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Submit a ProposalHyperautomation for Next-Generation Industries
 | Edited by Rajesh Kumar Dhanaraj, M. Nalini, A. Daniel, Ali Kashif Bashir, and Balamurugan Balusamy Copyright: 2024 | Status: Published ISBN: 9781394185825 | Hardcover | 342 pages Price: $225 USD  |
One Line Description
This book is essential for anyone looking to understand how hyperautomation can revolutionize businesses by simplifying operations, reducing errors, and creating more intelligent and adaptable workplaces through the use of automation technologies such as artificial intelligence, machine learning, and robotic process automation.
Audience
Researchers in hyperautomation implementation and its applications, system designers for automating industries and workspaces, professionals in related industries, and undergraduate and postgraduate students studying recent trends in the Internet of Everything
Researchers in hyperautomation implementation and its applications, system designers for automating industries and workspaces, professionals in related industries, and undergraduate and postgraduate students studying recent trends in the Internet of Everything
Description
The use of automation technologies to simplify any and every activity conceivable in a business, allowing repeated operations to operate without manual intervention, is known as hyperautomation. Hyperautomation transforms current and old processes and equipment by utilizing artificial intelligence, machine learning, and robotic process automation. This digital transformation may assist a business in gaining cost and resource efficiency, allowing it to prosper in a more competitive environment. With the advancement of automation technologies, hyperautomation is becoming more prevalent. Companies are shifting their methods to create more human-centered and intelligent workplaces. This change has ushered in a new era for organizations that rely on technology and automation tools to stay competitive. Businesses may move beyond technologys distinct advantages to genuine digital agility and scale adaptability when all forms of automation operate together in close partnership.
Automation tools must be simple to incorporate into the current technological stack while not requiring too much effort from IT. A platform must be able to plug and play with a wide range of technologies to achieve hyperautomation. The interdependence of automation technologies is a property that is connected to hyperautomation. Hyperautomation saves individuals time and money by reducing errors. Hyperautomation has the potential to create a workplace that is intelligent, adaptable, and capable of making quick, accurate decisions based on data and insights. Model recognition is used to determine what to do next and to optimize processes with the least amount of human engagement possible.
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The use of automation technologies to simplify any and every activity conceivable in a business, allowing repeated operations to operate without manual intervention, is known as hyperautomation. Hyperautomation transforms current and old processes and equipment by utilizing artificial intelligence, machine learning, and robotic process automation. This digital transformation may assist a business in gaining cost and resource efficiency, allowing it to prosper in a more competitive environment. With the advancement of automation technologies, hyperautomation is becoming more prevalent. Companies are shifting their methods to create more human-centered and intelligent workplaces. This change has ushered in a new era for organizations that rely on technology and automation tools to stay competitive. Businesses may move beyond technologys distinct advantages to genuine digital agility and scale adaptability when all forms of automation operate together in close partnership.
Automation tools must be simple to incorporate into the current technological stack while not requiring too much effort from IT. A platform must be able to plug and play with a wide range of technologies to achieve hyperautomation. The interdependence of automation technologies is a property that is connected to hyperautomation. Hyperautomation saves individuals time and money by reducing errors. Hyperautomation has the potential to create a workplace that is intelligent, adaptable, and capable of making quick, accurate decisions based on data and insights. Model recognition is used to determine what to do next and to optimize processes with the least amount of human engagement possible.
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Author / Editor Details
Rajesh Kumar Dhanaraj, PhD is a professor at the School of Computing Science and Engineering at Galgotias University, Greater Noida, India. He has contributed more than 25 books on various technologies, 21 patents, and 53 articles and papers in various refereed journals and international conferences, as well as contributed chapters books. He is a senior member of the Institute of Electrical and Electronics Engineers and is a member of the Computer Science Teacher Association and International Association of Engineers. He is also an expert advisory panel member of Texas Instruments Inc.
Rajesh Kumar Dhanaraj, PhD is a professor at the School of Computing Science and Engineering at Galgotias University, Greater Noida, India. He has contributed more than 25 books on various technologies, 21 patents, and 53 articles and papers in various refereed journals and international conferences, as well as contributed chapters books. He is a senior member of the Institute of Electrical and Electronics Engineers and is a member of the Computer Science Teacher Association and International Association of Engineers. He is also an expert advisory panel member of Texas Instruments Inc.
