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

Note: Intro -- 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 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 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.