Artificial Intelligence-Enabled Digital Twin for Smart Manufacturing E-Book

• Herausgeber: John Wiley & Sons
• Kategorie: Wissenschaft und neue Technologien
• Sprache: Englisch

Seitenzahl: 1073

Veröffentlichungsjahr: 2024

Beschreibung

An essential book on the applications of AI and digital twin technology in the smart manufacturing sector.

In the rapidly evolving landscape of modern manufacturing, the integration of cutting-edge technologies has become imperative for businesses to remain competitive and adaptive. Among these technologies, Artificial Intelligence (AI) stands out as a transformative force, revolutionizing traditional manufacturing processes and making the way for the era of smart manufacturing. At the heart of this technological revolution lies the concept of the Digital Twin—an innovative approach that bridges the physical and digital realms of manufacturing. By creating a virtual representation of physical assets, processes, and systems, organizations can gain unprecedented insights, optimize operations, and enhance decision-making capabilities.

This timely book explores the convergence of AI and Digital Twin technologies to empower smart manufacturing initiatives. Through a comprehensive examination of principles, methodologies, and practical applications, it explains the transformative potential of AI-enabled Digital Twins across various facets of the manufacturing lifecycle. From design and prototyping to production and maintenance, AI-enabled Digital Twins offer multifaceted advantages that redefine traditional paradigms. By leveraging AI algorithms for data analysis, predictive modeling, and autonomous optimization, manufacturers can achieve unparalleled levels of efficiency, quality, and agility.

This book explains how AI enhances the capabilities of Digital Twins by creating a powerful tool that can optimize production processes, improve product quality, and streamline operations. Note that the Digital Twin in this context is a virtual representation of a physical manufacturing system, including machines, processes, and products. It continuously collects real-time data from sensors and other sources, allowing it to mirror the physical system’s behavior and performance.

What sets this Digital Twin apart is the incorporation of AI algorithms and machine learning techniques that enable it to analyze and predict outcomes, recommend improvements, and autonomously make adjustments to enhance manufacturing efficiency. This book outlines essential elements, like real-time monitoring of machines, predictive analytics of machines and data, optimization of the resources, quality control of the product, resource management, decision support (timely or quickly accurate decisions).

Moreover, this book elucidates the symbiotic relationship between AI and Digital Twins, highlighting how AI augments the capabilities of Digital Twins by infusing them with intelligence, adaptability, and autonomy. Hence, this book promises to enhance competitiveness, reduce operational costs, and facilitate innovation in the manufacturing industry. By harnessing AI’s capabilities in conjunction with Digital Twins, manufacturers can achieve a more agile and responsive production environment, ultimately driving the evolution of smart factories and Industry 4.0/5.0.

Audience

This book has a wide audience in computer science, artificial intelligence, and manufacturing engineering, as well as engineers in a variety of industrial manufacturing industries. It will also appeal to economists and policymakers working on the circular economy, clean tech investors, industrial decision-makers, and environmental professionals.

