{"product_id":"automated-machine-learning-and-industrial-applications-hardback-9781394272396","title":"Automated Machine Learning and Industrial Applications (Hardback) 9781394272396","description":"\u003cfont face=\"Georgia\"\u003e\r\n\u003cp\u003e\u003cfont size=\"6\"\u003eAutomated Machine Learning and Industrial Applications\u003c\/font\u003e\u003cbr\u003e\r\n\r\n\r\n\r\n\r\n\r\n\u003c\/p\u003e\n\u003cp\u003e\u003cfont size=\"4\"\u003eE. Gangadevi (Edited by), Gangadevi (Author), M. Lawanya Shri (Edited by), Balamurugan Balusamy (Edited by), Rajesh Kumar Dhanaraj (Edited by)\u003c\/font\u003e\u003c\/p\u003e\r\n\r\n\u003cp\u003e\u003cfont size=\"3\"\u003e9781394272396, Wiley\u003c\/font\u003e\u003c\/p\u003e\r\n\r\n\u003cp\u003e\u003cfont size=\"3\"\u003eHardback, published 5 August 2025\u003c\/font\u003e\u003c\/p\u003e\r\n\r\n\u003cp\u003e\u003cfont size=\"3\"\u003e352 pages\u003cbr\u003e28 x 19 x 2.6 cm, 0.666 kg\u003c\/font\u003e\u003c\/p\u003e\r\n\r\n\r\n\r\n\r\n\r\n\u003cp align=\"justify\"\u003e\u003cstrong\u003e\u003cfont size=\"3\"\u003e\u003cp\u003e\u003cb\u003eThe 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.\u003c\/b\u003e \u003c\/p\u003e\n\u003cp\u003eAutomated 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.  \u003c\/p\u003e\n\u003cp\u003e\u003ci\u003eAutomated Machine Learning and Industrial Applications \u003c\/i\u003eoffers 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. \u003c\/p\u003e\n\u003cp\u003eReaders will find the book: \u003c\/p\u003e\n\u003cul\u003e\n\u003cli\u003eAims to explore current trends such as augmented reality, virtual reality, blockchain, open-source platforms, and Industry 4.0;\u003c\/li\u003e \u003cli\u003eServes as an effective guide for professionals, researchers, industrialists, data scientists, and application developers; \u003c\/li\u003e \u003cli\u003eExplores technologies such as IoT, blockchain, artificial intelligence, and robotics, serving as a core guide for undergraduate and postgraduate students.\u003c\/li\u003e\n\u003c\/ul\u003e \u003cp\u003e\u003cb\u003eAudience\u003c\/b\u003e \u003c\/p\u003e\n\u003cp\u003eData and computer scientists, research scholars, professionals, and industrialists interested in technology for Industry 4.0 applications.\u003c\/p\u003e\u003c\/font\u003e\u003c\/strong\u003e\u003c\/p\u003e\r\n\r\n\u003cp\u003e\u003cfont size=\"3\"\u003e\u003cp\u003ePreface xv\u003c\/p\u003e \u003cp\u003e\u003cb\u003e1 Design and Architecture of AutoML for Data Science in Next-Generation Industries 1\u003c\/b\u003e\u003cbr\u003e\u003ci\u003eE. Gangadevi, K. Santhi and M. Lawanya Shri\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e1.1 Introduction 1\u003c\/p\u003e \u003cp\u003e1.2 Modular Design 2\u003c\/p\u003e \u003cp\u003e1.3 Data Handling 3\u003c\/p\u003e \u003cp\u003e1.4 Model Training and Selection 4\u003c\/p\u003e \u003cp\u003e\u003cb\u003e2 Automated Machine Learning Model in Secure Data Transmission in Sustainable Healthcare Sensor Network Using Quantum Blockchain Architecture 17\u003c\/b\u003e\u003cbr\u003e\u003ci\u003eKaavya Kanagaraj, A. Sheryl Oliver, Kavitha V.P., S. Magesh and R. Manikandan\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e2.1 Introduction 18\u003c\/p\u003e \u003cp\u003e2.2 Related Works 19\u003c\/p\u003e \u003cp\u003e2.3 Proposed Model 21\u003c\/p\u003e \u003cp\u003e2.4 Results and Discussion 32\u003c\/p\u003e \u003cp\u003e2.5 Conclusion 36\u003c\/p\u003e \u003cp\u003e\u003cb\u003e3 Automated Machine Learning in the Biological and Medical Healthcare Industries: Analysis Interpretation and Evaluation 41\u003c\/b\u003e\u003cbr\u003e\u003ci\u003eIram Fatima, Naved Ahmed, Mehtab Alam, Ihtiram Raza Khan and Veena Grover\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e3.1 Introduction 42\u003c\/p\u003e \u003cp\u003e3.2 Methodology for Effective Data Management 43\u003c\/p\u003e \u003cp\u003e3.3 Foundations of Automated Machine Learning 45\u003c\/p\u003e \u003cp\u003e3.4 Applications in Healthcare 47\u003c\/p\u003e \u003cp\u003e3.5 Case Studies and Success Stories 50\u003c\/p\u003e \u003cp\u003e3.6 Ethical Implications 53\u003c\/p\u003e \u003cp\u003e3.7 Practical Implementation: From Concept to Application 53\u003c\/p\u003e \u003cp\u003e3.8 Future Directions and Trends 56\u003c\/p\u003e \u003cp\u003e3.9 Conclusion 57\u003c\/p\u003e \u003cp\u003e\u003cb\u003e4 Advancements in AI and AutoML for Plant Leaf Disease Identification in Sustainable Agriculture 63\u003c\/b\u003e\u003cbr\u003e\u003ci\u003eRanichandra C., Senthilkumar N. C., Senthil Kumar Narayanasamy and Atilla Elci\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e4.1 Introduction 64\u003c\/p\u003e \u003cp\u003e4.2 Literature Survey 65\u003c\/p\u003e \u003cp\u003e4.3 Preliminary Analysis for Agricultural Diseases 67\u003c\/p\u003e \u003cp\u003e4.4 Proposed Methods 70\u003c\/p\u003e \u003cp\u003e4.5 Conclusion 75\u003c\/p\u003e \u003cp\u003e\u003cb\u003e5 Predictive Maintenance in Industrial Settings: Video Analytics at the Edge with AutoML 79\u003c\/b\u003e\u003cbr\u003e\u003ci\u003eMadala Guru Brahmam and Vijay Anand R.\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e5.1 Introduction 80\u003c\/p\u003e \u003cp\u003e5.2 Literature Review 83\u003c\/p\u003e \u003cp\u003e5.3 Proposed Design of an Efficient Model for Enhancing Predictive Maintenance in Industrial Settings 87\u003c\/p\u003e \u003cp\u003e5.4 Result Evaluation and Comparative Analysis 95\u003c\/p\u003e \u003cp\u003e5.5 Conclusion and Future Scope 100\u003c\/p\u003e \u003cp\u003e\u003cb\u003e6 AutoCRM--An Automated Customer Relationship Management Learning System with Random Search Hyper-Parameter Optimization 105\u003c\/b\u003e\u003cbr\u003e\u003ci\u003eS. Rajeswari and S. Gomathi\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e6.1 Introduction 106\u003c\/p\u003e \u003cp\u003e6.2 Literature Review 113\u003c\/p\u003e \u003cp\u003e6.3 Methodology 122\u003c\/p\u003e \u003cp\u003e6.4 Results and Discussions 127\u003c\/p\u003e \u003cp\u003e6.5 Conclusion 136\u003c\/p\u003e \u003cp\u003e\u003cb\u003e7 The Competence of Customer Support Team for Sentiment Analysis in Chatbots Using AutoML 141\u003c\/b\u003e\u003cbr\u003e\u003ci\u003eG. Pradeep and M. Devi Sri Nandhini\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e7.1 Introduction 142\u003c\/p\u003e \u003cp\u003e7.2 Literature Survey 148\u003c\/p\u003e \u003cp\u003e7.3 Methodology for Chatbot Sentiment Analysis 154\u003c\/p\u003e \u003cp\u003e7.4 Experimentation and Results 163\u003c\/p\u003e \u003cp\u003e7.5 Conclusion 166\u003c\/p\u003e \u003cp\u003e\u003cb\u003e8 Financial Risk Prediction with Banking Monitoring for Cyber Security Analysis Using Automated Machine Learning 171\u003c\/b\u003e\u003cbr\u003e\u003ci\u003eK. Rajkumar, Prassanna Jayachandran, Kannan Chakrapani, S. Magesh and R. Manikandan\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e8.1 Introduction 172\u003c\/p\u003e \u003cp\u003e8.2 Related Works 173\u003c\/p\u003e \u003cp\u003e8.3 System Model 175\u003c\/p\u003e \u003cp\u003e8.4 Results and Discussion 183\u003c\/p\u003e \u003cp\u003e8.5 Conclusion 188\u003c\/p\u003e \u003cp\u003e\u003cb\u003e9 AutoML Ecosystem and Open-Source Platforms: Challenges and Limitations 191\u003c\/b\u003e\u003cbr\u003e\u003ci\u003eM. Anitha, J. Dhilipan, P.M. Kavitha and E. Gangadevi\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e9.1 Introduction 192\u003c\/p\u003e \u003cp\u003e9.2 Related Study 193\u003c\/p\u003e \u003cp\u003e9.3 Ecosystem of AutoML 194\u003c\/p\u003e \u003cp\u003e9.4 AutoML Frameworks 195\u003c\/p\u003e \u003cp\u003e9.5 Open-Source AutoML Libraries 200\u003c\/p\u003e \u003cp\u003e9.6 Types of AutoML Approaches 203\u003c\/p\u003e \u003cp\u003e9.7 Benefits of AutoML 203\u003c\/p\u003e \u003cp\u003e9.8 Challenges and Limitations 204\u003c\/p\u003e \u003cp\u003e9.9 Conclusion 204\u003c\/p\u003e \u003cp\u003e\u003cb\u003e10 Plant Disease Identification Using Extended-EfficientNet Deep Learning Model in Smart Farming 207\u003c\/b\u003e\u003cbr\u003e\u003ci\u003eK. Sathya, K. Kanmani, M. Revathy Meenal, D. Suganthi and T. S. Lakshmi\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e10.1 Introduction 208\u003c\/p\u003e \u003cp\u003e10.2 Literature Review 215\u003c\/p\u003e \u003cp\u003e10.3 Materials and Methods 220\u003c\/p\u003e \u003cp\u003e10.4 Methodology--E-ENet 223\u003c\/p\u003e \u003cp\u003e10.5 Experimental Analysis 228\u003c\/p\u003e \u003cp\u003e10.6 Results 230\u003c\/p\u003e \u003cp\u003e10.7 Comparative Test 233\u003c\/p\u003e \u003cp\u003e10.8 Summary 235\u003c\/p\u003e \u003cp\u003e\u003cb\u003e11 AutoML-Driven Deep Learning for Fake Currency Recognition 243\u003c\/b\u003e\u003cbr\u003e\u003ci\u003eT. Bhaskar and E. Gangadevi\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e11.1 Introduction 244\u003c\/p\u003e \u003cp\u003e11.2 Literature Review 244\u003c\/p\u003e \u003cp\u003e11.3 Proposed System 246\u003c\/p\u003e \u003cp\u003e11.4 Methodology 248\u003c\/p\u003e \u003cp\u003e11.5 Convolutional Neural Network 249\u003c\/p\u003e \u003cp\u003e11.6 Analysis Modeling 252\u003c\/p\u003e \u003cp\u003e11.7 Software Testing 254\u003c\/p\u003e \u003cp\u003e11.8 Results and Discussions 257\u003c\/p\u003e \u003cp\u003e11.9 Conclusion 260\u003c\/p\u003e \u003cp\u003e\u003cb\u003e12 Blockchain and Automated Machine Learning-Based Advancements for Banking and Financial Sectors 263\u003c\/b\u003e\u003cbr\u003e\u003ci\u003eK. Santhi, M. Lawanya Shri, Pranesh L., Dhanush T. and Suneel P.V.