Tabla de Contenidos: “…Turaga and Mihaela van der Schaar / /10.1 Introduction: Smarter Planet 234 / /10.2 Illustrative Problem: Transportation 237 / /10.3 Stream Processing Characteristics 238 / /10.4 Distributed Stream Processing Systems 239 / /10.4.1 State of the Art 239 / /10.4.2 Stream Processing Systems 240 / /10.5 Distributed Online Learning Frameworks 244 / /10.5.1 State of the Art 244 / /10.5.2 Systematic Framework for Online Distributed Ensemble Learning 247 / /10.5.3 Online Learning of the Aggregation Weights 250 / /10.5.4 Collision Detection Application 254 / /10.6 What Lies Ahead 257 / /Acknowledgment 258 / /References 258 / /11 Securing the Internet of Things: Need for a New Paradigm and Fog Computing 261 /Tao Zhang, Yi Zheng, Raymond Zheng, and Helder Antunes / /11.1 Introduction 261 / /11.2 New IoT Security Challenges That Necessitate Fundamental Changes to the Existing Security /Paradigm 263 / /11.2.1 Many Things Will Have Long Life Spans but Constrained and Difficult-to-Upgrade Resources 264 / /11.2.2 Putting All IoT Devices Inside Firewalled Castles Will Become Infeasible or Impractical 264 / /11.2.3 Mission-Critical Systems Will Demand Minimal-Impact Incident Responses 265 / /11.2.4 The Need to Know the Security Status of a Vast Number of Devices 266 / /11.3 A New Security Paradigm for the Internet of Things 268 / /11.3.1 Help the Less Capable with Fog Computing 269 / /11.3.2 Scale Security Monitoring to Large Number of Devices with Crowd Attestation 272 / /11.3.3 Dynamic Risk / Benefit-Proportional Protection with Adaptive Immune Security 277 / /11.4 Summary 281 / /Acknowledgment 281 / /References 281 / /INDEX 285.…”

Tabla de Contenidos: “…3.3 Machine learning for classification -- 3.3.1 Logistic regression -- 3.3.2 Training logistic regression -- 3.3.3 Model interpretation -- 3.3.4 Using the model -- 3.4 Next steps -- 3.4.1 Exercises -- 3.4.2 Other projects -- Summary -- Answers to exercises -- 4 Evaluation metrics for classification -- 4.1 Evaluation metrics -- 4.1.1 Classification accuracy -- 4.1.2 Dummy baseline -- 4.2 Confusion table -- 4.2.1 Introduction to the confusion table -- 4.2.2 Calculating the confusion table with NumPy -- 4.2.3 Precision and recall -- 4.3 ROC curve and AUC score -- 4.3.1 True positive rate and false positive rate -- 4.3.2 Evaluating a model at multiple thresholds -- 4.3.3 Random baseline model -- 4.3.4 The ideal model -- 4.3.5 ROC Curve -- 4.3.6 Area under the ROC curve (AUC) -- 4.4 Parameter tuning -- 4.4.1 K-fold cross-validation -- 4.4.2 Finding best parameters -- 4.5 Next steps -- 4.5.1 Exercises -- 4.5.2 Other projects -- Summary -- Answers to exercises -- 5 Deploying machine learning models -- 5.1 Churn-prediction model -- 5.1.1 Using the model -- 5.1.2 Using Pickle to save and load the model -- 5.2 Model serving -- 5.2.1 Web services -- 5.2.2 Flask -- 5.2.3 Serving churn model with Flask -- 5.3 Managing dependencies -- 5.3.1 Pipenv -- 5.3.2 Docker -- 5.4 Deployment -- 5.4.1 AWS Elastic Beanstalk -- 5.5 Next steps -- 5.5.1 Exercises -- 5.5.2 Other projects -- Summary -- 6 Decision trees and ensemble learning -- 6.1 Credit risk scoring project -- 6.1.1 Credit scoring dataset -- 6.1.2 Data cleaning -- 6.1.3 Dataset preparation -- 6.2 Decision trees -- 6.2.1 Decision tree classifier -- 6.2.2Decision tree learning algorithm -- 6.2.3 Parameter tuning for decision tree -- 6.3 Random forest -- 6.3.1 Training a random forest -- 6.3.2 Parameter tuning for random forest -- 6.4 Gradient boosting -- 6.4.1 XGBoost: Extreme gradient boosting…”

