Publications
This page lists publications by researchers in the AUT Knowledge Engineering and Discovery Research Institute. Titles are ordered from newest to oldest within their respective categories.
Journal papers
- N.Kasabov, Y.Tao, M.Doborjeh, E.Tu, J.Yang, Transfer Learning and Knowledge Representation of Time-Space Data Using the NeuCube Brain-Inspired Spiking Neural Network Architecture, IEEE Transacions of neural networks and learning systems, (submitted May 2021, under review).
- 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, COGN-D-20-00511R2, https://www.springer.com/journal/12559; https://doi.org/10.1007/s12559-021-09975-x, https://link.springer.com/content/pdf/10.1007/s12559-021-09975-x.pdf
- Doborjeh, Z., Hemmington, N., Doborjeh, M. and Kasabov, N. (2021), "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
- 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, COGN-D-20-00511R2, 26, Nov.2021, https://www.springer.com/journal/12559.
- 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
- 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,
- 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
- 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
- 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
- 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
- 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
- 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
- Aiwen Jia, Zhenhong Jia, Jie Yang, and Nikola k. Kasabov, Single-image snow removal based on an 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.
- 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.
- 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.
- 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
- 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
- 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.
- 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
- 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
- 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
- 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
- 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
- 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
- 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.
- 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/
- 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.
- 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
- 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
- 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.
- 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
- 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
- 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
- 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
- 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.
- 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
- 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
- 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
- 2020 - Jansari, V., & Pears,R. (2020). A Positive-Confidence based approach to Classifying Imbalanced data: A Case Study on Hepatitis. American Journal of Biomedical Science & Research, 8, 457–461. https://doi.org/10.34297/AJBSR.2020.08.001319
- 2020 - Kaushalya Kumarasinghe, Nikola Kasabov, Denise Taylor: Deep learning and deep knowledge representation in Spiking Neural Networks for Brain-Computer Interfaces. Neural Networks 121: 169-185 (2020)
- 2020 - A. Vanarse, J.I. Espinosa-Ramos, A. Osseiran, A. Rassau, A. Kasabov, “Application of a Brain-Inspired Spiking Neural Network Architecture to Odor Data Classification”, Sensors. May, 2020, 20(10), 2756, doi: https://doi.org/10.3390/s20102756
- 2019 - Capecci, E., Lobo, J. L., Laña, I., Espinosa-Ramos, J. I., & Kasabov, N. (2019). Modelling gene interaction networks from time-series gene expression data using evolving spiking neural networks. Evolving Systems, 1-15.
- 2019 - Maciąg, P. S., Kasabov, N., Kryszkiewicz, M., & Bembenik, R. (2019). Air pollution prediction with clustering-based ensemble of evolving spiking neural networks and a case study for London area. Environmental Modelling & Software, 118, 262-280.
- 2019 - Li, L., Wang, L., Jia, Z., Si, Y., Yang, J., & Kasabov, N. (2019). A Practical Medical Image Enhancement Algorithm Based on Nonsubsampled Contourlet Transform. Journal of Medical Imaging and Health Informatics, 9(5), 1046-1056.
- 2019 - Ren, R., Guo, Z., Jia, Z., Yang, J., Kasabov, N. K., & Li, C. (2019). Speckle noise Removal in image-based Detection of Refractive index changes in porous Silicon Microarrays. Scientific reports, 9(1), 1-14.
- 2019 - A. Arriandiaga, E. Portillo, J. I. Espinosa-Ramos and N. K. Kasabov, "Pulsewidth Modulation-Based Algorithm for Spike Phase Encoding and Decoding of Time-Dependent Analog Data", IEEE Transactions on Neural Networks and Learning Systems, November, 2019, doi: 10.1109/TNNLS.2019.2947380.
- 2019 - J. I. 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.
- 2019 - J. Behrenbeck1, Z. Tayeb C. Bhiri, C. Richter, O. Rhodes, N. Kasabov, J. I. Espinosa-Ramos, S. Furber, G. Cheng and J. Conradt, "Classification and regression of spatio-temporal signals using NeuCube and its realization on SpiNNaker neuromorphic hardware", Journal of Neural Engineerging, February, 2019, https://doi.org/10.1088/1741-2552/aafabc.
- 2019 - 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. (Q1, artificial intelligence)
- 2019 - 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 (Q1, artificial intelligence)
- 2019 - 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(Q1, artificial intelligence)
- 2019 - 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 (Q1, neurology)
- 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 (Q1, clinical psychology; mental health)
- 2019 - 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. (Q1, artificial intelligence)
- 2019 - P. S. P Maciaga, N. K. Kasabov, M. Kryszkiewicza, R. Benbenik, Prediction of Hourly Air Pollution in London Area Using Evolving Spiking Neural Networks, Environmental Modelling and Software, Elsevier, vol.118, 262-280, 2019, https://www.sciencedirect.com/science/article/pii/S1364815218307448?dgcid=author. (Q1, ecological modelling; environmental engineering, software)
- 2019 - 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 (Q1, automotive engineering; computer science)
- 2019 - 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 Print ISSN: 2162-237X Online ISSN: 2162-2388, DOI: 10.1109/TNNLS.2019.2947380. (Q1, artificial intelligence)
- 2019 - 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 (Q1 computer science, information systems, modelling and simulation)
- 2019 - 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. (Q1 computer science; Engineering)
- 2019 - 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. (Q1 computer science; Engineering)
- 2019 - 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 (Q1 computer science; Engineering)
- 2019 - 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 (Q1 computer science; Engineering)
- 2019 - 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 (Q1 computer science; Engineering)
- 2019 - 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 (Q2, applied mathematics)
- 2019 - 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://doi.org/10.1007/s12530-019-09269-6, 2019. (Q2, computer science applications)
- 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 (Q2, analytical chemistry)
- 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 (Q2, analytical chemistry)
- 2019 - Merkin, A. G., Medvedev, O. N., Sachdev, P. S., Tippett, L., Krishnamurthi, R., Mahon, S., Doborjeh, M., ... & Doborjeh, Z. . (2019). New avenue for the Geriatric Depression Scale: Rasch transformation enhances reliability of assessment. Journal of Affective Disorders.
- 2019 - Doborjeh, M., Kasabov, N., Doborjeh, Z., Enayatollahi, R., Tu, E., & Gandomi, A. H. (2019). Personalised modelling with spiking neural networks integrating temporal and static information. Neural Networks, 119, 162. https://www.sciencedirect.com/science/article/pii/S0893608019302175
- 2019 - Doborjeh, Z., Doborjeh, M., Taylor, T., Kasabov, N., Wang, G. Y., Siegert, R., & Sumich, A. (2019). Spiking Neural Network Modelling Approach Reveals How Mindfulness Training Rewires the Brain. Nature Scientific reports, 9(1), 6367.https://www.nature.com/articles/s41598-019-42863-x.
- 2019 - Capecci, E., Lobo, J. L., Laña, I., Espinosa-Ramos, J. I., & Kasabov, N. (2019). Modelling gene interaction networks from time-series gene expression data using evolving spiking neural networks. Evolving Systems, 1-15. https://link.springer.com/article/10.1007/s12530-019-09269-6
- 2018 - Peng, C., Liu, F., Yang, J., & Kasabov, N. (2018). Robust Visual Tracking via Dirac Weighted Cascading Correlation Filters. IEEE Signal Processing Letters.
- 2018 - 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.
- 2018 - Huang, X., Jia, Z., Zhou, J., Yang, J., & Kasabov, N. (2018). Speckle reduction of reconstructions of digital holograms using Gamma-correction and filtering. IEEE Access, 6, 5227-5235.
- 2018 - Peng, C., Liu, F., Yang, J., & Kasabov, N. (2018). Densely Connected Discriminative Correlation Filters for Visual Tracking. IEEE Signal Processing Letters.
- 2018 - 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 Modeling. IEEE Transactions on Neural Networks and Learning Systems, (99), 1-15.
- 2018 - Paulun, L., Wendt, A., & Kasabov, N. K. (2018). A retinotopic spiking neural network system for accurate recognition of moving objects using NeuCube and dynamic vision sensors. Frontiers in Computational Neuroscience, 12, 42. https://www.frontiersin.org/articles/10.3389/fncom.2018.00042/full
- 2018 - Doborjeh, Z. G., Kasabov, N., Doborjeh, M. G., & Sumich, A. (2018). Modelling peri-perceptual brain processes in a deep learning spiking neural network architecture. Nature Scientific reports, 8(1), 8912. https://www.nature.com/articles/s41598-018-27169-8
- 2018 - Doborjeh, Z. G., Doborjeh, M. G., & Kasabov, N. (2018). Attentional bias pattern recognition in spiking neural networks from spatio-temporal EEG data. Cognitive Computation, 10(1), 35-48. https://link.springer.com/article/10.1007/s12559-017-9517-x
- 2018 - Kasabov, N. K., Doborjeh, M. G., & Doborjeh, Z. G. (2017). Mapping, learning, visualization, classification, and understanding of fMRI Data in the NeuCube evolving spatiotemporal data machine of spiking neural networks. IEEE transactions on neural networks and learning systems, 28(4), 887-899. https://www.ncbi.nlm.nih.gov/pubmed/27723607
- 2017 - Doborjeh, M. G., 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. https://link.springer.com/article/10.1007/s12530-017-9178-8
- 2017 - Kasabov, N., Zhou, L., Doborjeh, M. G., Doborjeh, Z. G., & Yang, J. (2017). New Algorithms for Encoding, Learning and Classification of fMRI Data in a Spiking Neural Network Architecture: A Case on Modeling and Understanding of Dynamic Cognitive Processes. IEEE Transactions on Cognitive and Developmental Systems, 9(4), 293-303. https://ieeexplore.ieee.org/document/7776755/
- 2017 - Espinosa-Ramos, J. I., Capecci, E., & Kasabov, N. (2017). A Computational Model of Neuroreceptor Dependent Plasticity (NRDP) Based on Spiking Neural Networks. IEEE Transactions on Cognitive and Developmental Systems. doi: 10.1109/TCDS.2017.2776863
- 2017 - Wang, X., Jia, Z., Yang, J., & Kasabov, N. (2017). Change detection in SAR images based on the logarithmic transformation and total variation denoising method. Remote Sensing Letters, 8(3), 214-223.
- 2017 - Ren, D., Jia, Z., Yang, J., & Kasabov, N. K. (2017). A Practical GrabCut Color Image Segmentation Based on Bayes Classification and Simple Linear Iterative Clustering. IEEE Access, 5, 18480-18487.
- 2017 - Zhou, F., Jia, Z., Yang, J., & Kasabov, N. (2017). Method of improved fuzzy contrast combined adaptive threshold in NSCT for medical image enhancement. BioMed Research International, 2017.
- 2017 - Liu, L., Jia, Z., Yang, J., & Kasabov, N. (2017). A remote sensing image enhancement method using mean filter and unsharp masking in non-subsampled contourlet transform domain. Transactions of the Institute of Measurement and Control, 39(2), 183-193.
- 2017 - Chen, P., Zhang, Y., Jia, Z., Yang, J., & Kasabov, N. (2017). Remote Sensing Image Change Detection Based on NSCT-HMT Model and Its Application. Sensors, 17(6), 1295.
- 2017 - Guo, Z., Jia, Z., Yang, J., Kasabov, N., & Li, C. (2017). Image Processing of Porous Silicon Microarray in Refractive Index Change Detection. Sensors, 17(6), 1335.
- 2017 - Doborjeh, G, Z., Doborjeh, M., Kasabov, N. (2017). Attentional Bias Pattern Recognition in Spiking Neural Networks from Spatio-Temporal EEG Data. Cognitive Computation, Springer. DOI: 10.1007/s12559-017-9517-x
- 2017 - 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
- 2017 - Alvi, F. B., Pears, R., & Kasabov, N. (2017). An evolving spatio-temporal approach for gender and age group classification with Spiking Neural Networks. Evolving Systems, 1-12
- 2017 - Kasabov, N. (Fellow IEEE). From Multilayer Perceptrons and Neuro-Fuzzy Systems to Deep Learning Machines: Which Method to Use? – A Survey. International Journal on Information Technologies and Security, No. 2 (vol. 9), 2017, pp. 3-24.
- 2017 - Tu, E., Kasabov, N., & Yang, J. (2017). Mapping temporal variables into the neucube for improved pattern recognition, predictive modeling, and understanding of stream data. IEEE transactions on neural networks and learning systems, 28(6), 1305-1317.
- 2017 - Liu, L., Jia, Z., Yang, J., & Kasabov, N. (2017). A remote sensing image enhancement method using mean filter and unsharp masking in non-subsampled contourlet transform domain. Transactions of the Institute of Measurement and Control, 0142331215604210.
- 2017 - Wang, X., Jia, Z., Yang, J., & Kasabov, N. (2017). Change detection in SAR images based on the logarithmic transformation and total variation denoising method. Remote Sensing Letters, 8(3), 214-223.
- 2017 - Ge, C., Kasabov, N., Liu, Z., & Yang, J. (2017). A spiking neural network model for obstacle avoidance in simulated prosthetic vision. Information Sciences, 399, 30-42.
- 2017 - Sengupta, N., & Kasabov, N. (2017). Spike-time encoding as a data compression technique for the pattern recognition of temporal data. Information Sciences.
- 2017 - Kasabov, N. K., Doborjeh, M. G., & Doborjeh, Z. G. (2017). Mapping, Learning, Visualization, Classification, and Understanding of fMRI Data in the NeuCube Evolving Spatiotemporal Data Machine of Spiking Neural Networks. IEEE Transactions on Neural Networks and Learning Systems, 28(4), 887-899.
- 2016 - Lv, D., Jia, Z., Yang, J., & Kasabov, N. (2016). Remote sensing image enhancement based on the combination of nonsubsampled shearlet transform and guided filtering. Optical Engineering, 55(10), 103104-103104.
- 2016 - Kasabov, N., Zhou, L., Doborjeh, M. G., Doborjeh, Z. G., & Yang, J. (2016). 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 Transactions on Cognitive and Developmental Systems.
- 2016 - H. Wu, L. Gao and N. K. Kasabov, "Network-Based Method for Inferring Cancer Progression at the Pathway Level from Cross-Sectional Mutation Data," in IEEE/ACM Transactions on Computational Biology and Bioinformatics, vol. 13, no. 6, pp. 1036-1044, Nov.-Dec. 1 2016.
- 2016 - Gholami Doborjeh,M., Wang, G., Kasabov, N., Kydd, R., Roy Russell, B., 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 Transactions on Biomedical Engineering, vol. 63, no. 9,pp. 1830-1841. doi:10.1109/TBME.2015.2503400
- 2016 - Cheng, Y., Jin, Z., Gao, T., Chen, H., & Kasabov, N. (2016). An improved collaborative representation based classification with regularized least square (CRC–RLS) method for robust face recognition. Neurocomputing, 215, 250-259. doi:http://dx.doi.org/10.1016/j.neucom.2015.06.117
- 2016 - Gao, T., & Kasabov, N. (2016). Adaptive cow movement detection using evolving spiking neural network models. Evolving Systems, 1-9.
- 2016 - Bose, P., Kasabov, N. K., Bruzzone, L., & Hartono, R. N. (2016). Spiking Neural Networks for Crop Yield Estimation Based on Spatiotemporal Analysis of Image Time Series. IEEE Transactions on Geoscience and Remote Sensing. doi:10.1109/TGRS.2016.2586602
- 2016 - Li, L., Jia, Z., Yang, J., & Kasabov, N. (2016). Noisy Remote Sensing Image Segmentation with Wavelet Shrinkage and Graph Cuts. Journal of the Indian Society of Remote Sensing, 1-8. doi:10.1007/s12524-016-0561-x
- 2016 - Tu, E., Zhang, Y., Zhu, L., Yang, J., & Kasabov, N. (2016). A graph-based semi-supervised k nearest-neighbor method for nonlinear manifold distributed data classification. Information Sciences, 367-368, 673-688. doi:10.1016/j.ins.2016.07.016
- 2016 - Tu, E., Kasabov, N., & Yang, J. (2016). Mapping Temporal Variables Into the NeuCube for Improved Pattern Recognition, Predictive Modeling, and Understanding of Stream Data. IEEE Trans Neural Netw Learn Syst. doi:10.1109/TNNLS.2016.2536742
- 2016 - Wu, H., Gao, L., & Kasabov, N. (2016). Network-based method for inferring cancer progression at the pathway level from cross-sectional mutation data.. IEEE/ACM Trans Comput Biol Bioinform. doi:10.1109/TCBB.2016.2520934
- 2016 - Liu, R., Jia, Z., Qin, X., Yang, J., & Kasabov, N. (2016). SAR Image Change Detection Method Based on Pulse-Coupled Neural Network. Journal of the Indian Society of Remote Sensing, 1-8. doi:10.1007/s12524-015-0507-8
- 2016 - Kasabov, N., Scott, N., Tu, E., Marks, S., Sengupta, N., Capecci, E., . . . Yang, J. (2016). Evolving spatio-temporal data machines based on the NeuCube neuromorphic framework: Design methodology and selected applications. Neural Networks, 78, 1-14. doi:10.1016/j.neunet.2015.09.011
- 2016 - Wu, H., Gao, L., & Kasabov, N. (2015). Inference of cancer progression from somatic mutation data. ScienceDirect, 234-238. doi:10.1016/j.ifacol.2015.12.131
- 2015 - Gao, T., & Kasabov, N. (2015). A method used for Dotted Data Matrix image processing. Journal of Computational Methods in Science and Engineering, 15, 685-693. doi:10.3233/JCM-150581
- 2015 - Zhang, Y. -C., Jia, Z. -H., Qin, X. -Z., Yang, J., & Kasabov, N. (2015). Unsupervised detection of different SAR images based on improved NSCT domain image fusion algorithm. Guangdianzi Jiguang/Journal of Optoelectronics Laser, 26(10), 2023-2030. doi:10.16136/j.joel.2015.10.0252
- 2015 - Wang, J., Li, Q., Jia, Z., Kasabov, N., & Yang, J. (2015). A novel multi-focus image fusion method using PCNN in nonsubsampled contourlet transform domain. Optik, 126(20), 2508-2511. doi:10.1016/j.ijleo.2015.06.019
- 2015 - Liu, L., Jia, Z., Yang, J., & Kasabov, N. (2015). A medical image enhancement method using adaptive thresholding in NSCT domain combined unsharp masking. International Journal of Imaging Systems and Technology, 25(3), 199-205. doi:10.1002/ima.22137
- 2015 - Capecci, E., Kasabov, N., & Wang, G. Y. (2015). Analysis of connectivity in NeuCube spiking neural network models trained on EEG data for the understanding of functional changes in the brain: A case study on opiate dependence treatment. Neural Networks, 68, 62-77. doi:10.1016/j.neunet.2015.03.009
- 2015 - Breen, V., Kasabov, N., Kamat, A. M., Jacobson, E., Suttie, J. M., O'Sullivan, P. J., . . . Darling, D. G. (2015). 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. BMC Medical Research Methodology, 15(1). doi:10.1186/s12874-015-0036-8
- 2015 - Wu, H., Gao, L., Li, F., Song, F., Yang, X., & Kasabov, N. (2015). Identifying overlapping mutated driver pathways by constructing gene networks in cancer. BMC Bioinformatics. doi:10.1186/1471-2105-16-S5-S3
- 2015 - Tu, E., Yang, J., Kasabov, N., & Zhang, Y. (2015). Posterior Distribution Learning (PDL): A novel supervised learning framework using unlabeled samples to improve classification performance. Neurocomputing, 157, 173-186. doi:10.1016/j.neucom.2015.01.020
- 2015 - Feigin, V., Krishnamurthi, R., Bhattacharjee, R., Parmar, P., Theadom, A., Hussein, P., . . . Tobias, M. (2015). A new strategy to reduce global burden of Stroke. Stroke, 46, 1740-1747.
