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Produktbild: Automated Machine Learning and Industrial Applications

Automated Machine Learning and Industrial Applications

Fr. 263.00

inkl. gesetzl. MwSt., Versandkostenfrei

Beschreibung

Produktdetails

Einband

Gebundene Ausgabe

Erscheinungsdatum

02.09.2025

Herausgeber

E. Gangadevi + weitere

Verlag

Wiley

Seitenzahl

352

Maße (L/B/H)

23.4/15.6/2.5 cm

Gewicht

612 g

Sprache

Englisch

ISBN

978-1-394-27239-6

Beschreibung

Produktdetails

Einband

Gebundene Ausgabe

Erscheinungsdatum

02.09.2025

Herausgeber

Verlag

Wiley

Seitenzahl

352

Maße (L/B/H)

23.4/15.6/2.5 cm

Gewicht

612 g

Sprache

Englisch

ISBN

978-1-394-27239-6

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  • Produktbild: Automated Machine Learning and Industrial Applications

  • 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