The book provides a comprehensive understanding of Automated Machine Learning's transformative potential across various industries, empowering users to seamlessly implement advanced machine learning solutions without needing extensive expertise. Automated Machine Learning (AutoML) is a process to automate the responsibilities of machine learning concepts for real-world problems. The AutoML process is comprised of all steps, beginning with a raw dataset and concluding with the construction of a machine learning model for deployment. The purpose of AutoML is to allow non-experts to work with…mehr
The book provides a comprehensive understanding of Automated Machine Learning's transformative potential across various industries, empowering users to seamlessly implement advanced machine learning solutions without needing extensive expertise. Automated Machine Learning (AutoML) is a process to automate the responsibilities of machine learning concepts for real-world problems. The AutoML process is comprised of all steps, beginning with a raw dataset and concluding with the construction of a machine learning model for deployment. The purpose of AutoML is to allow non-experts to work with machine learning models and techniques without requiring much knowledge in machine learning. This advancement enables data scientists to produce the easiest solutions and most accurate results within a short timeframe, allowing them to outperform normal machine learning models. Meta-learning, neural network architecture, and hyperparameter optimization, are applied based on AutoML. Automated Machine Learning and Industrial Applications offers an overview of the basic architecture, evolution, and applications of AutoML. Potential applications in healthcare, banking, agriculture, aerospace, and security are discussed in terms of their frameworks, implementation, and evaluation. This book also explores the AutoML ecosystem, its integration with blockchain, and various open-source tools available on the AutoML platform. It serves as a practical guide for engineers and data scientists, offering valuable insights for decision-makers looking to integrate machine learning into their workflows. Readers will find the book: * Aims to explore current trends such as augmented reality, virtual reality, blockchain, open-source platforms, and Industry 4.0; * Serves as an effective guide for professionals, researchers, industrialists, data scientists, and application developers; * Explores technologies such as IoT, blockchain, artificial intelligence, and robotics, serving as a core guide for undergraduate and postgraduate students. Audience Data and computer scientists, research scholars, professionals, and industrialists interested in technology for Industry 4.0 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.
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Preface xv 1 Design and Architecture of AutoML for Data Science in Next-Generation Industries 1 E. Gangadevi, K. Santhi and M. Lawanya Shri 1.1 Introduction 1 1.2 Modular Design 2 1.3 Data Handling 3 1.4 Model Training and Selection 4 2 Automated Machine Learning Model in Secure Data Transmission in Sustainable Healthcare Sensor Network Using Quantum Blockchain Architecture 17 Kaavya Kanagaraj, A. Sheryl Oliver, Kavitha V.P., S. Magesh and R. Manikandan 2.1 Introduction 18 2.2 Related Works 19 2.3 Proposed Model 21 2.4 Results and Discussion 32 2.5 Conclusion 36 3 Automated Machine Learning in the Biological and Medical Healthcare Industries: Analysis Interpretation and Evaluation 41 Iram Fatima, Naved Ahmed, Mehtab Alam, Ihtiram Raza Khan and Veena Grover 3.1 Introduction 42 3.2 Methodology for Effective Data Management 43 3.3 Foundations of Automated Machine Learning 45 3.4 Applications in Healthcare 47 3.5 Case Studies and Success Stories 50 3.6 Ethical Implications 53 3.7 Practical Implementation: From Concept to Application 53 3.8 Future Directions and Trends 56 3.9 Conclusion 57 4 Advancements in AI and AutoML for Plant Leaf Disease Identification in Sustainable Agriculture 63 Ranichandra C., Senthilkumar N. C., Senthil Kumar Narayanasamy and Atilla Elci 4.1 Introduction 64 4.2 Literature Survey 65 4.3 Preliminary Analysis for Agricultural Diseases 67 4.4 Proposed Methods 70 4.5 Conclusion 75 5 Predictive Maintenance in Industrial Settings: Video Analytics at the Edge with AutoML 79 Madala Guru Brahmam and Vijay Anand R. 5.1 Introduction 80 5.2 Literature Review 83 5.3 Proposed Design of an Efficient Model for Enhancing Predictive Maintenance in Industrial