Intertwined

PUBLICATIONS

Prof. Kasabov and his team have published extensively in international journals and have many patents listed internationally.

Examples of relevant patents, journal publications and books are listed below.

Links to Full Lists of publications can be found here

patents

 

  1. N.Kasabov, Data Analysis and Predictive Systems and Related Methodologies, US patent 9,002,682 B2, 7 April 2015.
     

  2. N.Kasabov, V.Feigin, Z.Hou, Y.Chen, Improved method and system for predicting outcomes based on spatio/spectro-temporal data, PCT patent WO2015/030606 A2, US2016/0210552 A1. Granted/Publication date: 21 July 2016.  

     

 

journal publications

  1. N.K. Kasabov, Spiking neural networks for deep learning and knowledge representation: Editorial. Neural Networks 119 (2019), 341-342, https://doi.org/10.1016/j.neunet.2019.08.019.
     

  2. M. Doborjeh, N. Kasabov, Z. Doborjeh, R. Enayatollahi, E. Tu, A. H. Gandomi, Personalised modelling with spiking neural networks integrating temporal and static information, Neural Networks, 119 (2019),162-177.
     

  3. K.Kumarasinghe, N.Kasabov, D.Taylor, Deep Learning and Deep Knowledge Representation in Spiking Neural Networks for Brain-Computer Interfaces, Neural Networks (2019), doi: https://doi.org/10.1016/j.neunet.2019.08.029.
     

  4. Z. DoborjehM. Doborjeh, T. Taylor, N. Kasabov, G. Y. Wang, R. Siegert, A. Sumich, Spiking Neural Network Modelling Approach Reveals How Mindfulness Training Rewires the Brain, Nature, Scientific Reports, (2019) 9: 6367, https://www.nature.com/articles/s41598-019-42863-x.
     

  5. N.Kasabov, M. Doborjeh, A.Merkin, V. Feigin, Brain-Inspired AI for Personalised Predictive Modelling of Neurological Diseases, Neuroepidemiology 2019;52:3–16, Karger Publisher, DOI: 10.1159/000495016 (Abstracts)
     

  6. E.Capecci, J. L. Lobo, I.Lana, J. I. Espinosa Ramos, N.Kasabov, Modelling Gene Interaction Networks from Time-Series Gene Expression Data using Evolving Spiking Neural Networks, Evolving Systems, Springer, https://link.springer.com/article/10.1007%2Fs12530-019-09269-6, 2019,
     

  7. J. L. Lobo, I.Laña, J. Del Ser, M.N.Bilbao, N.Kasabov Evolving Spiking Neural Networks for online learning over drifting data streams, Neural Networks, 108, 1-19 (2018).
     

  8. J.Behrenbeck, Z.Tayeb, C.Bhiri, C.Richter, O.Rhodes, N.Kasabov, S.Furber, G.Cheng, J.Conradt. Classification and Regression of Spatio-Temporal EMG Signals using NeuCube Spiking Neural Network and its implementation on SpiNNaker Neuromorphic Hardware", Journal of Neural Engineering, IOP Press, 2018, Article reference: JNE-102499, http://iopscience.iop.org/journal/1741-2552
     

  9. .Doborjeh, N. Kasabov, M. Doborjeh & A. Sumich, Modelling Peri-Perceptual Brain Processes in a Deep Learning Spiking Neural Network Architecture, Scientific REPORTS, Nature, | (2018) 8:8912 | DOI:10.1038/s41598-018-27169-8; https://www.nature.com/articles/s41598-018-27169-8
     

  10. Sengupta, N., McNabb, C. B., Kasabov, N., & Russell, B. R. (2018). Integrating Space, Time, and Orientation in Spiking Neural Networks: A Case Study on Multimodal Brain Data Modelling. IEEE Transactions on Neural Networks and Learning Systems, 29(11). doi:10.1109/TNNLS.2018.2796023
     

  11. Doborjeh, G, Z., Doborjeh, M., Kasabov, N. (2017). Attentional Bias Pattern Recognition in Spiking Neural Networks from Spatio-Temporal EEG Data., Cognitive Computation, 10:35-48 2018, Springer. DOI: 10.1007/s12559-017-9517-x
     

  12. Doborjeh, M., Kasabov, N., & Doborjeh, Z. G. (2017). "Evolving, dynamic clustering of spatio/spectro-temporal data in 3D spiking neural network models and a case study on EEG data". Evolving Systems, 1-17, DOI: 10.1007/s12530-017-9178-8
     

  13. N. Kasabov, M. Doborjeh, Z. Doborjeh, Mapping, learning, visualisation, classification and understanding of fMRI data in the NeuCube Spatio Temporal Data Machine, IEEE Transactions of Neural Networks and Learning Systems, vol. 28,4, 887-899, 2017, DOI: 10.1109/TNNLS.2016.2612890
     

  14. N. Kasabov, L. Zhou, M. Gholami Doborjeh, J. Yang, “New Algorithms for Encoding, Learning and Classification of fMRI Data in a Spiking Neural Network Architecture: A Case on Modelling and Understanding of Dynamic Cognitive Processes”, IEEE Transaction on Cognitive and Developmental Systems, 2017, DOI: 10.1109/TCDS.2016.2636291.
     

