Tabla de Contenidos: “…Integration with Tivoli Configuration Manager -- 10.1 Introduction to IBM Tivoli Configuration Manager -- 10.2 Inventory - determine who has got which version -- 10.2.1 Create an inventory profile in Tivoli Framework -- 10.3 Software distribution -- 10.3.1 Build software package with Software Package Editor -- 10.3.2 Create software distribution profile in Tivoli Framework -- Chapter 11. …”

Tabla de Contenidos: “…3.2 A new approach to the application development process -- 3.3 Components of the Eclipse GUI framework -- 3.3.1 Workbench -- 3.3.2 Projects -- 3.3.3 Perspectives -- 3.3.4 Views -- 3.3.5 Editors -- 3.3.6 Crash recovery -- 3.4 Development Studio Client features -- 3.4.1 Tools -- 3.4.2 Common features -- 3.4.3 iSeries-specific features -- 3.5 Online help -- Summary -- Key terms and concepts -- Other helpful resources -- Questions -- Exercises -- On-going study -- Connecting to a server from the Remote System Explorer -- Chapter 4. …”

Tabla de Contenidos: “…8.7 Transforming Airline Industry With Internet of Things -- 8.7.1 How the IoT Is Improving the Aviation Industry -- 8.7.2 Applications of AI in the Aviation Industry -- 8.8 Revolution of Change (Paradigm Shift) -- 8.9 The Following Diagram Shows the Design of the Application -- 8.10 Discussion, Limitations, Future Research, and Conclusion -- 8.10.1 Growth of Aviation IoT Industry -- 8.10.2 IoT Applications-Benefits -- 8.10.3 Operational Efficiency -- 8.10.4 Strategic Differentiation -- 8.10.5 New Revenue -- 8.11 Present and Future Scopes -- 8.11.1 Improving Passenger Experience -- 8.11.2 Safety -- 8.11.3 Management of Goods and Luggage -- 8.11.4 Saving -- 8.12 Conclusion -- References -- 9 IoT-Based Water Management System for a Healthy Life -- 9.1 Introduction -- 9.1.1 Human Activities as a Source of Pollutants -- 9.2 Water Management Using IoT -- 9.2.1 Water Quality Management Based on IoT Framework -- 9.3 IoT Characteristics and Measurement Parameters -- 9.4 Platforms and Configurations -- 9.5 Water Quality Measuring Sensors and Data Analysis -- 9.6 Wastewater and Storm Water Monitoring Using IoT -- 9.6.1 System Initialization -- 9.6.2 Capture and Storage of Information -- 9.6.3 Information Modeling -- 9.6.4 Visualization and Management of the Information -- 9.7 Sensing and Sampling of Water Treatment Using IoT -- References -- 10 Fuel Cost Optimization Using IoT in Air Travel -- 10.1 Introduction -- 10.1.1 Introduction to IoT -- 10.1.2 Processing IoT Data -- 10.1.3 Advantages of IoT -- 10.1.4 Disadvantages of IoT -- 10.1.5 IoT Standards -- 10.1.6 Lite Operating System (Lite OS) -- 10.1.7 Low Range Wide Area Network (LoRaWAN) -- 10.2 Emerging Frameworks in IoT -- 10.2.1 Amazon Web Service (AWS) -- 10.2.2 Azure -- 10.2.3 Brillo/Weave Statement -- 10.2.4 Calvin -- 10.3 Applications of IoT -- 10.3.1 Healthcare in IoT…”

“…GAM.16 conceives of spatial formation as a permeable framework for sociospatial structures in various architectural, sociological, and cultural contexts, aiming to increase awareness of common resources and again make them one focus of sociopolitical discourse. …”

