Main Reference PaperDynamic Talent Flow Analysis with Deep Sequence Prediction Modeling, IEEE transactions on knowledge and data engineering, 2018 [Java/Python/R]
  • The framework of talent flow prediction integrates the several sources of data for forecasting the forthcoming talent flow. It comprises the two phases referred to formalization phase and prediction phase. The Dynamic Job Transition Network (DJTN) is built using the job trajectories in the formalization phase. It exploits the presented the Deep Sequence Prediction (DSP) to forecast the forthcoming talent flow.

+ Description
  • The framework of talent flow prediction integrates the several sources of data for forecasting the forthcoming talent flow. It comprises the two phases referred to formalization phase and prediction phase. The Dynamic Job Transition Network (DJTN) is built using the job trajectories in the formalization phase. It exploits the presented the Deep Sequence Prediction (DSP) to forecast the forthcoming talent flow.

  • To forecast the talent flow based on deep neural networks

  • To yield reasonable performance

+ Aim & Objectives
  • To forecast the talent flow based on deep neural networks

  • To yield reasonable performance

  • This work further enhanced by including the additional sources such as job description and job salary.

+ Contribution
  • This work further enhanced by including the additional sources such as job description and job salary.

  • 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.

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