  15. N. Kasabov, N. Scott, E.Tu, S. Marks, N.Sengupta, E.Capecci, M.Othman,M. Doborjeh, N.Murli,R.Hartono, J.Espinosa-Ramos, L.Zhou, F.Alvi, G.Wang, D.Taylor, V. Feigin,S. Gulyaev, M.Mahmoudh, Z-G.Hou, J.Yang, Design methodology and selected applications of evolving spatio- temporal data machines in the NeuCube neuromorphic framework, Neural Networks, v.78, 1-14, 2016. http://dx.doi.org/10.1016/j.neunet.2015.09.011.
     

  16. M.G.Doborjeh, N.Kasabov, Z.Doborjeh, A Spiking Neural Network Methodology and System for Learning and Comparative Analysis of EEG Data from Healthy versus Addiction Treated versus Addiction Not Treated Subjects, IEEE Trans. on Biomedical Engineering, Volume 63, Issue 9, Page: 1830-1841, 2016.
     

  17. E. Capecci,G.Wang , N. Kasabov, Analysis of connectivity in a NeuCube spiking neural network trained on EEG data for the understanding and prediction of functional changes in the brain: A case study on opiate dependence treatment, Neural Networks, vol.68,62-77, 2015.
     

  18. Kasabov, N. Evolving connectionist systems for adaptive learning and knowledge discovery: Trends and Directions, Knowledge Based Systems, 2015, (2015), http://dx.doi.org/10.1016/j.knosys.2014.12.032.
     

  19. Breen V;Kasabov N;Kamat AM;Jacobson E;Suttie JM;O'Sullivan PJ;Kavalieris L;Darling DG A holistic comparative analysis of diagnostic tests for urothelial carcinoma: A study of Cxbladder Detect, UroVysion® FISH, NMP22® and cytology based on imputation of multiple datasets,  (10.1186/s12874-015-0036-8),  BMC Medical Research Methodology 15(1):45 Article number ARTN 45 12 May 2015
     

  20. Kasabov, N. NeuCube: A Spiking Neural Network Architecture for Mapping, Learning and Understanding of Spatio-Temporal Brain Data, Neural Networks vol.52 (2014), pp. 62-76, http://dx.doi.org/10.1016/j.neunet.2014.01.006
     

  21. Liang., Hu., & Kasabov, N. (2013). Evolving Personalized Modelling System for Integrated Feature, Neighbourhood and Parameter Optimization utilizing Gravitational Search Algorithm. Evolving Systems. May, 2013, doi:10.1007/s12530-013-9081-x
     

  22. Kasabov, N., & Hu, Y. (2010, December). Integrated optimisation method for personalised modelling and case studies for medical decision support. International Journal of Functional Informatics and Personalised Medicine, 3(3), 236-256. doi:10.1504/IJFIPM.2010.039123
     

  23. N.Kasabov, Global, local and personalised modelling and profile discovery in Bioinformatics: An integratedapproach, Pattern Recognition Letters, Vol. 28, Issue 6 , April 2007, 673-685

Books

  1. Kasabov, N., Time-Space, Spiking Neural Networks and Brain-Inspired Artificial Intelligence,
    Springer (2018) 750p.,https://www.springer.com/gp/book/9783662577134


    More information, reviews and errata




     

  2. N. Kasabov (ed) Spring Handbook of Bio/and Neuroinformatics, Springer, 2014,
    https://link.springer.com/book/10.1007%2F978-3-642-30574-£


    More information and reviews


     

  3. Kasabov, N. Springer Verlag, London, (2007) 458p
     

  4. Benuskova, L. and N.Kasabov, Computational neuro-genetic modelling: Integrating bioinformatics and brain science data, information and knowledge via computational intelligence, Springer, New York, 2007, 290 pages
     

  5. Kasabov, N. Evolving connectionist systems: Methods and applications in bioinformatics, brain study and intelligent machines, Springer Verlag, London, (2003) 308p

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