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: 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.

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.

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: Title Page -- Preface -- Organizations -- Contents -- Managing Collaborative Sessions in WSNs -- Introduction -- Related Work -- Collaboration Hierarchy in WSNs -- Types of Collaboration -- Collaboration Hierarchy -- Sessions -- WISE-MANager -- Case Study -- Advantages and Disadvantages -- Conclusions -- References -- OGRE-Coder: An Interoperable Environment for the Automatic Generation of OGRE Virtual Scenarios -- Introduction -- OGRE Markup Language (OGREML) -- OGRE-Coder: Design and Implementation Issues -- Requirements and Use Cases -- Architecture -- Implementation Tools -- OGRE-Coder Functionalities -- Authoring OGRE Virtual Environments -- Generating OGRE Code with OGRE-Coder -- Conclusions -- References -- Natural and Intuitive Video Mediated Collaboration -- Current Systems for Enabling Remote Collaboration -- Videoconferencing and Telepresence Systems -- Desktop Video Conferencing -- Interactive Tables and Smart Whiteboards -- The Importance of Usability -- Building Prototypes -- First Prototype -- Second Prototype -- Our Design Concept -- Permanent Connection, Invisible User Interface -- Natural Collaborative Tools -- Discussion -- Is Permanent Connection Too Limited? -- Where Could This Concept Be Applied? -- Challenges of Permanent Video Connections -- Future Development Challenges -- References -- Evolutionary System Supporting Music Composition -- Introduction -- Evolutionary System Supporting Music Composition -- Architecture of the System -- Genetic Algorithm -- The Process of Music Composition -- Experimental Results -- Summary and Conclusions -- References -- Usability Inspection of Informal Learning Environments: The HOU2LEARN Case -- Introduction -- Literature Review -- The HOU2LEARN Platform -- Usability Evaluation -- General -- The Method Applied -- The Experiment -- Conclusions - Future Goals -- References. , Procedural Modeling of Broad-Leaved Trees under Weather Conditions in 3D Virtual Reality -- Introduction -- Related Work -- Procedural Modeling of Broad-Leaved Trees -- Modeling of Tree under Weather Conditions -- Forest Modeling -- Experimental Program -- Conclusion -- References -- New Method for Adaptive Lossless Compression of Still Images Based on the Histogram Statistics -- Introduction -- Basic Principles of the АRL Coding Method -- Evaluation of the Lossless Coding Method Efficiency -- Experimental Results -- Conclusions -- References -- Scene Categorization with Class Extendibility and Effective Discriminative Ability -- Introduction -- Category-Specific Approach -- Whole-Construction/Whole-Representation Strategy -- Category-Specific-Construction/Whole-Representation Strategy -- Category-Specific-Construction/ Category-Specific-Representation Strategy -- Experimental Results -- Conclusions -- References -- Adaptive Navigation in a Web Educational System Using Fuzzy Techniques -- Introduction -- Adaptive Navigation Support -- The Domain Knowledge -- Student Modeling -- Discussion on the Fuzzy Cognitive Maps and Fuzzy User Modeling Used -- Conclusion -- References -- Visualization Tool for Scientific Gateway -- Introduction -- Visual Representation of Datasets -- VT as a New Discovery for Presenting Academic Research Results -- Architecture of Visualization Tool -- Directly Visual Education Form -- Conclusion -- References -- Digital Text Based Activity: Teaching Geometrical Entities at the Kindergarten -- Introduction -- Review Standards -- Method - Data Collection and Observations -- Digital Based Activities at the Kindergarten -- Using Graphical Programs (Mspaint) -- Using Slide Shows (PowerPoint) -- Using Digital Cameras -- Using Spreadsheets (EXCEL) -- Summary and Conclusions -- References. , Cross Format Embedding of Metadata in Images Using QR Codes -- Introduction -- QRCodes -- Our Proposal -- Results -- Applications -- Conclusions -- References -- An Empirical Study for Integrating Personality Characteristics in Stereotype-Based Student Modelling in a Collaborative Learning Environment for UML -- Introduction -- Personality Related Stereotypes -- Empirical Study for Defining the Triggers -- Implementation of Triggers -- Conclusion -- References -- An Efficient Parallel Architecture for H.264/AVC Fractional Motion Estimation -- Introduction -- H.264/AVC FME Observations -- Encoding with INTER8x8 Mode or above -- Statistic Charactistics of Motion Vectors -- The Proposed Architecture -- Reference Pixel Array -- Integer Pixel Sampler in Reference Array -- 14-Input FME Engine -- Data Processing Order -- 3-Stages Processing -- Simulation