- 2015 - Kasabov, N., & Capecci, E. (2015). Spiking neural network methodology for modelling, classification and understanding of EEG spatio-temporal data measuring cognitive processes. Information Sciences, 294, 565-575. doi:10.1016/j.ins.2014.06.028
- 2015 - Wang, J. -J., Jia, Z. -H., Qin, X. -Z., Yang, J., & Kasabov, N. (2015). Medical image enhancement algorithm based on NSCT and the improved fuzzy contrast. International Journal of Imaging Systems and Technology, 25(1), 7-14. doi:10.1002/ima.22115
- 2015 - Kasabov, N. (2015). Evolving Connectionist Systems for Adaptive Learning and Knowledge Discovery: Trends and Directions. Knowledge-Based Systems. doi:10.1016/j.knosys.2014.12.032
- 2014 - Wubuli, A., Zhen-Hong, J., Xi-Zhong, Q., Jie, Y., & Kasabov, N. Medical image enhancement based on shearlet transform and unsharp masking. Journal of Medical Imaging and Health Informatics, 4(5), 814-818. doi:10.1166/jmihi.2014.1326
- 2014 - Ling-Ling, L., Zhen-Hong, J., Xi-Zhong, Q., Jie, Y., & Kasabov, N. White matter lesions change detection in MR images based on fuzzy nearness and non-subsampled shear waves. Journal of Medical Imaging and Health Informatics, 4(6), 953-956. doi:10.1166/jmihi.2014.1348
- 2014 - Tu, E., Cao, L., Yang, J., & Kasabov, N. A novel graph-based k-means for nonlinear manifold clustering and representative selection. Neurocomputing. doi:10.1016/j.neucom.2014.05.067
- 2014 - Feigin, V. L., Parmar, P. G., Barker-Collo, S., Bennett, D. A., Anderson, C. S., Thrift, A. G., Kasabov, N. Geomagnetic storms can trigger stroke: Evidence from 6 large population-based studies in Europe and Australasia. Stroke, 45(6), 1639-1645. doi:10.1161/STROKEAHA.113.004577
- 2014 - Kasabov, N. NeuCube: A spiking neural network architecture for mapping, learning and understanding of spatio-temporal brain data. Neural Networks, 52, 62-76. doi:10.1016/j.neunet.2014.01.006
- 2014 - Kasabov, N., Feigin, V., Hou, Z. -G., Chen, Y., Liang, L., Krishnamurthi, R., Parmar, P. Evolving spiking neural networks for personalised modelling, classification and prediction of spatio-temporal patterns with a case study on stroke. Neurocomputing, 134, 269-279. doi:10.1016/j.neucom.2013.09.049
- 2013 - Yi, X., Hu, Y., Jia, Z., Wang, L., Yang, J., & Kasabov, N. An enhanced multiphase Chan–Vese model for the remote sensing image segmentation. Concurrency and Computation: Practice and Experience.
- 2013 - Erogbogbo, F., May, J., Swihart, M., Prasad, P., Smart, K., Jack, S., Gladding, P., Schliebs, S., Hu, Raphel, et al. Bioengineering Silicon Quantum Dot Theranostics using a Network Analysis of Metabolomic and Proteomic Data in Cardiac Ischemia. Theranostics, 3(9), 719-728.
- 2013 - Kageyama, Y., Momose, A., Takahashi, T., Ishii, M., Nishida, M., Mohemmed, A., . Kasabov, N. Analysis of Lip Motion Change Arising due to Amusement Feeling. IEEJ Transactions on Electrical and Electronic Engineering, 8(5). doi:10.1002/tee.21892
- 2013 - Pears, R., Widiputra, H., & Kasabov, N. Evolving integrated multi-model framework for on line multiple time series prediction. Evolving Systems, 4(2), 99-117. doi:10.1007/s12530-012-9069-y
- 2013 - Liang., Hu., & Kasabov, N. Evolving Personalized Modeling System for Integrated Feature, Neighborhood and Parameter Optimization utilizing Gravitational Search Algorithm. Evolving Systems. doi:10.1007/s12530-013-9081-x
- 2013 - Schliebs, S., & Kasabov, N. Evolving spiking neural network-a survey. Evolving Systems, 4(2), 87-98. doi:10.1007/s12530-013-9074-9
- 2013 - Kasabov, N., Dhoble, K., Nuntalid, N., & Indiveri, G. Dynamic evolving spiking neural networks for on-line spatio- and spectro-temporal pattern recognition. Neural Networks, 41, 188-201.
- 2013 - Tu, E., Yang, J., Fang, J., Jia, Z., & Kasabov, N. An experimental comparison of semi-supervised learning algorithms for multispectral image classification. Photogrammetric Engineering and Remote Sensing, 79(4), 347-357.
- 2013 - Mohemmed, A., Schliebs, S., Matsuda, S., & Kasabov, N. Training spiking neural networks to associate spatio-temporal input-output spike patterns. Neurocomputing, 107, 3-10. doi:10.1016/j.neucom.2012.08.034
- 2013 - Jordanov, I., Apolloni, B., & Kasabov, N. (2013). Special Issue: Contemporary development of neural computation and applications. Neural Computing and Applications, 22(1), 1-2. doi:10.1007/s00521-012-0903-8
- 2012 - Kageyama, Y., Momose, A., Takahashi, T., Ishii, M., Nishida, M., Mohemmed, A., Kasabov, N., Analysis of Lip Motion Change Arising due to Amusement Feeling, IEEJ Transactions on Electrical and Electronic Engineering, (8)5:1-2, [September issue].
- 2012 - Wen Liang, Yingjie Hu, Nikola Kasabov, Evolving Personalized Modeling System for Integrated Feature, Neighborhood and Parameter Optimization utilizing Gravitational Search Algorithm. Evolving System. (Accepted and to be published).
- 2012 - Pears, R. and Widiputra, H. and Kasabov, N., Evolving integrated multi-model framework for on-line multiple time series prediction, Evolving Systems, Springer-Verlag Berlin Heidelberg, DOI: 10.1007/s12530-012-9069-y
- 2012 - Mohemmed, A. and S.Schliebs and S.Matsuda and N. Kasabov, SPAN: Spike Pattern Association Neuron for Learning Spatio-Temporal Sequences, International Journal of Neural Systems, Vol. 22, No. 4 (2012) 1-16.
- 2012 - Mohemmed, A.and S. Schliebs and S. Matsuda and N. Kasabov, Training Spiking Neural Networks to Associate Spatio-temporal Input-Output Spike Patterns, Neurocomputing, DOI: 10.1016/j.neucom.2012.08.034, ISSN 0925-2312.
- 2012 - Schliebs, S. and N.Kasabov, Evolving spiking neural networks: A Survey, Evolving Systems, Special Issue on Applications of Evolving Connectionist Systems, M.Watts (ed), Springer, accepted, in print, 2012
- 2012 - Kasabov, N., NeuCube EvoSpike Architecture for Spatio-Temporal Modelling and Pattern Recognition of Brain Signals, in: Mana, Schwenker and Trentin (Eds) ANNPR, Springer LNAI, 2012, 225-243.
- 2012 - Gladding, P., Erogbogbo, F., Swihart, M., Smart, K., Stewart, R., Zeng, I., Jullig, M., Bakeev, K., Hu, R., Schliebs, S., Gopalan, B., El-Jack, S.,. Bioengineering silicon quantum dot theranostics using a network analysis of metabolomic and proteomic data in cardiac ischaemia. Journal of the American College of Cardiology. 2012;59(13):E453-E453.
- 2012 - Kasabov, N., Dhoble, K., Nuntalid, N. and Indiveri, G. Dynamic Evolving Spiking Neural Networks for On-line Spatio- and Spectro-Temporal Pattern Recognition, Neural Networks.
- 2012 - Kasabov, N. Evolving, Probabilistic Spiking Neural Networks and Neurogenetic Systems for Spatio- and Spectro-Temporal Data Modelling and Pattern Recognition. In INNS Magazine of Natural Intelligence, 1(2): 23-37. Winter 2012
- 2012 - Shaoning Pang, Tao Ban, Youki Kadobayashi and Nikola K. Kasabov, LDA Merging and Splitting with Applications to Multi-agent Cooperative Learning and System Alteration, IEEE Transactions On Systems, Man And Cybernetics, -Part B. 42(2): 552-564.
- 2011 - Mohemmed, A., Johnston, M., Zhang, M.J., Particle swarm optimisation based AdaBoost for object detection, Springer, Journal of Soft Computing 15(9): 1793-1805. Sep 2011
- 2011 - Kasabov, N., Schliebs, R., Kojima, H., Probabilistic Computational Neurogenetic Framework: From Modelling Cognitive Systems to Alzheimer’s Disease. IEEE Transactions of Autonomous Mental Development, 3(4):300-3011, 2011
- 2011 - Fang J.X., Yang J., Tu E.M., Jia Z.H., Kasabov N., Multilayer level set method for multiregion image segmentation. Opt. Eng. Volume 50, Issue 6, 2011
- 2011 - Fang J.X., Yang J., Tu E.M., Jia Z.H., Kasabov N., Efficient multiresolution level set image segmentation with multiple regions. Opt. Eng. Volume 50, Issue 6, 2011
- 2011 – N. Kasabov, H.N.A. Hamed, Quantum-inspired Particle Swarm Optimisation for Integrated Feature and Parameter Optimisation of Evolving Spiking Neural Networks. International Journal of Artificial Intelligence, Volume 7, Number A11, Page 114-124, 2011. ISSN: 0974-0635, 2011
- 2011 - Widiputra, H., Pears, R., & Kasabov, N., Multiple time-series prediction through multiple time-series relationships profiling and clustered recurring trends. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 6635 LNAI(PART 2), 161-172, 2011
- 2011 - H.Widiputra, R. Pears, N. Kasabov, Dynamic Interaction Network versus Localized Trends Model for Multiple Time-Series Prediction, Cybernetics and Systems, Cybernetics and Systems, Vol. 42, No. 2 : 100-123, 2011
- 2011 - Seed, P. T., Chappell, L. C., Black, M. A., Poppe, K. K., Hwang, Y. C., Kasabov, N., North, R. A., Prediction of preeclampsia and delivery of small for gestational age babies based on a combination of clinical risk factors in high-risk women. Hypertens Pregnancy, 30(1), 58-73, 2011
- 2011 - S. Pang, T. Ban, Y. Kadobayashi, N. Kasabov, Personalized Mode Transductive Spanning SVM Classification Tree, Information Sciences, Vol. 181, No. 11: 2071-2085, June 2011
- 2010 - S. Pang, L. Song, and N. Kasabov, N., Correlation Aided Support Vector Regression for Forex Time Series Prediction, Neural Computing and Application, Springer London, 1-11, 2010
- 2010 - S.Soltic, N.Kasabov, Knowledge extraction from evolving spiking neural networks with a rank order population coding, Int.J.Neural Systems, 20 (6), 437–445, World Scientific Publishing Company, 2010
- 2010 - S. Schliebs, M. Defoin-Platel, N. Kasabov, On the Probabilistic Optimization of Spiking Neural Networks, International Journal of Neural Systems, 20(6):481-500, December 2010.
- 2010 - P.Gladding, J.Mackay, M.Wesbster, H.White, K.Ellis, M.Lee,N.Kasabov and R.Stewart, Longitudal study of a 9p21.3 SNP using a national electronic healthcare database, Personalised Medicine, 7(4), 361-369, 2010
- 2010 - S.Wysoski, L.Benuskova, N.Kasabov, Evolving Spiking Neural Networks for Audi-Visual Information Processing, Neural Networks, 2010
- 2010 - Masayuki Hisada, Seiichi Ozawa, Kau Zhang, Nikola Kasabov: Incremental linear discriminant analysis for evolving feature spaces in multitask pattern recognition problems, Evolving Systems, Vol. 1, No. 1: 17-27, 2010
- 2010 - N. Kasabov, To spike or not to spike: A probabilistic spiking neuron model, Neural Networks, Vol. 23, Issue 1, pp. 16-19, 2010
- 2010 - Haza Nuzly Abdull Hamed, Nikola Kasabov and Siti Mariyam Shamsuddin, Probabilistic Evolving Spiking Neural Network Optimization Using Dynamic Quantum-inspired Particle Swarm Optimization, Australian Journal of Intelligent Information Processing Systems, Vol. 11, No. 1, 2010
- 2010 - Kasabov, N., & Hu, Y., 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, Dec. 2010
- 2009 - H. Widiputra, R. Pears, A. Serguieva, N. Kasabov, Dynamic Interaction Networks In Modelling And Predicting The Behaviour of Multiple Interactive Stock Markets, Intelligent Systems in Accounting, Finance and Management, Volume 16, 189-205, 2009
- 2009 - M. Defoin-Platel, S.Schliebs, N.Kasabov, Quantum-inspired Evolutionary Algorithm: A multi-model EDA, IEEE Trans. Evolutionary Computation, vol.13, No.6, pp. 1218-1232 , 2009
- 2009 - S. Schliebs, M. Defoin-Platel, S. Worner, N. Kasabov, Integrated Feature and Parameter Optimization for Evolving Spiking Neural Networks: Exploring Heterogeneous Probabilistic Models, Neural Networks, Volume 22, Issues 5-6, 623-632, 2009
- 2009 - Kelly R. Atkinson1, Marion Blumenstein, Michael A. Black, Steven H. Wu, Nikola Kasabov, Rennae S. Taylor, Garth J. S. Cooper, Robyn A. North on behalf of the SCOPE Consortium, An altered pattern of circulating apolipoprotein E3 isoforms is implicated in preeclampsia, Journal of Lipid Research, Vol. 50, 71-80, January 2009
- 2009 - S.Pang, N.Kasabov, Encoding and decoding the knowledge of association rules over SVM classification trees, Knowledge and Information Systems, Springer, Vol 19, No 1, pp. 79-105, 2009
- 2009 - N.Kasabov, Integrative Connectionist Learning Systems Inspired by Nature: Current Models, Future Trends and Challenges, Natural Computing, Springer, pp. 199-218, Vol 8, No 2, 2009
- 2008 - Naoki Shimo, Shaoning Pang, Nikola Kasabov and Takeshi Yamakawa, Curiosity-driven Multi-agent Competitive and Cooperative LDA Learning, International Journal of Innovative Computing, Information and Control (IJICIC), 4(7): 11537-1552, 2008
- 2008 - Kasabov, N., "Evolving Intelligence in Humans and Machines: Integrative Evolving Connectionist Systems Approach," Computational Intelligence Magazine, IEEE, vol.3, no.3, pp.23-37, August 2008
- 2008 - N.Kasabov, Adaptive Modelling and Discovery in Bioinformatics: The Evolving Connectionist Approach, International Journal of Intelligent Systems, 23(5): 545-555, 2008
- 2008 - S.Wysoski, L.Benuskova, N.Kasabov, Fast and Adaptive Network of Spiking Neurons for Multi-view Visual Pattern Recognition, Neurocomputing, Volume 71, Issue 13-15,Pages 2563-2575 2008
- 2008 - Nikola Kasabov, Vishal Jain, Lubica Benuskova, Integrating evolving brain-gene ontology and connectionist-based system for modeling and knowledge discovery, Neural Networks, Volume 21, Issues 2-3, Advances in Neural Networks Research: IJCNN '07, 2007 International Joint Conference on Neural Networks IJCNN '07, March-April 2008, Pages 266-275, ISSN 0893-6080
- 2008 - Benuskova, L., Kasabov, N. Modeling brain dynamics using computational neurogenetic approach, Cognitive Neurodynamics, Springer Netherlands, vol. 2, no. 4, pp. 319-334, 2008
- 2008 - Zeke S. H. Chan, Ilkka Havukkala, Vishal Jain, Yingjie Hu and Nikola Kasabov, Soft Computing Methods to predict Gene Regulatory Networks: An Integrative approach on Time-Series Gene Expression Data, Applied Soft Computing, Volume 8, Issue 3, June 2008, Pages 1189-1199
- 2008 - S Ozawa, S Pang and N Kasabov, Incremental Learning of Chunk Data for On-line Pattern Classification Systems, IEEE Transactions of Neural Networks, vol.19, no.6, pp.1061-1074, June 2008
- 2008 - L.Benuskova and N.Kasabov, Modelling Brain Dynamics Using Computational Neurogenetic Approach, Cognitive Neurodynamics, Springer, vol.2, Num.4, 319-334, December,2008
- 2008 - Huang, L., Q.Song and N.Kasabov, Evolving connectionist system based role allocation for robotic soccer, Int. J. Advanced Robotic Systems, Vol. 5, Number 1, March 2008, 59-62
- 2007 - Yu-Hsin Lin, Jan Friederichs, Michael A. Black, Jörg Mages, Robert Rosenberg, Parry J. Guilford1, Vicky Phillips, Mark Thompson-Fawcett, Nikola Kasabov, Tumi Toro, Arend E. Merrie, Andre van Rij, Han-Seung Yoon, John L. McCall, Jörg Rüdiger Siewert, Bernhard Holzmann and Anthony E. Reeve, Multiple Gene Expression Classifiers from Different Array Platforms Predict Poor Prognosis of Colorectal Cancer, Clinical Cancer Research 13, 498-507, January 15, 2007
- 2007 - S.Pang,I.Havukkala, Y.Hu, N.Kasabov, Classification consistency analysis for bootstrapping gene selection, Neural Computing & Applications, Springer, Volume 16, Number 6, p.p.527-539
- 2007 - Kasabov, Nikola, Jain, Vishal, Gottgtroy, Paulo C. M., Benuskova, Lubica & Joseph, Frances. Brain gene ontology and simulation system (BGOS) for a better understanding of the brain. Cybernetics and Systems, June 2007, Vol. 38 (5), pp 495-508.
- 2007 - Lubica Benuskova and Nikola Kasabov, Modeling L-LTP based on changes in concentration of pCREB transcription factor, Neurocomputing, Volume 70, Issues 10-12, June 2007, Pages 2035-2040, ISSN: 0925-2312
- 2007 - Nikola Kasabov, Global, local and personalised modeling and pattern discovery in bioinformatics: An integrated approach, Pattern Recognition Letters, Volume 28, Issue 6, 15 April 2007, Pages 673-685
- 2007 - S.H. Chan, Lesley Collins and N. Kasabov, Bayesian learning of sparse gene regulatory networks, Biosystems, Volume 87, Issues 2-3, February 2007, Pages 299-306
- 2006 - Zeke S.H. Chan, H.W. Ngan, A.B. Rad, A.K. David and N. Kasabov, Short-term ANN load forecasting from limited data using generalization learning strategies, Neurocomputing, Volume 70, Issues 1-3, December 2006, Pages 409-419
- 2006 - Song, Q. and Kasabov, N. TWNFI- a transductive neuro-fuzzy inference system with weighted data normalisation for personalised modelling, Neural Networks, Vol.19, Issue 10, Dec. 2006, pp. 1591-1596
- 2006 - Chan, Z., Lesley Collins, N.Kasabov, An Efficient Greedy K-means Algorithm for Global Gene Trajectory clustering, Expert Systems with Applications: An International Journal. Volume 30, Issue 1, January 2006, Pages 137-141.
- 2006 - Chan, Z.,N.Kasabov, Lesley Collins, A Two-Stage Methodology for Gene Regulatory Network Extraction from Time-Course Gene Expression Data, Expert Systems with Applications: An International Journal, Volume 30, Issue 1, January 2006, Pages 59-63.
- 2006 - Ozawa, S., S. Pang and N. Kasabov, Online Feature Selection for Adaptive Evolving Connectionist Systems, International Journal of Innovative Computing, Information and Control, Volume 2, No. 1, 2006 pp181-192
- 2006 - Ozawa, S., Shaoning Pang and Nikola Kasabov, Incremental learning of feature space and classifier for on-line pattern recognition, International Journal of Knowledge based and Intelligent Engineering Systems, Volume 10, 2006, pp 57-65
- 2006 - Kasabov, N. Adaptation and Interaction in Dynamical Systems: Modelling and Rule Discovery Through Evolving Connectionist Systems, Applied Soft Computing, 2006, Volume 6, Issue 3, pages 307-322.
