.2 Evaluation Mode -- 4.5.3 Computer Training Mode -- 4.5.4 Training Data Collection Sub-mode. , 4.5.5 Machine Learning Sub-mode -- 4.6 Training-Testing Process and Results -- 4.6.1 Training Data -- 4.6.2 Training and Testing on Various Data Set Groups -- 4.6.3 Genetic Algorithm Results -- 4.6.4 Artificial Neural Network Training Results -- 4.7 Conclusions -- References -- 5 Exploiting Semi-supervised Learning in the Education Field: A Critical Survey -- 5.1 Introduction -- 5.2 Semi-supervised Learning -- 5.3 Literature Review -- 5.3.1 Performance Prediction -- 5.3.2 Dropout Prediction -- 5.3.3 Grade Level Prediction -- 5.3.4 Grade Point Value Prediction -- 5.3.5 Other Studies -- 5.3.6 Discussion -- 5.4 The Potential of SSL in the Education Field -- 5.5 Conclusions -- References -- Part III Machine Learning/Deep Learning in Security -- 6 Survey of Machine Learning Approaches in Radiation Data Analytics Pertained to Nuclear Security -- 6.1 Introduction -- 6.2 Machine Learning Methodologies in Nuclear Security -- 6.2.1 Nuclear Signature Identification -- 6.2.2 Background Radiation Estimation -- 6.2.3 Radiation Sensor Placement -- 6.2.4 Source Localization -- 6.2.5 Anomaly Detection -- 6.3 Conclusion -- References -- 7 AI for Cybersecurity: ML-Based Techniques for Intrusion Detection Systems -- 7.1 Introduction -- 7.1.1 Why Does AI Pose Great Importance for Cybersecurity? -- 7.1.2 Contribution -- 7.2 ML-Based Models for Cybersecurity -- 7.2.1 K-Means -- 7.2.2 Autoencoder (AE) -- 7.2.3 Generative Adversarial Network (GAN) -- 7.2.4 Self Organizing Map -- 7.2.5 K-Nearest Neighbors (k-NN) -- 7.2.6 Bayesian Network -- 7.2.7 Decision Tree -- 7.2.8 Fuzzy Logic (Fuzzy Set Theory) -- 7.2.9 Multilayer Perceptron (MLP) -- 7.2.10 Support Vector Machine (SVM) -- 7.2.11 Ensemble Methods -- 7.2.12 Evolutionary Algorithms -- 7.2.13 Convolutional Neural Networks (CNN) -- 7.2.14 Recurrent Neural Network (RNN) -- 7.2.15 Long Short Term Memory (LSTM). , 7.2.16 Restricted Boltzmann Machine (RBM) -- 7.2.17 Deep Belief Network (DBN) -- 7.2.18 Reinforcement Learning (RL) -- 7.3 Open Topics and Potential Directions -- 7.3.1 Novel Feature Representations -- 7.3.2 Unsupervised Learning Based Detection Systems -- References -- Part IV Machine Learning/Deep Learning in Time Series Forecasting -- 8 A Comparison of Contemporary Methods on Univariate Time Series Forecasting -- 8.1 Introduction -- 8.2 Related Work -- 8.3 Theoretical Background -- 8.3.1 ARIMA -- 8.3.2 Prophet -- 8.3.3 The Holt-Winters Seasonal Models -- 8.3.4 N-BEATS: Neural Basis Expansion Analysis -- 8.3.5 DeepAR -- 8.3.6 Trigonometric BATS -- 8.4 Experiments and Results -- 8.4.1 Datasets -- 8.4.2 Algorithms -- 8.4.3 Evaluation -- 8.4.4 Results -- 8.5 Conclusions -- References -- 9 Application of Deep Learning in Recurrence Plots for Multivariate Nonlinear Time Series Forecasting -- 9.1 Introduction -- 9.2 Related Work -- 9.2.1 Background on Recurrence Plots -- 9.2.2 Time Series Imaging and Convolutional Neural Networks -- 9.3 Time Series Nonlinearity -- 9.4 Time Series Imaging -- 9.4.1 Dimensionality Reduction -- 9.4.2 Optimal Parameters -- 9.5 Convolutional Neural Networks -- 9.6 Model Pipeline and Architecture -- 9.6.1 Architecture -- 9.7 Experimental Setup -- 9.8 Results -- 9.9 Conclusion -- References -- Part V Machine Learning in Video Coding and Information Extraction -- 10 A Formal and Statistical AI Tool for Complex Human Activity Recognition -- 10.1 Introduction -- 10.2 The Hybrid Framework-Formal Languages -- 10.3 Formal Tool and Statistical