M. Nalini, PhD is a professor at the Sri Sairam Engineering College, Chennai, Tamil Nadu, India. She has more than 14 years of experience working in teaching and research. Dr. Nalini is the author of more than 2 book and over 25 international journals and conferences. She has also received invitations to address international conferences as a keynote speaker and session chair and is a member of the Institute of Electrical and Electronics Engineers and a life member in Indian Society for Technical Education.
A. Daniel, PhD is an associate professor at the School of Computing Science and Engineering in Galgotias University, Greater Noida, Uttar Pradesh, India. He has published several articles in reputed international journals and is a member of the Institute of Electrical and Electronics Engineers, Association of Computing Machinery, Institute for Educational Research and Publication, International Association of Engineers, and the Computer Science Teachers Association.
Ali Kashif Bashir, PhD is affiliated with the School of Computing and Mathematics, Manchester Metropolitan University, United Kingdom. Additionally, he is an adjunct professor for the School of Electrical Engineering and Computer Science, National University of Science and Technology, Islamabad, an honorary professor at the School of Information and Communication Engineering, University of Electronics Science and Technology of China, and a chief advisor at the Visual Intelligence Research Center, UESTC. He is the author of over 100 peer-reviewed articles and has served as a chair for several conferences and workshops, delivering several invited and keynote talks.
Balamurugan Balusamy, PhD, is an associate dean to students at Shiv Nadar University at the Delhi-NCR Campus in Noida, India. He has authored/edited more than 80 books as well as over 200 contributions to international journals and conferences.