- 2006 - Gevrey, M., Sue Worner, Nikola Kasabov, Joel Pitta and Jean-Luc Giraudel, Estimating Risk of Events Using SOM Models: A Case Study on invasive species establishment, Ecological Modelling, 197, 2006, 361-372
- 2006 - Zeke S.H. Chan, H.W. Ngan, A.B. Rad, A.K. David and N. Kasabov, Short-term ANN load forecasting from limited data using generalization learning strategies, Neurocomputing, Volume 70, Issues 1-3, December 2006, Pages 409-419
- 2006 - Benuskova L, Jain V, Wysoski SG and Kasabov N (2006) Computational neurogenetic modeling: a pathway to new discoveries in genetic neuroscience. Intl. Journal of Neural Systems, 16(3): 215-226. ISSN 0129-0657.
- 2006 - Qun Song, Nikola Kasabov, Tianmin Ma and Mark Roger Marshall, Integrating regression formulas and kernel functions into locally adaptive knowledge-based neural networks: A case study on renal function evaluation, Artificial Intelligence in Medicine, Volume 36, Issue 3, , March 2006, Pages 235-244.
- 2005 - Liang Goh, Nikola Kasabov, An integrated feature selection and classification method to select minimum number of variables on the case study of gene expression data, Journal of Bioinformatics and Computational Biology, Vol. 3, No. 5 (2005) 1107-1136
- 2005 - Tsankova, Diana; Georgieva, Velichka; Kasabov, Nikola, Artificial Immune Networks as a Paradigm for Classification and Profiling of Gene Expression Data, Journal of Computational and Theoretical Nanoscience, Volume 2, Number 4, December 2005, pp. 543-550(8)
- 2005 - Nikola Kasabov, Igor A. Sidorov, and Dimiter S. Dimitrov, Computational Intelligence, Bioinformatics and Computational Biology: A Brief Overview of Methods, Problems and Perspectives, Journal of Computational and Theoretical Nanoscience, Volume 2, Number 4 (December 2005), Bioinformatics, ISSN: 1546-1963, pp. 473-491(19)
- 2005 - Ilkka Havukkala, Shaoning Pang, Vishal Jain, and Nikola Kasabov, Classifying MicroRNAs by Gabor Filter Features from 2D Structure Bitmap Images on a Case Study of Human MicroRNAs, Journal of Computational and Theoretical Nanoscience, Volume 2, Number 4 (December 2005) Bioinformatics, ISSN: 1546-198X, pp. 506-513
- 2005 - Nikola Kasabov, Lubica Benuskova, and Simei Gomes Wysoski, Biologically Plausible Computational Neurogenetic Models: Modeling the Interaction Between Genes, Neurons and Neural Networks, Journal of Computational and Theoretical Nanoscience, Volume 2, Number 4, December 2005, pp. 569-573(5) ISSN: 1546-1963
- 2005 - Shaoning Pang, Seiichi Ozawa and Nik Kasabov, Incremental Linear Discriminant Analysis for Classification of Data Streams, IEEE Trans. on System, Man, and Cybernetics-Part B, 35(5),905-914
- 2005 - Seiichi Ozawa, Soon Lee Toh, Shigeo Abe, Shaoning Pang and Nikola Kasabov , Incremental learning of feature space and classifier for face recognition, Neural Networks, Volume 18, Issues 5-6, June-July 2005, Pages 575-584
- 2005 - Goh, L., Kasabov, N., An Integrated Feature Selection and Classification Method to Select Minimum Number of Variables on the Case Study of Gene Expression Data, Journal of Bioinformatics and Computational Biology, Vol. 3, No. 5 (2005) 1107-1136
- 2005 - Q. Song and N. Kasabov, NFI: A Neuro-Fuzzy Inference Method for Transductive Reasoning, IEEE Transactions on Fuzzy Systems, IEEE Transactions on Fuzzy Systems, Vol 13, Issue 6, ISSN: 1063-6706, Pages 799- 808.
- 2005 - Z. Chan and N.Kasabov, A Preliminary Study on Negative Correlation Learning via Correlation-Corrected Data (NCCD), Neural Processing Letters, Springer, 2005, 21(3)207-214
- 2005 - M.R. Marshall, Q. Song, T.M. Ma, S. MacDonell, N.Kasabov, Evolving Connectionist System versus Algebraic Formulas for Prediction of Renal Function from Serum Creatinine, Kidney International, 2005, Vol. 67, Issue 5, pp.1944-1954
2005 - Z. Chan, N.Kasabov and L.Collins, A hybrid genetic algorithm and expectation maximization method for global gene trajectory clustering, Journal of Bioinformatics and Computational Biology, Imperial College Press, Vol. 3, No. 5 (2005) 1227-1242 - 2005 - Z. Chan and N.Kasabov, Fast Neural Network Ensemble Learning via Negative-Correlation Data Correction, IEEE Trans. Neural Networks, 2005, 16(6)1707-1710
- 2005 - Nikola Kasabov, Lubica Benuskova, Simei Gomes Wysoski, Computational Neurogenetic Modeling: Integration of Spiking Neural Networks, Gene Networks, and Signal Processing Techniques, Lecture Notes in Computer Science, Volume 3697, Aug 2005, Pages 509 - 514
- 2005 - Shaoning Pang, Seiichi Ozawa, Nikola Kasabov, Chunk Incremental LDA Computing on Data Streams, Lecture Notes in Computer Science, Volume 3497, Jan 2005, Pages 51 - 56
- 2005 - Snjezana Soltic, Shaoning Pang, Nikola Kasabov, Sue Worner, Lora Peackok, Dynamic Neuro-fuzzy Inference and Statistical Models for Risk Analysis of Pest Insect Establishment, Lecture Notes in Computer Science, Volume 3316, Jan 2004, Pages 971 - 976
- 2004 - Shaoning Pang, Seiichi Ozawa and Nikola Kasabov, One-Pass Incremental Membership Authentication by Face Classification, in Lecture Notes in Computer Science, Volume 3072, David Zhang and Anil Jain (eds), Springer-Verlag, Berlin, Heidelberg, pp: 155-161, 2004
- 2004 - Seiichi Ozawa, Shaoning Pang and Nikola Kasabov, A Modified Incremental Principal Component Analysis for On-Line Learning of Feature Space and Classifier, Lecture Notes in Artificial Intelligence, Volume 3157, pp: 231-240, 2004, Springer-Verlag, Berlin, Heidelberg, 2004
- 2004 - N. Kasabov, Z. S. H. Chan, V. Jain, I. Sidorov, and D. S. Dimitrov, "Gene Regulatory Network Discovery from Time-Series Gene Expression Data - A Computational Intelligence Approach," Lecture Notes in Computer Science, vol. 3316/2004, pp. 1344-1353, 2004.
- 2004 - Song Qun and Kasabov Nikola, TWRBF - transductive RBF Neural Network with Weighted Data Normalization, Lecture Notes in Computer Science, Vol.3316, ICONIP’2004, Springer Verlag, 2004
- 2004 - Z. Chan and N. Kasabov, "Evolutionary Computation for On-line and Off-line Parameter Tuning of Evolving Fuzzy Neural Networks," International Journal of Computational Intelligence and Applications, Imperial College Press, vol. 4, pp. 309-319, 2004.
- 2004 - Q. Song and N.Kasabov, NFI: Transductive neuro-fuzzy inference method for personalized modelling, IEEE Transactions on Fuzzy Systems, accepted.
- 2004 - N. Kasabov, Knowledge based neural networks for gene expression data analysis, modelling and profile discovery, Drug Discovery Today: BIOSILICO, vol. 2, No. 6, November 2004, pp. 253-261.
- 2004 - Z. S. H. Chan and N. Kasabov, "Efficient global clustering using the greedy elimination method," Electronics Letters, vol. 40, pp. 1611-1612, 2004.
- 2004 - Nikola Kasabov and Lubica Benuskova, Computational Neurogenetics, Journal of Theoretical and Computational Nanoscience, Vol. 1 (1) American Scientific Publisher, 2004, pp.47-61
- 2004 - N. Kasabov and S. N. Pang, Transductive Support Vector Machines and Applications in Bioinformatics for Promoter Recognition, Neural Information Processing-Letters & Review Vol.3, No.2, May 2004 pp. 31-38 (IEEE ICNNSP Best Paper Award)
- 2004 - Nikola Kasabov and Akbar Ghobakhlou, A Methodology and a System for Adaptive Integrated Speech and Image Learning and Recognition, International Journal of Computers, Systems and Signals, ISSN 1608-5655, Volume 5, No. 2, 2004
- 2003 - M. Futschik, M. Sullivan, A. Reeve, N. Kasabov, Prediction of clinical behaviour and treatment of cancers, Applied Bioinformatics 2003 (3 Suppl) S53-S58
- 2003 - M. Futschik, A.Reeve, and Kasabov, N. Evolving connectionist systems for knowledge discovery from gene expression data of cancer tissue, Artificial Intelligence in Medicine, 28 (2003) 165-189
- 2003 - T.Cohen, D.Hegg, Mde Michele, Q.Song, and N. Kasabov, An intelligent controller for automated operation of sequencing batch reactors, Water Science & Technology, IWA Publishing, Vol 47, No 12 (2003) 57-63
- 2003 - N.Kasabov, Spoken Language Analysis, Modeling And Recognition - Statistical And Adaptive Connectionist Approaches, Preface to a Special Issue of Information Sciences 2003, Volume 156 Numbers 1-2
- 2003 - M. Laws, R. Kilgour and N. Kasabov, Modelling the emergence of bilingual acoustic clusters: a preliminary case study, Information Sciences, 156 (2003) 85-107
- 2003 - A.Ghobakhlou, M. Watts and N. Kasabov, Adaptive speech recognition with evolving connectionist systems, Information Sciences, 156 (2003) 71-83
- 2003 - W. Abdulla and N. Kasabov, Reduced feature-set based parallel CHMM speech recognition systems, Information Sciences, 156 (2003) 23-38
- 2003 - Rizzi, R., Bazzana, F., Kasabov, N., Fedrizzi, M. and Erzegovesi, L. 2003, Simulation of ECB decisions and forecast of short term Euro rate with an adaptive fuzzy expert system, European Journal of Operation Research, 145 (2003) 363-381
- 2003 - Deng, D. and N.Kasabov, On-line pattern analysis by evolving self-organising maps, Neurocomputing, vol. 51, April (2003) 87-103
- 2002 - Kasabov, N., and Song, Q., DENFIS: Dynamic Evolving Neural-Fuzzy Inference System and its Application for Time Series Prediction, IEEE Transactions on Fuzzy Systems, vol. 10, no.2, April, (2002) 144-154.
- 2002 - Futschik, M., A.Jeffs, S.Pattison, N.Kasabov, M.Sullivan, A.Merrie, A.Reeve, Gene expression profiling of metastatic and non-metastatic colorectal cancer cell-lines, Genome Letters, vol.1, No.1 (2002), 1-9
- 2001 - Kasabov, N., Evolving Fuzzy Neural Networks for Supervised/Unsupervised On-Line, Knowledge-Based Learning, IEEE Transactions on Systems, Man and Cybernetics, Part B: Cybernetics, Vol 31, No. 6 Issue (December 2001, pp.902-918)
- 2001 - Kasabov, N., Artificial Neural Networks for Intelligent Information Processing, Transactions of Chemical Engineering, London, June 2001, 27:28
- 2001 - Kasabov, N. On-line learning, reasoning, rule extraction and aggregation in locally optimised evolving fuzzy neural networks, Neurocomputing, 41 (2001) 25-41
- 2000 - Brown, C., Jacobs, G., Schreiber, M., Magnum, J., McNaughton, J., Cambray, M., Futschik, M., Major, L., Rackham, O., Tate, W., Stockwell, P., Thompson, C., and Kasabov, N. Using bioinformatics to investigate post-trascriptional control of gene expression, NZ Bio Science, 7 (4):11-12 (2000) 1999
- 2000 - Kasabov, N., Israel, S., and Woodford, B.J., Hybrid evolving connectionist systems for image classification, Journal of Advanced Computational Intelligence, vol.4, No.1, (2000), 57-65
- 2000 - Kasabov, N., Postma, E. and van den Herik, J. AVIS: a connectionist-based framework for integrated auditory and visual information processing, Information Sciences, vol. 123, 127-148 (2000)
- 2000 - Kasabov, N., and Kozma, R., Methods and systems for intelligent human computer interaction - Editorial, Information Sciences, vol. 123 (2000), 1-2
- 2000 - Kim, J., A. Mowat, P. Poole, and N. Kasabov, Linear and non-linear pattern recognition models for classification of fruit from visible-near infrared spectra, Chemometrics and intelligent laboratory systems, 51 (2000) 201-216
- 1999 - Kasabov, N., Kilgour, R. and Sinclair, S. From hybrid adjustable neuro-fuzzy systems to adaptive connectionist-based systems for phoneme and word recognition. Fuzzy Sets and Systems, 130 (2):349-367 (1999)
- 1999 - Kim, J.S. and Kasabov, N. HyFIS: adaptive neuro-fuzzy systems and their application to non-linear dynamical systems, Neural Networks, 12 (9) 1301-1319 (1999)
- 1999 - Purvis, M., Kasabov, N., Benwell, G., Zhou, Q., and Zhang, F. Neuro-fuzzy methods for Environmental Modelling, System Research and Information Systems, 8 (4): 221-239 (1999)
- 1998 - Kasabov, N. Evolving fuzzy neural networks: Theory and Applications for on-line adaptive prediction, decision making and control, Australian Journal of Intelligent Information Processing Systems, 5 (3): 154-160 (1998)
- 1998 - Kasabov, N. Connectionist-based information systems: Methods and applications (Guest editorial), Australian Journal of Intelligent Information Processing Systems, 5 (3): 153 (1998)
- 1998 - Kasabov, N., Kim, J.S. and Kozma, R. A Fuzzy neural network for knowledge acquisition in complex time series, International Journal of Control and Cybernetics, 4 (27): 594-611 (1998)
- 1998 - Kasabov, N. The ECOS framework and the 'eco' training method for evolving connectionist systems. Journal of Advanced Computational Intelligence (1998) vol.2, No.6, 195-202
- 1998 - Kasabov, N. and Kozma, R. Self-organisation and adaptation in intelligent systems - preface Journal of Advanced Computational Intelligence (1998) vol.2, No.6, 177
- 1998 - Kasabov, N. and Kozma, R. Hybrid intelligent adaptive systems: a framework and a case study on speech recognition International Journal of Intelligent Systems 13 (6): 455-466 (1998)
- 1998 - Kasabov, N. and Kozma, R. Introduction: Hybrid intelligent adaptive systems. International Journal of Intelligent Systems 13 (6): 453-454 (1998)
- 1998 - Kozma, R., Kasabov, N., Kim, J. and Cohen, T. Integration of connectionist methods and chaotic time series analysis for the prediction of process data. International Journal of Intelligent Systems 13 (6): 520-538 (1998)
- 1998 - Kasabov, N. Fuzzy neural networks, rules extraction and fuzzy synergistic reasoning. Systems Research and Information Systems 8, 45-59 (1998)
- 1997 - Cohen, T. and Kasabov, N. Application of computational intelligence for on-line control of a Sequencing Batch Reactor (SBR) at Morrinsville Sewage Treatment Plant Water Science Technology, vol.35, No.10, 63-73 (1997)
- 1997 - Israel, S. and Kasabov, N. Statistical, connectionist and fuzzy inference techniques for image classification. Journal of Electronic Imaging 6 (3):1-11 (1997)
- 1997 - Kasabov, N., Kim, JS, Watts, M. and Gray, A. FuNN/2 - A fuzzy neural network architecture for adaptive learning and knowledge acquisition. Information Sciences 101(3-4): 155-175 (1997)
- 1997 - Kasabov, N. and Hirota, K. Special issue on advanced neuro-fuzzy techniques and their applications: introduction. Information Sciences 101(3-4): 153-154 (1997)
- 1997 - Kasabov, N. Learning strategies for modular neuro-fuzzy systems: a case study on phoneme-based speech recognition. Journal of Intelligent & Fuzzy Systems 5, 345-354 (1997)
- 1996 - Kasabov, N. Adaptable connectionist production systems. Neurocomputing 13(2-4):95-117 (1996)
- 1996 - Kasabov, N. Fril - fuzzy and evidential reasoning in artificial intelligence (a book review). Journal of the American Society for Information Science. 47 (10):790-791 (1996)
- 1996 - Kasabov, N. Learning fuzzy rules and approximate reasoning in fuzzy neural networks and hybrid systems. Fuzzy Sets and Systems 82(2):2-20 (1996)
- 1996 - Kasabov, N., Purvis, M., Zhang, F., and Benwell, G. Neuro-fuzzy engineering for spatial information processing. Australian Journal of Intelligent Information Processing Systems 3(2): 35-44 (1996)
- 1996 - Israel, S. and Kasabov, N. Improved learning strategies for multimodular fuzzy neural network systems: A case study on image classification. Australian Journal of Intelligent Information Processing Systems 3(2): 62-70 (1996)
- 1995 - Kasabov, N., Lavington S., Li S. and Wang C. A model for exploiting parallel associative matching in AI production systems. Knowledge-Based Systems 8 (1): 1-7 (1995)
- 1995 - Kasabov, N. Hybrid connectionist fuzzy systems for speech recognition. Lecture Notes in Computer Science/ Artificial Intelligence 1011:19-33 (1995)
- 1995 - Kasabov, N. Hybrid Connectionist Fuzzy Production Systems - Towards Building Comprehensive AI. Intelligent Automation and Soft Computing 1(4): 351-360 (1995)
- 1994 - Kasabov, N. Connectionist fuzzy production systems. Lecture Notes in Computer Science/ Artificial Intelligence 847:114-128 (1994)
- 1993 - Kasabov, N. Hybrid connectionist production systems. Journal of Systems Engineering 3(1): 15-21 (1993)
- 1993 - Kasabov, N. and Shishkov, S. A connectionist production system with partial match and its use for approximate reasoning. Connection Science 5(3/4): 275-305 (1993)
- 1991 - Kasabov, N. Incorporating neural networks into production systems and a practical approach towards the realisation of fuzzy expert systems. Computer Science and Informatics 21(2): 26-34 (1991)
- 1990 - Kasabov, N. Neural networks and genetic algorithms. Avtomatica i Informatica, 8/9:51-60 (1990) (in Bulgarian)
- 1990 - Kasabov, N. and Nikolaev, N. Parallel production systems. Avtomatica i Informatica, 7:37-45 (1990) (in Bulgarian)
- 1985 - Kasabov, N. Functionally reconfigurable general purpose parallel machines and some image processing and pattern recognition applications. Pattern Recognition Letters, 3:215-223 (1985)
- 1985 - Kasabov, N. A method for SIMD/MIMD functionally reconfigurable multi-microprocessor system design and parallel data exchange algorithms. Parallel Computing, 2:73-78 (1985)
- 1983 - Kasabov, N. A general approach to parallel processing in homogeneous multi-register, multi-processor and commutation structures. Computers and Artificial Intelligence 2(4): 349-359 (1983)
- 1983 - Kasabov, N. A multi- microprocessor system with a functional reconfiguration and parallel computations. Avtomatica i Ischislitelna Technika, 1:38-46 (1983) (in Bulgarian)
- 1983 - Karaivanova, M., Kasabov, N. and Hristov I. Predicting the scope of effect of anti-cancer medicines. Experimentalnaja Oncologija 5(1): 51-54 (1983) (in Russian)
- 1983 - Kasabov, N. Register commutation structures and algorithms for data exchange in multi-microprocessor systems. Avtomatica i Ischilitelna Technika, 5:17-24 (1983) (in Bulgarian)
- 1983 - Karaivanova, M., and Kasabov, N. Experimental Tumours as Prognostic Systems for Determining the Antitumour effect, Comptes rendus de l'Academie Bulgare des Sciences 35(11): 1595 -1598 (1983)
- 1981 - Kasabov, N., Bidjev, G. and Jechev, B. Hierarchical discrete systems and the realisation of parallel algorithms. Lecture Notes in Computer Science, 111:415-422 (1981)
- 1981 - Karaivanova, M. and Kasabov, N. On the selection of tumour models for the screening of anti-tumour substances (AS). Comptes rendus de l'Academie Bulgare des Sciences 34(2): 299-302 (1981)
- 1981 - Kasabov, N., Method and algorithm for permutation of data records, Systemi i Upravlenie, Bulgaria, 1: 39 - 43 (1981) (in Bulgarian)
- 1980 - Kasabov, N. and Bidjev, G. Minimal representation of the symmetrical group close to the compact one. Cybernetika, 3:135-136 (1980) (in Russian)
- Alexander Hui Xiang Yang, Nikola Kasabov and Yusuf Ozgur Cakmak, Machine Learning Methods for the Study of Cybersickness: A Systematic Review, Brain Informatics, Spinger-Nature, 2022
- Iman AbouHassan, Vinayak G.M. Jagtap, Parag Kulkarni, Nikola K. Kasabov, Time Series Predictive Modelling Using Online News: A Systematic Review of Methods, Open Problems, and Future Directions, with a Reference to Financial Data, Intelligent Systems with Applications, Elsevier, 2022 (under review)
- Qing Dong, Shihua Zhou, Qiang Zhang, Nikola K. Kasabov, A class of 5D Hamiltonian conservative hyperchaotic systems with symmetry and multistability, Nonlinear Dynamic, 2022, https://doi.org/10.1007/s11071-022-07735-6
- 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 22, no. 1: 85. https://doi.org/10.3390/s22010085
- Chen, W.; Jia, Z.; Yang, J.; Kasabov, N.K. Multispectral Image Enhancement Based on the Dark Channel Prior and Bilateral Fractional Differential Model. Remote Sens. 2022, 14, 233. https://doi.org/ 10.3390/rs14010233
Books and book chapters
- 2018 - N.Kasabov, Time-Space, Spiking Neural Networks and Brain-Inspired Artificial Intelligence, Springer Nature, 2018, 711pages. https://www.springer.com/gp/book/9783662577134
- 2016 - Hadjiski, M., Kasabov, N. K., Filev, D., & Jotsov, V. (Eds.). (2016). Novel Applications of Intelligent Systems (Vol. 586). Springer. doi: 10.1007/978-3-319-14194-7
- 2015 - Kasabov, N. (2015). Evolving Connectionist Systems: From Neuro-Fuzzy-, to Spiking- and Neuro-Genetic. In J. Kacprzyk, & W. Pedrycz (Eds.), Springer Handbook of Computational Intelligence (pp. 771-782). Springer Berlin Heidelberg. doi:10.1007/978-3-662-43505-2_40
- 2014 - Koprinkova-Hristova, P., Mladenov, V., & Kasabov, N. Artificial Neural Networks Methods and Applications in Bio-/Neuroinformatics (Vol. 4). Springer. doi:10.1007/978-3-319-09903-3
- 2014 - Kasabov, N. Handbook of Bio-/Neuroinformatics, Berlin: Springer Verlag. doi:10.1007/978-3-642-30574-0
- 2013 - Mladenov, V., Koprinkova-Hristova, P., Palm, G., Villa, A., Appolloni, B., & Kasabov, N. Artificial Neural Networks and Machine Learning - ICANN 2013 - 23rd International Conference on Artificial Neural Networks, Sofia, Bulgaria, September 10-13, 2013. Proceedings (Vol. 8131). V. Mladenov, P. D. Koprinkova-Hristova, G. Palm, A. E. P. Villa, B. Appollini, & N. Kasabov (Eds.), Springer. doi:10.1007/978-3-642-40728-4
- 2010 - Angelov, P., Filev, D., and Kasabov, N., Evolving intelligent systems: methodology and applications. John Wiley, New York. ISBN 978-0470287194
- 2008 - Yingjie Hu, Gene Selection Based On Consistency Modelling, Algorithms And Applications - Genetic Algorithm Application In Bioinformatics Data Analysis. Saarbrücken, Germany: Vdm Verlag, 2008. ISBN: 3639008839
- 2007 - Benuskova L and Kasabov N, Computational Neurogenetic Modeling. Springer, New York.,XII,pp. 292, ISBN:978-0-387-48353-5, 2007
- 2007 - Kasabov N. Evolving Connectionist Systems. The Knowledge Engineering Approach. 2nd edition, XXII, pp. 451, Springer, New York. [ISBN-10: 1-84628-345-0, ISBN 978-1-84628-345-1], 2007
- 2002 - Kasabov, N. Evolving connectionist systems: Methods and applications in bioinformatics, brain study and intelligent machines, Springer, London (2002)
- 2000 - Kasabov, N., ed.Future Directions for Intelligent Systems and Information Sciences, Heidelberg, Physica-Verlag (Springer Verlag) (2000), 420pp
- 1999 - Nikola Kasabov and Robert Kozma, editors (1999). Neuro-Fuzzy Techniques for Intelligent Information Systems (Studies in Fuzziness and Soft Computing, Vol.30), Springer Verlag.
