Bruce Mathew, P. Sumathi, GRD College of Science, Coimbatore, Tamilnadu, India.
M. U. Kharat, MET Institute of Engineering, Bhujbal Knowledge City, Nashik , India.
DOI : 01.0401/ijaict.2014.05.02
International Journal of Advanced Information and Communication Technology
Received On : October 09, 2016
Revised On : November 18, 2016
Accepted On : December 13, 2016
Published On : January 05, 2016
Volume 03, Issue 01
Pages : 425-430
Abstract
Knowledge Management is a very hot domain these days due to the increasing asset value for knowledge available within the organization. These days, knowledge is treated as a vital asset that can increase organization’s competitive advantage. The potential that knowledge management has for improving fisheries management is increasingly being recognized. There are relatively few studies that have specifically addressed the challenges of knowledge management in fisheries. Knowledge about various fishery resources has become a major focal point of all stakeholders in the investment of time and effort. In this paper, we propose a predictive knowledge management model that is specific to the availability of Oil Sardine along the south-west coast of Kerala. This model can be applied for gaining business intelligence as well as improving competitive effectiveness. This model is processed for attribute reduction with the help of rough set theory and accordingly decision rules have been defined.
Keywords
Knowledge, Rough Set Theory, Rule Induction, Reduction, Reducts, Local Covering.
Cite this article
Bruce Mathew, P. Sumathi, M. U. Kharat, “A Generic Predictive Knowledge Management Model for Fisheries with Special Emphasis Catch of Oil-Sardine along the South-West Coast of India” INTERNATIONAL JOURNAL OF ADVANCED INFORMATION AND COMMUNICATION TECHNOLOGY, pp.425-430, January 05, 2016.
Copyright
© 2016 Bruce Mathew, P. Sumathi, M. U. Kharat. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.