Optimization of Computer Network Performance Using Machine Learning
Table Of Contents
- <p>
- 1.Introduction<br>
- 1.1Background<br>
- 1.2Objectives<br>
- 1.3Scope<br>
- 2.Literature Review<br>
- 2.1Network Performance Metrics and Challenges<br>
- 2.2Machine Learning for Network Optimization<br>
- 2.3Data Preprocessing for Network Traffic Analysis<br>
- 2.4Model Evaluation for Network Performance<br>
- 3.Network Data Collection and Preprocessing<br>
- 3.1Data Sources and Collection Methods<br>
- 3.2Feature Extraction and Selection<br>
- 3.3Data Normalization and Transformation<br>
- 4.Machine Learning Model Development<br>
- 4.1Anomaly Detection Models<br>
- 4.2Predictive Performance Optimization Models<br>
- 4.3Model Training and Validation<br>
- 5.Testing and Evaluation<br>
- 5.1Performance Metrics<br>
- 5.2Model Accuracy and Efficiency<br>
- 5.3Real-world Network Performance Testing<br></p>
Project Abstract
<p> This project focuses on the optimization of computer network performance through the application of machine learning techniques. The project aims to develop predictive models that can analyze network traffic patterns, identify anomalies, and proactively optimize network resources to improve overall performance, reliability, and security. The project will explore different machine learning algorithms, data preprocessing techniques, and model evaluation methods to achieve efficient network management. <br></p>Project Overview
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