Hyperautomation for Next-Generation Industries

Inbunden, Engelska, 2024

AvRajesh Kumar Dhanaraj,M. Nalini,A. Daniel,Ali Kashif Bashir,Balamurugan Balusamy

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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. 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 technology’s 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.

Produktinformation

Utgivningsdatum2024-09-17
Vikt816 g
FormatInbunden
SpråkEngelska
Antal sidor352
FörlagJohn Wiley & Sons Inc
ISBN9781394185825

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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 to 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 two books 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 of the 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.

Preface xv1 Journey To Hyperautomation: The Pathway of Today’s Industries to Next Generation Industries 1T. Kavitha, S. Saraswathi and G. Senbagavalli1.1 Introduction: What is Hyperautomation (HA)? 21.1.1 What Makes Hyperautomation Quite Crucial? 41.1.2 Benefits of Hyperautomation for Employees and Businesses 41.1.3 How Does It Work? 61.2 Technologies Associated with HA 81.2.1 Robotic Process Automation (RPA) 91.2.2 Artificial Intelligence/Machine Learning (AI/ML) 111.2.3 Optical Character Recognition (OCR) 111.2.4 Natural Language Processing (NLP) 121.2.5 Digital Twin of an Organization (DTO) 131.2.6 Process Mining 131.2.7 RPA Bot Example 151.3 Potential Benefits/Abilities of HA 181.4 Challenges in Hyperautomation 191.5 Applications 211.6 Case Studies 241.6.1 Touchless Accounts Reconciliation 241.6.2 AI-Enabled Healthcare Claims Processing 251.6.3 Investment Consultant 261.6.4 AI-Enabled Vacation Rental Site 271.6.5 Detection of Forged Signatures 281.6.6 IT Automation 281.6.7 Customer Service Based on Social Media Marketing and Sentiment Analysis 291.6.8 Data Center Migration 301.7 Conclusion 31References 312 Software Robot for Next Generation Industries Using RPA 35S.V. Juno Bella Gracia, J. Godwin Ponsam, S. Sheeba Rachel and R. Gayathiri2.1 Introduction 362.2 Evolution of Industries 372.2.1 Industry 4.0 372.2.2 Industry 5.0: The Future 372.2.2.1 What is the Purpose of Industry 5.0? 382.2.2.2 How to Make It Happen? 382.2.3 RPA and Industry 4.0 392.2.4 RPA and Industry 5.0 392.3 Robotic Process Automation 392.3.1 Features of Robotic Process Operation 402.3.2 Types of Robotic Process Automation 412.3.3 The Key Performance Indicators of RPA 412.3.4 Benefits of Robotic Process Automation 422.3.5 Business Automation Using RPA 432.3.6 Features and Capabilities of RPA 442.3.7 RPA: The Fastest Growing Enterprise Software in the World 442.3.8 The Role of API Integration in Process Automation 442.4 Case Studies 452.4.1 Enhancing the Platform for End-to-End Automation 452.4.2 RPA for Finance and Accounting 462.4.3 Intelligent Document Processing 472.4.4 A Three-Way Model for Automation 482.4.5 A Three-Stage, Basic Model 482.4.6 The Essential Elements 492.5 Debunking RPA Rumors 502.6 Conclusion 52References 533 Artificial Intelligence-Based Hyperautomation for Smart Factory Process Automation 55Balasubramaniam S., A. Prasanth, K. Satheesh Kumar and Seifedine Kadry3.1 Introduction 563.1.1 Smart Factory 573.1.2 Advantages of Smart Factory 583.1.3 Smart Factory Levels 583.1.3.1 Simple Access to Information 583.1.3.2 Proactively Analyzing Data 583.1.3.3 Present Data 583.1.3.4 Actionable Information 583.1.4 Technologies Used in Smart Factory 593.1.4.1 Sensors 593.1.4.2 Cloud Computing 593.1.4.3 Big Data Analytics 593.1.4.4 Virtual and Augmented Reality 593.1.4.5 Digital Twins 593.1.4.6 IoTs 593.1.5 The Fundamentals of a Smart Factory 603.1.6 Challenges 603.1.7 Industry 5.0 603.2 Hyperautomation 603.2.1 What is Hyperautomation? 