Handbook of Intelligent Automation Systems Using Computer Vision and Artificial Intelligence

The book is essential for anyone seeking to understand and leverage the transformative power of intelligent automation technologies, providing crucial insights into current trends, challenges, and effective solutions that can significantly enhance operational efficiency and decision-making within organizations. Intelligent automation systems, also called cognitive automation, use automation technologies such as artificial intelligence, business process management, and robotic process automation, to streamline and scale decision-making across organizations. Intelligent automation simplifies processes, frees up resources, improves operational efficiencies, and has a variety of applications. Intelligent automation systems aim to reduce costs by augmenting the workforce and improving productivity and accuracy through consistent processes and approaches, which enhance quality, improve customer experience, and address compliance and regulations with confidence. Handbook of Intelligent Automation Systems Using Computer Vision and Artificial Intelligence explores the significant role, current trends, challenges, and potential solutions to existing challenges in the field of intelligent automation systems, making it an invaluable guide for researchers, industry professionals, and students looking to apply these innovative technologies. Readers will find the volume: Offers comprehensive coverage on intelligent automation systems using computer vision and AI, covering everything from foundational concepts to real-world applications and ethical considerations; Provides actionable knowledge with case studies and best practices for intelligent automation systems, computer vision, and AI; Explores the integration of various techniques, including facial recognition, natural language processing, neuroscience and neuromarketing. Audience The book is designed for AI and data scientists, software developers and engineers in industry and academia, as well as business leaders and entrepreneurs who are interested in the applications of intelligent automation systems.

Table of Contents:
Preface xix 1 Toward a Smarter Future: The Role of AI in Transforming Automation Systems 1 Manish Kumar Singla, Rupali Gill, Ramesh Kumar, Jyoti Gupta and Gaurav Sharma 1.1 Introduction 1 1.2 The Power of AI in IAS 3 1.3 Transforming Automation: A Multifaceted Impact 4 1.4 Benefits and Impact of IAS 6 1.5 The Spectrum of Applications: From Manufacturing to Beyond 7 1.6 Challenges and Considerations 8 1.7 Strategies for Mitigating Negative Impacts 10 1.8 Ethical Considerations of IAS 11 1.9 Discussion 12 1.10 Conclusion 13 References 14 2 Industry 5.0: Mapping the Lens from Know How to Realization 17 Upinder Kumar, Mahender Singh Kaswan and Rakesh Kumar 2.1 Introduction 17 2.2 Basic Principles of Industry 5.0 19 2.3 Technologies and Their Roles in Industry 5.0 20 2.4 Operator 5.0 31 2.5 Education 5.0 33 2.6 Industry 5.0 and Sustainability 36 2.7 Conclusion 37 References 38 3 Intelligent Automation System Integration in Mobile and Industrial Robotics for Enhanced Performance and Efficiency 47 Abdullah Bin Queyam, Ramesh Kumar, Anupma Gupta and Vipin Kumar 3.1 Introduction 47 3.2 Industrial Robotics 49 3.3 Anthropomorphic Robot: Bridging the Gap Between Humans and Machines 49 3.4 Case Study 54 3.5 Conclusion 72 References 72 4 Automation of Data Flow Management Based on Artificial Intelligence in Systems with an Internal Distribution Mechanism 75 A.E. Rashidov, A.R. Akhatov, F.M. Nazarov and I.N. Turakulov 4.1 Introduction 75 4.2 Methodology 83 4.3 Results 93 4.4 Discussion 96 4.5 Conclusion 97 References 97 5 Robotic Process Automation (RPA) and Virtual Reality Implementation in Engineering Education 103 Jabar H. Yousif , Ahmad Kayed and Maryam G. Aljabri 5.1 Introduction 103 5.2 Ethical Factors 105 5.3 Research Design 106 5.4 Research Questions 107 5.5 Experimental Design 108 5.6 Results and Discussion 110 5.7 Conclusion 115 References 116 6 Ethical Issues of Intelligent Automation Systems 119 V. Punitha, R. Sivanesan, P. Sharmila and G. Nithyakala 6.1 Introduction 119 6.2 Intelligent Automation Systems 123 6.3 The Ethical Implications of Intelligent Automation Systems 127 6.4 Case Studies