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Automated Machine Learning and Industrial Applications

Edited by E. Gangadevi, M. Lawanya Shri, Balamurugan Balusamy, Rajesh Kumar Dhanaraj
Copyright: 2025   |   Expected Pub Date:2025/06/30
ISBN: 9781394272396  |  Hardcover  |  
384 pages

One Line 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.

Audience
Data and computer scientists, research scholars, professionals, and industrialists interested in technology for Industry 4.0 applications.

Description
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.

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Author / Editor Details
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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Table of Contents
Preface
1. Design and Architecture of AutoML for Data Science in Next-Generation Industries
E. Gangadevi, K. Santhi and M. Lawanya Shri
1.1 Introduction
1.2 Modular Design
1.3 Data Handling
1.4 Model Training and Selection
Conclusion
References
2. Automated Machine Learning Model in Secure Data Transmission in Sustainable Healthcare Sensor Network Using Quantum Blockchain Architecture
Kaavya Kanagaraj, A. Sheryl Oliver, Kavitha V.P., S. Magesh and R. Manikandan
2.1 Introduction
2.2 Related Works
2.3 Proposed Model
2.4 Results and Discussion
2.5 Conclusion
References
3. Automated Machine Learning in the Biological and Medical Healthcare Industries: Analysis Interpretation and Evaluation
Iram Fatima, Naved Ahmed, Mehtab Alam, Ihtiram Raza Khan and Veena Grover
3.1 Introduction
3.1.1 Rise of AutoML
3.1.2 Significance of AutoML in Biological and Medical Healthcare
3.2 Methodology for Effective Data Management
3.3 Foundations of Automated Machine Learning
3.3.1 Understanding Automated Machine Learning
3.3.2 Components and Workflow
3.3.3 Pros of AutoML Implementation
3.3.4 Cons of AutoML Implementation
3.4 Applications in Healthcare
3.4.1 Disease Diagnosis
3.4.2 Drug Discovery and Development
3.4.3 Personalized Medicine
3.4.4 Predictive Analytics in Healthcare
3.5 Case Studies and Success Stories
3.5.1 Noteworthy Implementations
3.5.2 Impact on Patient Outcomes
3.5.3 Challenges Encountered and Overcome
3.6 Ethical Implications
3.6.1 Data Privacy and Security
3.6.2 Fairness and Bias Considerations
3.7 Practical Implementation: From Concept to Application
3.7.1 Problem Formulation and Data Preparation
3.7.2 Tool Selection
3.7.3 Training and Evaluation
3.7.4 Explainability and Interpretability
3.7.5 Deployment and Monitoring
3.8 Future Directions and Trends
3.8.1 Integration with Emerging Technologies
3.8.2 AutoML in Clinical Trials and Research
3.9 Conclusion
References
4. Advancements in AI and AutoML for Plant Leaf Disease Identification in Sustainable Agriculture
Ranichandra C., Senthilkumar N. C., Senthil Kumar Narayanasamy and Atilla Elci
4.1 Introduction
4.2 Literature Survey
4.3 Preliminary Analysis for Agricultural Diseases
4.3.1 Datasets and Descriptions
4.3.2 Normalization and Scaling
4.3.3 Feature Extraction and Classification
4.3.4 Spectral Image Analysis
4.4 Proposed Methods
4.4.1 Leaf Disease Identification Using ResNet
4.4.2 Pixel-Based Information Extraction and Ant Colony Optimization
4.4.3 Image Enhancement and Segmentation
4.5 Conclusion
References
5. Predictive Maintenance in Industrial Settings: Video Analytics at the Edge with AutoML
Madala Guru Brahmam and Vijay Anand R.
5.1 Introduction
5.2 Literature Review
5.3 Proposed Design of an Efficient Model for Enhancing Predictive Maintenance in Industrial Settings
5.4 Result Evaluation and Comparative Analysis
5.5 Conclusion and Future Scope
Future Scope
References
6. AutoCRM—An Automated Customer Relationship Management Learning System with Random Search Hyper-Parameter Optimization