Leseprobe

Table of Contents

Cover

Table of Contents

Series Page

Title Page

Copyright Page

Preface

Part 1: Fundamentals of AI-Based Smart Manufacturing

1 Machine Learning Fundamentals

1.1 Introduction

1.2 Classification

1.3 Regression

1.4 Clustering

1.5 Conclusion

References

2 Industry 4.0 in Manufacturing, Communication, Transportation, Healthcare

2.1 Introduction

2.2 Industry 4.0 in Manufacturing: Overview

2.3 Industry 4.0 in Communication: Overview

2.4 Industry 4.0 in Transportation: Overview

2.5 Industry 4.0 in Healthcare: Overview

2.6 Future of Industry 4.0 in Terms of Emerging Trends and Technologies

2.7 Implications of Industry 4.0 on Various Sectors

2.8 Opportunities for Businesses and Industries

2.9 Conclusion

References

3 Data Analytics and Big Data Analytics

3.1 Introduction to Data Analytics

3.2 Literature Survey

3.3 An Overview of Big Data Analytics

3.4 Process of Generation of Big Data Analytics for Manufacturing

3.5 Utilizing Big Data Analytics for Manufacturing Market Analysis

3.6 Global Big Data Insights for the Manufacturing Sector

3.7 Conclusion

References

4 Artificial Intelligence Empowered Smart Manufacturing for Modern Society: A Review

4.1 Introduction to AI, Smart Manufacturing

4.2 AI Applications in Manufacturing

4.3 Benefits and Challenges with AI

4.4 Emerging Technologies Enabling Smart Manufacturing

4.5 AI-Driven Smart Manufacturing

4.6 Popular Challenges and Issues Towards AI-Based Smart Manufacturing Systems

4.7 AI-Based Smart Manufacturing Systems for the Future

4.8 Improving Operational Efficiency and Environmental Sustainability in AI Based Smart Manufacturing

4.9 Future Research Opportunities and Research Gaps Towards AI-Empowered Smart Manufacturing

4.10 The Evolution of Industry 5.0 and Industry 6.0

4.11 Conclusion

References

5 Use Cases of Digital Twin in Smart Manufacturing

5.1 Introduction

5.2 Review of Relevant Literature

5.3 Various Use Cases of Digital Twin in Smart Manufacturing

5.4 Information Management System-Based Digital Twins and Big Data for Sustainable Smart Manufacturing

5.5 Challenges and Future Avenues

5.6 Conclusion

References

Part 2: Methods and Applications

6 Distributed Systems and Distributed Ledger Technology - An Introduction

6.1 An Introduction

6.2 Related Work

6.3 Blockchain – In General

6.4 Evolution of Blockchain

6.5 Generic Elements of a Blockchain

6.6 Benefits and Limitations of Blockchain

6.7 Tiers of Blockchain Technology

6.8 Features of a Blockchain

6.9 Types of Blockchain

6.10 Open Issues in Blockchain Technology

6.11 Important Challenges with Blockchain Technology

6.12 Conclusion

References

7 Digital Twins Tools and Technologies in Smart Manufacturing

7.1 Introduction

7.2 Applications and Characteristics of DT

7.3 DT in Manufacturing

7.4 Related Work

7.5 Case Study: Challenge Advisory [30]

7.6 Challenges to Implement DT [36]

7.7 Open Research

7.8 Conclusion

References

8 Blockchain Based Digital Twin for Smart Manufacturing

8.1 Introduction to Blockchain, Digital Twin, and Smart Manufacturing

8.2 Issues and Challenges in Conventional Manufacturing Processes

8.3 Digital Twins and Blockchain in Manufacturing

8.4 Synergy of Blockchain and Digital Twins for Smart Manufacturing

8.5 AI, Blockchain, IoT and Other Emerging Technologies: Role in Smart Manufacturing

8.6 Key Technologies for Blockchain-Based Digital Twins

8.7 Applications of Blockchain-Based Digital Twins in Smart Manufacturing

8.8 Security and Data Privacy in Smart Manufacturing - In General

8.9 Case Studies

8.10 Future Research Towards Integration of AI and Blockchain for Autonomous Manufacturing

8.11 Sustainability and Environmental Impact via Smart Manufacturing

8.12 Conclusion

References

9 Blockchain for Internet of Things and Machine Learning-Based Automated Sectors

9.1 Introduction

9.2 Evolution Variants and Architecture of Internet of Things

9.3 Evolution, Variants, and Architecture Machine Learning

9.4 Blockchain for Internet of Things and Machine Learning

9.5 Blockchain-Based Learning Automated Analytics Platforms

9.6 Blockchain Inclusion in Internet of Things Architecture and Machine Learning

9.7 Features Benefits Limitations Applications and Challenges of Internet of Things

9.8 Features Benefits Limitations Applications and Challenges of Machine Learning

9.9 Physical, Network, Software Attacks in Internet of Things and Machine Learning-Based Applications

9.10 Countermeasures for Raised Challenges and Reliability of Blockchain in Machine Learning/Artificial Intelligence

9.11 Countermeasures for Raised Challenges and Reliability of Blockchain in IoT

9.12 Adoption of Blockchain with Internet of Things Systems

9.13 Adoption of Blockchain with Machine Learning-Based Computing Environment

9.14 Blockchain- and Machine Learning-Based Solutions for Big Data Challenges

9.15 Blockchain-Enabled Internet of Things (IoTs) Platforms for Automation in Intelligent Transportation Systems

9.16 Blockchain-Enabled Internet of Things (IoTs) Platforms for Automation in Software Development

9.17 Blockchain-Enabled Internet of Things (IoTs) Platforms for Automation in Protecting Systems

9.18 Issues and Challenges Towards Blockchain Based IoT–ML Applications

9.19 Open Challenges of Blockchain, Internet of Things, and Machine Learning Integration

9.20 Future Research Opportunities for Blockchain, Internet of Things, and Machine Learning Integration

9.21 Conclusion

References

10 An Enhanced Threat Detection Model to Assist Supply Chain Management Using Artificial Intelligence

10.1 Introduction

10.2 Background

10.3 Literature Survey

10.4 Methodology Adopted

10.5 Analysis of the Work

10.6 Future Work

10.7 Conclusion

References

11 Role of AI and Digital Twin in Smart Manufacturing

11.1 Introduction

11.2 Digital Twins

11.3 Digital Twin for Smart Manufacturing

11.4 Artificial Intelligence Enabled with Digital Twin

11.5 AI and DT Lifecycle

11.6 AI-Enabled Digital Twins in Manufacturing

11.7 Digital Twins Used in Manufacturing

11.8 AI Impacting Digital Twins

11.9 Organizations Succeed Deploying Digital Twins

Summary

References

12 Data Analytics and Visualization in Smart Manufacturing Using AI-Based Digital Twins

12.1 Introduction

12.2 Smart Manufacturing and Digital Twins

12.3 Data Collection and Integration

12.4 AI-Based Analytics

12.5 Visualization Techniques

12.6 Case Studies and Applications

12.7 Challenges and Future Directions

12.8 Conclusion

References

Part 3: Issues and Challenges Towards AI and Digital Twin-Based Smart Manufacturing

13 The IoT of Robotics: The Frontier of Automation

13.1 Internet of Robotic Things Automation

13.2 Converging Sensing/Actuating Information Network

13.3 Marketplace for an IORT Ecosystem

13.4 IORT Practical Applications in Commerce

13.5 Healthcare Robotics Process Automation Paradigm

13.6 Operative Robotics

13.7 Vibrotactile Stimulation

13.8 IoT in Transportation

13.9 Applications of IoT and AI in Agriculture Automation

13.10 Sustainable Agriculture

13.11 Machine Intelligence

13.12 Virtual and Augmented Reality

13.13 Integration of Digital Twins with IoT

13.14 Biomedical Application

13.15 Smart Cities

13.16 Energy Management

13.17 Intelligent Connectivity

13.18 Continual Adaptation with Safety Guarantees

13.19 Multimodal Dialogue

13.20 Industrial IoT

13.21 IoT Fire Forecast Detectors

13.22 IoT-Based Greenhouse Management

13.23 IoT Architecture Domain

13.24 Emergent Interfaces

References

14 Real-Time Monitoring and Predictive Maintenance

14.1 Introduction

14.2 Fundamentals

14.3 Type of Predictive Maintenance

14.4 Real-Time Monitoring of Industrial Components

14.5 Predictive Maintenance with Real-Time Actions for Industrial Components

14.6 Challenges and Ethical Considerations

14.7 Conclusion

References

15 Advanced Topics for Blockchain-Based Applications: Open Issues, Technical, Legal, and Research Challenges

15.1 Introduction

15.2 Open Issues Towards Blockchain-Based Applications

15.3 Critical Challenges Towards Blockchain-Based Applications

15.4 Future Work

15.5 Conclusion

References

16 Issues and Challenges in Implementing Smart Manufacturing in the Current Scenario

16.1 Introduction

16.2 Challenges in Smart Manufacturing

16.3 Difficulties and Prospects for Further Research on Blockchain Application

16.4 Obstacles in Industry 4.0

16.5 The Internet of Things: Security Threats and Challenges (IoT)

16.6 Conclusion

References

Part 4: Near-Future Developments Towards AI and Digital Twin-Based Smart Manufacturing

