Rupali Gill, PhD is an associate professor and Dean at the Chitkara University Institute of Engineering and Technology. She has published over 40 technical research papers in leading journals, as well as a patent granted. Her interests include image processing, cloud computing, artificial intelligence, and machine learning. Susheela Hooda, PhD is an associate professor and Head of Academic Delivery at Chitkara University Institute of Engineering and Technology with over 15 years of teaching and research experience. She has published over thirty technical research papers in leading international journals and conferences and more than ten international patents. Her research interests include software engineering, aspect-oriented software development, software testing, cloud computing, artificial intelligence, and machine learning. Durgesh Srivastava, PhD is an associate professor and Head of Academic Operations at Chitkara University Institute of Engineering and Technology with over 15 years of research and academic experience. He has published over 30 papers in reputed national and international journals and conferences, as well as several patents and copyrights in the field of computer software. His research interests include machine learning, soft computing, pattern recognition, and software engineering, modeling, and design. Shilpi Harnal, PhD is an assistant professor at Chitkara University, Punjab. She specializes in cloud computing with over 13 years of teaching experience. She has published over 30 research papers in various national and international peer-reviewed journals, books, and conferences. Her research interests include fog computing, underwater wireless sensor networks (UWSN), and artificial intelligence.

• Preface xix1 Toward a Smarter Future: The Role of AI in Transforming Automation Systems 1Manish Kumar Singla, Rupali Gill, Ramesh Kumar, Jyoti Gupta and Gaurav Sharma1.1 Introduction 11.2 The Power of AI in IAS 31.3 Transforming Automation: A Multifaceted Impact 41.4 Benefits and Impact of IAS 61.5 The Spectrum of Applications: From Manufacturing to Beyond 71.6 Challenges and Considerations 81.7 Strategies for Mitigating Negative Impacts 101.8 Ethical Considerations of IAS 111.9 Discussion 121.10 Conclusion 13References 142 Industry 5.0: Mapping the Lens from Know How to Realization 17Upinder Kumar, Mahender Singh Kaswan and Rakesh Kumar2.1 Introduction 172.2 Basic Principles of Industry 5.0 192.3 Technologies and Their Roles in Industry 5.0 202.4 Operator 5.0 312.5 Education 5.0 332.6 Industry 5.0 and Sustainability 362.7 Conclusion 37References 383 Intelligent Automation System Integration in Mobile and Industrial Robotics for Enhanced Performance and Efficiency 47Abdullah Bin Queyam, Ramesh Kumar, Anupma Gupta and Vipin Kumar3.1 Introduction 473.2 Industrial Robotics 493.3 Anthropomorphic Robot: Bridging the Gap Between Humans and Machines 493.4 Case Study 543.5 Conclusion 72References 724 Automation of Data Flow Management Based on Artificial Intelligence in Systems with an Internal Distribution Mechanism 75A.E. Rashidov, A.R. Akhatov, F.M. Nazarov and I.N. Turakulov4.1 Introduction 754.2 Methodology 834.3 Results 934.4 Discussion 964.5 Conclusion 97References 975 Robotic Process Automation (RPA) and Virtual Reality Implementation in Engineering Education 103Jabar H. Yousif , Ahmad Kayed and Maryam G. Aljabri5.1 Introduction 1035.2 Ethical Factors 1055.3 Research Design 1065.4 Research Questions 1075.5 Experimental Design 1085.6 Results and Discussion 1105.7 Conclusion 115References 1166 Ethical Issues of Intelligent Automation Systems 119V. Punitha, R. Sivanesan, P. Sharmila and G. Nithyakala6.1 Introduction 1196.2 Intelligent Automation Systems 1236.3 The Ethical Implications of Intelligent Automation Systems 1276.4 Case Studies of Ethical Issues in IAS Decision-Making 1356.5 Environmental Impacts of IAS 1396.6 Existing Ethical Frameworks of IAS 1406.7 Conclusion 141References 1417 IAS and Facial Recognition System 145Ritu, Yogesh Shahare, Dinesh Singh Dhakar and