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: 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 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 -- Series Editor's Foreword -- Preface -- Contents -- 1 Introduction -- 1.1 Subject of the Book -- 1.2 Purpose of the Book and Contribution to Science -- 1.3 Structure of the Book -- Further Reading -- 2 e-Government: The Concept, the Environment and Critical Issues for the Back-Office Systems -- 2.1 Introduction -- 2.2 The Environment of Public Administration -- 2.3 Developing e-Government-Models and Levels of Development -- 2.3.1 The Three Rings Model -- 2.3.2 The Model of Focus and Centrality -- 2.3.3 The 5 Levels of e-Government Development -- 2.3.4 The Model of 13 Levels of Digital Service Integration -- 2.4 Towards an Electronic Public Administration: The Roadmap -- 2.5 Critical Issues for e-Government -- 2.6 Back-Office Systems Development-Critical Issues for the Greek Case -- 2.6.1 e-Administration-The Current Back-Office in Greece -- 2.6.2 The Current Situation for the Personnel of the Public Administration in Greece -- 2.6.3 Description of the Support System -- 2.6.4 Benefits of the System-Perspectives -- 2.6.5 Critical Issues for the System Development -- References -- 3 Semantic Web: The Evolution of the Web and the Opportunities for the e-Government -- 3.1 The Internet as the Foundation for Service Providing -- 3.2 The Web as an Internet Service-Historical and Technological References -- 3.3 From Web to Web 2.0 -- 3.4 The Road to Web 3.0 and Web X.0 -- 3.5 Web 2.0 Versus Web 3.0: A Comparative Analysis -- 3.5.1 Web 2.0 as a Collaborative Interactive Platform -- 3.5.2 Web 2.0 Technologies -- 3.5.3 The Semantic Web: Making Data Meaningful -- 3.5.4 The Levels of the Semantic Web -- 3.5.5 The Basic Technologies Used in the Semantic Web -- 3.5.6 Web 1.0-2.0-3.0: A Comparative Presentation -- 3.6 Semantic Web Applications -- 3.7 Advantages and Challenges for the Semantic Web -- 3.8 Benefits from the Application of the Semantic Web. , 3.9 Opportunities for e-Government from the Implementation of Semantic Web Solutions -- References -- 4 Representation and Knowledge Management for the Benefit of e-Government-Opportunities Through the Tools of the Semantic Web -- 4.1 Knowledge and e-Government-Competitive Advantage for the Public Sector -- 4.1.1 Knowledge as a Concept -- 4.1.2 Knowledge Creation -- 4.1.3 Knowledge Coding -- 4.2 Knowledge Management in Terms of Semantic Web-Critical Issues for Their Application in e-Government -- 4.2.1 The Concept "Knowledge Management" -- 4.2.2 Critical Issues for the Implementation of Knowledge Management in the Public Sector -- 4.2.3 Knowledge Management Procedures -- 4.2.4 Knowledge Management Systems -- 4.3 Semantic Tools for Knowledge Management in the Domain of Public Administration -- 4.3.1 The RDF Data Model -- 4.3.2 RDF Schema Specification Language -- 4.3.3 The URI and URI's Use -- 4.3.4 Web Ontology Language-OWL -- 4.3.5 Reasoning Tools -- 4.4 Modelling and Extraction of Knowledge in the Field of e-Government-Our Proposal as "The e-Government Ontology" -- 4.4.1 The e-Government Ontology Motivation -- 4.4.2 The Ontology Development in Protégé 4.3 -- 4.5 Knowledge Acquisition from "The e-Government Ontology" -- 4.5.1 SPARQL -- 4.5.2 SPARQL-DL in OWL2 Query Tab of Protégé -- 4.5.3 DL Query Tool of Protégé -- 4.6 Evaluation of Ontology -- 4.6.1 Categorization of the Ontology -- 4.6.2 Basic Principles of Design -- 4.6.3 Methodology of the Ontology Development -- 4.7 Semantic Modelling in the Domain of Official Statistics -- 4.7.1 The Official Statistics Domain -- 4.7.2 Developing an Ontology for the Modelling of Knowledge in the Field of Official Statistics of the ELS -- 4.7.3 Results -- 4.7.4 Assessment and Evaluation of Ontology -- 4.8 Knowledge Representation in the Internal Audit Field -- 4.8.1 Introduction and Motivation. , 4.8.2 Presentation of the Audit Field -- 4.8.3 Modelling of Knowledge Within the Auditing Sector -- 4.8.4 Examples of the Application of Restrictions, Rules and Queries in Ontology -- 4.8.5 Evaluation-Assessment of Ontology -- 4.8.6 Conclusions -- References -- 5 Towards Open Data and Open Governance-Representation of Knowledge and Triplification of Data in the Field of the Greek Open Government Data -- 5.1 From e-Government Towards Open Government -- 5.2 Benefits-Perspectives from the Opening of Government Data -- 5.3 The Case of Greek Open Government Data -- 5.4 Critical Issues for Opening Government Data -- 5.4.1 Basic Rules -- 5.4.2 Basic Steps for Opening Government Data -- 5.4.3 A Proposal for Linked Open Government Data -- 5.5 The Open Data Ontology-Our Proposal for the Case of the Greek Open Data Repository -- 5.5.1 Introduction and Motivation -- 5.5.2 The Ontology Implementation in Protége -- 5.5.3 Ontology Evaluation -- 5.6 Open Data Triplification-The Case of the Greek Open Data from the "Diavgeia Program" -- 5.6.1 Introduction -- 5.6.2 Relevant Tools -- 5.6.3 Triplification-The Case of Open Data by the "Diavgeia Program -- 5.6.4 Conclusions -- 5.7 Open Data Repositories-The Case of the e-Government Ontology Publishing as an Open Ontology in CKAN -- 5.7.1 Methodology-Steps for the Development of the CKAN Repository -- 5.7.2 Opening Data Through CKAN: The Case of Publishing-Opening e-Government Ontology -- References -- 6 Production and Publication of Linked Open Data: The Case of Open Ontologies -- 6.1 Linked Open Data -- 6.1.1 Semantic Web, Linked Data and Linked Open Data-The Fundamental Rules -- 6.1.2 The Way That Linked Data Work -- 6.1.3 Linking Information Resources to Other Resources Through Redirection -- 6.1.4 Basic Steps for Publishing Linked Open Data -- 6.2 Linked Open Data Tools -- 6.2.1 Apache Jena and Apache Fuseki Server. , 6.2.2 Eclipse RDF4J Framework -- 6.2.3 Pubby -- 6.2.4 Apache Tomcat -- 6.2.5 D2R Server and D2RQ Mapping Language -- 6.2.6 OpenLink Virtuoso Server and Virtuoso Open Source Server -- 6.2.7 Comparison of the Basic Tools -- 6.3 Triplification-The Case of Production of RDF Triples from Data in Relational Databases in National Municipal Registry -- 6.3.1 Motivation -- 6.3.2 The Case of the National Municipal Registry -- 6.3.3 Triplification-Steps and Methodology -- 6.3.4 Conclusions and Future Work -- 6.4 RDF Serialisation from JSON Data-The Case of JSON Data in Diavgeia.gov.gr -- 6.4.1 Introduction -- 6.4.2 JSON Versus JSON-LD -- 6.4.3 Producing RDF Triples out of JSON Data -- 6.4.4 Conclusion-Future Work -- 6.5 Publication of Linked Data: The Case of the Open Ontology for Open Government -- 6.5.1 Create SparqL EndPoint and Publish Linked Open Data Using Fuseki and Pubby Server -- 6.5.2 Ontology Publish as Linked Open Data -- 6.6 Publication of Open Ontology in the LOD Cloud -- 6.6.1 Create a SparqL Endpoint Through OpenLink Virtuoso -- 6.6.2 Publication of the e-Government Ontology in the LOD Cloud -- 6.6.3 Publication of the Ontology in Linked Open Vocabularies -- 6.7 Conclusions -- 6.7.1 Pubby Operation -- 6.7.2 Benefits of Publishing the Ontology for Open Government as Open Data -- References -- 7 Education and e-Government-The Case of a Moodle Based Platform for the Education and Evaluation of Civil Servants -- 7.1 Introduction -- 7.2 Current Situation in the Education of Civil Servants in Greece -- 7.3 e-Training in e-Government -- 7.4 The Wiki as a Means of Education and Knowledge Management in Public Institutions -- 7.5 Suggested Wiki Implementation Through Moodle -- References -- 8 Conclusions-Future Work -- Appendix -- A.1 Customization of the Configuration CKAN File (production.ini). , A.2 Configuration File of Pubby Server (Access in a SparqL Endpoint in the Same Machine) -- A.3 Configuration File of Pubby Server (Access in a Remote SparqL Endpoint) -- A.4 Summary of Key Open Government Licence Sites.

