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 -- Preface -- Contents -- 1 Machine Learning Paradigms -- References -- Learning Analytics with the Purpose to Measure Student Engagement, to Quantify the Learning Experience and to Facilitate Self-Regulation -- 2 Using a Multi Module Model for Learning Analytics to Predict Learners' Cognitive States and Provide Tailored Learning Pathways and Assessment -- 2.1 Introduction -- 2.2 Related Work -- 2.3 Multi Module Model and Logical Architecture of the System -- 2.4 Learners Clustering, Using the K-Means Algorithm, Supporting System's Modules -- 2.5 Evaluation and Discussion of Experimental Results -- 2.6 Ethics and Privacy for Learning Analytics -- 2.7 Conclusions and Future Work -- References -- 3 Analytics for Student Engagement -- 3.1 Effects of Student Engagement -- 3.2 Conceptualizing Student Engagement -- 3.3 Measuring Student Engagement -- 3.4 Analytics for Student Engagement -- 3.4.1 Early Alert Analytics -- 3.4.2 Dashboard Visualization Analytics -- 3.5 Dashboard Visualizations of Student Engagement -- 3.6 Comparative Reference Frame -- 3.7 Challenges and Potential Solutions for Analytics of Student Engagement: -- 3.7.1 Challenge 1: Connecting Engagement Analytics to Recommendations for Improvement -- 3.7.2 Potential Solutions: Using Diverse Metrics of Engagement to Improve Feedback Provided -- 3.7.3 Challenge 2: Quantifying Meaningful Engagement -- 3.7.4 Potential Solutions: Analytics Reflecting Quantity and Quality of Student Engagement -- 3.7.5 Challenge 3: Purposeful Engagement Reflection -- 3.7.6 Potential Solutions: Options for Purposeful Engagement Reflection -- 3.7.7 Challenge 4: Finding an Appropriate Reference Norm -- 3.7.8 Potential Solutions: Alternative Reference Frames -- 3.8 Conclusion -- References -- 4 Assessing Self-regulation, a New Topic in Learning Analytics: Process of Information Objectification. , 4.1 Introduction -- 4.2 Math Learning Process -- 4.3 Analyzing Empirical Evidence -- 4.3.1 Observations on a Learning Episode -- 4.3.2 Setting the Task -- 4.3.3 Students and Knowing Math -- 4.4 Math Meaningfulness and Three Modes of Manipulating the Blue Graph -- 4.4.1 The Adaptation Process: Dragging Points and Using Sliders -- 4.4.2 Typing the Parameters Values -- 4.4.3 Perceiving the 'a' Parameter and Its Properties -- 4.4.4 Typing Values Without Immediate Feedback -- 4.5 Discussion -- 4.5.1 Metacognitive Enactivism -- 4.6 As a Conclusion -- 4.6.1 Objectification as a Condition for Academic Knowing -- References -- Learning Analytics to Predict Student Performance -- 5 Learning Feedback Based on Dispositional Learning Analytics -- 5.1 Introduction -- 5.2 Related Work -- 5.2.1 Educational Context -- 5.2.2 The Crucial Predictive Power of Cognitive Data -- 5.2.3 An Unexpected Source of Variation: National Cultural Values -- 5.2.4 LA, Formative Assessment, Assessment of Learning and Feedback Preferences -- 5.2.5 LA and Learning Emotions -- 5.3 The Current Study -- 5.3.1 Participants -- 5.3.2 E-tutorial Trace Data -- 5.3.3 Performance Data -- 5.3.4 Disposition Data -- 5.3.5 Analyses -- 5.4 Results -- 5.4.1 Performance -- 5.4.2 National Cultural Values -- 5.4.3 Cognitive Learning Processing Strategies -- 5.4.4 Metacognitive Learning Regulation Strategies -- 5.4.5 Attitudes and Beliefs Towards Learning Quantitative Methods -- 5.4.6 Epistemic Learning Emotions -- 5.4.7 Activity Learning Emotions -- 5.4.8 Adaptive Motivation and Engagement -- 5.4.9 Maladaptive Motivation and Engagement -- 5.5 Discussion and Conclusion -- References -- 6 The Variability of the Reasons for Student Dropout in Distance Learning and the Prediction of Dropout-Prone