Ritu Jain7.1 Introduction 1457.2 Literature Review 1467.3 Understanding Intelligent Automation Systems (IASs) 1487.4 Advancements in Facial Recognition Technology 1507.5 Integration with Intelligent Automation Systems 1537.6 Challenges and Limitations 1557.7 Future Prospects and Emerging Trends 1577.8 Security and Surveillance Applications 1587.9 Ethical and Societal Implications 1597.10 Conclusion 160References 1618 An Image Synthesis Using Progressive Generative Adversarial Networks (PGANs) 163Ajay Pal Singh, Parvez Rahi and Vinod Kumar8.1 Introduction 1638.2 How Does GAN Work? 1658.3 The Birth of GANs: Recognizing the Need for Adversarial Frameworks 1678.4 Proposed Solutions 1688.5 Deep Learning Structures 1728.6 Analysis and Feature Finalization Subject to Constraints 1738.7 Design Flow 1748.8 The Comprehensive Design Flows 1768.9 Principal Results 1788.10 Training Stability 1798.11 GAN Applications 1808.12 Conclusion 181References 1819 Future Direction in Sign Language Recognition: A Review 185Nidhi Goel, Lekha Rani and Pradeepta Kumar Sarangi9.1 Introduction 1859.2 Sign Languages Around the World 1879.3 Sign Language Linguistics 1929.4 Motivation 1939.5 Objective 1939.6 Related Work 1949.7 Approaches 1969.8 Proposed Methodology 1989.9 Conclusion and Future Scope 200References 20110 Understanding Computer Vision for Intelligent Autonomous Systems 203Summiya Parveen and Aruna Tomar10.1 Introduction 20310.2 Fundamentals of Computer Vision 20510.3 Applications of Computer Vision in IAS 21010.4 Challenges and Emerging Techniques 21710.5 Future Directions and Conclusion 221References 22211 Computer Vision and Artificial Intelligence for Intelligence Automation Systems (IAS) 227Dharmendra Dangi, Vaibhav Suman, Amit Bhagat and Dheeraj Kumar Dixit11.1 Introduction 22711.2 Artificial Intelligence 22911.3 Computer Vision 23711.4 Conclusion 24211.5 Future Scope 243References 24312 Neural Network Approaches for Intelligent Decision-Making in Automation 247S.Z. Rufai, Inam Ul Haq, H.A. Shah and Mir Abrar Fayaz12.1 Introduction 24712.2 Role of Neural Networks in Modern Automation 24812.3 Fundamental Principles of Neural Networks 24912.4 Neural Network Architectures in Automation Systems 25612.5 Comparative Analysis of Different Architectures 26112.6 Neural Network Applications in Automation 26312.7 Training Strategies for Neural Networks 26612.8 Practical Considerations for Deployment 27012.9 Conclusion 273References 27313 A Novel Approach for Object Detection Technique Using Deep Learning 277Kumud Sachdeva and Rajan Sachdeva13.1 Introduction 27813.2 Literature Survey 27913.3 Deep Learning Methods 28113.4 Deep Learning Models 28413.5 Experimental Results 28713.6 Conclusion and Future Scope 289Bibliography 29014 Role of AI in Mental Health Care 295Kala K.U., Prabhakaran Mathialagan, Solomon Jebaraj N.R. and Sambath Kumar S.14.1 Introduction 29514.2 Significance of Addressing Mental Health Challenges 29614.3 Prevalent Mental Health Disorders 29814.4 Impact of Mental Health on Physical Well-Being 29914.5 The Societal Implications of Mental Health Disorders 30014.6 Significance of Early Recognition of Mental Health Matters 30214.7 Strategies for the Early Recognition of Mental Health Challenges 30314.8 Role of Technology in Mental Health Care 30414.9 AI in Mental Health Care 30514.10 AI in Screening and Assessment of Mental Health Issues 30614.11 AI in Personalized Treatment Planning 30714.12 AI in Digital Therapeutic Interventions 31014.13 AI Chatbot’s and Virtual Assistants in Mental Health Care 31314.14 Data Analysis and Predictive Modeling in Mental Health Care 31514.15 AI in Mental Health Monitoring 31714.16 Conclusion and Future Work 320References 32115 Application Areas of Computer Vision and AI in Intelligent Automation Systems 327Vinod Kumar, Chander Prabha, Ajay Pal Singh and Raj Kumar15.1 Introduction 32715.2 Advanced Techniques in CV and AI for IAS 32815.3 Why We Use AI in Research and Services Today 33115.4 The Association Across AI, ML, and dl 33215.5 Exploring Deep Learning and Neural Systems 