Tabla de Contenidos: “…-- 7.5.10 Regulation and Standardisation -- 7.6 Multi-Angle Stakeholders IoT Ecosystem -- 7.6.1 Technology and People -- 7.6.2 Ethics and Accountability -- 7.6.3 Regulation and Standardisation -- 7.6.4 Contractual Relationships -- 7.6.5 Risk Allocation -- 7.7 Conclusion and Recommendations -- Chapter 8 - IoT Standards - State-of-the-Art Analysis -- 8.1 Introduction -- 8.2 Analysing the IoT Standards Landscape -- 8.2.1 AIOTI WG03 -- 8.2.2 ETSI STF 505 -- 8.2.3 UNIFY-IoT CSA -- 8.3 A Framework to Analyse IoT Standardization -- 8.3.1 Horizontal and Vertical Domains -- 8.3.1.1 Vertical Domains -- 8.3.1.2 IoT SDOs and Alliance Landscape -- 8.3.2 Knowledge Areas -- 8.3.3 High Level Architecture (HLA) -- 8.3.3.1 The AIOTI HLA -- 8.3.3.2 Mapping of the HLA:The Example of oneM2M -- 8.3.3.3 The STF 505 Enterprise IoT Framework -- 8.3.4 Status of Standardization in IoT -- 8.3.5 Overview of IoT Standards Landscape -- 8.3.5.1 Generic Cross Domain Standards -- 8.3.5.2 Domain Specific Standards -- 8.3.6 Identifying IoT Standards Gaps…”

Tabla de Contenidos: “…. -- 8.7 Conclusions -- References -- 9 An Overview of the Challenges for Cost-Effective and Energy-Efficient Retrofits of the Existing Building Stock -- 9.1 Introduction -- 9.2 Challenges in Building Energy Retrofitting -- 9.2.1 Priorities of Stakeholders -- 9.2.2 Time Period -- 9.2.3 Capital Investment -- 9.2.4 Cost Effectiveness -- 9.2.5 Risk Analysis -- 9.2.6 Technology -- 9.2.7 Government Policies -- 9.2.8 Reliable Prediction of Building Energy Performance -- 9.3 Optimization Approaches for the Design of Building Energy Retrofit -- 9.4 Building Energy Retrofit and Sustainability -- 9.5 Conclusions -- Acknowledgment -- References -- 10 Smart Heating Systems for Cost-Effective Retrofitting -- 10.1 Introduction -- 10.2 Technology -- 10.2.1 "Smartness" in the Primary Systems -- 10.2.2 "Smartness" in the Secondary Systems -- 10.2.3 The Control and the Building Automation -- 10.2.4 The Heat Metering -- 10.2.5 The Users Interfaces…”

Tabla de Contenidos: “…(RQ2c) -- 8.5.5 Effects on Understanding of the Data Linkage Process (RQ3) -- 8.5.6 Effects on Perceptions of the Risk of Data Linkage (RQ4) -- 8.6 Discussion -- References -- Chapter 9 Mixing Modes in Household Panel Surveys: Recent Developments and New Findings -- 9.1 Introduction -- 9.2 The Challenges of Mixing Modes in Household Panel Surveys -- 9.3 Current Experiences with Mixing Modes in Longitudinal Household Panels -- 9.3.1 The German Socio‐Economic Panel (SOEP) -- 9.3.2 The Household, Income, and Labour Dynamics in Australia (HILDA) Survey -- 9.3.3 The Panel Study of Income Dynamics (PSID) -- 9.3.4 The UK Household Longitudinal Study (UKHLS)…”