Pipeline Architecture -- 10.4 DATA Pipeline -- 10.5 Tools for Implementation -- 10.6 Experimentation with Datasets to Identify the Ideal Model -- 10.6.1 KINISIS-Single Human Activity Recognition Modeling -- 10.6.2 DRASIS-Change of Human Activity Recognition Modeling -- 10.7 Conclusions. , References -- 11 A CU Depth Prediction Model Based on Pre-trained Convolutional Neural Network for HEVC Intra Encoding Complexity Reduction -- 11.1 Introduction -- 11.2 H.265 High Efficiency Video Coding -- 11.2.1 Coding Tree Unit Partition -- 11.2.2 Rate Distortion Optimization -- 11.2.3 CU Partition and Image Texture Features -- 11.3 Proposed Methodology -- 11.3.1 The Hierarchical Classifier -- 11.3.2 The Methodology of Transfer Learning -- 11.3.3 Structure of Convolutional Neural Network -- 11.3.4 Dataset Construction -- 11.4 Experiments and Results -- 11.5 Conclusion -- References.

Note: Title -- Foreword -- Editors -- Preface -- Table of Contents -- Advances in Multimedia Services in Intelligent Environments - Software Development Challenges and Solutions -- Introduction -- Conclusions -- References and Further Reading -- Evaluating the Generality of a Life-Cycle Framework for Incorporating Clustering Algorithms in Adaptive Systems -- Introduction -- The Software Life Cycle -- Inception -- Defining Requirements for the Prototype System and Analysis and Design of the Prototype Adaptive Recommender System -- Building and Evaluating the Prototype Adaptive Recommender System -- Elaboration -- Computing the Resemblance Coefficients for the Data Set and Developing the Clustering Algorithm -- Execute the Clustering Method for the Prototype and Evaluating the Results of the Clustering Algorithm used in the Prototype -- Construction -- The Most Efficient Algorithm and Designing Stereotypes Based on This Algorithm -- Building the User Modeling Component Based on the Stereotypes and Incorporating Them into the System -- Transition-Dynamically Improving System Performance while Used by Real Users -- Conclusion about the Life Cycle Process -- References -- A Distributed Multimedia Data Management over the Grid -- Introduction -- Related Work -- Overall Framework -- Replicated Multidimensional Index Structure -- Distributed Query Processing -- Automatic Load Balancing -- Empirical Study -- Conclusion and Future Works -- References -- A View of Monitoring and Tracing Techniques and Their Application to Service-Based Environments -- Introduction -- Execution Monitoring and Tracing -- Categories of Execution Monitoring -- What Can Be Traced? -- Monitoring Techniques -- Comparison -- Monitoring Challenges for Multimedia Services -- Behavioral Change -- Information Access -- Timing -- Synchronization of Event Traces -- Distributed Monitoring. , Trace Transmission -- Conclusion -- References -- An Ontological SW Architecture SupportingContribution and Retrieval of Service and Process Models -- Introduction -- An Application in the Healthcare Domain -- Ontological SW Architecture -- Three Tier with Ontologies -- Encoding Procedures through Ontologies -- Enabling Direct Contribution through Ontologies -- Enabling Semantic Queries -- Enabling Adapted Presentation through Ontologies -- Conclusions -- References -- Model-Driven Quality Engineering of Service-Based Systems -- Introduction -- Related Work -- QoS-Enabled WSDL (Q-WSDL) -- QoS Characteristics of Web Services -- Q-WSDL Metamodel -- Composite Web Services -- QoS Prediction of Composite Web Services -- Model-Driven Reliability Prediction -- Example Application -- Conclusions -- References -- Application of Logic Models for Pervasive Computing Environments and Context-Aware Services Support -- Introduction -- Contextual Models Requirements for Pervasive Computing -- The LogiPerSe First Order Logic Model -- Partial Validation and Ambiguity -- Scalability -- Composability -- Pervasive Service Scenario -- Logic-Based System Architecture -- Logic Model - Object-Oriented Model - Ontology Integration -- Conclusions -- References -- Intelligent Host Discovery from Malicious Software -- Introduction -- Instant Message Clients -- Search Engines -- Social Networks -- Torrent Trackers -- Conclusions -- References -- Swarm Intelligence: The Ant Paradigm -- Introduction -- Swarm Intelligence -- Principles of Natural Swarms -- The Biological Origins of Ant Algorithms -- Ant Algorithms of the Ant Colony Optimization Framework -- Ant System -- MAX MIN Ant System -- Ant Colony System -- Conclusion -- References -- Formulating Discrete Geometric Random Sums for Facilitating Intelligent Behaviour of a Complex System under a Major Risk -- Introduction. , Formulation of a Discrete Geometric Random Sum -- Stochastic Derivation of a Discrete Geometric Random Sum -- Risk and Crisis Management Applications and Computational Intelligence Interpretation of a Discrete Geometric Random Sum -- Conclusions -- References -- Incorporating a Discrete Renewal Random Sum in Computational Intelligence and Cindynics -- Introduction -- Formulation of a Discrete Renewal Random Sum -- Stochastic Derivation of a Discrete Renewal Random Sum -- Application in Cindynics and Interpretation in Computational Intelligence of a Discrete Renewal Random Sum -- Conclusions -- References -- Author Index.

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 -- 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 Introduction to Advances in Artificial Intelligence-Based Technologies -- References -- Part I Advances in Artificial Intelligence Tools and Methodologies -- 2 Synthesizing 2D Ground Images for Maps Creation and Detecting Texture Patterns -- 2.1 Introduction -- 2.2 Synthesizing 2D Consecutive Region-Images for Space Map Generation -- 2.3 Texture Paths Detection -- 2.4 Simulated Case Study and Comparison with Other Methods -- 2.5 Discussion -- References -- 3 Affective Computing: An Introduction to the Detection, Measurement, and Current Applications -- 3.1 Introduction -- 3.2 Background -- 3.3 Detection and Measurement Devices for Affective Computing -- 3.3.1 Brain Computer Interfaces (BCIs) -- 3.3.2 Facial Expression and Eye Tracking Technologies -- 3.3.3 Galvanic Skin Response -- 3.3.4 Multimodal Input Devices -- 3.3.5 Emotional Speech Recognition and Natural Language Processing -- 3.4 Application Examples -- 3.4.1 Entertainment -- 3.4.2 Chatbots -- 3.4.3 Medical Applications -- 3.5 Conclusions -- References -- 4 A Database Reconstruction Approach for the Inverse Frequent Itemset Mining Problem -- 4.1 Introduction -- 4.2 Related Work -- 4.3 Problem Definition -- 4.3.1 Frequent Itemset Hiding Problem -- 4.3.2 Inverse Frequent Itemset Hiding Problem -- 4.4 Hiding Approach -- 4.5 Conclusion and Future Steps -- References -- 5 A Rough Inference Software System for Computer-Assisted Reasoning -- 5.1 Introduction -- 5.2 Basic Concepts -- 5.2.1 Rough Sets -- 5.2.2 Information System -- 5.2.3 Decision System -- 5.2.4 Indiscernibility Relation -- 5.3 The Approximate Algorithms for Information Systems -- 5.3.1 The Approximate Algorithm for Attribute Reduction -- 5.3.2 The Algorithm for