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Introduction

Smart contracts have gained popularity in recent years due to their ability to automate and facilitate secure transactions on blockchain platforms. In the field of machine learning, smart contracts can be used to automate the process of creating, training, and deploying machine learning models. This thesis explores the use of smart contracts for automated machine learning, aiming to improve the efficiency, transparency, and security of machine learning processes.

1.1. Introduction
1.2. Background of Study
1.3. Problem Statement
1.4. Objective of Study
1.5. Limitation of Study
1.6. Scope of Study
1.7. Significance of Study
1.8. Structure of the Thesis
1.9. Definition of Terms

Chapter 1: Introduction
– Introduction
– Background of Study
– Problem Statement
– Objective of Study
– Limitation of Study
– Scope of Study
– Significance of Study
– Structure of the Thesis
– Definition of Terms

Chapter 2: Literature Review
– Overview of Smart Contracts
– Overview of Machine Learning
– Smart Contracts in Machine Learning
– Automation in Machine Learning
– Blockchain Technology
– Smart Contracts and Security
– Existing Systems in Automated Machine Learning
– Challenges in Automated Machine Learning
– Opportunities in Smart Contracts for Machine Learning
– Future Trends

Chapter 3: System Design and Methodology
– System Architecture
– Smart Contract Design
– Data Acquisition and Preprocessing
– Model Training and Evaluation
– Model Deployment
– Integration with Blockchain Platform
– Security Considerations
– Testing and Validation

Chapter 4: System Implementation
– Implementation of Smart Contracts
– Implementation of Machine Learning Algorithms
– Deployment on Blockchain Platform
– Performance Evaluation
– Case Studies
– Use Cases
– Challenges Faced
– Lessons Learned

Chapter 5: Conclusion and Summary
– Summary of Findings
– Contributions of the Study
– Future Research Directions
– Conclusion

Thesis Overview on Smart Contracts for Automated Machine Learning

Smart contracts have revolutionized the way transactions are conducted on blockchain platforms, ensuring transparency, security, and efficiency. In the field of machine learning, automation is key to improving the speed and accuracy of creating and deploying models. This thesis explores the use of smart contracts for automated machine learning, aiming to streamline the process and enhance the security of machine learning operations.

Chapter 1 provides an introduction to the topic, outlining the background of the study, the problem statement, objectives, limitations, scope, significance, and structure of the thesis. Chapter 2 conducts a thorough literature review on smart contracts, machine learning, automation, blockchain technology, and existing systems in automated machine learning. Chapter 3 delves into the system design and methodology, detailing the system architecture, smart contract design, data acquisition, model training, deployment, integration with blockchain platforms, security considerations, and testing.

Chapter 4 focuses on system implementation, covering the implementation of smart contracts, machine learning algorithms, deployment on blockchain platforms, performance evaluation, case studies, use cases, challenges faced, and lessons learned. Finally, Chapter 5 provides a conclusion and summary of the findings, highlighting the contributions of the study, suggesting future research directions, and wrapping up the thesis. Through this research, the potential of smart contracts for automated machine learning is explored to optimize machine learning processes and ensure secure and efficient transactions.

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