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Submit a ProposalIntelligent Automation and Analytics for Business Resilience
 | Edited by Daniel Arockiam, Balamurugan Balusamy, Prasenjit Chatterjee and Abhijit Saha Series: Sustainable Computing and Optimization Copyright: 2027 | Expected Pub Date:11/30/2026 ISBN: 9781394271856 | Hardcover | 300 pages Price: $195 USD  |
One Line Description
Unlock the full potential of AI-driven automation with this comprehensive guide to building agile, resilient, and sustainable organizations through real-world applications.
Audience
Researchers, academics, IT professionals, and policymakers in the fields of computer science, engineering, management, and data analytics.
Researchers, academics, IT professionals, and policymakers in the fields of computer science, engineering, management, and data analytics.
Description
In an era defined by rapid technological advancements and unprecedented global challenges, businesses must embrace innovation to remain resilient, agile, and competitive. This book explores the transformative role of AI-driven automation in enabling organizations to improve intelligence. It covers a wide spectrum of topics including the integration of AI in automation, cybersecurity, sustainability, performance measurement, and ethical applications in healthcare. It provides readers with insights into how automation technologies are reshaping industries, improving scalability and flexibility, and driving measurable returns on investment. The volume emphasizes practical applications, such as using AI analytics to assess green equity markets, employing automation in education, and ensuring responsible adoption of intelligent systems. By combining theoretical frameworks with real-world case studies, this book serves as a practical reference for academics, industry leaders, and professionals seeking to leverage intelligent automation to achieve resilience, sustainability, and competitive advantage.
Readers will find the volume:
• Provides a multi-disciplinary perspective on intelligent automation, covering AI, machine learning, cybersecurity, and ROI evaluation;
• Bridges academia and industry with applied case studies, frameworks, and research insights;
• Addresses timely topics such as sustainability, ethical AI, and automation in healthcare and education;
• Highlights future trends and research directions for business resilience in a rapidly changing technological landscape.
Back to Top
In an era defined by rapid technological advancements and unprecedented global challenges, businesses must embrace innovation to remain resilient, agile, and competitive. This book explores the transformative role of AI-driven automation in enabling organizations to improve intelligence. It covers a wide spectrum of topics including the integration of AI in automation, cybersecurity, sustainability, performance measurement, and ethical applications in healthcare. It provides readers with insights into how automation technologies are reshaping industries, improving scalability and flexibility, and driving measurable returns on investment. The volume emphasizes practical applications, such as using AI analytics to assess green equity markets, employing automation in education, and ensuring responsible adoption of intelligent systems. By combining theoretical frameworks with real-world case studies, this book serves as a practical reference for academics, industry leaders, and professionals seeking to leverage intelligent automation to achieve resilience, sustainability, and competitive advantage.
Readers will find the volume:
• Provides a multi-disciplinary perspective on intelligent automation, covering AI, machine learning, cybersecurity, and ROI evaluation;
• Bridges academia and industry with applied case studies, frameworks, and research insights;
• Addresses timely topics such as sustainability, ethical AI, and automation in healthcare and education;
• Highlights future trends and research directions for business resilience in a rapidly changing technological landscape.
Back to Top
Author / Editor Details
Daniel Arockiam, PhD, School of Engineering and IT, Manipal Academy of Higher Education, Dubai Campus, Dubai, UAE, with more than 14 years of academic and research experience. He has published more than 100 papers in international journals, authored six books, and filed 30 patents. His work spans AI, Internet of Things, and cybersecurity, with certifications in Python, Quantum Computing, and UiPath.
Daniel Arockiam, PhD, School of Engineering and IT, Manipal Academy of Higher Education, Dubai Campus, Dubai, UAE, with more than 14 years of academic and research experience. He has published more than 100 papers in international journals, authored six books, and filed 30 patents. His work spans AI, Internet of Things, and cybersecurity, with certifications in Python, Quantum Computing, and UiPath.
Balamurugan Balusamy, PhD is a Professor and the Dean of the School of Engineering and IT at the Manipal Academy of Higher Education in Dubai Campus, UAE. Over his career, he has given more than 300 talks at international conferences and published more than 200 books and 300 journal articles. His research focuses on XAI, data science, blockchain technology, and the Internet of Things.
