Tabla de Contenidos: “…Intro -- Table of Contents -- Title Page -- Copyright -- Preface -- 1 Comprehensive and Self-Contained Introduction to Deep Reinforcement Learning -- 1.1 Introduction -- 1.2 Comprehensive Study -- 1.3 Deep Reinforcement Learning: Value-Based and Policy-Based Learning -- 1.4 Applications and Challenges of Applying Reinforcement Learning to Real-World -- 1.5 Conclusion -- References -- 2 Impact of AI in 5G Wireless Technologies and Communication Systems -- 2.1 Introduction -- 2.2 Integrated Services of AI in 5G and 5G in AI -- 2.3 Artificial Intelligence and 5G in the Industrial Space -- 2.4 Future Research and Challenges of Artificial Intelligence in Mobile Networks -- 2.5 Conclusion -- References -- 3 Artificial Intelligence Revolution in Logistics and Supply Chain Management -- 3.1 Introduction -- 3.2 Theory-AI in Logistics and Supply Chain Market -- 3.3 Factors to Propel Business Into the Future Harnessing Automation -- 3.4 Conclusion -- References -- 4 An Empirical Study of Crop Yield Prediction Using Reinforcement Learning -- 4.1 Introduction -- 4.2 An Overview of Reinforcement Learning in Agriculture -- 4.3 Reinforcement Learning Startups for Crop Prediction -- 4.4 Conclusion -- References -- 5 Cost Optimization for Inventory Management in Blockchain and Cloud -- 5.1 Introduction -- 5.2 Blockchain: The Future of Inventory Management -- 5.3 Cost Optimization for Blockchain Inventory Management in Cloud -- 5.4 Cost Reduction Strategies in Blockchain Inventory Management in Cloud -- 5.5 Conclusion -- References -- 6 Review of Deep Learning Architectures Used for Identification and Classification of Plant Leaf Diseases -- 6.1 Introduction -- 6.2 Literature Review -- 6.3 Proposed Idea -- 6.4 Reference Gap -- 6.5 Conclusion -- References -- 7 Generating Art and Music Using Deep Neural Networks -- 7.1 Introduction -- 7.2 Related Works…”

Tabla de Contenidos: “…Artificial Intelligence -- 2.2.2. Types of AI -- 2.2.3. Machine Learning -- 2.2.4. Types of Machine Learning -- 2.3. …”

Tabla de Contenidos: “…Risk Management: A New Challenge -- Areas of Concern -- Synthesis -- Key Takeaways -- About the Authors -- Selected References -- Chapter 10 A Critique of the Investment Fund Governance Regime in Canada -- Introduction -- Investment Funds -- Types of Investment Fund -- Relationship with the Fund Manager -- How Funds Are Constituted -- The Problem -- Addressing the Problem -- Fund Governance in the United States -- Termination of the Management Agreement -- Approval of the Management Agreement -- Management of Conflicts of Interest -- Oversight of the Compliance Function -- Other Responsibilities -- The Fund Governance Regime in Canada -- Comparison with the United States -- Standard of Care -- Mandate -- Scope of Authority -- Membership -- Impact of the IRC -- Limitations of the IRC -- In Sum -- About the Author -- Notes -- Chapter 11 Third Stage Corporate Governance: Investors and Companies Divided by a Common Language -- About the Authors -- Notes -- Part III Technology Governance -- Chapter 12 Technology and the Corporate Board: Critical Considerations Going Forward -- Introduction -- History -- Exponential Thinking -- Emerging Technologies -- Artificial Intelligence -- Blockchain -- Cloud/Edge -- Telecommunications 5G/6G -- IoT and the Metaverse -- Quantum Computing -- Cybercrime -- Other Technologies -- Business Models and Disruptive Technology -- Conclusion -- References -- About the Author -- Chapter 13 Questions the Board Should Ask About Artificial Intelligence -- 1. Understanding AI -- 2. Unprecedented Growth in AI -- 3. Societal Concerns About AI -- 4. …”