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Table of Contents
Preface
1. Journey To Hyperautomation: The Pathway of Today’s Industries to Next Generation Industries
T. Kavitha, S. Saraswathi and G. Senbagavalli
1.1 Introduction: What is Hyperautomation (HA)?
1.2 Technologies Associated with HA
1.2.1 Robotic Process Automation (RPA)
1.2.2 Artificial Intelligence/Machine Learning (AI/ML)
1.2.3 Optical Character Recognition (OCR)
1.2.4 Natural Language Processing (NLP)
1.2.5 Digital Twin of an Organization (DTO)
1.2.6 Process Mining
1.2.7 RPA Bot Example
1.3 Potential Benefits/Abilities of HA
1.4 Challenges in Hyperautomation
1.5 Applications
1.6 Case Studies
1.7 Conclusion
References
2. Software Robot for Next Generation Industries Using RPA
S.V. Juno Bella Gracia, J. Godwin Ponsam, S. Sheeba Rachel and R. Gayathiri
2.1 Introduction
2.2 Evolution of Industries
2.3 Robotic Process Automation
2.4 Case Studies
2.5 Debunking RPA Rumors
2.6 Conclusion
References
3. Artificial Intelligence-Based Hyperautomation for Smart Factory Process Automation
Balasubramaniam S., A. Prasanth, K. Satheesh Kumar and Seifedine Kadry
3.1 Introduction
3.1.1 Smart Factory
3.1.2 Advantages of Smart Factory
3.1.3 Smart Factory Levels
3.1.3.1 Simple Access to Information
3.1.3.2 Proactively Analyzing Data
3.1.3.3 Present Data
3.1.3.4 Actionable Information
3.1.4 Technologies Used in Smart Factory
3.1.4.1 Sensors
3.1.4.2 Cloud Computing
3.1.4.3 Big Data Analytics
3.1.4.4 Virtual and Augmented Reality
3.1.4.5 Digital Twins
3.1.4.6 IoTs
3.1.5 The Fundamentals of a Smart Factory
3.1.6 Challenges
3.1.7 Industry 5.0
3.2 Hyperautomation
3.2.1 What is Hyperautomation?
3.2.2 Basic Components of Hyperautomation
3.2.3 RPA
3.2.3.1 Selecting the Appropriate Business Processes for Automation
3.2.3.2 Scaling Up Operations and Programs for Digital Process Automation
3.2.3.3 Considering Regulatory and Corporate Restrictions, i.e., Competing Initiatives
3.2.3.4 Controlling and Observing Automation in an Efficient Manner
3.2.3.5 Prior to Automating Business Processes, Improve Them
3.2.4 Hyperautomation’s Emergence
3.2.5 Advanced Technologies
3.2.5.1 Artificial Intelligence (AI)
3.2.5.2 Advanced Analytics
3.2.5.3 Intelligent Automation
3.2.5.4 Administration of Information
3.2.6 Hyperautomation Advantages
3.2.6.1 Employee Empowerment
3.2.6.2 Employee Training
3.2.6.3 Integration of Systems
3.2.6.4 Digital Nimbleness
3.2.6.5 Return on Investment
3.2.7 Increasing Trends in Hyperautomation
3.2.8 Hyperautomation Roadmap
3.2.9 Hyperautomation Work Flow Methodology
3.3 Hyperautomation-Based Industrial Ecosystem
3.3.1 Robotic Process Automation
3.3.2 Process Mining
3.3.3 AI
3.3.4 iBPMSs
3.3.5 Advanced Analytics
3.3.6 Approach to Delivery for Hyperautomation
3.3.6.1 Imagine
3.3.6.2 Deliver
3.3.6.3 Run
3.3.6.4 Benefits of Delivery Approach for Hyperautomation
3.4 Artificial Intelligence
3.4.1 Concepts of Artificial Intelligence
3.4.2 Shallow Learning
3.4.2.1 Support Vector Machine (SVM)
3.4.2.2 RF
3.4.2.3 KNN
3.4.3 Deep Learning
3.4.4 Deep Reinforcement Learning
3.4.5 NLP
3.4.5.1 Practical Applications for NLP in RPA
3.5 Hyperautomation Use Cases and Examples in Industry/Factory Processes
3.5.1 Payables Accounts
3.5.2 Journey and Expense
3.5.3 Handling Claims
3.5.3.1 Insurance Occurrences
3.5.3.2 Occupational Claims
3.5.4 Cash Order (O2C)
3.5.5 Additional Document Handling
3.5.6 Operations for Customer Service
3.5.7 Obtaining Leads From Anonymous Website Visitors
3.5.8 Processing for Underwriting
3.5.9 Redaction to Protect Privacy
3.5.10 Anti-Money Laundering (AML)
3.5.11 Underwriting a Loan
3.5.12 Customer Onboarding for Banks
