Note: Intro -- Foreword -- Preface -- Contents -- 1 Introduction to Handbook on Artificial Intelligence-Empowered Applied Software Engineering-VOL.1: Novel Methodologies to Engineering Smart Software Systems -- 1.1 Editorial Note -- 1.2 Book Summary and Future Volumes -- Bibliography for Further Reading -- Part I Survey of Recent Relevant Literature -- 2 Synergies Between Artificial Intelligence and Software Engineering: Evolution and Trends -- 2.1 Introduction -- 2.2 Methodology -- 2.3 The Evolution of AI in Software Engineering -- 2.4 Top Authors and Topics -- 2.5 Trends in AI Applications to Software Engineering -- 2.5.1 Machine Learning and Data Mining -- 2.5.2 Knowledge Representation and Reasoning -- 2.5.3 Search and Optimisation -- 2.5.4 Communication and Perception -- 2.5.5 Cross-Disciplinary Topics -- 2.6 AI-Based Tools -- 2.7 Conclusion -- References -- Part II Artificial Intelligence-Assisted Software Development -- 3 Towards Software Co-Engineering by AI and Developers -- 3.1 Introduction -- 3.2 Software Development Support and Automation Level by Machine Learning -- 3.2.1 Project Planning: Team Composition -- 3.2.2 Requirements Engineering: Data-Driven Persona -- 3.2.3 Design: Detection of Design Patterns -- 3.2.4 Categorization of Initiative and Level of Automation -- 3.3 Quality of AI Application Systems and Software -- 3.3.1 Metamorphic Testing -- 3.3.2 Improving Explainability -- 3.3.3 Systems and Software Architecture -- 3.3.4 Integration of Goals, Strategies, and Data -- 3.4 Towards Software Co-Engineering by AI and Developers -- 3.5 Conclusion -- References -- 4 Generalizing Software Defect Estimation Using Size and Two Interaction Variables -- 4.1 Introduction -- 4.2 Background -- 4.3 A Proposed Approach -- 4.3.1 Selection of Sample Projects -- 4.3.2 Data Collection -- 4.3.3 The Scope and Decision to Go with 'Interaction' Variables. , 4.3.4 Data Analysis and Results Discussion -- 4.3.5 The Turning Point -- 4.3.6 Models Performance-Outside Sample -- 4.4 Conclusion and Limitations -- 4.5 Future Research Directions -- 4.6 Annexure-Model Work/Details -- References -- 5 Building of an Application Reviews Classifier by BERT and Its Evaluation -- 5.1 Background -- 5.2 The Process of Building a Machine Learning Model -- 5.3 Dataset -- 5.4 Preprocessing -- 5.5 Feature Engineering -- 5.5.1 Bag of Words (BoW) [4] -- 5.5.2 FastText [5, 6] -- 5.5.3 Bidirectional Encoder Representations from Transformers (BERT) Embedding [7] -- 5.6 Machine-Learning Algorithms -- 5.6.1 Naive Bayes -- 5.6.2 Logistic Regression -- 5.6.3 BERT -- 5.7 Training and Evaluation Methods -- 5.8 Results -- 5.9 Discussion -- 5.9.1 Comparison of Classifier Performances -- 5.9.2 Performance of the Naive Bayes Classifiers -- 5.9.3 Performance of the Logistic Regression Classifiers -- 5.9.4 Visualization of Classifier Attention Using the BERT -- 5.10 Threats to Validity -- 5.10.1 Labeling Dataset -- 5.10.2 Parameter Tuning -- 5.11 Summary -- References -- 6 Harmony Search-Enhanced Software Architecture Reconstruction -- 6.1 Introduction -- 6.2 Related Work -- 6.3 HS Enhanced SAR -- 6.3.1 SAR Problem -- 6.3.2 HS Algorithm -- 6.3.3 Proposed Approach -- 6.4 Experimentation -- 6.4.1 Test Problems -- 6.4.2 Competitor approaches -- 6.5 Results and Discussion -- 6.6 Conclusion and Future Work -- References -- 7 Enterprise Architecture-Based Project Model for AI Service System Development -- 7.1 Introduction -- 7.2 Related Work -- 7.3 AI Servie System and Enterprise Architecture -- 7.3.1 AI Service System -- 7.3.2 Enterprise Architecture and AI Service System -- 7.4 Modeling Business IT Alignment for