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 -- Organization -- Preface -- Contents -- About the Editors -- Multimedia -- Reversible Data Hiding in Encrypted Image Based on MSB Inversion -- 1 Introduction -- 2 Related Works -- 2.1 Secret Embedding Procedure -- 2.2 Message Extracting and Image Recovery -- 3 The Proposed Method -- 3.1 Image Encryption and Secret Embedding -- 3.2 Message Extracting and Image Recovery -- 4 Experimental Result -- 5 Conclusion -- References -- Comments on the Visual Binary QR Code -- 1 Introduction -- 2 Schemes of Visual Binary QR Code -- 2.1 Unitag -- 2.2 QArt -- 2.3 Halftone QR Code -- 3 Comparisons -- 4 Conclusions -- References -- NLP-Based Hardware Solution for Censoring Audio on Over-the-Top (OTT) Media Services -- 1 Introduction -- 2 Related Work -- 3 Proposed Method -- 3.1 Hardware Architecture -- 3.2 Software Architecture -- 4 Experiments & -- Results -- 4.1 Application Components & -- Input Dataset -- 4.2 Accommodations for Lag -- 4.3 Results -- 5 Conclusion and Future Work -- References -- Efficient Steganographic Method Based on Modulo Cube -- 1 Introduction -- 2 Related Works -- 3 Proposed Method -- 3.1 Preliminary Phase -- 3.2 Embedding Phase -- 3.3 Extraction phase -- 4 Experimental Results -- 5 Conclusions -- References -- A High Capacity Reversible Data Hiding in Encrypted Images Using Multi-MSB Prediction and Huffman Coding -- 1 Introduction -- 2 Proposed Method -- 2.1 Apply the Huffman Tree Building Algorithm -- 2.2 Reduce Reference Bytes -- 2.3 Remove the Redundant Length Column -- 3 Experimental Results -- 3.1 Commonly Used Test Images -- 4 Conclusion -- References -- Reversible Data Hiding Based on Bidirectional Generalized Integer Transform -- 1 Introduction -- 2 Qiu et al. Scheme -- 3 Proposed Scheme -- 4 Experimental Results -- 5 Conclusions -- References -- Network and System Security (I). , A Prototype Design on Privacy-Preserving Outsourced Bayesian Network -- 1 Introduction -- 2 Preliminaries -- 3 System Model -- 4 Construction -- 4.1 Building Blocks -- 4.2 Round Trick -- 4.3 Main Protocol -- 5 Analysis and Discussions -- 6 Conclusions -- References -- The Security Challenge of Consumers' Mobile Payment -- 1 Introduction -- 2 Consumers' Mobile Payment Use Intention -- 3 Consumers' Mobile Payment Security -- 4 Research Methodology -- 4.1 Sample and Data Sources -- 4.2 Variables and Measures -- 4.3 Reliability Analysis -- 4.4 Validity Analysis -- 5 Results -- 5.1 The Structural Equation Modeling (SEM) Results for Mobile Payment Security-Mobile Payment Use Intention -- 5.2 The Impact of Consumers' Mobile Payment Security on Consumers' Mobile Payment Use Intention -- 6 Conclusions -- References -- Research on the Analysis of Key Attack Modes in a Wireless Environment -- 1 Introduction -- 2 The Proposed Scheme -- 2.1 Method Architecture -- 2.2 Test Flow Chart -- 2.3 Introduction to WPA2 Four-Way Handshakes Process -- 3 Analysis of Key Attack Modes -- 3.1 Dictionary Cracking Mode -- 3.2 Script Cracking Mode -- 3.3 Discussion and Comparison -- 4 Conclusion -- References -- Default Risk Prediction Using Random Forest and XGBoosting Classifier -- 1 Introduction -- 2 Literature Review -- 3 Methodology -- 3.1 Data -- 3.2 Data Cleaning -- 3.3 Exploratory Data Analysis -- 3.4 Feature Selection Method -- 3.5 Data Split & -- Sampling -- 3.6 Classification Models -- 4 Experiments -- 4.1 XGBoosting Classifier -- 4.2 Random Forest (RF) -- 5 Conclusion -- References -- An RFID Ownership Transfer Based on Multiple Owners with Different Weights -- 1 Introduction -- 2 The Proposed Method -- 2.1 The Initial Phase -- 2.2 The Ownership Transfer Request Phase -- 2.3 The Ownership Agreement Phase -- 2.4 The Ownership Transfer Phase. , 2.5 Mutual Authentication Between the TTP and the Tag -- 2.6 Tag Verification -- 3 Conclusion -- References -- Network and System Security (II) -- Comments on a Scalable Healthcare Authentication Protocol with Attack-Resilience and Anonymous Key-Agreement -- 1 Introduction -- 2 Review of Hajian et al.'s Scheme -- 2.1 System Setup Phase -- 2.2 Registration Phase -- 2.3 Authentication Phase -- 2.4 Password Change Phase -- 