Browsing KEDRI - the Knowledge Engineering and Discovery Research Institute by Title
Now showing items 9-28 of 54
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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 ... -
Evolving intelligent systems: methods, learning, & applications
(IEEE, 2006)The basic concept, formulation, background, and a panoramic view over the recent research results and open problems in the newly emerging area of Evolving Intelligent Systems are summarized in this short communication. ... -
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 ... -
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. ... -
Fast Neural Network Ensemble Learning via Negative-Correlation Data Correction
(IEEE, 2005)This letter proposes a new negative correlation (NC) learning method that is both easy to implement and has the advantages that: 1) it requires much lesser communication overhead than the standard NC method and 2) it is ... -
Gene trajectory clustering with a hybrid genetic algorithm and expectation maximization method
(IEEE, 2004)Clustering time course gene expression data (gene trajectories) is an important step towards solving the complex problem of gene regulatory network (GRN) modeling and discovery as it significantly reduces the dimensionality ... -
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, ... -
Incremental learning for online face recognition
(IEEE, 2005)In this paper, a new approach to face recognition is presented in which not only a classifier but also a feature space of input variables is learned incrementally to adapt to incoming training samples. A benefit of this ... -
Incremental learning in autonomous systems: evolving connectionist systems for on-line image and speech recognition
(IEEE, 2005)The paper presents an integrated approach to incremental learning in autonomous systems, that includes both pattern recognition and feature selection. The approach utilizes evolving connectionist systems (ECoS) and is ...