- 1998 - Shun-Ichi Amari and Nikola K. Kasabov, editors (1998). Brain-Like Computing and Intelligent Information Systems, Springer Verlag.
- 1997 - Kasabov, N., Kozma, R., Ko, K., O'Shea, R., Coghill, G., Gedeon, T., (Eds) Progress in Connectionist-based Information Systems, Vol. 1-2, pp.1355, Springer Verlag (1997) Volume 1: [ISBN 981-3083-61-1], Volume 2: [ISBN 981-3083-63-8]
- 1996 - Kasabov, N.K. Foundations of Neural Networks, Fuzzy Systems and Knowledge Engineering. Cambridge, Massachussets, MIT Press (1996) 570p [ISBN 0 -262-11212-4]
- 2018 - Sengupta, N., Ramos, J.I.E., Tu, E., Marks, S., Scott, N., Weclawski, J., Gollahalli, A.R., Doborjeh, M.G., Doborjeh, Z.G., Kumarasinghe, K. and Breen, V., (2018). From von Neumann Architecture and Atanasoffs ABC to Neuro-Morphic Computation and Kasabov’s NeuCube: Principles and Implementations. In Learning Systems: From Theory to Practice (pp. 1-28). Springer, Cham. https://link.springer.com/chapter/10.1007/978-3-319-75181-8_1
- 2015 - Kasabov, N. (2015). Evolving Connectionist Systems: From Neuro-Fuzzy-, to Spiking- and Neuro-Genetic. In J. Kacprzyk, & W. Pedrycz (Eds.), Springer Handbook of Computational Intelligence (pp. 771-782). Springer Berlin Heidelberg. doi:10.1007/978-3-662-43505-2_40
- 2015 - Kasabov, N. (2015). Integrative computational neurogenetic modelling. In Brain Mapping: An Encyclopedic Reference (Vol. 1, pp. 667-674). Academic press: Elsevier. doi:10.1016/B978-0-12-397025-1.00349-3
- 2014 - Kasabov, N. Understanding Nature Through the Symbiosis of Information Science, Bioinformatics and Neuroinformatics. In N. Kasabov (Ed.), Springer Handbook of Bio-/Neuroinformatics (pp. 1-13). Berlin: Springer-Verlag. doi:10.1007/978-3-642-30574-0
- 2014 - Kasabov, N. Brain, Gene, and Quantum Inspired Computational Intelligence. In N. Kasabov (Ed.), Springer Handbook of Bio-/Neuroinformatics (pp. 1083-1098). Berlin: Springer-Verlag. doi:10.1007/978-3-642-30574-0
- 2014 - Georgieva, P., Silva, F., Milanova, M., & Kasabov, N. EEG Signal Processing for Brain-Computer Interfaces. In N. Kasabov (Ed.), Springer Handbook for Bio-/Neuroinformatics (pp. 797-812). Berlin: Springer-Verlag. doi:10.1007/978-3-642-30574-0
- 2014 - Schliebs, S., & Kasabov, N. Computational Modeling with Spiking Neural Networks. In N. Kasabov (Ed.), Springer Handbook of Bio-/Neuroinformatics (pp. 625-646). Heidelberg: Springer-Verlag Berlin. doi:10.1007/978-3-642-30574-0
- 2014 - Tegginmath, S., Pears, R., & Kasabov, N. Ontologies and Machine Learning Systems. In N. Kasabov (Ed.), Springer Handbook of Bio-/Neuroinformatics (pp. 865-872). Berlin: Springer Verlag. doi:10.1007/978-3-642-30574-0
- 2014 - Liang, L., Krishnamurthi, R., Kasabov, N., & Feigin, V. Information methods for predicting risk and outcome of stroke. In N. Kasabov (Ed.), Springer Handbook of Bio-/Neuroinformatics (pp. 993-1001). Berlin: Springer Verlag. doi:10.1007/978-3-642-30574-0
- 2014 - Hu, Y., Kasabov, N., & Liang, W. Personalised Information Modelling Technologies for Personalised Medicine. In N. Kasabov (Ed.), Springer Handbook of Bio-/Neuroinformatics (pp. 533-553). Berlin: Springer Verlag.
- 2013 - Kasabov, N. The Evolution of the Evolving Neuro-Fuzzy Systems: From Expert Systems to Spiking-, Neurogenetic-, and Quantum Inspired. In R. Seising, E. Trillas, C. Moraga, & S. Termini (Eds.), On Fuzziness: A Homage to Lotfi A Zadeh (Vol. 298, pp. 271-280). Springer.
- 2012 - Kasabov, N., Evolving Spiking Neural Networks and Neurogenetic Systems for Spatio- and Spectro-Temporal Data Modelling and Pattern Recognition, Springer-Verlag Berlin Heidelberg 2012, J. Liu et al. (Eds.): IEEE WCCI 2012, LNCS 7311, pp. 234–260.
- 2012 - Widiputra, H., Pears, R., and Kasabov, N., Dynamic learning of multiple time series in a non-stationary environment, In: Sayed-Mouchaweh, Moamar; Lughofer, Edwin (Eds.), Learning in Non Stationary Environments: Methods and Applications, ISBN 978-1-4419-8019-9, Springer.
- 2011 - S.Soltic, N.Kasabov (2011) A Biologically Inspired Evolving Spiking Neural Model with Rank-Order Population Coding and a Taste Recognition System Case Study, Chapter 7 in : Turgay Temel (Ed) System and Circuit Design for Biologically-Inspired Intelligent Learning, IGI Global, 136-155, ISBN13: 9781609600181, 2011
- 2011 - Haza Nuzly Abdull Hamed, Nikola K. Kasabov and Siti Mariyam Shamsuddin., Quantum-Inspired Particle Swarm Optimization for Feature Selection and Parameter Optimization in Evolving Spiking Neural Networks for Classification Tasks, Evolutionary Algorithms, Eisuke Kita (Ed.),pp 133-148, ISBN: 978-953-307-171-8, InTech
- 2011 - Harya Widiputra, Russel Pears, Nikola Kasabov, Kalman Filter to Estimate Dynamic and Important Patterns of Interaction between Multiple Variables, in: Joaquín M. Gomez (ed) Kalman Filtering, Nova Science-New York, pp. 289-320, ISBN: 978-1-61761-462-0, 2011
- 2010 - S Ozawa, S Pang and N Kasabov, Online Feature Extraction for Evolving Intelligent Systems, in: P.Angelov, D.Filev, and N.Kasabov (eds) Evolving intelligent systems, IEEE Press and Wiley,pp. 151-172, 2010
- 2010 - S. G. Wysoski, L. Benuskova and N. Kasabov, Brain-Like Evolving Spiking Neural Network for Multimodal Information Processing, Studies in Computational Intelligence, 266: 15-27, ISBN 978-3-642-04025-2_3, 2010
- 2010 - Shimo N, Pang S, Horio K, Kasabov N, Tamukoh H, Koga T, Sonoh S, Isogai H, Yamakawa T, Effective and Adaptive Learning Based on Diversive/Specific Curiosity.In Brain-Inspired Information Technology. Editors: Hanazawa A, Miki T, Horio K. 266: 171-175. Springer 2010
- 2010 - Kasabov. N., Integrative Probabilistic Evolving Spiking Neural Networks Utilising Quantum Inspired Evolutionary Algorithm: A Computational Framework, Advances in Machine Learning II, 263: 415-425, ISBN-13: 978-3-642-05178-4. Springer 2010
- 2009 - Kasabov, N., Soft computing methods for global, local and personalized modeling and applications in bioinformatics. In Soft Computing Based Modeling in Intel. Systems, 196:1-18, 2009
- 2008 - N Kasabov, V Jain, L Benuskova, P Gottgtroy and F Joseph, Integration of Brain-Gene Ontology and Simulation Systems for Learning, Modelling and Discovery, In: Computational Intelligence in Medical Informatics, Series: Studies in Computational Intelligence, Vol. 85: 221-234, Editors: Arpad Kelemen, Ajith Abraham, Yulan Liang, ISBN: 978-3-540-75766-5, 2008
- 2008 - Nikola Kasabov, Qun Song, Lubica Benuskoval, Paulo Gottgtroy, Vishal Jain, Anju Verma, , Ilkka Havukkala, Elaine Rush, Russel Pears, Alex Tjahjana, Yingjie Hu, Stephen MacDonell, Integrating Local and Personalised Modelling with Global Ontology Knowledge Bases for Biomedical and Bioinformatics Decision Support, in: Smolin et al (eds) Computational Intelligence in Bioinformatics, 151(4): 93-116, Springer, 2008
- 2008 - Pang, S., Havukkala, I., Hu, Yingjie, Kasabov, N.: Bootstrapping Consistency Method for Optimal Gene Selection from Microarray Gene Expression Data for Classification Problems. Ch 4, pp.89-110, In: Zhang, Y.-Q., Rajapakse, J.C. (eds.): Machine Learning for Bioinformatics. John Wiley & Sons, Inc., New Jersey, ISBN: 978-0-470-11662-3, 2008
- 2008 - Kasabov, N., Song, Q., & Ma, T. M., Fuzzy-neuro systems for local and personalized modelling. In Forging New Frontiers: Fuzzy Pioneers II, 218: 175-197, Berlin / Heidelberg: Springer, 2008
- 2008 - Pang, S., & Kasabov, N. (2008). SVMT-rule: Association rule mining over SVM classification trees. In Rule Extraction from Support Vector Machines, 80:135-162, 2008
- 2007 - Ravi, V., Kumar, P.R, Srinivas, E.R., Kasabov, N.K. A Semi-Online Training Algorithm for Radial Basis Function Neural Networks: Application to Bankruptcy Prediction in Banks, Chapter XV in: V.Ravi (ed) Advances in Banking Technology and Management, Information Science Reference, Hashley-New York, pp. 243-260
- 2007 - N.Kasabov, Brain-, Gene-, and Quantum Inspired Computational Intelligence: Challenges and Opportunities, in: W. Duch and J. Manzduk (eds) Challenges in Computational Intelligence, Springer, ISBN: 978-3-540-71983-0, pp193-219, 2007
- 2006 - N.Kasabov, Brain-, Gene-, and Quantum Inspired Computational Intelligence: Challenges and Opportunities, in: Reusch. B., (eds) Computational Intelligence, Theory and Applications, Springer, ISBN: 978-3-540-34780-4, pp 521-544, 2006,
- 2006 - Gottgtroy P., Kasabov N., Macdonell S., Evolving Ontologies for Intelligent Decision Support, Elsevier, Fuzzy Logic And The Semantic Web, Chapter 21, pp 415-439, 2006
- 2005 - N. Kasabov, Liang Goh and Mike Sullivan, Integrated Prognostic Profiles: Combining Clinical and Gene Expression Information through Evolving Connectionist Approach, Vladimir B Bajic, Tan Tin Wee (eds), Information Processing in Living Systems in Series on Advances in Bioinformatics and Computational Biology - Vol. 2, World Scient. Publ., Singapore, 2005. pp 695-707 ISBN: 1-86094-563-5
- 2005 - N. Kasabov, Z. S. H. Chan, Vishal Jain, I. Sidorov and D. S. Dimitrov, Computational Modelling of Gene Regulatory Networks, Book Chapter in "Series on Advances in Bioinformatics and Computational Biology (Information Processing and Living Systems)", Vol. 2, pp 673-686.
- 2005 - N.Kasabov, Z.Chan, Q.Song and D.Greer, Evolving Connectionist Systems with Evolutionary Self-Optimisation, chapter in: Do smart adaptive systems exist - Best practice for selection and combination of intelligent methods, Gabrys, Bogdan; Leiviskä Kauko; Strackeljan, Jens (eds), Springer Verlag, Series Study in Fuzziness, vol.173, 2005
- 2005 - Kasabov N and Benuskova L (2005) Theoretical and Computational Models for Neuro, Genetic, and Neuro-Genetic Information Processing. In: Handbook of Computational and Theoretical Nanoscience, M. Rieth and W. Schommers (eds), vol 10, Chapter 41, American Scientific Publishers, Los Angeles. pp 1-38, ISBN: 1-58883-042-X
- 2005 - Dimiter S Dimitrov, Igor A. Sidorov and Nikola Kasabov, Computational Biology, in: M. Eieth and W. Schommers(eds) Handbook of Theoretical and Computational Nanotechnology, Vol. 1 (1) American Scientific Publisher, Chapter 21, 2005, ISBN: 1-58883-042-X
- 2004 - Kasabov, N. and D. Dimitrov, Evolving connectionist systems for gene regulatory network modelling and discovery, chapter in: Neural Information Processing: Research and Development, Rajapakse, Jagath C.; Wang, Lipo (Eds.), Springer-Verlag, 2004, X, 477 p., Hardcover, ISBN: 3-540-21123-3
- 2003 - T.Cohen, D.Hegg, M.de Michele, Q.Song, and N. Kasabov, An intelligent controller for automated operation of sequencing batch reactors, Water Science & Technology, IWA Publishing, Vol 47, No 12 (2003) 57-63
- 2003 - Kasabov, N. Evolving connectionist-based decision support systems, in: X.Yu, J.Kacprzyk (eds), Applied Decision Support with Soft Computing, series: Studies in Fuzziness and Soft Computing, vol. 124, Springer, 2003.