Results -- Conclusions -- References -- Fast Two-Stage Global Motion Estimation: A Blocks and Pixels Sampling Approach -- Introduction -- Motion Models -- Global Motion Estimation -- Initial Translation Estimation -- Block Sampling and Limited Block Matching -- Initial Estimation of Perspective Model GM Parameters -- Subsampling Pixels and Levenberg-Marquardt Algorithm -- Simulation -- Conclusion -- References -- Frame Extraction Based on Displacement Amount for Automatic Comic Generation from Metaverse Museum Visit Log -- Introduction -- Comic Generation System -- Evaluation -- Implementation -- Evaluation Outline -- Results and Discussions -- Related Work -- Conclusions and Future Work -- References -- Knowledge-Based Authoring Tool for Tutoring Multiple Languages -- Introduction -- Related Work -- Architecture of Our System -- Description of the System -- Authoring Domain Knowledge -- Authoring Student Model -- Authoring of Teaching Model -- Case Study for the Instructor -- Case Study for the Student. , Student Modeling and Error Diagnosis -- Modeling the System's Authoring Process -- Conclusions -- References -- Evaluating an Affective e-Learning System Using a Fuzzy Decision Making Method -- Introduction -- Fuzzy Simple Additive Weighting -- Overall Description of the System -- Evaluation Experiment -- Results -- Conclusions -- References -- Performance Evaluation of Adaptive Content Selection in AEHS -- Introduction -- Performance Evaluation Metrics for Decision-Based AEHS -- Evaluation Methodology for Decision-Based AEHS -- Setting Up the Experiments -- Designing the Media Space -- Designing the Learner Model -- Simulating the AM of an AEHS -- Experimental Results and Discussion -- Extracting the AM of existing AEHS -- Scaling Up the Experiments -- Conclusions -- References -- AFOL: Towards a New Intelligent Interactive Programming Language for Children -- Introduction -- General Architecture of the AFOL Programming Environment -- Overview of the AFOL Programming Learning System -- AFOL Language Commands and Object Oriented Structure -- Conclusions -- References -- Multimedia Session Reconfiguration for Mobility-Aware QoS Management: Use Cases and the Functional Model -- Introduction -- Session Reconfiguration and Use Cases -- Functional Model -- Performance Evaluation -- Conclusions and Future Work -- References -- LSB Steganographic Detection Using Compressive Sensing -- Introduction -- Steganalysis -- Compressive Sensing and BM3D -- The Proposed Method -- Results -- Conclusions -- References -- Analysis of Histogram Descriptor for Image Retrieval in DCT Domain -- Introduction -- Description of the Method -- Pre-processing -- Construction of the AC-Pattern Histogram -- Construction of DC-Pattern Histogram -- Application to Image Retrieval -- Paramaters of Descriptor -- Performance Analysis -- Application to GTF Database. , Application to ORL Database -- Conclusions -- References -- A Representation Model of Images Based on Graphs and Automatic Instantiation of Its Skeletal Configuration -- Introduction -- Related Works -- A Model for Images -- Instantiating the Model -- Experiments -- Conclusion and Outlook -- References -- Advice Extraction fromWeb for Providing Prior Information Concerning Outdoor Activities -- Introduction -- Characteristics Analysis of Advices -- The Definition of Advices -- Construction of Development Data -- Characteristics of Advices -- Characteristics of Advices Suitable for Situations -- Prior Advice Acquisition -- Preprocessing -- Advice Acquisition -- Situation Classification of Advices -- Experiment -- Evaluation Data -- Experiment for Acquiring Advices -- Experiment for Classifying Situation of Advices -- Conclusion -- References -- Automatic Composition of Presentation Slides, Based on Semantic Relationships among Slide Components -- Introduction -- Approach -- Document Structure -- Processing Flow -- Slide Editing -- Semantic Relationship -- Editing Operation -- Slide Composition -- Grouping of Slide Components -- Template-Based Slide Composition -- Prototype System -- Component Editing Interface -- Display Interface -- Conclusion -- References -- Sustainable Obsolescence Management - A Conceptual Unified Framework to Form Basis of an Interactive Intelligent Multimedia System -- Introduction -- Definitions -- Sustainability / Sustainable Development -- Obsolescence -- Sustainability versus Obsolescence - Built Environment Context -- Social -- Environmental -- Economic -- Holistic Sustainable Obsolescence Management -- Obsolescence Assessment (OA) -- Obsolescence Reduction (OR) -- Concluding Remarks -- References -- Automatic Text Formatting for Social Media Based on Linefeed and Comma Insertion -- Introduction. , Text Formatting by Comma and Linefeed Insertion.

