Artificial Intelligence in Industry 4.0 and 5G Technology

Name: Artificial Intelligence in Industry 4.0 and 5G Technology
Brand: Jossey-Bass
Price: 115.99 EUR
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Pandian Vasant Elias Munapo J. Joshua Thomas Gerhard-Wilhelm Weber(Herausgeber*in)

Jossey-Bass (Verlag)

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Erschienen am 24. Juni 2022

352 Seiten

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Beschreibung

Artificial Intelligence in Industry 4.0 and 5G Technology

Explores innovative and value-added solutions for application problems in the commercial, business, and industry sectors

As the pace of Artificial Intelligence (AI) technology innovation continues to accelerate, identifying the appropriate AI capabilities to embed in key decision processes has never been more critical to establishing competitive advantage. New and emerging analytics tools and technologies can be configured to optimize business value, change how an organization gains insights, and significantly improve the decision-making process across the enterprise.

Artificial Intelligence in Industry 4.0 and 5G Technology helps readers solve real-world technological engineering optimization problems using evolutionary and swarm intelligence, mathematical programming, multi-objective optimization, and other cutting-edge intelligent optimization methods. Contributions from leading experts in the field present original research on both the theoretical and practical aspects of implementing new AI techniques in a variety of sectors, including Big Data analytics, smart manufacturing, renewable energy, smart cities, robotics, and the Internet of Things (IoT).

* Presents detailed information on meta-heuristic applications with a focus on technology and engineering sectors such as smart manufacturing, smart production, innovative cities, and 5G networks.

* Offers insights into the use of metaheuristic strategies to solve optimization problems in business, economics, finance, and industry where uncertainty is a factor.

* Provides guidance on implementing metaheuristics in different applications and hybrid technological systems.

* Describes various AI approaches utilizing hybrid meta-heuristics optimization algorithms, including meta-search engines for innovative research and hyper-heuristics algorithms for performance measurement.

Artificial Intelligence in Industry 4.0 and 5G Technology is a valuable resource for IT specialists, industry professionals, managers and executives, researchers, scientists, engineers, and advanced students an up-to-date reference to innovative computing, uncertainty management, and optimization approaches.