- 2003 - Kasabov, N.Decision support systems and expert systems, in: M.Arbib (ed) Handbook of brain study and neural networks, MIT Press (2003)
- 2001 - Kasabov, N. Brain-like functions in evolving connectionist systems for on-line, knowledge-based learning, in: T. Kitamura (ed) What should be Computed to Understand and Model Brain Function, FLSI Soft Computing Series, Volume 3, World Scientific (2001), 77-113
- 2000 - Iliev, G. and Kasabov, N., Dual-Tone Multiple Frequency Detection Using Adaptive Filters and Neural Network Classifiers in: P. Sincak, J. Vascak, V. Kvasnicka, R. Mesiar (eds) The State of the Art in Computational Intelligence, Physica-Verlag, 2000, 302-307
- 2000 - Kasabov N., and G. Iliev, A methodology and a system for adaptive speech recognition in a noisy environment based on adaptive noise cancellation and evolving fuzzy neural networks, in: Neuro-Fuzzy Pattern Recognition, H. Bunke and A. Kandel, eds., World Scientific 2000, 179-203
- 2000 - Kasabov, N., Evolving and Evolutionary Connectionist Systems for On-Line Learning and Knowledge Engineering in: Peter Sincak, Jan Vascak (eds) Quo Vadis Computational Intelligence? New Trends and Approaches in Computational Intelligence, Physica-Verlag, 2000, 361-369
- 2000 - Kasabov, N., Erzegovezi, L, Fedrizzi, M, Beber, A, and Deng, D, Hybrid Intelligent Decision Support Systems and Applications for Risk Analysis and Prediction of Evolving Economic Clusters in Europe, in: N. Kasabov (ed) Future directions for intelligent information systems and information sciences, Springer Verlag, 2000, 347-372
- 2000 - Kasabov, N., Evolving connectionist systems - the new-Old AI Paradigm, in: N. Kasabov (ed) Future directions for intelligent information systems and information sciences, Springer Verlag, 2000, 3-12
- 2000 - Swope, J.A., Kasabov, N., and Williams, M., Neuro-fuzzy modelling of heart rate signals and applications to diagnostics, in: P.S. Szczepaniak, P.J.G. Lisboa, J. Kacprzyk, (eds), Fuzzy Systems in Medicine, Physica Verlag (2000) 519-542
- 2000 - Taylor, J. and Kasabov, N, Modelling the Emergence of Speech and Language through Evolving Connectionist Systems, in: N. Kasabov (ed) Future directions for intelligent information systems and information sciences, Springer Verlag, 2000, 102-126
- 1999 - Kasabov, N. and Kozma, R. Multi-scale analysis of time series based on neuro-fuzzy-chaos methodology applied to financial data. in: A. Refenes, A. Burges, and B. Moody, (eds) Computational Finance 1997, Kluwer Academic (1999), ISBN 0-7923-8308-7
- 1999 - Kasabov, N., Israel, S., and Woodford, B.J., Methodology and evolving connectionist architecture for image pattern recognition, in: Pal, Ghosh and Kundu (eds) Soft Computing and Image Processing, Heidelberg, Physica-Verlag (Springer Verlag) (1999), 318-336
- 1999 - Kasabov, N. Evolving connectionist and fuzzy connectionist systems - theory and applications for adaptive, on-line intelligent systems, in: Neuro-Fuzzy Techniques for Intelligent Information Systems, N. Kasabov and R.Kozma, (eds) Heidelberg, Physica Verlag (1999) 111-146
- 1999 - Kasabov, N., Kozma, R., Kilgour, R., Laws, M., Taylor, J., Watts, M., and Gray, A. Hybrid connectionist-based methods and systems for speech data analysis and phoneme-based speech recognition. in: Neuro-Fuzzy Techniques for Intelligent Information Systems, N. Kasabov and R. Kozma, (eds) Heidelberg, Physica Verlag (1999) 241-264
- 1999 - Kasabov, N., Evolving connectionist and fuzzy connectionist systems for on-line adaptive decision making and control, in: Advances in Soft Computing - Engineering Design and Manufacturing, R. Roy, T. Furuhashi and P.K. Chawdhry (eds.) Springer-Verlag, London Limited, 1999 [ISBN 1-85233-062-7] 638 pages
- 1999 - Kozma, R. and Kasabov, N., Generic neuro-fuzzy-chaos methodologies and techniques for intelligent time-series analysis. in: Soft Computing in Financial Engineering. R. Ribeiro, R.Yager, H. J. Zimmermann and J. Kacprzyk (eds) Heidelberg, Physica-Verlag (1999)
- 1999 - Watts, M., and Kasabov, N., Neuro-genetic tools and techniques, in: Neuro-Fuzzy Techniques for Intelligent Information Systems, N. Kasabov and R. Kozma, (eds) Heidelberg, Physica Verlag (1999) 97-110
- 1998 - Kasabov, N., Advanced Neuro-Fuzzy Engineering for Building Intelligent Adaptive Information Systems. in: Fuzzy Systems Design: Social and Engineering Applications. L.Reznik, V.Dimitrov and J.Kacprzyk (eds) Heidelberg, Physica-Verlag (1998) 249-262
- 1998 - Kasabov, N. A framework for intelligent conscious machines and its application to multilingual speech recognition systems, in: Brain-like computing and intelligent information systems. S. Amari and N. Kasabov (eds) Singapore, Springer Verlag (1998) 106-126
- 1998 - Kozma, R. and Kasabov, N., Chaos and fractal analysis of irregular time series embedded into connectionist structure, in: Brain-like computing and intelligent information systems. S. Amari and N. Kasabov (eds) Singapore, Springer Verlag (1998) 213-237
- 1997 - Kasabov, N., Kozma, R. Neuro-fuzzy-chaos engineering for building intelligent adaptive information systems. In: Intelligent Systems: Fuzzy Logic, Neural Networks and Genetic Algorithms. Da Ruan ed., Boston/London/Dordrecht, Kluwer Academic Publishers (1997) 213-237
- 1995 - Kasabov, N. and Clarke, G. A template-based implementation of connectionist knowledge based systems for classification and learning, in: Advances in Neural Networks, Vol.3. O. Omidvar (ed) New Jersey, Ablex Publishing Company (1995) 137-156
- 1995 - Kasabov, N., Building comprehensive AI and the task of speech recognition, in: Applications of Neural Networks to Telecommunications, 2. J. Alspector, R. Goodman and T. Brown (eds) New Jersey, Laurence Erlbaum (1995) 178-187
- 1992 - Kasabov, N., and Nikovski, D. Prognostic expert systems on a hybrid connectionist environment, in: Artificial Intelligence V Methodology, Systems, Applications, B. du Boulay and V.Sgurev (eds) Amsterdam, North Holland (1992) 141-148
- 1990 - Kasabov N., Hybrid connectionist rule based systems, in: Artificial Intelligence IV Methodology, Systems, Applications, P. Jorrand and V. Sgurev (eds) Amsterdam, North Holland (1990) 227-235
- 1990 - Kasabov, N, and Demirev, G., Neural networks and genetic algorithms, in: Izkustven Intelect, I. Popchev and L. Dakovski (eds) Sofia, Technika (1990) 200-210 (in Bulgarian)
- 1990 - Stankulova, B., Dakovski, L., Pavlov, R and Kasabov, N. Intelligent tutoring systems, in: Izkustven Intelect, I. Popchev and L. Dakovski (eds), Sofia, Technika (1990) 281-290 (in Bulgarian)
Conference outputs
- 2015 - Angelov, P., Atanassov, K.T., Doukovska, L., Hadjiski, M., Jotsov, V., Kacprzyk, J., Kasabov, N., Sotirov, S., Szmidt, E., Zadrożny, S. (Eds.) Proceedings of the 7th IEEE International Conference Intelligent Systems IS’2014, September 24‐26, 2014, Warsaw, Poland, Volume 1: Mathematical Foundations, Theory, Analyses, Springer, 2015.
- 2013 - Mladenov, V., Koprinkova-Hristova, P., Palm, G., Villa, A., Appolloni, B., & Kasabov, N. Artificial Neural Networks and Machine Learning - ICANN 2013 - 23rd International Conference on Artificial Neural Networks, Sofia, Bulgaria, September 10-13, 2013. Proceedings (Vol. 8131). V. Mladenov, P. D. Koprinkova-Hristova, G. Palm, A. E. P. Villa, B. Appolloni, & N. Kasabov (Eds.), Springer. doi:10.1007/978-3-642-40728-4
- 2009 - M.Koeppen, N.Kasabov and G.Coghill, Advancements in Neural Information Processing, Proc. off ICONIP 2008, Springer LNCS, vol. 5506/5507,pp.1257/1087, ISBN: 3-642-03039-4, 2009
- 2007 - J.Si, R.Sun, D.Brown, I.King and N.Kasabov (eds) Proceedings of the Int Joint Conference on Neutal Networks – IJCNN, 12-16 August 2007, IEEE Press, 2007
- 2007 - A.Koenig, M.Koeppen, A.Abraham, C.Igel and N.Kasabov, Proc. Seventh Int. Conference on Hybrid Intelligent Systems – HIS 2007, 17-19 Sept.2007, IEEE Comp.Soc.Press, pp.378, ISBN: 978-0-7695-2946-2
- 2006 - P.Angelov, D.Filev, N.Kasabov, O.Cordon (eds) Proc. 2006 Int. Symp. Evolving Fuzzy Systems, Lancaster, UK, IEEE Press, 2006
- 2004 - N. Pal, Nikola Kasabov et al, (eds) Proc. of the Int. Conf. on Neuro Information Processing, Calcutta, November 2004, Springer Verlag, Vol. 3316, ICONIP’2004, Heidelberg, 2004
- 2003 - Kasabov, Nik, Zeke S.H. Chan, Proceedings of the Conference on Neuro-Computing and Evolving Intelligence, November 2003, Auckland University of Technology, (2003) 122 pages
- 2003 - Ken Chen, Shu Heng Chen, Heng Da Cheng, David K.Y. Chiu, Sanjoy Das, Richard Duro, Zhen Jiang, Nik Kasabov, Etiene Kerre, Hong Va Leong, Qing Li,, Mi Lu, Manuel Grana Romay, Don Ventura, Paul P. Wang, Jie Wu, Proceedings of the 7th Joint Conference on Information Sciences, JCIS 2003 1780 pages
- 2002 - Kasabov, N. ed. Proceedings of the Neurocomputing Colloquium and Workshop, October, Auckland University of Technology, (2002) 85 pages
- 2001 - Kasabov, N., and B.Woodford, (eds) Proceedings of the ANNES’2001 conference on Artifical Neural Networks and Experts Systems, Dunedin, Nov. 2001, University of Otago (2001)
- 1999 - Gedeon, T., P.Wong, S.Halgamuge, N.Kasabov, D.Nauck, and K.Fukushima (eds) ICONIP’99-Proceedings of the 6th International Conference on Neural Information Processing, 16-20 November 1999, IEEE Press (1999), Vol I & II, 842 pages
- 1999 - Kasabov, N., and K.Ko, (eds) Emerging Knowledge Engineering and Connectionist-based Information Systems, Proceedings of the ICONIP/ANZIIS/ANNES’99 Workshop "Future directions for intelligent systems and information sciences, Dunedin, 22-23 Nov.1999, University of Otago (1999)
- 1998 - Kasabov, N., Kozma, R., O’Shea, R., Ko, K., Coghill, G., and Gedeon, T., (eds) Advances in Connectionist-Based Information Systems Proceedings of the International Conference on Neural Information Processing ICONIP’97, Springer Verlag Singapore (1998), 1550 pages
- 1993 - Kasabov, N. (ed.) The First New Zealand International Conference on Artificial Neural Networks and Expert Systems, Proceedings of ANNES'93 Dunedin, IEEE Computer Society Press (1993) 346 pages
- 2019 - Kaushalya Kumarasinghe, Denise Taylor, Nikola Kasabov: eSPANNet: Evolving Spike Pattern Association Neural Network for Spike-based Supervised Incremental Learning and Its Application for Single-trial Brain Computer Interfaces. IJCNN 2019
- 2018 - Lu Wang, Chao Ma, Enmei Tu, Jie Yang and Nikola Kasabov, “Discrete Sparse Hashing for Cross-Modal Similarity Search”, 25th International Conference on Neural Information Processing (ICONIP) 2018.
- 2018 - Mingjian Chen, Hao Zheng, Changsheng Lu, Enmei Tu, Jie Yang, Nikola Kasabov, “A Spatio-Temporal Fully Convolutional Network for Breast Lesion Segmentation in DCE-MRI,” 25th International Conference on Neural Information Processing (ICONIP) 2018
- 2018 - Doborjeh, G, Z., Doborjeh, G, M., Kasabov, N. (2018). EEG Pattern Recognition using Brain-Inspired Spiking Neural Networks for Modelling Human Decision Processes. IEEE WCCI, IJCNN conference in Rio, Brazil.
- 2017 - Liu, F., Huang, X., Peng, C., Yang, J., & Kasabov, N. (2017, November). Robust Kernel Approximation for Classification. In International Conference on Neural Information Processing(pp. 289-296). Springer, Cham.
- 2017 - Peng, C., Liu, F., Yang, H., Yang, J., & Kasabov, N. (2017, November). Correlation Filters with Adaptive Memories and Fusion for Visual Tracking. In International Conference on Neural Information Processing (pp. 170-179). Springer, Cham.
- 2017 - Omori, Y., Kawano, H., Seo, A., Doborjeh, Z. G., Kasabov, N., & Doborjeh, M. G. (2017, November). EEG Comparison Between Normal and Developmental Disorder in Perception and Imitation of Facial Expressions with the NeuCube. In International Conference on Neural Information Processing(pp. 596-601). Springer, Cham.
- 2016 - Capecci, E., Doborjeh, Z. G., Mammone, N., La Foresta, F., Morabito, F. C., & Kasabov, N. (2016, July). Longitudinal study of alzheimer's disease degeneration through EEG data analysis with a NeuCube spiking neural network model. In Neural Networks (IJCNN), 2016 International Joint Conference on (pp. 1360-1366). IEEE. https://ieeexplore.ieee.org/document/7727356/
- 2016 - Doborjeh G, Z., Doborjeh, M., Kasabov, N. (2016) .Efficient Recognition of Attentional Bias using EEG data and the NeuCube Evolving Spatio-Temporal Data Machine”, ICONIP in Kyoto, vol. 9950, pp. 645-653,. https://link.springer.com/chapter/10.1007/978-3-319-46681-1_76
- 2016 - Kawano, H., Seo, A., Doborjeh, Z. G., Kasabov, N., & Doborjeh, M. G. (2016). Analysis of similarity and differences in brain activities between perception and production of facial expressions using EEG DATA and the NeuCube spiking neural network architecture. In International Conference on Neural Information Processing (pp. 221-227). Springer, Cham. https://link.springer.com/chapter/10.1007/978-3-319-46681-1_27
- 2016 - Arya, A., Ravi, V., Tejasviram, V., Sengupta, N., Kasabov, N (2016). Cyber Fraud Detection using Evolving Spiking Neural Network. In IEEE 11th International Conference on Industrial and Information Systems (ICIIS) 2016. (Accepted)
- 2016 - Kasabov, N., Sengupta, N., Scott, N. (2016). From von neumann, John Atanasoff and ABC to Neuromorphic computation and the NeuCube spatio-temporal data machine. In IEEE-Intelligent Systems 2016.
- 2016 - Abbott, A., Sengupta, N., Kasabov, N. (2016). Which method to use for optimal structure and function representation of large spiking neural networks: A case study on the NeuCube architecture. In IJCNN 2016.
- 2016 - Breen, V., Kasabov, N., Du, P., & Calder, S. (2016). A spiking neural network for personalised modelling of electrogastrography (EGG). In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) Vol. 9896 LNAI (pp. 18-25). Springer Verlag. doi:10.1007/978-3-319-46182-3_2
- 2016 - Kawano, H., Seo, A., Gholami Doborjeh, Z., Kasabov, N., & Gholami, M. (2016). Analysis of Similarity and Differences in Brain Activities between Perception and Production of Facial Expressions Using EEG Data and the NeuCube Spiking Neural Network Architecture. In ICONIP 2016.
- 2016 - Gholami Doborjeh, Z., Gholami, M., & Kasabov, N. (2016). Efficient Recognition of Attentional Bias using EEG data and the NeuCube Evolving Spatio-Temporal Data Machine. In ICONIP 2016. Kyoto.
- 2016 - Gholami, M., & Kasabov, N. (2016). Personalised Modelling on Integrated Clinical and EEG Spatio-Temporal Brain Data in the NeuCube Spiking Neural Network System. International Joint Conference on Neural Networks (IJCNN) (pp. 1373-1378). Vancouver, Canada: IEEE.
- 2016 - Capecci, E., Gholami Doborjeh, Z., Mammone, N., Foresta, F., Morabito, F., & Kasabov, N. (2016). Longitudinal Study of Alzheimer’s Disease Degeneration through EEG Data Analysis with a NeuCube Spiking Neural Network Model. IEEE WCCI 2016 (pp. 1360-1366).
- 2015 - Wu, H., Gao, L., & Kasabov, N. (2015). Inference of cancer progression from somatic mutation data. IFAC-PapersOnLine, 48(28), 234-238.
- 2015 - Peng, L., Hou, Z. -G., Kasabov, N., Hu, J., Peng, L., & Wang, W. (2015). sEMG-based torque estimation for robot-assisted lower limb rehabilitation.. In IJCNN. IEEE. doi:10.1109/IJCNN.2015.7280449
- 2015 - Peng, L., Hou, Z. -G., Kasabov, N., Peng, L., Hu, J., & Wang, W. (2015). Implementation of active training for an upper-limb rehabilitation robot based on impedance control. In Proceedings of the 2015 27th Chinese Control and Decision Conference, CCDC 2015 (pp. 5453-5458). Institute of Electrical and Electronics Engineers Inc.. doi:10.1109/CCDC.2015.7161769
- 2015 - Kasabov, N. (2015). Neuromorphic Predictive Systems Based on Deep Learning. In Engineering Applications of Neural Networks Vol. 1. Springer. doi:10.1007/978-3-319-23983-5
- 2015 - E. Capecci, J. I. Espinosa-Ramosy, N. Mammone, N. Kasabov, J. Duun-Henriksenx, T. Wesenberg Kjaer, M. Campolok, F. La Forestak, F. C. Morabito, Modelling Absence Epilepsy Seizure Data in the NeuCube Evolving Spiking Neural Network Architecture, Proc. IJCNN 2015, Killarney, 12-17 July 2015, pages 1-8, DOI: 10.1109/IJCNN.2015.7280764
- 2015 - Gholami Doborjeh, M., & Kasabov, N. Dynamic 3D Clustering of Spatio-Temporal Brain data in the NeuCube Spiking Neural Network Architecture on a Case Study of fMRI Data. Neural Information Processing. ICONIP 2015, Part IV, LNCS 9492, pp. 191-198. DOI: 10.1007/978-3-319-26561-2_23.
- 2015 - Jia, S., Liang, Y., Chen, X., Gu, Y., Yang, J., Kasabov, N., & Qiao, Y. Adaptive Location for Multiple Salient Objects Detection. Neural Information Processing. ICONIP 2015, Part III, LNCS 9491, pp. 411-418. DOI: 10.1007/978-3-319-26561-2_46.
- 2015 - Zhao, Y., Qiao, Y., Yang, J., & Kasabov, N. Abnormal Activity Detection Using Spatio-Temporal Feature and Laplacian Sparse Representation. Neural Information Processing. ICONIP 2015, Part IV, LNCS 9492, pp. 410-418. DOI: 10.1007/978-3-319-26561-2_49.
- 2015 - Li, L., Kasabov, N., Yang, J., Yao, L., & Jia, Z. Poisson Image Denoising Based on BLS-GSM Method. Neural Information Processing. ICONIP 2015, Part IV, LNCS 9492, pp. 513-522. DOI: 10.1007/978-3-319-26561-2_61.
- 2015 - Wu, H., Gao, L., Li, F., Song, F., Yang, X., & Kasabov, N. Identifying overlapping mutated driver pathways by constructing gene networks in cancer. 10th International Symposium on Bioinformatics Research and Applications (ISBRA-14) Zhangjiajie, China. 28-30 June 2014, BMC Bioinformatics 2015, 16(Suppl 5):S3.
- 2015 - Capecci, E., Morabito, F., Campolo, M., Mammone, N., Labate, D., & Kasabov, N. A Feasibility Study of Using the NeuCube Spiking Neural Network Architecture for Modelling Alzheimer's Disease EEG data. In Smart Innovation, System and Technologies. Italy: Springer. Volume 37. pp 159-172. doi:10.1007/978-3-319-18164-6_16
- 2015 - Sengupta, N., Scott, N., & Kasabov, N. (2015). Framework for knowledge driven optimisation based data encoding for brain data modelling using spiking neural network architecture. In Advances in Intelligent Systems and Computing Vol. 415 (pp. 109-118). Springer Verlag. doi:10.1007/978-3-319-27212-2_92015
- 2015 - Peng, L., Hou, Z. -G., Kasabov, N., Bian, G. -B., Vladareanu, L., & Yu, H. (2015). Feasibility of NeuCube spiking neural network architecture for EMG pattern recognition. In Proceedings of the 2015 International Conference on Advanced Mechatronic Systems, ICAMechS Vol. 2015-October (pp. 365-369). Beijing, China: IEEE Computer Society. doi:10.1109/ICAMechS.2015.7287090
- 2015 - Wang, G. Y., Kasabov, N., Capecci, E., Doborjeh, M. G., Kydd, R., & Russell, B. (2015). EEG-based application of the NeuCube framework in addiction research. In 13th International Conference on Neuro- Computing and Evolving Intelligence (NCEI) 2015. Auckland, New Zealand. Retrieved from http://kedri.aut.ac.nz/conferences/ncei15/book-of-abstracts
- 2014 - Zhang, W., Yang, J., Jia, W., Kasabov, N., Jia, Z., & Zhou, L. Unsupervised Segmentation Using Cluster Ensembles. 21st International Conference, ICONIP 2014. Kuching, Malaysia, November 3-6,2014. Neural Information Processing, Lecture Notes in Computer Science, Volume 8836, part III, pp 76-84. doi: 10.1007/978-3-319-12643-2_10.
- 2014 - Murli, N., Kasabov, N., & Handaga, B. Classification of fMRI Data in the NeuCube Evolving Spiking Neural Network Architecture. 21st International Conference, ICONIP 2014. Kuching, Malaysia, November 3-6,2014. Neural Information Processing, Lecture Notes in Computer Science, Volume 8834, part I, pp 421-428. doi: 10.1007/978-3-319-12637-1_53.
