Maintaining Security and Privacy in Health Care System Using Learning Based Deep-Q-Networks.

Clicks: 200
ID: 39475
2018
Article Quality & Performance Metrics
Overall Quality
Not rated
Combines reader engagement with the AI quality analysis. This article has not been analysed, so there is no overall score — reader engagement is measured and shown alongside.
AI Quality Assessment
Not analyzed
Readership in this journal
Steady

Ranked #112 of 134 articles by views in Journal of medical systems

Most read Least read

Bar heights use a square-root scale. Only the 120 most-read articles are drawn; the journal has 134 in total.

Mint this article as an NFT
Not yet minted

Create a permanent, verifiable on-chain record of this article on the Scimatic Network. The NFT is held in your Journament account, and you can withdraw it to your own wallet at any time.

5 SUSD one-off · no wallet required
Sign in to mint
Abstract
In the recent past, Internet of Things (IoT) plays a significant role in different applications such as health care, industrial sector, defense and research etc.… It provides effective framework in maintaining the security, privacy and reliability of the information in internet environment. Among various applications as mentioned health care place a major role, because security, privacy and reliability of the medical information is maintained in an effective way. Even though, IoT provides the effective protocols for maintaining the information, several intermediate attacks and intruders trying to access the health information which in turn reduce the privacy, security and reliability of the entire health care system in internet environment. As a result and to solve the issues, in this research Learning based Deep-Q-Networks has been introduced for reducing the malware attacks while managing the health information. This method examines the medical information in different layers according to the Q-learning concept which helps to minimize the intermediate attacks with less complexity. The efficiency of the system has been evaluated with the help of experimental results and discussions.
Reference Key
mohamed-shakeel2018maintainingjournal Use this key to autocite in the manuscript while using SciMatic Manuscript Manager or Thesis Manager
Authors Mohamed Shakeel, P;Baskar, S;Sarma Dhulipala, V R;Mishra, Sukumar;Jaber, Mustafa Musa;
Journal Journal of medical systems
Year 2018
DOI
10.1007/s10916-018-1045-z
URL
Keywords Keywords not found

Citations

No citations found. To add a citation, contact the admin at info@scimatic.org

Comments

No comments yet. Be the first to comment on this article.