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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



  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. J. Li, J. Liu, S. Zhou, Q. Zhang and N. K. Kasabov, "Learning a Coordinated Network for Detail-Refinement Multiexposure Image Fusion," in IEEE Transactions on Circuits and Systems for Video Technology, vol. 33, no. 2, pp. 713-727, Feb. 2023, doi: 10.1109/TCSVT.2022.3202692, (JSR, Q1, Electrical and Electronic Engineering, Media Technology).

  2. Z.Doborjeh, M.Doborjeh, A.Sumich, B.Singh, A.Merkin, S.Budhraja, W.Bin Goh, E.Lai, M.Williams, S.Tan, J.Lee, and N.Kasabov, Investigation of Social and Cognitive Predictors in Non-Transition Ultra-High-Risk’ Individuals for Psychosis Using Spiking Neural Networks, Schizophrenia, 9, 10 (2023),

  3. Wei Zhang, Zhenhong Jia, Jie Yang, Nikola K. Kasabov, A dual channel decomposition and remapping fusion model for low illumination images with a wide field of view, Signal Processing: Image Communication, Elsevier, Vol. 113, 2023, 116925, ISSN 0923-5965,  (, (SJR, Q1, Electrical and electronic engineering)

  4. Jiawei Lia, Jinyuan Liub, Shihua Zhoua, Qiang Zhang and Nikola K.Kasabov, Infrared and visible image fusion based on residual dense network and gradient loss, Infrared Physics and Technology, vol.128, Jan.2023, 104486,, (SJR, Q2, Atomic and Molecular Physics and Optics, Electronic, optical and magnetic materials).

  5. B.Singh, M.Doborjeh, Z.Doborjeh, S.Budhraja, S.Tan, A.Sumich, W.Goh, J.Lee, E.Lai and  N. Kasabov, Constrained Neuro-Fuzzy Inference Methodology for Explainable Personalized Modelling with Applications on Gene Expression Data, Scientific Reports, 13-456, 2023,, Nature Publ., 2023,

  6. Kasabov, Nikola; Tan, Yongyao Tan; Doborjeh, Maryam; Tu, Enmei; Yang, Jie (2023): Transfer Learning of Fuzzy Spatio-Temporal Rules in the NeuCube Brain-Inspired Spiking Neural Network: A Case Study on EEG Spatio-temporal Data. TechRxiv. Preprint.,, license CC BY 4.0), submitted to IEEE Transactions of  Fuzzy Systems, (SJR, Q1, Applied mathematics, Artificial intelligence, Computational theory and mathematics, Control and system engineering).

  7. Nikola K. Kasabov, Iman AbouHassan, Vinayak G.M. Jagtap, Parag Kulkarni, Spiking neural networks for predictive and explainable modelling of multimodal streaming data on the Case Study of Financial Time Series Data and online news, SREP, Nature, pre-print on the Research Square, DOI:, license CC BY 4.0, IEEE Xplore Full-Text PDF, (SJR, Q1, Multidisciplinary).

  8. Yang AHX, Kasabov NK, Cakmak YO. Prediction and Detection of Virtual Reality induced Cybersickness: A Spiking Neural Network Approach Using Spatiotemporal EEG Brain Data and Heart Rate Variability. Research Square; 2022. DOI: 10.21203/, Brain Informatics, Springer-Nature, 2023, (JSR Q1, Cognitive neuroscience, Computer science applications, Neurology).

  9. Samaneh Saeedinea, J., N.Kasabov, Proceedings Royal Society A: Engineering, Mathematics and Physics,  2023, submitted, (Q1, Engineering, Mathematics, Physics)

  10.  Mark Crook-Rumsey, Christina J. Howard, Zohreh Doborjeh, Maryam Doborjeh, Josafath Israel Espinosa Ramos, Nikola Kasabov & Alexander Sumich, Spatiotemporal EEG Dynamics of Prospective Memory in Ageing and Mild Cognitive Impairment, Cognitive Computation, Springer-Nature, COGN-D-21-00108, 1-16, 2022, (SJR, Q1, Cognitive neuroscience, computer science applications, computer vision and pattern recognition)

  11. Sensen Song, Zhenhong Jia, JieYang, Nikola Kasabov, Salient detection via the fusion of background-based and multiscale frequency-domain features, Information Sciences, Vol. 618, December 2022, Pages 53-71, (SJR Q1, Artificial Intelligence, Computer science applications, Control and system engineering, Information systems and management, Software, Theoretical computer science).

  12. Sensen Song, Zhenhong Jia, JieYang, Nikola Kasabov, Image Segmentation Based on Fuzzy Low-Rank Structural Clustering, IEEE Transactions on Fuzzy Systems, Nov. 2022, doi: 10.1109/TFUZZ.2022.3220925, (SJR, Q1, Applied mathematics, Artificial intelligence, Computational theory and mathematics, Control and system engineering)

  13. Baoqiang Shi, Zhenhong Jia, Jie Yang and Nikola Kasabov, Unsupervised Change Detection in Wide-Field Video Images Under Low Illumination, IEEE Transactions on Circuits and Systems for Video Technology, October 2022, doi: 10.1109/TCSVT.2022.3216457, (JSR, Q1, Electrical and Electronic Engineering, Media Technology).