17 Artificial Intelligence for Malware Analysis: A Systematic Study

17.1 Introduction to AI, Malware and Cybersecurity Fundamentals

17.2 AI Applications in Cybersecurity

17.3 Existed Techniques for Malware Analysis

17.4 AI-Powered Malware Analysis

17.5 Available Malware Datasets and Benchmarks for Malware Analysis

17.6 Open Issues and Challenges in Dataset Creation in Malware Analysis

17.7 Metrics for Evaluating AI-Based Malware Detection

17.8 Technical, Legal Challenges and Issues Towards AI-Based Malware Analysis

17.9 Future Trends and Innovations Towards AI-Based Malware Analysis

17.10 Conclusion

References

18 Artificial Intelligence-Based Cyber Security and Digital Forensics: A Review

18.1 Introduction to Cybersecurity Fundamentals and Digital Forensics Basics

18.2 Background and Motivation

18.3 Digital Forensics and AI

18.4 AI for Threat Detection, and Prevention

18.5 Important Challenges and Ethical Issues for AI-Powered Cyber Security and Digital Forensics

18.6 Future Trends and Innovations AI-Powered Cyber Security and Digital Forensics

18.7 Conclusion

References

19 Blockchain Application in Industry 5.0

19.1 Introduction

19.2 Blockchain for Industry 5.0 Transformative Potential

19.3 Applications of Blockchain in Industry 5.0

19.4 Supply Chain Management and Traceability

19.5 Decentralized Manufacturing

19.6 Energy Sector Transformation

19.7 Intelligent Health Care and Medical Records

19.8 Financial Services and Payments

19.9 Case Studies

19.10 Open Issue and Critical Challenges

19.11 Conclusion

References

20 Blockchain-Empowered Internet of Things (IoTs) Platforms for Automation in Various Sectors

20.1 Introduction

20.2 Emerging Trends and Visions Towards Blockchain and Internet of Things

20.3 Usage of Blockchain in 5G-Enabled Internet of Things

20.4 Internet of Things and Industrial Internet of Things

20.6 Blockchain Platform for Industrial Internet of Things

20.7 Benefits and Features of Industrial Internet of Things

20.8 Application and Future Work of Blockchain Platform for Industrial Internet of Things

20.9 Future of Blockchain-Based Internet of Things

20.10 Blockchain-Enabled Internet of Things (IoTs) Platforms for Supply Chain Functions

20.11 Blockchain-Enabled Internet of Things (IoTs) Platforms for Smart Energy and Smart Grids

20.12 Blockchain-Enabled Internet of Things (IoTs) Platforms for Industrial Control Systems

20.13 Blockchain-Enabled Internet of Things (IoTs) Platforms for IoT-Based Customer Relationship Management (CRM) and Logistics CRM

20.14 Blockchain-Enabled Internet of Things (IoTs) Platforms for Smart Healthcare

20.15 Blockchain-Enabled Internet of Things (IoTs) Platforms for Digital Marketing and Online Social Networking

20.16 Blockchain-Enabled Internet of Things (IoTs) Platforms for Online Social Networking

20.17 Blockchain-Enabled Internet of Things (IoTs) Platforms for Textile Industry

20.18 Blockchain-Enabled Internet of Things (IoTs) Platforms for Smart Banking and Financial Services

20.19 Blockchain-Enabled Internet of Things (IoTs) Platforms for Military Services

20.20 Blockchain-Enabled Internet of Things (IoTs) Platforms for Smart Agricultural Services

20.21 Blockchain Deployment in 5G-Enabled Smart Industrial Automation

20.22 Open Issues in Blockchain-Empowered Internet of Things

20.23 Critical Challenges Towards Using Blockchain-Empowered Internet of Things

20.24 Conclusion

References

21 Digital Twin-Enabled Smart Manufacturing: Challenges and Future Directions

21.1 Introduction

21.2 Related Works

21.3 Current Landscape of Digital Twin Adoption

21.4 Challenges in Digital Twin Implementations

21.5 Future Work

21.6 Conclusion

References

22 Future of Computer Vision and Industrial Robotics in Smart Manufacturing

22.1 Introduction to Computer Vision and Industrial Robotics, and Smart Manufacturing

22.2 Role of Computer Vision in Smart Manufacturing

22.3 Industrial Robotics in Smart Manufacturing

22.4 Convergence of Computer Vision and Robotics in Today’s Smart Era: Applications and Opportunities

22.5 Emerging Technologies for Computer Vision and Robotics in Smart Manufacturing

22.6 Applications of Computer Vision and Robotics in Smart Manufacturing

22.7 Security and Data Privacy Towards Computer Vision and Robotics-Based Smart Manufacturing

22.8 Case Studies in Computer Vision and Robotics in Manufacturing

22.9 Future Opportunities Towards Computer Vision and Robotics-Based Smart Manufacturing

22.10 Sustainability and Environmental Impact Towards Computer Vision and Robotics-Based Smart Manufacturing

22.11 Conclusion

References

23 The Future of Manufacturing with AI and Data Analytics

23.1 Introduction

23.2 Different Types of Maintenance Strategies

23.3 New Research Trends in Manufacturing

23.4 Conception of Different AI Technologies

23.5 Digital Twins

23.6 Role of Artificial Intelligence in Predictive Maintenance

23.7 Limitations and Challenges

23.8 Opportunities and Future Scope

23.9 Concluding Remarks

References

24 Artificial Intelligence Techniques in Predictive Maintenance, Their Applications, Challenges, and Prospects

24.1 Introduction

24.2 Techniques of Predictive Maintenance

24.3 Conclusion

References

Index

End User License Agreement

List of Tables

Chapter 3

Table 3.1 Difference between data science, data analytics and big data.

Table 3.2 Examples of actual applications of big data in manufacturing.

Chapter 4

Table 4.1 Challenges and issues towards AI-based smart manufacturing systems.

Table 4.2 Research opportunities and research gaps towards AI-empowered smart ...

Chapter 8

Table 8.1 Digital Twin, types and applications of Digital Twins in manufacturi...

Table 8.2 Benefits and key use cases of Digital Twin in manufacturing.

Table 8.3 Security and data integrity issues in digital and blockchain in smar...

Chapter 10

Table 10.1 Algorithm to generate the hash key.

Table 10.2 Algorithm to generate the final hash key.

Table 10.3 Dataset information.

Table 10.4 Algorithm to use trial datasets for comparison.

Table 10.5 Simulation details.

Chapter 17

Table 17.1 AI-powered malware analysis.

Table 17.2 Issues and challenges towards AI based malware analysis.

Table 17.3 Research gaps and opportunity towards AI-based malware analysis.

Chapter 18

Table 18.1 Digital forensics and AI.

Table 18.2 Challenge and ethical issues towards AI-powered cyber security and ...

Table 18.3 Research gaps and opportunity for future AI based cybersecurity.

Chapter 19

Table 19.1 Case studies findings.

Chapter 21

Table 21.1 Comparison of related works.

Chapter 22

Table 22.1 Industrial robots: introduction, types, benefits and key applicatio...

Table 22.2 Sustainability and environmental impact towards computer vision and...

Chapter 23

Table 23.1 State-of-the-art AI models and approaches utilized in the manufactu...

Table 23.2 State-of-the-art digital twins utilized in the manufacturing indust...

Table 23.3 Table illustrating different data types utilized for the predictive...

List of Illustrations

Chapter 1

Figure 1.1 Classification of test data using algorithm.