2.5 User Identity Change Phase -- 3 Security Analysis -- 3.1 Gateway Authentication Failure -- 3.2 Vulnerability to Denial-of-Service Attack -- 3.3 Failed Password Change and User Identity Change -- 3.4 Compromised User Anonymity and Untraceability -- 4 Conclusions -- References -- A LWE-Based Receiver-Deniable Encryption Scheme -- 1 Introduction -- 2 Related Works -- 2.1 Deniable Encryption -- 2.2 LWE-Based Encryption -- 3 Receiver-Deniable LWE-Based Encryption -- 3.1 Concept -- 3.2 Construction -- 4 Evaluation -- 4.1 Correctness -- 4.2 Deniability -- 5 Conclusion and Future Works -- References -- Privacy-Preserved Hierarchical Authentication and Key Agreement for AI-Enabled Telemedicine Systems -- 1 Introduction -- 2 Related Works -- 2.1 AI Systems -- 2.2 Telemedicine Systems -- 2.3 Chebyshev Chaotic Maps -- 3 Proposed Scheme -- 4 Security Analysis -- 4.1 Security of Secret Key -- 4.2 Session Key Confirmation and Security of Session Key -- 4.3 Mutual Authentication -- 4.4 Unforgeability -- 4.5 Without Assistance of Registration Center (RC) -- 5 Conclusion -- References -- Fuzzy C-Means Based Feature Selection Mechanism for Wireless Intrusion Detection -- 1 Introduction -- 2 Related Works -- 3 Methodology -- 3.1 Research Design -- 3.2 Difference of FCM Center Distances -- 3.3 Auto Encoder -- 3.4 Deep Neural Network -- 3.5 Data Preprocessing -- 4 Experimental Results -- 5 Conclusion -- References. , An Active User-Side Detector for Evil Twins -- 1 Introduction -- 2 System Principle -- 2.1 Monitor Mode -- 2.2 Retransmission and Forwarding -- 2.3 Principle -- 3 Evaluation -- 3.1 Time Efficiency -- 3.2 Limitations -- 3.3 Discussion -- 4 Conclusion -- References -- AI ad Big Data Analysis (I) -- Evaluation of Recurrent Neural Network Model Training for Health Care Suggestions -- 1 Introduction -- 2 Related Work -- 3 Proposed Solution -- 3.1 LSTM-Based Total Care Prediction System -- 3.2 Feature Selection -- 3.3 Feature Encoding -- 4 System Evaluation -- 4.1 Study Population -- 4.2 The Performance Comparison of Different RNNs -- 5 Conclusion -- References -- E-learning Behavior Analytics in the Curriculum of Big Data Visualization Application -- 1 Introduction -- 2 Teaching Materials Design and Research Methods -- 2.1 Participants and Teaching Environment -- 2.2 E-leaning Variables and Analyzing Methods -- 3 Results -- 3.1 Hotspots of Online Teaching Materials -- 3.2 E-learning Behavior Analysis -- 3.3 Data Mining and Modeling -- 4 Discussion and Conclusion -- 4.1 Hotspots -- 4.2 E-learning Behavior Analysis -- 4.3 Model Prediction -- References -- Malware Detection Based on Image Conversion -- 1 Introduction -- 2 Related Work -- 2.1 Review the Image Texture Analysis Method [1] -- 2.2 Review the Classification of Convolutional Neural Network (CNN) Method [12] -- 3 Proposed Method -- 3.1 System Architecture Diagram -- 3.2 Generative Adversarial Network -- 3.3 Discrete Cosine Transform -- 3.4 Discrete Wavelet Transform -- 3.5 Convolutional Neural Network -- 4 Experiments and Results -- 5 Conclusion -- References -- Automobile Theft Detection by Driving Behavior Identification Using Deep Autoencoder -- 1 Introduction -- 2 Proposed Methods -- 2.1 Model Autoencoder -- 2.2 Deep Autoencoder for Anomaly Detection -- 2.3 Important Features -- 2.4 Dataset. , 2.5 Performance Measurement -- 3 Experimental Results -- 3.1 Performance of Anomaly Detection -- 3.2 Analysis of Important Features -- 4 Conclusions and Future Directions -- References -- Combining a Bi-LSTM-Based Siamese Network with Word2Vec Algorithm for Classifying High-Dimensional Dataset -- 1 Introduction -- 2 Related Work -- 2.1 Natural Language Processing -- 2.2 Recurrent Neural Network -- 2.3 Text Classification -- 2.4 Dimensionality Reduction -- 2.5 Siamese Network -- 3 Proposed Methods -- 3.1 Problem Definition -- 3.2 Proposed Method -- 4 Experiments -- 4.1 Proposed Method Experiment Data Source -- 4.2 Experiment 1: News Category Dataset Classification Precision After Dimensionality