RETRACTED: Reinforcement Learning for Cloud Computing Digital Library

Lin Xu

doi:10.4028/www.scientific.net/AMM.571-572.105

Paper Titles

An Identity-Based Conditional Proxy Re-Encryption in Cloud Computing Environments
p.74

Fine-Grained Access Control with Efficient Revocation in Cloud Storage
p.79

Integrity and Fidelity Evaluation of Digital Evidence in Live Forensics
p.90

MANET-Based Stable Clustering Algorithm and its Performance Analysis
p.100

RETRACTED: Reinforcement Learning for Cloud Computing Digital Library
p.105

Research on Audit Model of Users’ Operation Normalization Based on Graph Theory in Power System
p.109

Research on Design and Construction Method of Bent Functions
p.114

A Novelty Model for Reliability Assessment of Complex System
p.118

A Multi-Attribute Decision-Making Method Based on SPAOWA Operator
p.124

HomeApplied Mechanics and MaterialsApplied Mechanics and Materials Vols. 571-572RETRACTED: Reinforcement Learning for Cloud...

RETRACTED: Reinforcement Learning for Cloud Computing Digital Library

Retracted:

This paper has been retracted by publisher.
This paper was found to be in violation of the scope and quality criteria. The document is now considered retracted. Due to strong violation, necessary effort should be made to remove all further references to this paper.
We regret any inconvenience this publication might cause you.

Abstract:

Retracted paper: This paper proposes a new framework of combining reinforcement learning with cloud computing digital library. Unified self-learning algorithms, which includes reinforcement learning, artificial intelligence and etc, have led to many essential advances. Given the current status of highly-available models, analysts urgently desire the deployment of write-ahead logging. In this paper we examine how DNS can be applied to the investigation of superblocks, and introduce the reinforcement learning to improve the quality of current cloud computing digital library. The experimental results show that the method works more efficiency.

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Info:

Periodical:

Applied Mechanics and Materials (Volumes 571-572)

Pages:

105-108

DOI:

https://doi.org/10.4028/www.scientific.net/AMM.571-572.105

Online since:

June 2014

Authors:

Lin Xu*

Keywords:

Bayesian Observation, Cloud Computing Digital Library, Data Exchange, Reinforce Learning

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RIS, BibTeX

Permissions:

Creative Commons CC BY 4.0

Citation:

* - Corresponding Author

References

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