Figure 1.2 Data representation in logistic regression.

Figure 1.3 SVM system.

Figure 1.4 Categorization of set using K-nearest neighbor.

Figure 1.5 Classification of decision rule.

Figure 1.6 Classification of data set.

Figure 1.7 Plotting of multiple linear regression.

Figure 1.8 Logistic regression representation in graph form.

Figure 1.9 Plotting of polynomial regression using equation.

Figure 1.10 Vector generation using vector machine.

Figure 1.11 Tree selection representation.

Figure 1.12 Flow diagram of random forest algorithm.

Figure 1.13 Data before and after of K-means.

Figure 1.14 Example for DBSCAN clustering.

Chapter 3

Figure 3.1 Utilizing Big Data Analytics for manufacturing market analysis

Figure 3.2 Big Data in the Manufacturing Sector: The market size was estimated...

Figure 3.3 Global Big Data Insights for the Manufacturing Sector

Chapter 4

Figure 4.1 AI in manufacturing.

Figure 4.2 AI benefits in manufacturing.

Chapter 5

Figure 5.1 A production-oriented digital twin framework.

Figure 5.2 Smart manufacturing streams that utilize DT.

Figure 5.3 Facilitating technologies for digital twin-based smart manufacturin...

Figure 5.4 General structure of data-driven smart factory.

Figure 5.5 Flow process of cyber physical systems (CPS).

Figure 5.6 An architecture for digital twins in human–robot interaction.

Figure 5.7 General architecture of adaptive federated learning technique with ...

Figure 5.8 EIMI operational mechanism driven by digital twins and big data.

Chapter 6

Figure 6.1 Areas of related work in distributed systems.

Figure 6.2 Key aspects related to blockchain.

Figure 6.3 Evolution of blockchain [11].

Figure 6.4 Key aspects related to blockchain.

Figure 6.5 Benefits of blockchain.

Figure 6.6 Limitation of blockchain.

Figure 6.7 Key Features of blockchain technology.

Figure 6.8 Key issues in blockchain technology.

Figure 6.9 Key challenges with blockchain technology.

Chapter 7

Figure 7.1 Applications of digital twin [11].

Figure 7.2 Digital twin in manufacturing.

Chapter 8

Figure 8.1 Importance of Digital Twins in manufacturing.

Figure 8.2 Issues and challenges in conventional manufacturing processes.

Figure 8.3 Synergy of blockchain and Digital Twins for smart manufacturing.

Figure 8.4 Key Technologies for blockchain-based Digital Twins.

Figure 8.5 Applications of blockchain-based Digital Twins in smart manufacturi...

Figure 8.6 Future research towards integration of AI and blockchain.

Chapter 9

Figure 9.1 Key aspects related to blockchain.

Figure 9.2 Key aspects related to blockchain.

Figure 9.3 Key aspects related to blockchain.

Figure 9.4 Key aspects related to blockchain.

Figure 9.5 Key aspects related to blockchain.

Figure 9.6 Key aspects related to blockchain.

Figure 9.7 Key aspects related to blockchain.

Figure 9.8 Blockchain for Internet of Things and machine learning [11].

Figure 9.9 Blockchain-based learning automated analytics platforms.

Figure 9.10 IoT blockchain integration [19].

Figure 9.11 Machine learning blockchain integration.

Figure 9.12 Key benefits of blockchain-enabled IoT platforms in ITS automation...

Figure 9.13 Benefits for automation in software development.

Figure 9.14 Blockchain-enabled IoT platforms automate system protection.

Figure 9.15 Issues and challenges towards blockchain based IoT–ML applications...

Chapter 10

Figure 10.1 A preliminary framework for supply chain management [9].

Figure 10.2 Merkle tree [14].

Figure 10.3 Security comparison in the network.

Figure 10.4 Early threat detection.

Chapter 11

Figure 11.1 Digital twinning concept.

Figure 11.2 Smart manufacturing using artificial intelligence.

Figure 11.3 Digital twin network architecture.

Figure 11.4 Application scenarios of smart manufacturing cell.

Figure 11.5 Digital twin system with an interface to a physical and external d...

Figure 11.6 Setup phase.

Figure 11.7 Run phase.

Figure 11.8 Maintenance phase.

Chapter 12

Figure 12.1 Key concepts of smart manufacturing.

Figure 12.2 Overall process of sensor data acquisition.

Figure 12.3 Optimization algorithms.

Chapter 13

Figure 13.1 Applications of IoT.

Figure 13.2 Yearly shift from non-IoT to IoT devices.

Figure 13.3 Robotics evolution.

Figure 13.4 Machine intelligence.

Chapter 15

Figure 15.1 Open issues towards blockchain-based applications.

Figure 15.2 Technical challenges blockchain-based applications.

Figure 15.3 Non-technical challenges blockchain-based applications.

Figure 15.4 Legal challenges blockchain-based applications.

Figure 15.5 Blockchain-based applications research challenges.

Figure 15.6 Future work in blockchain-based applications.

Chapter 16

Figure 16.1 Challenges in smart manufacturing.

Chapter 17

Figure 17.1 Importance of AI and malware analysis in cybersecurity.

Figure 17.2 Advanced malware analysis.

Figure 17.3 Malware datasets and benchmarks.

Figure 17.4 Technical, legal challenges and issues towards AI-based malware an...

Figure 17.5 Future trends and innovations towards AI based malware analysis.

Chapter 18

Figure 18.1 Role of AI in cybersecurity.

Figure 18.2 AI-driven digital forensics.

Figure 18.3 Simulators, algorithms/methods for threat intelligence in cybersec...

Figure 18.4 AI-driven cybersecurity solutions for next generation society.

Figure 18.5 Unexplored areas in AI-based cybersecurity.

Chapter 19

Figure 19.1 Structure of Industry 5.0.

Figure 19.2 Workflow of blockchain in Industry 5.0.

Figure 19.3 Applications of blockchain in Industry 5.0.

Figure 19.4 Blockchain decentralized network.

Figure 19.5 Blockchain grid management.

Figure 19.6 Patient data chain.

Figure 19.7 Blockchain in financial service and payment.

Figure 19.8 Challenges of blockchain in Industry 5.0.

Chapter 20

Figure 20.1 Emerging trends and visions towards blockchain and Internet of Thi...

Figure 20.2 Key areas where blockchain can enhance the capabilities of 5G-enab...

Figure 20.3 Key characteristics of IIoT.

Figure 20.4 Key areas where blockchain can enhance the capabilities of 5G-enab...

Figure 20.5 Application of blockchain platform for Industrial Internet of Thin...