AI Service System -- 7.4.1 Generic Business-AI Alignment Model -- 7.4.2 Comparison with Project Canvas Model. , 7.5 Business Analysis Method for Constructing Domain Specific Business-AI Alignment Model -- 7.5.1 Business Analysis Tables -- 7.5.2 Model Construction Method -- 7.6 Practice -- 7.6.1 Subject Project -- 7.6.2 Result -- 7.7 Discussion -- 7.8 Conclusion -- References -- Part III Software Engineering Tools to Develop Artificial Intelligence Applications -- 8 Requirements Engineering Processes for Multi-agent Systems -- 8.1 Introduction -- 8.2 Background -- 8.2.1 Agents, Multiagent Systems, and the BDI Model -- 8.2.2 Requirements Engineering -- 8.3 Techniques and Process of Requirements Engineering for Multiagent Systems -- 8.3.1 Elicitation Requirements Techniques for Multiagent Systems -- 8.3.2 Requirements Engineering Processes for Multiagent Systems -- 8.3.3 Requirements Validation -- 8.4 Conclusion -- References -- 9 Specific UML-Derived Languages for Modeling Multi-agent Systems -- 9.1 Introduction -- 9.2 Backgroud -- 9.2.1 UML -- 9.2.2 Agents, Multiagent Systems, and the BDI Model -- 9.2.3 BDI Models -- 9.3 AUML-Agent UML -- 9.4 AORML-Agent-Object-Relationship Modeling Language -- 9.4.1 Considerations About AORML -- 9.5 AML-Agent Modeling Language -- 9.5.1 Considerations About AML -- 9.6 MAS-ML-Multiagent System Modeling Language -- 9.6.1 Considerations About MAS-ML -- 9.7 SEA-ML-Semantic Web Enabled Agent Modeling Language -- 9.7.1 Considerations -- 9.8 MASRML-A Domain-Specific Modeling Language for Multi-agent Systems Requirements -- 9.8.1 Considerations -- References -- 10 Methods for Ensuring the Overall Safety of Machine Learning Systems -- 10.1 Introduction -- 10.2 Related Work -- 10.2.1 Safety of Machine Learning Systems -- 10.2.2 Conventional Safety Model -- 10.2.3 STAMP and Its Related Methods -- 10.2.4 Standards for Software Lifecycle Processes and System Lifecycle Processes -- 10.2.5 Social Technology Systems and Software Engineering. , 10.2.6 Software Layer Architecture -- 10.2.7 Assurance Case -- 10.2.8 Autonomous Driving -- 10.3 Safety Issues in Machine Learning Systems -- 10.3.1 Eleven Reasons Why We Cannot Release Autonomous Driving Cars -- 10.3.2 Elicitation Method -- 10.3.3 Eleven Problems on Safety Assessment for Autonomous Driving Car Products -- 10.3.4 Validity to Threats -- 10.3.5 Safety Issues of Automatic Operation -- 10.3.6 Task Classification -- 10.3.7 Unclear Assurance Scope -- 10.3.8 Safety Assurance of the Entire System -- 10.3.9 Machine Learning and Systems -- 10.4 STAMP S& -- S Method -- 10.4.1 Significance of Layered Modeling of Complex Systems -- 10.4.2 STAMP S& -- S and Five Layers -- 10.4.3 Scenario -- 10.4.4 Specification and Standard -- 10.5 CC-Case -- 10.5.1 Definition of CC-Case -- 10.5.2 Technical Elements of CC-Case -- 10.6 Measures for Autonomous Driving -- 10.6.1 Relationship Between Issues and Measures Shown in This Section -- 10.6.2 Measure 1: Analyze Various Quality Attributes in Control Action Units -- 10.6.3 Measure 2: Modeling the Entire System -- 10.6.4 Measure 3: Scenario Analysis and Specification -- 10.6.5 Measure 4: Socio-Technical System -- 10.7 Considerations in Level 3 Autonomous Driving -- 10.7.1 Example of Autonomous Driving with the 5-layered Model of STAMP S& -- S -- 10.8 Conclusion -- References -- 11 MEAU: A Method for the Evaluation of the Artificial Unintelligence -- 11.1 Introduction -- 11.2 Machine Learning and Online Unintelligence: Improvisation or Programming? -- 11.3 The New Paradigm of Information from Digital Media and Social Networks -- 11.4 Numbers, Images and Texts: Sources of