Tabla de Contenidos: “…2.4.2.2 Variety -- 2.4.2.3 Velocity -- 2.4.2.4 Veracity -- 2.4.2.5 Value -- 2.4.3 Architectural Framework of Big Data -- 2.4.4 Data Protection Versus Data Security in Healthcare -- 2.4.4.1 Phishing Attacks -- 2.4.4.2 Malware and Ransomware -- 2.4.4.3 Cloud Threats -- 2.4.5 Technology in Use to Secure the Healthcare Data -- 2.4.5.1 Access Control Policy -- 2.4.6 Monitoring and Auditing -- 2.4.7 Standard for Data Protection -- 2.4.7.1 Healthcare Standard in India -- 2.4.7.2 Security Technical Standards -- 2.4.7.3 Administrative Safeguards Standards -- 2.4.7.4 Physical Safeguard Standards -- References -- 3 Ontology-Based System for Patient Monitoring -- 3.1 Introduction -- 3.1.1 Basics of Ontology -- 3.1.2 Need of Ontology in Patient Monitoring -- 3.2 Literature Review -- 3.2.1 Uses of Ontology in Various Domains -- 3.2.2 Ontology in Patient Monitoring System -- 3.3 Architectural Design -- 3.3.1 Phases of Patient Monitoring System -- 3.3.2 Reasoner in Patient Monitoring -- 3.4 Experimental Results -- 3.4.1 SPARQL Results -- 3.4.2 Comparison Between Other Systems -- 3.5 Conclusion and Future Enhancements -- References -- 4 Semantic Web Solutions for Improvised Search in Healthcare Systems -- 4.1 Introduction -- 4.1.1 Key Benefits and Usage of Technology in Healthcare System -- 4.2 Background -- 4.2.1 Significance of Semantics in Healthcare Systems -- 4.2.2 Scope and Benefits of Semantics in Healthcare Systems -- 4.2.3 Issues in Incorporating Semantics -- 4.2.4 Existing Semantic Web Technologies -- 4.3 Searching Techniques in Healthcare Systems -- 4.3.1 Keyword-Based Search -- 4.3.2 Controlled Vocabularies Based Search -- 4.3.3 Improvising Searches With Semantic Web Solutions -- 4.3.4 Health Domain-Specific Resources for Semantic Search -- 4.3.4.1 Ontologies -- 4.3.4.2 Libraries -- 4.3.4.3 Search Engines…”

Tabla de Contenidos: “…Cover -- Title Page -- Copyright Page -- Dedication Page -- Contents -- Preface -- Acknowledgement -- Chapter 1 Band Reduction of HSI Segmentation Using FCM -- 1.1 Introduction -- 1.2 Existing Method -- 1.2.1 K-Means Clustering Method -- 1.2.2 Fuzzy C-Means -- 1.2.3 Davies Bouldin Index -- 1.2.4 Data Set Description of HSI -- 1.3 Proposed Method -- 1.3.1 Hyperspectral Image Segmentation Using Enhanced Estimation of Centroid -- 1.3.2 Band Reduction Using K-Means Algorithm -- 1.3.3 Band Reduction Using Fuzzy C-Means -- 1.4 Experimental Results -- 1.4.1 DB Index Graph -- 1.4.2 K-Means-Based PSC (EEOC) -- 1.4.3 Fuzzy C-Means-Based PSC (EEOC) -- 1.5 Analysis of Results -- 1.6 Conclusions -- References -- Chapter 2 A Fuzzy Approach to Face Mask Detection -- 2.1 Introduction -- 2.2 Existing Work -- 2.3 The Proposed Framework -- 2.4 Set-Up and Libraries Used -- 2.5 Implementation -- 2.6 Results and Analysis -- 2.7 Conclusion and Future Work -- References -- Chapter 3 Application of Fuzzy Logic to the Healthcare Industry -- 3.1 Introduction -- 3.2 Background -- 3.3 Fuzzy Logic -- 3.4 Fuzzy Logic in Healthcare -- 3.5 Conclusions -- References -- Chapter 4 A Bibliometric Approach and Systematic Exploration of Global Research Activity on Fuzzy Logic in Scopus Database -- 4.1 Introduction -- 4.2 Data Extraction and Interpretation -- 4.3 Results and Discussion -- 4.3.1 Per Year Publication and Citation Count -- 4.3.2 Prominent Affiliations Contributing Toward Fuzzy Logic -- 4.3.3 Top Journals Emerging in Fuzzy Logic in Major Subject Areas -- 4.3.4 Major Contributing Countries Toward Fuzzy Research Articles -- 4.3.5 Prominent Authors Contribution Toward the Fuzzy Logic Analysis -- 4.3.6 Coauthorship of Authors -- 4.3.7 Cocitation Analysis of Cited Authors -- 4.3.8 Cooccurrence of Author Keywords…”

