Spatial-temporal data modelling and processing for personalised decision support

The purpose of this research is to undertake the modelling of dynamic data without losing any of the temporal relationships, and to be able to predict likelihood of outcome as far in advance of actual occurrence as possible. To this end a novel computational architecture for personalised ( individua...

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Main Author:	Othman, Muhaini
Format:	Thesis
Language:	English
Published:	2015
Subjects:	QA Mathematics
Online Access:	http://eprints.uthm.edu.my/1739/1/24p%20MUHAINI%20OTHMAN.pdf
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id	my-uthm-ep.1739
record_format	uketd_dc
spelling	my-uthm-ep.17392021-10-10T04:31:57Z Spatial-temporal data modelling and processing for personalised decision support 2015-05 Othman, Muhaini QA Mathematics QA273-280 Probabilities. Mathematical statistics The purpose of this research is to undertake the modelling of dynamic data without losing any of the temporal relationships, and to be able to predict likelihood of outcome as far in advance of actual occurrence as possible. To this end a novel computational architecture for personalised ( individualised) modelling of spatio-temporal data based on spiking neural network methods (PMeSNNr), with a three dimensional visualisation of relationships between variables is proposed. In brief, the architecture is able to transfer spatio-temporal data patterns from a multidimensional input stream into internal patterns in the spiking neural network reservoir. These patterns are then analysed to produce a personalised model for either classification or prediction dependent on the specific needs of the situation. The architecture described above was constructed using MatLab© in several individual modules linked together to form NeuCube (M1). This methodology has been applied to two real world case studies. Firstly, it has been applied to data for the prediction of stroke occurrences on an individual basis. Secondly, it has been applied to ecological data on aphid pest abundance prediction. Two main objectives for this research when judging outcomes of the modelling are accurate prediction and to have this at the earliest possible time point. The implications of these findings are not insignificant in terms of health care management and environmental control. As the case studies utilised here represent vastly different application fields, it reveals more of the potential and usefulness of NeuCube (M1) for modelling data in an integrated manner. This in turn can identify previously unknown (or less understood) interactions thus both increasing the level of reliance that can be placed on the model created, and enhancing our human understanding of the complexities of the world around us without the need for over simplification. Read less Keywords Personalised modelling; Spiking neural network; Spatial-temporal data modelling; Computational intelligence; Predictive modelling; Stroke risk prediction 2015-05 Thesis http://eprints.uthm.edu.my/1739/ http://eprints.uthm.edu.my/1739/1/24p%20MUHAINI%20OTHMAN.pdf text en public phd doctoral University of Technology, Auckland School of Computer and Mathematical Sciences
institution	Universiti Tun Hussein Onn Malaysia
collection	UTHM Institutional Repository
language	English
topic	QA Mathematics QA Mathematics
spellingShingle	QA Mathematics QA Mathematics Othman, Muhaini Spatial-temporal data modelling and processing for personalised decision support
description	The purpose of this research is to undertake the modelling of dynamic data without losing any of the temporal relationships, and to be able to predict likelihood of outcome as far in advance of actual occurrence as possible. To this end a novel computational architecture for personalised ( individualised) modelling of spatio-temporal data based on spiking neural network methods (PMeSNNr), with a three dimensional visualisation of relationships between variables is proposed. In brief, the architecture is able to transfer spatio-temporal data patterns from a multidimensional input stream into internal patterns in the spiking neural network reservoir. These patterns are then analysed to produce a personalised model for either classification or prediction dependent on the specific needs of the situation. The architecture described above was constructed using MatLab© in several individual modules linked together to form NeuCube (M1). This methodology has been applied to two real world case studies. Firstly, it has been applied to data for the prediction of stroke occurrences on an individual basis. Secondly, it has been applied to ecological data on aphid pest abundance prediction. Two main objectives for this research when judging outcomes of the modelling are accurate prediction and to have this at the earliest possible time point. The implications of these findings are not insignificant in terms of health care management and environmental control. As the case studies utilised here represent vastly different application fields, it reveals more of the potential and usefulness of NeuCube (M1) for modelling data in an integrated manner. This in turn can identify previously unknown (or less understood) interactions thus both increasing the level of reliance that can be placed on the model created, and enhancing our human understanding of the complexities of the world around us without the need for over simplification. Read less Keywords Personalised modelling; Spiking neural network; Spatial-temporal data modelling; Computational intelligence; Predictive modelling; Stroke risk prediction
format	Thesis
qualification_name	Doctor of Philosophy (PhD.)
qualification_level	Doctorate
author	Othman, Muhaini
author_facet	Othman, Muhaini
author_sort	Othman, Muhaini
title	Spatial-temporal data modelling and processing for personalised decision support
title_short	Spatial-temporal data modelling and processing for personalised decision support
title_full	Spatial-temporal data modelling and processing for personalised decision support
title_fullStr	Spatial-temporal data modelling and processing for personalised decision support
title_full_unstemmed	Spatial-temporal data modelling and processing for personalised decision support
title_sort	spatial-temporal data modelling and processing for personalised decision support
granting_institution	University of Technology, Auckland
granting_department	School of Computer and Mathematical Sciences
publishDate	2015
url	http://eprints.uthm.edu.my/1739/1/24p%20MUHAINI%20OTHMAN.pdf
_version_	1747830859634835456

Spatial-temporal data modelling and processing for personalised decision support

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