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Full Description
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.
Contents
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