Settings 87 5.4 Result Evaluation and Comparative Analysis 95 5.5 Conclusion and Future Scope 100 6 AutoCRM--An Automated Customer Relationship Management Learning System with Random Search Hyper-Parameter Optimization 105 S. Rajeswari and S. Gomathi 6.1 Introduction 106 6.2 Literature Review 113 6.3 Methodology 122 6.4 Results and Discussions 127 6.5 Conclusion 136 7 The Competence of Customer Support Team for Sentiment Analysis in Chatbots Using AutoML 141 G. Pradeep and M. Devi Sri Nandhini 7.1 Introduction 142 7.2 Literature Survey 148 7.3 Methodology for Chatbot Sentiment Analysis 154 7.4 Experimentation and Results 163 7.5 Conclusion 166 8 Financial Risk Prediction with Banking Monitoring for Cyber Security Analysis Using Automated Machine Learning 171 K. Rajkumar, Prassanna Jayachandran, Kannan Chakrapani, S. Magesh and R. Manikandan 8.1 Introduction 172 8.2 Related Works 173 8.3 System Model 175 8.4 Results and Discussion 183 8.5 Conclusion 188 9 AutoML Ecosystem and Open-Source Platforms: Challenges and Limitations 191 M. Anitha, J. Dhilipan, P.M. Kavitha and E. Gangadevi 9.1 Introduction 192 9.2 Related Study 193 9.3 Ecosystem of AutoML 194 9.4 AutoML Frameworks 195 9.5 Open-Source AutoML Libraries 200 9.6 Types of AutoML Approaches 203 9.7 Benefits of AutoML 203 9.8 Challenges and Limitations 204 9.9 Conclusion 204 10 Plant Disease Identification Using Extended-EfficientNet Deep Learning Model in Smart Farming 207 K. Sathya, K. Kanmani, M. Revathy Meenal, D. Suganthi and T. S. Lakshmi 10.1 Introduction 208 10.2 Literature Review 215 10.3 Materials and Methods 220 10.4 Methodology--E-ENet 223 10.5 Experimental Analysis 228 10.6 Results 230 10.7 Comparative Test 233 10.8 Summary 235 11 AutoML-Driven Deep Learning for Fake Currency Recognition 243 T. Bhaskar and E. Gangadevi 11.1 Introduction 244 11.2 Literature Review 244 11.3 Proposed System 246 11.4 Methodology 248 11.5 Convolutional Neural Network 249 11.6 Analysis Modeling 252 11.7 Software Testing 254 11.8 Results and Discussions 257 11.9 Conclusion 260 12 Blockchain and Automated Machine Learning-Based Advancements for Banking and Financial Sectors 263 K. Santhi, M. Lawanya Shri, Pranesh L., Dhanush T. and Suneel P.V. 12.1 Introduction 263 12.2 Understanding Blockchain and AutoML 264 12.3 Need of Blockchain 264 12.4 Synergies Between Blockchain and AutoML 265 12.5 Applications in Banking and Finance 265 12.6 Applications of AutoML in Industries 266 12.7 Case Studies and Real-World Applications 267 12.8 Blockchain in Finance 268 12.9 Real-World Examples and Case Studies 269 12.10 Benefits and Challenges 270 12.11 Discussion 270 12.12 Limitations 272 12.13 Recommendations for Implementation 273 12.14 Ethical Considerations and Responsible AI 274 12.15 Future Directions and Emerging Trends 275 12.16 Future Scope 276 12.17 Conclusion 277 13 Advances in Automated Machine Learning for Precision Healthcare and Biomedical Discoveries 281 Aryan Chopra, Lawanya Shri M. and Santhi K. 13.1 Introduction 281 13.2 Current Day Usage of AI 284 13.3 Data Management and Security in Healthcare AI 286 13.4 Challenges in Integrating AI into Healthcare Systems 288 13.5 Challenges and Ethical Concerns 290 13.6 Case Study 291 13.6.1 PharmEasy 291 13.6.2 Qure.ai 291 13.7 Implementing AutoML Techniques 292 13.8 Conclusion 293 14 Democratizing Machine Learning: The Rise of Automated Machine Learning (AutoML) 297 Debarati Dutta and Priya G. 14.1 Introduction 298 14.2 Flow of AutoML 299 14.3 AutoML Components 308 14.4 Application 309 14.5 Future Scope 311 14.6 Conclusion 311 15 Open-Source Tools in Automated Machine Learning 319 Malaserene I., K. Santhi and M. Lawanya Shri References 326 Index 329
Preface xv 1 Design and Architecture of AutoML for Data Science in Next-Generation Industries 1 E. Gangadevi, K. Santhi and M. Lawanya Shri 1.1 Introduction 1 1.2 Modular Design 2 1.3 Data Handling 3 1.4 Model Training and Selection 4 2 Automated Machine Learning Model in Secure Data Transmission in Sustainable Healthcare Sensor Network Using Quantum Blockchain Architecture 17 Kaavya Kanagaraj, A. Sheryl Oliver, Kavitha V.P., S. Magesh and R. Manikandan 2.1 Introduction 18 2.2 Related Works 19 2.3 Proposed Model 21 2.4 Results and Discussion 32 2.5 Conclusion 36 3 Automated Machine Learning in the Biological and Medical Healthcare Industries: Analysis Interpretation and Evaluation 41 Iram Fatima, Naved Ahmed, Mehtab Alam, Ihtiram Raza Khan and Veena Grover 3.1 Introduction 42 3.2 Methodology for Effective Data Management 43 3.3 Foundations of Automated Machine