Optimization of Computer Network Performance Using Machine Learning
Table Of Contents
<p>1. Introduction<br> 1.1 Background<br> 1.2 Objectives<br> 1.3 Scope<br>2. Literature Review<br> 2.1 Network Performance Metrics and Challenges<br> 2.2 Machine Learning for Network Optimization<br> 2.3 Data Preprocessing for Network Traffic Analysis<br> 2.4 Model Evaluation for Network Performance<br>3. Network Data Collection and Preprocessing<br> 3.1 Data Sources and Collection Methods<br> 3.2 Feature Extraction and Selection<br> 3.3 Data Normalization and Transformation<br>4. Machine Learning Model Development<br> 4.1 Anomaly Detection Models<br> 4.2 Predictive Performance Optimization Models<br> 4.3 Model Training and Validation<br>5. Testing and Evaluation<br> 5.1 Performance Metrics<br> 5.2 Model Accuracy and Efficiency<br> 5.3 Real-world Network Performance Testing<br></p>
Project Abstract
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.
Project Overview
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