Approximate Rule Generation -- 5.4 Implementation of the Rough Inference System. , 5.5 An Application in Electrical Engineering-A Case Study -- 5.6 Conclusions -- References -- Part II Advances in Artificial Intelligence-based Applications and Services -- 6 Context Representation and Reasoning in Robotics-An Overview -- 6.1 Introduction -- 6.2 Context -- 6.2.1 Definitions of Context -- 6.2.2 Context Aware Systems -- 6.2.3 Context Representation -- 6.3 Context Reasoning -- 6.3.1 Reasoning Approaches and Techniques -- 6.3.2 Reasoning Tools -- 6.4 Conclusions and Future Work -- References -- 7 Smart Tourism and Artificial Intelligence: Paving the Way to the Post-COVID-19 Era -- 7.1 Introduction -- 7.2 Methodology and Research Approach -- 7.3 Artificial Intelligence and Smart Tourism -- 7.3.1 Artificial Intelligence -- 7.3.2 AI Smart Tourism Recommender Systems -- 7.3.3 Deep Learning -- 7.3.4 Augmented Reality In tourism -- 7.3.5 AI Autonomous Agents -- 7.4 Smart Tourism in COVID-19 Pandemic -- 7.5 Conclusions and Future Directions -- References -- 8 Challenges and AI-Based Solutions for Smart Energy Consumption in Smart Cities -- 8.1 Introduction -- 8.2 Smart Energy in Smart Cities -- 8.3 Energy Consumption Challenges and AI Solutions -- 8.3.1 End-User Consumers in Smart Cities -- 8.3.2 Demand Forecasting -- 8.3.3 Prosumers Management -- 8.3.4 Consumption Privacy -- 8.4 Discussion -- References -- 9 How to Make Different Thinking Profiles Visible Through Technology: The Potential for Log File Analysis and Learning Analytics -- 9.1 Introduction -- 9.2 The Development of Log File Analysis and Learning Analytics -- 9.3 Analysing Log File Data in Researching Dynamic Problem-Solving -- 9.4 Extracting, Structuring and Analysing Log File Data to Make Different Thinking Profiles Visible -- 9.4.1 Aims -- 9.4.2 Methods -- 9.5 Participants -- 9.6 Instruments -- 9.7 Procedures -- 9.8 Results -- 9.9 Discussion -- 9.10 Conclusions and Limitations. , References -- 10 AI in Consumer Behavior -- 10.1 Introduction -- 10.2 Literature Review -- 10.3 Artificial Intelligence (AI) in Consumer Behavior -- 10.3.1 Artificial Intelligence -- 10.3.2 Consumer Behavior -- 10.3.3 AI in Consumer Behavior -- 10.3.4 AI and Ethics -- 10.4 Conclusion -- References -- Part III Theoretical Advances in Computation and System Modeling -- 11 Coupled Oscillator Networks for von Neumann and Non-von Neumann Computing -- 11.1 Introduction -- 11.2 Basic Unit, Network Architecture and Computational Principle -- 11.3 Nonlinear Oscillator Networks and Phase Equation -- 11.3.1 Example -- 11.4 Oscillator Networks for Boolean Logic -- 11.4.1 Registers -- 11.4.2 Logic Gates -- 11.5 Conclusions -- References -- 12 Design and Implementation in a New Approach of Non-minimal State Space Representation of a MIMO Model Predictive Control Strategy-Case Study and Performance Analysis -- 12.1 Introduction -- 12.2 Centrifugal Chiller-System Decomposition -- 12.2.1 Centrifugal Chiller Dynamic Model Description -- 12.2.2 Centrifugal Chiller Dynamic MIMO ARMAX Model Description -- 12.2.3 Centrifugal Chiller Open Loop MIMO ARMAX Discrete-Time Model -- 12.2.4 Centrifugal Chiller Dynamic MIMO ARMAX Model Nonminimal State Space Description -- 12.3 MISO MPC Strategy Design in a Minimal State Space Realization -- 12.3.1 MIMO MPC Optimization Problem Formulation -- 12.3.2 MIMO MPC Parameters Design -- 12.3.3 MIMO MPC MATLAB SIMULINK Simulation Results -- 12.4 MIMO MPC Strategy Design in a Nonminimal State Space Realization -- 12.5 Conclusions -- References.