Figure 20.6 Future work of blockchain platform for Industrial Internet of Thin...

Figure 20.7 Future of blockchain-based IoT.

Figure 20.8 Blockchain-enabled Internet of Things for the supply chain.

Figure 20.9 Blockchain enabled IoTs for Smart Energy and Smart grids [Shahinza...

Figure 20.10 Blockchain-enabled Internet of Things (IoTs) platforms for indust...

Figure 20.11 Blockchain-enabled IoTs for IoT-based CRM and logistics CRM.

Figure 20.12 Blockchain-enabled IoTs for smart healthcare.

Figure 20.13 Blockchain-enabled IoTs for digital marketing and online social n...

Figure 20.14 Blockchain-enabled IoTs for online social networking.

Figure 20.15 Blockchain-enabled IoTs for textile industry.

Figure 20.16 Blockchain-enabled IoTs for smart banking, financial services and...

Figure 20.17 Blockchain-enabled IoTs for agricultural services.

Figure 20.18 Blockchain deployment in 5G-enabled smart industrial automation.

Figure 20.19 Open issues in blockchain-empowered Internet of Things.

Figure 20.20 Critical challenges towards using blockchain-empowered IoT.

Chapter 21

Figure 21.1 Industrial implementation.

Figure 21.2 Benefits in using digital twin technologies.

Figure 21.3 Key challenges during implementation.

Figure 21.4 Technical challenges during implementation.

Figure 21.5 Organizational challenges.

Chapter 22

Figure 22.1 Evolution of manufacturing and automation.

Figure 22.2 Limitations of traditional manufacturing processes.

Figure 22.3 Emerging technologies for computer vision and robotics.

Figure 22.4 Security and data privacy towards computer vision and robotics bas...

Figure 22.5 Collaborative robots in electronics assembly in manufacturing.

Chapter 23

Figure 23.1 Schematic representation for different type of maintenance with po...

Figure 23.2 Schematic representation of the frequency of the work vs. cost for...

Figure 23.3 Schematic representation of an automated maintenance process using...

Figure 23.4 Schematic representation showing the hierarchy of different AI met...

Figure 23.5 Schematic representation showing a brief overview of the different...

Figure 23.6 Schematic representation showing brief working of the convolutiona...

Figure 23.7 Schematic representation showing transformer architecture introduc...

Figure 23.8 Illustration of an approach where reinforcement learning (RL) can ...

Figure 23.9 Schematic representation illustrating the concept of transfer lear...

Figure 23.10 Schematic representation illustrating architecture of the generat...

Chapter 24

Figure 24.1 Implementation of time screening.

Figure 24.2 AI technologies in predictive maintenance.

Figure 24.3 Intelligence in AI.

Guide

Cover Page

Table of Contents

Series Page

Title Page

Copyright Page

Preface

Begin Reading

Index

WILEY END USER LICENSE AGREEMENT

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Scrivener Publishing100 Cummings Center, Suite 541JBeverly, MA 01915-6106

Publishers at ScrivenerMartin Scrivener ([email protected])Phillip Carmical ([email protected])

Artificial Intelligence-Enabled Digital Twin for Smart Manufacturing

Edited by

Amit Kumar Tyagi

Department of Fashion Technology, National Institute of Fashion Technology, New Delhi, India

Shrikant Tiwari

School of Computing Science and Engineering (SCSE), Galgotias University, Greater Noida, Uttar Pradesh, India

Senthil Kumar Arumugam

Department of Professional Studies, Christ University, Bengaluru, India

and

Avinash Kumar Sharma

Dept. of Computer Science and Engineering, Sharda University, Greater Noida, India

This edition first published 2024 by John Wiley & Sons, Inc., 111 River Street, Hoboken, NJ 07030, USA and Scrivener Publishing LLC, 100 Cummings Center, Suite 541J, Beverly, MA 01915, USA© 2024 Scrivener Publishing LLCFor more information about Scrivener publications please visit www.scrivenerpublishing.com.

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Library of Congress Cataloging-in-Publication Data

ISBN 978-1-394-30357-1

Cover image courtesy of Adobe FireflyCover design by Russell Richardson

Preface

In the rapidly evolving landscape of modern manufacturing, the integration of cutting-edge technologies has become imperative for businesses to remain competitive and adaptive. Among these technologies, Artificial Intelligence (AI) stands out as a transformative force, revolutionizing traditional manufacturing processes and making the way for the era of smart manufacturing. At the heart of this technological revolution lies the concept of the Digital Twin—an innovative approach that bridges the physical and digital realms of manufacturing. By creating a virtual representation of physical assets, processes, and systems, organizations can gain unprecedented insights, optimize operations, and enhance decision-making capabilities.

This timely book explores the convergence of AI and Digital Twin technologies to empower smart manufacturing initiatives. Through a comprehensive examination of principles, methodologies, and practical applications, we explain the transformative potential of AI-enabled Digital Twins across various facets of the manufacturing lifecycle. From design and prototyping to production and maintenance, AI-enabled Digital Twins offer multifaceted advantages that redefine traditional paradigms. By leveraging AI algorithms for data analysis, predictive modeling, and autonomous optimization, manufacturers can achieve unparalleled levels of efficiency, quality, and agility.

This book explains how AI enhances the capabilities of Digital Twins by creating a powerful tool that can optimize production processes, improve product quality, and streamline operations. Note that the Digital Twin in this context is a virtual representation of a physical manufacturing system, including machines, processes, and products. It continuously collects real-time data from sensors and other sources, allowing it to mirror the physical system’s behavior and performance.

What sets this Digital Twin apart is the incorporation of AI algorithms and machine learning techniques that enable it to analyze and predict outcomes, recommend improvements, and autonomously make adjustments to enhance manufacturing efficiency. This book outlines essential elements, like real-time monitoring of machines, predictive analytics of machines and data, optimization of the resources, quality control of the product, resource management, decision support (timely or quickly accurate decisions).

Moreover, this book elucidates the symbiotic relationship between AI and Digital Twins, highlighting how AI augments the capabilities of Digital Twins by infusing them with intelligence, adaptability, and autonomy. Hence, this book promises to enhance competitiveness, reduce operational costs, and facilitate innovation in the manufacturing industry. By harnessing AI’s capabilities in conjunction with Digital Twins, manufacturers can achieve a more agile and responsive production environment, ultimately driving the evolution of smart factories and Industry 4.0/5.0.

We want to express our deepest appreciation to everyone who dedicated their time and efforts to make this book a success. Furthermore, we wish to gratefully acknowledge the suggestions, help, and support of Martin Scrivener and the team at Scrivener Publishing.

