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.  
   
    

 

CONFERENCES 2024

1. Invited talk, Unconventional Computing and Natural Computation (UCNC)2024, Pohang, 17-21.06.2024, https://sites.google.com/view/ucnc-2024

2. Keynote, 2024 6th International Conference on Computer Communication and the Internet (ICCCI 2024), Web: https://www.iccci.org/index.html , June 14-16, 2024, Tokyo Metropolitan University.

3. Keynote talk, 2024 3rd International Conference on Networks, Communications and Information Technology (CNCIT 2024), Xi'an, China, June 7-9, 2024, http://www.cncit.net/ 

4. Invited talk, Symposium on Advanced Computational Intelligence (SACI), Hungary, 21 May 2024.

5. Keynote talk, 2024 2nd Asia Conference on Computer Vision, Image Processing and Pattern Recognition (CVIPPR 2024), Xiamen, China, April 26-28, 2024, http://www.cvippr.net

6. Keynote Speaker and Advisory Chair at 2024 6th International Conference on Natural Language Processing (ICNLP 2024, www.icnlp.net), Xi'an, March 22-24, 2024. 

7. Keynote, 2024, 2nd International Conference on Mathematics, Computation and Modeling (CMCM 2024), Chengdu, China, March 15-17, 2024, https://cmcm2024.net, Keynote Speaker and Conference Chair .

 

publications

2024

Chapter

1. Kasabov, N. (2024) Brain-inspired evolving and spiking connectionist systems, Chapter 8  in:  “Artificial intelligence in the age of neural networks and brain computing”, edited by Robert Kozma, Cesare Alippi, Yoonsuck Choe, Francesco Carlo Morabito, Academic Press, Elsevier, pp.124-145, https://doi.org/10.1016/B978-0-323-96104-2.00007-5, https://www.sciencedirect.com/science/article/abs/pii/B9780323961042000075?via%3Dihub, ISBN 978-0-323-96104-2.

​

Journal Publications

1. Z. Guo, Z. Jia, L. Wang, D. Wang, G. Yang and N. Kasabov, "Constructing New Backbone Networks via Space-Frequency Interactive Convolution for Deepfake Detection," in IEEE Transactions on Information Forensics and Security, vol. 19, pp. 401-413, 2024, https://doi:10.1109/TIFS.2023.3324739.

2. Mishaim Malik, Benjamin Chong, Justin Fernandez, Vickie Shim, Nikola Kirilov Kasabov, Alan Wang , ID: bioengineering-2796461, Stroke lesion segmentation and deep learning: A comprehensive review, MDPI, Bioengineering 2024, 11, 86, https://doi.org/10.3390/ bioengineering1101008

3. D. Ni, Z. Jia, J. Yang and N. K. Kasabov, "Online Low-Light Sand-Dust Video Enhancement Using Adaptive Dynamic Brightness Correction and a Rolling Guidance Filter," in IEEE Transactions on Multimedia, vol. 26, pp. 2192-2206, 2024, doi: 10.1109/TMM.2023.3293276.

4. Nikola Kasabov, Basabdatta Sen Bhattacharya, Dharmik Patel, Naman Aggarwal, Tanmay Bankar, Iman AbouHassan, Cognitive Audio-Visual Associative Memories using Brain-inspired Spiking Neural Networks with Case Studies on Moving Object Recognition (submitted to IEEE Trans. Cognitive and Developmental Systems, 2023). 

 

2023

1. Xiangyang Ning, Qing Dong, Shihua Zhou,  Qiang Zhang, Nikola K. Kasabov, Construction of new 5D Hamiltonian conservative hyperchaotic system and its application in image encryption, Nonlinear Dynamics, Springer-Nature, 2023,  https://doi:10.1007/s111071-023-08866-0

2. Samuel Ming Xuan Tan,  Jie Yin Yee, Sugam Budhraja, Balkaran Singh, Zohreh Doborjeh, Maryam Doborjeh, Nikola Kasabov, Edmund Lai, Alexander Sumich, Jimmy Lee, Wilson Wen Bin Goh, RNA-sequencing of peripheral whole blood of individuals at ultra-high-risk for psychosis – A longitudinal perspective, Asian Journal of Psychiatry, Elsevier, 89 (2023) 103796,  https://doi.org/10.1016/j.ajp.2023.103796.