Tabla de Contenidos: “…1.4 Benefits of Using Linux -- 1.4.1 Free of Cost -- 1.4.2 Time to Market -- 1.4.3 No "Vendor Lock-in" -- 1.4.4 Highly Stable Operating System -- 1.4.5 Low Maintenance -- 1.4.5.1 Supporting Software Releases After Shipping the Product -- 1.4.5.2 Keeping Your Own Drivers up to Date With the Latest Kernel -- 1.5 Linux Architecture -- 1.5.1 Linux Kernel Components -- 1.5.1.1 Device Driver Framework -- 1.5.1.2 Schedulers -- 1.5.1.3 Interrupt -- 1.5.1.4 Memory Management -- 1.5.1.5 Communication Protocol Stack -- 1.5.1.6 User Interface (UI) -- 1.6 Build Environment -- 1.6.1 Kernel Compilation -- 1.6.2 Root Filesystem -- 1.6.2.1 Yocto -- 1.7 Customizing Linux -- 1.7.1 Low Memory Footprint -- 1.7.2 Boot Performance -- 1.7.3 High Throughput Performance -- 1.7.3.1 Core Affinity -- 1.7.3.2 Interrupt Coalescing -- 1.7.3.3 User Space Mapping of Buffers -- 1.7.3.4 User Space Drivers -- 1.7.4 Latencies -- 1.8 Linux Development and its Open-Source Ecosystem -- 1.8.1 Linux Versions -- 1.8.2 Long-Term Support (LTS) Linux Version -- 1.8.3 Related Open-Source Communities -- 1.8.3.1 Linux Foundation -- 1.8.3.2 Linaro -- 1.8.4 Linux-Based Distributions -- 1.8.4.1 Android -- 1.8.4.2 Ubuntu -- 1.9 Coding Guidelines -- 1.10 Code Review in the Upstream Community -- 1.11 License -- 2 U-Boot -- 2.1 U-Boot and its Applicability to Several Types of Devices -- 2.2 Major Features of U-Boot -- 2.2.1 Multiple Boot Source Support -- 2.2.2 Shell (User Interface) -- 2.2.3 Environment Variables -- 2.2.4 Scripts -- 2.2.5 Stand-Alone Applications -- 2.2.6 Operating System Boot Commands -- 2.2.7 Autoboot -- 2.2.8 Sandbox U-Boot -- 2.3 U-Boot Directory Organization -- 2.4 U-Boot Architecture and Memory Footprint -- 2.4.1 Single-Stage Boot Loader -- 2.4.2 Two-Stage Boot Loader -- 2.5 Fast Boot Approach -- 2.6 Secure Boot…”