Amit Kumar Tyagi

Shrikant Tiwari

Senthil Kumar Arumugam

Avinash Kumar Sharma

Part 1FUNDAMENTALS OF AI-BASED SMART MANUFACTURING

1Machine Learning Fundamentals

Renugadevi A. S.1*, R. Jayavadivel2, Charanya J.1, Kaviya P.1 and Guhan R.1

1Department of Electronics and Communication Engineering, Kongu Engineering College, Erode, India

2Department of CSE, Alliance College of Engineering and Design, Alliance University, Karnataka, India

Abstract

Machine learning (ML) is a topic of study focused on comprehending and developing “learning” methods, or methods that use data to enhance performance on a certain set of tasks. It is considered to be a component of artificial intelligence. The Types of learning in Machine Learning are Supervised Learning: uses labeled data for model training, Unsupervised Learning: uses unlabeled data for model training. When labeled data is not available (there is no result to predict), the learning purpose is to find hidden similarities, groups or clusters among examples, or to determine characteristics in the data structure. Reinforcement Learning: consists of a trained agent that learns on the basis of rewards or penalties. The Model techniques used in machine learning based models are: 1) Classification: prediction task of categorical values in supervised learning. 2) Regression: prediction task of continuous values in supervised learning. 3) Clustering: find groups or similarities in data in unsupervised learning. 4) Dimensionality reduction (DR): reduce the number of variables/features in data in unsupervised learning. Among the types of learning, each machine learning consists of variety of algorithms and performance measures, which is aligned with various model techniques. This chapter focuses on all the types of machine learning algorithms such as Support vector machine, Discriminant Analysis, Naïve Bayes, K nearest neighbor, K Means, Decision tree, principal component analysis, etc.

Keywords: Machine learning, supervised learning, unsupervised learning, reinforcement learning

1.1 Introduction

Computational models known as machine learning algorithms allow computers to learn from data and make judgments or predictions without the need for explicit programming. They may be divided into three primary groups: clustering, regression, and classification. Classification algorithms, like Decision Trees and Support Vector Machines, give labels or categories to incoming data. Regression methods, such as Polynomial and Linear regression, forecast continuous numerical values. Clustering methods such as K-Means and Hierarchical Clustering combine related data points without the need for predetermined labels. These algorithms are essential for a wide range of applications, such as natural language processing and picture identification, since they can adjust to the specific issue and goal at hand. New methods keep coming up as the area develops, which carries on the machine learning progress.

1.2 Classification

Classification algorithms are used to classify test data based on prior learning. Among them, the model learns from previous data and separates the test data into different groups.

Types of Classifiers

Binary classifier: A classifier whose results have exactly two categories is called a binary classifier [1]. Example: Normal or abnormal, yes or no

Multi-class classifier: When the results of the classifier have more than two classes, it is called a binary classifier based on multiple classifiers. Example: Different stages of skin cancer, different types of products

The classification algorithm can be further divided as shown in Figure 1.1.

1.2.1 Linear Model

1.2.1.1 Logistic Regression

The probability of test items is predicted using supervised machine learning techniques like logistic regression. A binary classifier is what logistic regression is. The data is represented as 1 or 0 in the binary output variable. However, the resulting value is between 0 and 1. The S-shaped sigmoid function is used here. Linear regression models theoretically predict values as a function of X. The general linear regression model has a simple equation expressed as shown in below Figure 1.2.

Figure 1.1 Classification of test data using algorithm.

Figure 1.2 Data representation in logistic regression.

1.2.1.2 Support Vector Machine

SVM is a supervised learning system that can transform input data into a higher-order space. Data classification is done using a hyperplane with maximum. SVM can handle large amounts of data and is a widely used tool in machine learning for binary classification problems. Classification methods usually involve a set of training-test datasets.

There are many specifics of the training process and cost estimates for each event. The purpose of SVM is to obtain a model that predicts the objective value of the given data based on training data as shown in Figure 1.3. This function converts the training vectors to higher order. SVM finds the maximum marginal linear separating hyperplane in highdimensional space. If more than one feature is available, you need to select part of the input before moving to SVM.

1.2.2 Nonlinear Model

1.2.2.1 K-Nearest Neighbor

A supervised machine learning technique called K-Nearest Neighbor is used to categorize an example within a set of kNNs shown clearly in Figure 1.4. At first, the K value corresponds to a low value. Sort the test data into groups based on increased similarity by comparing how similar the test data is to each category. Regression and classification issues can benefit from its application. KNN does not learn by training, which is why it is referred to as a lazy learner.

Figure 1.3 SVM system.

Figure 1.4 Categorization of set using K-nearest neighbor.

The Euclidean distance between two points is [2]:

1.2.2.2 Naive Bayes

Naive Bayes is a hedonic method based on the Bayesian manager making predictions and assigning data x to list i with the largest return probability P. It specifically reduces the overhead of including each feature in the prediction class that contains it. Naive Bayes shows a competitive advantage over more traditional and advanced classification methods such as decision trees and neural networks. Due to its short learning time, it is also a good classifier that can easily process high-dimensional data. [3] Bayesian classification is about tracking learning and actual grouping strategies. It takes simple prediction models and allows us to express model uncertainty in a way that calculates the consequences of the event. It can solve many diagnostic and prognostic problems. Bayesian inference is used as a learning method that appears in Naive Bayes text.

A simple statement of Bayes’ theorem is as follows:

1.2.2.3 Decision Tree

The decision tree classifier provides a clear classification model that is valid in many applications.

The nodes of the tree represent tests for a particular item and branches in different directions depending on the value shown in Figure 1.5. A page represents one category of notes. According to the test results, the lines of the educational system are separated by transferring data from the roots to the leaves.

Figure 1.5 Classification of decision rule.

1.2.2.4 Random Forest

A supervised machine learning approach called Random Forest is used to address regression and classification issues. It’s an ensemble learning technique that uses many classifiers to tackle challenging issues. To boost accuracy, test data is classified using numerous decision trees in random forests, and then the data is averaged as shown in Figure 1.6. Accuracy will increase with more wood utilized, but overfitting will become an issue.

1.3 Regression

Data scientists can use regression to predict a continuous variable (y) using the mathematical standard for the outcomes of one or more predictions (x). Linear regression is probably the most commonly used form of regression analysis given its ease of estimation and forecasting [4].

Figure 1.6 Classification of data set.