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Inhalt

Cover
Title Page
Copyright
Contents
List of Contributors
Preface
Profile of Editors
Acknowledgments
Chapter 1 Dynamic Key-based Biometric End-User Authentication Proposal for IoT in Industry 4.0
1.1 Introduction
1.2 Literature Review
1.3 Proposed Framework
1.3.1 Enrolment Phase
1.3.2 Authentication Phase
1.3.2.1 Pre-processing
1.3.2.2 Minutiae Extraction and False Minutiae Removal
1.3.2.3 Key Generation from extracted Minutiae points
1.3.2.4 Encrypting the Biometric Fingerprint Image Using AES
1.4 Comparative Analysis
1.5 Conclusion
References
Chapter 2 Decision Support Methodology for Scheduling Orders in Additive Manufacturing
2.1 Introduction
2.2 The Additive Manufacturing Process
2.3 Some Background
2.4 Proposed Approach
2.4.1 A Mathematical Model for the Initial Printing Scheduling
2.4.1.1 Considerations
2.4.1.2 Sets
2.4.2 Parameters
2.4.2.1 Orders
2.4.2.2 Parts
2.4.2.3 Printing Machines
2.4.2.4 Process
2.4.3 Decision Variables
2.4.4 Optimization Criteria
2.4.5 Constrains
2.5 Results
2.5.1 Orders
2.6 Conclusions
References
Chapter 3 Significance of Consuming 5G-Built Artificial Intelligence in Smart Cities
3.1 Introduction
3.2 Background and Related Work
3.3 Challenges in Smart Cities
3.3.1 Data Acquisition
3.3.2 Data Analysis
3.3.3 Data Security and Privacy
3.3.4 Data Dissemination
3.4 Need for AI and Data Analytics
3.5 Applications of AI in Smart Cities
3.5.1 Road Condition Monitoring
3.5.2 Driver Behavior Monitoring
3.5.3 AI-Enabled Automatic Parking
3.5.4 Waste Management
3.5.5 Smart Governance
3.5.6 Smart Healthcare
3.5.7 Smart Grid
3.5.8 Smart Agriculture
3.6 AI-based Modeling for Smart Cities
3.6.1 Smart Cities Deployment Model
3.6.2 AI-Based Predictive Analytics
3.6.3 Pre-processing
3.6.4 Feature Selection
3.6.5 Artificial Intelligence Model
3.7 Conclusion
References
Chapter 4 Neural Network Approach to Segmentation of Economic Infrastructure Objects on High-Resolution Satellite Images
4.1 Introduction
4.2 Methodology for Constructing a Digital Terrain Model
4.3 Image Segmentation Problem
4.4 Segmentation Quality Assessment
4.5 Existing Segmentation Methods and Algorithms
4.6 Classical Methods
4.7 Neural Network Methods
4.7.1 Semantic Segmentation of Objects in Satellite Images
4.8 Segmentation with Neural Networks
4.9 Convolutional Neural Networks
4.10 Batch Normalization
4.11 Residual Blocks
4.12 Training of Neural Networks
4.13 Loss Functions
4.14 Optimization
4.15 Numerical Experiments
4.16 Description of the Training Set
4.17 Class Analysis
4.18 Augmentation
4.19 NN Architecture
4.20 Training and Results
4.21 Conclusion
Acknowledgments
References
Chapter 5 The Impact of Data Security on the Internet of Things
5.1 Introduction
5.2 Background of the Study
5.3 Problem Statement
5.4 Research Questions
5.5 Literature Review
5.5.1 The Data Security on IoT
5.5.2 The Security Threats and Awareness of Data Security on IoT
5.5.3 The Different Ways to Assist with Keeping Your IoT Device Safer from Security Threats
5.6 Research Methodology
5.6.1 Population and Sampling
5.6.2 Data Collection
5.6.3 Reliability and Validity
5.7 Chapter Results and Discussions
5.7.1 The Demographic Information
5.7.1.1 Age, Ethnic Group, and Ownership of a Smart Device
5.7.2 Awareness of Users About Data Security of the Internet of Things
5.7.3 The Security Threats that are Affecting the Internet of Things Devices
5.7.3.1 The Architecture of IoT Devices
5.7.3.2 The botnets Attack
5.7.4 The Effects of Security Threats on IoT Devices that are Affecting Users
5.7.4.1 The Slowness or Malfunctioning of the IoT Device
5.7.4.2 The Trust of Users on IoT
5.7.4.3 The Safety of Users
5.7.4.4 The Guaranteed Duration of IoT Devices
5.7.5 Different Ways to Assist with Keeping IoT Smart Devices Safer from Security Threats
5.7.5.1 The Change Default Passwords
5.7.5.2 The Easy or Common Passwords
5.7.5.3 On the Importance of Reading Privacy Policies
5.7.5.4 The Bluetooth and Wi-Fi of IoT Devices
5.7.5.5 The VPN on IoT
5.7.5.6 The Physical Restriction
5.7.5.7 Two-Factor Authentication
5.7.5.8 The Biometric Authentication
5.8 Answers to the Chapter Questions
5.8.1 Objective 1: Awareness on Users About Data Security of Internet of Things (IoT)
5.8.2 Objective 2: Determine the Security Threats that are Involved in the Internet of Things (IoT)
5.8.3 Objective 3: The Effects of Security Threats on IoT Devices that are Affecting Users
5.8.4 Objective 4: Different Ways to Assist with Keeping IoT Devices Safer from Security Threats
5.8.5 Other Descriptive Analysis (Mean)
5.8.5.1 Mean 1 - Awareness on Users About Data Security on IoT
5.8.5.2 The Effects of Security Threats on IoT Devices that are Affecting Users
5.8.5.3 Different Ways to Assist with Keeping an IoT Device Safer
5.9 Chapter Recommendations
5.10 Conclusion
References
Chapter 6 Sustainable Renewable Energy and Waste Management on Weathering Corporate Pollution
6.1 Introduction
6.2 Literature Review
6.2.1 Energy Efficiency
6.2.2 Waste Minimization
6.2.3 Water Consumption
6.2.4 Eco-Procurement
6.2.5 Communication
6.2.6 Awareness
6.2.7 Sustainable and Renewable Energy Development
