Machine learning for network traffic and video quality analysis : develop and deploy applications using JavaScript and Node. js / Tulsi Pawan Fowdur, Lavesh Babooram
- Author
- Fowdur, Tulsi Pawan
- Published
- Berkeley, CA : Apress L. P., 2024.
- Physical Description
- 1 online resource (xiii, 465 pages) : illustrations
- Additional Creators
- Babooram, Lavesh
Access Online
- Contents
- Intro -- Table of Contents -- About the Authors -- About the Technical Reviewer -- Chapter 1: Introduction -- 1.1 Overview of Network Traffic Monitoring and Analysis -- 1.1.1 Importance of NTMA -- 1.1.2 Key Objectives of NTMA -- 1.1.3 Network Traffic Components -- 1.1.4 NTMA Techniques and Methodologies -- 1.1.5 Challenges of NTMA -- 1.1.6 Use Cases of NTMA -- 1.1.7 Emerging Trends in NTMA -- 1.1.8 Bridging the Gap between NTMA and User Experience -- 1.2 Overview of Video Quality Assessment -- 1.2.1 Significance of VQA -- 1.2.2 Factors Affecting Video Quality, 1.2.3 Evolution of VQA Approaches -- 1.2.4 Real-World Applications of VQA -- 1.2.5 Challenges in VQA -- 1.2.6 Emerging Trends in VQA -- 1.3 Machine Learning in JavaScript -- 1.3.1 Introduction to Machine Learning -- 1.3.2 Coupling JavaScript with Machine Learning -- 1.3.3 Data Preparation and Preprocessing in JavaScript -- 1.3.4 Supervised Learning with JavaScript -- 1.3.5 Unsupervised Learning with JavaScript -- 1.3.6 Deep Learning in JavaScript -- 1.3.7 Deploying Machine Learning Models in Web Applications -- 1.4 Node.js and Networking -- 1.5 Book Overview -- 1.6 References - Chapter 1, Chapter 2: Network Traffic Monitoring and Analysis -- 2.1 NTMA Fundamentals -- 2.1.1 Data Sources and Collection -- 2.1.2 Key Metrics -- 2.1.3 Data Preprocessing and Cleaning -- 2.1.4 Network Topology and Architecture -- 2.1.5 Data-Driven Analytics -- 2.1.6 Supervised Learning for Traffic Classification -- 2.1.7 Unsupervised Learning for Anomaly Detection -- 2.1.8 Predictive Analytics -- 2.1.9 Real-time AI-Based Decision Support -- 2.2 Existing NTMA Applications -- 2.2.1 SolarWinds NetFlow Traffic Analyzer -- 2.2.2 Paessler PRTG Network Monitor -- 2.2.3 Wireshark, 2.2.4 ManageEngine NetFlow Analyzer -- 2.2.5 Site24x7 Network Monitoring -- 2.2.6 Prometheus -- 2.2.7 Commercial vs. Open-Source Solutions -- 2.2.8 Challenges and Considerations -- 2.3 State-of-the-Art Review of NTMA -- 2.3.1 Background of NTMA -- 2.3.2 The Rise of Machine Learning -- 2.3.3 Machine Learning Algorithms to Classify Network Traffic -- 2.3.4 Machine Learning Algorithms to Predict Network Traffic -- 2.4 Summary -- 2.5 References - Chapter 2 -- Chapter 3: Video Quality Assessment -- 3.1 VQA Fundamentals -- 3.1.1 Video Quality Metrics -- 3.1.2 Human Perception in Video Quality, and 3.1.3 Video Quality Attributes -- 3.1.4 The Optimal VQA Strategy -- 3.1.5 Quality of Experience (QoE) Metrics -- 3.1.6 Quality of Service (QoS) Metrics -- 3.1.7 Quality of Performance (QoP) Metrics -- 3.1.8 Subjective VQA -- 3.1.9 Objective VQA -- 3.1.10 Quality Metrics for Network, Video, and Streaming -- 3.1.11 Video Quality Databases and Benchmarking -- 3.1.12 Temporal and Spatial Considerations in VQA -- 3.1.13 VQA for Evolving Video Content -- 3.2 Existing VQA Applications -- 3.2.1 Sentry by Telestream -- 3.2.2 Real-Time Media Assessment (RTMA) by ThinkTel -- 3.2.3 Witbe -- 3.2.4 ViCue Soft
- Summary
- This book offers both theoretical insights and hands-on experience in understanding and building machine learning-based Network Traffic Monitoring and Analysis (NTMA) and Video Quality Assessment (VQA) applications using JavaScript. JavaScript provides the flexibility to deploy these applications across various devices and web browsers. The book begins by delving into NTMA, explaining fundamental concepts and providing an overview of existing applications and research within this domain. It also goes into the essentials of VQA and offers a survey of the latest developments in VQA algorithms. The book includes a thorough examination of machine learning algorithms that find application in both NTMA and VQA, with a specific emphasis on classification and prediction algorithms such as the Multi-Layer Perceptron and Support Vector Machine. The book also explores the software architecture of the NTMA client-server application. This architecture is meticulously developed using HTML, CSS, Node.js, and JavaScript. Practical aspects of developing the Video Quality Assessment (VQA) model using JavaScript and Java are presented. Lastly, the book provides detailed guidance on implementing a complete system model that seamlessly merges NTMA and VQA into a unified web application, all built upon a client-server paradigm. By the end of the book, you will understand NTMA and VQA concepts and will be able to apply machine learning to both domains and develop and deploy your own NTMA and VQA applications using JavaScript and Node.js. What You Will Learn What are the fundamental concepts, existing applications, and research on NTMA? What are the existing software and current research trends in VQA? Which machine learning algorithms are used in NTMA and VQA? How do you develop NTMA and VQA web-based applications using JavaScript, HTML, and Node.js? Who This Book Is For Software professionals and machine learning engineers involved in the fields of networking and telecommunications.
- Subject(s)
- ISBN
- 9798868803543 (electronic bk.)
9798868803536
9789798868801
9798868803 - Note
- Description based upon print version of record.
3.2.5 AccepTV Video Quality Monitor
View MARC record | catkey: 44802345