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 Introduction to Handbook on Artificial Intelligence-Empowered Applied Software Engineering-Vol. 2: Smart Software Applications in Cyber-Physical Systems -- 1.1 Editorial Note -- 1.2 Book Summary and Future Volumes -- Bibliography for Further Reading -- Part I Smart Software Applications in Scientific Document Processing -- 2 Detection, Extraction and SPN Representation of Pseudo-Algorithms in Scientific Documents -- 2.1 Introduction -- 2.2 Visual Detection of Pseudo-Codes in Documents -- 2.2.1 Extraction of Different Text Blocks in Documents -- 2.2.2 Pyramidal Image Representation -- 2.2.3 Decomposition and Classification of the Pseudo Code Sections -- 2.3 Learning -- 2.3.1 The Dataset -- 2.3.2 Evaluation of Learning Process -- 2.4 Translation of Algorithms to Graphs and SPNs -- 2.4.1 Generation of a Graph -- 2.4.2 Stochastic Petri Net Representation -- 2.5 Conclusion and Future Work -- References -- 3 A Recommender Engine for Scientific Paper Peer-Reviewing System -- 3.1 Introduction -- 3.2 Related Works -- 3.3 Dataset and Feature -- 3.4 Methodology -- 3.4.1 Create Training Dataset -- 3.4.2 The Architecture of Recommender Engine -- 3.4.3 Final Recommendation Section -- 3.5 Result and Analysis -- 3.6 Conclusion(s) -- References -- Part II Smart Software Applications in Enterprise Modeling -- 4 Visualization of Digital-Enhanced Enterprise Modeling -- 4.1 Introduction -- 4.2 A Meta Model for Describing Value of Digital Service -- 4.3 Visualization Patterns -- 4.3.1 Service Definition Pattern -- 4.3.2 Value Proposition Pattern -- 4.3.3 Use Process Refinement pattern -- 4.4 Application Example -- 4.4.1 Healthcare Example -- 4.4.2 Application of Visualization Patterns -- 4.5 Discussions -- 4.5.1 Effectiveness -- 4.5.2 Novelty -- 4.5.3 Mapping to BMC -- 4.5.4 Limitation -- 4.6 Related Work -- 4.7 Conclusion. , References -- 5 Know-linking: When Machine Learning Meets Organizational Tools Analysis to Generate Shared Knowledge in Large Companies -- 5.1 Introduction -- 5.2 State of the Art -- 5.2.1 Profiling -- 5.2.2 Organizational Tools Analysis -- 5.2.3 Indexing -- 5.3 Related Works -- 5.4 Know-linking Approach -- 5.4.1 Presentation -- 5.5 Environment Study -- 5.6 Know-linking in Aerospace Manufacturer -- 5.6.1 Technical Audit -- 5.6.2 Extracting Profiles -- 5.6.3 Generating Semantic Models for Each Profile -- 5.6.4 Hidden Semantic Links Between Profiles -- 5.6.5 Indexing Based Profiles -- 5.7 Conclusion and Future Work -- References -- 6 Changes in Human Resources Management with Artificial Intelligence -- 6.1 Introduction -- 6.2 The Effect of AI in Human Resources Management -- 6.2.1 Recruitment Process with AI -- 6.2.2 Training Process with AI -- 6.2.3 Performance Assessment Process with AI -- 6.2.4 Talent Management Process with AI -- 6.2.5 Salary Management Process with AI -- 6.3 Conclusion -- References -- Part III Smart Software Applications in Education -- 7 Promoting Reading Among Teens: Analyzing the Emotional Preferences of Teenage Readers -- 7.1 Introduction -- 7.2 Related Works -- 7.3 Our Emotion Trait Analysis Approach -- 7.3.1 Processing a Book Description -- 7.3.2 Calculating an Emotion Vector -- 7.3.3 Emotion Trait -- 7.3.4 Partitioning Books by Average Ratings -- 7.3.5 Reducing Objective Values by Comparing Synonyms -- 7.3.6 Implementation -- 7.4 Conclusions and Future Works -- References -- 8 A Multi-institutional Analysis of CS1 Students' Common Misconceptions of Key Programming Concepts -- 8.1 Introduction -- 8.2 Literature Review -- 8.3 Study Design -- 8.3.1 Research Objective -- 8.3.2 Research Questions -- 8.3.3 Data Collection -- 8.3.4 Reliability of Pre-post-test Instrument -- 8.3.5 Study Procedure -- 8.4 Experimental Results. , 8.5 Discussion of Results -- 8.6 Conclusion -- References -- Part IV Smart Software Applications in Healthcare and Medicine -- 9 Clustering-Based Scaling for Healthcare Data -- 9.1 Introduction -- 9.2 Fuzzy Clustering -- 9.3 Fuzzy Clustering for 3-Way Data -- 9.4 Fuzzy Cluster-Scaled Regression Analysis -- 9.5 Numerical Examples -- 9.6 Conclusions -- References -- 10 Normative and Fuzzy Components of Medical AI Applications -- 10.1 Preliminaries -- 10.2 Normative Issues -- 10.3 Fuzziness and Norms -- 10.4 Conclusions -- References -- Part V Smart Software Applications in Infrastructure Monitoring -- 11 Adaptive Structural Learning of Deep Belief Network and Its Application to Real Time Crack Detection of Concrete Structure Using Drone -- 11.1 Introduction -- 11.2 Adaptive Learning Method of Deep Belief Network -- 11.2.1 Restricted Boltzmann Machine and Deep Belief Network -- 11.2.2 Neuron Generation and Annihilation Algorithm of RBM -- 11.2.3 Layer Generation Algorithm of DBN -- 11.3 SDNET 2018 -- 11.3.1 Data Description -- 11.3.2 The Classification Results -- 11.4 Crack Detection for Japanese Concrete Structure -- 11.4.1 Data Collection -- 11.4.2 Detection Results -- 11.5 Real-Time Detection and Visualization System Using Drone -- 11.5.1 Embedded System -- 11.5.2 Demonstration Experiment -- 11.6 Conclusion -- References.

