Automated Machine Learning and Industrial Applications

E. Gangadevi, PhD is an assistant professor in the Department of Computer Science at Loyola College, Chennai, India. She has published two patents, six books, over 18 research papers in international journals, and many book chapters. Her areas of research are machine learning, deep learning, IoT, and cloud computing. M. Lawanya Shri, PhD is an associate professor in the School of Information Technology and Engineering at Vellore Institute of Technology, India. She has published two books, two patents, and over 50 articles and papers in refereed journals and international conferences. Her research interests include blockchain technology, machine learning, cloud computing, and IOT. Balamurugan Balusamy, PhD is an associate dean at Shiv Nadar University, Delhi, India with over 12 years of teaching experience. He has published more than 200 papers in international journals, 80 books, and given over 195 talks at various international events and symposia. His contributions focus on engineering education, blockchain, and data sciences. Rajesh Kumar Dhanaraj, PhD is a professor in the School of Computing Science and Engineering at Symbiosis University, Pune, India. He has contributed to over 25 books on various technologies, 21 patents, and 53 articles and papers in various refereed journals and conferences. His research interests include machine learning, cyber-physical systems, and wireless sensor networks.

• Preface xv1 Design and Architecture of AutoML for Data Science in Next-Generation Industries 1E. Gangadevi, K. Santhi and M. Lawanya Shri1.1 Introduction 11.2 Modular Design 21.3 Data Handling 31.4 Model Training and Selection 42 Automated Machine Learning Model in Secure Data Transmission in Sustainable Healthcare Sensor Network Using Quantum Blockchain Architecture 17Kaavya Kanagaraj, A. Sheryl Oliver, Kavitha V.P., S. Magesh and R. Manikandan2.1 Introduction 182.2 Related Works 192.3 Proposed Model 212.4 Results and Discussion 322.5 Conclusion 363 Automated Machine Learning in the Biological and Medical Healthcare Industries: Analysis Interpretation and Evaluation 41Iram Fatima, Naved Ahmed, Mehtab Alam, Ihtiram Raza Khan and Veena Grover3.1 Introduction 423.2 Methodology for Effective Data Management 433.3 Foundations of Automated Machine Learning 453.4 Applications in Healthcare 473.5 Case Studies and Success Stories 503.6 Ethical Implications 533.7 Practical Implementation: From Concept to Application 533.8 Future Directions and Trends 563.9 Conclusion 574 Advancements in AI and AutoML for Plant Leaf Disease Identification in Sustainable Agriculture 63Ranichandra C., Senthilkumar N. C., Senthil Kumar Narayanasamy and Atilla Elci4.1 Introduction 644.2 Literature Survey 654.3 Preliminary Analysis for Agricultural Diseases 674.4 Proposed Methods 704.5 Conclusion 755 Predictive Maintenance in Industrial Settings: Video Analytics at the Edge with AutoML 79Madala Guru Brahmam and Vijay Anand R.5.1 Introduction 805.2 Literature Review 835.3 Proposed Design of an Efficient Model for Enhancing Predictive Maintenance in Industrial Settings 875.4 Result Evaluation and Comparative Analysis 955.5 Conclusion and Future Scope 1006 AutoCRM--An Automated Customer Relationship Management Learning System with Random Search Hyper-Parameter Optimization 105S. Rajeswari and S. Gomathi6.1 Introduction 1066.2 Literature Review 1136.3 Methodology 1226.4 Results and Discussions 1276.5 Conclusion 1367 The Competence of Customer Support Team for Sentiment Analysis in Chatbots Using AutoML 141G. Pradeep and M. Devi Sri Nandhini7.1 Introduction 1427.2 Literature Survey 1487.3 Methodology for Chatbot Sentiment Analysis 1547.4 Experimentation and Results 1637.5 Conclusion 1668 Financial Risk Prediction with Banking Monitoring for Cyber Security Analysis Using Automated Machine Learning 171K. Rajkumar, Prassanna Jayachandran, Kannan Chakrapani, S. Magesh and R. Manikandan8.1 Introduction 1728.2 Related Works 1738.3 System Model 1758.4 Results and Discussion 1838.5 Conclusion 1889 AutoML Ecosystem and Open-Source Platforms: Challenges and Limitations 191M. Anitha, J. Dhilipan, P.M. Kavitha and E. Gangadevi9.1 Introduction 1929.2 Related Study 1939.3 Ecosystem of AutoML 1949.4 AutoML Frameworks 1959.5 Open-Source AutoML Libraries 2009.6 Types of AutoML Approaches 2039.7 Benefits of AutoML 2039.8 Challenges and Limitations 2049.9 Conclusion 20410 Plant Disease Identification Using Extended-EfficientNet Deep Learning Model in Smart Farming 207K. Sathya, K. Kanmani, M. Revathy Meenal, D. Suganthi and T. S. Lakshmi10.1 Introduction 20810.2 Literature Review 21510.3 Materials and Methods 22010.4 Methodology--E-ENet 22310.5 Experimental Analysis 22810.6 Results 23010.7 Comparative Test 23310.8 Summary 23511 AutoML-Driven Deep Learning for Fake Currency Recognition 243T. Bhaskar and E. Gangadevi11.1 Introduction 24411.2 Literature Review 24411.3 Proposed System 24611.4 Methodology 24811.5 Convolutional Neural Network 24911.6 Analysis Modeling 25211.7 Software Testing 25411.8 Results and Discussions 25711.9 Conclusion 26012 Blockchain and Automated Machine Learning-Based Advancements for Banking and Financial Sectors 263K. Santhi, M. Lawanya Shri, Pranesh L., Dhanush T. and Suneel P.V.12.1 Introduction 26312.2 Understanding Blockchain and AutoML 26412.3 Need of Blockchain 26412.4 Synergies Between Blockchain and AutoML 26512.5 Applications in Banking and Finance 26512.6 Applications of AutoML in Industries 26612.7 Case Studies and Real-World Applications 26712.8 Blockchain in Finance 26812.9 Real-World Examples and Case Studies 26912.10 Benefits and Challenges 27012.11 Discussion 27012.12 Limitations 27212.13 Recommendations for Implementation 27312.14 Ethical Considerations and Responsible AI 27412.15 Future Directions and Emerging Trends 27512.16 Future Scope 27612.17 Conclusion 27713 Advances in Automated Machine Learning for Precision Healthcare and Biomedical Discoveries 281Aryan Chopra, Lawanya Shri M. and Santhi K.13.1 Introduction 28113.2 Current Day Usage of AI 28413.3 Data Management and Security in Healthcare AI 28613.4 Challenges in Integrating AI into Healthcare Systems 28813.5 Challenges and Ethical Concerns 29013.6 Case Study 29113.6.1 PharmEasy 29113.6.2 Qure.ai 29113.7 Implementing AutoML Techniques 29213.8 Conclusion 29314 Democratizing Machine Learning: The Rise of Automated Machine Learning (AutoML) 297Debarati Dutta and Priya G.14.1 Introduction 29814.2 Flow of AutoML 29914.3 AutoML Components 30814.4 Application 30914.5 Future Scope 31114.6 Conclusion 31115 Open-Source Tools in Automated Machine Learning 319Malaserene I., K. Santhi and M. Lawanya ShriReferences 326Index 329