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 Machine Learning Paradigms: Introduction to Deep Learning-Based Technological Applications -- 1.1 Editorial Note -- References -- Part IDeep Learning in Sensing -- 2 Vision to Language: Methods, Metrics and Datasets -- 2.1 Introduction -- 2.2 Challenges in Image Captioning -- 2.2.1 Understanding and Predicting `Importance' in Images -- 2.2.2 Visual Correctness of Words -- 2.2.3 Automatic Evaluation Metrics -- 2.2.4 Image Specificity -- 2.2.5 Natural-Sounding Descriptions -- 2.3 Image Captioning Models and Their Taxonomy -- 2.3.1 Example Lookup-Based Models -- 2.3.2 Generation-based Models -- 2.4 Assessment of Image Captioning Models -- 2.4.1 Human Evaluation -- 2.4.2 Automatic Evaluation Metrics -- 2.4.3 Distraction Task(s) Based Methods -- 2.5 Datasets for Image Captioning -- 2.5.1 Generic Captioning Datasets -- 2.5.2 Stylised Captioning Datasets -- 2.5.3 Domain Specific Captioning Datasets -- 2.6 Applications of Visual Captioning -- 2.6.1 Medical Image Captioning -- 2.6.2 Life-Logging -- 2.6.3 Commentary for Sports' Videos -- 2.6.4 Captioning for Newspapers -- 2.6.5 Captioning for Assistive Technology -- 2.6.6 Other Applications -- 2.7 Extensions of Image Captioning to Other Vision-to-Language Tasks -- 2.7.1 Visual Question Answering -- 2.7.2 Visual Storytelling -- 2.7.3 Video Captioning -- 2.7.4 Visual Dialogue -- 2.7.5 Visual Grounding -- 2.8 Conclusion and Future Works -- References -- 3 Deep Learning Techniques for Geospatial Data Analysis -- 3.1 Introduction -- 3.2 Deep Learning: A Brief Overview -- 3.2.1 Deep Learning Architectures -- 3.2.2 Deep Neural Networks -- 3.2.3 Convolutional Neural Network (CNN) -- 3.2.4 Recurrent Neural Networks (RNN) -- 3.2.5 Auto-Encoders (AE) -- 3.3 Geospatial Analysis: A Data Science Perspective -- 3.3.1 Enabling Technologies for Geospatial Data Collection. , 3.3.2 Geospatial Data Models -- 3.3.3 Geospatial Data Management -- 3.4 Deep Learning for Remotely Sensed Data Analytics -- 3.4.1 Data Pre-processing -- 3.4.2 Feature Engineering -- 3.4.3 Geospatial Object Detection -- 3.4.4 Classification Tasks in Geospatial Analysis -- 3.5 Deep Learning for GPS Data Analytics -- 3.6 Deep Learning for RFID Data Analytics -- 3.7 Conclusion -- References -- 4 Deep Learning Approaches in Food Recognition -- 4.1 Introduction -- 4.2 Background -- 4.2.1 Popular Deep Learning Frameworks -- 4.3 Deep Learning Methods for Food Recognition -- 4.3.1 Food Image Datasets -- 4.3.2 Approach #1: New Architecture Development -- 4.3.3 Approach #2: Transfer Learning and Fine-Tuning -- 4.3.4 Approach #3: Deep Learning Platforms -- 4.4 Comparative Study -- 4.4.1 New Architecture Against Pre-trained Models -- 4.4.2 Deep Learning Platforms Against Each Other -- 4.5 Conclusions -- References -- Part IIDeep Learning in Social Media and IOT -- 5 Deep Learning for Twitter Sentiment Analysis: The Effect of Pre-trained Word Embedding -- 5.1 Introduction -- 5.2 Related Work -- 5.3 Evaluation Procedure -- 