Tabla de Contenidos: “…AIoT in everything -- 1 Current developments and trends in video surveillance -- 1.1 Introduction -- 1.2 Related studies -- 1.2.1 The summary of the findings -- 1.2.1.1 Improvements in the video surveillance system to our well-being -- 1.2.1.2 The technical challenges of video surveillance and applications -- 1.2.1.3 The improvement of neural networks in video surveillance systems -- 1.3 Result and discussion -- 1.4 Conclusion and recommendation -- References -- 2 Application of artificial intelligence in mobile networks: a survey -- 2.1 Introduction -- 2.2 Trends of mobile networks -- 2.3 Mobile network issues -- 2.3.1 Planning process issues -- 2.3.2 Maintenance issues -- 2.4 Motivations for AI-enabled mobile network -- 2.5 Possibilities of using AI technology to operate mobile networks -- 2.5.1 The PHY and MAC layers using AI -- 2.5.1.1 Prediction and channel estimation -- 2.5.1.2 Processing of reception -- 2.5.1.3 Decoding a channel -- 2.5.2 Applying AI to planning and maintenance process -- 2.5.2.1 Planning process -- 2.5.2.2 Maintenance process -- 2.6 Solutions to challenges of AI mobile networks -- 2.7 Expected development/deployment -- 2.8 Conclusion -- Acknowledgment -- References -- 3 Metaverse technology in education: an enhancement for the future -- 3.1 Introduction -- 3.2 Application of metaverse in education -- 3.2.1 Augmented reality usage in education -- 3.2.2 Virtual reality usage in education -- 3.2.3 Lifelogging usage in education -- 3.2.4 Mirrow world usage in education -- 3.3 How does metaverse change education? …”

Tabla de Contenidos: “…2.6.1 IT Support -- 2.7 I4.0 in Functional Field -- 2.7.1 I4.0 Logistics -- 2.7.2 Resource Planning -- 2.7.3 Systems for Warehouse Management -- 2.7.4 Transportation Management Systems -- 2.7.5 Transportation Systems with Intelligence -- 2.7.6 Information Security -- 2.8 Existing Technology in I4.0 -- 2.8.1 Applications of I4.0 in Existing Industries -- 2.8.2 Additive Manufacturing (AM) -- 2.8.3 Intelligent Machines -- 2.8.4 Robots that are Self-Aware -- 2.8.5 Materials that are Smart -- 2.8.6 IoT -- 2.8.7 The Internet of Things in Industry (IIoT) -- 2.8.8 Sensors that are Smart -- 2.8.9 System Using a Smart Programmable Logic Controller (PLC) -- 2.8.10 Software -- 2.8.11 Augmented Reality (AR)/Virtual Reality (VR) -- 2.8.12 Gateway for the Internet of Things -- 2.8.13 Cloud -- 2.8.14 Applications of Additive Manufacturing in I4.0 -- 2.8.15 Artificial Intelligence (AI) -- 2.9 Applications in Current Industries -- 2.9.1 I4.0 in Logistics -- 2.9.2 I4.0 in Manufacturing Operation -- 2.10 Future Scope of Research -- 2.10.1 Theoretical Framework of I4.0 -- 2.11 Discussion and Implications -- 2.11.1 Hosting: Microsoft -- 2.11.2 Platform for the Internet of Things (IoT): Microsoft, GE, PTC, and Siemens -- 2.11.3 A Systematic Computational Analysis -- 2.11.4 Festo Proximity Sensor -- 2.11.5 Connectivity Hardware: HMS -- 2.11.6 IT Security: Claroty -- 2.11.7 Accenture Is a Systems Integrator -- 2.11.8 Additive Manufacturing: General Electric -- 2.11.9 Augmented and Virtual Reality: Upskill -- 2.11.10 ABB Collaborative Robots -- 2.11.11 Connected Vision System: Cognex -- 2.11.12 Drones/UAVs: PINC -- 2.11.13 Self-Driving in Vehicles: Clear Path Robotics -- 2.12 Conclusion -- References -- Chapter 3 Interaction of Internet of Things and Sensors for Machining -- 3.1 Introduction -- 3.2 Various Sensors Involved in Machining Process -- 3.2.1 Direct Method Sensors…”

