KEDRI - the Knowledge Engineering and Discovery Research Institute
KEDRI - the Knowledge Engineering and Discovery Research Institute of Auckland University of Technology was established in June 2002 and since then has been developing novel information processing methods, technologies and their applications to enhance discoveries across different areas of science and engineering. The methods are mainly based on principles from Nature, such as brain information processing, evolution, genetics, quantum physics.
Items in these collections are protected by the Copyright Act 1994 (New Zealand). These works may be consulted by you, provided you comply with the provisions of the Act and the following conditions of use:
- Any use you make of these works must be for research or private study purposes only, and you may not make them available to any other person.
- Authors control the copyright of their works. You will recognise the author’s right to be identified as the author of the work, and due acknowledgement will be made to the author where appropriate.
- You will obtain the author’s permission before publishing any material from the work.
Recently Added
-
Machine Learning Methods for the Study of Cybersickness: A Systematic Review
(Springer, 2022)This systematic review offers a world-first critical analysis of machine learning methods and systems, along with future directions for the study of cybersickness induced by virtual reality (VR). VR is becoming increasingly ... -
The Role of Event Related Potentials in Pre-comprehension Processing of Consumers to Marketing Logos
(Guilan University of Medical Sciences, and co-published by Negah Institute for Scientific Communication, 2019)Background: In human behavior study, by peering directly into the brain and assessing distinct patterns, evoked neurons and neuron spike can be more understandable by taking advantages of accurate brain analysis. Objectives: ... -
Classification and segmentation of fMRI Spatio-temporal Brain Data with a NeuCube Evolving Spiking Neural Network Model
(IEEE, 2014)The proposed feasibility analysis introduces a new methodology for modelling and understanding functional Magnetic Resonance Image (fMRI) data recorded during human cognitive activity. This constitutes a type of Spatio-Temporal ... -
Dynamic 3D Clustering of Spatio-temporal Brain Data in the NeuCube Spiking Neural Network Architecture on a Case Study of fMRI and EEG Data
(Springer, 2015)The paper presents a novel clustering method for dynamic Spatio-Temporal Brain Data (STBD) on the case study of functional Magnetic Resonance Image (fMRI). The method is based on NeuCube spiking neural network (SNN) ... -
Modelling peri-perceptual brain processes in a deep learning spiking neural network architecture
(Macmillan Publishers Limited, 2018)Familiarity of marketing stimuli may affect consumer behaviour at a peri-perceptual processing level. The current study introduces a method for deep learning of electroencephalogram (EEG) data using a spiking neural network ... -
Evolving, Dynamic Clustering of Spatio/Spectro-Temporal Data in 3D Spiking Neural Network Models and a Case Study on EEG Data
(Springer, 2017)Clustering is a fundamental data processing technique. While clustering of static (vector based) data and of fixed window size time series have been well explored, dynamic clustering of spatiotemporal data has been little ... -
New Algorithms for Encoding, Learning and Classification of fMRI Data in a Dpiking Neural Network Architecture: A Case on Modelling and Understanding of Dynamic Cognitive
(IEEE, 2016)The paper argues that, the third generation of neural networks – the spiking neural networks (SNN), can be used to model dynamic, spatio-temporal, cognitive brain processes measured as functional magnetic resonance imaging ... -
Personalised Modelling on Integrated Clinical and EEG Spatio-Temporal Brain Data in the NeuCube Spiking Neural Network System
(IEEE, 2016)This paper introduces a novel personalised modelling framework and system for analysing Spatio-Temporal Brain Data (STBD) along with person clinical static data. For every individual, based on selected subset of similar ... -
Longitudinal Study of Alzheimer’s Disease Degeneration through EEG Data Analysis with aNeuCube Spiking Neural Network Model
(IEEE, 2016)Motivated by the dramatic rise of neurological disorders, we propose a SNN technique to model electroencephalography (EEG) data collected from people affected by Alzheimer’s Disease (AD) and people diagnosed with mild ... -
A graph-based semi-supervised k nearest-neighbor method for nonlinear manifold distributed data classification
(Elsevier Inc., 2016)k nearest neighbors (kNN) is one of the most widely used supervised learning algorithms to classify Gaussian distributed data, but it does not achieve good results when it is applied to nonlinear manifold distributed data, ... -
Mapping temporal variables into the NeuCube for improved pattern recognition, predictive modeling, and understanding of stream data
(IEEE, 2016)This paper proposes a new method for an optimized mapping of temporal variables, describing a temporal stream data, into the recently proposed NeuCube spiking neural network (SNN) architecture. This optimized mapping extends ... -
Network-based method for inferring cancer progression at the pathway level from cross-sectional mutation data
(IEEE, 2016)Large-scale cancer genomics projects are providing a wealth of somatic mutation data from a large number of cancer patients. However, it is difficult to obtain several samples with a temporal order from one patient in ... -
Evolving Connectionist Systems for Adaptive Learning and Knowledge Discovery: Trends and Directions
(Elsevier, 2015)This paper follows the 25 years of development of methods and systems for knowledge-based neural network systems and more specifically the recent evolving connectionist systems (ECOS). ECOS combine the adaptive/evolving ... -
SPAN: Spike Pattern Association Neuron for learning spatio-temporal sequences
(World Scientific Publishing Company, 2012)Spiking Neural Networks (SNN) were shown to be suitable tools for the processing of spatio-temporal information. However, due to their inherent complexity, the formulation of efficient supervised learning algorithms for ... -
A PSO based adaboost approach to object detection
(Springer Verlag, 2008)This paper describes a new approach using particle swarm optimisation (PSO) within AdaBoost for object detection. Instead of using the time consuming exhaustive search for finding good features to be used for constructing ... -
Mobile robot navigation - some issues in controller design and implementation
(IEEE Instrumentation and Measurement, Malaysia (IM), 2009) -
Robotics for engineering education
(Robocup - Singapore, 2010)Most products are the integration of modules from different engineering areas – mechanical, electrical and electronics, computing etc. Engineering graduates are expected to design, manufacture and control those ... -
Modelling the effect of genes on the dynamics of probabilistic spiking neural networks for computational neurogenetic modelling
(AUT University, 2011)Computational neuro-genetic models (CNGM) combine two dynamic models – a gene regulatory network (GRN) model at a lower level, and a spiking neural network (SNN) model at a higher level to model the dynamic interaction ... -
Exploring associations between changes in ambient temperature and stroke occurrence: comparative analysis using global and personalised modelling methods
(Springer, 2011)Stroke is a major cause of disability and mortality in most economically developed countries that increasing global importance. Up till now, there is uncertainty regarding the effect of weather conditions on stoke occurrence. ... -
A novel microarray gene selection method based on consistency
(IEEE Computer Society Press, 2006)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 ...