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  • Browsing School of Engineering, Computer and Mathematical Sciences - Te Kura Mātai Pūhanga, Rorohiko, Pāngarau by Author
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Browsing School of Engineering, Computer and Mathematical Sciences - Te Kura Mātai Pūhanga, Rorohiko, Pāngarau by Author "Kasabov, N"

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    • A novel evolving clustering algorithm with polynomial regression for chaotic time-series prediction 

      Widiputra, H; Kho, H; Lukas; Pears, R; Kasabov, N (Springer Berlin Heidelberg, 2009)
      Time-series prediction has been a very well researched topic in recent studies. Some popular approaches to this problem are the traditional statistical methods e.g. multiple linear regression and moving average, and neural ...
    • Application of an Improved Focal Loss in Vehicle Detection 

      He, X; Yang, J; Kasabov, N (Springer, 2020)
      Object detection is an important and fundamental task in computer vision. Recently, the emergence of deep neural network has made considerable progress in object detection. Deep neural network object detectors can be grouped ...
    • Bioengineering silicon quantum dot theranostics using a network analysis of metabolomic and proteomic data in cardiac ischemia 

      Erogbogbo, F; May, J; Swihart, M; Prasad, P; Smart, K; Jack, S; Korcyk, D; Webster, M; Stewart, R; Zeng, I; Jullig, M; Bakeev, K; Jamieson, M; Kasabov, N; Gopalan, B; Liang, L; Hu, R; Schliebs, S; Villas-Boas, S; Gladding, P (Ivyspring International Publisher, 2013)
      Metabolomic profiling is ideally suited for the analysis of cardiac metabolism in healthy and diseased states. Here, we show that systematic discovery of biomarkers of ischemic preconditioning using metabolomics can be ...
    • Brain, gene, and quantum inspired computational intelligence 

      Kasabov, N (Springer-Verlag, 2014)
      This chapter discusses opportunities and challenges for the creation of methods of computational intelligence (CI) and more specifically – artificial neural networks (ANN), inspired by principles at different levels of ...
    • Classification and Segmentation of fMRI Spatio-temporal Brain Data With a Neucube Evolving Spiking Neural Network Model 

      Doborjeh, MG; Capecci, E; Kasabov, N (Institute of Electrical and Electronics Engineers Inc., 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 modeling with spiking neural networks 

      Schliebs, S; Kasabov, N (Springer-Verlag Berlin, 2014)
      This chapter reviews recent developments in the area of spiking neural networks (SNN) and summarizes the main contributions to this research field. We give background information about the functioning of biological neurons, ...
    • Contemporary developments in neural networks: spiking neural networks for adaptive spatio-/spectro temporal pattern recognition 

      Kasabov, N (International Conference on Artificial Neural Networks (ICANN), 2013)
      No abstract.
    • Deep Learning of Explainable EEG Patterns as Dynamic Spatiotemporal Clusters and Rules in a Brain-Inspired Spiking Neural Network 

      Doborjeh, M; Doborjeh, Z; Kasabov, N; Barati, M; Wang, GY
      The paper proposes a new method for deep learning and knowledge discovery in a brain-inspired Spiking Neural Networks (SNN) architecture that enhances the model’s explainability while learning from streaming spatiotemporal ...
    • Deep Semi-supervised Learning via Dynamic Anchor Graph Embedding in Latent Space 

      Tu, E; Wang, Z; Yang, J; Kasabov, N (Elsevier BV, 2021)
      Recently, deep semi-supervised graph embedding learning has drawn much attention for its appealing performance on the data with a pre-specified graph structure, which could be predefined or empirically constructed based ...
    • Design of MRI Structured Spiking Neural Networks and Learning Algorithms for Personalized Modelling, Analysis, and Prediction of EEG Signals 