Prasenjit Chatterjee, PhD is a Professor of Mechanical Engineering and the Dean of Research and Consultancy at the MCKV Institute of Engineering, West Bengal, India. He has published more than 155 research papers in international journals and peer-reviewed conferences, authored and edited more than 55 books. His research interests include intelligent decision-making, fuzzy computing, supply chain management, optimization techniques, risk management, and sustainability modelling.
Abhijit Saha, PhD is an Assistant Professor in the Department of Computing Technologies at the SRM Institute of Science and Technology, Chennai, Tamil Nadu, India, with more than ten years of teaching and research experience. He has published more than 50 research articles in various journals of international repute. His areas of research interest are fuzzy set theory, soft set theory, aggregation operators, optimization, and decision-making.
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Table of Contents
Preface
Acknowledgement
1. Intelligent Automation and Analytics for Business Resilience
E. Murali, D. Deepa, R. Vignesh, P. Keerthana and S. Sreeji
1.1 Introduction
1.2 Evolution of Intelligent Automation
1.2.1 The Pre-Industrial Era (Before 1760)
1.2.2 Key Milestones
1.2.3 The Industrial Revolution (1760–1840)
1.3 The Beginning of the Modern Era
1.3.1 The Computing Era (1945–1980)
1.4 The Information Age (1980–2010)
1.5 Intelligent Automation Age (2010–Present)
1.6 Intelligent Automation Maturity (2018–2020)
1.7 Hyperautomation: A New Era (2021–Present)
1.8 Current Trends in Intelligent Automation
1.8.1 Convergence of AI and Robotic Process Automation (RPA)
1.8.2 Workflow Automation
1.8.3 Business Process Management (BPM)
1.8.4 Low-Code/No-Code Solutions
1.8.5 AI in Business Process Automation (BPA)
1.9 Dominant Technologies in Intelligent Automation
1.9.1 Artificial Intelligence (AI) and Machine Learning (ML)
1.9.2 Natural Language Processing (NLP)
1.9.3 Computer Vision
1.9.4 Process Mining
1.9.5 Intelligent Document Processing (IDP)
1.9.6 Cloud Computing and Edge Computing
1.9.7 Blockchain
1.9.8 Internet of Things (IoT)
1.10 Future Goals and Transformation in Intelligent Automation
1.10.1 Rise of Generative AI
1.10.2 Cognitive Automation
1.11 Conclusion
References
2. Integrating AI in Automation
G. Arun Prasath, S. Dinesh Krishnan, Priscilla Joy and Daniel Arockiam
2.1 An Introduction to Artificial Intelligence in the Field of Automation
2.2 An Overview of the Development of Automation
2.2.1 Enhancing the Collaboration between Humans and Machines
2.2.2 Various Forms of Artificial Intelligence
2.2.3 Implementations of Artificial Intelligence in Industrial Automation
2.2.4 The Following are Examples of AI-Driven Industrial Automation Case Studies
2.3 Collaboration between Humans and Robots in the Manufacturing Industry
2.4 The Use of Autonomous Vehicles in the Logistics Industry
2.5 Decision-Making in Industrial Operations Driven by Artificial Intelligence
2.6 Edge Computing in Artificial Intelligence–Driven Automation
2.7 Adaptability in Environments That are Always Changing
2.8 Applications Tailored to Specific Industries
2.9 XAI, or Explainable Artificial Intelligence, in the Context of Automation
2.10 Establishing Trust in Artificial Intelligence Systems
2.11 Collaboration between Humans and Artificial Intelligence
2.12 Improving the Capability to Identify and Correct Errors
2.13 Artificial Intelligence for Environmentally Responsible
Automation
2.14 The Importance of Energy Conservation and Efficiency
2.15 The Optimization of Resources in Agricultural Production
2.15.1 Reducing Waste and Promoting a Circular Economy
2.15.2 Concerning the Conservation and Monitoring of Biodiversity
2.16 International Partnerships for Alternatives to Environmental Problems
2.16.1 Human-Cantered Design in Artificial Intelligence Interfaces
2.16.2 Making Decisions Openly and Honestly
2.16.3 Considerations of an Ethical Nature and the Empowerment of Users
2.16.4 Inclusive Accessibility
2.16.5 Continuous Feedback from Users and Iterative Modifications
2.17 Automation of Artificial Intelligence in Cybersecurity
2.17.1 Threat Detection and the Identification of Anomalies
