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Introduction

Modern computer networks are becoming increasingly complex and mission-critical, with the demand for high performance and reliability continuing to grow. As the number of connected devices and the volume of data traffic increase, traditional network management approaches are struggling to keep pace. In response to this challenge, researchers and industry practitioners are exploring the use of Artificial Intelligence (AI) solutions to enhance network performance.

This thesis focuses on the application of AI-powered solutions to optimize network performance. By leveraging AI technologies such as machine learning and deep learning, network administrators can automate routine tasks, detect anomalies, predict network failures, and optimize resource allocation, leading to improved network efficiency, reliability, and security.

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 Networking
2.2 Machine Learning Algorithms for Network Optimization
2.3 Deep Learning Techniques for Network Performance
2.4 AI-powered Network Security Solutions
2.5 AI-driven Network Management Tools
2.6 Case Studies on AI in Network Performance Enhancement
2.7 Challenges and Opportunities in AI for Networking
2.8 Current Trends in AI-powered Network Optimization
2.9 Ethical and Privacy Considerations in AI-driven Networking
2.10 Future Directions in AI for Network Performance Improvement

Chapter 3: System Design and Methodology
3.1 Research Framework
3.2 Data Collection and Preprocessing
3.3 AI Model Selection
3.4 Training and Testing Procedures
3.5 Performance Evaluation Metrics
3.6 Integration with Existing Network Infrastructure
3.7 Scalability and Deployment Considerations
3.8 Ethical and Legal Compliance Measures

Chapter 4: System Implementation
4.1 Data Acquisition and Storage
4.2 AI Model Development
4.3 Integration with Network Monitoring Tools
4.4 Performance Testing and Optimization
4.5 Resource Allocation and Management
4.6 Security Integration
4.7 User Interface Design and Usability Testing
4.8 Documentation and Knowledge Transfer

Chapter 5: Conclusion and Summary
5.1 Summary of Findings
5.2 Contributions to the Field
5.3 Implications for Practice
5.4 Limitations and Future Research Directions
5.5 Concluding Remarks

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