Title of article :
A novel deep learning framework for improving the quality of services using block chain technology
Author/Authors :
Parthiban, P. Department of CSE - IFET Colleg of Engineering, Villupuram, India , Santhosh Kumar, K. Department of IT - Annamalai University, Chidambaram, India
Abstract :
Electronic Health Record (EHR) holds immensely sensitive information and consisting of crucial
data related to the patients. Storing and organising such data is highly arduous task. Researches
still going on to improve the Quality of Service (QOS) of such data. Existing study focused only on
improving the system throughput, privacy and latency issues. But they did not tend to scrutinize
the scalability and privacy of such data records. In this paper, we propose a novel deep learning
framework to improve the Quality of Service using block chain technology. Initially the data is
classified into high priority and low priority based on its nature by using Recurrent Neural Network
(RNN). Then, the classified high priority data is further allowed to each block of a block chain and
the low priority data is stored and maintained as log file. Finally, the results are compared based
on the evaluation metrics which demonstrates our proposed novel deep learning framework achieves
better accuracy.
Keywords :
Electronic Health Record (EHR) , Quality of Service (QOS) , Recurrent Neural Network (RNN) , Block chain
Journal title :
International Journal of Nonlinear Analysis and Applications