LEADER 01356nam--2200397---450- 001 990002080000203316 005 20090724123404.0 010 $a88-14-10627-4 035 $a000208000 035 $aUSA01000208000 035 $a(ALEPH)000208000USA01 035 $a000208000 100 $a20041014d2003----km-y0enga50------ba 101 $aita 102 $aIT 105 $a||||||||001yy 200 1 $aFormulario ragionato della nuova s.r.l.$eschemi e modelli per tutte le operazioni societarie illustrate con la recente legge di riforma$fGiuseppe Stassano, Matteo Stassano 210 $aMilano$cA. Giuffrè$d2003 215 $aXIII, 239 p.$d24 cm$e1 CD-ROM 225 2 $aCosa & come$iSocietà 410 0$12001$aCosa & come$iSocietà 606 0 $aSocietà a responsabilità limtata 676 $a346.450668 700 1$aSTASSANO,$bGiuseppe$0105680 701 1$aSTASSANO,$aMatteo$0253001 801 0$aIT$bsalbc$gISBD 912 $a990002080000203316 951 $aXXV.3.E 290 (IG II 1017)$b42517 G.$cXXV.3.E 290 (IG II)$d00136112 959 $aBK 969 $aGIU 979 $aALINA$b90$c20041014$lUSA01$h0924 979 $aMARIASEN$b90$c20050309$lUSA01$h1510 979 $aRSIAV3$b90$c20090724$lUSA01$h1121 979 $aRSIAV3$b90$c20090724$lUSA01$h1234 996 $aFormulario ragionato della nuova s.r.l$91042090 997 $aUNISA 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