2.17.2 The Correlation between Behavioral Analysis and Predictive Security
2.17.3 Protocols for an Adaptive Security System
2.17.4 Identifying and Addressing Vulnerabilities That are Known as Zero-Day Vulnerabilities
2.17.5 The Following Technologies are Examples of Adversarial Machine Learning and Deception
2.17.6 Continuous Monitoring and Integration of Threat Intelligence Consist of the Following
2.17.7 Analytics of User Behavior and Detection of Threats from Within the Organization
2.18 Artificial Intelligence and Predictive Analytics
2.18.1 Risk Management and the Planning of Different Scenarios
2.18.2 Insights Gained from Customers and Personalization
2.18.3 Utilization of Supply Chain Optimization
2.18.4 Continuous Education and the Capacity to Adapt in AI-Driven Predictive Analytics
2.18.5 The Application of Artificial Intelligence to Supply
Chain Management
2.19 Artificial Intelligence in Agriculture Automation
2.19.1 Precision Agriculture and Crop Monitoring
2.20 Conclusion
References
3. AI-Based Automatic Flow Controller Using Machine Learning
B. Kalaiselvi, T. Vijayan, A. Kumaravel and K. Sivachandar
3.1 Introduction
3.1.1 Design Complexity
3.1.2 Challenges in Conventional PID Control
3.1.3 Adaptive Strategies
3.1.4 Practical Implications
3.2 Related Works
3.2.1 Organization of the Article
3.3 Dataset Description
3.3.1 The Key Steps in Data Preprocessing
3.3.2 The Soft Tool WEKA
3.3.3 Data Acquisition Report
3.4 Materials and Methods
3.4.1 Machine Learning Techniques
3.4.2 Strategy for Training with Ground Truth Data
3.4.3 Key Features of Intelligent Flow Controllers
3.4.4 Experiment Flow
3.5 Results and Discussion
3.5.1 Output Performance Criteria
3.5.1.1 Base Model Training (Bagging)
3.5.1.2 Meta-Model
3.5.1.3 Aggregation
3.6 Conclusion and Future Scope
References
4. Scalability and Flexibility in Intelligence Automation Solution
Rahin Batcha R., D. Saravanan, Vijay Ramalingam, Arul Prakash A. and S. Vignesh
4.1 Introduction
4.2 The Significance of Gen AI in Various Industries
4.3 Data Privacy and Security in Gen AI Implementations
4.4 The Need for a Comprehensive AI Governance Framework
4.5 Key Components of a Successful Gen AI Governance Framework
4.5.1 Risks and Challenges on Misinformation and Ethical Use of Generative AI
4.5.2 Enabling APIs and RPA for Healthcare and Life Sciences Industry
4.6 Key Challenges Faced by HLS
4.6.1 Data Management and Integration
4.6.2 Patient Engagement
4.6.3 Supply Chain Management
4.6.4 Research and Development
4.6.5 Regulatory Compliance
4.6.6 APIs in Healthcare and Lifesciences
4.7 Streamlined Data Sharing
4.7.1 Remote Patient Monitoring
4.7.2 Patient Engagement
4.7.3 Clinical Trials
4.7.4 Healthcare Analytics
4.7.5 RPA in Healthcare and Lifesciences
4.7.6 Streamlined Claims Processing
4.7.7 Streamlined EHR Management
4.7.8 Regulatory Compliance
4.8 Automation and APIs for Banking, Finance, and Insurance
4.9 Robotic Desktop Automation
4.10 Conclusion
References
5. AI Analytics for Assessing the Performance of Green Equity Market
Shyam S. Gupta, Binod Soni and Daniel Arockiam
5.1 Introduction
5.2 AI Analytics
5.3 Impact of AI in Green Fintech
5.4 Technologies Driving Green Fintech
5.5 Applications and Analysis of AI in Fintech
5.6 Techniques of AI Analytics Applied in Fintech Method
5.7 Outcome of AI Analytics
5.8 Future Prospects
5.9 Conclusions
References
6. Challenges and Trends of Measuring the ROI of Intelligent Automation
Ghanshyam Prasad Dubey, Ayush Giri, Diksha Batham, Anushka Sharma, Ashish Singh and Daniel Arockiam
6.1 Introduction
6.1.1 Challenges in Measuring IA ROI
6.2 Literature Review
6.2.1 Theoretical Frameworks
6.2.2 Empirical Studies
6.3 Methodology
6.3.1 Methodologies for Measuring IA ROI
6.3.2 Data Collection Methods
6.3.3 ROI Calculation Framework
6.3.4 Risk Assessment and Sensitivity Analysis
6.3.5 Sensitivity Analysis
6.3.6 Evaluation of Non-Financial Benefits
6.3.7 Challenges in Measuring IA ROI
6.3.8 Limitations
6.3.9 Findings and Discussion
6.3.10 Discussion
6.4 Future Trends in Intelligent Automation and ROI Measurement
6.5 Conclusion
References
7. An In-Depth Analysis of the Return on Investment in Software Test Automation
Sathiyaprasad. B., Menaka. D., Sreeji. S., Tamezheneal. R.
and Gopal. R.