of Ethical Issues in IAS Decision-Making 135 6.5 Environmental Impacts of IAS 139 6.6 Existing Ethical Frameworks of IAS 140 6.7 Conclusion 141 References 141 7 IAS and Facial Recognition System 145 Ritu, Yogesh Shahare, Dinesh Singh Dhakar and Ritu Jain 7.1 Introduction 145 7.2 Literature Review 146 7.3 Understanding Intelligent Automation Systems (IASs) 148 7.4 Advancements in Facial Recognition Technology 150 7.5 Integration with Intelligent Automation Systems 153 7.6 Challenges and Limitations 155 7.7 Future Prospects and Emerging Trends 157 7.8 Security and Surveillance Applications 158 7.9 Ethical and Societal Implications 159 7.10 Conclusion 160 References 161 8 An Image Synthesis Using Progressive Generative Adversarial Networks (PGANs) 163 Ajay Pal Singh, Parvez Rahi and Vinod Kumar 8.1 Introduction 163 8.2 How Does GAN Work? 165 8.3 The Birth of GANs: Recognizing the Need for Adversarial Frameworks 167 8.4 Proposed Solutions 168 8.5 Deep Learning Structures 172 8.6 Analysis and Feature Finalization Subject to Constraints 173 8.7 Design Flow 174 8.8 The Comprehensive Design Flows 176 8.9 Principal Results 178 8.10 Training Stability 179 8.11 GAN Applications 180 8.12 Conclusion 181 References 181 9 Future Direction in Sign Language Recognition: A Review 185 Nidhi Goel, Lekha Rani and Pradeepta Kumar Sarangi 9.1 Introduction 185 9.2 Sign Languages Around the World 187 9.3 Sign Language Linguistics 192 9.4 Motivation 193 9.5 Objective 193 9.6 Related Work 194 9.7 Approaches 196 9.8 Proposed Methodology 198 9.9 Conclusion and Future Scope 200 References 201 10 Understanding Computer Vision for Intelligent Autonomous Systems 203 Summiya Parveen and Aruna Tomar 10.1 Introduction 203 10.2 Fundamentals of Computer Vision 205 10.3 Applications of Computer Vision in IAS 210 10.4 Challenges and Emerging Techniques 217 10.5 Future Directions and Conclusion 221 References 222 11 Computer Vision and Artificial Intelligence for Intelligence Automation Systems (IAS) 227 Dharmendra Dangi, Vaibhav Suman, Amit Bhagat and Dheeraj Kumar Dixit 11.1 Introduction 227 11.2 Artificial Intelligence 229 11.3 Computer Vision 237 11.4 Conclusion 242 11.5 Future Scope 243 References 243 12 Neural Network Approaches for Intelligent Decision-Making in Automation 247 S.Z. Rufai, Inam Ul Haq, H.A. Shah and Mir Abrar Fayaz 12.1 Introduction 247 12.2 Role of Neural Networks in Modern Automation 248 12.3 Fundamental Principles of Neural Networks 249 12.4 Neural Network Architectures in Automation Systems 256 12.5 Comparative Analysis of Different Architectures 261 12.6 Neural Network Applications in Automation 263 12.7 Training Strategies for Neural Networks 266 12.8 Practical Considerations for Deployment 270 12.9 Conclusion 273 References 273 13 A Novel Approach for Object Detection Technique Using Deep Learning 277 Kumud Sachdeva and Rajan Sachdeva 13.1 Introduction 278 13.2 Literature Survey 279 13.3 Deep Learning Methods 281 13.4 Deep Learning Models 284 13.5 Experimental Results 287 13.6 Conclusion and Future Scope 289 Bibliography 290 14 Role of AI in Mental Health Care 295 Kala K.U., Prabhakaran Mathialagan, Solomon Jebaraj N.R. and Sambath Kumar S. 14.1 Introduction 295 14.2 Significance of Addressing Mental Health Challenges 296 14.3 Prevalent Mental Health Disorders 298 14.4 Impact of Mental Health on Physical Well-Being 299 14.5 The Societal Implications of Mental Health Disorders 300 14.6 Significance of Early Recognition of Mental Health Matters 302 14.7 Strategies for the Early Recognition of Mental Health Challenges 303 14.8 Role of Technology in Mental Health Care 304 14.9 AI in Mental Health Care 305 14.10 AI in Screening and Assessment of Mental Health Issues 306 14.11 AI in Personalized Treatment Planning 307 14.12 AI in Digital Therapeutic Interventions 310 14.13 AI Chatbot’s and Virtual Assistants in Mental Health Care 313 14.14 Data Analysis and Predictive Modeling in Mental Health Care 315 14.15 AI in Mental Health Monitoring 317 14.16 Conclusion and Future Work 320 References 321 15 Application Areas of Computer Vision and AI in Intelligent Automation Systems 327 Vinod Kumar, Chander Prabha, Ajay Pal Singh and Raj Kumar 15.1 Introduction 327 15.2 Advanced Techniques in CV and AI for IAS 328 15.3 Why We Use AI in Research and Services Today 331 15.4 