Tabla de Contenidos: “…2.6.1 IT Support -- 2.7 I4.0 in Functional Field -- 2.7.1 I4.0 Logistics -- 2.7.2 Resource Planning -- 2.7.3 Systems for Warehouse Management -- 2.7.4 Transportation Management Systems -- 2.7.5 Transportation Systems with Intelligence -- 2.7.6 Information Security -- 2.8 Existing Technology in I4.0 -- 2.8.1 Applications of I4.0 in Existing Industries -- 2.8.2 Additive Manufacturing (AM) -- 2.8.3 Intelligent Machines -- 2.8.4 Robots that are Self-Aware -- 2.8.5 Materials that are Smart -- 2.8.6 IoT -- 2.8.7 The Internet of Things in Industry (IIoT) -- 2.8.8 Sensors that are Smart -- 2.8.9 System Using a Smart Programmable Logic Controller (PLC) -- 2.8.10 Software -- 2.8.11 Augmented Reality (AR)/Virtual Reality (VR) -- 2.8.12 Gateway for the Internet of Things -- 2.8.13 Cloud -- 2.8.14 Applications of Additive Manufacturing in I4.0 -- 2.8.15 Artificial Intelligence (AI) -- 2.9 Applications in Current Industries -- 2.9.1 I4.0 in Logistics -- 2.9.2 I4.0 in Manufacturing Operation -- 2.10 Future Scope of Research -- 2.10.1 Theoretical Framework of I4.0 -- 2.11 Discussion and Implications -- 2.11.1 Hosting: Microsoft -- 2.11.2 Platform for the Internet of Things (IoT): Microsoft, GE, PTC, and Siemens -- 2.11.3 A Systematic Computational Analysis -- 2.11.4 Festo Proximity Sensor -- 2.11.5 Connectivity Hardware: HMS -- 2.11.6 IT Security: Claroty -- 2.11.7 Accenture Is a Systems Integrator -- 2.11.8 Additive Manufacturing: General Electric -- 2.11.9 Augmented and Virtual Reality: Upskill -- 2.11.10 ABB Collaborative Robots -- 2.11.11 Connected Vision System: Cognex -- 2.11.12 Drones/UAVs: PINC -- 2.11.13 Self-Driving in Vehicles: Clear Path Robotics -- 2.12 Conclusion -- References -- Chapter 3 Interaction of Internet of Things and Sensors for Machining -- 3.1 Introduction -- 3.2 Various Sensors Involved in Machining Process -- 3.2.1 Direct Method Sensors…”

Tabla de Contenidos: “…Cover -- Half Title -- Series Page -- Title Page -- Copyright Page -- Table of Contents -- Preface -- Acknowledgments -- Editors -- Contributors -- Chapter 1: IoT architecture and design -- 1.1 Introduction -- 1.1.1 Challenges: IoT architecture drivers -- 1.1.2 Classical IoT architecture models -- 1.1.2.1 oneM2M architecture -- 1.1.2.2 IoT world forum reference model -- 1.1.3 Emerging IoT architecture models -- 1.1.4 Conclusion -- References -- Chapter 2: Application of Artificial Intelligence (AI) and the Internet of Things (IoT) in process industries toward Industry 4.0 -- 2.1 Introduction -- 2.1.1 1st industrial revolution -- 2.1.2 2nd industrial revolution -- 2.1.3 3rd industrial revolution -- 2.1.4 4th industrial revolution -- 2.2 Industry 4.0 -- 2.2.1 Need for Industry 4.0 -- 2.2.2 The Significance of Industry 4.0 -- 2.2.3 Technologies of Industry 4.0 -- 2.2.4 Nine components that are bringing about the transformation to Industry 4.0 -- 2.2.4.1 Cloud computing -- 2.2.4.2 Big data -- 2.2.4.3 The Internet of Things -- 2.2.4.4 Augmented reality -- 2.2.4.5 System integration: horizontal and vertical system integration -- 2.2.4.6 Cybersecurity -- 2.2.4.7 Simulation -- 2.2.4.8 Autonomous robot -- 2.2.4.9 Additive manufacturing -- 2.2.5 Issues and challenges faced by Industry 4.0 -- 2.3 Artificial intelligence -- 2.3.1 Artificial Intelligence: a brief history -- 2.3.2 Approach of Artificial Intelligence in Industry 4.0 -- 2.3.3 Key elements in Industrial AI -- 2.3.4 Industrial AI ecosystem -- 2.3.5 Limitations of industrial Artificial Intelligence -- 2.4 Examples -- 2.4.1 Automotive -- 2.4.1.1 Description -- 2.4.2 Food and beverage -- 2.4.2.1 Description -- 2.4.3 Medical equipment -- 2.4.3.1 Description -- 2.5 Conclusion -- Acknowledgements -- References…”

