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Introduction:

The advancement of Artificial Intelligence (AI) has revolutionized various industries, including industrial safety monitoring. Edge AI, which involves performing AI algorithms on devices at the edge of the network, has emerged as a promising technology for enhancing safety monitoring in industrial settings. This thesis aims to explore the application of Edge AI in industrial safety monitoring, with a focus on its benefits, challenges, and implications.

Chapter 1: Introduction
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 2: Literature Review
2.1 Overview of AI in industrial safety monitoring
2.2 Edge computing and its applications in safety monitoring
2.3 Challenges and limitations of traditional safety monitoring systems
2.4 Case studies of Edge AI implementation in industrial safety monitoring
2.5 Benefits of using Edge AI for safety monitoring
2.6 Comparison of Edge AI with cloud-based AI systems
2.7 Security and privacy considerations in Edge AI for safety monitoring
2.8 Future trends in Edge AI for industrial safety monitoring
2.9 Regulatory and compliance issues related to Edge AI implementation
2.10 Training and education requirements for Edge AI adoption in industrial safety monitoring

Chapter 3: Research Methodology
3.1 Research design
3.2 Data collection methods
3.3 Data analysis techniques
3.4 Sampling techniques
3.5 Ethical considerations
3.6 Research limitations
3.7 Pilot testing
3.8 Validity and reliability of research findings

Chapter 4: Discussion of Findings
4.1 Analysis of data collected
4.2 Comparison of findings with existing literature
4.3 Implications of research findings
4.4 Recommendations for future research
4.5 Practical implications for industry stakeholders
4.6 Challenges and limitations encountered during the research process
4.7 Opportunities for further exploration in the field of Edge AI for industrial safety monitoring
4.8 Contribution of the study to the existing body of knowledge

Chapter 5: Conclusion and Summary
5.1 Summary of key findings
5.2 Conclusions drawn from the research
5.3 Practical implications for industrial safety monitoring
5.4 Recommendations for industry stakeholders
5.5 Future research directions
5.6 Reflections on the research process

Thesis Overview:

The industrial sector is constantly looking for innovative technologies to enhance safety monitoring practices. Edge AI presents a unique opportunity to revolutionize safety monitoring by enabling real-time data processing at the edge of the network. This thesis explores the application of Edge AI in industrial safety monitoring, addressing key issues such as benefits, challenges, and implications. Through a thorough literature review, research methodology, and discussion of findings, this thesis aims to provide valuable insights for industry stakeholders and researchers interested in leveraging Edge AI for improved safety monitoring practices. The potential impact of Edge AI on industrial safety monitoring is significant, and this thesis contributes to the growing body of knowledge in this field.

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