3. Qing Dong, Shihua  Zhou, Qiang Zhang, Nikola K. Kasabov,  A new five-dimensional non-Hamiltonian conservative hyperchaos system with multistability and transient properties, Chaos, Solitons and Fractals 175 (2023) 113998,  https://doi.org/10.1016/j.chaos.2023.113998, https://www.sciencedirect.com/science/article/abs/pii/S0960077923008998?via%3Dihub

4. Maciag, Piotr; Bembenik, Robert; Piekarzewicz Aleksandra,  Del Ser Lorente, Javier; Lopez Lobo, Jesus; Nikola Kasabov;, Effective Air Pollution Prediction by Combining Time Series Decomposition with Stacking and Bagging Ensembles of Evolving Spiking Neural Networks, Environmental Modelling and Software, vol.170, on line: 16.10.2023, Dec 2023, 105851,  https://doi.org/10.1016/j.envsoft.2023.105851; https://www.sciencedirect.com/science/article/pii/S1364815223002372

5. Ravi , N. Kasabov et al, (2023). From Quantum Computing to Quantum-inspired Computation for Neuromorphic Advancement – A Survey. TechRxiv. Preprint. https://doi.org/10.36227/techrxiv.24053250.v1 

6. Alexander Hui Xiang Yang, Cristian Galán-Augé, Nikola Kasabov, Yusuf Ozgur Cakmak  Machine Learning-Guided High-Definition Transcranial Direct Current Stimulation Prevents Cybersickness, Brain Stimulation, Elsevier, https://www.brainstimjrnl.com/, submitted 2023.

7. Alexander Hui Xiang Yang, Cristian Galán-Augé, Nikola Kasabov, Yusuf Ozgur Cakmak  Machine Learning-Guided High-Definition Transcranial Direct Current Stimulation Prevents Cybersickness, Nuromodulation, submitted 2023.

8. Nikola K. Kasabov, Helena Bahrami, Maryam Doborjeh, Alan Wang, Brain Inspired Spatio-Temporal Associative Memories for Neuroimaging Data: EEG and fMRI,  Bioengineering 2023, MDPI 10(12), 1341 https://doi.org/10.3390/bioengineering10121341, www.mdpi.com/journal/bioengineering (OA);  

9. AbouHassan, Iman; Kasabov, Nikola; Bankar, Tanmay; Garg, Rishabh; Sen Bhattacharya, Basabdatta (2023). PAMeT-SNN: Predictive Associative Memory for Multiple Time Series based on Spiking Neural Networks with Case Studies in Economics and Finance.  TechRxiv. Preprint. https://doi.org/10.36227/techrxiv.24063975.v1, preprint https://papers.ssrn.com/sol3/papers.cfm?abstract_id=4665533

10. Li, Jiawei; Liu, Jinyuan; Zhou, Shihua; Zhang, Qiang; Kasabov, Nikola, , "GeSeNet: A General Semantic-guided Network with Couple Mask Ensemble for Medical Image Fusion" , IEEE Transactions on Neural Networks and Learning Systems, DOI: https://doi.org/10.1109/TNNLS.2023.3293274, 21 July 2023.

11. S. Song, Z. Jia, J. Yang and N. Kasabov, "Image Segmentation Based on Fuzzy Low-Rank Structural Clustering," in IEEE Transactions on Fuzzy Systems, vol. 31, no. 7, pp. 2153-2166, July 2023, doi: https://doi.org/10.1109/TFUZZ.2022.3220925

12. Grace Wen, Vickie Shim, Samantha Jane Holdsworth, Justin Fernandez, Miao Qiao, Nikola Kasabov, Alan Wang Artificial Intelligence for Brain MRI Data Harmonization: A Systematic Review, Bioengineering, 10(4):397, Publisher MDPI, No.2259748, 2023, https://doi.org/10.3390/bioengineering10040397. (OA)

13. 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: https://doi.org/10.1109/TCSVT.2022.3202692. 

14. Zohreh Doborjeh, Maryam Doborjeh, Alexander Sumich, Balkaran Singh, Alexander Merkin, Sugam Budhraja, Wilson Wen Bin Goh, Edmund Lai, Margaret Williams, Samuel Tan, Jimmy Lee, and Nikola Kasabov, Investigation of Social and Cognitive Predictors in Non-Transition Ultra-High-Risk’ Individuals for Psychosis Using Spiking Neural Networks, Schizophrenia, 9, 10 (2023), https://doi.org/10.1038/s41537-023-00335-2 (OA) 

15. 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, https://doi.org/10.1016/j.image.2023.116925.

16. (https://www.sciencedirect.com/science/article/pii/S0923596523000073).

17. 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, https://doi.org/10.1016/j.infrared.2022.104486.