Students -- 6.1 Introduction -- 6.2 Literature Review -- 6.3 HOU Distance Learning Methodology and Data Description. , 6.4 Interview Based Survey Results -- 6.5 Machine Learning Techniques, Experiments and Results -- 6.5.1 Machine Learning Techniques, Experiments and Results -- 6.5.2 The Experiments -- 6.5.3 Results -- 6.5.4 Student Behavior Tool -- 6.6 Discussion -- 6.7 Conclusion -- Appendix -- References -- Learning Analytics Incorporated in Tools for Building Learning Materials and Educational Courses -- 7 An Architectural Perspective of Learning Analytics -- 7.1 Introduction -- 7.2 What is an Architectural Perspective? -- 7.3 Functional Viewpoints -- 7.3.1 Knowledge Discovery Functions -- 7.3.2 Analytical Functions -- 7.3.3 Predictive Functions -- 7.3.4 Generative Functions -- 7.4 Quality Attributes -- 7.5 Information Viewpoint -- 7.6 Architectural Patterns and Styles -- 7.6.1 Model-View-Control (MVC) -- 7.6.2 Publisher-Subscriber -- 7.6.3 Microservices -- 7.6.4 An Architecture for Learning Analytics -- 7.7 Discussion -- References -- 8 Multimodal Learning Analytics in a Laboratory Classroom -- 8.1 Introduction -- 8.2 Classroom Research -- 8.3 The Science of Learning Research Classroom -- 8.4 The Social Unit of Learning Project -- 8.5 Conceptualization(s) of Engagement -- 8.6 Multimodal Learning Analytics of Engagement in Classrooms -- 8.7 Observation Data -- 8.8 Features Selection, Extraction and Evaluation -- 8.8.1 Multimodal Behavioral Features -- 8.8.2 Feature Visualization -- 8.8.3 Feature Extraction Conclusions -- 8.9 Illustration of High Level Construct Based on Features Extracted -- 8.9.1 Attention to Teacher Speech -- 8.9.2 Teacher Attention -- 8.9.3 Student Concentration During Individual Task -- 8.9.4 Engagement During Pair and Group Work -- 8.10 Implications -- 8.11 Conclusion -- References -- 9 Dashboards for Computer-Supported Collaborative Learning -- 9.1 The Emergence of Learning Analytics and Dashboards -- 9.2 Collaborative Learning Theories. , 9.2.1 Group Cognition (GC) -- 9.2.2 Shared Mental Models (SMMs) -- 9.2.3 Situational Awareness (SA) -- 9.2.4 Socially Shared Regulation of Learning (SSRL) -- 9.3 Tools for CSCL -- 9.3.1 Group Awareness Tools (GATs) -- 9.3.2 Shared Mirroring Systems -- 9.3.3 Ambient Displays -- 9.4 Learning Dashboards for CSCL -- 9.5 How Can Collaborative Learning Dashboards Be Improved? -- 9.5.1 Principle 1: Adopt Iterative, User-Centred Design -- 9.5.2 Principle 2: Navigate the Theoretical Space -- 9.5.3 Principle 3: Visualize to Support Decision-Making -- References -- Learning Analytics as Tools to Support Learners and Educators in Synchronous and Asynchronous e-Learning -- 10 Learning Analytics in Distance and Mobile Learning for Designing Personalised Software -- 10.1 Introduction -- 10.2 Distance Learning -- 10.3 Mobile Learning and Mobile Learning Analytics -- 10.4 Personalised Learning Software -- 10.5 Data Collection -- 10.5.1 Modalities of Interaction in PCs -- 10.5.2 Modalities of Interaction in Smartphones -- 10.6 Multi-criteria Analysis -- 10.6.1 Combining Modalities of Interaction in HCI -- 10.6.2 Combining Modalities of Interaction in Smartphones -- 10.7 Conclusions -- References -- 11 Optimizing Programming Language Learning Through Student Modeling in an Adaptive Web-Based Educational Environment -- 11.1 Introduction -- 11.2 Related Work -- 11.3 Description of the Student Model -- 11.3.1 Analyzing Data That Have Been Gathered by the Implementation of ELaC -- 11.3.2 The Improved Student Model of ELaCv2 -- 11.4 Description of the Operation of the Student Model -- 11.4.1 Examples of Operation -- 11.5 Evaluation-Results -- 11.6 Conclusion -- References.