Errors, Misinformation and Unintelligence -- 11.5 MEAU: A Method for the Evaluation of the Artificial Unintelligence -- 11.6 Results -- 11.7 Lessons Learned -- 11.8 Conclusions -- Appendix 1 -- Appendix 2 -- Appendix 3 -- Appendix 4. , References -- 12 Quantum Computing Meets Artificial Intelligence: Innovations and Challenges -- 12.1 Introduction -- 12.1.1 Benefits of Quantum Computing for AI -- 12.2 Quantum Computing Motivations -- 12.2.1 What Does ``Quantum'' Mean? -- 12.2.2 The Wave-Particle Duality -- 12.2.3 Qubit Definition -- 12.2.4 The Schrödinger Equation -- 12.2.5 Superposition -- 12.2.6 Interference -- 12.2.7 Entanglement -- 12.2.8 Gate-Based Quantum Computing -- 12.3 Quantum Machine Learning -- 12.3.1 Variational Quantum Algorithms -- 12.3.2 Data Encoding -- 12.3.3 Quantum Neural Networks -- 12.3.4 Quantum Support Vector Machine -- 12.3.5 Variational Quantum Generator -- 12.4 Quantum Computing Limitations and Challenges -- 12.4.1 Scalability and Connectivity -- 12.4.2 Decoherence -- 12.4.3 Error Correction -- 12.4.4 Qubit Control -- 12.5 Quantum AI Software Engineering -- 12.5.1 Hybrid Quantum-Classical Frameworks -- 12.5.2 Friction-Less Development Environment -- 12.5.3 Quantum AI Software Life Cycle -- 12.6 A new Problem Solving Approach -- 12.6.1 Use Case 1: Automation and Transportation Sector -- 12.6.2 Use Case 2: Food for the Future World -- 12.6.3 Use Case 3: Cheaper Reliable Batteries -- 12.6.4 Use Case 4: Cleaner Air to Breathe -- 12.6.5 Use Case 5: AI-Driven Financial Solutions -- 12.7 Summary and Conclusion -- References.

Note: Intro -- 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 -- Foreword -- References -- Preface -- Contents -- 1 Introduction to Fusion of Machine Learning Paradigms -- 1.1 Editorial -- References -- Part I Recent Application Areas of Fusion of Machine Learning Paradigms -- 2 Artificial Intelligence as Dual-Use Technology -- 2.1 Introduction -- 2.2 What Is DUT -- 2.3 AI: Concepts, Models and Technology -- 2.4 Agent-Based AI and Autonomous System -- 2.4.1 Basic Model of Agent-Based AI -- 2.4.2 Conceptual Model of Autonomous Weapon System -- 2.5 Dual-Use Technology and DARPA -- 2.5.1 Historical View and Role of DARPA -- 2.5.2 DARPA's Contribution to DUT R& -- D on AI -- 2.6 DARPA-Like Organizations in Major Countries -- 2.7 Dual-Use Dilemma -- 2.8 Concluding Remarks -- References -- 3 Diabetic Retinopathy Detection Using Transfer and Reinforcement Learning with Effective Image Preprocessing and Data Augmentation Techniques -- 3.1 Introduction -- 3.2 Background -- 3.2.1 Deep Learning for Diabetic Retinopathy -- 3.2.2 Image Preprocessing Techniques -- 3.2.3 Reinforcement Learning and Deep Learning -- 3.3 Data Augmentation Techniques -- 3.3.1 Traditional Data Augmentation -- 3.3.2 SMOTE-Based Data Augmentation -- 3.3.3 Data Augmentation Using Generative Adversarial Networks -- 3.4 Datasets of Eye Fundus Images -- 3.5 Transfer Learning Experiments -- 3.5.1 Dataset -- 3.5.2 Image Preprocessing -- 3.5.3 Image Augmentation -- 3.5.4 Deep Learning Experiments -- 3.5.5 Reinforcement Learning Experiments -- 3.6 Conclusion and Future Work -- References -- 4 A Novel Approach for Non-linear Deep Fuzzy Rule-Based Model and Its Applications in Biomedical Analyses -- 4.1 Introduction -- 4.2 Method -- 4.2.1 Preliminaries -- 4.2.2 Hierarchical Fuzzy Structure -- 4.2.3 Stacked Deep Fuzzy Rule-Based System (SD-FRBS) -- 4.2.4 Adaptation of the First-Order TSK Structure in SD-FRBS. , 4.2.5 Concatenated Deep Fuzzy Rule-Based System (CD-FRBS) -- 4.3 Data Description and Results -- 4.3.1 MIMIC-III Dataset -- 4.3.2 SD-FRBS as a Multivariate Regressor