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

Note: Intro -- 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.

Note: Intro -- Foreword -- Preface -- Contents -- 1 Introduction to Advances in Selected Artificial Intelligence Areas -- 1.1 Editorial Note -- 1.2 Book Summary and Future Volumes -- References -- Part I Advances in Artificial Intelligence Paradigms -- 2 Feature Selection: From the Past to the Future -- 2.1 Introduction -- 2.2 The Need for Feature Selection -- 2.3 History of Feature Selection -- 2.4 Feature Selection Techniques -- 2.4.1 Filter Methods -- 2.4.2 Embedded Methods -- 2.4.3 Wrapper Methods -- 2.5 What Next in Feature Selection? -- 2.5.1 Scalability -- 2.5.2 Distributed Feature Selection -- 2.5.3 Ensembles for Feature Selection -- 2.5.4 Visualization and Interpretability -- 2.5.5 Instance-Based Feature Selection -- 2.5.6 Reduced-Precision Feature Selection -- References -- 3 Application of Rough Set-Based Characterisation of Attributes in Feature Selection and Reduction -- 3.1 Introduction -- 3.2 Background and Related Works -- 3.2.1 Estimation of Feature Importance and Feature Selection -- 3.2.2 Rough Sets and Decision Reducts -- 3.2.3 Reduct-Based Feature Characterisation -- 3.2.4 Stylometry as an Application Domain -- 3.2.5 Continuous Versus Nominal Character of Input Features -- 3.3 Setup of Experiments -- 3.3.1 Preparation of Input Data and Datasets -- 3.3.2 Decision Reducts Inferred -- 3.3.3 Rankings of Attributes Based on Reducts -- 3.3.4 Classification Systems Employed -- 3.4 Obtained Results of Feature Reduction -- 3.5 Conclusions -- References -- 4 Advances in Fuzzy Clustering Used in Indicator for Individuality -- 4.1 Introduction -- 4.2 Fuzzy Clustering -- 4.3 Convex Clustering -- 4.4 Indicator of Individuality -- 4.5 Numerical Examples -- 4.6 Conclusions and Future Work -- References -- 5 Pushing the Limits Against the No Free Lunch Theorem: Towards Building General-Purpose (GenP) Classification Systems -- 5.1 Introduction. , 5.2 Multiclassifier/Ensemble Methods -- 5.2.1 Canonical Model of Single Classifier Learning -- 5.2.2 Methods for Building Multiclassifiers -- 5.3 Matrix Representation of the Feature Vector -- 5.4 GenP Systems Based on Deep Learners -- 5.4.1 Deep Learned Features -- 5.4.2 Transfer Learning -- 5.4.3 Multiclassifier System Composed of Different CNN Architectures -- 5.5 Data Augmentation -- 5.6 Dissimilarity Spaces -- 5.7 Conclusion -- References -- 6 Bayesian Networks: Theory and Philosophy -- 6.1 Introduction -- 6.2 Bayesian Networks -- 6.2.1 Bayesian Networks Background -- 6.2.2 Bayesian Networks Defined -- 6.3 Maximizing Entropy for Missing Information -- 6.3.1 Maximum Entropy Formalism -- 6.3.2 Maximum Entropy Method -- 6.3.3 Solving for the Lagrange Multipliers -- 6.3.4 Independence -- 6.3.5 Overview -- 6.4 Philosophical Considerations -- 6.4.1 Thomas Bayes and the Principle of Insufficient Reason -- 6.4.2 Objective Bayesianism -- 6.4.3 Bayesian Networks Versus Artificial Neural Networks -- 6.5 Bayesian Networks in Practice -- References -- Part II Advances in Artificial Intelligence Applications -- 7 Artificial Intelligence in Biometrics: Uncovering Intricacies of Human Body and Mind -- 7.1 Introduction -- 7.2 Background and Literature Review -- 7.2.1 Biometric Systems Overview -- 7.2.2 Classification and Properties of Biometric Traits -- 7.2.3 Unimodal and Multi-modal Biometric Systems -- 7.2.4 Social Behavioral Biometrics and Privacy -- 7.2.5 Deep Learning in Biometrics -- 7.3 Deep Learning in Social Behavioral Biometrics -- 7.3.1 Research Domain Overview of Social Behavioral Biometrics -- 7.3.2 Social Behavioral Biometric Features -- 7.3.3 General Architecture of Social Behavioral Biometrics System -- 7.3.4 Comparison of Rank and Score Level Fusion -- 7.3.5 Deep Learning in Social Behavioral Biometrics -- 7.3.6 Summary and Applications. , 7.4 Deep Learning in Cancelable Biometrics -- 7.4.1 Biometric Privacy and Template Protection -- 7.4.2 Unimodal and Multi-modal Cancelable Biometrics -- 7.4.3 Deep Learning Architectures for Cancelable Multi-modal Biometrics -- 7.4.4 Performance of Cancelable Biometric System -- 7.4.5 Summary and Applications -- 7.5 Applications and Open Problems -- 7.5.1 User Authentication and Anomaly Detection -- 7.5.2 Access Control -- 7.5.3 Robotics -- 7.5.4 Assisted Living -- 7.5.5 Mental Health -- 7.5.6 Education -- 7.6 Summary -- References -- 8 Early Smoke Detection in Outdoor Space: State-of-the-Art, Challenges and Methods -- 8.1 Introduction -- 8.2 Problem Statement and Challenges -- 8.3 Conventional Machine Learning Methods -- 8.4 Deep Learning Methods -- 8.5 Proposed Deep Architecture for Smoke Detection -- 8.6 Datasets -- 8.7 Comparative Experimental Results -- 8.8 Conclusions -- References -- 9 Machine Learning for Identifying Abusive Content in Text Data -- 9.1 Introduction -- 9.2 Abusive Content on Social Media and Their Identification -- 9.3 Identification of Abusive Content with Classic Machine Learning Methods -- 9.3.1 Use of Word Embedding in Data Representation -- 9.3.2 Ensemble Model -- 9.4 Identification of Abusive Content with Deep Learning Models -- 9.4.1 Taxonomy of Deep Learning Models -- 9.4.2 Natural Language Processing with Advanced Deep Learning Models -- 9.5 Applications -- 9.6 Future Direction -- 9.7 Conclusion -- References -- 10 Toward Artifical Intelligence Tools for Solving the Real World Problems: Effective Hybrid Genetic Algorithms Proposal -- 10.1 Introduction -- 10.2 University Course Timetabling UCT -- 10.2.1 Problem Statement and Preliminary Definitions -- 10.2.2 Related Works -- 10.2.3 Problem Modelization and Mathematical Formulation -- 10.2.4 An Interactive Decision Support System (IDSS) for the UCT Problem. , 10.2.5 Empirical Testing -- 10.2.6 Evaluation and Results -- 10.3 Solid Waste Management Problem -- 10.3.1 Related Works -- 10.3.2 The Mathematical Formulation Model -- 10.3.3 A Genetic Algorithm Proposal for the SWM -- 10.3.4 Experimental Study and Results -- 10.4 Conclusion -- References -- 11 Artificial Neural Networks for Precision Medicine in Cancer Detection -- 11.1 Introduction -- 11.2 The fLogSLFN Model -- 11.3 Parallel Versus Cascaded LogSLFN -- 11.4 Adaptive SLFN -- 11.5 Statistical Assessment -- 11.6 Conclusions -- References -- Part III Recent Trends in Artificial Intelligence Areas and Applications -- 12 Towards the Joint Use of Symbolic and Connectionist Approaches for Explainable Artificial Intelligence -- 12.1 Introduction -- 12.2 Literature Review -- 12.2.1 The Explainable Interface -- 12.2.2 The Explainable Model -- 12.3 New Approaches to Explainability -- 12.3.1 Towards a Formal Definition of Explainability -- 12.3.2 Using Ontologies to Design the Deep Architecture -- 12.3.3 Coupling DNN and Learning Classifier Systems -- 12.4 Conclusions -- References -- 13 Linguistic Intelligence As a Root for Computing Reasoning -- 13.1 Introduction -- 13.2 Language as a Tool for Communication -- 13.2.1 MLW -- 13.2.2 Sounds and Utterances Behavior -- 13.2.3 Semantics and Self-expansion -- 13.2.4 Semantic Drifted Off from Verbal Behavior -- 13.2.5 Semantics and Augmented Reality -- 13.3 Language in the Learning Process -- 13.3.1 Modeling Learning Profiles -- 13.3.2 Looking for Additional Teaching Tools in Academy -- 13.3.3 LEARNITRON for Learning Profiles -- 13.3.4 Profiling the Learning Process: Tracking Mouse and Keyboard -- 13.3.5 Profiling the Learning Process: Tracking Eyes -- 13.3.6 STEAM Metrics -- 13.4 Language of Consciousness to Understand Environments -- 13.4.1 COFRAM Framework -- 13.4.2 Bacteria Infecting the Consciousness. , 13.5 Harmonics Systems: A Mimic of Acoustic Language -- 13.5.1 HS for Traffic's Risk Predictions -- 13.5.2 HS Application to Precision Farming -- 13.6 Conclusions and Future Work -- References -- 14 Collaboration in the Machine Age: Trustworthy Human-AI Collaboration -- 14.1 Introduction -- 14.2 Artificial Intelligence: An Overview -- 14.2.1 The Role of AI-Definitions and a Short Historic Overview -- 14.2.2 AI and Agents -- 14.2.3 Beyond Modern AI -- 14.3 The Role of AI for Collaboration -- 14.3.1 Human-Computer Collaboration Where AI is Embedded -- 14.3.2 Human-AI Collaboration (Or Conversational AI) -- 14.3.3 Human-Human Collaboration Where AI Can Intervene -- 14.3.4 Challenges of Using AI: Toward a Trustworthy AI -- 14.4 Conclusion -- References.