Reduction -- 4.3 Experiment 2: Classification Precision After IMDb Dimensionality Reduction -- 5 Conclusion -- References -- Real Time Drowsiness Detection Based on Facial Dynamic Features -- 1 Introduction -- 2 Literature Review -- 3 The Proposed Method -- 4 Experimental Results -- 5 Conclusions -- References -- AI ad Big Data Analysis (II) -- Gradient Deep Learning Boosting and Its Application on the Imbalanced Datasets Containing Noises in Manufacturing -- 1 Introduction -- 2 Literature Review -- 3 Materials and Methods -- 3.1 Materials -- 3.2 Methods -- 4 Materials and Methods -- 4.1 Experiments -- 4.2 Results -- 5 Discussions and Conclusions -- References -- Fabric Defect Detection by Applying Structural Similarity Index to the Combination of Variational Autoencode and Generative Adversarial Network -- 1 Introduction -- 2 The Proposed Scheme -- 2.1 The Architecture of Proposed Model -- 2.2 Loss Function -- 3 Experimental Results -- 3.1 Introduction to Environment Configuration and Data Set -- 3.2 Evaluation Index -- 3.3 Performance Evaluation After Training the Model with Fabric -- 4 Conclusions -- References. , A Novel Defense Mechanism Against Label-Flipping Attacks for Support Vector Machines.

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 -- Preface -- Contents -- 1 Introduction to Advances in Assistive Technologies -- 1.1 Editorial Note -- 1.2 Book Summary and Future Volumes -- References -- Part I Advances in Assistive Technologies in Healthcare -- 2 Applications of AI in Healthcare and Assistive Technologies -- 2.1 Introduction -- 2.2 Healthcare and Biomedical Research -- 2.2.1 Controlled Monitoring Environment -- 2.2.2 Evolving Healthcare Techniques -- 2.2.3 Diagnosis -- 2.3 Assistive Technologies -- 2.3.1 Smart Homes and Cities -- 2.3.2 Assistive Robotics -- 2.4 Analysis and Forecasting -- 2.5 Conclusions -- References -- 3 A Research Agenda for Dementia Care: Prevention, Risk Mitigation and Personalized Interventions -- 3.1 Introduction -- 3.2 Mild Behavioral Impairments (MBI) and Dementia -- 3.3 Biometric Data -- 3.4 Caring for Caregivers -- 3.5 Tests -- 3.6 Conclusions -- References -- 4 Machine Learning and Finite Element Methods in Modeling of COVID-19 Spread -- 4.1 Introduction -- 4.1.1 Physiology of Human Respiratory System -- 4.1.2 Spreading of SARS-CoV-2 Virus Infection -- 4.1.3 Machine Learning for SARS-CoV-2 -- 4.2 Methods -- 4.2.1 Finite Element Method for Airways and Lobes -- 4.2.2 Machine Learning Method -- 4.3 Results -- 4.3.1 Simulation of Virus Spreading by Finite Element Analysis -- 4.3.2 Machine Learning Results -- 4.4 Conclusions -- References -- Part II Advances in Assistive Technologies in Medical Diagnosis -- 5 Towards Personalized Nutrition Applications with Nutritional Biomarkers and Machine Learning -- 5.1 Introduction -- 5.1.1 Summary -- 5.1.2 Chapter Synopsis -- 5.1.3 Goals and Perspective -- 5.2 Basic Concepts -- 5.2.1 Personalized Medicine -- 5.2.2 Next Generation Sequencing -- 5.2.3 Obesity -- 5.2.4 Nutritional Biomarkers -- 5.3 Neural Networks, Pattern Recognition and Datasets -- 5.3.1 Neural Network. , 5.3.2 Implementation Environment -- 5.3.3 Proposed System -- 5.4 Implementation and Evaluation of the Proposed System -- 5.4.1 Deep Back Propagation Neural Network -- 5.4.2 Standard Biochemistry Profile Neural Network (SBPNN) -- 5.4.3 Neural Network Dietary Profile -- 5.5 Conclusions and Future Research -- 5.5.1 Prevention -- 5.5.2 Modelling -- 5.5.3 Automation -- 5.5.4 Perspective -- 5.5.5 Discussion on Feature Research -- 5.6 Appendix -- References -- 6 Inductive Machine Learning and Feature Selection for Knowledge Extraction from Medical Data: Detection of Breast Lesions in MRI -- 6.1 Introduction -- 6.2 Detailed Literature Review -- 6.3 Presentation of the Data -- 6.3.1 Data Collection -- 6.3.2 Description of Variables -- 6.3.3 Dataset Preprocessing -- 6.4 Methodology -- 6.4.1 Modeling Methodology -- 6.4.2 Feature Selection Process -- 6.4.3 Classification Method -- 