Tabla de Contenidos: “…(c) Taking account of diagnostic coverage -- (d) Subdividing the checklists according to the effect of diagnostics -- (e) Establishing a model -- (f) Nonlinearity -- (g) Equipment type -- (h) Calibration -- 5.2.3 Fault Tree Analysis -- 5.3 Taking Account of Auto Test -- 5.4 Human Factors -- 5.4.1 Addressing Human Factors -- 5.4.2 Human Error Rates -- "HEART" method -- "TESEO" method -- 5.4.3 A Rigorous Approach -- 6 - Failure Rate and Mode Data -- 6.1 Data Accuracy -- 6.2 Sources of Data -- 6.2.1 Electronic Failure Rates -- 6.2.2 Other General Data Collections -- 6.2.3 Some Older Sources -- 6.2.4 Manufacturer's Data -- 6.2.5 Anecdotal Data -- 6.3 Data Ranges and Confidence Levels -- 6.4 Conclusions -- 7 - Demonstrating and Certifying Conformance -- 7.1 Demonstrating Conformance -- 7.2 The Current Framework for Certification -- 7.3 Self-Certification (Including Some Independent Assessment) -- 7.3.1 Showing Functional Safety Capability (FSM) as Part of the Quality Management System -- 7.3.2 Application of IEC 61508 to Projects/Products -- 7.3.3 Rigor of Assessment -- 7.3.4 Independence -- 7.4 Preparing for Assessment -- 7.5 Summary -- B - Specific Industry Sectors -- 8 - Second Tier Documents-Process, Oil and Gas Industries -- 8.1 IEC International Standard 61511: Functional Safety-Safety Instrumented Systems for the Process Industry Sector (Second Edition to be Published in 2016) -- 8.1.1 Organizing and Managing the Life Cycle -- 8.1.2 Requirements Involving the Specification -- 8.1.3 Requirements for Design and Development -- (a) Selection of components and subsystems -- (b) Architecture (i.e., safe failure fraction) -- (c) Predict the random hardware failures -- (d) Software (referred to as "program") -- (i) Requirements -- (ii) Software library modules -- (iii) Software design specification -- (iv) Code -- (v) Programming support tools…”

Tabla de Contenidos: “…4.1 Introduction -- 4.2 Conceptual Framework -- 4.3 Previous Research -- 4.4 Data and Methods -- 4.5 Results -- 4.6 Conclusions -- Acknowledgements -- References -- Chapter 5 The Effects of Biological Data Collection in Longitudinal Surveys on Subsequent Wave Cooperation -- 5.1 Introduction -- 5.2 Literature Review -- 5.3 Biological Data Collection and Subsequent Cooperation: Research Questions -- 5.4 Data -- 5.5 Modelling Steps -- 5.6 Results -- 5.7 Discussion and Conclusion -- 5.8 Implications for Survey Researchers -- References -- Chapter 6 Understanding Data Linkage Consent in Longitudinal Surveys -- 6.1 Introduction -- 6.2 Quantitative Research: Consistency of Consent and Effect of Mode of Data Collection -- 6.2.1 Data and Methods -- 6.2.2 Results -- 6.2.2.1 How Consistent Are Respondents about Giving Consent to Data Linkage between Topics? …”

Tabla de Contenidos: “…Cover -- Title Page -- Copyright -- Contents -- Biography -- List of Contributors -- Chapter 1 Artificial Intelligence and Cybersecurity: Tale of Healthcare Applications -- 1.1 Introduction -- 1.2 A Brief History of AI -- 1.3 AI in Healthcare -- 1.4 Morality and Ethical Association of AI in Healthcare -- 1.5 Cybersecurity, AI, and Healthcare -- 1.6 Future of AI and Healthcare -- 1.7 Conclusion -- References -- Chapter 2 Data Analytics for Smart Cities: Challenges and Promises -- 2.1 Introduction -- 2.2 Role of Machine Learning in Smart Cities -- 2.3 Smart Cities Data Analytics Framework -- 2.3.1 Data Capturing -- 2.3.2 Data Analysis -- 2.3.2.1 Big Data Algorithms and Challenges -- 2.3.2.2 Machine Learning Process and Challenges -- 2.3.2.3 Deep Learning Process and Challenges -- 2.3.2.4 Learning Process and Emerging New Type of Data Problems -- 2.3.3 Decision‐Making Problems in Smart Cities -- 2.3.3.1 Traffic Decision‐Making System -- 2.3.3.2 Safe and Smart Environment -- 2.4 Conclusion -- References -- Chapter 3 Embodied AI‐Driven Operation of Smart Cities: A Concise Review -- 3.1 Introduction -- 3.2 Rise of the Embodied AI -- 3.3 Breakdown of Embodied AI -- 3.3.1 Language Grounding -- 3.3.2 Language Plus Vision -- 3.3.3 Embodied Visual Recognition -- 3.3.4 Embodied Question Answering -- 3.3.5 Interactive Question Answering -- 3.3.6 Multi‐agent Systems -- 3.4 Simulators -- 3.4.1 MINOS -- 3.4.2 Habitat -- 3.5 Future of Embodied AI -- 3.5.1 Higher Intelligence -- 3.5.2 Evolution -- 3.6 Conclusion -- References -- Chapter 4 Analysis of Different Regression Techniques for Battery Capacity Prediction -- 4.1 Introduction -- 4.2 Data Preparation -- 4.2.1 Dataset -- 4.2.2 Feature Extraction -- 4.2.3 Noise Addition -- 4.3 Experiment Design and Machine Learning Algorithms -- 4.4 Result and Analysis -- 4.5 Threats to Validity -- 4.6 Conclusions…”