3.6 Artificial Intelligence-Based Hyperautomation for Smart Factory Process Automation
3.6.1 Industry 4.0 Intelligent Automation Solution Based on the IoTs and Machine Learning
3.6.2 Soft Sensors Powered by Deep Learning to Increase Industrial Automation’s Adaptability
3.6.3 Analysis of Robot Control System Optimization Design Using Artificial Intelligence
3.6.4 Automation of the Power Distribution Network Using AI
3.6.5 Modular Deep RL and Policy Transfer Allow for Flexible Automation
3.6.6 Hyperautomation in the Auto Industry
3.6.7 Hyperautomation in Transforming Under Writing Operation in the Life Insurance Industry
3.7 Conclusion
References
4. Intelligent Assistants Using Natural Language Processing for Hyperautomation
M. Nalini, Rajesh Kumar Dhanraj, Balamurugan Balusamy, Abirami, V. and Kavya, K.
4.1 Introduction
4.1.1 Hyperautomation
4.1.2 Automation vs Hyperautomation
4.1.3 Natural Language Processing (NLP)
4.1.4 Components of Natural Language Processing
4.1.4.1 Natural Language Generation (NLG)
4.1.4.2 Natural Language Understanding (NLU)
4.1.5 NLP in Intelligent Automation
4.1.6 NLP in Robotic Process Automation (RPA)
4.2 Phases of NLP
4.2.1 Morphological Analysis
4.2.2 Syntactic Analysis
4.2.2.1 Parsing
4.2.2.2 Derivation
4.2.3 Semantics Analysis
4.2.4 Discourse Integration
4.2.5 Pragmatic Analysis
4.3 NLP Application in Web/Android Services
4.3.1 Chatbox
4.3.2 AI Assistant
4.3.3 Search Result
4.3.4 Digital Phone Call
4.3.5 Machine Translation
4.4 Role of NLP in Internet Protocol
4.4.1 Market Intelligent
4.4.2 Text Analytics
4.4.3 Data Analysis
4.4.4 E-mail Analysis
4.4.5 Predictive Text
4.4.6 Auto Correct
References
5. Digital Twins for Hyperautomation for Next Generation
V. Divya M.Sc., M. Phil., A. Prasanth, K. K. Devi Sowndarya and Chien Thang Pham
5.1 Introduction
5.2 Hyperautomation Requirement
5.3 Literature Review
5.4 Hyperautomation Methodology
5.5 Background of Hyperautomation
5.6 Hyperautomation Enhancement
5.7 Association of Versatile Technologies with Hyperautomation
5.8 Hyperautomation Workflow
5.9 Hyperautomation Domains
5.10 Path to Hyperautomation
5.11 Automation Process Categories
5.12 Sophistication of the Automation
5.13 Technologies in Hyperautomation
5.14 Technological Ecosystem of Hyperautomation
5.15 Future Scope of Hyperautomation
5.16 Conclusion
References
6. IQ Bot for Intelligent Document Process and Mail Processing
Vinora A., Lloyds E., Nancy Deborah R., Sivakarthi G. and Mohanad Alfiras
6.1 Introduction to IQ Bot
6.2 Understanding the Internal Operations of the IQ Bot
References
7. Bot-Based Process Triggering by Incoming E-mails and Documents
M. Nalini, Rajesh Kumar Dhanraj, Balamurugan Balusamy, Abirami, V., Kavya, K. and Aishwaryalakshmi, G.
7.1 Introduction of Bot
7.1.1 What are Bots?
7.1.2 Chat Bots and their Influence in the Industry
7.1.3 Bot Based on RPA
7.2 Bot Triggering by Incoming E-mail and Incoming Document
7.2.1 The Technologies Behind E-mail Bots
7.2.2 E-mail Bots and Other Systems
7.2.3 E-mail Bot Expected Results
7.2.4 Automate Handling of Incoming Documents with Documentbot
7.3 Types of Bots
7.3.1 Malicious and Non-Malicious Bot Activity
7.3.2 Scraper Bots
7.3.3 Spam Bots
7.3.4 Social Media Bots
7.3.5 Spider Bots
7.3.6 Ticketing Bots
7.3.7 Download Bots
7.4 Various Other Bot Triggers
7.4.1 Add a Hotkey Trigger
7.4.2 Add an Interface Trigger
7.4.3 Add a Process Trigger
7.4.4 Add a Service Trigger
7.5 Applications of Bot Trigger
7.5.1 Software Application for Particular Task—Performance
7.5.2 Instant Messenger Applications
7.5.3 Bots Used on Applications
References
8. Hyperautomation for Automating the Customer Service Operations
Nancy Deborah R., Alwyn Rajiv S., Vinora A. and Sivakarthi G.