The Association Across AI, ML, and dl 332 15.5 Exploring Deep Learning and Neural Systems 333 15.6 Delving into Deep Neural Networks’ Learning Approaches 334 15.7 Rule-Based Modeling: A Cornerstone of AI Development 336 15.8 The Role of Fuzzy Logic and Distributed Logic in AI 337 15.9 AI and CV Technologies for Advancing Manufacturing Industries 338 15.10 AI and CV Revolutionizing Healthcare Innovations 338 15.11 Innovative Solutions for Agriculture and Environment 340 15.12 Innovative Solutions for Retail and Consumer Goods 341 15.13 Revolutionizing Transportation and Logistics with AI and cv 342 15.14 Advancing AI through Case-Based Reasoning (CBR) 345 15.15 Text Mining and NLP in IAS 346 15.16 Exploring Artificial Intelligence Applications and Challenges 360 15.17 Exploring Artificial Intelligence in Computer Vision Tasks 360 15.18 Conclusion 362 References 362 16 A Real-Time Speech-Text Conversion System Using Deep Learning Technique 371 K. Saranya and P. Jeevananthan 16.1 Introduction 371 16.2 Related Works 373 16.3 Problem Definition 375 16.4 System Specification 375 16.5 Methodology and Flowchart 378 16.6 Audio Conversion 380 16.7 Results and Discussion 384 16.8 Conclusion 386 References 387 17 Transforming the Evaluation: The Crucial Role of Natural Language Processing in Intelligent Automation System 389 Pratibha, Bhavna Sharma, Sana Bharti, Susheela Hooda and Shilpi Harnal 17.1 Introduction 389 17.2 Natural Language Processing (NLP) as the Foundation of Intelligent Automation 396 17.3 Exploring Current Applications 397 17.4 Future Directions for NLP in an Automated Environment 399 17.5 Challenges and Opportunities 402 17.6 Developing Talent: Cultivating Natural Language Processing Masters of Tomorrow 404 17.7 Conclusion and Future Scope 404 References 404 18 IAS and Its Impact in Neuroscience 407 G. Vijaya and K. Ramesh 18.1 Introduction 407 18.2 Neuroscience 411 18.3 Integration of Neuroscience with Intelligent Automation Systems (IAS) 412 18.4 Challenges in Integrating IAS in Neuroscience Applications 414 18.5 Application Areas of Neuroscience in Intelligent Automation Systems 415 18.6 Conclusion 419 References 419 19 Intelligent Automation Systems (IAS) and Its Application in Neuroscience 423 Bikram Kar and Amit Kumar 19.1 Introduction 423 19.2 Understanding Neurosciences 430 19.3 How Neuroscience Can Help in Understanding Intelligent Automation Systems (IAS) 434 19.4 Application Areas of Neuroscience in IAS 437 19.5 Connecting IAS and Neuroscience 440 19.6 Challenges and Future Directions 443 19.7 Conclusion 445 References 446 20 A Neuromarketing Framework for Data-Driven Intelligent Automation in Marketing 449 Jyoti Kesarwani, Himanshu Rai and Rahul Kesarwani 20.1 Introduction 449 20.2 Literature Review 451 20.3 Proposed Neuromarketing Framework 457 20.4 Benefits and Applications of Neuromarketing 460 20.5 Real-Time Campaign Optimization Using Biometric Feedback 462 20.6 Mitigating Bias in AI Through Neuromarketing Data 464 20.7 Other Potential Applications 465 20.8 Conclusion 466 References 467 21 Neuroscience and Intelligent Automation System 471 Harpreet Kaur and Pannem Shreya 21.1 Introduction 471 21.2 Intelligent Automation 473 21.3 Technologies and Software Associated with IA Systems 474 21.4 History of Developments in AI and Neuroscience 476 21.5 Essential Technologies for Developing IAS 476 21.6 Discoveries Related to Neuroscience 477 21.7 Applications of Artificial Intelligence in Neuroscience 478 21.8 Artificial Neural Network Versus Biological Neural Network 480 21.9 Developments of Intelligent Automation Systems Models 481 21.10 AI for Neuroscience Development 484 21.11 Neuromarketing 487 21.12 AI Inspired by Brain Science 488 21.13 Current State 489 21.14 Conclusion 490 References 490 22 Unveiling the Visual World Through AI-Powered Computer Vision 493 Sonia Kumari Shishodia, Shuchi Sharma, Eram Khan and Logesh Babu 22.1 Introduction 493 22.2 The Human Eye Anatomy 494 22.3 Key Techniques 503 22.4 Applications of AI-Powered Computer Vision Across Industries 504 22.5 Threat Detection and Monitoring in Surveillance and Security 506 22.6 Trends and Future Directions in AI-Powered Computer Vision 506 22.7 Conclusion 507 References 507 Index 511

About the Author :
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.