7.1 Introduction
7.2 Literature Survey
7.3 In-Depth Analysis
7.4 Steps to Calculate ROI for Automation Testing
7.5 Maximization of ROI of Automation Testing
7.6 Robotic Process Automation (RPA) for Process Optimization
7.6.1 Understanding Robotic Process Automation (RPA)
7.6.2 Key Components of RPA
7.6.3 Applications of RPA in Process Optimization
7.6.4 Benefits of RPA for Process Optimization
7.6.5 Best Practices for Implementing RPA for Process Optimization
7.6.6 Challenges in RPA and How to Address Them
7.6.7 Future Trends in RPA for Process Optimization
7.7 Survey Validity
7.8 Conclusion
References
8. Exploring Sustainable Intelligent Automation in Educational Environments: The Role of Smartphones and Service Robots for Child Development
Menaka D., Sathiyaprasad B., Sreeji S., Sundar R. and Gopal R.
8.1 Introduction
8.1.1 Rise of Robots
8.1.2 Consequences for Babies and Children
8.2 Literature Survey
8.2.1 Models of General Education
8.2.2 The Robot Platform
8.2.3 Global Robotic Technology Market
8.3 A Robot-Based Learning Approach That Combines an Instructional Robot with a Smartphone
8.3.1 The System’s Basic Idea
8.3.2 Necessary Features for a Robot and Smartphone to Communicate
8.4 Overview
8.4.1 Enhanced Learning Engagement through Interactive Technology
8.4.2 Role of Intelligent Automation in Safety and Monitoring
8.4.3 Encouraging Social Skills and Emotional Development
8.4.4 Promoting Sustainable Learning Models
8.4.5 Supporting Cognitive Development through Multimodal Learning
8.5 Conclusion
References
9. Research Areas in Intelligent Automation
Mohini Sharma, Ghanshyam Prasad Dubey, Shailendra Mourya, Anushka Sharma, Ananya Likhar and Daniel Arockiam
9.1 Introduction
9.2 Literature Review
9.2.1 Current State of Intelligent Automation
9.2.2 Techniques
9.3 Security Concern
9.3.1 Why is Machine Learning Important for Cybersecurity Today?
9.3.2 Requirements for Security
9.4 Challenges for Organizations with Intelligent Automation
9.5 Future Trends and Predictions
9.5.1 Future Researches in Semiconductor and Equipment
9.5.2 Sectoral Analysis of Future Trends in Intelligent Automation
9.6 Conclusion
References
10. Ethical AI and Responsible Automation: Ethical Integration of Artificial Intelligence in Healthcare: Balancing Innovation and Patient Well-Being
Usama Abdur Rahman, S. Sreeji, M. Arshiya Mobeen, Daniel Arockiam and Anwar Basha
10.1 Introduction to AI in Healthcare
10.2 Historical Context and Evolution of AI in Healthcare
10.3 Ethical Considerations in AI Implementation
10.3.1 Ethical Principles for AI Technology in Healthcare
10.4 Data Confidentiality and Security
10.5 Accountability in AI Systems
10.6 Interdisciplinary Collaboration in Ethical AI
10.6.1 Examples and Case Studies
10.6.1.1 Human-in-the-Loop (HITL) Approach
10.6.1.2 Multidisciplinary Digital Health Research
10.6.1.3 Medical Students’ Intelligent Tutoring System
10.6.1.4 Healthcare AI’s Quality Management Systems (QMS)
10.6.2 The Diverse Functions of Healthcare, Industry, and Academic Professionals
10.7 Healthcare Professionals’ Role in Ethical AI
10.7.1 The Present Situation of AI Education Initiatives
10.7.2 Principles of Guideline
10.7.2.1 Guideline1: Necessity for Regulatory Strategies
10.7.2.2 Guideline 2: Multidisciplinary Approach to Design and Delivery
10.7.2.3 Guideline 3: Competency-Driven Curriculum Design
10.7.2.4 Guideline 4: Interaction between Patients and Clinicians
10.8 Regulatory Frameworks and Governance
10.9 Conclusion
References
Index
Back to Top
Preface
Acknowledgement
1. Intelligent Automation and Analytics for Business Resilience
E. Murali, D. Deepa, R. Vignesh, P. Keerthana and S. Sreeji