Note: Intro -- Foreword -- Preface -- Contents -- 1 Applications of Learning and Analytics in Intelligent Systems -- References -- Learning and Analytics in Intelligent Medical Systems -- 2 A Comparison of Machine Learning Techniques to Predict the Risk of Heart Failure -- 2.1 Introduction -- 2.2 Theoretical Background -- 2.3 Data and Methods -- 2.3.1 Dataset -- 2.3.2 Evaluation Process -- 2.3.3 Weka -- 2.4 Overview of Proposed Systems -- 2.4.1 Logistic Regression -- 2.4.2 Decision Tree -- 2.4.3 Random Forest -- 2.4.4 K-Nearest Neighbor -- 2.4.5 Artificial Neuronal Network -- 2.4.6 SVM -- 2.4.7 Naïve Bayes -- 2.4.8 OneR -- 2.4.9 ZeroR -- 2.4.10 Hybrid -- 2.5 Comparison Results -- 2.6 Conclusions -- References -- 3 Differential Gene Expression Analysis of RNA-seq Data Using Machine Learning for Cancer Research -- 3.1 Introduction -- 3.2 Materials and Methods -- 3.2.1 RNAseq -- 3.2.2 Classical Approach -- 3.2.3 Machine Learning -- 3.2.4 Comparative Workflow -- 3.3 Code and Results of an Analysis with Real Data -- 3.3.1 Loading Packages -- 3.3.2 Loading and Searching the Data from TCGA -- 3.3.3 Patient Selection -- 3.3.4 Dependent Variable Definition -- 3.3.5 Biological Gene Filter -- 3.3.6 Graphics -- 3.3.7 Classical Statistical Analysis -- 3.3.8 Machine Learning Analysis -- 3.4 Conclusions -- References -- 4 Machine Learning Approaches for Pap-Smear Diagnosis: An Overview -- 4.1 Introduction -- 4.2 Cervical Cancer and Pap-Test -- 4.3 The Pap-Smear Databases -- 4.3.1 A Basic Data Analysis of New Data -- 4.4 The Used Methodologies -- 4.4.1 Adaptive Network-Based Fuzzy Inference System (ANFIS) -- 4.4.2 Artificial Neural Networks -- 4.4.3 Heuristic Classification -- 4.4.4 Minimum Distance Classification -- 4.4.5 Hard C-Means Clustering -- 4.4.6 Fuzzy C-Means Clustering -- 4.4.7 Gustafson and Kessel Clustering -- 4.4.8 k-Nearest Neighborhood Classification. , 4.4.9 Weighted k-Nearest Neighborhood Classification -- 4.4.10 Tabu Search -- 4.4.11 Genetic Programming -- 4.4.12 Ant Colony -- 4.5 The Pap-Smear Classification Problem -- 4.5.1 Classification with ANFIS -- 4.5.2 Heuristic Classification Based on GP -- 4.5.3 Classification Using Defuzzification Methods -- 4.5.4 Direct and Hierarchical Classification -- 4.5.5 Classification Using Feed-Forward Neural Network -- 4.5.6 Nearest Neighborhood Classification Based on GP Feature Selection -- 4.5.7 Nearest Neighborhood Classification Using Tabu Search for Feature Selection -- 4.5.8 Nearest Neighborhood Classification Using ACO for Feature Selection -- 4.5.9 Minimum Distance Classifier -- 4.6 Conclusion and Future Work -- References -- Learning and Analytics in Intelligent Power Systems -- 5 Multi-kernel Analysis Paradigm Implementing the Learning from Loads Approach for Smart Power Systems -- 5.1 Introduction -- 5.2 Background -- 5.2.1 Kernel Machines -- 5.2.2 Gaussian Processes -- 5.3 Multi-kernel Paradigm for Load Analysis -- 5.3.1 Problem Statement -- 5.3.2 Multi-kernel Paradigm -- 5.4 Results -- 5.4.1 Problem Statement -- 5.4.2 Further Results -- 5.5 Conclusion and Future Work -- References -- 6 Conceptualizing and