1.3.1 Linear Regression

One of the most popular and simple machine learning methods used for predictive analytics is linear regression. Predictive regression describes what is predicted, while linear regression estimates constants such as age, salary, and other variables. The variance of the variable (y) varies as a function of the independent variable; this shows the relationship between the variable and the variable (x). A regression line is a line that attempts to fit data between the variables and the independent variables.

The equation of the inverted line is y = a0 + a * x + b.

Here,

y is a variable.

x = foreign variable

a0 is the intersection point of the line.

The following highlights other differences between the two forms of linear regression: Basic Linear Regression An independent variable is utilized in simple linear regression to forecast the value of the dependent variable.

1.3.2 Multiple Linear Regression

Using many independent variables, this technique forecasts a variable’s value as shown Figure 1.7.

Several uses for linear regression include:

Sales forecasting and model analysis

Salary forecasting

Real estate forecasting

ETA-related traffic.

1.3.3 Logistic Regression

Learning is facilitated by regression law. It is employed to ascertain or quantify the likelihood of the equation’s result (yes/no). One of the applications of artificial intelligence is determining whether a person will catch coronavirus or not, as an example of reverse calculation [5]. Binary classification means that there are only two answers to this question: disease or infection. In this example, a person’s ability to contract COVID-19 depends on the virus, symptoms, presence of antibodies, etc. It may depend. These factors (independent variables) may include the disease, symptoms, and immune responses, all of which may have an impact on the predicted outcome (dependent variables).

Figure 1.7 Plotting of multiple linear regression.

1.3.3.1 Three Types of Logistic Regression

There are three types of logistic regression.

Binary logistic regression is used when there are only two outcomes; like the old example of whether a 19-year-old is likely to contract COVID-19.

Multinomial logistic regression is used when there is more than one outcome, such as determining whether a person will get a cold, allergies, flu, or COVID-19.

Using logistic regression techniques when ranking scores, such as data on classifying COVID-19 infections as minor, medium, and major as shown in

Figure 1.8

.

1.3.4 Polynomial Regression

Polynomial regression and other probability approaches employ direct models as indirect data. It directly involves a recurring variable even if there is a non-linear curve between the value of x and the relative or positive value of y. [6] Consider a dataset containing data points that are not directly accessible. In this case, direct duplication will not provide the best results for the content of this document. The content of this document requires further elaboration. In polynomial rendering, key points are converted to polynomial highlights to a predetermined degree and then a direct model is used to display them. This indicates that the linear polynomial provides the best fit to the data of interest.

The polynomial iteration formula is added directly with equations; where the polynomial iteration Y = b0 + b1x + b2x2 + b3x3 + ... + bnxn is derived directly from the conditions repeating Y = b0 + b1x.

Therefore Y is the normal or desired result and b0, b1, ..., bn are the iteration coefficients. Our independent variable or input variable is x.

Figure 1.8 Logistic regression representation in graph form.

Figure 1.9 Plotting of polynomial regression using equation.

Since the coefficients are linear with the quadratic term, the model is still linear as shown in Figure 1.9.

1.3.5 Support Vector Regression

Support any supervised learning technique called Vector Machine is appropriate for issues that are descriptive and recurrent. If we use it to create related problems in this way, it is called help vector generation. The optimization method for continuous data is support vector reconstruction as shown in Figure 1.10. Next, a few words about propagating the help vector: use the fragment function to index lower-level data to higher levels. In SVM, the hyperplane is usually a line separating two groups; however, one row in the SVR supports the prediction of change and covers most of the data of interest.

1.3.6 Decision Tree Regression

Train with AutoML tools to use decision trees and artificial intelligence technology to group data using true or false answers for ambiguous or repetitive questions. Considering its structure, it looks like a tree with roots, inside and certain parts of the leaves as shown in Figure 1.11. The selected tree starts from the central hub and branches towards the inner hubs and leaf hubs. The final group or actual quality is tracked in the middle of the page. Tree selection is easy to understand and easy to understand. Start by defining the material that will be the center of the selected tree [7].

Figure 1.10 Vector generation using vector machine.

Figure 1.11 Tree selection representation.

In general, no one can predict the final course with complete certainty; This is called transmission. This spread is estimated using methods such as the Gini coefficient, entropy, and information gain, which indicate how well items relate to the given data. At each stage, the core is selected as the least polluting component. For equipment with mathematics, the Gini coefficient is determined by analyzing the data on the request ramp before calculating the average of the continuous equipment. To find the Gini coefficient, use the following formulas: K is the total number of classes, and p is the level of classes that are now in existence.

The weighted normally distributed Gini coefficient is not the same for all values of the leaf. Select the price with the lowest price for this trademark. The tool has been restructured according to some important values so that points and values similar to the base can be selected. This cycle is repeated at each depth and each location until all records are identified. After creating the tree, you can predict the values or properties of important data by pulling the tree from each location. When tree selection is used for replication, transmission is estimated using transformed or squared residuals rather than the Gini coefficient. The remaining steps of the interaction can be compared.

1.3.7 Random Forest Regression

Random forest is a well-liked technique for regression and classification. Given that regression and classification are the two most crucial components of machine learning, the random forest method is among the most significant algorithms in the field. Determining whether a user would purchase a product or how to grant credit are just two examples of the numerous commercial applications where the capacity to do precise analysis comes in extremely handy. Naive Bayes classifiers, logistic regression, decision trees, and support vector machines are examples of data science classifiers. Conversely, the Random Forest classifier occupies a position close to the summit of the classifier hierarchy.

1.3.7.1 Random Forest Algorithm in Practice

The Random Forest algorithm functions rather sensibly. [8] The method is used in two steps: first, a random forest is generated and N decision trees are combined; second, a prediction is produced for each tree generated in the first step as shown in Figure 1.12.

The steps listed below can be used to illustrate how this approach functions:

Step 1: Choose M spots at random from the training.

Step 2: For the data points (subset) you choose in Step 1, create a decision tree.

Step 3: Every decision tree will provide results. Look at this for sure.

Step 4: A majority vote or average, whichever is suitable, determines the categorization and regression’s ultimate outcome.

1.3.7.2 Random Forest Algorithm Application Examples

For example, the following four industries frequently use the Random Forest Algorithm: credit risk of the banking industry.

Hospital: Identify dangerous diseases and their consequences.

Land use: To find regions with similar land use patterns, a random forest distribution technique was also employed.

Business trends: Business trends may be found using this method.

The random forest algorithm has the following advantages:

Overfitting is eliminated because the results are based on the mean or majority vote.

The parallel feature is seen with the independence of each decision tree.

It preserves diversity by not taking into account all advantages, although this is not always the case when creating every decision tree.

