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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

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.

Library	Material Type	Item Barcode	Shelf Number	[[missing key: search.ChildField.HOLDING]]	Status
Online Library	E-Book	600063-1001	Q325.5 .A98 2025		Wiley E-Kitap Koleksiyonu