Embodied Artificial Intelligence

R. Rajmohan, PhD is an Assistant Professor at the SRM Institute of Science and Technology’s Kattankulathur Campus, Tamil Nadu, India. He has published more than 60 papers in various reputed journals, 20 book chapters, six edited books, one authored book, and three patents. His fields of interest include artificial intelligence, data science, medical imaging, machine learning, wireless networks, deep learning, and IoT. Subrata Chowdhury, PhD is an Associate Professor in the Department of Computer Science and Machine Learning, Sreenivasa Institute of Technology and Management Studies, Chittoor, India. He has four books and more than 100 research papers, copyrights, and patents to his credit, in addition to his extensive work as an editor. He has handled various projects in AI, blockchain, and cloud computing for national and international clients. G. Glorindal Selvam, PhD is a is a highly experienced and accomplished academic and computer engineering professional with over 20 years of expertise and is recognized for her leadership in both academic and research roles. Her professional highlights include patent publications in fields such as IoT, cancer detection, and agricultural technology, alongside numerous awards for innovation. Her diverse roles reflect his commitment to academic excellence, research, and collaboration with institutions worldwide. T. Ananth Kumar, PhD is an Associate Professor and Head of Research at the IFET College of Engineering, Tamil Nadu, India. He has published one authored and six edited books, as well as many book chapters, patents, and articles in international journals and conferences. His fields of interest include networks on chips, computer architecture, and application-specific integrated circuit design. S. Balamurugan, PhD is the Director of Research, iRCS, an Indian Technological Research and Consulting Firm. He has published 100 books, 300 papers in international journals and conferences, and 300 patents. With 20 years of research on various cutting-edge technologies, he provides expert guidance in technology forecasting and decision making for leading companies and startups.

• Series Preface xixPreface xxiAcknowledgments xxiiiPart I: Foundations and Frontiers of Embodied Artificial Intelligence 11 Key Components of Neural Models for Embodied AI 3P. Deivendran, G. Arun Sampaul Thomas, S. Muthukaruppasamy, J. Nandha Gopal and R. Senthilkumar1.1 Introduction 41.2 Agent Architecture and Embodied Neural Agents 81.3 Methods and Task Models 91.4 Multi-Agent Simulations 121.5 Methods 191.6 Simulated Agent Design 201.7 Performance Analysis 221.8 Conclusion 232 Energy Efficiency in Embodied AI Systems 27J. Dhanalakshmi, Aryan Raj, Aryan Nair, Chin-Shiuh Shieh and A. Prabhu Chakkaravarthy2.1 Introduction 282.2 Energy Consumption Challenges in Embodied AI 302.3 Hardware Optimization for Energy-Efficient Embodied AI 342.4 Software Innovations for Energy Efficiency 382.5 Architectural Enhancements for Power Management 412.6 Recent Advancements and Case Studies 432.7 Results and Discussion 45Contents vii2.8 Future Directions in Energy-Efficient Embodied AI 472.9 Conclusion 483 Embodied AI in Healthcare: From Clinical Applications to Assistive Living 53N. A. Natraj and P. Ashok3.1 Introduction 543.2 Foundations of Healthcare Robotics 583.3 Applications in Clinical Settings 643.4 Assistive Robotics for Daily Living 703.5 Future Directions and Challenges 764 Embodied AI for Human Robot Collaboration Using Agentic AI and Attention Economy 85Robin Rohit Vincent, Nisha F., Varalaskhmi T. and Anand Muthukumarappan4.1 Introduction 864.2 Fundamentals of Embodied AI 874.3 Agentic AI: Empowering Autonomous Collaboration 904.4 Attention Economy and Human-Robot Interaction 934.5 System Architecture and Implementation 974.6 Methodology 984.7 Applications and Case Studies 1094.8 Limitations 1144.9 Future Directions and Ethical Implications 1154.10 Summary 1195 Enhancing Urban Intelligence Through Adaptive Systems and Embodied AI 125D. Devi Aruna, Prativa Mishra, B. Ramya, P. Dinesh Kumar and C. Kumuthini5.1 Introduction of Smart Cities and IoT 1265.2 Literature Review 1335.3 Hybrid AI Model for Smart City Adaptation 1375.4 Methodology 