Main Reference PaperParallel computing method of deep belief networks and its application to traffic flow prediction, Knowledge-Based Systems, 2019.[Python]
  • The proposed master-slave parallel computing model effectively learn the traffic flow by utilizing the deep belief network. In the proposed method, the slave computing node captures and learns the features from the sub dataset and fed as input to the master computing node that synthesizes the features as long as learning is completed.

Description
  • The proposed master-slave parallel computing model effectively learn the traffic flow by utilizing the deep belief network. In the proposed method, the slave computing node captures and learns the features from the sub dataset and fed as input to the master computing node that synthesizes the features as long as learning is completed.

  • To forecast the traffic flow

  • To acquire entire features within the dataset

Aim & Objectives
  • To forecast the traffic flow

  • To acquire entire features within the dataset

  • The integration of the proposed system with the feature scaling technique will improve the performance

Contribution
  • The integration of the proposed system with the feature scaling technique will improve the performance

  • 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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