An improved computational model for classification of 3D spatio temporal FMRI data

3D spatial temporal functional magnetic resonance imaging (fMRI) for classification has gained wide attention in the literature to be applied in the application of data mining techniques. Similarly, Spiking Neural Networks (SNN) has successfully applied in many problems to process and classify fMRI...

Full description

Saved in:

Bibliographic Details
Main Author:	Saharuddin, Shaznoor Shakira
Format:	Thesis
Language:	English English English
Published:	2018
Subjects:	QA299.6-433 Analysis
Online Access:	http://eprints.uthm.edu.my/522/1/24p%20SHAZNOOR%20SHAKIRA%20BINTI%20SAHARUDDIN.pdf http://eprints.uthm.edu.my/522/2/SHAZNOOR%20SHAKIRA%20BINTI%20SAHARUDDIN%20COPYRIGHT%20DECLARATION.pdf http://eprints.uthm.edu.my/522/3/SHAZNOOR%20SHAKIRA%20BINTI%20SAHARUDDIN%20WATERMARK.pdf
Tags:	Add Tag No Tags, Be the first to tag this record!

id	my-uthm-ep.522
record_format	uketd_dc
spelling	my-uthm-ep.5222021-07-25T08:39:12Z An improved computational model for classification of 3D spatio temporal FMRI data 2018-11 Saharuddin, Shaznoor Shakira QA299.6-433 Analysis 3D spatial temporal functional magnetic resonance imaging (fMRI) for classification has gained wide attention in the literature to be applied in the application of data mining techniques. Similarly, Spiking Neural Networks (SNN) has successfully applied in many problems to process and classify fMRI data. However, the network still has a drawback in terms of processing noise, redundant and irrelevant features especially in fMRI data. To an extent, standard machine learning techniques has effectively process and classify fMRI data. Although, these techniques are only best at dealing spatial data, which completely neglect the temporal information inside the data. In order to achieve higher classification accuracy, there is a need to filter out noise from the dataset. Studies have shown that the presence of noise in the data effects the classification process thereby reducing the classification accuracy. In this study, the feature selection technique has been used as a filter at the pre-processing part of the dataset. Thus, this study proposed a feature selection technique called iReliefF to overcome the complexity in selecting the important features in fMRI dataset. This technique has been trained and tested by using StarPlus dataset. Based on the obtained results, the new computational model with proposed method iReliefF has shown better performance by achieving 85% accuracy compared to the existing model which is 80%. Therefore, it can be concluded that the proposed iReliefF has achieved reasonable accuracy and is very effective as well as ideal for fMRI dataset. 2018-11 Thesis http://eprints.uthm.edu.my/522/ http://eprints.uthm.edu.my/522/1/24p%20SHAZNOOR%20SHAKIRA%20BINTI%20SAHARUDDIN.pdf text en public http://eprints.uthm.edu.my/522/2/SHAZNOOR%20SHAKIRA%20BINTI%20SAHARUDDIN%20COPYRIGHT%20DECLARATION.pdf text en staffonly http://eprints.uthm.edu.my/522/3/SHAZNOOR%20SHAKIRA%20BINTI%20SAHARUDDIN%20WATERMARK.pdf text en validuser mphil masters Universiti Tun Hussein Onn Malaysia Faculty of Computer Science and Information Technology
institution	Universiti Tun Hussein Onn Malaysia
collection	UTHM Institutional Repository
language	English English English
topic	QA299.6-433 Analysis
spellingShingle	QA299.6-433 Analysis Saharuddin, Shaznoor Shakira An improved computational model for classification of 3D spatio temporal FMRI data
description	3D spatial temporal functional magnetic resonance imaging (fMRI) for classification has gained wide attention in the literature to be applied in the application of data mining techniques. Similarly, Spiking Neural Networks (SNN) has successfully applied in many problems to process and classify fMRI data. However, the network still has a drawback in terms of processing noise, redundant and irrelevant features especially in fMRI data. To an extent, standard machine learning techniques has effectively process and classify fMRI data. Although, these techniques are only best at dealing spatial data, which completely neglect the temporal information inside the data. In order to achieve higher classification accuracy, there is a need to filter out noise from the dataset. Studies have shown that the presence of noise in the data effects the classification process thereby reducing the classification accuracy. In this study, the feature selection technique has been used as a filter at the pre-processing part of the dataset. Thus, this study proposed a feature selection technique called iReliefF to overcome the complexity in selecting the important features in fMRI dataset. This technique has been trained and tested by using StarPlus dataset. Based on the obtained results, the new computational model with proposed method iReliefF has shown better performance by achieving 85% accuracy compared to the existing model which is 80%. Therefore, it can be concluded that the proposed iReliefF has achieved reasonable accuracy and is very effective as well as ideal for fMRI dataset.
format	Thesis
qualification_name	Master of Philosophy (M.Phil.)
qualification_level	Master's degree
author	Saharuddin, Shaznoor Shakira
author_facet	Saharuddin, Shaznoor Shakira
author_sort	Saharuddin, Shaznoor Shakira
title	An improved computational model for classification of 3D spatio temporal FMRI data
title_short	An improved computational model for classification of 3D spatio temporal FMRI data
title_full	An improved computational model for classification of 3D spatio temporal FMRI data
title_fullStr	An improved computational model for classification of 3D spatio temporal FMRI data
title_full_unstemmed	An improved computational model for classification of 3D spatio temporal FMRI data
title_sort	improved computational model for classification of 3d spatio temporal fmri data
granting_institution	Universiti Tun Hussein Onn Malaysia
granting_department	Faculty of Computer Science and Information Technology
publishDate	2018
url	http://eprints.uthm.edu.my/522/1/24p%20SHAZNOOR%20SHAKIRA%20BINTI%20SAHARUDDIN.pdf http://eprints.uthm.edu.my/522/2/SHAZNOOR%20SHAKIRA%20BINTI%20SAHARUDDIN%20COPYRIGHT%20DECLARATION.pdf http://eprints.uthm.edu.my/522/3/SHAZNOOR%20SHAKIRA%20BINTI%20SAHARUDDIN%20WATERMARK.pdf
_version_	1747830630714966016

An improved computational model for classification of 3D spatio temporal FMRI data

Similar Items