18. Balkaran Singh, Maryam Doborjeh, Zohreh Doborjeh, Sugam Budhraja, Samuel Tan, Alexander Sumich, Wilson Goh, Jimmy Lee, Edmund Lai, Nikola Kasabov, Constrained Neuro Fuzzy Inference Methodology for Explainable Personalised Modelling with Applications on Gene Expression Data, Scientific Reports, 13-456, 2023, https://doi.org/10.1038/s41598-022-27132-8, https://www.nature.com/articles/s41598-022-27132-8, Nature Publ., 2023 (OA). 

19. Kasabov, Nikola; Tan, Yongyao Tan; Doborjeh, Maryam; Tu, Enmei; Yang, Jie; Goh, Wilson; Lee, Jimmy  (2023): Transfer Learning of Fuzzy Spatio-Temporal Rules in the NeuCube Brain-Inspired Spiking Neural Network: A Case Study on EEG Spatio-temporal Data. IEEE Transactions on Fuzzy Systems, vol.31, issue 12, Dec.2023, 4542-4552, Print ISSN: 1063-6706, Online ISSN: 1941-0034, DOI: https://doi.org/10.1109/TFUZZ.2023.3292802, https://ieeexplore.ieee.org/document/10175605.

20. Martinez Seras Aitor, Del Ser Lorente Javier, Lopez Lobo Jesus, Pablo Garcia Bringas, Nikola Kasabov,  A Novel Out-of-Distribution Detection Approach for Spiking Neural Networks: Design, Fusion, Performance Evaluation and Explainability", Information Fusion, vol.100, Dec. 2023, 101943, https://doi.org/10.1016/j.inffus.2023.101943.

21. Iman AbouHassan, Nikola K. Kasabov, 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 on-line news, SREP, Springer-Nature, Sci Rep 13, 18367 (2023). https://doi.org/10.1038/s41598-023-42605-0 (pre-print on Research Square, https://www.researchsquare.com/article/rs-2262084/v1) (OA)

22. 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. Brain  Informatics, Springer-Nature (2023) 10:15, https://doi.org/10.1186/s40708-023-00192-w, July 2023. (OA)

23.  J. Lei, J. Li, J. Liu, S. Zhou, Q. Zhang and N. K. Kasabov, "GALFusion: Multi-Exposure Image Fusion via a Global–Local Aggregation Learning Network," in IEEE Transactions on Instrumentation and Measurement, vol. 72, pp. 1-15, 2023, Art no. 5011915, https://doi.org/10.1109/TIM.2023.3267525.

24. Wang, X.; Yang, J.; Kasabov, N.K. Integrating Spatial and Temporal Information for Violent Activity Detection from Video Using Deep Spiking Neural Networks. Sensors 2023, 23, 4532, https://doi.org/10.3390/s23094532. (OA)

25.  Sensen Song, Zhenhong Jia, Fei Shi, Junnan Wang, Jie Yang, Nikola Kasabov, Saliency optimization fused background feature with frequency domain features, Multimedia Tools and Applications, October 2023, doi: https://doi.org/10.1007/s11042-023-16760-5.

26.  Jiajun Lin, Zhenhong Jia, Jie Yang, and Nikola Kasabov, Two-stage change detection for low-illumination wide-field high-resolution video images based on the pixel extremum log-ratio operator, IEEE Tr CSVT, 2023, submitted.

27.  Samaneh Alsadat Saeedinia, Mohammad Reza Jahed Motlagh, Abbas Tafakhori, Nikola Kirilov Kasabov, Diagnostic biomarker discovery from brain EEG data with LSTM, reservoir-SNN and NeuCube: Methods and a pilot study on epilepsy vs migraine,  IEEE archive,  https://doi.org/10.36227/techrxiv.23514486.v1

28.   Sugam Budhraja, Maryam Doborjeh , Balkaran Singh, TAN MING XUAN SAMUEL, Zohreh Doborjeh, Edmund Lai, Alexandr Merkin, Jimmy Lee, Wilson Goh, Nikola Kasabov, Filter and Wrapper Stacking Ensemble (FWSE): A Robust Approach for Reliable Biomarker Discovery in High-Dimensional Omics Data" (BIB-23-0262.R2), Briefings in Bioionfrmatics, 2023.    Volume 24, Issue 6, November 2023, bbad382, https://doi.org/10.1093/bib/bbad382 (OA)

29.  Kasabov, Nikola (2023). STAM-SNN: Spatio-Temporal Associative Memories in Brain-inspired Spiking Neural Networks: Concepts and Perspectives. TechRxiv. Preprint. https://doi.org/10.36227/techrxiv.23723208.v1

30. Xuanle Zhou, Maryam Doborjeh, Nikola Kirilov Kasabov, Zohreh Doborjeh, Integrating Local STDP and Global Backpropagation Learning Using Spike-Time and Spike-Rate Representations for Improved Classification of Spatiotemporal Data in the NeuCube Brain Inspired Evolving Spiking Neural Network Architecture: A Case Study on EEG Brain Data, January 2023, preprint, DOI:10.2139/ssrn.4617064.