“…Probability, Random Variables, and Random Processes is a comprehensive textbook on probability theory for engineers that provides a more rigorous mathematical framework than is usually encountered in undergraduate courses. …”

Tabla de Contenidos: “…4.4.1 Mathematical Model -- 4.4.2 Advantages of the Proposed Model -- 4.5 Discussion and Conclusion -- References -- Chapter 5 Integrating IoT and Deep Learning-The Driving Force of Industry 4.0 -- 5.1 Motivation and Background -- 5.2 Bringing Intelligence Into IoT Devices -- 5.3 The Foundation of CR-IoT Network -- 5.3.1 Various AI Technique in CR-IoT Network -- 5.3.2 Artificial Neural Network (ANN) -- 5.3.3 Metaheuristic Technique -- 5.3.4 Rule-Based System -- 5.3.5 Ontology-Based System -- 5.3.6 Probabilistic Models -- 5.4 The Principles of Deep Learning and Its Implementation in CR-IoT Network -- 5.5 Realization of CR-IoT Network in Daily Life Examples -- 5.6 AI-Enabled Agriculture and Smart Irrigation System-Case Study -- 5.7 Conclusion -- References -- Chapter 6 A Systematic Review on Blockchain Security Technology and Big Data Employed in Cloud Environment -- 6.1 Introduction -- 6.2 Overview of Blockchain -- 6.3 Components of Blockchain -- 6.3.1 Data Block -- 6.3.2 Smart Contracts -- 6.3.3 Consensus Algorithms -- 6.4 Safety Issues in Blockchain Technology -- 6.5 Usage of Big Data Framework in Dynamic Supply Chain System -- 6.6 Machine Learning and Big Data -- 6.6.1 Overview of Shallow Models -- 6.6.1.1 Support Vector Machine (SVM) -- 6.6.1.2 Artificial Neural Network (ANN) -- 6.6.1.3 K-Nearest Neighbor (KNN) -- 6.6.1.4 Clustering -- 6.6.1.5 Decision Tree -- 6.7 Advantages of Using Big Data for Supply Chain and Blockchain Systems -- 6.7.1 Replenishment Planning -- 6.7.2 Optimizing Orders -- 6.7.3 Arranging and Organizing -- 6.7.4 Enhanced Demand Structuring -- 6.7.5 Real-Time Management of the Supply Chain -- 6.7.6 Enhanced Reaction -- 6.7.7 Planning and Growth of Inventories -- 6.8 IoT-Enabled Blockchains -- 6.8.1 Securing IoT Applications by Utilizing Blockchain -- 6.8.2 Blockchain Based on Permission -- 6.8.3 Blockchain Improvements in IoT…”

Tabla de Contenidos: “…Introduction -- 1.2. Decision-making framework/models for operations research -- 1.3. Operations research in IoT, IIoT, edge and smart edge computing, sensor data -- 1.4. …”