1.1 Introduction
1.2 Evolution of Intelligent Automation
1.2.1 The Pre-Industrial Era (Before 1760)
1.2.2 Key Milestones
1.2.3 The Industrial Revolution (1760–1840)
1.3 The Beginning of the Modern Era
1.3.1 The Computing Era (1945–1980)
1.4 The Information Age (1980–2010)
1.5 Intelligent Automation Age (2010–Present)
1.6 Intelligent Automation Maturity (2018–2020)
1.7 Hyperautomation: A New Era (2021–Present)
1.8 Current Trends in Intelligent Automation
1.8.1 Convergence of AI and Robotic Process Automation (RPA)
1.8.2 Workflow Automation
1.8.3 Business Process Management (BPM)
1.8.4 Low-Code/No-Code Solutions
1.8.5 AI in Business Process Automation (BPA)
1.9 Dominant Technologies in Intelligent Automation
1.9.1 Artificial Intelligence (AI) and Machine Learning (ML)
1.9.2 Natural Language Processing (NLP)
1.9.3 Computer Vision
1.9.4 Process Mining
1.9.5 Intelligent Document Processing (IDP)
1.9.6 Cloud Computing and Edge Computing
1.9.7 Blockchain
1.9.8 Internet of Things (IoT)
1.10 Future Goals and Transformation in Intelligent Automation
1.10.1 Rise of Generative AI
1.10.2 Cognitive Automation
1.11 Conclusion
References
2. Integrating AI in Automation
G. Arun Prasath, S. Dinesh Krishnan, Priscilla Joy and Daniel Arockiam
2.1 An Introduction to Artificial Intelligence in the Field of Automation
2.2 An Overview of the Development of Automation
2.2.1 Enhancing the Collaboration between Humans and Machines
2.2.2 Various Forms of Artificial Intelligence
2.2.3 Implementations of Artificial Intelligence in Industrial Automation
2.2.4 The Following are Examples of AI-Driven Industrial Automation Case Studies
2.3 Collaboration between Humans and Robots in the Manufacturing Industry
2.4 The Use of Autonomous Vehicles in the Logistics Industry
2.5 Decision-Making in Industrial Operations Driven by Artificial Intelligence
2.6 Edge Computing in Artificial Intelligence–Driven Automation
2.7 Adaptability in Environments That are Always Changing
2.8 Applications Tailored to Specific Industries
2.9 XAI, or Explainable Artificial Intelligence, in the Context of Automation
2.10 Establishing Trust in Artificial Intelligence Systems
2.11 Collaboration between Humans and Artificial Intelligence
2.12 Improving the Capability to Identify and Correct Errors
2.13 Artificial Intelligence for Environmentally Responsible
Automation
2.14 The Importance of Energy Conservation and Efficiency
2.15 The Optimization of Resources in Agricultural Production
2.15.1 Reducing Waste and Promoting a Circular Economy
2.15.2 Concerning the Conservation and Monitoring of Biodiversity
2.16 International Partnerships for Alternatives to Environmental Problems
2.16.1 Human-Cantered Design in Artificial Intelligence Interfaces
2.16.2 Making Decisions Openly and Honestly
2.16.3 Considerations of an Ethical Nature and the Empowerment of Users
2.16.4 Inclusive Accessibility
2.16.5 Continuous Feedback from Users and Iterative Modifications
2.17 Automation of Artificial Intelligence in Cybersecurity
2.17.1 Threat Detection and the Identification of Anomalies
2.17.2 The Correlation between Behavioral Analysis and Predictive Security
2.17.3 Protocols for an Adaptive Security System
2.17.4 Identifying and Addressing Vulnerabilities That are Known as Zero-Day Vulnerabilities
2.17.5 The Following Technologies are Examples of Adversarial Machine Learning and Deception
2.17.6 Continuous Monitoring and Integration of Threat Intelligence Consist of the Following
2.17.7 Analytics of User Behavior and Detection of Threats from Within the Organization
2.18 Artificial Intelligence and Predictive Analytics
2.18.1 Risk Management and the Planning of Different Scenarios