 

2022

1. 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  Mild Cognitive Impairment, Cognitive Computation, Spinger-Nature, COGN-D-21-00108, 1-16, 2022 https://link.springer.com/article/10.1007/s12559-022-10075-7. (OA)

2.  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, https://doi.org/10.1016/j.ins.2022.10.103

3. 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)

4. 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).

5. 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, https://doi.org/10.1186/s40708-022-00172-6, (JSR Q1, Cognitive neuroscience, Computer science applications, Neurology). (OA)

6. Qing Dong, Shihua Zhou, Qiang Zhang, Nikola K. Kasabov, A class of 5D Hamiltonian conservative hyperchaotic    systems with symmetry and multistability, Nonl. Dynamics, 2022, https://doi.org/10.1007/s11071-022-07735-6.

7. 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, https://doi.org/10.1007/s12559-021-09975-x, (SJR, Q1, Cognitive neuroscince, computer science applications, computer vision and pattern recognition) (OA)

8. 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, https://doi.org/10.3390/s22010085, (SJR, Q1, Instrumentation). (OA)

9. 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, https://doi.org/10.1108/IJCHM-06-2021-0767, 34, no. 3 (2022), 1154-1176.

10. 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, https://doi.org/10.3390/rs14010233,

11.  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,

12. https://doi: 10.1109/JPHOT.2022.3227118, VOL. 14, NO. 6, DECEMBER 2022.  

 

2021

1. Dora S, Kasabov N. Spiking Neural Networks for Computational Intelligence: An Overview. Big Data and Cognitive Computing. 2021; 5(4):67. https://doi.org/10.3390/bdcc5040067

2. 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 ofr Dementia, Neural Networks, vol.144, Dec.2021, 522-539, https://doi.org/10.1016/j.neunet.2021.09.013,  

3. Doborjeh, M.; Doborjeh, Z.; Kasabov, N.; Barati, M.; Wang, G.Y. Deep Learning of Explainable EEG Patterns as Dynamic Spatiotemporal Clusters and Rules in a Brain-Inspired Spiking Neural Network. Sensors 2021, 21, 4900. https://doi.org/10.3390/s21144900 

4. Patrick A Gladding, Zina Ayar, Kevin Smith, Prashant Patel, Julia Pearce, Shalini Puwakdandawa, Dianne Tarrant, Jon Atkinson, Elizabeth McChlery, Merit Hanna, Nick Gow, Hasan Bhally, Kerry Read, Prageeth Jayathissa, Jonathan Wallace, Sam Norton, Nikola K Kasabov, Cristian S Calude, Deborah Steel, Colin Mckenzie, A machine learning PROGRAM to identify COVID-19 and other diseases from hematology data, Future Science, OA, Published Online:12 Jun 2021, https://doi.org/10.2144/fsoa-2020-0207

5. Samaneh Alsadat Saeedinia, Mohammad Reza Jahed-Motlagh1, Abbas Tafakhori, Nikola Kasabov, Design of MRI Structured Spiking Neural Networks and Learning Algorithms for Personalized Modelling, Analysis,  and Prediction of EEG Signals, Nature,  Scientific Reports, June (2021) 11:12064, https://doi.org/10.1038/s41598-021-90029-5

6. C.Tan, M.Sarlija, N.Kasabov, NeuroSense: Short-Term Emotion Recognition and Understanding Based on Spiking Neural Network Modelling of Spatio-Temporal EEG Patterns, Neurocomputing, paper No.23238, 2021, https://authors.elsevier.com/sd/article/S0925-2312(20)32010-5

7. Sanders, P.J.; Doborjeh, Z.G.; Doborjeh, M.G.; Kasabov, N.K.; Searchfield, G.D. Prediction of Acoustic Residual Inhibition of Tinnitus Using a Brain-Inspired Spiking Neural Network Model. Brain Sci. 2021, 11, 52. https://doi.org/10.3390/brainsci11010052

8. Kumarasinghe, K., Kasabov, N. & Taylor, D. Brain-inspired spiking neural networks for decoding and understanding muscle activity and kinematics from electroencephalography signals during hand movements. Sci Rep 11, 2486 (2021). https://doi.org/10.1038/s41598-021-81805-4; https://www.nature.com/articles/s41598-021-81805-4

9. Aiwen Jia, Zhenhong Jia, Jie Yang, and Nikola k. Kasabov, Single-image snow removal based on an

10. attention mechanism and a generative adversarial network, IEEE Access, 6 Jan. 2021, vol.9, 12852-12860, ISSN: 2169-3536, DOI: 10.1109/ACCESS.2021.3051359.

