Main Reference PaperMultiple Attributes QoS Prediction via Deep Neural Model with Contexts, IEEE Transactions on Service Computing, 2018 [Java/J2EE]
  • A multi-layered neural network is proposed for context-aware multi-attributes QoS prediction. When training a multi-layered neural network, the continuous changes of parameters in previous layers lead to subsequent changes at each successive layer. In the task-specific layer, the candidate services are ranked using response-time, throughput, and reliability.

Description
  • A multi-layered neural network is proposed for context-aware multi-attributes QoS prediction. When training a multi-layered neural network, the continuous changes of parameters in previous layers lead to subsequent changes at each successive layer. In the task-specific layer, the candidate services are ranked using response-time, throughput, and reliability.

  • To achieve the context and multiple attributes based QoS prediction.

  • To improve the prediction accuracy.

Aim & Objectives
  • To achieve the context and multiple attributes based QoS prediction.

  • To improve the prediction accuracy.

  • To improve the prediction accuracy of QoS data is preprocessed.

Contribution
  • To improve the prediction accuracy of QoS data is preprocessed.

  • Operating system: Ubuntu / Windows

  • Language: Java/J2EE

Software Tools & Technologies
  • Operating system: Ubuntu / Windows

  • Language: Java/J2EE

  • 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.

Leave Comment

Your email address will not be published. Required fields are marked *

clear formSubmit