Tabla de Contenidos: “…-- 1.1.1 Academic AI -- 1.1.2 Game AI -- 1.2 Model of Game AI -- 1.2.1 Movement -- 1.2.2 Decision Making -- 1.2.3 Strategy -- 1.2.4 Infrastructure -- 1.2.5 Agent-Based AI -- 1.2.6 In the Book -- 1.3 Algorithms and Data Structures -- 1.3.1 Algorithms -- 1.3.2 Representations -- 1.3.3 Implementation -- 1.4 Layout of the Book -- CHAPTER 2: GAME AI -- 2.1 The Complexity Fallacy -- 2.1.1 When Simple Things Look Good -- 2.1.2 When Complex Things Look Bad -- 2.1.3 The Perception Window -- 2.1.4 Changes of Behavior -- 2.2 The Kind of AI in Games -- 2.2.1 Hacks -- 2.2.2 Heuristics -- 2.2.3 Algorithms -- 2.3 Speed and Memory Constraints -- 2.3.1 Processor Issues -- 2.3.2 Memory Concerns -- 2.3.3 Platforms -- 2.4 The AI Engine -- 2.4.1 Structure of an AI Engine -- 2.4.2 Tool Concerns -- 2.4.3 Putting It All Together -- PART II: Techniques -- CHAPTER 3: MOVEMENT -- 3.1 The Basics of Movement Algorithms -- 3.1.1 Two-Dimensional Movement -- 3.1.2 Statics -- 3.1.3 Kinematics -- 3.2 Kinematic Movement Algorithms -- 3.2.1 Seek -- 3.2.2 Wandering -- 3.3 Steering Behaviors -- 3.3.1 Steering Basics -- 3.3.2 Variable Matching -- 3.3.3 Seek and Flee -- 3.3.4 Arrive -- 3.3.5 Align -- 3.3.6 Velocity Matching -- 3.3.7 Delegated Behaviors -- 3.3.8 Pursue and Evade -- 3.3.9 Face -- 3.3.10 Looking Where You're Going -- 3.3.11 Wander -- 3.3.12 Path Following -- 3.3.13 Separation -- 3.3.14 Collision Avoidance -- 3.3.15 Obstacle and Wall Avoidance -- 3.3.16 Summary -- 3.4 Combining Steering Behaviors -- 3.4.1 Blending and Arbitration -- 3.4.2 Weighted Blending -- 3.4.3 Priorities -- 3.4.4 Cooperative Arbitration -- 3.4.5 Steering Pipeline -- 3.5 Predicting Physics -- 3.5.1 Aiming and Shooting -- 3.5.2 Projectile Trajectory…”

Tabla de Contenidos: “…Introduction -- 1.1 Language and speech disorders: Types, diagnosis, and treatment -- 1.1.1 Fluency speech disorders -- 1.1.2 Resonance disorders -- 1.1.3 Voice disorders -- 1.1.4 Motor speech disorders -- 1.1.5 Language disorders -- 1.1.6 Acquired language disorders -- 1.1.7 Speech sound disorders -- 1.1.7.1 Deep learning and machine learning in AI -- 1.1.7.1 Deep learning and machine learning in AI -- 2. Research methodology -- 2.1 Research questions -- 2.2 Data collection and data analysis process -- 3. …”

Tabla de Contenidos: “…Cover -- Series Page -- Title Page -- Copyright Page -- Contents -- Preface -- Chapter 1 Artificial Intelligence in Risk Management -- 1.1 Introduction -- 1.1.1 Context and the Driving Force Behind It -- 1.1.2 Aim of This Chapter -- 1.1.3 Outline of This Chapter -- 1.2 The Role of AI in Risk Management -- 1.2.1 The Significance of Risk Management -- 1.2.2 Deficiencies in Conventional Methods of Risk Management -- 1.2.3 The Requirement for Advanced Methods -- 1.3 Role of Artificial Intelligence in Risk Management -- 1.3.1 An Overview of Artificial Intelligence and Its Applications -- 1.3.2 Applications of AI-Based Methods in Risk Management -- 1.4 The Challenges of Implementing AI-Based Risk Management Systems -- 1.5 The Benefits of Using Artificial Intelligence in Risk Management -- 1.6 Conclusions and Future Considerations of AI in Risk Management -- 1.6.1 A Brief Review of the Role of AI in Risk Management -- 1.6.2 Perspectives on the Future -- 1.6.3 The Transformative Power of AI in Risk Management -- 1.7 The Implications and Factors to Take Into Account While Using AI in Risk Management -- 1.8 Overcoming Obstacles and Putting AI to Work in Risk Management -- 1.9 Conclusion -- References -- Chapter 2 Application of Artificial Intelligence in Risk Assessment and Mitigation in Banks -- 2.1 Introduction -- 2.2 Transitions in Banking Due to AI -- 2.3 Risk Assessment and Mitigation through Artificial Intelligence -- 2.3.1 Fraud Recognition -- 2.3.2 Regulatory Compliance Management -- 2.3.3 Credit Risk Modeling -- 2.3.4 Insider Threat Prevention -- 2.4 General Banking Regulations Pertaining to Artificial Intelligence -- 2.5 Methodology -- 2.5.1 Bibliometric Analysis -- 2.5.2 Co-Occurrence Analysis -- 2.6 Theoretical Implications -- 2.7 Managerial Implications -- 2.8 Future Scope -- 2.9 Conclusion -- References…”

