國立高雄大學圖資館 |

Language: English

Back

Machine learning for network traffic...

Babooram, Lavesh.

Machine learning for network traffic and video quality analysisdevelop and deploy applications using JavaScript and Node.js /

Record Type:	Electronic resources : Monograph/item
Title/Author:	Machine learning for network traffic and video quality analysisby Tulsi Pawan Fowdur, Lavesh Babooram.
Reminder of title:	develop and deploy applications using JavaScript and Node.js /
Author:	Fowdur, Tulsi Pawan.
other author:	Babooram, Lavesh.
Published:	Berkeley, CA :Apress :2024.
Description:	xiii, 465 p. :ill., digital ;24 cm.
Contained By:	Springer Nature eBook
Subject:	Computer networksManagement.
Online resource:	https://doi.org/10.1007/979-8-8688-0354-3
ISBN:	9798868803543$q(electronic bk.)

Machine learning for network traffic and video quality analysisdevelop and deploy applications using JavaScript and Node.js /
Fowdur, Tulsi Pawan.

Machine learning for network traffic and video quality analysisdevelop and deploy applications using JavaScript and Node.js /[electronic resource] :by Tulsi Pawan Fowdur, Lavesh Babooram. - Berkeley, CA :Apress :2024. - xiii, 465 p. :ill., digital ;24 cm.

Chapter 1: Introduction to NTMA and VQA -- Chapter 2: Network Traffic Monitoring and Analysis -- Chapter 3: Video Quality Assessment -- Chapter 4: Machine Learning Techniques for NTMA and VQA -- Chapter 5: NTMA Application with JavaScript -- Chapter 6: Video Quality Assessment Application Development with JavaScript -- Chapter 7: NTMA and VQA Integration.

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?

ISBN: 9798868803543$q(electronic bk.)

Standard No.: 10.1007/979-8-8688-0354-3doiSubjects--Uniform Titles:

Node.js.
Subjects--Topical Terms:

182253
Computer networks
--Management.

LC Class. No.: TK5105.5 / .F68 2024

Dewey Class. No.: 004.6

Machine learning for network traffic and video quality analysisdevelop and deploy applications using JavaScript and Node.js /
LDR:03313nmm a2200325 a 4500 001 658650
003 DE-He213
005 20240620125246.0
006 m d
007 cr nn 008maaau
008 240923s2024 cau s 0 eng d
020 $a 9798868803543$q(electronic bk.)
020 $a 9798868803536$q(paper)
024 7 $a 10.1007/979-8-8688-0354-3 $2 doi
035 $a 979-8-8688-0354-3
040 $a GP $c GP
041 0 $a eng
050 4 $a TK5105.5 $b .F68 2024
072 7 $a UYQM $2 bicssc
072 7 $a MAT029000 $2 bisacsh
072 7 $a UYQM $2 thema
082 0 4 $a 004.6 $2 23
090 $a TK5105.5 $b .F784 2024
100 1 $a Fowdur, Tulsi Pawan. $3 931005
245 1 0 $a Machine learning for network traffic and video quality analysis $h [electronic resource] : $b develop and deploy applications using JavaScript and Node.js / $c by Tulsi Pawan Fowdur, Lavesh Babooram.
260 $a Berkeley, CA : $b Apress : $b Imprint: Apress, $c 2024.
300 $a xiii, 465 p. : $b ill., digital ; $c 24 cm.
505 0 $a Chapter 1: Introduction to NTMA and VQA -- Chapter 2: Network Traffic Monitoring and Analysis -- Chapter 3: Video Quality Assessment -- Chapter 4: Machine Learning Techniques for NTMA and VQA -- Chapter 5: NTMA Application with JavaScript -- Chapter 6: Video Quality Assessment Application Development with JavaScript -- Chapter 7: NTMA and VQA Integration.
520 $a 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?
630 0 0 $a Node.js. $3 684234
650 0 $a Computer networks $x Management. $3 182253
650 0 $a Machine learning. $3 188639
650 0 $a JavaScript (Computer program language) $3 237616
650 1 4 $a Machine Learning. $3 833608
650 2 4 $a Artificial Intelligence. $3 212515
650 2 4 $a Programming Language. $3 913494
700 1 $a Babooram, Lavesh. $3 969890
710 2 $a SpringerLink (Online service) $3 273601
773 0 $t Springer Nature eBook
856 4 0 $u https://doi.org/10.1007/979-8-8688-0354-3
950 $a Professional and Applied Computing (SpringerNature-12059)