\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e12.1 Introduction 263\u003c\/p\u003e \u003cp\u003e12.2 Understanding Blockchain and AutoML 264\u003c\/p\u003e \u003cp\u003e12.3 Need of Blockchain 264\u003c\/p\u003e \u003cp\u003e12.4 Synergies Between Blockchain and AutoML 265\u003c\/p\u003e \u003cp\u003e12.5 Applications in Banking and Finance 265\u003c\/p\u003e \u003cp\u003e12.6 Applications of AutoML in Industries 266\u003c\/p\u003e \u003cp\u003e12.7 Case Studies and Real-World Applications 267\u003c\/p\u003e \u003cp\u003e12.8 Blockchain in Finance 268\u003c\/p\u003e \u003cp\u003e12.9 Real-World Examples and Case Studies 269\u003c\/p\u003e \u003cp\u003e12.10 Benefits and Challenges 270\u003c\/p\u003e \u003cp\u003e12.11 Discussion 270\u003c\/p\u003e \u003cp\u003e12.12 Limitations 272\u003c\/p\u003e \u003cp\u003e12.13 Recommendations for Implementation 273\u003c\/p\u003e \u003cp\u003e12.14 Ethical Considerations and Responsible AI 274\u003c\/p\u003e \u003cp\u003e12.15 Future Directions and Emerging Trends 275\u003c\/p\u003e \u003cp\u003e12.16 Future Scope 276\u003c\/p\u003e \u003cp\u003e12.17 Conclusion 277\u003c\/p\u003e \u003cp\u003e\u003cb\u003e13 Advances in Automated Machine Learning for Precision Healthcare and Biomedical Discoveries 281\u003c\/b\u003e\u003cbr\u003e\u003ci\u003eAryan Chopra, Lawanya Shri M. and Santhi K.\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e13.1 Introduction 281\u003c\/p\u003e \u003cp\u003e13.2 Current Day Usage of AI 284\u003c\/p\u003e \u003cp\u003e13.3 Data Management and Security in Healthcare AI 286\u003c\/p\u003e \u003cp\u003e13.4 Challenges in Integrating AI into Healthcare Systems 288\u003c\/p\u003e \u003cp\u003e13.5 Challenges and Ethical Concerns 290\u003c\/p\u003e \u003cp\u003e13.6 Case Study 291\u003c\/p\u003e \u003cp\u003e13.6.1 PharmEasy 291\u003c\/p\u003e \u003cp\u003e13.6.2 Qure.ai 291\u003c\/p\u003e \u003cp\u003e13.7 Implementing AutoML Techniques 292\u003c\/p\u003e \u003cp\u003e13.8 Conclusion 293\u003c\/p\u003e \u003cp\u003e\u003cb\u003e14 Democratizing Machine Learning: The Rise of Automated Machine Learning (AutoML) 297\u003c\/b\u003e\u003cbr\u003e\u003ci\u003eDebarati Dutta and Priya G.\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e14.1 Introduction 298\u003c\/p\u003e \u003cp\u003e14.2 Flow of AutoML 299\u003c\/p\u003e \u003cp\u003e14.3 AutoML Components 308\u003c\/p\u003e \u003cp\u003e14.4 Application 309\u003c\/p\u003e \u003cp\u003e14.5 Future Scope 311\u003c\/p\u003e \u003cp\u003e14.6 Conclusion 311\u003c\/p\u003e \u003cp\u003e\u003cb\u003e15 Open-Source Tools in Automated Machine Learning 319\u003c\/b\u003e\u003cbr\u003e\u003ci\u003eMalaserene I., K. Santhi and M. Lawanya Shri\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003eReferences 326\u003c\/p\u003e \u003cp\u003eIndex 329\u003c\/p\u003e\u003c\/font\u003e\u003c\/p\u003e\r\n\r\n\u003cp\u003e\u003cfont size=\"3\"\u003eSubject Areas: Computer science [\u003ca title=\"See our other books on Computer science\" href=\"https:\/\/freshlyprintedbooks.co.uk\/search?q=%22Computer%20science%20%5BUY%5D%22\"\u003eUY\u003c\/a\u003e]\u003c\/font\u003e\u003c\/p\u003e\r\n\r\n\r\n\u003c\/font\u003e","brand":"Wiley-Scrivener","offers":[{"title":"Brand New","offer_id":52433243472152,"sku":"9781394272396","price":144.19,"currency_code":"GBP","in_stock":true}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/0730\/2037\/5320\/files\/9781394272396.jpg?v=1784852909","url":"https:\/\/freshlyprintedbooks.co.uk\/products\/automated-machine-learning-and-industrial-applications-hardback-9781394272396","provider":"Freshly Printed Books","version":"1.0","type":"link"}