Browsing KEDRI - the Knowledge Engineering and Discovery Research Institute by Title
Now showing items 1-20 of 56
-
A computational neurogenetic model of a spiking neuron
(IEEE, 2005)The paper presents a novel, biologically plausible spiking neuronal model that includes a dynamic gene network. Interactions of genes in neurons affect the dynamics of the neurons and the whole network through neuronal ... -
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 ... -
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 ... -
A two-stage methodology for gene regulatory network extraction from time-course gene expression data
(IEEE, 2004)The discovery of gene regulatory networks (GRN) from time-course gene expression data (gene trajectory data) is useful for (1) identifying important genes in relation to a disease or a biological function; (2) gaining an ... -
A versatile quantum-inspired evolutionary algorithm
(IEEE, 2007)This study points out some weaknesses of existing Quantum-Inspired Evolutionary Algorithms (QEA) and explains in particular how hitchhiking phenomenons can slow down the discovery of optimal solutions and encourage premature ... -
An adaptive model of person identification combining speech and image information
(IEEE, 2004)The paper introduces a combination of adaptive neural network systems and statistical method for integrating speech and face image information for person identification. The method allows for the development of models of ... -
An incremental principal component analysis for chunk data
(IEEE, 2006)This paper presents a new algorithm of dynamic feature selection by extending the algorithm of Incremental Principal Component Analysis (IPCA), which has been originally proposed by Hall and Martin. In the proposed IPCA, ... -
Are probabilistic spiking neural networks suitable for reservoir computing?
(AUT University, 2011)This study employs networks of stochastic spiking neurons as reservoirs for liquid state machines (LSM). We experimentally investigate the separation property of these reservoirs and show their ability to generalize classes ... -
Bioinformatics: a knowledge engineering approach
(IEEE, 2004)The paper introduces the knowledge engineering (KE) approach for the modeling and the discovery of new knowledge in bioinformatics. This approach extends the machine learning approach with various rule extraction and other ... -
Brain-gene ontology: integrating bioinformatics and neuroinformatics data, information and knowledge to enable discoveries
(IEEE, 2006)The paper presents some preliminary results on the brain-gene ontology (BGO) project that is concerned with the collection, presentation and use of knowledge in the form of ontology. BGO includes various concepts, facts, ... -
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 ... -
Computational neurogenetic modeling: a methodology to study gene interactions underlying neural oscillations
(IEEE, 2006)We present new results from Computational Neurogenetic Modeling to aid discoveries of complex gene interactions underlying oscillations in neural systems. Interactions of genes in neurons affect the dynamics of the whole ... -
Computational neurogenetic modelling: gene networks within neural networks
(IEEE, 2004)This paper introduces a novel connectionist approach to neural network modelling that integrates dynamic gene networks within neurons with a neural network model. Interaction of genes in neurons affects the dynamics of the ... -
DENFIS: dynamic evolving neural-fuzzy inference system and its application for time series prediction
(IEEE, 2002)This paper introduces a new type of fuzzy inference systems, denoted as dynamic evolving neural-fuzzy inference system (DENFIS), for adaptive online and offline learning, and their application for dynamic time series ... -
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) ... -
Efficient global clustering using the greedy elimination method
(IEEE, 2004)A novel global clustering method called the greedy elimination method is presented. Experiments show that the proposed method scores significantly lower clustering errors than the standard K-means over two benchmark and ... -
Evolutionary Computation for Dynamic Parameter Optimisation of Evolving Connectionist Systems for On-line Prediction of Time Series with Changing Dynamics
(IEEE, 2003)The paper describes a method of using evolutionary computation technique for parameter optimisation of evolving connectionist systems (ECOS) that operate in an online, life-long learning mode. ECOS evolve their structure ... -
Evolving connectionist systems based role allocation of robots for soccer playing
(IEEE, 2005)For a group of robots (multi-agents) to complete a task, it is important for each of them to play a certain role changing with the environment of the task. One typical example is robotic soccer in which a team of mobile ... -
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 ...