      Saeedinia, SA; Jahed-Motlagh, MR; Tafakhori, A; Kasabov, N (Nature Publishing Group, 2021)
      This paper proposes a novel method and algorithms for the design of MRI structured personalized 3D spiking neural network models (MRI-SNN) for a better analysis, modeling, and prediction of EEG signals. It proposes a novel ...
    • Dynamic Interaction Networks in modelling and predicting the behaviour of multiple interactive stock markets 

      Widiputra, H; Pears, R; Serguieva, A; Kasabov, N (John Wiley & Sons, 2009)
      The behaviour of multiple stock markets can be described within the framework of complex dynamic systems. A representative technique of the framework is the dynamic interaction network (DIN), recently developed in the ...
    • EEG signal processing for brain-computer interfaces 

      Georgieva, P; Silva, F; Milanova, M; Kasabov, N (Springer-Verlag, 2014)
      This chapter is focused on recent advances in electroencephalogram (EEG) signal processing for brain computer interface (BCI) design. A general overview of BCI technologies is first presented, and then the protocol for ...
    • Emotion Recognition and Understanding Using EEG Data in a Brain-inspired Spiking Neural Network Architecture 

      Alzhrani, W; Doborjeh, M; Doborjeh, Z; Kasabov, N (Ulster University, 2021)
      This paper is in the scope of emotion recognition by employing a brain-inspired recurrent spiking neural network (BI-SNN) architecture for modelling, mapping, learning, classifying, visualising, and understanding of ...
    • Erratum: Lou, X.; Jia, Z.; Yang, J.; Kasabov, N. Change Detection in SAR Images Based on the ROF Model Semi-Implicit Denoising Method. Sensors 2019, 19, 1179 

      Lou, X; Jia, Z; Yang, J; Kasabov, N (MDPI, 2019)
      The authors wish to make the following erratum to this paper [https://www.mdpi.com/1424-8220/19/10/2314/htm].
    • Evolving computational intelligence: methods, systems, applications 

      Kasabov, N (International Conference on Intelligent Computing (ICIC), 2013)
      The talk presents an overview of current methods of computational intelligence (CI) called evolving CI (eCI) and how they can be used in to create adaptive, computational intelligence (CI) systems across areas of applications. ...
    • Evolving integrated multi-model framework for on line multiple time series prediction 

      Pears, R; Widiputra, H; Kasabov, N (Springer, 2013)
      Time series prediction has been extensively researched in both the statistical and computational intelligence literature with robust methods being developed that can be applied across any given application domain. A much ...
    • Evolving spiking neural network - a survey 

      Schliebs, S; Kasabov, N (Springer, 2013)
      This paper provides a comprehensive literature survey on the evolving Spiking Neural Network (eSNN) architecture since its introduction in 2006 as a further extension of the ECoS paradigm introduced by Kasabov in 1998. We ...
    • Evolving Spiking Neural Network Model for PM2.5 Hourly Concentration Prediction Based on Seasonal Differences: A Case Study on Data from Beijing and Shanghai 

      Liu, H; Lu, G; Wang, Y; Kasabov, N (Taiwan Association for Aerosol Research, 2021)
      In recent years, the dangers that air pollutants pose to human health and the environment have received widespread attention. Although accurately predicting the air quality is essential to managing pollution and developing ...
    • Evolving spiking neural networks for personalised modelling, classification and prediction of spatio-temporal patterns with a case study on stroke 

      Kasabov, N; Feigin, V; Hou, Z-G; Chen, Y; Liang, L; Krishnamurthi, R; Othman, M; Parmar, P (Elsevier, 2014)
      The paper presents a novel method and system for personalised (individualised) modelling of spatio/spectro-temporal data (SSTD) and prediction of events. A novel evolving spiking neural network reservoir system (eSNNr) is ...
    • Evolving, probabilistic spiking neural networks and neurogenetic systems for spatio- and spectro-temporal data modelling and pattern recognition 

      Kasabov, N (The International Neural Network Society (INNS), 2012)
      Spatio- and spectro-temporal data (SSTD) are the most common types of data collected in many domain areas, including engineering, bioinformatics, neuroinformatics, ecology, environment, medicine, economics, etc. However, ...

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