Main Reference PaperLow-rank Multi-view Embedding Learning for Micro-video Popularity Prediction, IEEE transactions on knowledge and data engineering, 2018.
  • The proposed framework effectively forecasts the popularity of micro-video by exploiting the low-rank multi-view embedding and regression analysis methods. It exploits the augmented Lagrangian multiplier technique for the optimization purpose.

+ Description
  • The proposed framework effectively forecasts the popularity of micro-video by exploiting the low-rank multi-view embedding and regression analysis methods. It exploits the augmented Lagrangian multiplier technique for the optimization purpose.

  • To forecast the popularity of micro-video

  • To mitigate heterogeneous, interrelated, and noise issues.

+ Aim & Objectives
  • To forecast the popularity of micro-video

  • To mitigate heterogeneous, interrelated, and noise issues.

  • To construct the scalable learning model for the large-scale data

+ Contribution
  • To construct the scalable learning model for the large-scale data

  • Operating system: Ubuntu / Windows

  • Java/Python/R

+ Software Tools & Technologies
  • Operating system: Ubuntu / Windows

  • Java/Python/R

  • M.E / M.Tech/ MS / Ph.D.- Customized according to the client requirements.

+ Project Recommended For
  • M.E / M.Tech/ MS / Ph.D.- Customized according to the client requirements.

  • No Readymade Projects-project delivery Depending on the complexity of the project and requirements.

+ Order To Delivery
  • No Readymade Projects-project delivery Depending on the complexity of the project and requirements.

Professional Ethics: We S-Logix would appreciate the students those who willingly contribute with atleast a line of thinking of their own while preparing the project with us. It is advised that the project given by us be considered only as a model project and be applied with confidence to contribute your own ideas through our expert guidance and enrich your knowledge.

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