Measuring Energy Security: Geopolitical Dimensions, Data Availability, Quantitative and Qualitative Methods -- 6.1 Preamble -- 6.1.1 Structure of Chapter -- 6.2 Review of Energy Security Literature -- 6.2.1 Brief History of Energy Security -- 6.2.2 Defining Security and Energy Security -- 6.2.3 Energy Security Since the 20th Century -- 6.2.4 Energy Security and Geopolitics -- 6.2.5 Dimensions of Energy Security -- 6.3 Methodology -- 6.3.1 Research Questions -- 6.4 Analyses and Results -- 6.4.1 Milestone Time Periods -- 6.4.2 Data -- 6.4.3 Measuring Energy Security -- 6.4.4 Creating a Geopolitical Energy Security Index. , 6.4.5 Using Cluster Analysis -- 6.4.6 Looking at Case Studies of Key Countries -- 6.4.7 Carrying Out Interviews of Energy Experts -- 6.4.8 Forecasting Energy Security -- 6.5 Closing Comments -- References -- Learning and Analytics in Performance Assessment -- 7 Automated Stock Price Motion Prediction Using Technical Analysis Datasets and Machine Learning -- 7.1 Introduction -- 7.2 Technical Analysis Synopsis -- 7.3 Machine Learning Component -- 7.3.1 SVM Algorithm -- 7.3.2 Adaptive Boost Algorithm -- 7.4 System Implementation -- 7.4.1 System Structure -- 7.4.2 Training Data Set -- 7.4.3 Selection of Machine Learning Algorithm and Implementation -- 7.4.4 Android Client Application -- 7.5 System Evaluation -- 7.6 Conclusions and Future Work -- References -- 8 Airport Data Analysis Using Common Statistical Methods and Knowledge-Based Techniques -- 8.1 Introduction -- 8.2 Literature Review -- 8.3 Airport Data Analysis -- 8.3.1 Data Collection and Cleansing -- 8.3.2 Case Study Description and Scope of Current Analysis -- 8.3.3 Demand Seasonality -- 8.3.4 International Passenger Connectivity Matrix -- 8.3.5 Weekly and Daily Airport Operating Patterns -- 8.3.6 Airplane Types and Associated Runway Length Requirements -- 8.4 Conclusions -- References -- 9 A Taxonomy and Review of the Network Data Envelopment Analysis Literature -- 9.1 Introduction -- 9.2 DMU's Internal Network Structures and Assessment Paradigms -- 9.3 Assessment Paradigms -- 9.3.1 Independent Assessments -- 9.3.2 Joint Assessments -- 9.4 Classification of Network DEA Studies -- 9.5 Conclusion -- References -- Learning and Analytics in Intelligent Safety and Emergency Response Systems -- 10 Applying Advanced Data Analytics and Machine Learning to Enhance the Safety Control of Dams -- 10.1 Introduction -- 10.2 The Data Lifecycle in the Safety Control of Concrete Dams. , 10.2.1 Raw Data Collection -- 10.2.2 Processing and Data Storage -- 10.2.3 Data Quality Assessment and Outlier Detection -- 10.2.4 Data Analysis and Dam Safety Assessment Based on Quantitative Interpretation Models -- 10.2.5 Data Analysis and Dam Safety Assessment Based on Machine Learning Models -- 10.3 Data Analysis and Data Prediction Using Deep Learning Models-An Overview -- 10.4 Adopted Problem Solving Process-The Design Science Research Methodology -- 10.5 Proposed Methodology-Adding Value to the Interpretation of the Monitored Dam Behaviour Through the Use of Deep Learning Models -- 10.6 Demonstration and Evaluation-Assessment