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Table of Contents:

Chapter 1: Introduction
1.1 Background of the Study
1.2 Statement of the Problem
1.3 Research Questions
1.4 Objectives of the Study
1.5 Significance of the Study
1.6 Scope and Limitations of the Study

Chapter 2: Literature Review
2.1 Definition of Edge Computing
2.2 Overview of Edge AI Applications
2.3 Advances in Edge Computing Technologies
2.4 Challenges and Opportunities in Edge AI Applications
2.5 Previous Studies on Edge Computing for Edge AI Applications

Chapter 3: Research Methodology
3.1 Research Design
3.2 Data Collection Methods
3.3 Data Analysis Techniques
3.4 Sampling Strategy
3.5 Ethical Considerations

Chapter 4: Discussion of Findings
4.1 Analysis of Data
4.2 Comparison with Existing Literature
4.3 Implications of Findings
4.4 Recommendations for Future Research

Chapter 5: Conclusion and Summary
5.1 Summary of Findings
5.2 Conclusion
5.3 Recommendations for Practitioners

Brief Overview on Edge Computing for Edge AI Applications:

Edge computing is a distributed computing paradigm that brings computation and data storage closer to the location where it is needed, to improve response time and save bandwidth. Edge AI applications refer to artificial intelligence algorithms and models that are deployed at the edge of the network, enabling real-time decision-making and processing of data.

The combination of edge computing and AI has revolutionized the way data is processed and analyzed in various industries such as healthcare, manufacturing, and transportation. With the proliferation of IoT devices and the increasing demand for real-time processing, edge computing for edge AI applications has gained significant attention in recent years.

This brief overview will explore the basics of edge computing and its applications in AI, discuss the current trends and challenges in the field, and provide insights into the potential future developments in edge computing for edge AI applications.

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