It preserves diversity by not taking into account all advantages, although this is not always the case when creating every decision tree.

Unaffected by Curse of Dimension.

It is very stable since the average response of large trees is used. The unique position is reduced because not all attributes are determined for each tree.

Despite being among the greatest algorithms for handling regression and classification issues, Random Forest has a few drawbacks that you should be aware of before utilizing it.

Random forests are more complex than decision trees chosen by walking through the tree.

Due to its complexity, this model takes longer to train than other models. When a decision tree needs to make a prediction, it needs to produce input data.

Figure 1.12 Flow diagram of random forest algorithm.

1.3.8 Lasso Regression

The normal method is Rope Regression. It is preferred over recursive for more accurate numbers. In this model, shrinkage is used. [9] When data values jump towards the mean, this is called shrinkage. Simple, redundant models can be improved by coupling methods (i.e. models with fewer boundaries). This particular type of iteration works best when the sample shows a large difference or you want to take several steps on the selected sample, such as deciding and escaping the boundary. L1 normalization for string recovery. Since it expresses choice, it is used in situations where there are many things.

1.4 Clustering

Clustering, commonly known as cluster analysis, is a problem in unsupervised learning. It is often used as a data analysis tool to analyze interesting data, such as grouping customers based on their behavior. There are dozens of algorithms to choose from, and more than one algorithm works best in every situation. Instead, it’s good to work on different integration algorithms and different settings for each method.

1.4.1 Clustering Algorithms

There are many types of clustering algorithms. It uses many similar algorithms to find a dense observation area or measure the distance between samples at a particular location. That’s why it’s usually a good idea to scale data before using the integration.

1.4.2 K-Means Clustering Algorithm

Unmonitored Learning System Unsupervised data is divided into many groups using K-Means clustering. Here, K is the number of presets that should be generated during the process; for instance, if K = 2, there will be two groups, and if K = 3, there will be three groups.

It enables us to automatically recognize groups in unlabeled data without the need for training and to divide data into distinct groups. Every cluster is given a centroid since the method is based on them. This algorithm’s primary objective is to shorten the distance between each data point and its matching cluster. The method takes as input unlabeled data, splits it into k groups, and keeps going until it runs out of groups. The k value in this algorithm has to be known ahead of time.

The K-means clustering technique serves two primary purposes, which are:

To determine the ideal value for the K-means point or centroid, an iterative procedure is employed. The closest k-center is allocated to each data point. Data points that are near particular places k are used to construct clusters as shown in Figure 1.13.

So each group is different from the other groups and has some different data points.

Here are the steps that show how the K-word algorithm works:

Step 1: First, select K to determine the number of groups.

Step 2: Select random centroids or K locations. It may not be informative.

Step 3: Match each data point to the nearest center point; This will create K preset groups.

Step 4: Determine the number of changes and transfer them to the center of each group.

Step 5: Repeat step three to reassign all data points to the new center of each cluster.

Step 6: If the address exists, go to Step 4; otherwise go to Done.

Step 7: Complete the model.

1.4.3 Mean Shift Clustering Algorithm

Meanshift is a clustering algorithm that, unlike unsupervised learning, regroups data points by moving the points to mode (in the context of Meanshift, mode is the fastest data points in the region). For this reason it is sometimes called the pattern search algorithm. Contextchanging algorithms can be used in computer vision and image processing. [10] Meanshift clustering technique does not need a predefined number of clusters, in contrast to the popular K-Means clustering algorithm. This program counts the number of groups by analyzing the input.

Figure 1.13 Data before and after of K-means.

The process of the center transformation algorithm can be done as follows:

Make the data points cluster centers the first priority.

Repeat the following steps until you converge or reach a predetermined decision:

For each data center, select the average of all points within a specific radius (e.g., “core”) in the data center.

In the subject guide.

The point that does not change after convergence is the group center of gravity.

For the final cluster center and cluster function of the data points.

One significant benefit of average function clustering is that the number of clusters need not be predetermined. Furthermore, it is capable of managing groups of any size or form and does not assume any particular arrangement of items. It could, however, be dependent on the core’s radius and choice of core.

Mean-Shift clustering can be applied to many types of data, including object tracking, image and video processing, and bioinformatics.

1.4.4 DBSCAN Clustering

Convex or spherical clusters can be found using hierarchical clustering or partitioning techniques (K-means, PAM clustering). [11] Stated differently, they are only appropriate for small, evenly distributed groups as shown in Figure 1.14.

Figure 1.14 Example for DBSCAN clustering.

The data’s noise and outliers will also have a significant effect on this.

Real world data can contain errors, for example:

Clusters can have shapes like the example below.

The file may contain noise.

1.4.4.1 Two Elements Required by DBSCAN

The region surrounding a data point is represented by eps. Two points are said to be neighbors if their distance is less than or equal to eps. A substantial percentage of the data will be regarded as outliers if the Eps value is very low. The categories will be combined if it is determined to be too big, resulting in the majority of the data points falling into one category. an approach to eps value determination using k-distance graphs [10].

MinPts are the closest neighbors (data points) inside the Eps radius. The MinPts number should increase with the size of the file. Generally speaking, MinPts>= D + 1 may be used to determine the minimal MinPts based on the length D of the data collection. MinPts should be set to a minimum value of 3 [12].

1.4.4.2 Steps Followed to do DBSCAN Algorithm

Indicate landmarks or points that visit more than MinPts neighbors in eps by finding all neighbors.

If necessary, create a new category for each priority if not already done.

Recursively find all related items by velocity and add them to the same group.

Points a and b are said to as having a rapid connection if there is a point c, sufficient points are nearby, and both points are eps away. There are chains in this system. Consequently, b must be a neighbor of someone if b is a neighbor of c, c is a neighbor of d, d is a neighbor of e, and e is a neighbor of a.

Now, skip over sections of the file in search of unexplored material. All items that are not part of a group are considered noise.

1.5 Conclusion

This study concludes with a thorough examination of many machine learning methods, including approaches for grouping, regression, and classification. The significance of learning techniques and tactics in the context of artificial intelligence is highlighted in the abstract, which establishes the scene. The classification section explores both linear and non-linear models, including Decision Tree, Random Forest, K-Nearest Neighbor, Support Vector Machine, Naive Bayes, and Logistic Regression, and makes a distinction between binary and multi-class classifiers. The section on regression provides information on both linear and non-linear regression techniques, such as Random Forest, Polynomial, Decision Tree, Support Vector, and Logistic regression. In the section on clustering, the concepts of K-Means clustering, Mean Shift clustering, and DBSCAN are introduced along with clustering as an unsupervised learning issue.

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