11. Urtats Etxegarai, EvaPortillo, Jon Irazusta, LucienKoefoed, NikolaKasabov, A heuristic approach for lactate threshold estimation for training decision-making: An accessible and easy to use solution for recreational runners, European Jiournal of Operational Research, Elsevier, 291, 427-437, https://www.journals.elsevier.com/european-journal-of-operational-research.

12. Y. Xin, Z. Jia, J. Yang and N. K. Kasabov, "Specular Reflection Image Enhancement Based on a Dark Channel Prior," in IEEE Photonics Journal, vol. 13, no. 1, pp. 1-11, Feb. 2021, Art no. 6500211,  https://doi.org/10.1109/JPHOT.2021.3053906.

13. Hengyuan Liu, Guibin Lu,Yangjun Wang, Nikola Kasabov, Evolving spiking neural network model for PM2.5 hourly concentration prediction based on seasonal differences: A  case study on data from Beijing and Shanghai, Aerosol and Air Quality Research, vol.21, Issue 2, Feb. 2021, 200247, https://doi.org/10.4209/aaqr.2020.05.0247

14. Yongji Li, Rui Wu, Zhenhong Jia *, Jie Yang, Nikola Kasabov, Video Desnowing and Deraining via Saliency and Dual Adaptive Spatiotemporal Filtering, Sensors, MDPI, Nov.2021, 21, 7610. https:// doi.org/10.3390/s21227610

15. Z. Huang, Z. Jia, J. Yang and N. K. Kasabov, "An Effective Algorithm for Specular Reflection Image Enhancement," in IEEE Access, vol. 9, pp. 154513-154523, 2021, doi: 10.1109/ACCESS.2021.3128939.

16. Wei, Y.; Jia, Z.; Yang, J.; Kasabov, N.K. High-Brightness Image Enhancement Algorithm. Appl. Sci. 2021, 11, 11497,  https://doi.org/ 10.3390/app112311497

17. Shaoxia Xu, Yuan Liu, Shihua Zhou, Qiang Zhang, Nikola K. Kasabov, DNA Matrix Operation Based on the Mechanism of the DNAzyme Binding to Auxiliary Strands to Cleave the Substrate, Manuscript ID: biomolecules-1445615, Biomolecules, MDPI, 2021, 11, 1797,  https://doi.org/ 10.3390/biom11121797

18. Enmei Tu, Zihao Wang, Jie Yang, Nikola Kasabov, Deep Semi-Supervised Learning via Dynamic Anchor Graph Embedding in Latent Space, Neural Networks, Nov. 2021, NN5051, https://www.sciencedirect.com/science/article/pii/S0893608021004676, DOI: https://doi.org/10.1016/j.neunet.2021.11.026

 

2020

1. Zohreh Doborjeh, Maryam Doborjeh, Mark Crook‐Rumsey, Tamasin Taylor, Grace Y. Wang, David Moreau, Christian Krägeloh, Wendy Wrapson, Richard J. Siegert, Nikola Kasabov, Grant Searchfield, Alexander Sumich,  Interpretability of Spatiotemporal Dynamics of Brain Processes Followed by Mindfulness Intervention in a Brain‐Inspired Spiking Neural Network Architecture, Sensors, MDPI, Switzerland, December, 2020,  https://doi.org/10.3390/s20247354, https://www.mdpi.com/1424-8220/20/24/7354

2. M.Durai, P.Sanders, Z.Doborjeh, M.Doborjeh, N.Kasabov, G.D.Searchfield, Prediction of tinnitus masking benefit within a case series using a spiking neural network model, Progress in Brain Research, Elseviewer, 2020, https://doi.org/10.1016/bs.pbr.2020.08.003

3. Yaqiao Cheng, Zhenhong Jia, Huicheng Lai, Jie Yang, Nikola k. Kasabov, A Fast Sand-Dust Image Enhancement Algorithm by Blue Channel Compensation and Guided Image Filtering , IEEE Access, 2020, DOI10.1109/ACCESS.2020.3034151

4. Sensen Song, Zhenhong Jia, Jie Yang, and Nikola K. Kasabov, A Fast Image Segmentation Algorithm Based on Saliency Map and Neutrosophic Set Theory, IEEE Photonics journal, vol.12, No.5, 1-16, Oct.2020, Paper No. 3901016, 10.1109/JPHOT.2020.3026973.