6.4.4 Validation Process -- 6.5 Modeling Approaches -- 6.5.1 Experimental Process -- 6.5.2 Experimental Results -- 6.6 Conclusions and Further Search -- Annex 6.1-Abbreviations -- Annex 6.2-Variables Frequency Charts (Original Dataset) -- Annex 6.3-Variables' Values Range -- Annex 6.4-Classification Tree (Benign or Malignant) -- References -- 7 Learning Paradigms for Neural Networks for Automated Medical Diagnosis -- 7.1 Introduction -- 7.2 Classical Artificial Neural Networks -- 7.3 Learning Paradigms -- 7.3.1 Evolutionary Computation Learning Paradigm -- 7.3.2 Bayesian Learning Paradigm -- 7.3.3 Markovian Stimulus-Sampling Learning Paradigm -- 7.3.4 Logistic Regression Paradigm -- 7.3.5 Ant Colony Optimization Learning Paradigm -- 7.4 Conclusions and Future Outlook -- References -- Part III Advances in Assistive Technologies in Mobility and Navigation -- 8 Smart Shoes for Assisting People: A Short Survey -- 8.1 Smart Shoes for People in Need. , 8.1.1 Smart Shoes for Visually Impaired People [6] -- 8.1.2 Smart Shoes for Blind Individuals [13] -- 8.1.3 IoT Based Wireless Smart Shoes and Energy Harvesting System [7] -- 8.1.4 Smart Shoes for Sensing Force [8] -- 8.1.5 Smart Shoes for Temperature and Pressure [9] -- 8.1.6 Smart Shoes in IoT [10] -- 8.1.7 Smart Shoes for People with Walking Disorders [23] -- 8.2 Special Purpose Smart Shoes -- 8.2.1 Smart Shoes with Triboelectric Nanogenerator [11] -- 8.2.2 Smart Shoes Gait Analysis [12] -- 8.2.3 Smart Shoes for Biomechanical Energy Harvesting [14] -- 8.2.4 Smart Shoes with Embedded Piezoelectric Energy Harvesting [15] -- 8.2.5 Pedestrian Navigation Using Smart Shoes with Markers [16] -- 8.2.6 Smart Shoes with 3D Tracking Capabilities [17] -- 8.2.7 Pedestrian's Safety with Smart Shoes Sensing [18] -- 8.2.8 Smart Shoes Insole Tech for Injury Prevention [19] -- 8.3 Maturity Evaluation of the Smart Shoes -- 8.4 Conclusion -- References -- 9 Re-Examining the Optimal Routing Problem from the Perspective of Mobility Impaired Individuals -- 9.1 Introduction -- 9.2 Literature Review -- 9.2.1 Mobility Aspects for People with Special Needs -- 9.3 Related Work -- 9.3.1 Miller-Tucker-Zemlin Formulation of the Traveling Salesman Problem -- 9.3.2 Dantzig-Fulkerson-Johnson Formulation of the Traveling Salesman Problem -- 9.4 The Optimal Routing Problem from the Perspective of Mobility Impaired Individuals -- 9.4.1 Measuring Route Scores Based on the Degree of Accessibility -- 9.4.2 Problem Statement: The Optimal Routing Problem from the Perspective of Mobility Impaired Individuals -- 9.4.3 The Proposed Solution Approach -- 9.5 The Experimental Results and Discussion -- 9.6 Conclusions -- References -- 10 Human Fall Detection in Depth-Videos Using Temporal Templates and Convolutional Neural Networks -- 10.1 Introduction -- 10.2 Proposed Method. , 10.3 Experiments, Results and Discussion -- 10.3.1 SDU Fall Dataset -- 10.3.2 UP-Fall Detection Dataset -- 10.3.3 UR Fall Detection Dataset -- 10.3.4 MIVIA Action Dataset -- 10.4 Conclusions and Future Work -- 10.5 Compliance with Ethical Standards -- References -- 11 Challenges in Assistive Living Based on Tech Synergies: The Cooperation of a Wheelchair and A Wearable Device -- 11.1 Overall Description of the Challenges -- 11.2 Background and Significance -- 11.3 The Associated Research Challenges -- 11.3.1 Main Innovative Tasks -- 11.4 Discussion -- References -- 12 Human-Machine Requirements' Convergence for the Design of Assistive Navigation Software: Τhe Case of Blind or Visually Impaired People -- 12.1 Introduction -- 12.2 Related Work -- 12.3 Methodology -- 12.3.1 Interviews with BVI People and Requirements Classification -- 12.3.2 Description of the Participants -- 12.3.3 Requirements Classification -- 12.4 Analysis of the Elicited Requirements -- 12.4.1 Elicited Requirements of the BVI -- 12.5 Discussion -- 12.6 Conclusion -- Appendix A -- References -- Part IV Advances in Privacy and Explainability in Assistive Technologies -- 13 Privacy-Preserving Mechanisms with Explainability in Assistive AI Technologies -- 13.1 Introduction -- 13.1.1 Data Ethics -- 13.1.2 Data Privacy -- 13.1.3 Data Security -- 13.2 AI Applications in Assistive Technologies -- 13.2.1 Explainable AI (XAI) -- 13.3 Data Privacy and Ethical Challenges for Assistive Technologies -- 13.3.1 Data Collection and Data Sharing -- 13.3.2 Secure and Responsible Data Sharing Framework -- 13.4 AI Assistive Technologies with Privacy Enhancing -- 13.4.1 Privacy-Preserving Mechanisms for AI Assistive Technologies -- 13.5 Discussions -- References.