5.3.1 Datasets -- 5.3.2 Data Preprocessing -- 5.3.3 Pre-trained Word Embeddings -- 5.3.4 Deep Learning -- 5.4 Comparative Analysis and Discussion -- 5.5 Conclusion and Future Work -- References -- 6 A Good Defense Is a Strong DNN: Defending the IoT with Deep Neural Networks -- 6.1 Introduction -- 6.2 State of the Art in IoT Cyber Security -- 6.3 A Cause for Concern: IoT Cyber Security -- 6.3.1 Introduction to IoT Cyber Security -- 6.3.2 IoT Malware -- 6.4 Background of Machine Learning -- 6.4.1 Support Vector Machine (SVM) -- 6.4.2 Random Forest -- 6.4.3 Deep Neural Network (DNN) -- 6.5 Experiment -- 6.5.1 Training and Test Data -- 6.5.2 Baselines of the Machine Learning Models -- 6.6 Results and Discussion -- 6.6.1 Results -- 6.6.2 Discussion. , 6.7 Conclusion -- References -- Part IIIDeep Learning in the Medical Field -- 7 Survey on Deep Learning Techniques for Medical Imaging Application Area -- 7.1 Introduction -- 7.2 From Machine Learning to Deep Learning -- 7.3 Learning Algorithm -- 7.4 ANN -- 7.4.1 Activation Function in ANN -- 7.4.2 Training Process -- 7.5 DNN -- 7.5.1 Supervised Deep Learning -- 7.5.2 Unsupervised Learning -- 7.6 MRI Preprocessing -- 7.6.1 Inter-series Sorting -- 7.6.2 Registration -- 7.6.3 Normalization -- 7.6.4 Correction of the Bias Field -- 7.7 Deep Learning Applications in Medical Imagining -- 7.7.1 Classification -- 7.7.2 Detection -- 7.7.3 Segmentation -- 7.7.4 Registration -- 7.8 Conclusion -- References -- 8 Deep Learning Methods in Electroencephalography -- 8.1 Introduction -- 8.1.1 A Short Introduction to EEG -- 8.2 Literature Review -- 8.2.1 Public Datasets -- 8.2.2 Preprocessing Methods -- 8.2.3 Input Representation -- 8.2.4 Data Augmentation -- 8.2.5 Architectures -- 8.2.6 Features Visualization -- 8.2.7 Applications -- 8.3 Practical Example-Eriksen Flanker Task -- 8.3.1 Materials -- 8.4 Summary -- References -- Part IVDeep Learning in Systems Control -- 9 The Implementation and the Design of a Hybriddigital PI Control Strategy Based on MISO Adaptive Neural Network Fuzzy Inference System Models-A MIMO Centrifugal Chiller Case Study -- 9.1 Introduction -- 9.2 Centrifugal Chiller System Decomposition-Closed-Loop Simulations -- 9.3 MISO ARMAX and ANFIS Models of MIMO Centrifugal Chiller Plant -- 9.3.1 MISO ARMAX and ANFIS Evaporator Subsystem Models -- 9.3.2 MISO ARMAX and ANFIS Condenser Subsystem Models -- 9.4 Centrifugal Chiller PID Closed-Loop Control Strategies-Performance Analysis -- 9.5 Conclusions -- References -- 10 A Review of Deep Reinforcement Learning Algorithms and Comparative Results on Inverted Pendulum System -- 10.1 Introduction. , 10.2 Reinforcement Learning Background -- 10.2.1 Markov Decision Process -- 10.2.2 Deep-Q Learning -- 10.2.3 Double Deep-Q Learning -- 10.2.4 Double Dueling Deep-Q Learning -- 10.2.5 Reinforce -- 10.2.6 Asynchronous Deep Reinforcement Learning Methods -- 10.3 Inverted Pendulum Problem -- 10.4 Experimental Results -- 10.5 Conclusions -- References -- Part VDeep Learning in Feature