LEADER 04374nam 22006135 450 001 9910866580003321 005 20240620125246.0 010 $a9798868803543$b(electronic bk.) 010 $z9798868803536 024 7 $a10.1007/979-8-8688-0354-3 035 $a(MiAaPQ)EBC31497573 035 $a(Au-PeEL)EBL31497573 035 $a(CKB)32320329400041 035 $a(DE-He213)979-8-8688-0354-3 035 $a(OCoLC)1441721196 035 $a(OCoLC-P)1441721196 035 $a(CaSebORM)9798868803543 035 $a(EXLCZ)9932320329400041 100 $a20240620d2024 u| 0 101 0 $aeng 135 $aurcnu|||||||| 181 $ctxt$2rdacontent 182 $cc$2rdamedia 183 $acr$2rdacarrier 200 10$aMachine Learning For Network Traffic and Video Quality Analysis $eDevelop and Deploy Applications Using JavaScript and Node.js /$fby Tulsi Pawan Fowdur, Lavesh Babooram 205 $a1st ed. 2024. 210 1$aBerkeley, CA :$cApress :$cImprint: Apress,$d2024. 215 $a1 online resource (475 pages) 300 $aDescription based upon print version of record. 300 $a3.2.5 AccepTV Video Quality Monitor 311 08$aPrint version: Fowdur, Tulsi Pawan Machine Learning for Network Traffic and Video Quality Analysis Berkeley, CA : Apress L. P.,c2024 9798868803536 327 $aChapter 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. 330 $aThis 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? . 606 $aMachine learning 606 $aArtificial intelligence 606 $aProgramming languages (Electronic computers) 606 $aMachine Learning 606 $aArtificial Intelligence 606 $aProgramming Language 615 0$aMachine learning. 615 0$aArtificial intelligence. 615 0$aProgramming languages (Electronic computers). 615 14$aMachine Learning. 615 24$aArtificial Intelligence. 615 24$aProgramming Language. 676 $a006.31 700 $aFowdur$b Tulsi Pawan$01769543 701 $aBabooram$b Lavesh$01769544 801 0$bMiAaPQ 801 1$bMiAaPQ 801 2$bMiAaPQ 912 $a9910866580003321 996 $aMachine Learning For Network Traffic and Video Quality Analysis$94241127 997 $aUNINA