Tabla de Contenidos: “…3.3.3 Graceful terminations -- 3.3.4 Folder watcher -- 3.3.5 Action scripts -- 3.4 Running watchers -- 3.4.1 Testing watcher execution -- 3.4.2 Scheduling watchers -- Summary -- 4 Handling sensitive data -- 4.1 Principles of automation security -- 4.1.1 Do not store sensitive information in scripts -- 4.1.2 Principle of least privilege -- 4.1.3 Consider the context -- 4.1.4 Create role-based service accounts -- 4.1.5 Use logging and alerting -- 4.1.6 Do not rely on security through obscurity -- 4.1.7 Secure your scripts -- 4.2 Credentials and secure strings in PowerShell -- 4.2.1 Secure strings -- 4.2.2 Credential objects -- 4.3 Storing credentials and secure strings in PowerShell -- 4.3.1 The SecretManagement module -- 4.3.2 Set up the SecretStore vault -- 4.3.3 Set up a KeePass vault -- 4.3.4 Choosing the right vault -- 4.3.5 Adding secrets to a vault -- 4.4 Using credentials and secure strings in your automations -- 4.4.1 SecretManagement module -- 4.4.2 Using Jenkins credentials -- 4.5 Know your risks -- Summary -- 5 PowerShell remote execution -- 5.1 PowerShell remoting -- 5.1.1 Remote context -- 5.1.2 Remote protocols -- 5.1.3 Persistent sessions -- 5.2 Script considerations for remote execution -- 5.2.1 Remote execution scripts -- 5.2.2 Remote execution control scripts -- 5.3 PowerShell remoting over WSMan -- 5.3.1 Enable WSMan PowerShell remoting -- 5.3.2 Permissions for WSMan PowerShell remoting -- 5.3.3 Execute commands with WSMan PowerShell remoting -- 5.3.4 Connect to the desired version of PowerShell -- 5.4 PowerShell remoting over SSH -- 5.4.1 Enable SSH PowerShell remoting -- 5.4.2 Authenticating with PowerShell and SSH -- 5.4.3 SSH environment considerations -- 5.4.4 Execute commands with SSH PowerShell remoting -- 5.5 Hypervisor-based remoting -- 5.6 Agent-based remoting -- 5.7 Setting yourself up for success with PowerShell remoting…”

Tabla de Contenidos: “…4.7.3 Gamma GLMs for House Selling Price Data -- Appendix: GLM Analogs of Orthogonality Results for Linear Models -- Chapter Notes -- Exercises -- 5 Models for Binary Data -- 5.1 Link Functions for Binary Data -- 5.1.1 Ungrouped versus Grouped Binary Data -- 5.1.2 Latent Variable Threshold Model for Binary GLMs -- 5.1.3 Probit, Logistic, and Linear Probability Models -- 5.2 Logistic Regression: Properties and Interpretations -- 5.2.1 Interpreting : Effects on Probabilities and on Odds -- 5.2.2 Logistic Regression with Case-Control Studies -- 5.2.3 Logistic Regression is Implied by Normal Explanatory Variables -- 5.2.4 Summarizing Predictive Power: Classification Tables and ROC Curves -- 5.2.5 Summarizing Predictive Power: Correlation Measures -- 5.3 Inference About Parameters of Logistic Regression Models -- 5.3.1 Logistic Regression Likelihood Equations -- 5.3.2 Covariance Matrix of Logistic Parameter Estimators -- 5.3.3 Statistical Inference: Wald Method is Suboptimal -- 5.3.4 Conditional Logistic Regression to Eliminate Nuisance Parameters -- 5.4 Logistic Regression Model Fitting -- 5.4.1 Iterative Fitting of Logistic Regression Models -- 5.4.2 Infinite Parameter Estimates in Logistic Regression -- 5.5 Deviance and Goodness of Fit for Binary GLMS -- 5.5.1 Deviance and Pearson Goodness-of-Fit Statistics -- 5.5.2 Chi-Squared Tests of Fit and Model Comparisons -- 5.5.3 Residuals: Pearson, Deviance, and Standardized -- 5.5.4 Influence Diagnostics for Logistic Regression -- 5.6 Probit and Complementary Log-Log Models -- 5.6.1 Probit Models: Interpreting Effects -- 5.6.2 Probit Model Fitting -- 5.6.3 Log-Log and Complementary Log-Log Link Models -- 5.7 Examples: Binary Data Modeling -- 5.7.1 Example: Risk Factors for Endometrial Cancer Grade -- 5.7.2 Example: Dose-Response Study -- Chapter Notes -- Exercises -- 6 Multinomial Response Models…”