S. Rajeswari and S. Gomathi
6.1 Introduction
6.1.1 Opinion Mining or Sentiment Analysis
6.1.2 Machine Learning Approaches
6.1.3 Machine Learning Pipeline (ML)
6.1.4 Automated Machine Learning (AutoML)
6.1.4.1 AutoML Core Goals
6.1.4.2 AutoML Tools
6.1.5 Objectives of this Research
6.1.6 Outline
6.2 Literature Review
6.3 Methodology
6.3.1 Data Preparation
6.3.1.1 Data Collection
6.3.1.2 Data Cleaning and Labeling
6.3.1.3 Data Visualization
6.3.1.4 Feature Engineering
6.3.2 AutoKeras
6.3.2.1 Neural Architecture Search and Hyper-Parameter Tuning
6.3.3 Model Selection
6.4 Results and Discussions
6.4.1 Comparative Analysis: AutoML vs ML
6.5 Conclusion
References
7. The Competence of Customer Support Team for Sentiment Analysis in Chatbots Using AutoML
G. Pradeep and M. Devi Sri Nandhini
7.1 Introduction
7.1.1 Background
7.1.2 Problem Definition
7.1.3 Scope
7.1.4 Technical Highlights
7.1.5 Objectives
7.1.6 Common Chatbot Use Cases
7.1.7 The Basics of Sentiment Analysis
7.1.8 Levels of Sentiment Analysis
7.2 Literature Survey
7.3 Methodology for Chatbot Sentiment Analysis
7.3.1 AutoML-Based Exploratory Data Analysis and Subjectivity Detection
7.3.2 Trilateral Modifier Utilization
7.3.3 Sentiment Polarity Detection
7.3.4 Workflow of Customer Service Inquiry–Chatbot Response
7.3.5 Scoring
7.4 Experimentation and Results
7.4.1 Performance Metrics
7.4.2 Data Collection
7.4.3 Evaluation
7.5 Conclusion
References
8. Financial Risk Prediction with Banking Monitoring for Cyber Security Analysis Using Automated Machine Learning
K. Rajkumar, Prassanna Jayachandran, Kannan Chakrapani, S. Magesh and R. Manikandan
8.1 Introduction
8.2 Related Works
8.3 System Model
8.3.1 Cyber Security Detection Using Gaussian Encoder Belief Network
8.4 Results and Discussion
8.5 Conclusion
References
9. AutoML Ecosystem and Open-Source Platforms: Challenges and Limitations
M. Anitha, J. Dhilipan, P.M. Kavitha and E. Gangadevi
9.1 Introduction
9.2 Related Study
9.3 Ecosystem of AutoML
9.3.1 Data Preprocessing
9.3.2 Model Selection
9.3.3 Hyperparameter Tuning
9.3.4 Model Evaluation and Deployment
9.4 AutoML Frameworks
9.4.1 Google AutoML
9.4.2 IBM Watson AutoAI
9.4.3 Microsoft Azure AutoML
9.4.4 H2O.ai
9.4.5 Data Robot
9.4.6 Databricks AutoML
9.4.7 Tune
9.4.8 AutoKeras
9.4.9 H2O Driverless AI
9.4.10 RapidMiner
9.4.11 Google Cloud AutoML Tables
9.4.12 H2O Sparkling Water
9.4.13 Turi Create
9.4.14 Big ML
9.4.15 Hail
9.5 Open-Source AutoML Libraries
9.5.1 Auto-Sklearn
9.5.2 TPOT (Tree-Based Pipeline Optimization Tool)
9.5.3 AutoKeras
9.5.4 MLBox
9.5.5 AutoGluon
9.5.6 H2O AutoML
9.5.7 Auto-WEKA
9.5.8 AutoGluon Tabular
9.5.9 FLAML
9.5.10 Ludwig
9.6 Types of AutoML Approaches
9.6.1 Fully Automated
9.6.2 Human-in-the-Loop
9.6.3 Model Assisted
9.7 Benefits of AutoML
9.8 Challenges and Limitations
9.9 Conclusion
References
10. Plant Disease Identification Using Extended-EfficientNet Deep Learning Model in Smart Farming
K. Sathya, K. Kanmani, M. Revathy Meenal, D. Suganthi and T. S. Lakshmi
10.1 Introduction
10.1.1 Obstacles in the Agricultural Sector
10.1.1.1 Soil Erosion
10.1.1.2 Absence of High-Quality Seeds
10.1.1.3 Lack of Contemporary Farming Machinery
10.1.2 Challenges of AI in Agriculture
10.1.3 Existing Plant Disease Identification Methods
10.2 Literature Review
10.3 Materials and Methods
10.3.1 Dataset
10.3.2 Existing CNN Models
10.3.2.1 AlexNet
10.3.2.2 VGG16
10.3.2.3 ResNet50
10.3.2.4 Inception V3
10.4 Methodology—E-ENet
10.4.1 Localization of the Leaf
10.4.2 Segmentation of Leaf Image
10.4.3 The Diseased Leaf Identification
10.5 Experimental Analysis
10.5.1 The Acquisition of Data
10.5.2 The Parameter Setup