1385.5 Conclusion 152Part II: Techniques in Embodied Artificial Intelligence 1556 Real-Time Sign Language Translator with Dynamic Ensemble Learning Using Embodied Artificial Intelligence 157Ganesh Khekare, Pranay Batta, Tavishi Jain, Vincent Omollo Nyangaresi, Nischal Puri and Seema Raut6.1 Introduction 1586.2 Literature Review 1596.3 Methodology 1596.4 Results and Discussion 1776.5 Conclusion 1846.6 Future Scope 1877 Cognitive Neural Systems for Embodied Artificial Intelligence in Skin Disease Detection and Classification Framework 191Karthick Manoj R., S. Aasha Nandhini and M. Batumalay7.1 Introduction 1927.2 Literature Survey 1977.3 Proposed Methodology 2007.4 Results and Discussion 2047.5 Conclusion 2108 Enhanced AI-Powered Optical Character Recognition with CNN and Graph Theory-Based Arabic Handwriting Segmentation 213N. Nithiyanandam, N. Sivaranjani, R. Madhura and G. Malarselvi8.1 Introduction 2148.2 Segmentation Contribution to AI-OCR 2158.3 Methodology 2168.4 Implementation and Discussion of Results 2258.5 Conclusion 2299 A Novel XAI-Based Deep Learning Approach for Accurate Diagnosis of Knee Osteoarthritis 233Lavanya R. and Sudha Senthilkumar9.1 Introduction 2349.2 Symptoms and Progression of KOA 2369.3 Traditional Diagnostic Approaches for KOA 2389.4 Artificial Intelligence's Function in Medical Imaging 2419.5 Transformer-Based Explainable AI (XAI) for KOA Detection 2439.6 Methods 2449.7 Performance Evaluation and Comparative Analysis 2529.8 Benefits of AI-Powered KOA Diagnosis 2539.9 Ethical Considerations and Challenges in AI-Based Diagnosis 2569.10 Future Directions and Innovations in Transformer-Based Explainable AI for Knee Osteoarthritis Diagnosis 2579.11 Conclusion 25810 Artificial Intelligence-Based Monetary Administration in Institutions Employing Cognitive Fuzzy Logic and Sensors 265Gokulnath C. and Shibu N. V.10.1 Introduction 26610.2 Literature Survey 26910.3 Proposed Method 27010.4 Findings and Analysis 27710.5 Conclusion 28211 Applying AI Embodied Deep Learning Models for Real-Time Prediction and Classification of Dengue Virus Serotypes Using Complete Genome Data 285N. Sivaranjani, N. Nithiyanandam, V. Manickavasagam, G.K. Sandhia and S. Nikkath Bushra11.1 Introduction 28611.2 Resources and Procedures 28911.3 Discussion and Results 29511.4 Conclusion 303Part III: Applications of Embodied Artificial Intelligence 30712 Embodied Artificial Intelligence for Smart Homes: Towards Adaptive and Context-Aware Automation 309D. Divya, M. Kalaimani, C.P. Jetlin and K. Kavitha12.1 Introduction 31012.2 Materials and Methods 31712.3 Framework of IoT-Enabled Smart City 31812.4 Home Automation 32512.5 General Applications of Smart Cities 33012.6 Smart City Security Challenges 33312.7 Conclusion 33513 Smart Umbrella: An Embodied AI-Powered Forecasting Companion for Rainy Days 339Nivethitha V., G. Achuth, Dinesh R. and Lakshmanan L.13.1 Introduction 34113.2 Literature Survey 34313.3 Methodology 34513.4 Algorithm Design 35313.5 Workflow 35513.6 Use Cases 35813.7 Evaluation Results 35913.8 Conclusion 36414 TactiSense: An IoT-Based Skin Cancer Detection System Using Machine Learning and Skin Elasticity Analysis 367Arumuga Arun R. and Aniket Anand14.1 Introduction 36814.2 Literature Review 37014.3 Methodology 37414.4 System Architecture 37814.5 Implementation 38114.6 Results 39014.7 Discussion 39314.8 Conclusion 39415 The Future of Embodied AI in Smart Cities and IoT 399Manas Kumar Yogi and Subhasri Telkar15.1 Introduction 40015.2 Building Blocks of Embodied AI Systems for Smart Cities 40215.3 Human-Robot Interaction (HRI) in Urban Environments: A Hybrid Model 41215.4 Results 42315.5 Research Roadmap and Future Directions 42815.6 Conclusion 43216 Embodied AI in Marketing: The Rise and Impact of AI Influencers 437Rishi Satapathy and Abhishek Kumar16.1 Introduction 43816.2 Methodology 44116.3 Background and Evolution of Virtual Influencers 44316.4 Creation and Maintenance of Virtual Influencers 44716.5 The Rise of Virtual Influencers 45016.6 Impact on Consumer Behavior and Brand Loyalty 45316.7 Marketing Strategies Involving Virtual Influencers 45516.8 Challenges and Ethical Considerations 45816.9 Future Trends and Implications 46316.10 Conclusion 469References 473Index 475