Tabla de Contenidos: “…JavaScript analysis -- Specialized web technology fingerprinting libraries -- Proactive web security measures with Python -- Input validation and data sanitization -- Secure authentication and authorization -- Secure session management -- Secure coding practices -- Implementing security headers -- Summary -- Chapter 4: Exploiting Web Vulnerabilities Using Python -- Web application vulnerabilities - an overview -- SQL injection -- XSS -- IDOR -- A case study concerning web application security -- SQL injection attacks and Python exploitation -- Features of SQLMap -- How SQLMap works -- Basic usage of SQLMap -- Intercepting with MITMProxy -- XSS exploitation with Python -- Understanding how XSS works -- Reflected XSS (non-persistent) -- Stored XSS (persistent) -- Python for data breaches and privacy exploitation -- XPath -- CSS Selectors -- Summary -- Chapter 5: Cloud Espionage - Python for Cloud Offensive Security -- Cloud security fundamentals -- Shared Responsibility Model -- Cloud deployment models and security implications -- Encryption, access controls, and IdM -- Security measures offered by major cloud providers -- Access control in cloud environments -- Impact of malicious activities -- Python-based cloud data extraction and analysis -- Risks of hardcoded sensitive data and detecting hardcoded access keys -- Enumerating EC2 instances using Python (boto3) -- Exploiting misconfigurations in cloud environments -- Types of misconfigurations -- Identifying misconfigurations -- Exploring Prowler's functionality -- Enhancing security, Python in serverless, and infrastructure as code (IaC) -- Introducing serverless computing -- Introduction to IaC -- Summary -- Part 3: Python Automation for Advanced Security Tasks -- Chapter 6: Building Automated Security Pipelines with Python Using Third-Party Tools…”