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 -- References -- Preface -- Contents -- 1 Machine Learning Paradigms: Advances in Data Analytics -- Bibliography -- Data Analytics in the Medical, Biological and Signal Sciences -- 2 A Recommender System of Medical Reports Leveraging Cognitive Computing and Frame Semantics -- 2.1 Introduction -- 2.2 State of the Art -- 2.2.1 Biomedical Information Retrieval -- 2.2.2 Biomedical Classification -- 2.2.3 Biomedical Clustering -- 2.2.4 Biomedical Recommendation -- 2.2.5 Cognitive Computing and IBM Watson -- 2.2.6 Frame Semantics and Framester -- 2.3 Architecture of Our System -- 2.3.1 Content Analyzer Module -- 2.3.2 Machine Learning Module -- 2.3.3 Recommendation Module -- 2.4 Experiments -- 2.4.1 The Test Dataset -- 2.4.2 Experiment Setup -- 2.4.3 Recommendation Module Setup -- 2.4.4 Results -- 2.5 Conclusion and Future Trends -- References -- 3 Classification Methods in Image Analysis with a Special Focus on Medical Analytics -- 3.1 Introduction -- 3.2 Background -- 3.3 Feature Representation for Image Classification -- 3.3.1 Global Features -- 3.3.2 Local Features -- 3.3.3 Bag of Visual Words -- 3.3.4 Pixel-Level Features -- 3.4 Security and Biometrics -- 3.4.1 Supervised Classification -- 3.5 Aerospace and Satellite Monitoring -- 3.5.1 Supervised Classification -- 3.5.2 Unsupervised Classification -- 3.6 Document Analysis and Language Understanding -- 3.6.1 Supervised Classification -- 3.6.2 Unsupervised Classification -- 3.7 Information Retrieval -- 3.7.1 Supervised Classification -- 3.7.2 Unsupervised Classification -- 3.8 Classification in Image-Based Medical Analytics -- 3.8.1 Diagnostic Inspective Acquisition Imaging -- 3.8.2 Nuclear Medicine Imaging -- 3.8.3 Clinical Radiology Imaging -- 3.8.4 Horizon of the Research and Future Challenges -- 3.9 Conclusions -- 3.10 Further Readings -- References. , 4 Medical Data Mining for Heart Diseases and the Future of Sequential Mining in Medical Field -- 4.1 Introduction -- 4.2 Classical Data Mining Technics and Heart Diseases -- 4.2.1 Popular Data Mining Algorithms -- 4.2.2 Data Mining and Heart Diseases -- 4.3 Sequential Mining in Medical Domain -- 4.4 Sequential Mining -- 4.4.1 Important Terms and Notations -- 4.4.2 Sequential Patterns Mining -- 4.4.3 General and Specific Techniques Used by SPM Algorithms -- 4.4.4 Extensions of Sequential Pattern Mining Algorithms -- 4.5 Discussion -- 4.6 Conclusion -- References -- 5 Machine Learning Methods for the Protein Fold Recognition Problem -- 5.1 Introduction -- 5.2 Supervised Learning -- 5.3 Deep Learning Methods in Pattern Recognition -- 5.4 Features of the Amino Acid Sequence -- 5.5 Protein Fold Machine Learning-Based Classification Methods -- 5.5.1 Datasets Used in the Described Experiments -- 5.5.2 Methods -- 5.6 Discussion, Conclusions and Future Work -- References -- 6 Speech Analytics Based on Machine Learning -- 6.1 Introduction -- 6.2 Speech Phoneme Signal Analysis -- 6.3 Speech Signal Pre-processing -- 6.4 Speech Information Retrieval Scheme -- 6.5 Feature Extraction -- 6.5.1 Time Domain Features -- 6.5.2 Frequency Domain Features -- 6.5.3 Mel-Frequency Cepstral Coefficients -- 6.6 Data Preparation for Deep Learning -- 6.7 Experiment Results -- 6.7.1 Feature Vector Applied to Vowel Classification -- 6.7.2 Feature Vector Applied to Allophone Classification -- 6.7.3 Convolutional Neural Networks Applied to Allophone