Tabla de Contenidos: “…4.3.1 Drawbacks -- 4.3.1.1 Complexity and Expertise Dependency -- 4.3.1.2 Technological Hurdles -- 4.3.1.3 Performance and Verification -- 4.3.2 Input Data -- 4.4 Proposed System -- 4.4.1 Streamlined Expertise Requirements -- 4.4.2 Scalable Technology Implementation -- 4.4.3 Robust Performance and Validation -- 4.4.4 Advantages -- 4.4.4.1 Enhanced Accessibility -- 4.4.4.2 Improved Scalability -- 4.4.4.3 Consistently Reliable Performance -- 4.4.5 Proposed Algorithm Steps -- 4.5 Experimental Results -- 4.5.1 Performance Evaluation Methods -- 4.5.1.1 Accuracy -- 4.5.1.2 Precision -- 4.5.1.3 Recall -- 4.5.1.4 Sensitivity -- 4.5.1.5 Specificity -- 4.5.1.6 F1 Score -- 4.5.1.7 Area Under Curve (AUC) -- 4.5.1.8 Convolutional Neural Network (CNN) Architecture -- 4.5.1.9 Model Training and Validation -- 4.5.1.10 Data Augmentation and Regularization -- 4.5.1.11 Performance Metrics -- 4.6 Conclusion -- Conflicts of Interest -- References -- Chapter 5 Machine Learning-Enabled Digital Twins for Diagnostic and Therapeutic Purposes -- 5.1 Introduction -- 5.2 Conceptualization of Digital Twin and Machine Learning -- 5.2.1 Digital Twins -- 5.2.1.1 Working With the Digital Twins -- 5.2.2 Machine Learning -- 5.2.2.1 Deep Learning -- 5.2.2.2 Reinforcement Learning -- 5.3 State-of-the-Art Works -- 5.4 Applications of Digital Twins Enabled With Deep Learning Models and Reinforcement Learning -- 5.5 Limitations and Challenges -- 5.6 Opportunities/Future Scope -- 5.7 Concluding Remarks -- References -- Chapter 6 Blockchain as the Backbone of a Connected Ecosystem of Smart Hospitals -- 6.1 Introduction -- 6.2 Smart Hospitals -- 6.2.1 Key Technologies in Smart Hospital Environments -- 6.2.1.1 Internet of Things (IoT) -- 6.2.1.2 Artificial Intelligence (AI) -- 6.2.1.3 Big Data Analytics -- 6.2.1.4 Interoperable Systems…”

Tabla de Contenidos: “…Self-learning healthcare IoT -- 4.3.3. Towards Big Data in healthcare IoT -- 4.4. Impact of smartphones on edge computing -- 4.4.1. …”

Tabla de Contenidos: “…Cover -- Half Title -- Title Page -- Copyright Page -- Dedication -- Table of Contents -- Preface -- Acknowledgments -- Editors -- Contributors -- Section I -- Chapter 1: Secure Fog-Cloud of Things: Architectures, Opportunities and Challenges -- 1.1 Introduction -- 1.1.1 Chapter Road Map -- 1.2 Secure Fog-Cloud of Things -- 1.2.1 Environment -- 1.2.2 Architecture -- 1.3 Threats, Vulnerabilities and Exploits in Fog-Cloud of Things Ecosystems -- 1.4 Key Machine Learning Kits for Secure Fog-Cloud of Things Architecture -- 1.5 Applications -- 1.6 Opportunities and Challenges in Improving Security in Fog-Cloud of Things -- 1.6.1 Opportunities -- 1.6.2 Challenges -- 1.7 Future Trends -- 1.8 Conclusion -- References -- Chapter 2: Collaborative and Integrated Edge Security Architecture -- 2.1 Background -- 2.2 Edge Security Challenges -- 2.3 Perspectives of Edge Security Architecture -- 2.4 Emerging Trends and Enablers for Edge Security Architecture -- 2.4.1 The Edge Computing Architecture -- 2.4.2 Leveraging Fog-Based Security Architecture for Edge Networks -- 2.5 Collaborative and Integrated Security Architecture for Edge Computing -- 2.5.1 Overview -- 2.5.2 Distributed Virtual Firewall (DFWs) -- 2.5.3 Distributed Intrusion Detection Systems (IDSs) -- 2.6 Conclusion and Future Research -- References -- Chapter 3: A Systemic IoT-Fog-Cloud Architecture for Big-Data Analytics and Cyber Security Systems: A Review of Fog Computing -- 3.1 Introduction -- 3.2 Fog Computing Systems -- 3.2.1 Description of Fog -- 3.2.2 Characteristics of Fog -- 3.2.3 Systemic Architecture of IoT-Fog-Cloud -- 3.2.4 Applications of IoT, Fog and Cloud Systems -- 3.3 Cyber Security Challenges -- 3.4 Security Solutions and Future Directions -- 3.5 Conclusion -- References -- Chapter 4: Security and Organizational Strategy: A Cloud and Edge Computing Perspective -- 4.1 Introduction…”