5. Shihua Zhou, Pinyan He, Nikola Kasabov, A Dynamic DNA Color Image Encryption Method Based on SHA-512 Entropy, 2020, Entropy-936332, http://www.mdpi.com/journal/entropy/

6. J. Wang, Z. Jia, H. Lai, J. Yang and N. K. Kasabov, "A Multi-Information Fusion Correlation Filters Tracker," in IEEE Access, vol. 8, pp. 162022-162040, 2020, doi: 10.1109/ACCESS.2020.3021235.

7. Clarence Tan; Gerardo Ceballos; Nikola Kasabov; Narayan Subramaniyam, FusionSense: Emotion Classification using Feature Fusion of Multimodal Data and Deep learning in a Brain-inspired Spiking Neural Network, Sensors (ISSN 1424-8220), MDPI Publisher, September 2020

8. B.Kelsen, A.Sumich, N.Kasabov, S.Liang, G.Wang, What has social neuroscience learned from hyperscanning studies of spoken communication? A systematic review. Neuroscience&Biobehavioural Reviews, 3 September 2020, https://doi.org/10.1016/j.neubiorev.2020.09.008; https://www.sciencedirect.com/science/article/abs/pii/S0149763420305650

9. B. Yang, Z. Jia, J. Yang and N. K. Kasabov, "Video Snow Removal Based on Self-adaptation Snow Detection and Patch-based Gaussian Mixture Model," in IEEE Access, vol.8, 160188-160201, Print ISSN: 2169-3536, On-line ISSN: 2169-3536, doi: 10.1109/ACCESS.2020.3020619.

10. Tan, C., Šarlija, M. & Kasabov, N. Spiking Neural Networks: Background, Recent Development and the NeuCube Architecture. Neural Process Lett., 52, 1675-1701 (2020). https://doi.org/10.1007/s11063-020-10322-8

11. Yong Zhu, Zhenhong Jia, Jie Yang and Nikola K. Kasabov, Change Detection in Multitemporal Monitoring Images Under Low Illumination, IEEE Access, vol.8, 2020, 126700 – 126712,  DOI: https://doi.org/10.1109/ACCESS.2020.3008262

12. Anup Vanarse,  Josafath Israel Espinosa-Ramos, Adam Osseiran, Alexander Rassau, Nikola Kasabov Application of a Brain-Inspired Spiking Neural Network Architecture to Odour Data Classification, Manuscript ID: Sensors-778506; 2020.

13. Zhi Li, Zhenhong Jia, Jie Yang, Nikola Kasabov, Low Illumination Video Image Enhancement, IEEE Photonics Journal, Volume 12, Number 4, August 2020 (open access), DOI: 10.1109/JPHOT.2020.3010966

14. Y.Cheng, Z.Jia, H.Lai, J.Yang, N.Kasabov, Blue Channel and Fusion for Sandstorm Image Enhancement,  IEEE Access,  Issue date December 2020, vol.8, issue 1, 66931-66940, DOI: 10.1109/ACCESS.2020.2985869

15. Z.Li, Z.Jia, L.Liu, J.Yang, N.Kasabov, A method to improve the accuracy of SAR image change detection by using an image enhancement method, ISPRS Journal of Photogrametry and Remote Sensing, Elsevier, vol.163, May 2020, 137-151.

16. Zuo, J., Jia, Z., Yang, J., Kasabov, N.Moving object detection in video sequence images based on an improved visual background extraction algorithm, Multimedia Tools and Applications, 2020, 79(39-40), pp. 29663–29684. DOI:10.1007/s11042-020-09530-0, Corpus ID: 221110480

17. Li, Z., Jia, Z., Yang, J., Kasabov, N.An efficient and high quality medical CT image enhancement algorithm, International Journal of Imaging Systems and Technology, 2020, 30(4), pp. 939–949

18. Alexander G. Merkin, Oleg N. Medvedev, Perminder S. Sachdev, Lynette Tippett, Rita Krishnamurthi, Susan Mahon, Nikola Kasabov, Priya Parmar, John Crawford, Zohreh G. Doborjeh, Maryam G. Doborjeh, Kristan Kang, Nicole A.Kochan, Helena Bahrami, Henry Brodaty, Valery L.Feigin New avenue for the geriatric depression scale: Rasch transformation enhances reliability of assessment, Journal of Affective Disorders, Volume 264, 1 March 2020, Pages 7-14, https://doi.org/10.1016/j.jad.2019.11.100

 

2019

1. Ren, R., Guo, Z., Jia, Z., Yang, J., Kasabov, N., Li, G., Speckle Noise Removal in Image-based Detection of Refractive Index Changes in Porous Silicon Microarrays. Sci Rep 9, 15001 (2019). https://doi.org/10.1038/s41598-019-51435-y

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

3. Wei Q, Kasabov N, Polycarpou M, Zeng Z, Deep learning neural networks: Methods, systems, and applications, Neurocomputing 2019, https://doi.org/10.1016/j.neucom.2019.03.073

4. Lobo JL, Del Ser J, Bifet A, Kasabov N Spiking Neural Networks and online learning: An overview and perspectives, Neural Networks 121:88-100 2020, on-line publications 2019:  https://www.sciencedirect.com/science/article/pii/S0893608019302655?via%3Dihub

5. 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.