8.1 Introduction
8.2 Advantages of Hyperautomation
8.3 Issues With Hyperautomation
8.4 Use Cases of Hyperautomation
8.5 Customer Service Hyperautomation
8.6 Customer Support Hyperautomation: Where We are Now and Where We are Headed
8.7 Benefits of Customer Service Hyperautomation
8.8 Reforming the Future of Customer Service Operation through Hyperautomation
8.9 Case Study: Hyperautomation in Customer Onboarding
8.10 Conclusion
References
9. Applications of Hyperautomation in Finance and Banking Industries
S. Arunarani, A. Prasanth, N. Pushpalatha and Mariya Ouaissa
9.1 Introduction
9.2 History of Hyperautomation
9.3 Literature Review
9.3.1 RPA vs. Intelligent Automation
9.3.2 Tools and Techniques for Hyperautomation
9.3.3 Major Applications of Hyperautomation Across Industries
9.4 The Role of Hyperautomation in Banking and Finance
9.4.1 Benefits of Using Hyperautomation in Banking and Financial Sector
9.4.2 Challenges Associated with Hyperautomation in Banking and Finance Industry
9.4.3 Issues that Banks and Financial Institutions Face in the Absence of Hyperautomation
9.5 Dedicated Workflow Process for Hyperautomation
9.6 Case Study
9.6.1 Exploring the Case Study
9.7 Future of Hyperautomation
9.8 Conclusion
References
10. Application of Hyperautomation in COVID-19 Analysis and Management
P. Ashok, Pon Bharathi A., S. Rathika and Venkatesh Ramamurthy
10.1 Introduction
10.2 Literature Review
10.3 Summary
References
11. Application of Hyperautomation in Insurance and Retail Industries
A. Vinora, E. Lloyds, R. Nancy Deborah and Sivakarthi G.
Introduction
Conclusions
References
12. Application of Hyperautomation in Predictive Maintenance—A Technical Analysis
Sunith Babu L., Hemanth Kumar J., Madhusudhan B., Nitish Kumar, V. and Sujitha, R.