Tabla de Contenidos: “…. -- 1.1 Introduction -- 1.2 The Two Perceptive Elements -- 1.3 Immersive Tools and Technologies -- 1.4 The Methodology -- 1.5 Interaction Through Voice Assistant -- 1.6 Improved Distraction Tracking -- 1.7 Discussions -- 1.8 Market Analysis -- 1.9 Result -- 1.10 Conclusion -- 2 When Technology Meets Tradition: Rediscovering Tribes Through Virtual Reality 27 Debanjana Nag -- 2.1 Introduction -- 2.2 Concept of Communication and Virtual Reality as a Tool of Communication -- 2.3 The Tribes and the Concept of 'Modern' Society -- 2.4 Status of Women in Tribal Societies -- 2.5 Socio-Political Systems of Tribal Societies -- 2.6 Conditions of Education, Health and Hygiene -- 2.7 Application of Virtual Reality as a Learning Pedagogy to Understand Tribal Aspects -- 2.8 Problems in the Implementation of VR Among the Tribes -- 2.9 Utilization of Virtual Reality to Connect Tribes with the Mainstream -- 2.10 Epilogue -- 3 Post-Pandemic Approaches Through Various Advanced Toolkits for Online Teaching and Learning Confidence 47 Karthikeyan Kandasamy, Prakash Maran Jeganathan and Shalini Surajmani -- 3.1 Introduction -- 3.2 The Impact of Technology on Learning -- 3.3 MAXHUB UC BM35 Bluetooth Teleconference Speakerphone -- 3.4 4K Ultra HD -- 3.5 BenQ EdTech -- 3.6 Conclusion -- 4 Expanding Teaching Possibilities: Applications of Technological Products in Education 61 Jyoti Verma and Gagandeep -- 4.1 Introduction -- 4.2 Benefits of Technology in the Education Sector -- 4.3 Applications of Technological Products in Education -- 4.4 PESTLE Analysis of Technological Products in the Education Sector -- 4.5 Successive Growth of Technological Products in India -- 4.6 Conclusion -- 5 Evolutionary Advantages of Virtual Reality in Education 73 Richa Kapoor Mehra -- 5.1 Introduction -- 5.2 Differences Between Real and Virtual Space -- 5.3 Use of Virtual Reality: A Matter of Concern -- 5.4 Conclusion -- 6 Exploring Possibilities and Apprehensions About Application of Artificial Intelligence in Higher Education 87 Siddharth Shimpi -- 6.1 Introduction -- 6.2 The Current State of AI in Higher Education -- 6.3 Advantages of AI in Higher Education -- 6.4 Potential Risks of AI in Higher Education -- 6.5 Ethical Considerations in AI in Higher Education -- 6.6 Future of AI in Higher Education -- 6.7 Case Studies -- 6.8 Best Practices -- 6.9 Conclusion -- 7 Impact of Virtual Reality on Immersive Education 101 Manju Rani -- 7.1 Introduction -- 7.2 Techniques Used in Immersive Learning -- 7.3 Advantages of Immersive Learning -- 7.4 Disadvantages of Immersive Learning -- 7.5 Virtual Reality -- 7.6 Processing of VR -- 7.7 Types of Virtual Reality -- 7.8 Developing Content for VR -- 7.9 Creating a Virtual Reality Environment -- 7.10 VR in Education -- 7.11 E-Learning and VR -- 7.12 The Usefulness of VR Technology in the Field of Education -- 7.13 VR in Teacher Training and Pedagogy -- 7.14 Creating an Immersive VR Environment with MaxWhere -- 7.15 Applications or Software for Using VR Technology in the Field of Education -- 7.16 Growth of VR Technology in Education -- 7.17 Obstacles in Using VR Technology -- 7.18 Interpretation of Results -- Part 2: Reimaging Education with Metaverse -- 8 The Metaverse in Education: An Upcoming Future Trend 129 Priya Jindal and Ansh Jindal -- 8.1 Introduction -- 8.2 XR in Metaverse -- 8.3 Categories of Extended Reality (XR) -- 8.4 XR and Learning -- 8.5 Approaches of Learning in Metaverse -- 8.6 Comparison of Classes in Person, Virtual Classes, and in the Metaverse -- 8.7 Challenges of Metaverse in Education -- 8.8 Trends Supporting Metaverse in Education -- 8.9 Conclusion -- 9 Role of Virtual Reality in Education: Its Scope, Reach and Effect on Student Behavior 145 Shweta Kapoor and Gitanjali Kalia -- 9.1 Introduction -- 9.2 Types of Virtual Reality -- 9.3 Various Types of Augmented Reality -- 9.4 Virtual and Augmented Reality -- 9.5 Application of Virtual Reality -- 9.6 Implementation of VR in Education Sector -- 9.7 Effects of Virtual Reality on Student Behavior -- 9.8 Positive Effect of Virtual Reality on Students -- 9.9 Negative Effect of Virtual Reality on Students -- 9.10 Challenges Faced when Implementing VR in Education Sector -- 9.11 Conclusion -- 9.12 Forming New Perspectives -- 10 Virtual Reality in Education: Benefits, Applications and Challenges 165 Rajni Bala and Prachi Gupta -- 10.1 Introduction -- 10.2 Industry Key Players in VR in Education -- 10.3 VR Market Segmentation in the Education Sector -- 10.4 Application of VR in Education -- 10.5 Distance Learning -- 10.6 Advantages of VR in the Classroom -- 10.7 Disadvantages of VR in the Classroom -- 10.8 How VR Will Improve Education -- 10.9 Challenges of Implementing VR in Education -- 10.10 How to Maximize the Benefits of VR in Education -- 11 Exploring the Landscape of Virtual Reality in Education: A Bibliometric and Thematic Analysis 181 Natashaa Kaul and Chanakya Kumar -- 11.1 Introduction -- 11.2 Literature Review -- 11.3 Methodology -- 11.4 Findings and Results -- 11.5 Methodology and Research Perspectives -- 11.6 Temporal Analysis Using Word Clouds -- 11.7 Future Research -- 11.8 Conclusion -- 11.9 Implications for Research and Practice -- 11.10 Limitations of the Study -- 12 VR in Vocational Educational and Training: Conceptual Framework and Adoption Roadmap 201 Kumar Shalender, Babita Singla and Sandhir Sharma -- 12.1 Introduction -- 12.2 Benefits of VR in Training -- 12.3 VR Adoption Framework -- 12.4 Conclusion and Discussion -- 13 Virtual Reality in Education -- A Blessing or Curse? …”