and Interpretation of the Monitored Structural Behaviour of a Concrete Dam During Its Operation Phase -- 10.6.1 The Case Study-The Alto Lindoso Dam -- 10.6.2 The Dataset-Horizontal Displacements Measured by the Pendulum Method -- 10.6.3 Main Results and Discussion -- 10.7 Final Remarks -- References -- 11 Analytics and Evolving Landscape of Machine Learning for Emergency Response -- 11.1 Introduction -- 11.1.1 Emergency Management -- 11.1.2 Machine Learning -- 11.1.3 Scope and Organizations -- 11.2 Applications of Machine Learning in Emergency Response -- 11.2.1 Machine Learning Techniques for Emergency Management Cycles -- 11.2.2 Event Prediction -- 11.2.3 Warning Systems -- 11.2.4 Event Detection and Tracking -- 11.2.5 Situational Awareness -- 11.2.6 Emergency Evaluation -- 11.2.7 Crowdsourcing -- 11.3 Analysis of Emergency Data -- 11.3.1 Big Data in Emergency Management -- 11.3.2 Data Collection -- 11.3.3 Information Extraction and Filtering -- 11.3.4 Data Integration -- 11.3.5 Applications for Data Analysis in Emergency -- 11.4 Challenges and Opportunities of Machine Learning in Response -- 11.4.1 Data Collection -- 11.4.2 Information Extraction -- 11.4.3 Data Filtering -- 11.4.4 Data Integration. , 11.5 Crowdsourcing in Emergency Management -- 11.5.1 Crowdsourcing with Machine Learning for Emergency Management -- 11.5.2 Example: Crowdsourcing and Machine Learning for Tracking Emergency -- 11.6 Conclusions -- References -- Learning and Analytics in Intelligent Social Media -- 12 Social Media Analytics, Types and Methodology -- 12.1 Social Networks and Analytics -- 12.1.1 Descriptive Analytics -- 12.1.2 Diagnostic Analytics -- 12.1.3 Predictive Analytics -- 12.1.4 Prescriptive Analytics -- 12.2 Introduction to Social Network Mining -- 12.3 Data Structure -- 12.3.1 Structured Data -- 12.3.2 Semi-structured Data -- 12.3.3 Unstructured Data -- 12.4 Data Quality -- 12.4.1 Noise -- 12.4.2 Outliers -- 12.4.3 Missing Values -- 12.4.4 Duplicate Data -- 12.5 Data Preprocessing -- 12.5.1 Aggregation -- 12.5.2 Discretization -- 12.5.3 Feature Selection -- 12.5.4 Feature Extraction -- 12.5.5 Sampling -- 12.6 Network Modeling -- 12.6.1 Real World Networks -- 12.6.2 Random Graphs -- 12.6.3 Small World Model -- 12.6.4 Preferential Attachment Model -- 12.7 Network Schemas -- 12.7.1 Multi-relational Network with Single Typed Objects -- 12.7.2 Bipartite Network -- 12.7.3 Star-Schema Network -- 12.7.4 Multiple-hub Network -- 12.8 Task Categorization -- 12.9 Machine Learning -- 12.9.1 Supervised Learning -- 12.9.2 Unsupervised Learning -- 12.10 Conclusions -- References -- 13 Machine Learning Methods for Opinion Mining In text: The Past and the Future -- 13.1 Introduction -- 13.2 Terminology -- 13.3 Early Projects -- 13.4 The Fascinating Opportunities that Sentiment Analysis Raises -- 13.5 Natural Language Processing for Sentiment Analysis -- 13.5.1 Affective Information for Sentiment Analysis -- 13.5.2 Corpora Annotated for Sentiment Analysis Tasks -- 13.5.3 Distributional Semantics and Sentiment Analysis. , 13.6 Traditional Models Based on Lexica and Feature Engineering.