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An Efficient Method to Classify the Peer-to-Peer Network Videos and Video Servers Over Video on Demand Services

  • M. NarayananEmail author
Conference paper
Part of the Lecture Notes in Networks and Systems book series (LNNS, volume 65)

Abstract

As one of the wildest emerging technologies, P2P has attracted attention on live streaming and VoD. Video plays an energetic part in communication and any kind of relaxing activity for entertainment. In order to provide reasonable service across all seasons, two machine learning techniques are used where the availability of server depends on the hits across the season. Popular videos are sorted out based on the greatest number of hits primarily and the recovery phase selects solitary or many similar cases from the preceding popularity videos that are stored. The updated/modified video records are reused as per query. In the revised phase, the present popularity record is updated. Finally, the updated popularity records are preserved in the retaining phase. Application of AODE algorithm results in grouping video server as seasonal and nonseasonal. The content of the video is categorized on the basis of the hearer’s test at the commencing remains additionally scrutinized emerging in 90% clarity of classification.

Keywords

Average estimators Reasoning based on case Categorizing video server Peer-to-Peer networks Seasonal-based videos Video on Demand 

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Copyright information

© Springer Nature Singapore Pte Ltd. 2019

Authors and Affiliations

  1. 1.Department of Computer Science and EngineeringMalla Reddy College of EngineeringSecunderabad, HyderabadIndia

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