for Granger Causality Estimation-In EEG Connectivity Index Extraction -- 4.3.3 CD-FRBS in Staging Depression Severity -- 4.4 Discussion and Conclusion -- 4.4.1 Suggested Future Works -- References -- 5 Harmony Search-Based Approaches for Fine-Tuning Deep Belief Networks -- 5.1 Introduction -- 5.2 Theoretical Background -- 5.2.1 Deep Belief Networks -- 5.2.2 Harmony Search -- 5.3 Methodology -- 5.3.1 Datasets -- 5.3.2 Experimental Setup -- 5.4 Experimental Results -- 5.5 Conclusions -- References -- 6 Toward Smart Energy Systems: The Case of Relevance Vector Regression Models in Hourly Solar Power Forecasting -- 6.1 Introduction -- 6.2 Relevance Vector Regression -- 6.3 RVR Based Day Ahead Forecasting -- 6.4 Results -- 6.5 Conclusion -- References -- 7 Domain-Integrated Machine Learning for IC Image Analysis -- 7.1 Introduction -- 7.2 Hierarchical Multi-classifier System -- 7.2.1 Architecture of Hierarchical Multi-classifier System -- 7.2.2 Result and Discussion on Case Study -- 7.3 Deep Learning with Pseudo Labels -- 7.3.1 Methodology -- 7.3.2 Application to IC Image Analysis -- 7.4 Conclusions and Future Works -- References -- Part II Applications that Can Clearly Benefit from Fusion of Machine Learning Paradigms -- 8 Fleshing Out Learning Analytics and Educational Data Mining with Data and ML Pipelines -- 8.1 Introduction -- 8.2 Data and ML Pipelines -- 8.3 Related Work -- 8.4 An Automated EDM and LA Methodology -- 8.4.1 A Data Pipeline Scenario -- 8.4.2 An ML Pipeline Scenario -- 8.5 Experiments and Results -- 8.6 Conclusions and Future Work -- References -- 9 Neural Networks Based Throughput Estimation of Short Production Lines Without Intermediate Buffers -- 9.1 Introduction. , 9.2 Data Sets of i-Stage Production Line Problems -- 9.3 Deep Learning and Multilayer Perceptron -- 9.4 Experimental Process of Deep Learning Approach -- 9.5 Results of Deep Learning Approach -- 9.6 Conclusions -- References.

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

Note: Intro -- Foreword -- Preface -- Contents -- 1 Introduction to Handbook on Artificial Intelligence-Empowered Applied Software Engineering-Vol. 2: Smart Software Applications in Cyber-Physical Systems -- 1.1 Editorial Note -- 1.2 Book Summary and Future Volumes -- Bibliography for Further Reading -- Part I Smart Software Applications in Scientific Document Processing -- 2 Detection, Extraction and SPN Representation of Pseudo-Algorithms in Scientific Documents -- 2.1 Introduction -- 2.2 Visual Detection of Pseudo-Codes in Documents -- 2.2.1 Extraction of Different Text Blocks in Documents -- 2.2.2 Pyramidal Image Representation -- 2.2.3 Decomposition and Classification of the Pseudo Code Sections -- 2.3 Learning -- 2.3.1 The Dataset -- 2.3.2 Evaluation of Learning Process -- 2.4 Translation of Algorithms to Graphs and SPNs -- 2.4.1 Generation of a Graph -- 2.4.2 Stochastic Petri Net Representation -- 2.5 Conclusion and Future Work -- References -- 3 A Recommender Engine for Scientific Paper Peer-Reviewing System -- 3.1 Introduction -- 3.2 Related Works -- 3.3 Dataset and Feature -- 3.4 Methodology -- 3.4.1 Create Training Dataset -- 3.4.2 The Architecture of Recommender Engine -- 3.4.3 Final Recommendation Section -- 3.5 Result and Analysis -- 3.6 Conclusion(s) -- References -- Part II Smart Software Applications in Enterprise Modeling -- 4 Visualization of Digital-Enhanced Enterprise Modeling -- 4.1 Introduction -- 4.2 A Meta Model for Describing Value of Digital Service -- 4.3 Visualization Patterns -- 4.3.1 Service Definition Pattern -- 4.3.2 Value Proposition Pattern -- 4.3.3 Use Process Refinement