2.18.2 Insights Gained from Customers and Personalization
2.18.3 Utilization of Supply Chain Optimization
2.18.4 Continuous Education and the Capacity to Adapt in AI-Driven Predictive Analytics
2.18.5 The Application of Artificial Intelligence to Supply
Chain Management
2.19 Artificial Intelligence in Agriculture Automation
2.19.1 Precision Agriculture and Crop Monitoring
2.20 Conclusion
References
3. AI-Based Automatic Flow Controller Using Machine Learning
B. Kalaiselvi, T. Vijayan, A. Kumaravel and K. Sivachandar
3.1 Introduction
3.1.1 Design Complexity
3.1.2 Challenges in Conventional PID Control
3.1.3 Adaptive Strategies
3.1.4 Practical Implications
3.2 Related Works
3.2.1 Organization of the Article
3.3 Dataset Description
3.3.1 The Key Steps in Data Preprocessing
3.3.2 The Soft Tool WEKA
3.3.3 Data Acquisition Report
3.4 Materials and Methods
3.4.1 Machine Learning Techniques
3.4.2 Strategy for Training with Ground Truth Data
3.4.3 Key Features of Intelligent Flow Controllers
3.4.4 Experiment Flow
3.5 Results and Discussion
3.5.1 Output Performance Criteria
3.5.1.1 Base Model Training (Bagging)
3.5.1.2 Meta-Model
3.5.1.3 Aggregation
3.6 Conclusion and Future Scope
References
4. Scalability and Flexibility in Intelligence Automation Solution
Rahin Batcha R., D. Saravanan, Vijay Ramalingam, Arul Prakash A. and S. Vignesh
4.1 Introduction
4.2 The Significance of Gen AI in Various Industries
4.3 Data Privacy and Security in Gen AI Implementations
4.4 The Need for a Comprehensive AI Governance Framework
4.5 Key Components of a Successful Gen AI Governance Framework
4.5.1 Risks and Challenges on Misinformation and Ethical Use of Generative AI
4.5.2 Enabling APIs and RPA for Healthcare and Life Sciences Industry
4.6 Key Challenges Faced by HLS
4.6.1 Data Management and Integration
4.6.2 Patient Engagement
4.6.3 Supply Chain Management
4.6.4 Research and Development
4.6.5 Regulatory Compliance
4.6.6 APIs in Healthcare and Lifesciences
4.7 Streamlined Data Sharing
4.7.1 Remote Patient Monitoring
4.7.2 Patient Engagement
4.7.3 Clinical Trials
4.7.4 Healthcare Analytics
4.7.5 RPA in Healthcare and Lifesciences
4.7.6 Streamlined Claims Processing
4.7.7 Streamlined EHR Management
4.7.8 Regulatory Compliance
4.8 Automation and APIs for Banking, Finance, and Insurance
4.9 Robotic Desktop Automation
4.10 Conclusion
References
5. AI Analytics for Assessing the Performance of Green Equity Market
Shyam S. Gupta, Binod Soni and Daniel Arockiam
5.1 Introduction
5.2 AI Analytics
5.3 Impact of AI in Green Fintech
5.4 Technologies Driving Green Fintech
5.5 Applications and Analysis of AI in Fintech
5.6 Techniques of AI Analytics Applied in Fintech Method
5.7 Outcome of AI Analytics
5.8 Future Prospects
5.9 Conclusions
References
6. Challenges and Trends of Measuring the ROI of Intelligent Automation
Ghanshyam Prasad Dubey, Ayush Giri, Diksha Batham, Anushka Sharma, Ashish Singh and Daniel Arockiam
6.1 Introduction
6.1.1 Challenges in Measuring IA ROI
6.2 Literature Review
6.2.1 Theoretical Frameworks
6.2.2 Empirical Studies
6.3 Methodology
6.3.1 Methodologies for Measuring IA ROI
6.3.2 Data Collection Methods
6.3.3 ROI Calculation Framework
6.3.4 Risk Assessment and Sensitivity Analysis
6.3.5 Sensitivity Analysis
6.3.6 Evaluation of Non-Financial Benefits
6.3.7 Challenges in Measuring IA ROI
6.3.8 Limitations
6.3.9 Findings and Discussion
6.3.10 Discussion
6.4 Future Trends in Intelligent Automation and ROI Measurement
6.5 Conclusion
References
7. An In-Depth Analysis of the Return on Investment in Software Test Automation
Sathiyaprasad. B., Menaka. D., Sreeji. S., Tamezheneal. R.
and Gopal. R.