623.2.2 Basic Components of Hyperautomation 623.2.3 RPA 633.2.3.1 Selecting the Appropriate Business Processes for Automation 633.2.3.2 Scaling Up Operations and Programs for Digital Process Automation 643.2.3.3 Considering Regulatory and Corporate Restrictions, i.e., Competing Initiatives 643.2.3.4 Controlling and Observing Automation in an Efficient Manner 643.2.3.5 Prior to Automating Business Processes, Improve Them 643.2.4 Hyperautomation’s Emergence 653.2.5 Advanced Technologies 653.2.5.1 Artificial Intelligence (AI) 653.2.5.2 Advanced Analytics 653.2.5.3 Intelligent Automation 663.2.5.4 Administration of Information 663.2.6 Hyperautomation Advantages 663.2.6.1 Employee Empowerment 663.2.6.2 Employee Training 673.2.6.3 Integration of Systems 673.2.6.4 Digital Nimbleness 673.2.6.5 Return on Investment 673.2.7 Increasing Trends in Hyperautomation 673.2.8 Hyperautomation Roadmap 683.2.9 Hyperautomation Work Flow Methodology 683.3 Hyperautomation-Based Industrial Ecosystem 693.3.1 Robotic Process Automation 693.3.2 Process Mining 703.3.3 AI 703.3.4 iBPMSs 713.3.5 Advanced Analytics 713.3.6 Approach to Delivery for Hyperautomation 713.3.6.1 Imagine 713.3.6.2 Deliver 713.3.6.3 Run 713.3.6.4 Benefits of Delivery Approach for Hyperautomation 713.4 Artificial Intelligence 723.4.1 Concepts of Artificial Intelligence 733.4.2 Shallow Learning 743.4.2.1 Support Vector Machine (SVM) 743.4.2.2 Random Forest RF 743.4.2.3 KNN 743.4.3 Deep Learning 743.4.4 Deep Reinforcement Learning 753.4.5 NLP 753.4.5.1 Practical Applications for NLP in RPA 763.5 Hyperautomation Use Cases and Examples in Industry/ Factory Processes 763.5.1 Payables Accounts 773.5.2 Journey and Expense 783.5.3 Handling Claims 783.5.3.1 Insurance Occurrences 783.5.3.2 Occupational Claims 793.5.4 Cash Order (O2C) 793.5.5 Additional Document Handling 793.5.6 Operations for Customer Service 793.5.7 Obtaining Leads From Anonymous Website Visitors 793.5.8 Processing for Underwriting 803.5.9 Redaction to Protect Privacy 803.5.10 Anti-Money Laundering (AML) 803.5.11 Underwriting a Loan 803.5.12 Customer Onboarding for Banks 803.6 Artificial Intelligence-Based Hyperautomation for Smart Factory Process Automation 813.6.1 Industry 4.0 Intelligent Automation Solution Based on the IoTs and Machine Learning 813.6.2 Soft Sensors Powered by Deep Learning to Increase Industrial Automation’s Adaptability 823.6.3 Analysis of Robot Control System Optimization Design Using Artificial Intelligence 833.6.4 Automation of the Power Distribution Network Using AI 833.6.5 Modular Deep RL and Policy Transfer Allow for Flexible Automation 843.6.6 Hyperautomation in the Auto Industry 843.6.7 Hyperautomation in Transforming Under Writing Operation in the Life Insurance Industry 853.7 Conclusion 85References 874 Intelligent Assistants Using Natural Language Processing for Hyperautomation 91M. Nalini, Rajesh Kumar Dhanraj, Balamurugan Balusamy, Abirami, V. and Kavya, K.4.1 Introduction 924.1.1 Hyperautomation 924.1.2 Automation vs Hyperautomation 944.1.3 Natural Language Processing (NLP) 944.1.4 Components of Natural Language Processing 954.1.4.1 Natural Language Generation (NLG) 954.1.4.2 Natural Language Understanding (NLU) 954.1.5 NLP in Intelligent Automation 964.1.6 NLP in Robotic Process Automation (RPA) 964.2 Phases of NLP 974.2.1 Morphological Analysis 994.2.2 Syntactic Analysis 1014.2.2.1 Parsing 1024.2.2.2 Derivation 1034.2.3 Semantics Analysis 1044.2.4 Discourse Integration 1064.2.5 Pragmatic Analysis 1074.3 NLP Application in Web/Android Services 1084.3.1 Chatbox 1094.3.2 AI Assistant 1094.3.3 Search Result 1104.3.4 Digital Phone Call 1114.3.5 Machine Translation 1124.4 Role of NLP in Internet Protocol 1144.4.1 Market Intelligent 1144.4.2 Text Analytics 1164.4.2.1 Benefits of Text Analytics 1174.4.3 Data Analysis 1174.4.4 E-mail Analysis 1194.4.4.1 Dataset 1194.4.4.2 ETL Pipeline 1194.4.4.3 AI Pipeline 1204.4.5 Predictive Text 1204.4.6 Auto Correct 121References 1235 Digital Twins for Hyperautomation for Next Generation 127V. Divya, A. Prasanth, K. K. Devi Sowndarya and Chien Thang Pham5.1 Introduction 1285.2 Hyperautomation Requirement 1295.3 Literature Review 1295.4 Hyperautomation Methodology 1315.5 