Tabla de Contenidos: “….-4.4 Knowledge Work -- 4.5 Integration -- 4.6 Conclusions -- Chapter Five: Security, Risk Analysis and Intelligence -- Extract -- Introduction to Chapter Five -- 5.1 The Need For Profiles – and Using a Data Fusion Tool to Help -- 5.2 Case Studies -- 5.2.1 The Relationship of Drought and Political/Economic Crises in Africa -- 5.2.2 The 2009 Swine Slaughter in Egypt -- 5.2.3 Civil War -- 5.2.4 The Sinai Peninsula in 2012 -- 5.2.5 Poland in 1982.-5.3 Possible Annual Reports -- 5.3.1 Annual International Organization Report -- 5.3.2 Annual Voting/Consensus Practice Guide -- 5.4 Doveryay, no proveryay Дoвepяй, нo пpoвepяй: Trust But Verify -- Chapter Six: Legal Matters -- Extract: -- 6.1 Delegation Legal Adviser -- 6.2 Sanctions and Legality -- 6.2.1 United Nations Sanctions -- 6.2.2 National Sanctions and Restrictions: -- 6.2.3 Sanctions on Non-State Entities -- 6.2.4 International Courts: -- 6.3 Words Matter.…”

Tabla de Contenidos: “…28.2.2 Trends -- 28.3 Consequences for Switzerland -- 28.3.1 Implementation Possibilities: Make or Buy -- 28.3.2 Variation and Recommendation -- 28.4 Conclusion -- References -- 29 Authentication -- 29.1 Introduction -- 29.2 Analysis -- 29.2.1 Definition -- 29.2.2 Trends -- 29.3 Consequences for Switzerland -- 29.3.1 Implementation Possibilities: Make or Buy -- 29.4 Conclusion -- References -- Part V Use-Cases -- 30 Secure Media -- 30.1 Introduction -- 30.2 Analysis -- 30.2.1 Definition -- 30.2.2 Trends -- 30.3 Consequences for Switzerland -- 30.3.1 Implementation Possibilities: Make or Buy -- 30.3.2 Variations and Recommendation -- 30.4 Conclusion -- References -- 31 Secure Positioning and Localization -- 31.1 Introduction -- 31.2 Analysis -- 31.2.1 Definition -- 31.2.2 Trends -- 31.3 Consequences for Switzerland -- 31.3.1 Implementation Possibilities: Make or Buy -- 31.3.2 Variations and Recommendation -- 31.4 Conclusion -- References -- 32 Secure Payment -- 32.1 Introduction -- 32.2 Analysis -- 32.2.1 Definition -- 32.2.2 Trends -- 32.3 Consequences for Switzerland -- 32.3.1 Implementation Possibilities: Make or Buy -- 32.3.2 Variation and Recommendation -- 32.4 Conclusion -- References -- 33 Disk, File and Database Encryption -- 33.1 Introduction -- 33.2 Analysis -- 33.3 Definition -- 33.3.1 Full Disk Encryption (FDE) -- 33.3.2 File-Based Encryption (FBE) -- 33.3.3 Manual File Encryption -- 33.3.4 Database Encryption (DBE) -- 33.4 Security Considerations -- 33.4.1 Encryption Algorithms -- 33.4.2 Key Management -- 33.4.3 Coercion -- 33.5 OS Examples -- 33.6 Trends -- 33.7 Consequences for Switzerland -- 33.8 Conclusion -- References -- 34 WEB3 -- 34.1 Introduction -- 34.2 Analysis -- 34.2.1 Definition -- 34.2.2 Technologies -- 34.2.3 Risks -- 34.2.4 Trends -- 34.3 Consequences for Switzerland -- 34.3.1 Adoption and Efficacy -- 34.4 Conclusion…”