Tabla de Contenidos: “…IoT (Internet of Things) -- 13.6. Big Data…”

Tabla de Contenidos: “…Cover -- Series Page -- Title Page -- Copyright Page -- Contents -- Preface -- Chapter 1 Introduction to Smart Farming: Definition, Importance and Trends -- 1.1 Introduction -- 1.2 Smart Farming -- 1.3 Internet of Things -- 1.3.1 Fundamentals of IoT Applications in Smart Farming -- 1.4 Technologies Used in Smart Farming -- 1.4.1 Global Positioning System (GPS) -- 1.4.2 Sensor Technologies -- 1.4.3 Variable-Rate of Technology (VRT) and Grid Soil Sampling -- 1.4.4 Geographic Information System (GIS) -- 1.4.5 Crop Management -- 1.4.6 Soil and Plant Sensors -- 1.4.7 Rate Controllers -- 1.4.8 Precision Irrigation in Pressurized Systems -- 1.4.9 Yield Monitor -- 1.4.10 Software -- 1.5 Importance of Smart Farming -- 1.5.1 Soil Mapping and Plant Monitoring -- 1.5.2 Irrigation -- 1.5.3 Site-Specific Nutrient Management -- 1.5.4 Crop Pest and Disease Management -- 1.5.5 Yield Monitoring and Forecasting -- 1.5.6 Enhanced Production Rates -- 1.5.7 Water Conservation -- 1.5.8 Real-Time Data and Insights -- 1.5.9 Reduction in Operation Costs -- 1.5.10 High-Quality Production -- 1.5.11 Accurate Farm and Yield Evaluation -- 1.5.12 Improved Livestock Production -- 1.5.13 Reduced Environmental Footprint Impact -- 1.5.14 Remote Monitoring for Easy Management -- 1.5.15 Expensive Asset Monitoring -- 1.6 Role of IoT in Advanced Farming Practices -- 1.6.1 Greenhouse Farming and Protected Cultivation -- 1.6.2 Hydroponics -- 1.6.3 Vertical Farming -- 1.6.4 Phenotyping -- 1.7 Trends of Smart Farming -- 1.7.1 Precision Agriculture -- 1.7.2 Automation -- 1.7.3 Internet of Things (IoT) -- 1.7.4 Big Data Analytics -- 1.7.5 Vertical Farming -- 1.7.6 Aquaponics -- References -- Chapter 2 Overview of Robotics in Agriculture: Types and Applications -- 2.1 Introduction -- 2.2 Background -- 2.2.1 Historical Perspective of Agriculture and Technology…”