Tabla de Contenidos: “…Front cover -- Contents -- Notices -- Trademarks -- Preface -- The team that wrote this book -- Become a published author -- Comments welcome -- Part 1 Implementing a BI solution framework -- Chapter 1. The state of business integration technology -- 1.1 Overview -- 1.2 The evolution of business integration technology -- 1.3 Integration capabilities -- Chapter 2. …”

Tabla de Contenidos: “…Cover -- Contents -- Foreword -- Preface -- Acknowledgements -- About the Authors -- Chapter 1: Introduction to Human Resource Management -- Chapter Objectives -- 1.1 Evolution of HRM -- 1.2 Defining HRM -- 1.3 Changing Environment in the Marketplace -- 1.3.1 Shift Towards Services -- 1.3.2 The New Business Cult -- 1.3.3 Globalization -- 1.3.4 Technology Trends -- 1.3.5 Demographic Trends -- 1.4 The Harvard Framework -- 1.5 Whose Job is HRM? -- 1.5.1 Role of the Line Manager -- 1.5.2 Line and Staff Aspects of an HR Manager's Job -- 1.5.3 Collaboration Between Line Manager and HR Manager -- 1.6 Role of the HR Function -- 1.6.1 Employee Advocate -- 1.6.2 Human Capital Developer -- 1.6.3 Functional Expert -- 1.6.4 Strategic Partner -- 1.6.5 HR Leader -- 1.7 Competencies of the HR Professional -- 1.7.1 Strategic Contribution -- 1.7.2 Personal Credibility -- 1.7.3 HR Delivery -- 1.7.4 Business Knowledge -- 1.7.5 HR Technology -- 1.8 The HR Organization -- 1.8.1 HR Functional Organization -- 1.8.2 HR Shared Services -- 1.8.3 Embedded HR -- 1.9 Ethics in HRM -- Application Case -- In a Nutshell -- Drill Down -- Field Guide -- Review Questions -- Exercises -- References -- Web site -- Chapter 2: Strategic Human Resource Management -- Chapter Objectives -- 2.1 Strategy Defined -- 2.2 Strategic Management Process -- 2.2.1 Phase 1: Diagnosis -- 2.2.2 Phase 2: Formulation of Strategy -- 2.2.3 Phase 3: Implementation of the Strategy -- 2.3 Levels of Strategy -- 2.3.1 Corporate Strategy -- 2.3.2 Competitive Strategy -- 2.3.3 Functional Strategy -- 2.4 Defining Strategic Human Resource Management -- 2.5 Perspectives on SHRM -- 2.5.1 Universalistic Perspective or the Best Practice Approach -- 2.5.2 Contingency Perspective or the Best-fit Approach -- 2.5.3 Bundling Approach -- 2.6 HR Strategies -- 2.6.1 Overall Strategies -- 2.6.2 Specific HR Strategies…”

Tabla de Contenidos:

“…This work combines the potential of legal formalism with an analytical framework drawing on political theory. It analyses the argumentative strategies used by international lawyers, and developed them further, exploiting the interpretative methodology of international law as well as elaborate discrimination arguments. …”