Tabla de Contenidos: “…Cover -- Title Page -- Copyright Page -- Contents -- Preface -- Chapter 1 Quality Assurance in Data Science: Need, Challenges and Focus -- 1.1 Introduction -- 1.1.1 Quality Assurance and Testing -- 1.1.2 Data Science and Quality Assurance -- 1.1.3 Background -- 1.2 Testing and Quality Assurance -- 1.2.1 Key Terminologies Associated With Testing -- 1.3 Product Quality and Test Efforts -- 1.3.1 Testing Metrics -- 1.3.2 How to Improve the Business Value to Products Using Test Automation -- 1.3.3 Data Analysis and Management in Test Automation -- 1.3.4 Data Models in Data Science -- 1.4 Data Masking in Data Model and Associated Risks -- 1.5 Prediction in Data Science -- Case Study -- 1.6 Role of Metrics in Evaluation -- 1.7 Quantity of Data in Quality Assurance -- 1.8 Identifying the Right Data Sources -- 1.8.1 Need to Gather Up-to-Date Data -- 1.8.2 Synthesising Existing Advanced Technologies for Continuous Business Improvements -- 1.9 Conclusion -- References -- Chapter 2 Design and Implementation of Social Media Mining - Knowledge Discovery Methods for Effective Digital Marketing Strategies -- 2.1 Introduction -- 2.1.1 Objectives of the Study -- 2.2 Literature Review -- 2.3 Novel Framework for Social Media Data Mining and Knowledge Discovery -- 2.4 Classification for Comparison Analysis -- 2.5 Clustering Methodology to Provide Digital Marketing Strategies -- 2.5.1 Status (Text Form) -- 2.5.2 Images (Photos) -- 2.5.3 Video Post -- 2.5.4 Link Post -- 2.6 Experimental Results -- 2.7 Conclusion -- References -- Chapter 3 A Study on Big Data Engineering Using Cloud Data Warehouse -- 3.1 Introduction -- 3.2 Comparison Study of Different Cloud Data Warehouses -- 3.2.1 Amazon Redshift -- 3.2.2 High-Level Architecture of Amazon Redshift -- 3.2.3 Features of Amazon Redshift Cloud Data Warehouse -- 3.2.4 Pricing of Amazon Redshift Cloud Data Warehouse…”

Tabla de Contenidos: “…5.4 Healthcare Practitioners -- 5.4.1 Smart Medical Devices -- 5.4.2 Time to Diagnosis -- 5.4.3 Electronic Health Records (EHRs), the Electronic Medical Records (EMRs) -- 5.4.4 Using AI and Analytics to Determine Dental Group Practice Quality -- 5.4.5 Some Ways Various Healthcare Professionals Are Using Textual Analytics -- 5.5 Manufacturing Practitioners -- 5.5.1 Predictive Maintenance to Proactively Identify and Address Impending Equipment Failure -- 5.6 Retail Practitioners -- 5.7 Telecommunication Practitioners -- 5.8 Energy Practitioners -- 5.8.1 Major Energy Producer Unites Data Scientists and Subject Matter Experts -- 5.8.2 Large Utility Company Adds AI Project to Its Digital Transformation Project -- 5.9 Insurance Practitioners -- 5.9.1 CEO of Property and Casualty Insurance Company Delivers Analytics Mandate -- 5.10 Other Practitioners -- 5.11 Conclusion and Next Steps for Practitioners -- Reference -- 6 The Leaders -- 6.1 Introduction -- 6.2 Some Characteristics of Leaders -- 6.2.1 Organizational and Structural Differences -- 6.2.1.1 Notes on Organizational Structure and Design -- 6.2.2 Data Excellence -- 6.2.2.1 The Use of Unstructured Data-New Horizon Leaders Are Tapping Into -- 6.2.3 Self-Service That Enables Everyone in the Organization -- 6.3 Healthcare Leaders -- 6.3.1 Using Regulations and Mandates as a Catalyst for Innovation -- 6.4 Manufacturing Leaders -- 6.4.1 Smarter Identification of Defects with Text Analytics -- 6.5 Retail Leaders -- 6.5.1 Assortment -- 6.5.2 Right Value Proposition -- 6.5.3 Right Time/Location/Delivery -- 6.6 Energy Leaders -- 6.6.1 Global Top Five Oil Producer Manages Equipment with Text Analytics -- 6.7 Financial Services and Insurance Leaders -- 6.7.1 Insurance Holding Company Reduces Fraud Risk and Improves Operations with Text Mining (Analytics) -- 6.7.1.1 The Pilot -- 6.7.1.2 Results after Two Years…”

Tabla de Contenidos: “…Results and discussion -- 4.1 Fuel consumption -- 4.2 Emissions benchmark -- 4.3 Energy cost benchmark -- 4.4 Sustainability development indices -- 4.5 Financial viability -- 4.6 Cash flow -- 4.7 Financial risk impacts -- 5. Conclusion -- References…”

