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International Journal of Innovation and Applied Studies
ISSN: 2028-9324     CODEN: IJIABO     OCLC Number: 828807274     ZDB-ID: 2703985-7
 
 
Wednesday 23 May 2018

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  Call for Papers - May 2018     |     Now IJIAS is indexed in EBSCO, ResearchGate, ProQuest, Chemical Abstracts Service, Index Copernicus, IET Inspec Direct, Ulrichs Web, Google Scholar, CAS Abstracts, J-Gate, UDL Library, CiteSeerX, WorldCat, Scirus, Research Bible and getCited, etc.  
 
 
 

Naïve Bayesian Learning based Multi Agent Architecture for Telemedicine


Volume 2, Issue 4, April 2013, Pages 412–422

 Naïve Bayesian Learning based Multi Agent Architecture for Telemedicine

Ei Ei Chaw1

1 Information and Communication Technology Department, University of Technology, Pyin Oo Lwin City, Mandalay Division, Myanmar

Original language: English

Received 9 January 2013

Copyright © 2013 ISSR Journals. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Abstract


Agent-based systems are one of the most vibrant and important areas of the research and development to have emerged in Information Technology in recent years. They are one of the most promising approaches for designing and implementing autonomous, intelligent and social software assistants capable of supporting human decision-making. These kinds of systems are believed to be appropriate in many aspects of the healthcare domain. As a result, there is a growing interest of researchers in the application of agent-based techniques to problems in the healthcare domain. The adoption of agent technologies and multi-agent constitutes an emerging area in bioinformatics. Multi-agent based medical diagnosis systems may improve traditionally developed medical computational systems and may also support medical staff in decision-making. In this paper, we simulate the multi agent system for cancer classification. The proposed architecture consists of service provider agents as upper layer agent, coordinator agent as middle layer agent and initial agent lowest layer agent. Coordinator agent serves as matchmaker agent that uses Na

Author Keywords: Agent, Autonomous, Healthcare, Naive Bayesian, Communication.


How to Cite this Article


Ei Ei Chaw, “Naïve Bayesian Learning based Multi Agent Architecture for Telemedicine,” International Journal of Innovation and Applied Studies, vol. 2, no. 4, pp. 412–422, April 2013.