Tabla de Contenidos: “…References -- Chapter 3 Safe and Reliable Smart City Design Based on Blockchain Technology -- 3.1 Introduction -- 3.1.1 Blockchain Technology -- 3.1.2 Use of Blockchain in Smart City -- 3.1.3 Objective of Work -- 3.2 Related Work -- 3.2.1 Problem Statement -- 3.3 Blockchain Technology for Smart Cities -- 3.3.1 Application of the System -- 3.3.2 Blockchain Technology's Many Advantages in the Modern Smart City -- 3.4 Methods -- 3.4.1 Blockchain-Based Smart City Infrastructure -- 3.5 Conclusion -- References -- Chapter 4 Blockchain and Digital Twin for Enhancing Personal Security in Modern Cities -- 4.1 Introduction -- 4.2 Digital Twin -- 4.2.1 Digital Twins in Manufacturing Industry -- 4.2.2 Integration with Emerging Technologies -- 4.2.3 Real-World Reflection Through Sensors -- 4.2.4 Land Management and Global Concerns -- 4.2.5 Increasing Importance of Information Technologies -- 4.2.6 Advancements in IoT and Connectivity -- 4.2.7 Role of Artificial Intelligence (AI) in Digital Twins -- 4.2.8 Cloud Computing Empowering Digital Twins -- 4.2.9 Impact of 5G Technology -- 4.2.10 Smart Cities and the Growing Application Field -- 4.2.11 Big Data's Role in Informed Decision Making -- 4.3 Digital Twin and Metaverse -- 4.3.1 Virtual Prototyping and Design Optimization -- 4.3.2 Healthcare Integration for Personalized Medicine -- 4.3.3 The Intersection of Digital Twins and the Metaverse -- 4.3.4 Immersive Collaborative Environments -- 4.3.5 Enhanced Virtual Experiences Through Data Fusion -- 4.3.6 Challenges and Future Developments -- 4.3.7 Data Privacy and Security Concerns -- 4.3.8 Standardization and Interoperability -- 4.4 Blockchain Technology -- 4.4.1 Overview of Blockchain Technology -- 4.4.2 Data Checking and Analysis -- 4.4.3 Immutable History -- 4.4.4 Potential Problem Solving -- 4.4.5 Rapid Adoption by Major Companies…”

Tabla de Contenidos: “…IBM Systems -- 5.2. Big Data Applications at Medidata -- 6. Metrics for CT -- 7. …”

Tabla de Contenidos: “…14.2.1.4 Predicting Diabetes -- 14.2.1.5 Predicting Alzheimer's -- 14.2.2 Drug Development and Discovery -- 14.2.3 Clinical Decision Support (CDS) -- 14.2.4 Medical Image Examination -- 14.2.5 Monitoring of Health and Wearable Technology -- 14.2.6 Telemedicine and Remote Patient Monitoring -- 14.2.7 Chatbots and Virtual Medical Assistants -- 14.3 Why Machine Learning is Crucial in Healthcare -- 14.4 Challenges and Opportunities -- 14.5 Conclusion -- References -- Chapter 15 Enhancing Resource Management in Precision Farming through AI-Based Irrigation Optimization -- 15.1 Introduction to Precision Farming -- 15.1.1 Definition of Precision Farming -- 15.1.2 Importance of Precision Farming in Agriculture -- 15.2 Role of Artificial Intelligence (AI) in Precision Farming -- 15.2.1 Influence of AI in Precision Farming -- 15.2.2 Challenges and Limitations of AI in Precision Farming -- 15.3 Data Collection and Sensing for Precision Farming -- 15.3.1 Remote Sensing Techniques -- 15.3.2 Satellite Imagery Analysis -- 15.3.3 Unmanned Aerial Vehicles (UAVs) for Data Collection -- 15.3.4 Internet of Things (IoT) Sensors -- 15.3.5 Data Preprocessing and Integration -- 15.4 Crop Monitoring and Management -- 15.4.1 Crop Yield Prediction -- 15.4.2 Disease Detection and Diagnosis -- 15.4.3 Nutrient Management and Fertilizer Optimization -- 15.5 Precision Planting and Seeding -- 15.5.1 Variable Rate Planting -- 15.5.2 GPS and Auto-Steering Systems -- 15.5.3 Seed Singulation and Metering -- 15.5.4 Plant Health Monitoring and Care -- 15.6 Harvesting and Yield Estimation -- 15.6.1 Yield Estimation Models -- 15.6.2 Real-Time Crop Monitoring During Harvest -- 15.7 Data Analytics and Machine Learning -- 15.7.1 Predictive Analytics for Crop Yield -- 15.7.2 Machine Learning Algorithms for Precision Farming -- 15.7.3 Big Data Analytics in Precision Farming…”