Tabla de Contenidos: “…-- 1.3.7 Advantages and Disadvantages of AI in Post COVID Era -- 1.4 Conclusion -- References -- 2 Healthcare System 4.0 Perspectives on COVID-19 Pandemic -- 2.1 Introduction -- 2.2 Key Techniques of HCS 4.0 for COVID-19 -- 2.2.1 Artificial Intelligence (AI) -- 2.2.2 The Internet of Things (IoT) -- 2.2.3 Big Data -- 2.2.4 Virtual Reality (VR) -- 2.2.5 Holography -- 2.2.6 Cloud Computing -- 2.2.7 Autonomous Robots -- 2.2.8 3D Scanning -- 2.2.9 3D Printing Technology -- 2.2.10 Biosensors -- 2.3 Real World Applications of HCS 4.0 for COVID-19 -- 2.4 Opportunities and Limitations -- 2.5 Future Perspectives -- 2.6 Conclusion -- References -- 3 Analysis and Prediction on COVID-19 Using Machine Learning Techniques -- 3.1 Introduction -- 3.2 Literature Review -- 3.3 Types of Machine Learning…”

Tabla de Contenidos: “…Benchmarking your Go AI -- 2.7. Summary -- 3 Implementing your first Go bot -- 3.1. …”

Tabla de Contenidos: “…Astigmatism of Vision -- 2.13 The Illusion of Data, Delusion of Big Data, and the Absence of Intelligence in AI -- 2.14 In Service of Society -- 2.15 Data Science in Service of Society: Knowledge and Performance from PEAS -- 2.16 Temporary Conclusion -- Acknowledgements -- References -- Chapter 3 Machine Learning Techniques for IoT Data Analytics -- 3.1 Introduction -- 3.2 Taxonomy of Machine Learning Techniques -- 3.2.1 Supervised ML Algorithm -- 3.2.1.1 Classification -- 3.2.1.2 Regression Analysis -- 3.2.1.3 Classification and Regression Tasks -- 3.2.2 Unsupervised Machine Learning Algorithms -- 3.2.2.1 Clustering -- 3.2.2.2 Feature Extraction -- 3.2.3 Conclusion -- References -- Chapter 4 IoT Data Analytics Using Cloud Computing -- 4.1 Introduction -- 4.2 IoT Data Analytics -- 4.2.1 Process of IoT Analytics -- 4.2.2 Types of Analytics -- 4.3 Cloud Computing for IoT -- 4.3.1 Deployment Models for Cloud…”

