WorldCat Identities

Knowledge, Engineering and Discovery Research Institute

Works: 12 works in 14 publications in 1 language and 68 library holdings
Genres: Conference papers and proceedings  Academic theses 
Classifications: QE48.8, 006.32
Publication Timeline
Most widely held works by Engineering and Discovery Research Institute Knowledge
Proceedings of the Conference on Neuro-Computing and Evolving Intelligence : AUT Technology Park, Auckland, New Zealand, 20 and 21 November, 2003 by Conference on Neuro-Computing and Evolving Intelligence( Book )

1 edition published in 2003 in English and held by 9 WorldCat member libraries worldwide

Today's knowledge engineering : KEDRI's computational intelligence repository 2007( Book )

1 edition published in 2007 in English and held by 2 WorldCat member libraries worldwide

"The KEDRI ... repository contains original methods and software systems for intelligent data analysis"--Introduction
Neuro-Computing Colloquium & Workshop (NCC & W'02) : 30-31 October, 2002, Auckland, New Zealand : [Conference programme and abstracts]( Book )

1 edition published in 2002 in English and held by 2 WorldCat member libraries worldwide

Computational modelling of spatio-temporal EEG brain data with spiking neural networks : a thesis submitted to Auckland University of Technology in fulfilment of the requirements for the degree of Doctor of Philosophy (PhD), 2015 by Elisa Capecci( )

1 edition published in 2015 in English and held by 1 WorldCat member library worldwide

The research presented in this thesis is aimed at modelling, classification and understanding of functional changes in brain activity that forewarn of the onset and/or the progression of a neurodegenerative process that may result in a number of disorders, including cognitive impairments, opiate addiction, Epilepsy and Alzheimer's Disease. The study of neural plasticity and disease onset have been the centre of attention for researchers; especially as the population is ageing there is a need to deal with the increase in cognitive decline and the early onset of neurological diseases. As a consequence, large amounts of brain data has been collected and even more is expected to be collected, by means of novel computational techniques and biochemistry measurements. However, brain data is difficult to analyse and understand, especially since many of the traditional statistical and AI techniques are not able to deal with it appropriately. Driven by these issues and aiming to achieve the proposed goals, this study undertook to explore the potential of an evolving spatio-temporal data processing machine called the NeuCube architecture of spiking neurons, to analyse, classify and extract knowledge from electroencephalography spatio-temporal brain data. Firstly, the research undertaken in this thesis proposes a biologically plausible spiking neural network methodology for electroencephalography data classification and analysis. Secondly, it proposes a methodology for understanding functional changes in brain activity generated by the spatio-temporal data in the spiking neural network model. Thirdly, a new unsupervised learning rule is proposed for the investigation of the biological processes responsible for brain synaptic activity with the aim of targeting pharmacological treatments. The research undertaken achieved the following: high accuracy classification of electroencephalography data, even when fewer EEG channels and/or unprocessed data was used; personalised prognosis and early prediction of neurological events; the development of a tool for visualization and analysis of connectivity and spiking activity generated in the computational model; a better understanding of the impact of different drug doses on brain activity; a better understanding of specific neurological events by revealing the area of the brain where they occurred; and the analysis of the impact of biochemical processes on the neuronal synaptic plasticity of the model. Further improvement of the understanding and use of the proposed methodologies would contribute to the advancement of research in the area of prediction of neurological events and understanding of brain data related to neurological disorders, such as Alzheimer's Disease
Gene selection based on consistency modelling, algorithms and applications : thesis submitted in partial fulfillment of the requirements for the degee of Master of Computer and Information Sciences, Auckland University of Technology, July, 2006 by Yingjie Hu( )

2 editions published in 2006 in English and held by 1 WorldCat member library worldwide

Consistency modeling for gene selection is a new topic emerging from recent cancer bioinformatics research. The result of classification or clustering on a training set was often found very different from the same operations on a testing set. Here, the issue is addressed as a consistency problem. In practice, the inconsistency of microarray datasets prevents many typical gene selection methods working properly for cancer diagnosis and prognosis. In an attempt to deal with this problem, a new concept of performance-based consistency is proposed in this thesis. An interesting finding in our previous experiments is that by using a proper set of informative genes, we significantly improved the consistency characteristic of microarray data. Therefore, how to select genes in terms of consistency modelling becomes an interesting topic. Many previously published gene selection methods perform well in the cancer diagnosis domain, but questions are raised because of the irreproducibility of experimental results. Motivated by this, two new gene selection methods based on the proposed performance-based consistency concept, GAGSc (Genetic Algorithm Gene Selection method in terms of consistency) and LOOLSc (Leave-one-out Least-Square bound method with consistency measurement) were developed in this study with the purpose of identifying a set of informative genes for achieving replicable results of microarray data analysis. The proposed consistency concept was investigated on eight benchmark microarray and proteomic datasets. The experimental results show that the different microarray datasets have different consistency characteristics, and that better consistency can lead to an unbiased and reproducible outcome with good disease prediction accuracy. As an implementation of the proposed performance-based consistency, GAGSc and LOOLSc are capable of providing a small set of informative genes. Comparing with those traditional gene selection methods without using consistency measurement, GAGSc and LOOLSc can provide more accurate classification results. More importantly, GAGSc and LOOLSc have demonstrated that gene selection, with the proposed consistency measurement, is able to enhance the reproducibility in microarray diagnosis experiments
Novel integrated methods of evolving spiking neural network and particle swarm optimisation by Haza Nuzly Abdull Hamed( )

1 edition published in 2012 in English and held by 1 WorldCat member library worldwide

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  Kids General Special  
Audience level: 0.76 (from 0.59 for Gene selec ... to 1.00 for HIS '06, N ...)

Alternative Names
Auckland University of Technology Knowledge Engineering and Discovery Research Institute


English (14)