Tabla de Contenidos: “…4.2 Current State of Science -- 4.2.1 Theoretical Framework -- 4.2.2 Smoke Measurements -- 4.2.3 Smoke Plume Modeling -- 4.2.4 Interactive Processes -- 4.2.5 Smoke Decision Support Systems -- 4.3 Gaps in Understanding Plume Dynamics -- 4.3.1 Measurements -- 4.3.2 Plume Rise -- 4.3.3 Dispersion and Transport Modeling -- 4.3.4 Nighttime Smoke -- 4.3.5 Physics-Based Fire Models -- 4.3.6 Smoke Management for Prescribed Fires -- 4.4 Vision for Improving Plume Dynamics Science -- 4.4.1 New Research on Observational and Computational Capabilities -- 4.4.2 New Approaches and Tools -- 4.4.3 New Projects -- 4.4.4 Recent Policies and Integration with Smoke Impacts Research -- 4.5 Emerging Issues and Challenges -- 4.5.1 Coupled Modeling Systems -- 4.5.2 Improving Modeling Tools with Field Campaign Data -- 4.5.3 Real-Time Smoke Transport Modeling and Prediction -- 4.5.4 Smoke from Duff Burning Under Drought Conditions -- 4.5.5 Smoke Plume Dynamics and Climate Change -- 4.5.6 Smoke Dynamics in the Earth System -- 4.6 Conclusions -- 4.7 Key Findings -- References -- 5 Emissions -- 5.1 Introduction -- 5.2 Current State of the Science -- 5.2.1 Fuel Properties, Combustion Processes, and Emissions -- 5.2.2 Smoke Composition and Emission Factors -- 5.2.3 Emission Calculations -- 5.3 Existing Data, Tools, Models, and Other Technology -- 5.3.1 Emission Factors -- 5.3.2 Emission Inventories -- 5.3.3 Emission Models for Land Management -- 5.4 Gaps in Data, Understanding, and Tools/Technology -- 5.4.1 Emission Factors for Wildfires -- 5.4.2 Connecting Laboratory Studies with Field Observations -- 5.4.3 Variability of EFs with Combustion Conditions -- 5.4.4 Validation of Emission Inventories -- 5.4.5 Forecasting Wildfire Emissions -- 5.4.6 Measuring and Modeling PM2.5 -- 5.4.7 Emissions of Hazardous Air Pollutants -- 5.4.8 Emissions from Structure Fires -- 5.5 Conclusions…”

Tabla de Contenidos: “…References -- 4 An accurate deep learning-based computer-aided diagnosis system for early diagnosis of prostate cancer -- 4.1 Introduction -- 4.2 Methods -- 4.2.1 Feature Extraction -- 4.2.2 CNN-based classification -- 4.3 Experimental results -- 4.4 Conclusion -- References -- 5 Adaptive graph convolutional neural network and its biomedical applications -- 5.1 Introduction -- 5.2 Related work -- 5.2.1 Evolution of graph convolutional neural networks -- 5.2.1.1 Spatial graph convolutional neural networks -- 5.2.1.2 Spectral graph convolutional neural networks -- 5.2.2 Neural network on molecular graph -- 5.2.3 Attention on graph -- 5.2.4 Neural network for survival analysis -- 5.3 Method -- 5.3.1 Spectral graph convolution-LL layer -- 5.3.1.1 Learning residual graph Laplacian -- 5.3.1.2 Re-parameterization on feature transform -- 5.3.2 Adaptive graph convolution network architecture -- 5.3.3 Graph attention network on adaptive graph -- 5.3.4 DeepGraphSurv framework -- 5.4 Experiment -- 5.4.1 Drug-property prediction -- 5.4.1.1 Baseline model -- 5.4.1.2 Dataset -- 5.4.1.3 Experimental result -- 5.4.2 DeepGraphSurv and survival prediction -- 5.4.2.1 Dataset -- 5.4.2.2 Baseline model -- 5.4.2.3 Experimental result -- 5.5 Conclusion -- References -- Further reading -- 6 Deep slice interpolation via marginal super-resolution, fusion, and refinement -- 6.1 Introduction -- 6.2 Related work -- 6.2.1 Traditional slice interpolation methods -- 6.2.2 Learning-based super-resolution methods -- 6.3 Problem formulation and baseline convolutional neural networks approaches -- 6.4 The proposed algorithm -- 6.4.1 Marginal super-resolution -- 6.4.2 Two-view fusion and refinement -- 6.4.3 Comparison with baseline convolutional neural networks approaches -- 6.5 Experiments -- 6.5.1 Implementation details -- 6.5.2 Dataset -- 6.5.3 Evaluation metrics…”