Liu, Hongbo, Abraham, Ajith, Zhang, Weishi and McLoone, Sean F. (2011) A swarm-based rough set approach for FMRI data analysis. International Journal of Innovative Computing, Information and Control, 7 (6). pp. 3121-3132. ISSN 1349-4198
PDF
SM_Swarm-based_Rough_Set.pdf
Download (34kB)
SM_Swarm-based_Rough_Set.pdf
Download (34kB)
Abstract
The functional Magnetic Resonance Imaging (fMRI) is one of the most
important tools for exploring the operation of the brain as it allows the spatially localized
characteristics of brain activity to be observed. However, fMRI studies generate huge
volumes of data and the signals of interest have low signal to noise ratio making its
analysis a very challenging problem. There is a growing need for new methods that can
efficiently and objectively extract the useful information from fMRI data and translate it
into intelligible knowledge. In this paper, we introduce a swarm-based rough set approach
to fMRI data analysis. Our approach is based on exploiting the power of particle swarm
optimization to discover the feature combinations in an efficient manner by observing the
change in positive region as the particles proceed through the search space. The approach
supports multi-knowledge extraction. We evaluate the performance of the algorithm using
benchmark and fMRI datasets. The results demonstrate its potential value for cognition
research.
Item Type: | Article |
---|---|
Keywords: | Particle swarm; Swarm intelligence; Multi-knowledge; fMRI; |
Academic Unit: | Faculty of Science and Engineering > Electronic Engineering |
Item ID: | 3870 |
Depositing User: | Sean McLoone |
Date Deposited: | 17 Sep 2012 13:40 |
Journal or Publication Title: | International Journal of Innovative Computing, Information and Control |
Publisher: | ICIC International |
Refereed: | Yes |
URI: | https://mural.maynoothuniversity.ie/id/eprint/3870 |
Use Licence: | This item is available under a Creative Commons Attribution Non Commercial Share Alike Licence (CC BY-NC-SA). Details of this licence are available here |
Repository Staff Only (login required)
Downloads
Downloads per month over past year