Learning 45 3.4 Applications in Healthcare 47 3.5 Case Studies and Success Stories 50 3.6 Ethical Implications 53 3.7 Practical Implementation: From Concept to Application 53 3.8 Future Directions and Trends 56 3.9 Conclusion 57 4 Advancements in AI and AutoML for Plant Leaf Disease Identification in Sustainable Agriculture 63 Ranichandra C., Senthilkumar N. C., Senthil Kumar Narayanasamy and Atilla Elci 4.1 Introduction 64 4.2 Literature Survey 65 4.3 Preliminary Analysis for Agricultural Diseases 67 4.4 Proposed Methods 70 4.5 Conclusion 75 5 Predictive Maintenance in Industrial Settings: Video Analytics at the Edge with AutoML 79 Madala Guru Brahmam and Vijay Anand R. 5.1 Introduction 80 5.2 Literature Review 83 5.3 Proposed Design of an Efficient Model for Enhancing Predictive Maintenance in Industrial Settings 87 5.4 Result Evaluation and Comparative Analysis 95 5.5 Conclusion and Future Scope 100 6 AutoCRM--An Automated Customer Relationship Management Learning System with Random Search Hyper-Parameter Optimization 105 S. Rajeswari and S. Gomathi 6.1 Introduction 106 6.2 Literature Review 113 6.3 Methodology 122 6.4 Results and Discussions 127 6.5 Conclusion 136 7 The Competence of Customer Support Team for Sentiment Analysis in Chatbots Using AutoML 141 G. Pradeep and M. Devi Sri Nandhini 7.1 Introduction 142 7.2 Literature Survey 148 7.3 Methodology for Chatbot Sentiment Analysis 154 7.4 Experimentation and Results 163 7.5 Conclusion 166 8 Financial Risk Prediction with Banking Monitoring for Cyber Security Analysis Using Automated Machine Learning 171 K. Rajkumar, Prassanna Jayachandran, Kannan Chakrapani, S. Magesh and R. Manikandan 8.1 Introduction 172 8.2 Related Works 173 8.3 System Model 175 8.4 Results and Discussion 183 8.5 Conclusion 188 9 AutoML Ecosystem and Open-Source Platforms: Challenges and Limitations 191 M. Anitha, J. Dhilipan, P.M. Kavitha and E. Gangadevi 9.1 Introduction 192 9.2 Related Study 193 9.3 Ecosystem of AutoML 194 9.4 AutoML Frameworks 195 9.5 Open-Source AutoML Libraries 200 9.6 Types of AutoML Approaches 203 9.7 Benefits of AutoML 203 9.8 Challenges and Limitations 204 9.9 Conclusion 204 10 Plant Disease Identification Using Extended-EfficientNet Deep Learning Model in Smart Farming 207 K. Sathya, K. Kanmani, M. Revathy Meenal, D. Suganthi and T. S. Lakshmi 10.1 Introduction 208 10.2 Literature Review 215 10.3 Materials and Methods 220 10.4 Methodology--E-ENet 223 10.5 Experimental Analysis 228 10.6 Results 230 10.7 Comparative Test 233 10.8 Summary 235 11 AutoML-Driven Deep Learning for Fake Currency Recognition 243 T. Bhaskar and E. Gangadevi 11.1 Introduction 244 11.2 Literature Review 244 11.3 Proposed System 246 11.4 Methodology 248 11.5 Convolutional Neural Network 249 11.6 Analysis Modeling 252 11.7 Software Testing 254 11.8 Results and Discussions 257 11.9 Conclusion 260 12 Blockchain and Automated Machine Learning-Based Advancements for Banking and Financial Sectors 263 K. Santhi, M. Lawanya Shri, Pranesh L., Dhanush T. and Suneel P.V. 12.1 Introduction 263 12.2 Understanding Blockchain and AutoML 264 12.3 Need of Blockchain 264 12.4 Synergies Between Blockchain and AutoML 265 12.5 Applications in Banking and Finance 265 12.6 Applications of AutoML in Industries 266 12.7 Case Studies and Real-World Applications 267 12.8 Blockchain in Finance 268 12.9 Real-World Examples and Case Studies 269 12.10 Benefits and Challenges 270 12.11 Discussion 270 12.12 Limitations 272 12.13 Recommendations for Implementation 273 12.14 Ethical Considerations and Responsible AI 274 12.15 Future Directions and Emerging Trends 275 12.16 Future Scope 276 12.17 Conclusion 277 13 Advances in Automated Machine Learning for Precision Healthcare and Biomedical Discoveries 281 Aryan Chopra, Lawanya Shri M. and Santhi K. 13.1 Introduction 281 13.2 Current Day Usage of AI 284 13.3 Data Management and Security in Healthcare AI 286 13.4 Challenges in Integrating AI into Healthcare Systems 288 13.5 Challenges and Ethical Concerns 290 13.6 Case Study 291 13.6.1 PharmEasy 291 13.6.2 Qure.ai 291 13.7 Implementing AutoML Techniques 292 13.8 Conclusion 293 14 Democratizing Machine Learning: The Rise of Automated Machine Learning (AutoML) 297 Debarati Dutta and Priya G. 14.1 Introduction 298 14.2 Flow of AutoML 299 14.3 AutoML Components 308 14.4 Application 309 14.5 Future Scope 311 14.6 Conclusion 311 15 Open-Source Tools in Automated Machine Learning 319 Malaserene I., K. Santhi and M. Lawanya Shri References 326 Index 329
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