10.5.2.1 The Configuration of Parameters for Leaf Localization
10.5.2.2 The Configuration of Parameters for Leaf Segmentation
10.5.2.3 The Configuration of Parameters for Leaf Retrieval
10.6 Results
10.6.1 The Leaf Localization Outcome
10.6.2 The Outcomes of Leaf Segmentation
10.6.3 The Result of Disease Identification
10.7 Comparative Test
10.8 Summary
References
11. AutoML-Driven Deep Learning for Fake Currency Recognition
T. Bhaskar and E. Gangadevi
11.1 Introduction
11.2 Literature Review
11.2.1 Scope
11.2.2 Objectives
11.3 Proposed System
11.4 Methodology
11.5 Convolutional Neural Network
11.6 Analysis Modeling
11.6.1 Behavioral Modeling
11.7 Software Testing
11.7.1 Types of Testing
11.7.2 Test Cases
11.8 Results and Discussions
11.9 Conclusion
References
12. Blockchain and Automated Machine Learning-Based Advancements for Banking and Financial Sectors
K. Santhi, M. Lawanya Shri, Pranesh L., Dhanush T. and Suneel P.V.
12.1 Introduction
12.2 Understanding Blockchain and AutoML
12.3 Need of Blockchain
12.4 Synergies Between Blockchain and AutoML
12.5 Applications in Banking and Finance
12.6 Applications of AutoML in Industries
12.7 Case Studies and Real-World Applications
12.8 Blockchain in Finance
12.9 Real-World Examples and Case Studies
12.10 Benefits and Challenges
12.11 Discussion
12.12 Limitations
12.13 Recommendations for Implementation
12.14 Ethical Considerations and Responsible AI
12.15 Future Directions and Emerging Trends
12.16 Future Scope
12.17 Conclusion
References
13. Advances in Automated Machine Learning for Precision Healthcare and Biomedical Discoveries
Aryan Chopra, Lawanya Shri M. and Santhi K.
13.1 Introduction
13.1.1 Some of the Recent Publications and their Findings
13.2 Current Day Usage of AI
13.2.1 Deep Learning and Neural Networks
13.2.2 Natural Language Processing (NLP)
13.2.2.1 Clinical Documentation
13.2.2.2 Disease Prediction
13.2.2.3 Chatbots and Virtual Assistants
13.2.2.4 Report Analysis
13.2.3 Automation
13.2.3.1 Appointment Scheduling
13.2.3.2 Medication Dispensary
13.2.3.3 Robotic Surgeries
13.2.3.4 Inventory Management
13.3 Data Management and Security in Healthcare AI
13.3.1 Data Acquisition and Storage
13.3.2 Data Processing and Analysis
13.3.3 Data Protection and Privacy
13.3.4 Balancing Technological Advancements with Data Governance
13.3.5 The Evolving Role of AI in Data Security
13.3.6 Continuous Education in Data Management and AI
13.3.7 Preparing for the Future of Healthcare AI and Data Management
13.4 Challenges in Integrating AI into Healthcare Systems
13.4.1 Technical Challenges
13.4.2 Operational Challenges
13.4.3 Data Standardization and Interoperability
13.4.4 Ethical and Legal Challenges
13.4.5 Financial and Resource Constraints
13.5 Challenges and Ethical Concerns
13.5.1 Privacy and Data Security
13.5.2 Bias and Fairness
13.5.3 Doctor–Patient Relationship
13.5.4 Accountability and Liability
13.6 Case Study
13.6.1 PharmEasy
13.6.2 Qure.ai
13.7 Implementing AutoML Techniques
13.8 Conclusion
References
14. Democratizing Machine Learning: The Rise of Automated Machine Learning (AutoML)
Debarati Dutta and Priya G.
14.1 Introduction
14.2 Flow of AutoML
14.3 AutoML Components
14.4 Application
14.5 Future Scope
14.6 Conclusion
References
15. Open-Source Tools in Automated Machine Learning
Malaserene I., K. Santhi and M. Lawanya Shri
Introduction
AutoGluon
Strengths of AutoGluon
Auto-Sklearn
TPOT (Tree-Based Pipeline Optimization Tool)
AutoML-Docker
AutoKeras
H2O
Light AutoML
MLBox
Pycaret
MLJAR
Application of AutoML and XAI
Automated Traffic Analysis
AutoML for Genome-Wide Association Studies
Development of Prediction Model for Diesel Engines
Conclusion
References
Index

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