12.1 Introduction to Hyperautomation
12.2 Predictive Maintenance: An Overview
12.3 Application of Hyperautomation in Predictive Maintenance
12.4 Conclusion
References
Index
Back to Top
Preface
1. Journey To Hyperautomation: The Pathway of Today’s Industries to Next Generation Industries
T. Kavitha, S. Saraswathi and G. Senbagavalli
1.1 Introduction: What is Hyperautomation (HA)?
1.2 Technologies Associated with HA
1.2.1 Robotic Process Automation (RPA)
1.2.2 Artificial Intelligence/Machine Learning (AI/ML)
1.2.3 Optical Character Recognition (OCR)
1.2.4 Natural Language Processing (NLP)
1.2.5 Digital Twin of an Organization (DTO)
1.2.6 Process Mining
1.2.7 RPA Bot Example
1.3 Potential Benefits/Abilities of HA
1.4 Challenges in Hyperautomation
1.5 Applications
1.6 Case Studies
1.7 Conclusion
References
2. Software Robot for Next Generation Industries Using RPA
S.V. Juno Bella Gracia, J. Godwin Ponsam, S. Sheeba Rachel and R. Gayathiri
2.1 Introduction
2.2 Evolution of Industries
2.3 Robotic Process Automation
2.4 Case Studies
2.5 Debunking RPA Rumors
2.6 Conclusion
References
3. Artificial Intelligence-Based Hyperautomation for Smart Factory Process Automation
Balasubramaniam S., A. Prasanth, K. Satheesh Kumar and Seifedine Kadry
3.1 Introduction
3.1.1 Smart Factory
3.1.2 Advantages of Smart Factory
3.1.3 Smart Factory Levels
3.1.3.1 Simple Access to Information
3.1.3.2 Proactively Analyzing Data
3.1.3.3 Present Data
3.1.3.4 Actionable Information
3.1.4 Technologies Used in Smart Factory
3.1.4.1 Sensors
3.1.4.2 Cloud Computing
3.1.4.3 Big Data Analytics
3.1.4.4 Virtual and Augmented Reality
3.1.4.5 Digital Twins
3.1.4.6 IoTs
3.1.5 The Fundamentals of a Smart Factory
3.1.6 Challenges
3.1.7 Industry 5.0
3.2 Hyperautomation
3.2.1 What is Hyperautomation?
3.2.2 Basic Components of Hyperautomation
3.2.3 RPA
3.2.3.1 Selecting the Appropriate Business Processes for Automation
3.2.3.2 Scaling Up Operations and Programs for Digital Process Automation
3.2.3.3 Considering Regulatory and Corporate Restrictions, i.e., Competing Initiatives
3.2.3.4 Controlling and Observing Automation in an Efficient Manner
3.2.3.5 Prior to Automating Business Processes, Improve Them
3.2.4 Hyperautomation’s Emergence
3.2.5 Advanced Technologies
3.2.5.1 Artificial Intelligence (AI)
3.2.5.2 Advanced Analytics
3.2.5.3 Intelligent Automation
3.2.5.4 Administration of Information
3.2.6 Hyperautomation Advantages
3.2.6.1 Employee Empowerment
3.2.6.2 Employee Training
3.2.6.3 Integration of Systems
3.2.6.4 Digital Nimbleness
3.2.6.5 Return on Investment
3.2.7 Increasing Trends in Hyperautomation
3.2.8 Hyperautomation Roadmap
3.2.9 Hyperautomation Work Flow Methodology
3.3 Hyperautomation-Based Industrial Ecosystem
3.3.1 Robotic Process Automation
3.3.2 Process Mining
3.3.3 AI
3.3.4 iBPMSs
3.3.5 Advanced Analytics
3.3.6 Approach to Delivery for Hyperautomation
3.3.6.1 Imagine
3.3.6.2 Deliver
3.3.6.3 Run
3.3.6.4 Benefits of Delivery Approach for Hyperautomation
3.4 Artificial Intelligence
3.4.1 Concepts of Artificial Intelligence
3.4.2 Shallow Learning
3.4.2.1 Support Vector Machine (SVM)
3.4.2.2 RF
3.4.2.3 KNN
3.4.3 Deep Learning
3.4.4 Deep Reinforcement Learning
3.4.5 NLP
3.4.5.1 Practical Applications for NLP in RPA
3.5 Hyperautomation Use Cases and Examples in Industry/Factory Processes
3.5.1 Payables Accounts
3.5.2 Journey and Expense
3.5.3 Handling Claims
3.5.3.1 Insurance Occurrences
3.5.3.2 Occupational Claims
3.5.4 Cash Order (O2C)
3.5.5 Additional Document Handling
3.5.6 Operations for Customer Service
3.5.7 Obtaining Leads From Anonymous Website Visitors
3.5.8 Processing for Underwriting
3.5.9 Redaction to Protect Privacy
3.5.10 Anti-Money Laundering (AML)
3.5.11 Underwriting a Loan
3.5.12 Customer Onboarding for Banks
3.6 Artificial Intelligence-Based Hyperautomation for Smart Factory Process Automation
3.6.1 Industry 4.0 Intelligent Automation Solution Based on the IoTs and Machine Learning
3.6.2 Soft Sensors Powered by Deep Learning to Increase Industrial Automation’s Adaptability
3.6.3 Analysis of Robot Control System Optimization Design Using Artificial Intelligence
3.6.4 Automation of the Power Distribution Network Using AI
3.6.5 Modular Deep RL and Policy Transfer Allow for Flexible Automation
3.6.6 Hyperautomation in the Auto Industry
3.6.7 Hyperautomation in Transforming Under Writing Operation in the Life Insurance Industry
3.7 Conclusion
References
4. Intelligent Assistants Using Natural Language Processing for Hyperautomation
M. Nalini, Rajesh Kumar Dhanraj, Balamurugan Balusamy, Abirami, V. and Kavya, K.
4.1 Introduction
4.1.1 Hyperautomation
4.1.2 Automation vs Hyperautomation
4.1.3 Natural Language Processing (NLP)
4.1.4 Components of Natural Language Processing
