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Browsing KEDRI - the Knowledge Engineering and Discovery Research Institute by Title 
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  • KEDRI - the Knowledge Engineering and Discovery Research Institute
  • Browsing KEDRI - the Knowledge Engineering and Discovery Research Institute by Title
  •   Open Research
  • AUT Research Institutes, Centres and Networks
  • KEDRI - the Knowledge Engineering and Discovery Research Institute
  • Browsing KEDRI - the Knowledge Engineering and Discovery Research Institute by Title
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Browsing KEDRI - the Knowledge Engineering and Discovery Research Institute by Title

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Now showing items 45-56 of 56

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    • On-line evolving fuzzy clustering 

      Ravi, V.; Srinivas, E.; Kasabov, N (IEEE, 2007)
      In this paper, a novel on-line evolving fuzzy clustering method that extends the evolving clustering method (ECM) of Kasabov and Song (2002) is presented, called EFCM. Since it is an on-line algorithm, the fuzzy membership ...
    • Optimisation and modelling of spiking neural networks - Enhancing neural information processing systems through the power of evolution 

      Schliebs, S (LAP LAMBERT Academic Publishing, 2010)
      Motivated by the desire to better understand the truly remarkable information processing capabilities of the brain, numerous biologically plausible computational models have been explored in the recent decades. Already ...
    • Personalised Modelling on Integrated Clinical and EEG Spatio-Temporal Brain Data in the NeuCube Spiking Neural Network System 

      Gholami, M; Kasabov, N (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 ...
    • Quantum-inspired feature and parameter optimization of evolving spiking neural networks with a case study from ecological modelling 

      Schliebs, S; Defoin-Platel, M; Worner, S; Kasabov, N (IEEE, 2009)
      The paper introduces a framework and implementation of an integrated connectionist system, where the features and the parameters of an evolving spiking neural network are optimised together using a quantum representation ...
    • Quantum-inspired particle swarm optimization for feature selection and parameter optimization in evolving spiking neural networks for classification tasks 

      Abdull Hamed, HN; Kasabov, N; Shamsuddin, SM (InTech, 2011)
      Introduction: Particle Swarm Optimization (PSO) was introduced in 1995 by Russell Eberhart and James Kennedy (Eberhart & Kennedy, 1995). PSO is a biologically-inspired technique based around the study of collective behaviour ...
    • Robotics for engineering education 

      Huang, L (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 ...
    • The Role of Event Related Potentials in Pre-comprehension Processing of Consumers to Marketing Logos 

      Nazari, MA; Salehi Fadardi, J; Gholami Doborjeh, Z; Amanzadeh Oghaz, T; Saeedi, MT; Yazdi, SAA (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: ...
    • SPAN: Spike Pattern Association Neuron for learning spatio-temporal sequences 

      Mohemmed, A; Schliebs, S; Matsuda, S; Kasabov, N (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 ...
    • Transductive modeling with GA parameter optimization 

      Mohan, N.; Kasabov, N (IEEE, 2005)
      Introduction - While inductive modeling is used to develop a model (function) from data of the whole problem space and then to recall it on new data, transductive modeling is concerned with the creation of single model for ...
    • Transductive Support Vector Machines and Applications in Bioinformatics for Promoter Recognition 

      Kasabov, N; Pang, S. (IEEE, 2004)
      This paper introduces a novel transductive support vector machine (TSVM) model and compares it with the traditional inductive SVM on a key problem in bioinformatics - promoter recognition. While inductive reasoning is ...
    • TWNFC - Transductive neural-fuzzy classifier with weighted data normalization and its application in medicine 

      Ma, T.; Song, Q.; Marshall, M.; Kasabov, N (IEEE, 2005)
      This paper introduces a novel fuzzy model - transductive neural-fuzzy classifier with weighted data normalization (TWNFC), While inductive approaches are concerned with the development of a model to approximate data in the ...
    • WDN-RBF: weighted data normalization for radial basic function type neural networks 

      Song, Q.; Kasabov, N (IEEE, 2004)
      This paper introduces an approach of Weighted Data Normalization (WDN) for Radial Basis Function (RBF) type of neural networks. It presents also applications for medical decision support systems. The WDN method optimizes ...

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