pattern -- 4.4 Application Example -- 4.4.1 Healthcare Example -- 4.4.2 Application of Visualization Patterns -- 4.5 Discussions -- 4.5.1 Effectiveness -- 4.5.2 Novelty -- 4.5.3 Mapping to BMC -- 4.5.4 Limitation -- 4.6 Related Work -- 4.7 Conclusion. , References -- 5 Know-linking: When Machine Learning Meets Organizational Tools Analysis to Generate Shared Knowledge in Large Companies -- 5.1 Introduction -- 5.2 State of the Art -- 5.2.1 Profiling -- 5.2.2 Organizational Tools Analysis -- 5.2.3 Indexing -- 5.3 Related Works -- 5.4 Know-linking Approach -- 5.4.1 Presentation -- 5.5 Environment Study -- 5.6 Know-linking in Aerospace Manufacturer -- 5.6.1 Technical Audit -- 5.6.2 Extracting Profiles -- 5.6.3 Generating Semantic Models for Each Profile -- 5.6.4 Hidden Semantic Links Between Profiles -- 5.6.5 Indexing Based Profiles -- 5.7 Conclusion and Future Work -- References -- 6 Changes in Human Resources Management with Artificial Intelligence -- 6.1 Introduction -- 6.2 The Effect of AI in Human Resources Management -- 6.2.1 Recruitment Process with AI -- 6.2.2 Training Process with AI -- 6.2.3 Performance Assessment Process with AI -- 6.2.4 Talent Management Process with AI -- 6.2.5 Salary Management Process with AI -- 6.3 Conclusion -- References -- Part III Smart Software Applications in Education -- 7 Promoting Reading Among Teens: Analyzing the Emotional Preferences of Teenage Readers -- 7.1 Introduction -- 7.2 Related Works -- 7.3 Our Emotion Trait Analysis Approach -- 7.3.1 Processing a Book Description -- 7.3.2 Calculating an Emotion Vector -- 7.3.3 Emotion Trait -- 7.3.4 Partitioning Books by Average Ratings -- 7.3.5 Reducing Objective Values by Comparing Synonyms -- 7.3.6 Implementation -- 7.4 Conclusions and Future Works -- References -- 8 A Multi-institutional Analysis of CS1 Students' Common Misconceptions of Key Programming Concepts -- 8.1 Introduction -- 8.2 Literature Review -- 8.3 Study Design -- 8.3.1 Research Objective -- 8.3.2 Research Questions -- 8.3.3 Data Collection -- 8.3.4 Reliability of Pre-post-test Instrument -- 8.3.5 Study Procedure -- 8.4 Experimental Results. , 8.5 Discussion of Results -- 8.6 Conclusion -- References -- Part IV Smart Software Applications in Healthcare and Medicine -- 9 Clustering-Based Scaling for Healthcare Data -- 9.1 Introduction -- 9.2 Fuzzy Clustering -- 9.3 Fuzzy Clustering for 3-Way Data -- 9.4 Fuzzy Cluster-Scaled Regression Analysis -- 9.5 Numerical Examples -- 9.6 Conclusions -- References -- 10 Normative and Fuzzy Components of Medical AI Applications -- 10.1 Preliminaries -- 10.2 Normative Issues -- 10.3 Fuzziness and Norms -- 10.4 Conclusions -- References -- Part V Smart Software Applications in Infrastructure Monitoring -- 11 Adaptive Structural Learning of Deep Belief Network and Its Application to Real Time Crack Detection of Concrete Structure Using Drone -- 11.1 Introduction -- 11.2 Adaptive Learning Method of Deep Belief Network -- 11.2.1 Restricted Boltzmann Machine and Deep Belief Network -- 11.2.2 Neuron Generation and Annihilation Algorithm of RBM -- 11.2.3 Layer Generation Algorithm of DBN -- 11.3 SDNET 2018 -- 11.3.1 Data Description -- 11.3.2 The Classification Results -- 11.4 Crack Detection for Japanese Concrete Structure -- 11.4.1 Data Collection -- 11.4.2 Detection Results -- 11.5 Real-Time Detection and Visualization System Using Drone -- 11.5.1 Embedded System -- 11.5.2 Demonstration Experiment -- 11.6 Conclusion -- References.

Note: Intro -- Foreword -- Preface -- Contents -- 1 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 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.