- 2014 - Tu, E., Yang, J., Jia, Z., & Kasabov, N. Posterior Distribution Learning (PDL): A Novel Supervised Learning Framework. 21st International Conference, ICONIP 2014. Kuching, Malaysia, November 3-6,2014. Neural Information Processing, Lecture Notes in Computer Science, Volume 8834, part I, pp 86-94. doi: 10.1007/978-3-319-12637-1_11
- 2014 - Doborjeh, M., Capecci, E., & Kasabov, N. Classification and segmentation of fMRI Spatio-Temporal Brain Data with a NeuCube Evolving Spiking Neural Network Model, 2014 IEEE Symposium on Evolving and Autonomous Learning Systems (EALS). 9-12 Dec. 2014, Orlando, USA (pp. 73-80). doi: 10.1109/EALS.2014.7009506
- 2014 - Taylor, D., Scott, N., Kasabov, N., Capecci, E., Tu, E., Saywell, N., Hou, Z. Feasibility of NeuCube SNN architecture for detecting motor execution and motor intention for use in BCI applications. In 2014 International Joint Conference on Neural Networks (IJCNN) (pp. 3221-3225). Beijing, China: IEEE.
- 2014- Hartono, R., Pears, R., Kasabov, N., & Worner, S. (2014). Extracting Temporal Knowledge from Time Series: A Case Study in Ecological Data. In 2014 International Joint Conference on Neural Networks (IJCNN) (pp. 4237-4243). Beijing, China: IEEE. doi:10.1109/IJCNN.2014.6889918
- 2014 - Othman, M., Kasabov, N., Tu, E., Feigin, V., Krishnamurthi, R., Hou, Z., Hu, J. Improved predictive personalized modelling with the use of Spiking Neural Network System and a case study on stroke occurrences data. In 2014 International Joint Conference on Neural Networks (IJCNN) (pp. 3197-3204). Beijing, China: IEEE. doi:10.1109/IJCNN.2014.6889709
- 2014 - Tu, E., Kasabov, N., Othman, M., Li, Y., Worner, S., Yang, J., Jia, Z. NeuCube(ST) for Spatio-Temporal Data Predictive Modelling with a Case Study on Ecological Data. In 2014 International Joint Conference on Neural Networks (pp. 638-645). Beijing, China: IEEE. doi:0.1109/IJCNN.2014.6889717
- 2014 - Hu, J., Hou, Z., Chen, Y., Kasabov, N., & Scott, N. EEG-Based Classification of Upper-Limb ADL Using SNN for Active Robotic Rehabilitation. In 2014 5th IEEE RAS & EMBS International Conference on Biomedical Robotics and Biomechatronics (pp. 409-414). Sao Paolo, Brazil: IEEE. doi:10.1109/BIOROB.2014.6913811
- 2013 - Schliebs, S., Kasabov, N., Parry, D., & Hunt, D. Towards a Wearable Coach: Classifying Sports Activities with Reservoir Computing. In EANN 2013, Halkidiki Greece. Engineering Applications of Neural Networks Communications in Computer and Information Science, Vol. 383, pp. 233-242.
- 2013 - Schliebs, S., Capecci, E., & Kasabov, N. Spiking Neural Network for On-line Cognitive Activity Classification Based on EEG data. In ICONIP 2013, Daegu, Korea. Neural Information Processing Lecture Notes in Computer Science, Vol. 8228, pp. 55-62.
- 2013 - Scott, N., Kasabov, N., & Indiveri, G. NeuCube Neuromorphic Framework for Spatio-Temporal Brain Data and Its Python Implementation. In ICONIP 2013, Daegu, Korea. Neural Information Processing Lecture Notes in Computer Science, Vol. 8228, pp. 78-84.
- 2013 - Chen, Y., Hu, J., Kasabov, N., Hou, Z., & Cheng, L. NeuroCubeRehab: A Pilot Study for EEG Classification in Rehabilitation Practice Based on Spiking Neural Networks. In ICONIP 2013, Daegu, Korea. Neural Information Processing Lecture Notes in Computer Science, Vol. 8228, pp. 70-77.
- 2013 - Kasabov, N., Hu, J., Chen, Y., Scott, N., & Turkova, Y. Spatio-temporal EEG data classification in the NeuCube 3D SNN Environment: Methodology and Examples. In ICONIP 2013, Daegu, Korea. Neural Information Processing Lecture Notes in Computer Science, Vol. 8228, pp. 63-69.
- 2013 - Zhou, L., Gong, C., Li, Y., Qiao, Y., Yang, J., & Kasabov, N. Salient Object Segmentation Based on Automatic Labeling. In ICONIP 2013, Daegu, Korea. Neural Information Processing Lecture Notes in Computer Science, Vol. 8228, pp. 584-591.
- 2012 - Othman, M., Kasabov, N., Hu, R. Spatial-temporal data representation in ontology system for personalized decision support. In: Talent Management Symposium (TMS 2012) , 11-12 July 2012, Australia
- 2012 - Minghui Li, Zhenhong Jia, Jie Yang, Yingjie Hu, Dianjun Li, An Algorithm for Remote Sensing Image Denoising Based on the Combination of the Improved BiShrink and DTCWT, Procedia Engineering, Volume 24, 2011, Pages 470-474, ISSN 1877-7058, 10.1016/j.proeng.2011.11.2678.
- 2012 - Kasabov, N. Evolving Spiking Neural Networks for Spatio and Spectro-Temporal Pattern Recognition, 2012 IEEE 6th International Conference ‘Intelligent Systems’, IEEE Press, 978-1-4673-2278-2/12/$31.00 ©2012, vol.1. 27-32, 2012.
- 2012 - Mohemmed, A., Lu, Guoyu, Kasabov, N.: Evaluating SPAN Incremental Learning for Handwritten Digit Recognition . In Proceedings of the 19th international conference on Neural Information Processing - Volume Part III (ICONIP'12), Tingwen Huang, Zhigang Zeng, Chuandong Li, and Chi Sing Leung (Eds.), Vol. Part III. Springer-Verlag, Berlin, Heidelberg.
- 2012 - Schliebs, S. and M. Fiasch´e and N. Kasabov, Constructing Robust Liquid State Machines to Process Highly Variable Data Streams. Proceedings Editors: Villa AEP, Duch W, Érdi P, Masulli F, Palm G. ICANN (1). Springer, LNCS 7552, 604-611, 2012.
- 2012 - Dhoble, K., N. Nuntalid, G. Indivery and N.Kasabov, On-line Spatiotemporal Pattern Recognition with Evolving Spiking Neural Networks utilising Address Event Representation, Rank Oder- and Temporal Spike Learning, Proc. WCCI 2012 IEEE World Congress on Computational Intelligence, June, 10-15, 2012 - Brisbane, Australia, 554-560
- 2012 - Mohemmed, A. and N.Kasabov, Incremental learning algorithm for spike pattern classification, WCCI 2012 IEEE World Congress on Computational Intelligence, June, 10-15, 2012 - Brisbane, Australia, 1227- 1232
- 2011 - Wei Cui, Zhenhong Jia, Xizhong Qin, Jie Yang, Yingjie Hu, Multi-temporal Satellite Images Change Detection Algorithm Based on NSCT, Procedia Engineering, Volume 24, 2011, Pages 252-256, ISSN 1877-7058, 10.1016/j.proeng.2011.11.2636.
- 2011 - Qifan Wang, Zhenhong Jia, Xizhong Qin, Jie Yang, Yingjie Hu, A New Technique for Multispectral and Panchromatic Image Fusion, Procedia Engineering, Volume 24, 2011, Pages 182-186, ISSN 1877-7058.
- 2011 - Jihong Liu, Zhenhong Jia, Xizhong Qin, Jie Yang, Yingjie Hu, NSCT Remote Sensing Image Denoising Based on Threshold of Free Distributed FDR, Procedia Engineering, Volume 24, 2011, Pages 616-620, ISSN 1877-7058.
- 2011 - Ab Aziz, N.A., Mohemmed, A.W., Yusoff Alias, M., Ab Aziz, K., Syahali, S., Coverage maximization and energy conservation for mobile wireless sensor networks: A two phase particle swarm optimization algorithm. In Proceedings of the 6th International Conference on Bio-Inspired Computing: Theories and Applications, BIC-TA 2011. IEEE. 64-69. 2011
- 2011 - Kasabov, N., Dhoble, K., Nuntalid, N., & Mohemmed, A., Evolving probabilistic spiking neural networks for spatio- temporal pattern recognition: A preliminary study on moving object recognition .In 18th International Conference on Neural Information Processing. Shanghai, China, Springer, Heidelberg. LNCS 7064, 230-239.
- 2011 - Nuntalid, N., Dhoble, K., & Kasabov, N., EEG Classification with BSA Spike Encoding Algorithm and Evolving Probabilistic Spiking Neural Network. In 18th International Conference on Neural Information Processing. Shanghai, China, Springer, Heidelberg. LNCS 7062, 451-460.
- 2011 - Mohemmed, A., Schliebs, S., & Kasabov, N., SPAN: A Neuron for Precise-Time Spike Pattern Association. In 18th International Conference on Neural Information Processing. Shanghai, China. Shanghai, China. Springer, Heidelberg. LNCS 7063, pp.718-725.
- 2011 - Schliebs, S., Hamed, H. N. A., & Kasabov, N., A reservoir-based evolving spiking neural network for on-line spatio-temporal pattern learning and recognition.In 18th International Conference on Neural Information Processing. Shanghai, China, Springer, Heidelberg. LNCS 7063, pp.160-168.
- 2011 - Liang, W., Hu, Y., Kasabov, N., & Feigin, V., Exploring Associations between Changes in Ambient Temperature and Stroke Occurrence: Comparative Analysis using Global and Personalised Modelling Methods. In 18th International Conference on Neural Information Processing. Shanghai, China, Springer, Heidelberg. LNCS 7062, pp.129-137.
- 2011 - Hu, Y., & Kasabov, N., Personalised Modelling on SNPs Data for Crohn's Disease Prediction. In 18th International Conference on Neural Information Processing. Shanghai, China, Springer, Heidelberg. LNCS 7062, 646-653.
- 2011 – A. Mohemmed, S. Schliebs, S. Matsuda, K. Dhoble, and N. Kasabov, Optimization of Spiking Neural Networks with Dynamic Synapses for Spike Sequence Generation using PSO, International Joint Conference on Neural Networks – IJCNN’11, San Jose, California (pp. 2969-2974). USA, 2011
- 2011 - Hamed, H., Kasabov, N., Shamsuddin, S., Widiputra, H., & Dhoble, K., An Extended Evolving Spiking Neural Network Model for Spatio-Temporal Pattern Classification. In Proceedings of International Joint Conference on Neural Networks (pp. 2653-2656). California, USA: IEEE. 2011
- 2011 - Schliebs, S., Mohemmed, A., & Kasabov, N., Are Probabilistic Spiking Neural Networks Suitable for Reservoir Computing?. In International Joint Conference on Neural Networks, pp. 3156-3163. San Jose, USA, 2011
- 2011 - Kasabov, N., Schliebs, S., & Mohemmed, A., Modelling the Effect of Genes on the Dynamics of Probabilistic Spiking Neural Networks for Computational Neurogenetic Modelling. In 8th International Meeting on Computational Intelligence Methods for Bioinformatics and Biostatistics. Gargnano-Lago di Garda, Italy.2011
- 2011 - Widiputra, H., Pears, R., and Kasabov, N., Multiple Time-series Prediction Through Multiple Time-series Relationships Profiling and Clustered Recurring Trends, 15th Pacific-Asia Conference Knowledge Discovery and Data Mining , PAKDD’11. (pp. 161-172) 2011
- 2011 - Mohemmed, A., Schliebs, S., Matsuda, S., & Kasabov, N., Method for training a spiking neuron to associate input-output spike trains. In EANN/AIAI 2011, Part I, IFIP AICT 363. IFIP International Federation for Information Processing (2011) (pp. 219-228. Greece. 2011
- 2011 - Jin Y, Meng Y, Weng J, Kasabov N, Guest editorial special issue on computational modeling of neural and brain development, IEEE Transactions on Autonomous Mental Development, 3(4):273-274 2011
- 2010 - N. Gunasekara, S. Pang, and N. Kasabov, Tuning N-gram String Kernel SVMs via Meta Learning, Proc. of International Conference on Neural Information Processing - ICONIP'10, Springer-Verlag, Vol. Part II [ISBN: 3-642-17533-3 978-3-642-17533-6]:91-98, 2010
- 2010 - Ye Chen, Shaoning Pang, and Nikola Kasabov, Factorizing Class Characteristics via Group MEBs Construction, Proc. of International Conference on Neural Information Processing - ICONIP'10, Springer-Verlag, Vol. Part II [ISBN: 3-642-17533-3 978-3-642-17533-6]: 283-290, 2010
- 2010 - Stefan Schliebs, Nuttapod Nuntalid, Nikola Kasabov,Towards spatio-temporal pattern recognition using evolving spiking neural networks, Proc. of International Conference on Neural Information Processing - ICONIP'10, Springer-Verlag, Vol. Part I [ISBN:3-642-17536-8 978-3-642-17536-7]: 163-170, 2010
- 2010 - Haza Nuzly Abdull Hamed, Nikola Kasabov and Siti Mariyam Shamsuddin, Dynamic Quantum-inspired Particle Swarm Optimization as Feature and Parameter Optimizer for Evolving Spiking Neural Networks, Proc of International Conference on Computer and Software Modeling - ICCSM 2010, 2010 (in print)
- 2010 - S. Schliebs, M. Defoin-Platel, N. Kasabov, Analyzing the Dynamics of the Simultaneous Feature and Parameter Optimization of an Evolving Spiking Neural Network, Proc. of International Joint Conference on Neural Networks, pp 933-940, 2010
- 2010 - S. Pang, T. Ban, Y. Kadobayashi and N. Kasabov, Incremental and Decremental LDA Learning with Applications, Proc. of International Joint Conference on Neural Networks 2010, 2010.
- 2010 - S. Schliebs, Heterogeneous Probabilistic Models for Optimization and Modelling of Evolving Spiking Neural Networks, Proc. of 8th New Zealand Computer Science Research Student Conference, Wellington, New Zealand, 2010
- 2010 - H. Widiputra, Building an Integrated Multi-model Framework for Multiple Time-series Prediction, Proc. of 8th New Zealand Computer Science Research Student Conference, Wellington, New Zealand, 2010
- 2010 - K. Dhoble, Multi-example Image Retrieval on Active mode Incremental NDA Learning, Proc. of 8th New Zealand Computer Science Research Student Conference, Wellington, New Zealand, 2010
- 2009 – H.N.A. Hamed, N. Kasabov, S.M. Shamsuddin, Integrated Feature Selection and Parameter Optimization for Evolving Spiking Neural Networks using Quantum Inspired Particle Swarm Optimization, Proc. of SOCPAR 2009, IEEE, pp. 695 – 698, 2009
- 2009 - S. Schliebs, M. Defoin-Platel, S. Worner, N. Kasabov, Quantum-inspired Feature and Parameter Optimization of Evolving Spiking Neural Networks with a Case Study from Ecological Modelling, Proc. of International Joint Conference on Neural Networks, Atlanta, Georgia, USA, 2833-2840, 2009
- 2009 - Pang, S. Ban, T. Kadobayashi Y. & Kasabov, N, (2009) Spanning SVM Tree for Personalized Transductive Learning, Proc. of ICANN 2009, Part I, LNCS 5768, pp. 913-922, 2009
- 2009 - Widiputra, H., Kho, H., Lukas, Pears, R., Kasabov, N., A Novel Evolving Clustering Algorithm with Polynomial Regression for Chaotic Time-Series Prediction, (2009) Proc. of ICONIP 2009, Part II, LNCS 5864, pp. 114-121, 2009
- 2009 - Verma, A., Fiasche, M., Cuzzola, M., Iacopino, P., Morabito, F., & Kasabov, N., Ontology Based Personalized Modeling for Type 2 Diabetes Risk Analysis: An Integrated Approach, 2009 Proc. of ICONIP 2009, Part II, LNCS 5864, pp. 360-366, 2009
- 2009 - Hu, Y., Kasabov, N., Coevolutionary Method for Gene Selection and Parameter Optimization in Microarray Data Analysis, 2009, Proc. of ICONIP 2009, Part II, LNCS 5864, pp. 483-492, 2009
- 2009 - Michlovsky, Z. Pang, S. Kasabov, N. Ban, T. & Kadobayashi, Y., String Kernel Based SVM for Internet Security Implementation, (2009) Proc. of ICONIP 2009, Part II, LNCS 5864, pp. 530-539, 2009
- 2009 - Hamed, H. N. A. Kasabov, N. Michlovsky, Z. &Shamsuddin, S., String Pattern Recognition Using Evolving Spiking Neural Networks and Quantum Inspired Particle Swarm Optimization, Proc. of ICONIP 2009, Part II, LNCS 5864, pp. 611-619, 2009
- 2009 - Chen, Y. Pang, S. Kasabov, N. Ban, T. & Kadobayashi, Y, Hierarchical Core Vector Machines for Network Intrusion Detection, Proc. of ICONIP 2009, Part II, LNCS 5864, pp. 520-529, 2009
- 2009 - Pang, S. Dhoble , K. Chen, Y. Kasabov, N. Ban, T. & Kadobayashi, Y. Active Mode Incremental Nonparametric Discriminant Analysis Learning, Proc. of the Eighth International Conference on Information and Management Sciences, 407-412 July 2009
- 2009 - Pang, S. Ozawa, S. Kasabov, N. Curiosity driven incremental LDA agent active learning, Proc. Of 2009 International Joint Conference on Neural Networks, pp. 2401-2408, 14-19 June 2009
- 2009 - N. Kasabov, Integrative Probabilistic Evolving Spiking Neural Networks Utilising Quantum Inspired Evolutionary Algorithm: A Computational Framework, in: M. Koeppen, N. Kasabov, G. Goghill and M. Ishikawa (eds) Advances in neural information processing, Proc. of ICONIP 2008, Auckland, Springer LNCS-5506, 3-13, 2009
- 2009 - S. Gordon, S. Pang, R. Nishioka, N. Kasabov, T. Yamakawa, Vision Based Mobile Robot for Indoor Environmental Security, in: M. Koeppen, N. Kasabov, G. Goghill and M. Ishikawa (eds) Advances in neural information processing, Proc. of ICONIP 2008, Auckland, Springer LNCS-5506, 962-969, 2009
- 2009 - M. Hisada, S. Ozawa, K. Zhang, S. Pang, N. Kasabov, A Novel Incremental Linear Discriminant Analysis for Multitask Pattern Recognition Problems, in: M. Koeppen, N. Kasabov, G. Goghill and M. Ishikawa (eds) Advances in neural information processing, Proc. of ICONIP 2008, Auckland, Springer LNCS-5506, 1163-1171, 2009
- 2009 - S. Ozawa, K. Matsumoto, S. Pang, N. Kasabov, Incremental Principal Component Analysis Based on Adaptive Accumulation Ratio, in: M. Koeppen, N. Kasabov, G. Goghill and M. Ishikawa (eds) Advances in neural information processing, Proc. of ICONIP 2008, Auckland, Springer LNCS-5506, 1196-1203, 2009
- 2009 - A. Verma, N. Kasabov, E. Rush, Q. Song, Ontology Based Personalized Modeling for Chronic Disease Risk Analysis: An Integrated Approach, in: M. Koeppen, N. Kasabov, G. Goghill and M. Ishikawa (eds) Advances in neural information processing, Proc. of ICONIP 2008, Auckland, Springer LNCS-5506, 1204-1210, 2009
- 2009 - Y. Hu, Q. Song, K. Kasabov, Personalized Modeling Based Gene Selection for Microarray Data Analysis, in: M. Koeppen, N. Kasabov, G. Goghill and M. Ishikawa (eds) Advances in neural information processing, Proc. of ICONIP 2008, Auckland, Springer LNCS-5506, 1221-1228, 2009
- 2009 - S. Schliebs, M. Defoin-Platel, N. Kasabov, Integrated Feature and Parameter Optimization for an Evolving Spiking Neural Network, in: M. Koeppen, N. Kasabov, G. Goghill and M. Ishikawa (eds) Advances in neural information processing, Proc. of ICONIP 2008, Auckland, Springer LNCS-5506, 1229-1236, 2009
- 2009 - H. Widiputra, R. Pears, N. Kasabov, Personaliased Modelling for Multiple Time-series Data Prediction: A Preliminary Investigation in Asia Pacific Stock Market Indexes Movement, in: M. Koeppen, N. Kasabov, G. Goghill and M. Ishikawa (eds) Advances in neural information processing, Proc. of ICONIP 2008, Auckland, Springer LNCS-5506, 1237-1244, 2009
- 2009 - W. De Mulder, S. Schliebs, R. Boel, M. Kuiper, Initialization Dependence of Clustering Algorithms, in: M. Koeppen, N. Kasabov, G. Goghill and M. Ishikawa (eds) Advances in neural information processing, Proc. of ICONIP 2008, Auckland, Springer LNCS-5507, 615-622, 2009
- 2009 - Ozawa, S., Kawashima, Y., Pang, S., & Kasabov, N., Adaptive incremental principal component analysis in nonstationary online learning environments. In IJCNN (pp. 2394-2400). Atlanta, Georgia: IEEE, 2009
- 2009 - Fiasché, M., Verma, A., Cuzzola, M., Iacopino, P., Kasabov, N., & Morabito, F. C., Discovering diagnostic gene targets and early diagnosis of acute GVHD using methods of computational intelligence over gene expression data. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) 5769: 10-19. 2009
- 2008 - Soltic, S., Wysoski, S. G. & Kasabov, N. (2008). Evolving spiking neural networks for taste recognition. International Joint Conference on Neural Networks IJCNN’08, Hong Kong, 1-6 Jun, 2092-2098.