Vector Processing -- 11 Stock Market Forecasting by Using Support Vector Machines -- 11.1 Introduction -- 11.2 Support Vector Machines -- 11.3 Determinants of Risk and Volatility in Stock Prices -- 11.4 Predictions of Stock Market Movements by Using SVM -- 11.4.1 Data Processing -- 11.4.2 The Proposed SVM Model -- 11.4.3 Feature Selection -- 11.5 Results and Conclusions -- References -- 12 An Experimental Exploration of Machine Deep Learning for Drone Conflict Prediction -- 12.1 Introduction -- 12.1.1 Airspace and Traffic Assumptions -- 12.1.2 Methodological Assumptions -- 12.2 A Brief Introduction to Artificial Neural Networks (ANNs) -- 12.3 Drone Test Scenarios and Traffic Samples -- 12.3.1 Experimental Design -- 12.3.2 ANN Design -- 12.3.3 Procedures -- 12.4 Results -- 12.4.1 Binary Classification Accuracy -- 12.4.2 Classification Sensitivity and Specificity -- 12.4.3 The Extreme Scenario -- 12.4.4 ROC Analysis -- 12.4.5 Summary of Results -- 12.5 Conclusions -- References -- 13 Deep Dense Neural Network for Early Prediction of Failure-Prone Students -- 13.1 Introduction -- 13.2 Literature Review -- 13.3 The Deep Dense Neural Network -- 13.4 Experimental Process and Results -- 13.5 Conclusions -- References -- Part VIEvaluation of Algorithm Performance -- 14 Non-parametric Performance Measurement with Artificial Neural Networks -- 14.1 Introduction -- 14.2 Data Envelopment Analysis -- 14.3 Artificial Neural Networks -- 14.4 Proposed Approach. , 14.4.1 Data Generation-Training and Testing Samples -- 14.4.2 ANN Architecture and Training Algorithm -- 14.5 Results -- 14.6 Conclusion -- References -- 15 A Comprehensive Survey on the Applications of Swarm Intelligence and Bio-Inspired Evolutionary Strategies -- 15.1 Introduction -- 15.2 Nature Inspired Intelligence -- 15.2.1 Swarm Intelligence -- 15.2.2 Algorithms Inspired by Organisms -- 15.3 Application Areas and Open Problems for NII -- 15.3.1 Applications of Swarm Intelligent Methods -- 15.3.2 Applications of Organisms-Inspired Algorithms -- 15.3.3 Comparison and Discussion -- 15.3.4 Are All These Algorithms Actually Needed? -- 15.4 Suggestions and Future Work -- References -- 16 Detecting Magnetic Field Levels Emitted by Tablet Computers via Clustering Algorithms -- 16.1 Introduction -- 16.2 Measurement of the Tablet Magnetic Field -- 16.2.1 Magnetic Field -- 16.2.2 Measuring Devices -- 16.2.3 TCO Standard -- 16.2.4 The Realized Experiment -- 16.2.5 A Typical Way of Working with the Tablet -- 16.3 Magnetic Field Clustering -- 16.3.1 K-Means Clustering -- 16.3.2 K-Medians Clustering -- 16.3.3 Self-Organizing Map Clustering -- 16.3.4 DBSCAN Clustering -- 16.3.5 Expectation-Maximization with Gaussian Mixture Models -- 16.3.6 Hierarchical Clustering -- 16.3.7 Fuzzy-C-Means Clustering -- 16.4 Evaluation of the Tablet User Exposure to ELF Magnetic Field -- 16.5 Results and Discussion -- 16.5.1 Measurement Results -- 16.5.2 Clustering Results -- 16.5.3 The foe Results Measurement -- 16.6 Conclusions -- References.