Background of Hyperautomation 1325.6 Hyperautomation Enhancement 1335.7 Association of Versatile Technologies with Hyperautomation 1345.8 Hyperautomation Workflow 1365.9 Hyperautomation Domains 1385.10 Path to Hyperautomation 1405.11 Automation Process Categories 1405.12 Sophistication of the Automation 1415.13 Technologies in Hyperautomation 1435.14 Technological Ecosystem of Hyperautomation 1465.15 Future Scope of Hyperautomation 1475.16 Conclusion 147References 1486 IQ Bot for Intelligent Document Process and Mail Processing 153Vinora A., Lloyds E., Nancy Deborah R., Sivakarthi G. and Mohanad Alfiras6.1 Introduction to IQ Bot 1546.2 Understanding the Internal Operations of the IQ Bot 155References 1737 Bot-Based Process Triggering by Incoming E-mails and Documents 177M. Nalini, Rajesh Kumar Dhanraj, Balamurugan Balusamy, Abirami, V., Kavya, K. and Aishwaryalakshmi, G.7.1 Introduction of Bot 1787.1.1 What are Bots? 1787.1.2 Chat Bots and their Influence in the Industry 1787.1.3 Bot Based on RPA 1807.2 Bot Triggering by Incoming E-mail and Incoming Document 1827.2.1 The Technologies Behind E-mail Bots 1827.2.2 E-mail Bots and Other Systems 1837.2.3 E-mail Bot Expected Results 1847.2.4 Automate Handling of Incoming Documents with Documentbot 1857.3 Types of Bots 1867.3.1 Malicious and Non-Malicious Bot Activity 1867.3.2 Scraper Bots 1877.3.3 Spam Bots 1887.3.4 Social Media Bots 1897.3.5 Spider Bots 1917.3.6 Ticketing Bots 1917.3.7 Download Bots 1937.4 Various Other Bot Triggers 1957.4.1 Add a Hotkey Trigger 1957.4.2 Add an Interface Trigger 1957.4.3 Add a Process Trigger 1977.4.4 Add a Service Trigger 1977.5 Applications of Bot Trigger 1987.5.1 Software Application for Particular Task—Performance 1987.5.2 Instant Messenger Applications 2007.5.3 Bots Used on Applications 201References 2028 Hyperautomation for Automating the Customer Service Operations 207Nancy Deborah R., Alwyn Rajiv S., Vinora A. and Sivakarthi G.8.1 Introduction 2078.2 Advantages of Hyperautomation 2118.3 Issues With Hyperautomation 2128.4 Use Cases of Hyperautomation 2138.5 Customer Service Hyperautomation 2148.6 Customer Support Hyperautomation: Where We are Now and Where We are Headed 2168.7 Benefits of Customer Service Hyperautomation 2208.8 Reforming the Future of Customer Service Operation through Hyperautomation 2228.9 Case Study: Hyperautomation in Customer Onboarding 2248.10 Conclusion 227References 2289 Applications of Hyperautomation in Finance and Banking Industries 229S. Arunarani, A. Prasanth, N. Pushpalatha and Mariya Ouaissa9.1 Introduction 2309.2 History of Hyperautomation 2319.3 Literature Review 2339.3.1 RPA vs. Intelligent Automation 2349.3.2 Tools and Techniques for Hyperautomation 2359.3.3 Major Applications of Hyperautomation Across Industries 2399.4 The Role of Hyperautomation in Banking and Finance 2439.4.1 Benefits of Using Hyperautomation in Banking and Financial Sector 2449.4.2 Challenges Associated with Hyperautomation in Banking and Finance Industry 2459.4.3 Issues that Banks and Financial Institutions Face in the Absence of Hyperautomation 2469.5 Dedicated Workflow Process for Hyperautomation 2479.6 Case Study 2499.6.1 Exploring the Case Study 2519.7 Future of Hyperautomation 2529.8 Conclusion 253References 25310 Application of Hyperautomation in COVID-19 Analysis and Management 255P. Ashok, Pon Bharathi A., S. Rathika and Venkatesh Ramamurthy10.1 Introduction 25610.2 Literature Review 25710.3 Summary 274References 27511 Application of Hyperautomation in Insurance and Retail Industries 277A. Vinora, E. Lloyds, R. Nancy Deborah and Sivakarthi G.Introduction 277Conclusions 297References 29812 Application of Hyperautomation in Predictive Maintenance— A Technical Analysis 299Sunith Babu L., Hemanth Kumar J., Madhusudhan B., Nitish Kumar V. and Sujitha R.12.1 Introduction to Hyperautomation 30012.2 Predictive Maintenance: An Overview 30212.3 Application of Hyperautomation in Predictive Maintenance 30612.4 Conclusion 316References 318Index 325