33315.6 Delving into Deep Neural Networks’ Learning Approaches 33415.7 Rule-Based Modeling: A Cornerstone of AI Development 33615.8 The Role of Fuzzy Logic and Distributed Logic in AI 33715.9 AI and CV Technologies for Advancing Manufacturing Industries 33815.10 AI and CV Revolutionizing Healthcare Innovations 33815.11 Innovative Solutions for Agriculture and Environment 34015.12 Innovative Solutions for Retail and Consumer Goods 34115.13 Revolutionizing Transportation and Logistics with AI and cv 34215.14 Advancing AI through Case-Based Reasoning (CBR) 34515.15 Text Mining and NLP in IAS 34615.16 Exploring Artificial Intelligence Applications and Challenges 36015.17 Exploring Artificial Intelligence in Computer Vision Tasks 36015.18 Conclusion 362References 36216 A Real-Time Speech-Text Conversion System Using Deep Learning Technique 371K. Saranya and P. Jeevananthan16.1 Introduction 37116.2 Related Works 37316.3 Problem Definition 37516.4 System Specification 37516.5 Methodology and Flowchart 37816.6 Audio Conversion 38016.7 Results and Discussion 38416.8 Conclusion 386References 38717 Transforming the Evaluation: The Crucial Role of Natural Language Processing in Intelligent Automation System 389Pratibha, Bhavna Sharma, Sana Bharti, Susheela Hooda and Shilpi Harnal17.1 Introduction 38917.2 Natural Language Processing (NLP) as the Foundation of Intelligent Automation 39617.3 Exploring Current Applications 39717.4 Future Directions for NLP in an Automated Environment 39917.5 Challenges and Opportunities 40217.6 Developing Talent: Cultivating Natural Language Processing Masters of Tomorrow 40417.7 Conclusion and Future Scope 404References 40418 IAS and Its Impact in Neuroscience 407G. Vijaya and K. Ramesh18.1 Introduction 40718.2 Neuroscience 41118.3 Integration of Neuroscience with Intelligent Automation Systems (IAS) 41218.4 Challenges in Integrating IAS in Neuroscience Applications 41418.5 Application Areas of Neuroscience in Intelligent Automation Systems 41518.6 Conclusion 419References 41919 Intelligent Automation Systems (IAS) and Its Application in Neuroscience 423Bikram Kar and Amit Kumar19.1 Introduction 42319.2 Understanding Neurosciences 43019.3 How Neuroscience Can Help in Understanding Intelligent Automation Systems (IAS) 43419.4 Application Areas of Neuroscience in IAS 43719.5 Connecting IAS and Neuroscience 44019.6 Challenges and Future Directions 44319.7 Conclusion 445References 44620 A Neuromarketing Framework for Data-Driven Intelligent Automation in Marketing 449Jyoti Kesarwani, Himanshu Rai and Rahul Kesarwani20.1 Introduction 44920.2 Literature Review 45120.3 Proposed Neuromarketing Framework 45720.4 Benefits and Applications of Neuromarketing 46020.5 Real-Time Campaign Optimization Using Biometric Feedback 46220.6 Mitigating Bias in AI Through Neuromarketing Data 46420.7 Other Potential Applications 46520.8 Conclusion 466References 46721 Neuroscience and Intelligent Automation System 471Harpreet Kaur and Pannem Shreya21.1 Introduction 47121.2 Intelligent Automation 47321.3 Technologies and Software Associated with IA Systems 47421.4 History of Developments in AI and Neuroscience 47621.5 Essential Technologies for Developing IAS 47621.6 Discoveries Related to Neuroscience 47721.7 Applications of Artificial Intelligence in Neuroscience 47821.8 Artificial Neural Network Versus Biological Neural Network 48021.9 Developments of Intelligent Automation Systems Models 48121.10 AI for Neuroscience Development 48421.11 Neuromarketing 48721.12 AI Inspired by Brain Science 48821.13 Current State 48921.14 Conclusion 490References 49022 Unveiling the Visual World Through AI-Powered Computer Vision 493Sonia Kumari Shishodia, Shuchi Sharma, Eram Khan and Logesh Babu22.1 Introduction 49322.2 The Human Eye Anatomy 49422.3 Key Techniques 50322.4 Applications of AI-Powered Computer Vision Across Industries 50422.5 Threat Detection and Monitoring in Surveillance and Security 50622.6 Trends and Future Directions in AI-Powered Computer Vision 50622.7 Conclusion 507References 507Index 511