Tabla de Contenidos: “…3.1.2 Public Cloud -- 3.1.3 Private Cloud (Internal Cloud or Corporate Cloud) -- 3.1.4 Difference Between Public Cloud and Private Cloud -- 3.1.5 Public versus Private Cloud -- 3.1.6 Factors for Public versus Private Cloud -- 3.2 Cloud Infrastructure -- 3.2.1 Cloud Computing Infrastructure -- 3.2.2 Status of Cloud Computing in India -- 3.3 Cloud Application Architecture -- 3.3.1 Architectural Considerations -- Summary -- Key Terms -- Review Questions -- Chapter 4: Working of Cloud Computing -- 4.1 Trends in Computing -- 4.1.1 IT Maturity Analysis -- 4.1.2 Technology Trends to Watch -- 4.2 Cloud Service Models -- 4.2.1 Service Models -- 4.2.2 Cloud Service Models -- 4.3 Cloud Deployment Models -- 4.3.1 Cloud Computing Deployment Models -- 4.4 Pros and Cons of Cloud Computing -- 4.4.1 Risks in the Cloud -- 4.4.2 Cloud Storage as Service: Pros and Cons -- 4.5 Cloud Computing and Services: Pros and Cons -- 4.5.1 Centralized Data Storage in Cloud Computing -- 4.5.2 Cloud Servers Maintenance and Security -- 4.5.3 Data Access and Network Connectivity -- 4.5.4 Cost Factor -- 4.5.5 Cloud Servers Data Backup and Availability -- Summary -- Key Terms -- REVIEW QUESTIONS -- Part Two : Cloud Computing Architecture -- Chapter 5: Cloud Computing Technology -- 5.1 Cloud Lifecycle Model -- 5.1.1 Phases of CDLC -- 5.1.2 Case Study: Oracle Cloud Management-An Overview -- 5.2 Role of Cloud Modelling and Architecture -- 5.2.1 Cloud Computing Model -- 5.2.2 Necessary Characteristics -- 5.2.3 Service Models -- 5.2.4 Deployment Models -- 5.3 Reference Model for Cloud Computing -- 5.3.1 Reference Architecture, Frameworks and Models for Cloud Computing -- 5.3.2 Case Study 1: Cloud Computing Model of IBM -- 5.4 Cloud Industry Standard -- 5.4.1 IEEE Outlines Cloud Computing Standard for Industry -- Summary -- Key Terms -- Review Questions -- Chapter 6: Cloud Architecture…”

Tabla de Contenidos: “…8.2.2 Introduction to Arduino, Raspberry Pi, and Their Components -- 8.2.3 Recognizing the Input/Output -- 8.3 Open Service Platforms for IoT Cloud Computing -- 8.4 Resource Management Techniques for Cloud‐Based IoT Environments -- 8.5 Data Management for IoT -- 8.6 ThingSpeak for IoT -- References -- Chapter 9 The Internet of Things for Healthcare -- 9.1 IoT as a Key in the Digital Healthcare System -- 9.2 How IoT Helps in Healthcare Processes -- 9.3 Applications of IoT Technology in Healthcare -- 9.3.1 Remote Patient Monitoring -- 9.3.2 Smart Healthcare Systems -- 9.3.3 Telemedicine -- 9.3.4 Drug Development -- 9.3.5 Medical Robots -- 9.4 Healthcare IoT Challenges, Security Issues, and Risks -- 9.5 Wireless Body Area Network (WBAN) -- 9.6 WBAN Architecture -- 9.7 What Makes Wearables So Sexy? …”

“…The collection is inspired by the effort of making professional educators aware of the risks of the digital turn in educational storytelling but also of the opportunities and the conditions for critical engagements. …”

“…This book provides: a framework to understand risks posed by blindspots from a leadership perspective the four types of blinspots that are most important for leaders to be aware of a self-assessment and a team survey to help leaders in identifying/recognizing their blindspots corrective actions leaders can take to mitigate the resulting risks "--…”

“…However, intervention to steer innovation in this direction is merited so long as the risks of not attaining climate policy targets are seen as higher than the risks of intervention…”

“…Reflecting today's unique opportunities and challenges, Starting a Business All-In-One For Dummies is packed with everything you need to manage your personal and business risks and successfully navigate your first year in business. …”