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Introduction

The emergence of Edge AI technology has revolutionized the field of real-time analytics by enabling data processing and analysis to be performed closer to the source of data, thereby reducing latency and increasing efficiency. This thesis focuses on exploring the potential of Edge AI for real-time analytics, with a particular emphasis on its applications and benefits in various industries such as finance, healthcare, and manufacturing.

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 Two: Literature Review
2.1 Introduction to Edge AI
2.2 Real-time Analytics and its Importance
2.3 Edge Computing and its Evolution
2.4 Use Cases of Edge AI in Various Industries
2.5 Challenges and Limitations of Edge AI for Real-time Analytics
2.6 Comparison of Edge AI vs. Cloud Computing
2.7 Recent Advances in Edge AI Technology
2.8 Tools and Platforms for Implementing Edge AI
2.9 Security and Privacy Concerns in Edge AI
2.10 Future Trends in Edge AI for Real-time Analytics

Chapter Three: System Design and Methodology
3.1 Overview of System Architecture
3.2 Data Collection and Preprocessing
3.3 Machine Learning Models for Real-time Analytics
3.4 Deployment of AI Models on Edge Devices
3.5 Performance Evaluation Metrics
3.6 Data Visualization Techniques
3.7 Edge AI Infrastructure Setup
3.8 Integration with Cloud-based Systems

Chapter Four: System Implementation
4.1 Selection of Hardware and Software Components
4.2 Data Acquisition and Storage
4.3 Model Training and Optimization
4.4 Model Inference and Prediction
4.5 Integration with Existing Systems
4.6 Testing and Validation
4.7 Performance Tuning and Optimization
4.8 Documentation and User Training

Chapter Five: Conclusion and Summary
5.1 Summary of Findings
5.2 Contributions of the Study
5.3 Implications for Future Research
5.4 Practical Applications and Recommendations
5.5 Conclusion

Thesis Overview:

Edge AI has emerged as a disruptive technology that leverages the power of artificial intelligence and edge computing to enable real-time analytics at the network edge. This thesis explores the potential of Edge AI for real-time analytics and its applications in various industries. The literature review provides insights into the evolution of Edge AI, its benefits, challenges, and recent advances in the field. The system design and methodology chapter outline the architecture, data processing, machine learning models, and performance metrics for effective real-time analytics. The system implementation chapter details the hardware and software setup, model deployment, testing, and optimization strategies. The conclusion chapter summarizes the findings, contributions, and future research directions in the area of Edge AI for real-time analytics.

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