Note: Intro -- Foreword -- Preface -- Contents -- 1 Multimedia Services in Intelligent Environments: Recommendation Services -- Abstract -- 1…Introduction -- 2…Recommendation Services -- 3…Conclusions -- References and Further Readings -- 2 User Modeling in Mobile Learning Environments for Learners with Special Needs -- Abstract -- 1…Introduction -- 2…Overview of a Mobile Educational System for Students with Special Needs -- 2.1 Students with Moving Difficulties -- 2.2 Students with Sight Problems -- 2.3 Dyslexic Students -- 3…Mobile Coordination of People Who Support Children with Special Needs -- 4…Conclusions -- References -- 3 Intelligent Mobile Recommendations for Exhibitions Using Indoor Location Services -- Abstract -- 1…Introduction and Motivation -- 2…Related Experience -- 3…Design and Storyboard -- 4…Architectural Issues -- 4.1 Mobile Devices Subsystem at Visitor Level -- 4.1.1 Operational Specifications of Subsystem of Mobile Devices at Visitor Level -- 4.1.2 Underlying Infrastructure for the Smartphone App: Cell Application -- 4.1.3 Presentation Multimedia Data -- 4.1.4 Instant Messaging Application -- 4.1.5 Positioning Application -- 4.2 Mobile Devices Management and Disposal Subsystem -- 4.2.1 Operational Specifications of Mobile Devices Management and Disposal Subsystem -- 4.2.2 Interaction with Other Subsystems -- 4.2.3 Correct Operation Issues -- 4.3 Networking Services Subsystem -- 4.3.1 Operational Specifications of Networking Services Subsystem -- 4.3.2 Interaction with Other Subsystems -- 4.3.3 Communication Server -- 4.3.4 Monitoring and Management Visitors Administration Console -- 4.4 Data Management System -- 4.5 Wireless Network Infrastructure Subsystem -- 5…Indoor Positioning and Technologies -- 5.1 Microsoft .NET Compact Framework -- 5.2 XMPP Protocol -- 5.3 Openfire -- 5.4 Windows Media Player Mobile. , 5.5 Macromedia Flash Player -- 5.6 Microsoft Visual Studio -- 6…Case Study: The Digital Exhibition of the History of Ancient Olympic Games and Application Visual Evaluation -- 7…Conclusions and Future Steps -- References -- 4 Smart Recommendation Services in Support of Patient Empowerment and Personalized Medicine -- Abstract -- 1…Introduction -- 2…Review and Methods -- 2.1 What Information Constitutes a User Profile? -- 2.2 How Profiling Information is Represented? -- 2.3 How Profiling Information is Obtained? -- 3…Profiling Mechanisms for Patient Empowerment -- 3.1 Challenges and Opportunities -- 3.2 Patient Profiling Server -- 3.3 ALGA-C -- 3.3.1 Quality of Life and Perceived Health State -- 3.3.2 Quality of Life and Psychological Aspects -- 3.3.3 Quality of Life and Psychosocial Aspects -- 3.3.4 Quality of Life and Cognitive Aspects -- 4…p-Medicine Interactive Empowerment Services -- 4.1 p-Medicine Portal -- 4.2 PHR -- 4.3 HDOT Components -- 4.4 Recommendation Service -- 4.5 e-Consent -- 5…Conclusion -- Acknowledgments -- A.x(118). 