Tabla de Contenidos: “…2.3 Computer storage technologies -- 2.3.1 Punched cards - Hollerith and IBM -- 2.3.2 Punched paper tapes -- 2.3.3 Handwriting recognition -- 2.3.4 Delay line memories -- 2.3.5 Core memories -- 2.3.6 Semiconductor memories -- 2.3.7 Redundant array of Independent Memories - RAIM -- 2.3.8 Magnetic Random Access Memory - MRAM -- 2.3.9 Magnetic tapes and tape libraries -- 2.3.10 An analytical model for a tape library -- 2.3.11 Summary of a recent article on magnetic tapes -- 2.3.12 Origins of Hard Disk Drives - HDDs -- 2.3.13 HDD manufacturers -- 2.3.14 Storage technologies expected to replace disk drives -- 2.3.15 Magnetic bubble memories -- 2.3.16 Charged Couple Devices - CCDs -- 2.3.17 Micro-Electro-Mechanical Systems - MEMS -- 2.3.18 IBM Zurich millipede -- 2.3.19 Phase Change Memory - PCM -- 2.3.20 Flash memories -- 2.3.21 Companies producing flash memories -- 2.3.22 Elevating commodity storage with the SALSA host translation layer -- 2.3.23 Flash SSD versus magnetic HDD pricing -- 2.3.24 Pure Storage design of Purity -- 2.3.25 Intel/Micron 3D_XPoint Optane Memory -- 2.3.26 Processing In Memory - PIM -- 2.3.27 Universal memory technology - UltraRAM -- 2.3.28 Racetrack memory -- 2.3.29 Optical storage -- 2.3.30 Holographic memory -- 2.3.31 M-DISC and storage longevity -- 2.3.32 Persistent and NonVolatile Memory - NVM -- 2.3.33 Glass as a new storage medium -- 2.3.34 DNA based archival storage system -- 2.4 Reliability studies of DRAM, HDDs, &amp -- flash SSDs -- 2.4.1 Flash SSD reliability at Facebook, Google &amp -- NetApp -- 2.5 Storage Networking Industry Association - SNIA -- 2.5.1 Solid state storage performance -- 2.5.2 Persistent Memory Forum -- 2.5.3 Computational storage -- 2.6 Big data and its sources -- 2.7 Sources of storage content -- 2.8 Ranking and description of media companies -- 2.9 Sources of news: newspapers, radio and TV stations…”

Tabla de Contenidos: “…Front Cover -- Half Title Page -- RIVER PUBLISHERS SERIES IN AUTOMATION, CONTROL AND ROBOTICS -- Title Page -- Copyright Page -- Dedication Page -- Contents -- Acknowledgments -- Foreword -- List of Contributors -- List of Figures -- List of Tables -- List of Abbreviations -- Chapter 1 - Introduction -- 1.1 Maintenance Today -- 1.2 The Path to Proactive Maintenance -- 1.3 Why to Read this Book -- References -- Chapter 2 - Business Drivers of a Collaborative, Proactive Maintenance Solution -- 2.1 Introduction -- 2.1.1 CBM-based PM in Industry -- 2.1.2 CBM-based PM in Service Business -- 2.1.3 Life Cycle Cost and Overall Equipment Effectiveness -- 2.1.4 Integrating IoT with Old Equipment -- 2.1.5 CBM Strategy as a Maintenance Business Driver -- 2.2 Optimization of Maintenance Costs -- 2.3 Business Drivers for Collaborative Proactive Maintenance -- 2.3.1 Maintenance Optimisation Models -- 2.3.2 Objectives and Scope -- 2.3.3 Maintenance Standards -- 2.3.4 Maintenance-related Operational Planning -- 2.4 Economic View of CBM-based PM -- 2.5 Risks in CBM Plan Implementation -- 2.5.1 Technology -- 2.5.2 People -- 2.5.3 Processes -- 2.5.4 Organizational Culture -- References -- Chapter 3 - The MANTIS Reference Architecture -- 3.1 Introduction -- 3.1.1 MANTIS Platform Architecture Overview -- 3.2 The MANTIS Reference Architecture -- 3.2.1 Related Work and Technologies -- 3.2.1.1 Reference architecture for the industrial internet of things -- 3.2.1.2 Data processing in Lambda -- 3.2.1.3 Maintenance based on MIMOSA -- 3.2.2 Architecture Model and Components -- 3.2.2.1 Edge tier -- 3.2.2.2 Platform tier -- 3.2.2.3 Enterprise tier -- 3.2.2.4 Multi stakeholder interactions -- 3.3 Data Management -- 3.3.1 Data Quality Considerations -- 3.3.2 Utilization of Cloud Technologies -- 3.3.3 Data Storages in MANTIS -- 3.3.4 Storage Types -- 3.3.4.1 Big data file systems…”