7.1 Introduction
7.2 Literature Survey
7.3 In-Depth Analysis
7.4 Steps to Calculate ROI for Automation Testing
7.5 Maximization of ROI of Automation Testing
7.6 Robotic Process Automation (RPA) for Process Optimization
7.6.1 Understanding Robotic Process Automation (RPA)
7.6.2 Key Components of RPA
7.6.3 Applications of RPA in Process Optimization
7.6.4 Benefits of RPA for Process Optimization
7.6.5 Best Practices for Implementing RPA for Process Optimization
7.6.6 Challenges in RPA and How to Address Them
7.6.7 Future Trends in RPA for Process Optimization
7.7 Survey Validity
7.8 Conclusion
References
8. Exploring Sustainable Intelligent Automation in Educational Environments: The Role of Smartphones and Service Robots for Child Development
Menaka D., Sathiyaprasad B., Sreeji S., Sundar R. and Gopal R.
8.1 Introduction
8.1.1 Rise of Robots
8.1.2 Consequences for Babies and Children
8.2 Literature Survey
8.2.1 Models of General Education
8.2.2 The Robot Platform
8.2.3 Global Robotic Technology Market
8.3 A Robot-Based Learning Approach That Combines an Instructional Robot with a Smartphone
8.3.1 The System’s Basic Idea
8.3.2 Necessary Features for a Robot and Smartphone to Communicate
8.4 Overview
8.4.1 Enhanced Learning Engagement through Interactive Technology
8.4.2 Role of Intelligent Automation in Safety and Monitoring
8.4.3 Encouraging Social Skills and Emotional Development
8.4.4 Promoting Sustainable Learning Models
8.4.5 Supporting Cognitive Development through Multimodal Learning
8.5 Conclusion
References
9. Research Areas in Intelligent Automation
Mohini Sharma, Ghanshyam Prasad Dubey, Shailendra Mourya, Anushka Sharma, Ananya Likhar and Daniel Arockiam
9.1 Introduction
9.2 Literature Review
9.2.1 Current State of Intelligent Automation
9.2.2 Techniques
9.3 Security Concern
9.3.1 Why is Machine Learning Important for Cybersecurity Today?
9.3.2 Requirements for Security
9.4 Challenges for Organizations with Intelligent Automation
9.5 Future Trends and Predictions
9.5.1 Future Researches in Semiconductor and Equipment
9.5.2 Sectoral Analysis of Future Trends in Intelligent Automation
9.6 Conclusion
References
10. Ethical AI and Responsible Automation: Ethical Integration of Artificial Intelligence in Healthcare: Balancing Innovation and Patient Well-Being
Usama Abdur Rahman, S. Sreeji, M. Arshiya Mobeen, Daniel Arockiam and Anwar Basha
10.1 Introduction to AI in Healthcare
10.2 Historical Context and Evolution of AI in Healthcare
10.3 Ethical Considerations in AI Implementation
10.3.1 Ethical Principles for AI Technology in Healthcare
10.4 Data Confidentiality and Security
10.5 Accountability in AI Systems
10.6 Interdisciplinary Collaboration in Ethical AI
10.6.1 Examples and Case Studies
10.6.1.1 Human-in-the-Loop (HITL) Approach
10.6.1.2 Multidisciplinary Digital Health Research
10.6.1.3 Medical Students’ Intelligent Tutoring System
10.6.1.4 Healthcare AI’s Quality Management Systems (QMS)
10.6.2 The Diverse Functions of Healthcare, Industry, and Academic Professionals
10.7 Healthcare Professionals’ Role in Ethical AI
10.7.1 The Present Situation of AI Education Initiatives
10.7.2 Principles of Guideline
10.7.2.1 Guideline1: Necessity for Regulatory Strategies
10.7.2.2 Guideline 2: Multidisciplinary Approach to Design and Delivery
10.7.2.3 Guideline 3: Competency-Driven Curriculum Design
10.7.2.4 Guideline 4: Interaction between Patients and Clinicians
10.8 Regulatory Frameworks and Governance
10.9 Conclusion
References
Index
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