- 2008 - Yingjie Hu, Nikola Kasabov, "Ontology-Based Framework for Personalized Diagnosis and Prognosis of Cancer Based on Gene Expression Data " in Lecture Notes in Computer Science. vol. 4985 Heidelberg: Springer Berlin, 2008, pp. 846-855.
- 2008 - Yuan-Chun Hwang; Qun Song; Kasabov, N., "MUFIS: A neuro-fuzzy inference system using multiple types of fuzzy rules," Fuzzy Systems, 2008. FUZZ-IEEE 2008. (IEEE World Congress on Computational Intelligence). IEEE International Conference on , vol., no., pp.1411-1414, 1-6 June 2008
- 2008 - Yuan-Chu Hwang; Yuan-Chun Hwang; Chiu-Hung Su, "Experience Co-Creation on Ubiquitous Cultural e-Service Provision: A Case of Taiwan's Hakka Culture," Networked Computing and Advanced Information Management, 2008. NCM '08. Fourth International Conference on , vol.2, no., pp.438-443, 2-4 Sept. 2008
- 2008 - Boris Bacic, Nikola Kasabov, Stephen MacDonell, Shaoning Pang, Evolving Connectionist Systems for Adaptive Sport Coaching, ICONIP2007, Japan, 13-16 November 2007, LNCS, Part II, pp.416-425, Springer, 2008
- 2008 - Seiichi Ozawa, Shaoning Pang, Nikola Kasabov, Adaptive Face Recognition System Using Fast Incremental Principal Component Analysis, ICONIP2007, Japan, 13-16 November 2007, LNCS, Part II, 4985, pp.396-405, Springer, 2008
- 2008 - Kasabov, N., Data mining, modeling and knowledge discovery methods for personalised biomedical decision support systems. In IFMBE Proceedings Vol. 21 IFMBE (pp. 11-12). Kuala Lumpur, Malaysia: Springer.2008
- 2008 - Kasabov, N., & Benuskova, L., Dynamic Interaction Networks and Global Ontology-Based Modelling of Brain Dynamics. In R. Wang, F. Gu, & E. Shen (Eds.), Advances In Cognitive Neurodynamics, Proceedings (pp. 3-7). Shanghai, China: Springer.2008
- 2008 - Kasabov, N., Koprinska, I., & Iliev, G. (2008). Evolving connectionist systems for on-line pattern classification of multimedia data. In D. P. Dimitrov, V. Mladenov, S. Jordanova, & N. Mastorakis (Eds.), Proceedings of the 9th WSEAS International Conference on Neural Networks (NN' 08) (pp. 73-77). 2008
- 2008 - Pang, S., Ban, T., Kadobayashi, Y., & Kasabov, N., gSVMT: Aggregating SVMs over a dynamic grid learned from data. In Proceedings of 11th International Conference on Computer and Information Technology, ICCIT 2008 (pp. 72-79). Khulna, Bangladesh. 2008
- 2008 - Kasabov, N., Jain, V., & Benuskova, L., Integrating evolving brain-gene ontology and connectionist-based system for modeling and knowledge discovery. In Neural Networks Vol. 21 (pp. 266-275). 2008
- 2008 - Pang, S., & Kasabov, N., r-SVMT: Discovering the Knowledge of Association Rule over SVM classification trees. In Proceedings of the International Joint Conference on Neural Networks (pp. 2486-2493). Hongkong. 2008
- 2008 - Ozawa, S., Pang, S., & Kasabov, N., Adaptive Face Recognition System Using Fast Incremental Principal Component Analysis. In M. Ishikawa, K. Doya, H. Miyamoto, & T. Yamakawa (Eds.), ICONIP (2) Vol. 4985 (pp. 396-405). Kitakyushu, Japan: Springer. 2008
- 2007 - Kasabov, N., Evolving Connectionist and Hybrid Systems: Methods, Tools, Applications. In HIS (pp. 3). Germany: IEEE Computer Society. 2007
- 2007 - 2007 - Ravi, V., Srinivas, E. R., & Kasabov, N. K. (2007). On-Line Evolving Fuzzy Clustering. In Proceedings - International Conference on Computational Intelligence and Multimedia Applications, ICCIMA 2007 Vol. 1 (pp. 347-351). Tamil Nadu, India. 2007
- 2007 - Simei Gomes Wysoski, Lubica Benuskova, Nikola Kasabov, Adaptive Spiking Neural Networks for Audiovisual Pattern Recognition, ICONIP2007, Japan, 13-16 November 2007, LNCS, , Part II, pp.406-415 Springer, 2007
- 2007 - M. Defoin Platel, M. Schliebs, and N. Kasabov. A versatile quantum-inspired evolutionary algorithm. In IEEE Congress on Evolutionary Computation, 2007. CEC '07, pages 423–430, 2007.
- 2007 - Kasabov N, Jain V, Gottgtroy PCM, Benuskova L, Wysoski SG, Joseph F (2007) Evolving Brain-Gene Ontology System (EBGOS): Towards Integrating Bioinformatics and Neuroinformatics Data to Facilitate Discoveries. Proc. IJCNN'2007, pp.1054-1058.
- 2007 - Simei Gomes Wysoski, Lubica Benuskova and Nikola Kasabov, Text-Independent Speaker Authentication with Spiking Neural Networks, Artificial Neural Networks – ICANN 2007, pp.758-767
- 2006 - Kasabov, N., "Neuro-, Genetic-, and Quantum Inspired Evolving Intelligent Systems," Evolving Fuzzy Systems, 2006 International Symposium on , vol., no., pp.63-73, Sept. 2006
- 2006 - Kasabov, N.; Filev, D., "Evolving Intelligent Systems: Methods, Learning, & Applications," Evolving Fuzzy Systems, 2006 International Symposium on , vol., no., pp.8-18, Sept. 2006
- 2006 - Soltic, S. & Peacock, L. Evolving connectionist systems in ecological modelling. Presented at the 1st Korean-New Zealand Joint Workshop on Advance of Computational Intelligence methods and Applications, Auckland, New Zealand, 8 Feb. 2006
- 2006 - Soltic, S. & Peacock, L. A comparison of inductive and transductive models for predicting the establishment potential of the exotic scale, Aspidiella hartii (Cockerell), in New Zealand. Bulletin of Applied Computing and Information Technology, 4 (2). 2006
- 2007 - Song, Q. and Ma, T., “GAWDN-NFIS: Neural-Fuzzy Inference System with a Genetic Algorithm Based on Weighted Data Normalization and Its Application in Medicine”, Proc. of The Fourth International Conference on Fuzzy Systems and Knowledge Discovery (FSKD2007), Hailou, China,
August, 2007, pp603 - 607.
- 2006 - Shaoning Pang, Nikola Kasabov, Investigating LLE Eigenface on Pose and Face Identification, Lecture Notes in Computer Science, Volume 3972, Apr 2006, Pages 134 - 139
- 2006 - Shaoning Pang, Ilkka Havukkala, Nikola Kasabov, Two-Class SVM Trees (2-SVMT) for Biomarker Data Analysis, Lecture Notes in Computer Science, Volume 3973, Apr 2006, Pages 629 - 634
- 2006 - Qun Song, Tian Min Ma and Nikola Kasabov, TTLSC – Transductive Total Least Square Model for Classification and Its Application in Medicine, Advanced Data Mining and Applications, Lecture Notes in Computer Science, Volume 4093/2006, Pages 197-204
- 2006 - N.Kasabov, Brain-, Gene-, and Quantum Inspired Computational Intelligence: Challenges and Opportunities, in: W.Duch and J.Manzduk (eds) Computational Intelligence, Theory and Applications, pp 521-544, Springer, 2006
- 2006 - Benuskova L, Wysoski SG and Kasabov N (2006) Computational neurogenetic modeling: a methodology to study gene interactions underlying neural oscillations. Intl. Joint Conf. Neural Net., IJCNN 2006 , pp. 9388-9394. ISBN 0-7803-9490-9
- 2006 - Havukkala, I. Large-scale macromolecule classification and clustering by 2D structure bitmap image analysis. The 1st Korean-New Zealand Joint Workshop on Advance of Computational Intelligence Methods and Applications, 17 February 2006, Auckland, New Zealand. 17 February 2006, Proceedings, pp. 20-21.
- 2006 - Hu, Y., Pang, S., & Havukkala, I. J. Gene selection in terms of performance based on consistency. The 1st Korean-New Zealand Joint Workshop on Advance of Computational Intelligence Methods and Applications, Auckland, New Zealand, 17 February 2006, Proceedings, pp. 28-32.
- 2006 - Benuskova, L., Kroon, R. & Havukkala, I. A Compact 2D Representation and Visualization of Large Symbolic Sequences and Applications for Comparative Genome Studies. The 1st Korean-New Zealand Joint Workshop on Advance of Computational Intelligence Methods and Applications, 17 February 2006, Auckland, New Zealand, Proceedings, pp. 26-27.
- 2006 - Simei Gomes Wysoski, Lubica Benuskova and Nikola Kasabov, On-Line Learning with Structural Adaptation in a Network of Spiking Neurons for Visual Pattern Recognition, Artificial Neural Networks - ICANN 2006, Lecture Notes in Computer Science, Volume 4131/2006, Pages 61-70
- 2006 - Simei Gomes Wysoski, Lubica Benuskova and Nikola Kasabov, Adaptive Learning Procedure for a Network of Spiking Neurons and Visual Pattern Recognition, Artificial Advanced Concepts for Intelligent Vision Systems, Lecture Notes in Computer Science, Volume 4179/2006, Pages 1133-1142
- 2006 - Havukkala I, Benuskova L, Pang S, Jain V, Kroon R, Kasabov N (2006) Image and fractal information processing for large-scale chemoinformatics, genomics analyses and pattern discovery. In: J.C. Rajapakse, L. Wong, R. Acharya (Eds), Proc. Pattern Recognition in Bioinformatics, PRIB 2006. Lecture Notes in Bioinformatics, vol. 4146, pp. 163-173, Springer, Berlin/Heidelberg. ISBN 3-540-37446-9
- 2006 - Ozawa, S., Pang, S., & Kasabov, N., An incremental principal component analysis for chunk data. In IEEE International Conference on Fuzzy Systems (pp. 2278-2285). Vancouver. 2006
- 2006 - Kasabov, N., Computational intelligence for bioinformatics: The knowledge engineering approach. In M. Bramer, F. Coenen, & T. Allen (Eds.), Research and Development in Intelligent Systems XXII (pp. 3-4). Springer London. 2006
- 2006 - Kasabov, N., & Filev, D., Evolving intelligent systems: methods, learning, and applications. In 2006 International Symposium on Evolving Fuzzy Systems (pp. 8-18). United Kingdom. 2006
- 2005 - Kasabov N, Benuskova L and Wysoski SG (2005) Computational neurogenetic modeling: integration of spiking neural networks, gene networks, and signal processing techniques. In: Artificial Neural Networks: Formal Models and Their Applications - ICANN 2005, LNCS 3697, W. Duch, J. Kacprzyk, E. Oja, S. Zadrozny (eds), Springer-Verlag, Berlin Heidelberg, pp. 509-514. ISBN: 3-540-28755-8.
- 2005 - Kasabov, N. Global, Local and Personalized Modeling and Pattern Discovery in Bioinformatics: An Integrated Approach, In. Proc. IEEE International Workshop on Soft Computing Applications, Szeged-Hungary and Arad-Romania, 27-30 August, 2005. pp. 56-57
- 2005 - Huang, L.; Song, Q.; Kasabov, N., "Evolving Connectionist Systems Based Role Allocation of Robots for Soccer Playing," Intelligent Control, 2005. Proceedings of the 2005 IEEE International Symposium on, Mediterrean Conference on Control and Automation , pp. 36- 40, 27-29 June 2005
- 2005 - Mohan, N.; Kasabov, N., "Transductive modeling with GA parameter optimization," Neural Networks, 2005. IJCNN '05. Proceedings. 2005 IEEE International Joint Conference on , vol.2, no.pp. 839- 844 vol. 2, 31 July-4 Aug. 2005
- 2005 - Qun Song, Tianmin Ma, Nikola Kasabov, Transductive Knowledge Based Fuzzy Inference System for Personalized Modeling, Lecture Notes in Computer Science, Volume 3614, Dec
- 2005 - Ma, T. M., Song, Q., Kasabov, N., and Marshall, M. R. 2005. TWNFC - Transductive Neural-Fuzzy Classifier with Weighted Data Normalization and Its Application in Medicine. In Proceedings of the international Conference on Computational intelligence For Modelling, Control and Automation and international Conference on intelligent Agents, Web Technologies and internet Commerce Vol-1 (Cimca-Iawtic'06) - Volume 01 (November 28 - 30, 2005). CIMCA. IEEE Computer Society, Washington, DC, 479-484.
- 2005 - Qun Song, Tianmin Ma, Nikola Kasabov, Transductive Knowledge Based Fuzzy Inference System for Personalized Modeling, Lecture Notes in Computer Science, Volume 3614, Dec 2005, Pages 528 - 535
- 2005 - Kasabov, N.; Benuskova, L.; Wysoski, S.G., "A computational neurogenetic model of a spiking neuron," Neural Networks, 2005. IJCNN '05. Proceedings. 2005 IEEE International Joint Conference on Volume 1, 31 July-4 Aug. 2005 Page(s):446 - 451 vol. 1
- 2005 - Kasabov, N. , Zhang, D. , Pang, S. Incremental learning in autonomous systems: evolving connectionist systems for on-line image and speech recognition, In: Proc. of IEEE Workshop on Advanced Robotics and its Social Impacts, Nagoya Japan. June 12-15, 2005. pp. 120 - 125
- 2005 - Chan, S. H. , Collins, L. , Kasabov, N. Bayesian Inference of Sparse Gene Network, In: Proc. The Sixth International Workshop on Information Processing in Cells and Tissues, St William's College, York, United Kingdom, August 30 - September 1, 2005, pp. 333 - 347
- 2005 - Chan, S. H. , Collins, L. , Kasabov, N. Fast Global Clustering of Gene Expression Data using the Greedy Elimination Method, In: Proc. The Sixth International Workshop on Information Processing in Cells and Tissues, St William's College, York, United Kingdom, August 30 - September 1, 2005
- 2005 - Chan, S. H. , Kasabov, N. Global EM Learning of Finite Mixture Models using the Greedy Elimination Method, In: Proc. The twenty-fifth Annual International Conference of the British Computer Society's Specialist Group on Artificial Intelligence, Peterhouse College, Cambridge, UK, 12th-14th December 2005
- 2005 - Chan, S. H. , Kasabov, N. Fast Estimation of Distribution Algorithm (EDA) via Constrained Multi-Parent Recombination, In: Proc. The twenty-fifth Annual International Conference of the British Computer Society's Specialist Group on Artificial Intelligence, Peterhouse College, Cambridge, UK, 12th-14th December 2005
- 2005 - N. Kasabov and P. Angelov, Evolving Computational Intelligence Systems, IEEE Workshop on Genetic and Fuzzy Systems GFS2005, Grenada, Spain, March, 2005
- 2005 V. Jain, P Gottgtroy, N Kasabov - Modelling the interaction between 2G12 antibody and GP120 of HIV, Poster, Proceedings of NZ Bio, New Zealand.
- 2004 - Jain, V. and Kasabov N., Understanding the binding between 2G12 antibody and GP120 of HIV through molecular modelling and representing a crucial interacting residue ASN 295, Proceedings of conference on Neurocomputing and evolving intelligence (NCEI), New Zealand, Vol. 3, pp 60
- 2004 - Jain, V., Classifying unknown proteins and understanding substrate specificity of methyltransferases, Proceedings of the International Conference on Recent Advances in Biomedical and Therapeutic Sciences (ICRABTS 04'), Erasmus MC University Medical Centre, Netherlands and Institute of Biomedical sciences, BU, India, pp 168.
- 2004 - Kasabov, N., Chan, S. H., Jain, V., Sidirov, I., and Dimitrov, S. D., Gene Regulatory Network Modelling from short Time-course Gene Expression Data: Method, System and Applications, Proceedings of Biotech, India, Vol.2, pp 84.
- 2004 - Z. S. H. Chan and N. Kasabov, "Gene Trajectory Clustering with a Hybrid Genetic Algorithm and Expectation Maximization Method," presented at International Joint Conference on Neural Networks, Budapest, 2004.
- 2004 - Z. Chan, N. Kasabov, and L. Collins, "A two-stage methodology for gene regulatory network extraction from time-course gene expression data," presented at IEEE Workshop on Biomedical Applications of Circuits and Systems, Singapore, 2004.
- 2004 - Snjezana Soltic, Shaoning Pang, Nikola Kasabov, Sue Worner and Lora Peacock, Dynamic Neuro-fuzzy Inference and Statistical Models for Risk Analysis of Pest Insect Establishment, in International Conference on Neuro Information Processing ICONIP 2004, Calcutta 22-25 Nov 2004.
- 2004 - Ghobakhlou, D. Zhang and N. Kasabov, An Evolving Neural Network Model for Person Verification Combining Speech and Image in International Conference on Neuro Information Processing ICONIP 2004, Calcutta 22-25 Nov 2004.
- 2004 - D. Zhang, N. Kasabov, A. Ghobakhlou, An Adaptive Model of Person Identification Combining Speech and Image Information, in ICARCV 2004, Kunming, China
- 2004 - Nikola K. Kasabov, Zeke S. H. Chan, Vishal Jain, Igor Sidorov and Dimiter S. Dimitrov, Gene Regulatory Network Discovery from Time-Series Gene Expression Data: A Computational Intelligence Approach, ICONIP 2004, Springer-Verlag LNCS (Accepted)
- 2004 - Qun Song, Tianmin Ma and Nikola Kasabov. LR-KFNN: Logistic Regression-Kernel Function Neural Networks and the GFR-NN Model for Renal Function Evaluation in International Conference on Computational Intelligence for Modelling, Control & Automation (CIMCA 2004), July 2004, Gold Coast, Australia.
- 2004 - S. N. Pang, Seiichi Ozawa, Nikola Kasabov (2004) One-Pass Incremental Membership Authentication by Face Classification. ICBA 2004, LNCS 3072, Springer 155-161.
- 2004 - Gottgtroy, P., Kasabov, N. & MacDonell, S. (2004). An ontology driven approach for knowledge discovery in Biomedicine. Proceedings of the VIII Pacific Rim International Conferences on Artificial Intelligence (PRICAI) - Auckland, New Zealand.