6. K.Kumarasinghe, N.Kasabov, D.Taylor, Deep Learning and Deep Knowledge Representation in Spiking Neural Networks for Brain-Computer Interfaces, Neural Networks, vol.121, Jan 2020, 169-185, doi: https://doi.org/10.1016/j.neunet.2019.08.029.

7. 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, https://www.nature.com/articles/s41598-019-42863-x.

8. J.Espinosa-Ramos, E.Capecci, N.Kasabov, A Computational Model of Neuroreceptor-Dependent Plasticity (NRDP) Based on Spiking Neural Networks, IEEE Transactions on Cognitive and Developmental Systems, March, 2019,  Vol. 11, Issue:1, 63-72, DOI: 10.1109/TCDS.2017.2776863

9. 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)

10. B.Petro, N.Kasabov, R.Kiss, Selection and optimisation of spike encoding methods for spiking neural networks, algorithms, IEEE Transactions of  Neural Networks and Learning Systems, April 2019, DOI:10.1109/TNNLS.2019.2906158, vol.31, Issue 2, 358-370.

11. P. S. Maciag, N. K. Kasabov, M. Kryszkiewicz, R. Bembenik, Air pollution prediction with clustering-based ensemble of evolving spiking neural networks and a case study on London area, Environmental Modelling and Software, Elsevier, vol.118, 262-280,  2019, https://doi.org/10.1016/j.envsoft.2019.04.012

12. https://www.sciencedirect.com/science/article/pii/S1364815218307448?dgcid=author.

13. Laña I, Lobo JL, Capecci E, Del Ser J, Kasabov N, Adaptive long-term traffic state estimation with evolving spiking neural networks, Transportation Research Part C: Emerging Technologies 101:126-144 2019, https://doi.org/10.1016/j.trc.2019.02.011

14. 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 11, 599–613 (2020). https://doi.org/10.1007/s12530-019-09269-6; https://link.springer.com/article/10.1007%2Fs12530-019-09269-6

15. Arriandiaga, E. Portilio, I.Espinosa, N.Kasabov, Pulse-Width Modulation based Algorithm for Spike Phase Encoding and Decoding of Time Dependent  Analog Data, IEEE Transactions on Neural Networks and Learning Systems, vol 31, issue 10, 3920-3931, Print ISSN: 2162-237X Online ISSN: 2162-2388, DOI: 10.1109/TNNLS.2019.2947380.

16. Etxegarai U, Portillo E, Irazusta J, Koefoed LA, Kasabov N, A heuristic approach for lactate threshold estimation for training decision-making: An accessible and easy to use solution for recreational runners, European Journal of Operational Research, https://doi.org/10.1016/j.ejor.2019.08.023

17. L. Ma, Z. Jia, Y. Yu, J. Yang, and N.K. Kasabov, Multi-Spectral Image Change Detection Based on Band Selection and Single-Band Iterative Weighting, IEEE Access, vol.7, 2019, date of publication March 4, 2019, DOI:  10.1109/ACCESS.2019.2901286.

18. Unhui Zuo, Zhenhong Jia, Jie Yang, and Nikola Kasabov, Moving Target Detection Based on Improved Gaussian Mixture Background Subtraction in Video Images, October 31, 2019, DOI: 10.1109/ACCESS.2019.2946230.

19. L. Ma, Z. Jia, Y. Yu, J. Yang, and N.K. Kasabov, SAR Image Change Detection Based on Mathematical Morphology and the K-Means Clustering Algorithm, IEEE Access,  2019; Vol.7, 1, DOI: 10.1109/ACCESS.2019.2908282

20. Ma L, Zhenhong J, Yang J, Kasabov N Multi-spectral image change detection based on single-band iterative weighting and fuzzy C-means clustering European Journal of Remote Sensing 53(1):201, https://www.tandfonline.com/doi/full/10.1080/22797254.2019.1707124

21. Li L, Wang L, Wang Z, Jia Z, Si Y, Yang J, Kasabov N A novel medical image fusion approach based on nonsubsampled shearlet transform, Journal of Medical Imaging and Health Informatics 9(9):1815-1826 2019