Tabla de Contenidos: “…Maheswari, Mohankumar, and Banuroopa -- 2.1 5G - An Introduction -- 2.1.1 Industry Applications -- 2.1.2 Healthcare -- 2.1.3 Retail -- 2.1.4 Agriculture -- 2.1.5 Manufacturing -- 2.1.6 Logistics -- 2.1.7 Sustainability of 5G Networks -- 2.1.8 Implementation of 5G -- 2.1.9 Architecture of 5G Technology -- 2.2 5G and AI -- 2.2.1 Gaming and Virtual Reality -- 2.3 AI and 5G -- 2.3.1 Continuous Learning AI Model -- 2.4 Challenges and Roadmap -- 2.4.1 Technical Issues -- 2.4.2 Technology Roadmap -- 2.4.3 Deployment Roadmap -- 2.5 Mathematical Models -- 2.5.1 The Insights of Mathematical Modeling in 5G Networks -- 2.6 Conclusion -- References -- 3 Sustainable Paradigm for Computing the Security of Wireless Internet of Things: Blockchain Technology Sana Zeba, Mohammed Amjad, and Danish Raza Rizvi -- 3.1 Introduction -- 3.2 Research Background -- 3.2.1 The Internet of Things -- 3.2.1.1 Security Requirements in Wireless IoT -- 3.2.1.2 Layered Architecture of Wireless IoT -- 3.2.2 Blockchain Technology -- 3.2.2.1 Types of Blockchain -- 3.2.2.2 Integration of Blockchain with Wireless Internet of Things -- 3.3 Related Work -- 3.3.1 Security Issues in Wireless IoT System -- 3.3.2 Solutions of Wireless IoT Security Problem -- 3.4 Research Methodology -- 3.5 Comparison of Various Existing Solutions -- 3.6 Discussion of Research Questions -- 3.7 Future Scope of Blockchain in IoT -- 3.8 Conclusion -- References -- 4 Cognitive IoT Based Health Monitoring Scheme Using Non-Orthogonal MultipleAccess Ashiqur Rahman Rahul, Saifur Rahman Sabuj, Majumder Fazle Haider, andShakil Ahmed -- 4.1 Introduction -- 4.2 Related Work -- 4.3 System Model and Implementation -- 4.3.1 Network Description -- 4.3.2 Sensing and Transmission Analysis -- 4.3.3 Pathloss Model -- 4.3.4 Mathematical Model Evaluation -- 4.3.4.1 Effectual Throughput -- 4.3.4.2 Interference Throughput -- 4.3.4.3 Energy Efficiency -- 4.3.4.4 Optimum Power -- 4.3.4.4.1 Optimum Power Derivation for HRC -- 4.2.3.4.2 Optimum Power Derivation for MRC -- 4.4 Simulation Results -- 4.5 Conclusion -- 4.A Appendices -- 4.A.1 Proof of Optimum Power Transmission for HRC Device at EffectualState (z = 0) -- 4.A.2 Proof of Optimum Power Transmission for HRC Device inInterference State (z = 1) -- 4.A.3 Proof of Optimum Power Transmission for MRC Device at EffectualState (z = 0) -- 4.A.4 Proof of Optimum Power Transmission for MRC Device inInterference State (z = 1) -- References -- 5 Overview of Resource Management for Wireless Adhoc Network Mehajabeen Fatima and Afreen Khueaheed -- 5.1 Introduction -- 5.1.1 Wired and Wireless Network Design Approach -- 5.1.2 History -- 5.1.3 Spectrum of Wireless Adhoc Network -- 5.1.4 Enabling and Networking Technologies -- 5.1.5 Taxonomy of Wireless Adhoc Network (WANET) -- 5.2 Mobile Adhoc Network (MANET) -- 5.2.1 Introduction to MANET -- 5.2.2 Common Characteristics of MANET -- 5.2.3 Advantages and Disadvantages -- 5.2.4 Applications of MANET -- 5.2.5 Major Issues of MANET -- 5.3 Vehicular Adhoc Network (VANET) -- 5.3.1 Introduction of VANET -- 5.3.2 Common Features of VANET -- 5.3.3 Pros, Cons, Applications -- 5.4 Wireless Mesh Network (WMN) -- 5.4.1 Preface of WMN -- 5.4.2 Common Traits of WMN -- 5.4.3 WMN Has Many Open Issues and Research Challenges -- 5.4.4 Performance Metrics -- 5.4.5 Advantages and Disadvantages -- 5.4.6 Prominent Areas and Challenges of WMN -- 5.5 Wireless Sensor Network (WSN) -- 5.5.1 Overview of WSN -- 5.5.2 Common Properties of WSN -- 5.5.3 Benefits, Harms, and Usage of WSN -- 5.6 Intelligent Management in WANET -- 5.6.1 Major Issues of WANET -- 5.6.2 Challenges of MAC Protocols -- 5.6.3 Routing Protocols -- 5.6.3.1 Challenges of Routing Protocols -- 5.6.3.1.1 Scalability -- 5.6.3.1.2 Quality of Service -- 5.6.3.1.3 Security -- 5.6.4 Energy and Battery Management -- 5.7 Future Research Directions -- 5.8 Conclusion -- References -- 6 Survey: Brain Tumor Detection Using MRI Image with Deep Learning Techniques Chalapathiraju Kanumuri and C.H. …”

“…What You’ll Learn: Know the technologies underpinning Industry 4.0 and AI 2.0 Develop real-time solutions using IoT in Azure Bring the smart capabilities of AI 2.0 into your application using a simple API call Host and manage your solution on Azure Understand Blockchain as a Service Capture and analyze data on the fly Make predictions using existing data This book is for novice and intermediate .NET developers and architects who want to learn what it takes to create a modern or next-generation application. …”