Main Reference PaperSemi-supervised Deep Reinforcement Learning in Support of IoT and Smart City Services, IEEE Internet of Things Journal, June 2017 [Java]
  • To support smart city applications, a semi-supervised deep reinforcement learning model that consumes both labeled and unlabeled data by utilizing Variational Autoencoders (VAE) as the inference engine for generalizing optimal policies and learns the best action policies. Proposed approach is tested for indoor localization based on BLE signal strength.

+ Description
  • To support smart city applications, a semi-supervised deep reinforcement learning model that consumes both labeled and unlabeled data by utilizing Variational Autoencoders (VAE) as the inference engine for generalizing optimal policies and learns the best action policies. Proposed approach is tested for indoor localization based on BLE signal strength.

  • To extend deep reinforcement learning to the semi-supervised paradigm in order to improve the performance and accuracy of the learning agent.

  • To make a close estimation of the target locations with proposed model in indoor localization of smart city applications.

+ Aim & Objectives
  • To extend deep reinforcement learning to the semi-supervised paradigm in order to improve the performance and accuracy of the learning agent.

  • To make a close estimation of the target locations with proposed model in indoor localization of smart city applications.

  • A technique is contributed that improves the accuracy further.

+ Contribution
  • A technique is contributed that improves the accuracy further.

  • Java JDK 1.8, MySQL 5.5.40

  • Netbeans 8.0.1, & J2SE.

+ Software Tools & Technologies
  • Java JDK 1.8, MySQL 5.5.40

  • Netbeans 8.0.1, & J2SE.

  • 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-Depending on the complexity of the project and requirements.

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

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