4.1.4.1 Natural Language Generation (NLG)
4.1.4.2 Natural Language Understanding (NLU)
4.1.5 NLP in Intelligent Automation
4.1.6 NLP in Robotic Process Automation (RPA)
4.2 Phases of NLP
4.2.1 Morphological Analysis
4.2.2 Syntactic Analysis
4.2.2.1 Parsing
4.2.2.2 Derivation
4.2.3 Semantics Analysis
4.2.4 Discourse Integration
4.2.5 Pragmatic Analysis
4.3 NLP Application in Web/Android Services
4.3.1 Chatbox
4.3.2 AI Assistant
4.3.3 Search Result
4.3.4 Digital Phone Call
4.3.5 Machine Translation
4.4 Role of NLP in Internet Protocol
4.4.1 Market Intelligent
4.4.2 Text Analytics
4.4.3 Data Analysis
4.4.4 E-mail Analysis
4.4.5 Predictive Text
4.4.6 Auto Correct
References
5. Digital Twins for Hyperautomation for Next Generation
V. Divya M.Sc., M. Phil., A. Prasanth, K. K. Devi Sowndarya and Chien Thang Pham
5.1 Introduction
5.2 Hyperautomation Requirement
5.3 Literature Review
5.4 Hyperautomation Methodology
5.5 Background of Hyperautomation
5.6 Hyperautomation Enhancement
5.7 Association of Versatile Technologies with Hyperautomation
5.8 Hyperautomation Workflow
5.9 Hyperautomation Domains
5.10 Path to Hyperautomation
5.11 Automation Process Categories
5.12 Sophistication of the Automation
5.13 Technologies in Hyperautomation
5.14 Technological Ecosystem of Hyperautomation
5.15 Future Scope of Hyperautomation
5.16 Conclusion
References
6. IQ Bot for Intelligent Document Process and Mail Processing
Vinora A., Lloyds E., Nancy Deborah R., Sivakarthi G. and Mohanad Alfiras
6.1 Introduction to IQ Bot
6.2 Understanding the Internal Operations of the IQ Bot
References
7. Bot-Based Process Triggering by Incoming E-mails and Documents
M. Nalini, Rajesh Kumar Dhanraj, Balamurugan Balusamy, Abirami, V., Kavya, K. and Aishwaryalakshmi, G.
7.1 Introduction of Bot
7.1.1 What are Bots?
7.1.2 Chat Bots and their Influence in the Industry
7.1.3 Bot Based on RPA
7.2 Bot Triggering by Incoming E-mail and Incoming Document
7.2.1 The Technologies Behind E-mail Bots
7.2.2 E-mail Bots and Other Systems
7.2.3 E-mail Bot Expected Results
7.2.4 Automate Handling of Incoming Documents with Documentbot
7.3 Types of Bots
7.3.1 Malicious and Non-Malicious Bot Activity
7.3.2 Scraper Bots
7.3.3 Spam Bots
7.3.4 Social Media Bots
7.3.5 Spider Bots
7.3.6 Ticketing Bots
7.3.7 Download Bots
7.4 Various Other Bot Triggers
7.4.1 Add a Hotkey Trigger
7.4.2 Add an Interface Trigger
7.4.3 Add a Process Trigger
7.4.4 Add a Service Trigger
7.5 Applications of Bot Trigger
7.5.1 Software Application for Particular Task—Performance
7.5.2 Instant Messenger Applications
7.5.3 Bots Used on Applications
References
8. Hyperautomation for Automating the Customer Service Operations
Nancy Deborah R., Alwyn Rajiv S., Vinora A. and Sivakarthi G.
8.1 Introduction
8.2 Advantages of Hyperautomation
8.3 Issues With Hyperautomation
8.4 Use Cases of Hyperautomation
8.5 Customer Service Hyperautomation
8.6 Customer Support Hyperautomation: Where We are Now and Where We are Headed
8.7 Benefits of Customer Service Hyperautomation
8.8 Reforming the Future of Customer Service Operation through Hyperautomation
8.9 Case Study: Hyperautomation in Customer Onboarding
8.10 Conclusion
References
9. Applications of Hyperautomation in Finance and Banking Industries
S. Arunarani, A. Prasanth, N. Pushpalatha and Mariya Ouaissa
9.1 Introduction
9.2 History of Hyperautomation
9.3 Literature Review
9.3.1 RPA vs. Intelligent Automation
9.3.2 Tools and Techniques for Hyperautomation
9.3.3 Major Applications of Hyperautomation Across Industries
9.4 The Role of Hyperautomation in Banking and Finance
9.4.1 Benefits of Using Hyperautomation in Banking and Financial Sector
9.4.2 Challenges Associated with Hyperautomation in Banking and Finance Industry
9.4.3 Issues that Banks and Financial Institutions Face in the Absence of Hyperautomation
9.5 Dedicated Workflow Process for Hyperautomation
9.6 Case Study
9.6.1 Exploring the Case Study
9.7 Future of Hyperautomation
9.8 Conclusion
References
10. Application of Hyperautomation in COVID-19 Analysis and Management
P. Ashok, Pon Bharathi A., S. Rathika and Venkatesh Ramamurthy
10.1 Introduction
10.2 Literature Review
10.3 Summary
References
11. Application of Hyperautomation in Insurance and Retail Industries
A. Vinora, E. Lloyds, R. Nancy Deborah and Sivakarthi G.
Introduction
Conclusions
References
12. Application of Hyperautomation in Predictive Maintenance—A Technical Analysis
Sunith Babu L., Hemanth Kumar J., Madhusudhan B., Nitish Kumar, V. and Sujitha, R.
12.1 Introduction to Hyperautomation
12.2 Predictive Maintenance: An Overview
12.3 Application of Hyperautomation in Predictive Maintenance
12.4 Conclusion
References
Index
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