Tabla de Contenidos: “…Imaging Modalities -- 6.2.4 Suitable Technologies Before Machine Learning -- 6.2.5 MRI Brain Image Segmentation -- 6.3 Related Work -- 6.4 Gaps and Observations -- 6.5 Suggestions -- 6.6 Conclusion -- References -- 7 Challenges, Standards, and Solutions for Secure and Intelligent 5G Internet of Things (IoT) Scenarios Ayasha Malik and Bharat Bhushan -- 7.1 Introduction -- 7.2 Safety in Wireless Networks: Since 1G to 4G -- 7.2.1 Safety in Non-IP Networks -- 7.2.2 Safety in 3G -- 7.2.3 Security in 4G -- 7.2.4 Security in 5G -- 7.2.4.1 Flashy System Traffic and Radio Visual Security Keys -- 7.2.4.2 Authorized Network Security and Compliance with Subscriber Level Safety Policies -- 7.2.5 Security in 5G and Beyond -- 7.3 IoT Background and Requirements -- 7.3.1 IoT and Its Characteristics -- 7.3.2 Characteristics of IoT Infrastructure -- 7.3.3 Characteristics of IoT Applications -- 7.3.4 Expected Benefits of IoT Adoption for Organization -- 7.3.4.1 Benefits Correlated to Big Data Created by IoT -- 7.3.4.2 Benefits Interrelated to the Openness of IoT -- 7.3.4.3 BenefitsRelated to the Linked Aspect6 of IoT -- 7.4 Non 5G Standards Supporting IoT -- 7.4.1 Bluetooth Low Energy -- 7.4.2 IEEE 802.15.4 -- 7.4.3 LoRa -- 7.4.4 Sigfox -- 7.4.5. …”

Tabla de Contenidos: “…OTT Voice Service Quality 182 -- 6.2 QoS-enabled Video and IPTV Services 183 -- 6.2.1 IPTV and QoS 184 -- 6.3 QoE for VoIP and IPTV 188 -- 6.3.1 QoE for VoIP 188 -- 6.3.2 QoE for IPTV 190 -- 6.4 QoS for Popular Internet Services 192 -- 6.5 QoS for Business Users (VPN Services) 196 -- 6.6 QoS for Internet Access Service and Over-the-Top Data Services 198 -- 6.6.1 Traffic Management for OTT Services 200 -- 6.6.2 Traffic Management Approaches 200 -- 6.6.3 Traffic Management Influence on QoE for OTT Services 204 -- 6.7 Internet of Things (IoT) Services 205 -- 6.7.1 Mobile Cellular Internet of Things 206 -- 6.7.2 IoT Big Data and Artificial Intelligence 209 -- 6.8 Cloud Computing Services 210 -- 6.8.1 QoS Metrics for Cloud Services 212 -- 6.9 Business and Regulatory Challenges for Services Over Ultra-Broadband 214 -- 6.9.1 Business Aspects for Broadband Services 214 -- 6.9.2 Regulatory Challenges for Broadband Services 216 -- References 218 -- 7 Broadband QoS Parameters, KPIs, and Measurements 221 -- 7.1 QoS, QoE, and Application Needs 221 -- 7.2 Generic and Specific QoS Parameters 224.…”

