Automated machine learning and industrial applications /

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...

Full description

Bibliographic Details
Other Authors:	Gangadevi, E. (Editor)
Format:	eBook
Language:	English
Published:	Hoboken, NJ : Beverly, MA : John Wiley & Sons, Inc. ; Scrivener Publishing LLC, 2025.
Subjects:	Machine learning. Electronic books.
Online Access:	Connect to the full text of this electronic book

Table of Contents:

Cover
Series Page
Title Page
Copyright Page
Contents
Preface
Chapter 1 Design and Architecture of AutoML for Data Science in Next-Generation Industries
1.1 Introduction
1.2 Modular Design
1.3 Data Handling
1.4 Model Training and Selection
Conclusion
References
Chapter 2 Automated Machine Learning Model in Secure Data Transmission in Sustainable Healthcare Sensor Network Using Quantum Blockchain Architecture
2.1 Introduction
2.2 Related Works
2.3 Proposed Model
2.4 Results and Discussion
2.5 Conclusion
References
Chapter 3 Automated Machine Learning in the Biological and Medical Healthcare Industries: Analysis Interpretation and Evaluation
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
Chapter 4 Advancements in AI and AutoML for Plant Leaf Disease Identification in Sustainable Agriculture
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
Chapter 5 Predictive Maintenance in Industrial Settings: Video Analytics at the Edge with AutoML
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