Classification -- 6.7.4 Convolutional Neural Networks Applied to Vowel Classification -- 6.8 Conclusions -- References -- Data Analytics in Social Studies and Social Interactions -- 7 Trends on Sentiment Analysis over Social Networks: Pre-processing Ramifications, Stand-Alone Classifiers and Ensemble Averaging -- 7.1 Introduction. , 7.2 Research Methodology -- 7.3 Twitter Datasets -- 7.4 Evaluation of Data Preprocessing Techniques -- 7.5 Evaluation of Stand-Alone Classifiers -- 7.6 Evaluation of Ensemble Classifiers -- 7.7 The Case of Sentiment Analysis in Social e-Learning -- 7.8 Conclusions and Future Work -- References -- 8 Finding a Healthy Equilibrium of Geo-demographic Segments for a Telecom Business: Who Are Malicious Hot-Spotters? -- 8.1 Introduction -- 8.2 Geospatial and Geo-demographic Data -- 8.3 The Combinatorial Optimization Module -- 8.4 Infrastructure-Friendly and Stressing Clients -- 8.5 Experiments -- 8.6 Conclusions -- References -- Data Analytics in Traffic, Computer and Power Networks -- 9 Advanced Parametric Methods for Short-Term Traffic Forecasting in the Era of Big Data -- 9.1 Introduction -- 9.2 Traffic Data -- 9.2.1 Traffic Network -- 9.2.2 Traffic Descriptors -- 9.2.3 Traffic Data Sources -- 9.3 Traffic Data Preprocessing -- 9.3.1 Time Series Formulation -- 9.3.2 Outlier Detection -- 9.3.3 Missing Data Imputation -- 9.3.4 Map-Matching -- 9.4 Parametric Short-Term Traffic Forecasting -- 9.4.1 Autoregressive Moving Average (ARMA) -- 9.4.2 Autoregressive Integrated Moving Average (ARIMA) -- 9.4.3 Space-Time ARIMA (STARIMA) -- 9.4.4 Lag-STARIMA -- 9.4.5 Graph-Based Lag-STARIMA (GBLS) -- References -- 10 Network Traffic Analytics for Internet Service Providers-Application in Early Prediction of DDoS Attacks -- 10.1 Introduction -- 10.2 The Procedure Adopted -- 10.2.1 Related Work -- 10.3 The Proposed Approach -- 10.3.1 Mathematical Formulation -- 10.3.2 State Space Model-Autoregressive Model-Discrete-Time Kalman Filter -- 10.4 Structure and Parameters of the MRSP Algorithm -- 10.5 Results and Performance of the MRSP Algorithm -- 10.6 Detecting Anomalies -- 10.6.1 DETECTING a DDoS ATTACK -- 10.6.2 Detecting an Anomaly -- 10.6.3 Final Remarks. , 10.7 Conclusions -- References -- 11 Intelligent Data Analysis in Electric Power Engineering Applications -- 11.1 Introduction -- 11.2 Intelligent Techniques in Ground Resistance Estimation -- 11.2.1 Grounding Systems -- 11.2.2 Application of ANN Methodologies for the Estimation of Ground Resistance -- 11.2.3 Wavelet Networks Modeling for the Estimation of Ground Resistance -- 11.2.4 Inductive Machine Learning -- 11.2.5 Genetic and Gene Expression Programming Versus Linear Regression Models -- 11.3 Estimation of Critical Flashover Voltage of Insulators -- 11.3.1 Problem Description -- 11.3.2 Genetic Algorithms -- 11.3.3 Application of ANNs -- 11.3.4 Multilayer Perceptron ANNs -- 11.3.5 Genetic Programming -- 11.3.6 Gravitational Search Algorithm Technique -- 11.4 Other Applications of Electric Power Systems -- 11.4.1 Load Forecasting -- 11.4.2 Lightning Performance Evaluation in Transmission Lines -- 11.5 Conclusions and Further Research -- References -- Data Analytics for Digital Forensics -- 12 Combining Genetic Algorithms and Neural Networks for File Forgery Detection -- 12.1 Introduction -- 12.1.1 McKemmish Predominant Model -- 12.1.2 Kent Predominant Model -- 12.1.3 Digital Evidences -- 12.1.4 File Type Identification -- 12.2 Methodology of the Proposed Method -- 12.3 Experimental Setup and Results -- 12.4 Conclusions -- References -- Theoretical Advances and Tools for Data Analytics -- 13 Deep Learning Analytics -- 13.1 Introduction -- 13.2 Preliminaries and Notation -- 13.3 Unsupervised Learning -- 13.3.1 Deep Autoencoders -- 13.3.2 Autoencoder Variants -- 13.4 Supervised Learning -- 13.4.1 Multilayer Perceptrons -- 13.4.2 Convolutional Neural Networks -- 13.4.3 Recurrent Neural Networks -- 13.5 Deep Learning Frameworks -- 13.6 Concluding Remarks -- References.