Tabla de Contenidos: “…-- 6.7 Identifying bad data -- 6.8 Kinds of problems -- 6.9 Responses to bad data -- Techniques for fixing bad data -- Cleaning our data set -- 6.11.1 Rewriting bad rows -- 6.11.2 Filtering rows of data -- 6.11.3 Filtering columns of data -- Preparing our data for effective use -- 6.12.1 Aggregating rows of data -- 6.12.2 Combining data from different files using globby -- 6.12.3 Splitting data into separate files -- Building a data processing pipeline with Data-Forge -- Summary -- Chapter 7: Dealing with huge data files -- 7.1 Expanding our toolkit -- 7.2 Fixing temperature data -- 7.3 Getting the code and data -- 7.4 When conventional data processing breaks down -- 7.5 The limits of Node.js -- 7.5.1 Incremental data processing -- 7.5.2 Incremental core data representation -- 7.5.3 Node.js file streams basics primer -- 7.5.4 Transforming huge CSV files -- 7.5.5 Transforming huge JSON files -- 7.5.6 Mix and match -- Summary -- Chapter 8: Working with a mountain of data -- 8.1 Expanding our toolkit -- 8.2 Dealing with a mountain of data -- 8.3 Getting the code and data -- 8.4 Techniques for working with big data -- 8.4.1 Start small -- 8.4.2 Go back to small -- 8.4.3 Use a more efficient representation -- 8.4.4 Prepare your data offline -- 8.5 More Node.js limitations -- 8.6 Divide and conquer -- 8.7 Working with large databases -- 8.7.1 Database setup -- 8.7.2 Opening a connection to the database -- 8.7.3 Moving large files to your database -- 8.7.4 Incremental processing with a database cursor -- 8.7.5 Incremental processing with data windows -- 8.7.6 Creating an index…”

Tabla de Contenidos: “…2.7.2 Face Detection and Identification in Real-World Situations -- 2.8 Future Direction in Object Detection and Tracking -- 2.8.1 Future Plans for Object Tracking and Detection -- 2.8.1.1 Multiobject Tracking -- 2.8.2 3D Object Tracking and Detection -- 2.8.3 Real-Time Performance -- 2.9 Conclusion -- References -- Chapter 3 Printing Organs with 3D Technology -- 3.1 Introduction -- 3.2 Bioprinting in Three Dimensions (3D) -- 3.3 3D Printing Types -- 3.3.1 Inkjet Bioprinting -- 3.3.2 Microextrusion Bioprinting -- 3.3.3 Laser-Assisted Printing -- 3.3.4 Stereolithography -- 3.3.5 3D Bioprinting Materials and Cells -- 3.4 Applications for 3D Printing in Cells -- 3.4.1 Blood Vessels -- 3.4.2 Liver -- 3.4.3 Cartilage -- 3.4.4 Muscle -- 3.4.5 Bone -- 3.4.6 Skin -- 3.4.7 Neutralization of Neurons -- 3.4.8 Pancreas -- 3.5 New Developments -- 3.6 Progress in India -- 3.7 Limitation -- 3.8 A Future Point of View -- 3.9 Conclusion -- References -- Chapter 4 Comparative Evaluation of Machine Learning Algorithms for Bank Fraud Detection -- 4.1 Introduction -- 4.2 Proposed Framework -- 4.3 Results -- 4.4 Concluding Remarks and Future Scope -- References -- Chapter 5 An Overview of Computational-Based Strategies for Drug Repositioning -- 5.1 Introduction -- 5.2 Drug Repositioning -- 5.2.1 Computational Strategies for Drug Repositioning -- 5.2.1.1 IoT in Drug Repositioning -- 5.2.1.2 AI and ML in Drug Repositioning -- 5.2.1.3 Digital Twin in Drug Repurposing -- 5.2.1.4 Cloud Computing in Drug Repositioning -- 5.2.1.5 Big Data in Drug Repositioning -- 5.3 Challenges and Opportunities for Drug Repurposing -- 5.4 Conclusion -- References -- Chapter 6 Improving Performance With Feature Selection, Extraction, and Learning -- 6.1 Introduction -- 6.2 Feature Selection -- 6.2.1 Filter Methods -- 6.2.1.1 Procedure -- 6.2.1.2 Advantages -- 6.2.1.3 Disadvantages…”