  14. Alexander Hui Xiang Yang, Nikola Kasabov and Yusuf Ozgur Cakmak, Machine Learning Methods for the Study of Cybersickness: A Systematic Review, Brain Informatics, Springer-Nature, 9:24, 2022,, (JSR Q1, Cognitive neuroscience, Computer science applications, Neurology).

  15. Qing Dong, Shihua Zhou, Qiang Zhang, Nikola K. Kasabov, A class of 5D Hamiltonian conservative hyperchaotic systems with symmetry and multistability, Nonlinear Dynamics, 2022,, (JSR, Q1, Applied mathematics, Control and system engineering, Electrical and electronic engineering).

  16. Maryam Doborjeh, Zohreh Doborjeh, Alexander Merkin, Rita Krishnamurthi, Reza Enayatollahi, Valery Feigin, Nikola Kasabov, Personalised Spiking Neural Network Models of Clinical and Environmental Factors to Predict Stroke, Cognitive Computation, 14:2187–2202, 2022,, (SJR, Q1, Cognitive neuroscience, computer science applications, computer vision and pattern recognition)

  17. Guo, Lingli, Zhenhong Jia, Jie Yang, and Nikola K. Kasabov. 2022. "Detail Preserving Low Illumination Image and Video Enhancement Algorithm Based on Dark Channel Prior" Sensors, MDPI, 22, 85,1-20,, (SJR, Q1, Instrumentation).

  18. Doborjeh, Z., Hemmington, N., Doborjeh, M. and Kasabov, N. (2022), "Artificial intelligence: a systematic review of methods and applications in hospitality and tourism", International Journal of Contemporary Hospitality Management,, 34, no. 3 (2022), 1154-1176. (SJR, Q1, Tourism and hospitality management).  

  19. Chen, W.; Jia, Z.; Yang, J.; Kasabov, N.K. Multispectral Image Enhancement Based on the Dark Channel Prior and Bilateral Fractional Differential Model. Remote Sensing, MDPI, 2022, 14 (1), 233, 1-25,, (SJR, Q1, Earth and Planetary Sciences).

  20.  J. Wang, Z. Jia, H. Lai, J. Yang and N. K. Kasabov, "Object Tracking Based on a Time-Varying Spatio-Temporal Regularized Correlation Filter With Aberrance Repression," in IEEE Photonics Journal, doi: 10.1109/JPHOT.2022.3227118, VOL. 14, NO. 6, DECEMBER 2022 (SJR Q2, Atomic and molecular physics and optics, Electrical and Electronic Engineering).

  21. M. Doborjeh, Z.Doborjeh, A.Merkin, R.Krishnamurthi, R. Enayatollahi, V.Feigin, N.Kasabov, Personalised Spiking Neural Network Models of Clinical and Environmental Factors to Predict Stroke, Cognitive Computation,

  22. M. Doborjeh, Z.Doborjeh, A.Merkin, H.Bahrami, A.Sumich, R.Krishnamurthi, O. Medvedev, M.Crook-Rumsey, C.  Morgan, I.Kirk, P.Sachdev, H. Brodaty, K. Kang, W.Wen, V. Feigin, N. Kasabov, Personalised Predictive Modelling with Spiking Neural Networks of Longitudinal MRI Neuroimaging Cohort and the Case Study of Dementia, Neural Networks, vol.144, Dec.2021, 522-539,, 

  23. Dora S, Kasabov N. Spiking Neural Networks for Computational Intelligence: An Overview. Big Data and Cognitive Computing. 2021; 5(4):67.

  24. N.K. Kasabov, Spiking neural networks for deep learning and knowledge representation: Editorial. Neural Networks 119 (2019), 341-342,

  25. 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.

  26. K.Kumarasinghe, N.Kasabov, D.Taylor, Deep Learning and Deep Knowledge Representation in Spiking Neural Networks for Brain-Computer Interfaces, Neural Networks (2019), doi:

  27. Z. Doborjeh,  M. 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,

  28. 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)

  29. 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,, 2019,

  30. 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).

  31. 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,

  32. .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;

  33. 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

  34. 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

  35. 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

  36. N. Kasabov, M. Doborjeh, Z. Doborjeh, Mapping, learning, visualization, 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

  37. 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.

  38. 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.

  39. 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.

  40. 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.

  41. Kasabov, N. Evolving connectionist systems for adaptive learning and knowledge discovery: Trends and Directions, Knowledge Based Systems, 2015, (2015),

  42. 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

  43. 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,

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

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

  46. N.Kasabov, Global, local and personalized modelling and profile discovery in Bioinformatics: An integrated approach, Pattern Recognition Letters, Vol. 28, Issue 6 , April 2007, 673-685


  1. Kasabov, N., Time-Space, Spiking Neural Networks and Brain-Inspired Artificial Intelligence,
    Springer (2018) 750p.,

    More information, reviews and errata


  2. N. Kasabov (ed) Spring Handbook of Bio/and Neuroinformatics, Springer, 2014,£

    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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