6.3 Conceptual Framework
6.4 Conclusion
6.4.1 Energy Efficiency
6.4.2 Waste Minimization
6.4.3 Water Consumption
6.4.4 Eco-Procurement
6.4.5 Communication
6.4.6 Sustainable and Renewable Energy Development
Acknowledgment
References
Chapter 7 Adam Adaptive Optimization Method for Neural Network Models Regression in Image Recognition Tasks
7.1 Introduction
7.2 Problem Statement
7.3 Modifications of the Adam Optimization Method for Training a Regression Model
7.4 Computational Experiments
7.4.1 Model for Evaluating the Eye Image Blurring Degree
7.4.2 Facial Rotation Angle Estimation Model
7.5 Conclusion
Acknowledgments
References
Chapter 8 Application of Integer Programming in Allocating Energy Resources in Rural Africa
8.1 Introduction
8.1.1 Applications of the QAP
8.2 Quadratic Assignment Problem Formulation
8.2.1 Koopmans-Beckmann Formulation
8.3 Current Linearization Technique
8.3.1 The General Quadratic Binary Problem
8.3.2 Linearizing the Quadratic Binary Problem
8.3.2.1 Variable Substitution
8.3.2.2 Justification
8.3.3 Number of Variables and Constraints in the Linearized Model
8.3.4 Linearized Quadratic Binary Problem
8.3.5 Reducing the Number of Extra Constraints in the Linear Model
8.3.6 The General Binary Linear (BLP) Model
8.3.6.1 Convex Quadratic Programming Model
8.3.6.2 Transforming Binary Linear Programming (BLP) Into a Convex/Concave Quadratic Programming Problem
8.3.6.3 Equivalence
8.4 Algorithm
8.4.1 Making the Model Linear
8.5 Conclusions
References
Chapter 9 Feasibility of Drones as the Next Step in Innovative Solution for Emerging Society
9.1 Introduction
9.1.1 Technology and Business
9.1.2 Technological Revolution of the Twenty-first Century
9.2 An Overview of Drone Technology and Its Future Prospects in Indian Market
9.2.1 Utilities
9.2.1.1 Delivery
9.2.1.2 Media/Photography
9.2.1.3 Agriculture
9.2.1.4 Contingency and Disaster Management Scenarios
9.2.1.5 Civil and Military Services: Search and Rescue, Surveillance, Weather, and Traffic Monitoring, Firefighting
9.2.2 Complexities Involved
9.2.3 Drones in Indian Business Scenario
9.3 Literature Review
9.3.1 Absorption and Diffusion of New Technology
9.3.2 Leadership for Innovation
9.3.3 Social and Economic Environment
9.3.4 Customer Perceptions
9.3.5 Alliances with Other National and International Organizations
9.3.6 Other Influencers
9.4 Methodology
9.5 Discussion
9.5.1 Market Module
9.5.2 Technology Module
9.5.3 Commercial Module
9.6 Conclusions
References
Chapter 10 Designing a Distribution Network for a Soda Company: Formulation and Efficient Solution Procedure
10.1 Introduction
10.2 New Distribution System
10.3 The Mathematical Model to Design the Distribution Network
10.4 Solution Technique
10.4.1 Lagrangian Relaxation
10.4.2 Methods for Finding the Value of Lagrange Multipliers
10.4.3 Selecting the Solution Method
10.4.4 Used Notation
10.4.5 Proposed Relaxations of the Distribution Model
10.4.5.1 Relaxation 1
10.4.5.2 Relaxation 2
10.4.6 Selection of the Best Lagrangian Relaxation
10.5 Heuristic Algorithm to Restore Feasibility
10.6 Numerical Analysis
10.6.1 Scenario 2020
10.6.2 Scenario 2021
10.6.3 Scenario 2022
10.6.4 Scenario 2023
10.7 Conclusions
References
Chapter 11 Machine Learning and MCDM Approach to Characterize Student Attrition in Higher Education
11.1 Introduction
11.1.1 Background
11.2 Proposed Approach
11.3 Case Study
11.3.1 Intelligent Phase
11.3.2 Design Phase
11.3.3 Choice Phase
11.4 Results
11.5 Conclusion
References
Chapter 12 A Concise Review on Recent Optimization and Deep Learning Applications in Blockchain Technology
12.1 Background
12.2 Computational Optimization Frameworks
12.3 Internet of Things (IoT) Systems
12.4 Smart Grids Data Systems
12.5 Supply Chain Management
12.6 Healthcare Data Management Systems
12.7 Outlook
References
Chapter 13 Inventory Routing Problem with Fuzzy Demand and Deliveries with Priority
13.1 Introduction
13.2 Problem Description
13.3 Mathematical Formulation
13.4 Computational Experiments
13.4.1 Numerical Example
13.4.1.1 The Inventory Routing Problem Under Certainty
13.4.1.2 The Inventory Routing Problem Under Uncertainty in the Consumption Rate of Product
13.5 Conclusions and Future Work
References
Chapter 14 Comparison of Defuzzification Methods for Project Selection
14.1 Introduction
14.2 Problem Description
14.3 Mathematical Model
14.3.1 Sets and Parameters
14.3.2 Decision Variables
14.3.3 Objective Functions
14.4 Constraints
14.5 Methods of Defuzzification and Solution Algorithm
14.5.1 k-Preference Method
14.5.2 Integral Value
14.5.3 SAUGMECON Algorithm
14.6 Results
14.6.1 Results of k-Preference Method
14.6.2 Results of Integral Value Method
14.7 Conclusions
References
Chapter 15 Re-Identification-Based Models for Multiple Object Tracking
15.1 Introduction
15.2 Multiple Object Tracking Problem
15.3 Decomposition of Tracking into Filtering and Assignment Tasks
15.4 Cost Matrix Adjustment in Assignment Problem Based on Re-Identification with Pre-Filtering of Descriptors by Quality
15.5 Computational Experiments
15.6 Conclusion
Acknowledgments
References
Index
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