Main Reference PaperA Double Deep Q-learning Model for Energy-efficient Edge Scheduling, IEEE Transactions on Services Computing, 2018 [R]
  • To alleviate the energy consumption issue of edge computing devices, the work introduces a double deep Q-learning model. It exploits the Rectified Linear Units (ReLU) as the activation function to eliminate the vanishing gradient issue.

Description
  • To alleviate the energy consumption issue of edge computing devices, the work introduces a double deep Q-learning model. It exploits the Rectified Linear Units (ReLU) as the activation function to eliminate the vanishing gradient issue.

  • To mitigate the consumption of energy.

  • To overcome the vanishing gradient issue

Aim & Objectives
  • To mitigate the consumption of energy.

  • To overcome the vanishing gradient issue

  • To develop the system with the tensor-train network and the tensor decomposition enhances the efficiency of the system.

Contribution
  • To develop the system with the tensor-train network and the tensor decomposition enhances the efficiency of the system.

  • 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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