6…Appendix -- References -- 5 Ontologies and Cooperation of Distributed Heterogeneous Information Systems for Tracking Multiple Chronic Diseases -- Abstract -- 1…Introduction -- 2…Home Telemonitoring for Patients with Chronic Diseases -- 3…Ontology-Based Knowledge Representation -- 3.1 Knowledge Representation Formalisms -- 4…Heterogeneous Medical Knowledge -- 4.1 Techniques for Achieving Semantic Interoperability -- 5…Cooperation of Distributed Heterogeneous Information Systems -- 5.1 Ontologies and Cooperation -- 6…Ontology Construction for Monitoring Patients with Chronic Diseases -- 6.1 Ontology Development Methodologies -- 6.2 Steps for Creating an Ontology -- 6.3 Languages and Tools for Ontologies Reasoning -- 7…Summary -- References -- 6 Interpreting the Omics 'era' Data -- Abstract -- 1…Molecular Structures. , 2…Tree Hierarchies -- 3…Next Generation Sequencing -- 4…Network Biology -- 5…Visualization in Biology---the Present and the Future -- Acknowledgments -- References -- 7 Personalisation Systems for Cultural Tourism -- Abstract -- 1…Introduction -- 2…Personalising City Tours -- 3…Personalising Museum Tours -- 4…Technology -- 5…User Modeling -- 6…Discussion -- References -- Resource List -- 8 Educational Recommender Systems: A Pedagogical-Focused Perspective -- Abstract -- 1…Introduction -- 2…Recommender Systems and Educational Recommender Systems -- 3…Advantages of Introducing Recommender Systems in the Classroom -- 3.1 Student Performance -- 3.2 Social Learning Enhancement -- 3.3 Increased Motivation -- 4…Challenges -- 5…Conclusions -- A.x(118). Appendix A: Selected Resources -- References -- 9 Melody-Based Approaches in Music Retrieval and Recommendation Systems -- Abstract -- 1…Introduction -- 2…Music Similarity and Classification -- 2.1 Train/Test Tasks -- 2.2 Methods and Tools -- 2.3 Audio Tag Classification -- 2.4 Cover Song Classification -- 2.4.1 Methods and Tools -- 2.5 Audio Music Similarity and Retrieval -- 2.5.1 Methods and Tools -- 3…Rhythmical Analysis -- 3.1 Tempo Estimation -- 3.2 Beat Tracking -- 3.2.1 Methods and Tools -- 4…Structural Analysis -- 4.1 Key Detection -- 4.2 Chord Estimation -- 4.3 Audio Melody Extraction -- 4.4 Multiple F_0 Estimation -- 4.4.1 Overlapping Partials -- 4.4.2 Diverse Spectral Characteristics -- 4.4.3 Other Noise Sources -- 4.4.4 Evaluation -- 4.5 Structural Segmentation -- 4.5.1 Methods and Tools -- 5…Query-Based Music Identification -- 5.1 Query by Tapping -- 5.2 Query by Humming (QbH) and Melody Similarity -- 5.2.1 Note-Based Approaches -- 5.2.2 Direct Matching -- 5.2.3 Melody Matching -- 6…Summary -- 7…Resource List -- 7.1 Software -- 7.1.1 Weka -- 7.1.2 Torch -- 7.1.3 Marsyas -- 7.1.4 Praat. , 7.1.5 Audacity -- 7.2 Resources -- 7.2.1 EdaBoard -- 7.2.2 comp.dsp -- 7.2.3 iMIRSEL -- References -- 10 Composition Support of Presentation Slides Based on Transformation of Semantic Relationships into Layout Structure -- Abstract -- 1…Introduction -- 2…Related Work -- 2.1 Authoring Support of Semantic Contents -- 2.2 User Interfaces for Presentation Composition -- 2.3 Automatic Generation of Presentation Slides -- 3…Framework -- 3.1 Process of Presentation Preparation -- 3.2 Representation of Presentation Scenario -- 3.3 Generating Presentation Slides Using Layout Templates -- 4…Composition of Presentation Scenario -- 4.1 Data Structure of Presentation Scenario -- 4.2 Operations for Scenario Composition -- 5…Automatic Generation of Presentation Slides -- 5.1 Processing Flow -- 5.2 Grouping Slide Components -- 5.3 Allocating Slide Components on Slides -- 6…Prototype System -- 6.1 Scenario Composition Interface -- 6.2 Examples of Generated Slides -- 7…Comparison with Existing Systems -- 7.1 Systems Handling Semantic Contents -- 7.2 Systems Supporting Slide Composition -- 8…Conclusion -- References.