22. Chen M, Zheng H, Lu C, Tu E, Yang J, Kasabov N, Accurate breast lesion segmentation by exploiting spatio-temporal information with deep recurrent and convolutional network Journal of Ambient Intelligence and Humanized Computing 2019,  https://link.springer.com/article/10.1007%2Fs12652-019-01551-4

23. Ren R, Jia Z, Yang J, Kasabov N Applying Speckle Noise Suppression to Refractive Indices Change Detection in Porous Silicon Microarrays, Sensors (Basel) 19(13):2019, https://doi.org/10.3390/s19132975

24. Li L, Wang L, Jia Z, Si Y, Yang J, Kasabov N A Practical Medical Image Enhancement Algorithm Based on Nonsubsampled Contourlet Transform, Journal of Medical Imaging and Health Informatics 9(5):1046-1056 2019

25. Yang X, Jia Z, Yang J, Kasabov N, Change Detection of Optical Remote Sensing Image Disturbed by Thin Cloud Using Wavelet Coefficient Substitution Algorithm. Sensors (Basel, Switzerland) 19(9):2019, https://doi.org/10.3390/s19091972

26. Qingrong G, Zhenhong J, Jie Y, Kasabov N Contrast enhancement of medical images using fuzzy set theory and nonsubsampled shearlet transform, International Journal of Imaging Systems and Technology 29:483-490 2019, https://doi.org/10.1002/ima.22326.

27. Ren R, Jia Z, Yang J, Kasabov NK, Huang X, Quasi-noise-free and detail-preserved digital holographic reconstruction, IEEE Access 7:52155-52167 2019, DOI: 10.1109/ACCESS.2019.2910187

28. Liu L, Jia Z, Yang J, Kasabov NK SAR Image Change Detection Based on Mathematical Morphology and the K-Means Clustering Algorithm, IEEE Access 7:43970-43978 2019, DOI: 10.1109/ACCESS.2019.2908282

 

2018

1. 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).

2. 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, vol.16, No.2 2019,  https://iopscience.iop.org/article/10.1088/1741-2552/aafabc

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

4. Paulun, L., Wendt, A., & Kasabov, N. (2018). A retinotopic spiking neural network system for accurate recognition of moving objects using NeuCube and dynamic vision sensors. Frontiers in Computational Neuroscience, 12, p.42, doi:10.3389/fncom.2018.00042

5. 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

6. Doborjeh, G, Z., Doborjeh, M., Kasabov, N. (2018). 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

7. Al Zoubi, M.Awad, N.Kasabov, Anytime multipurpose emotion recognition from EEG data using a Liquid State Machine based framework, Artificial Intelligence in Medicine, 86,  2018. 1-8, https://www.sciencedirect.com/science/article/pii/S0933365717302804.

8. Chen P, Jia Z, Yang J, Kasabov N., Robust Visual Tracking via Dirac-Weighted Cascading Correlation Filters", IEEE Signal Processing Letters, IEEE Explore. Issue Date: November 2018 Volume: 25, Issue:11 On Page(s): 1700-1704·  Print , ISSN: 1070-9908 Online ISSN: 1558-2361, DOI: 10.1109/LSP.2018.2871883

9. Peng, C., Liu, F., Yang, J., & Kasabov, N. (2018). Densely Connected Discriminative Correlation Filters for Visual Tracking. IEEE Signal Processing Letters, 25(7). doi:10.1109/LSP.2018.2836360

10. Peng, C., Liu, F., Yang, J., & Kasabov, N. (2018). Robust Visual Tracking via Dirac-Weighted Cascading Correlation Filters. IEEE Signal Processing Letters, 25(11), 1700-1704. doi:10.1109/LSP.2018.2871883

11. Chen, P., Jia, Z., Yang, J., & Kasabov, N. (2018). Unsupervised Change Detection of SAR Images Based on an Improved NSST Algorithm. Journal of the Indian Society of Remote Sensing, 46(5), 801-808. doi:10.1007/s12524-017-0740-4

12. Huang X, Jia Z, Zhou J, Yang J, Kasabov N, Speckle Reduction of Reconstructions of Digital Holograms Using Gamma-Correction and Filtering,  IEEE Access 6:5227-5235, 2018.

13. Wenyan, Z., Zhenhong, J., Yu, Y., Yang, J., & Kasabov, N. (2018). SAR image change detection based on equal weight image fusion and adaptive threshold in the NSST domain. European Journal of Remote Sensing, 51(1), 785-794. doi:10.1080/22797254.2018.1491804

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