Tabla de Contenidos: “…4.1 Introduction -- 4.2 Definition -- 4.3 Define Phase -- 4.4 Measure Phase -- 4.5 Analyse Phase -- 4.6 Improve Phase -- 4.7 Control Phase -- 4.8 Sustain Phase -- 4.9 Application -- 4.9.1 Basic Steps -- 4.9.2 Worked-Out Example -- 4.9.3 Define -- 4.9.4 Measure -- 4.9.5 Analyse -- 4.9.6 Improve -- 4.9.7 Control -- 4.9.8 Sustain -- 4.9.9 Benefits -- 4.9.10 Pitfalls -- 4.9.11 Training Requirements -- 4.10 DMAICS and DMADV -- 4.10.1 Define -- 4.10.2 Measure -- 4.10.3 Analyse -- 4.10.4 Design -- 4.10.5 Verify -- 4.11 Summary -- 5 The Scope of Green Six Sigma Tools and Techniques -- 5.1 Introduction -- 5.2 The Drivers for Tools and Techniques -- 5.3 The Problems of Using Tools and Techniques -- 5.3.1 Inadequate Training -- 5.3.2 Management Commitment of Resources -- 5.3.3 Employee Mindset -- 5.3.4 Poor Application of Tools and Techniques -- 5.4 The Critical Success Factors -- 5.4.1 Top Management Commitment -- 5.4.2 Availability of Resources -- 5.4.3 Well-Designed Education and Training Programme -- 5.4.4 Rigorous Project Management Approach -- 5.5 Summary -- 6 The Digital Revolution and Climate Change -- 6.1 Introduction -- 6.2 Information Technology and Systems -- 6.2.1 IT Hardware Strategy -- 6.2.2 IT Software Strategy -- 6.2.3 Market-Making Applications -- 6.2.4 Enterprise Resource Planning Applications -- 6.2.5 Customer Relationships Management Solutions -- 6.2.6 Supply Chain Management Solutions -- 6.2.7 Implementation Strategy -- 6.3 E-business -- 6.3.1 E-commerce Solutions -- 6.4 Big Data and Artificial Intelligence -- 6.5 Digital Tools for Green Six Sigma -- 6.6 Quality 4.0 -- 6.7 Digital Technology Applications in Climate Change -- 6.8 Summary -- 7 Green Six Sigma in Manufacturing, Services, Projects and SMEs -- 7.1 Introduction -- 7.2 Green Six Sigma in Large Manufacturing Operations -- 7.3 Green Six Sigma in Service Operations…”

“…You’ll learn: How performance management software can reduce the risk of running modern data applications Methods for applying AI to provide insights, recommendations, and automation to operationalize big data systems and data applications How to plan, migrate, and operate big data workloads and data pipelines in the cloud and in hybrid deployment models…”

Tabla de Contenidos: “…Things (IoT) 327 -- 12.1.10 The Age of Big Data 328 -- 12.1.11 5G and Femtocells 328 -- 12.1.12 Antenna Design and Channel Modelling 328 -- References 330 -- Index 332 --…”

“…With this intelligence explosion, the influence of artificial intelligence technology and the key themes of machine learning, big data, and digital twin are evolving and creating the need for cyber-physical management professionals. …”

“…In addition, the book looks at the impact of big data on relational databases and the option of using NoSQL databases for that purpose. …”

“…Provides a background in data science fundamentals and preparing your data for analysis Details different data visualization techniques that can be used to showcase and summarize your data Explains both supervised and unsupervised machine learning, including regression, model validation, and clustering techniques Includes coverage of big data processing tools like MapReduce, Hadoop, Dremel, Storm, and Spark It's a big, big data world out there—let Data Science For Dummies help you harness its power and gain a competitive edge for your organization…”