Main Reference PaperA novel fuzzy deep-learning approach to traffic flow prediction with uncertain spatial–temporal data features, Future Generation Computer Systems, 2018 [R].
  • The framework of fuzzy logic and the deep learning methods proposed for the traffic flow forecasting that helps to resolve the uncertainty related issues. It explores the spatiotemporal features belongs to the traffic flow through the tensor data representation to improve accuracy.

Description
  • The framework of fuzzy logic and the deep learning methods proposed for the traffic flow forecasting that helps to resolve the uncertainty related issues. It explores the spatiotemporal features belongs to the traffic flow through the tensor data representation to improve accuracy.

  • To improve the accuracy of traffic flow forecasting

  • To overcome the shortcomings of shallow methods.

Aim & Objectives
  • To improve the accuracy of traffic flow forecasting

  • To overcome the shortcomings of shallow methods.

  • The system is developed using the reinforcement learning based on the external factor to improve the performance.

Contribution
  • The system is developed using the reinforcement learning based on the external factor to 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.

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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