- 2004 - Song, Q., Kasabov, N., "TWRBF - Transductive RBF Neural Network with Weighted Data Normalization", The 11th International Conference on Neural Information Processing (ICONIP2004), Calcutta, India, 22 - 25, 2004, accepted.
- 2004 - Song, and Kasabov , N., "WDN-RBF: Weighted Data Normalization for Radial Basic Function Type Neural Networks", Proc. of International Joint Conference on Neural Networks 2004 (IJCNN2004), Budapest, Hungary, 25 - 29, July, 2004, pp. 2095 - 2098.
- 2004 - Song, Q., Ma, T. and Kasabov, N., "LR-KFNN: Logistic Regression-Kernel Function Neural etworks and the GFR-NN Model for Renal Function Evaluation", Proc. Of International Conference on Computational Intelligence for modelling, Control and Automation 2004 (CIMCA2004), Gold Coast, Australia, 12 - 14, July, 2004, pp. 946 - 951.
- 2004 - Song, Q. and Kasabov, N., "TWNFI -Transductive Neural-Fuzzy Inference System with Weighted Data Normalization and Its Application in Medicine", Second Announcement International Workshop on Fuzzy Systems & Innovational Computing 2004 (FIC2004)", Kitakyushu, Japan, 2 - 3, June, 2004, accepted.
- 2004 - Zeke S. H. Chan and Nikola Kasabov, Gene Trajectory Clustering with a Hybrid Genetic Algorithm and Expectation Maximization Method in International Joint Conference on Neural Networks, IJCNN 2004, Budapest, 16-30 June 2004, IEEE Press
- 2004 - Shaoning Pang and Nikola Kasabov, Inductive vs Transductive Inference, Global vs Local Models: SVM, TSVM, and SVMT for Gene Expression Classification Problems in International Joint Conference on Neural Networks, IJCNN 2004, Budapest, 16-30 June 2004, IEEE Press
- 2004 - Kasabov N, Benuskova L and Wysoski SG (2004) Computational neurogenetic modelling: gene networks within neural networks In: Proc. IEEE Intl. Joint Conference on Neural Networks, vol. 2. pp. 1203-1208.
- 2004 - Liang Goh, Qun Song and Nikola Kasabov, A Novel Feature Selection Method to Improve Classification of Gene Expression Data in Proc. of Bioinformatics 2004 Second Asia-Pacific Bioinformatics Conference (APBC 2004) Dunedin, 18-22nd January 2004, Australian Computer Science Communications Volume 26, Number 4 (161-166)
- 2004 - Seiichi Ozawa, S. N. Pang, and Nikola Kasabov: A Modified Incremental Principal Component Analysis for On-line Learning of Feature Space and Classifier, PRICAI2004, LNAI, Springer.
- 2004 - Seiichi Ozawa, Shaoning Pang and Nikola Kasabov. On-line Feature Selection for Adaptive Evolving Connectionist Systems, Fuzzy Systems & Innovation Computing, Kitakyushu Japan
- 2003 - Jain, V., Modelling gene regulatory network (GRN): Problems and Approaches, Proceedings of conference on Neurocomputing and evolving intelligence (NCEI), New Zealand, ISBN: 0-476-00165-X, Vol. 2, pp 67
- 2003 - Gottgtroy, P., Kasabov, N. & MacDonell, S. (2003). Building Evolving Ontology Maps for Data Mining and Knowledge Discovery in Biomedical Informatics. Proceedings of the BIOMAT - Brazilian Symposium of Mathematical and Computational Biology . Rio de Janeiro.
- 2003 - Q. Song, N. Kasabov, Weighted Data Normalization and Feature Selection for Evolving Connectionist Systems Proceedings in Proc. of the Eight Australian and New Zealand Intelligent Information Systems Conference, Sydney, Australia Dec. 2003 (285-290)
- 2003 - Q. Song, T. Ma and N. Kasabov, A Novel Generic Higher-Order TSK Fuzzy Model for Prediction and Applications for Medical Decision Support in Proc. of the Eight Australian and New Zealand Intelligent Information Systems Conference, Sydney, Australia Dec. 2003 (241-245)
- 2003 - N. Kasabov, Shaoning Pang, Transductive Support Vector Machines And Applications In Bioinformatics For Promoter Recognition in Proc. of IEEE International Conference on Neural Networks and Signal Processing, Nanjing, China, Dec. 2003 (1-6)
- 2003 - N. Kasabov, Adaptive Neural Networks, Gene Networks, and Evolutionary Systems - Real and Artificial Evolving Intelligence, in Proc. of the 7th Joint Conference on Information Sciences, North Carolina, 26-30 September, 2003 (1381-1384)
- 2003 - D. Zhang, N. Kasabov, Q. Song, I. Nishikawa, Evolving Connectionist Modeling of Auditory, Visual and Combined Stimuli Perception Based on EEG Data, in Proc. of the 7th Joint Conference on Information Sciences, North Carolina, 26-30 September, 2003 (1361-1364)
- 2003 - G. Coghill, D. Zhang, A. Ghobakhlou, N. Kasabov, Connectionist Systems for Rapid Adaptive Learning: A Comparative Analysis on Speech Recognition, in Proc. of the 7th Joint Conference on Information Sciences, North Carolina, 26-30 September, 2003 (1365-1368)
- 2003 - G. Vachkov, N. Kasabov, Real-Time Recognition Of The Operating Modes Of Plants And Machines By Use of Self-Organizing Maps, in Proc. of the 7th Joint Conference on Information Sciences, North Carolina, 26-30 September, 2003 (1375-1380)
- 2003 - M.Futshick, A.Reeve, and N.Kasabov, Modular Decision System and Information Integration for Improved Disease Outcome Prediction in: Proc. of the European Conference on Computational Biology, France, 2003, in print
- 2003 - P.Gottgtroy, N.Kasabov, and S. Macdonell, An ontology engineering approach for knowledge discovery from data in evolving domains, Datamining 2003, 04-09 December, 2003.
- 2003 - N. Kasabov, Q. Song and I. Nishikawa. Evolutionary Computation for Dynamic Parameter Optimisation of Evolving Connectionist Systems for On-line Prediction of Time Series with Changing Dynamics, IEEE Proceedings, IJCNN'2003, Portland, Oregon, July 2003
- 2003 - L. Goh, N. Kasabov, Integrated Gene Expression Analysis of Multiple Microarray Data Sets Based on a Normalization Technique and on Adaptive Connectionist Model, IEEE Proceedings, IJCNN'2003, Portland, Oregon, July 2003
- 2003 - N. Kasabov, G. Venkov, and S. Minchev. Neural Systems for Solving the Inverse Problem of Recovering the Primary Signal Waveform in Potential Transformers, IEEE Proceedings, IJCNN'2003, Portland, Oregon, July 2003
- 2003 - N.Kasabov and L.Gogh, Supervised clustering for feature selection from microarray data, Proc. Of the Int. Conf. on Intelligent Systems for Molecular Biology - ISMB, Brisbane, June 2003
- 2003 - A. Ghobakhlou, Nikola Kasabov, A Methodology for Adaptive Speech Recognition Systems and a Development Environment in Proc. of Artificial Neural Networks and Neural Information
- Processing ICANN/ICONIP 2003 International Conference, Istanbul, Turkey, June 2003 (316-319)
- 2003 - W. Abdulla, V. Kecman, N. Kasabov, Speech-background classification by using SVM technique, in Proc. of Artificial Neural Networks and Neural Information Processing ICANN/ICONIP 2003 International Conference, Istanbul, Turkey, June 2003 (310-315)
- 2002 - N. Kasabov and Q. Song, "GA-Optimisation of evolving connectionist systems for classification with a case study from bioinformatics," Proc. of ICONIP’2002, Singapore, November, 2002.
- 2002- N. Kasabov, Evolving connectionist systems for dynamic modelling and knowledge discovery: methods, tools, applications, IEEE Int. Symposium on Intelligent Systems, Proc. IEEE, Bulgaria, 9-12 Sept. 2002
- 2002- N.Kasabov, Evolving Intelligence - methods and applications in adaptive control, data mining and decision support, Int. Conference SAER’2002, Varna, Bulgaria, 2-22 September, 2002
- 2002-N.Kasabov, and D.Dimitrov, A method for gene regulatory network modelling with the use of evolving connectionist systems, Proc. of ICONIP’2002 - International Conference on Neuro-Information Processing, Singapore, November 2002, IEEE Press, 2002
- 2002-N.Kasabov, Evolving connectionist systems for dynamic modelling and knowledge discovery, Proc. of ICONIP’2002 - International Conference on Neuro-Information Processing, Singapore, November 2002, IEEE Press, 2002
- 2002-N.Kasabov, and Q.Song, GA-parameter optimisation of evolving connectionist systems for classification and a case study from bioinformatics, Proc. of ICONIP’2002 - International Conference on Neuro-Information Processing, Singapore, November 2002, IEEE Press, 2002
- 2002 - Futschik, M. and N. Kasabov, Fuzzy clustering of gene expression data, Proc. of World Congress of Computational Intelligence WCCI’2002, Hawaii, 12-17 May,2002 IEEE Press.
- 2002 - Watts, M. and N. Kasabov, Evolutionary optimisation of evolving connectionist systems, Proc. of World Congress of Computational Intelligence WCCI’2002, Hawaii, 12-17 May, 2002 IEEE Press.
- 2001 - Deng, D. and Kasabov, N. Evolving localised learning for on-line colour image quantisation, accepted by Inter. Conf. on Image Processing, ICIP'2001, Thessaloniki, Greece, Oct. 2001.
- 2001 - Futschik M., and Kasabov, N., Evolving Fuzzy Neural Networks for Knowledge Discovery from Gene Expression Data - A Case Study, RECOMB'2001 Proceedings - Currents in Computational Molecular Biology 2001, Lengauer, T., Sankoff, D., (eds) 22-25 April 2001, Montreal, Canada, 175-178
- 2001 - Kasabov, N., Futschik, M.E., and Middlemiss, M.J., Knowledge Based Neural Networks for On-Line and Off-Line Modeling and Rule Extraction in Bioinformatics, CGBI'2001 Atlantic Symposium on Computational Biology, Genome Information Systems and Technology, 15-17 March 2001, Durham, North Carolina, USA
- 2001 - Watts, M., Major, L., Kasabov N. and Tate, W. (2001). Neural Network Analysis of Protein Termination Signal Efficiency. In: Proceedings of ICONIP 2001, Shanghai, China, November, 2001.
- 2001 - Watts, M. and Kasabov N. (2001). Dynamic Optimisation of Evolving Connectionist System Training Parameters by Pseudo-Evolution Strategy. In: Proceedings of Congress on Evolutionary Computation 2001 pg 1335-1342.
- 2001 - Woodford, B.J. and Kasabov, N.K. Ensembles of EFuNNs: An architecture for a multi module classifier. Accepted for publication in The proceedings of FUZZ-IEEE'2001. The 10th IEEE International Conference on Fuzzy Systems, December 2-5 2001, Melbourne, Australia.
- 2001 - Deng, D. and Kasabov, N. On-line Pattern Analysis by Evolving Self-Organizing Maps. In: Proceedings of the Fifth Biannual Conference on Artificial Neural Networks and Expert Systems (ANNES2001), 2001, 46-51.
- 2001 -Song, Q. and Kasasbov, N. ECM, A Novel On-line, Evolving Clustering Method and its Applications. In: Proceedings of the Fifth Biannual Conference on Artificial Neural Networks and Expert Systems (ANNES2001), 2001, 87-92.
- 2001 -Abdulla, W.H. and Kasabov, N.K. Improving speech recognition performance through gender separation. In: Proceedings of the Fifth Biannual Conference on Artificial Neural Networks and Expert Systems (ANNES2001), 2001, 218-222.
- 2000 - Abdulla, W. and Kasabov, N., Parallel CHMM speech recognition systems, Proceedings of Joint Conference of Information Sciences (JCIS), Atlantic City, New Jersey, February 2000 (accepted)
- 2000 - Deng, D., and Kasabov, N., Evolving Localised Learning for On-Line Colour Image Quantisation, Proceedings of the Internatio Conference on Vision Computing, November 2000, Hamilton, New Zealand, 186-191
- 2000 - Deng, D., and Kasabov, N., ESOM: An Algorithm to Evolve Self-Organizing Maps from On-Line Data Streams, Shun-Ichi Amari, C. Lee Giles, Marco Gori, Vincenzo Piuri (eds) Proceedings of the IJCNN'2000 on Neural Networks Neural Computing: New Challenges and Perspectives for the New Millennium, Como, Italy, July 24-27, 2000 Vol VI, 3-8
- 2000 - Ghobakhlou, A., Watts, M. and Kasabov, N. (2000). On-Line Expansion of Output Space in Evolving Fuzzy Neural Networks. In: Proceedings ICONIP 2000, Taejon, Korea, November, 2000.
- 2000 - Iliev, G., and Kasabov, N. Channel equalisation using adaptive filtering with averaging, in: Proceedings of Joint Conference of Information Sciences (JCIS), Atlantic City, New Jersey, February 2000, 870-873
- 2000 - Iliev, G. and Kasabov, N. (2000). Channel equalization using adaptive filtering with averaging,5th Joint Conference on Information Sciences (JCIS 2000), Atlantic City, USA, 27 Feb.-3 March 2000, 870-873.
- 2000 - Iliev, G., and Kasabov, N., Tracking of Narrow Band Signals Using Constrained Adaptive Second-Order Filters, Proceedings of ICONIP'2000, November 14-18, 2000, Taejon, Korea 1367-1370
- 2000 - Kasabov, N., and Iliev, G., Hybrid Systems for Robust Recognition of Noisy Speech Based on Evolving Fuzzy Neural Networks and Adaptive Filtering, Shun-Ichi Amari, C. Lee Giles, Marco Gori, Vincenzo Piuri (eds) Proceedings of the IJCNN'2000 on Neural Networks Neural Computing: New Challenges and Perspectives for the New Millennium, Como, Italy, July 24-27, 2000 Vol V, 91-96
- 2000 - Kasabov, N., Evolving Connectionist Systems - a Symbiosis of Learning and Evolution, Proceedings of ICONIP'2000, November 14-18, 2000, Taejon, Korea, 676-680
- 2000 - Kasabov, N., Deng, D., Erzegovesi, L., Fedrizzi, M., and Beber, A., On-line decision making and prediction of financial and macroeconomic parameters on the case study of the European Monetary Union, H. Bothe and R. Rojas (eds) Proceedings of the second ICSC Symposium on Neural Computation, May 23-26, 2000, Berlin, ISCS (International Computer Science Conventions, Canada/Switzerland), 301-307
- 2000 - Koprinska, I., and Kasabov, N., Evolving Fuzzy Neural Network for Camera Operations Recognition Proceedings of the International Conference on Pattern Recognition, September 3-7, 2000, ICPR, Barcelona Vol II, 523-526
- 2000 - Song, Q., and Kasabov, N., Dynamic Evolving Neuro-Fuzzy Inference System (DENFIS): On-Line Learning and Application for Time-Series Prediction
- Proceedings of the 6th International Conference on Soft Computing, October 1-4, 2000, Iizuka, Japan, 696-702
- 2000 - Taylor, J., Kasabov, N., and Kilgour, R., Modelling the Emergence of Speech Sound Categories in Evolving Connectionist Systems, Proceedings of the JCIS'2000 - the Joint Conference on Information Sciences, Atlantic City, February 2000, Association of Intelligent Machinery Inc., 844-848 (2000)
- 2000 - Watts, M. and Kasabov, N. (2000). Simple evolving connectionist systems and experiments on isolated phoneme recognition, First IEEE Symposium on Combinations of Evolutionary Computation and Neural Networks, San Antonio, May, 2000.
- 1999 - Abdulla, W. and Kasabov, N. (1999). Two pass Markov model for speech recognition systems, Intern. Conference of Information and Communication Systems (ICICS'99), December, Singapore, paper No 175.
- 1999 - Abdulla, W. and Kasabov, N. (1999). Speech recognition enchancement via robust CHMM speech background discrimination, ICONIP/ANZIIS/ANNES'99 Workshop, Dunedin, New Zealand, November 22-24, 65-70.
- 1999 - Deng, D., Koprinska, I., and Kasabov, N., RICBIS - New Zealand Repository for Intelligent Connectionist-Based Information Systems, in: Emerging Knowledge Engineering and Connectionist-based Systems Proceedings of the ICONIP/ANZIIS/ANNES’99 Workshop "Future directions for intelligent systems and information sciences, Dunedin, 22-23 Nov.1999, N.Kasabov and K.Ko (eds),182-185
- 1999 - Deng, D. and Kasabov, N. (1999). Evolving self-organizing map and its application in generation of a world macroeconomic map, ICONIP/ANZIIS/ANNES'99 Workshop, Dunedin, New Zealand, November 22-24, 7-12.
- 1999 - Futschik, M., Schreiber, M., Brown, C. and Kasabov, N. (1999). Comparative studies of neural network models for mRNA analysis, Intern. Conference on Intelligent Systems for Molecular Biology, Neidelberg, Germany, August 6-10.
- 1999 - Ghobakhlou, A., Song, Q. and Kasabov, N. (1999). ROKEL: the interactively learning and navigating robot of the knowledge engineering laboratory at Otago, ICONIP/ANZIIS/ANNES'99 Workshop, Dunedin, New Zealand, November 22-24, 57-59.
- 1999 - Hegg, D., Cohen, T., Kasabov, N., and Song, Q., Intelligent Control of Sequencing Batch Reactors (SBRs) for Biological Nitrogen Removal, in: Emerging Knowledge Engineering and Connectionist-based Systems, Proceedings of the ICONIP/ANZIIS/ANNES’99 Workshop "Future directions for intelligent systems and information sciences, Dunedin, 22-23 Nov.1999, N.Kasabov and K.Ko (eds), 152-155
- 1999 - Iliev, G. and Kasabov, N. (1999).Adaptive blind noise suppression in some speech processing applications, 6th Intern. Conference on Neural Information Processing (ICONIP'99), Perth, Australia, 192-197.
- 1999 - Iliev, G. and Kasabov, N. (1999). Adaptive filtering with averaging in noise cancellation for voice and speech recognition, ICONIP/ANZIIS/ANNES'99 Workshop, Dunedin, New Zealand, November 22-24, 71-75.
- 1999 - Kasabov, N. (1999). Evolving connectionist systems and applications for adaptive speech recognition, Intern. Joint Conference on Neural Networks (IJCNN'99), July 10-16, Washington DC, USA.
- 1999 - Kasabov, N. and Fedrizzi, M. (1999). Fuzzy neural networks and evolving connectionist systems for intelligent decision making, In: Proc. of the Eight International Fuzzy Systems Association World Congress, Taiwan, August 17-20, 30-35.
- 1999 - Kasabov, N., Deng, D., Erzegovezi, L., Fedrizzi, M. and Beber, A. (1999). Hybrid intelligent decision support systems and applications for risk analysis and prediction, Intern. Conference on Intelligent Systems for Investment Decision Making, Bond University, Gold Coast, Australia, December, pp. 172-177.
- 1999 - Kasabov, N. and Woodford, B. (1999). Rule insertion and rule extraction from evolving fuzzy neural networks: algorithms and applications for building adaptive, intelligent expert systems, IEEE Intern. Fuzzy Systems Conference, Seoul, Korea, August 22-25, 1406-1411.
- 1999 - Kasabov, N., Tuck, D. and Watts, W. (1999). Implementing knowledge and data fusion in a versatile software environment for adaptive learning and decision making, Intern. Conference on Data Fusion, San Jose, July, 455-462.
- 1999 - Kasabov, N. (1999). Evolving fuzzy neural networks for adaptive, on-line intelligent agents and systems, Intern. Conference on Recent Advances in Mechatronics, Istanbul, Turkey, May 24-26, 27-41.
- 1999 - Koprinska I., and Kasabov, N., An Application of Evolving Fuzzy Neural Network for Compressed Video Parsing, in: Emerging Knowledge Engineering and Connectionist-based Systems, Proceedings of the ICONIP/ANZIIS/ANNES’99 Workshop "Future directions for intelligent systems and information sciences, Dunedin, 22-23 Nov.1999, N.Kasabov and K.Ko (eds), 96-102
- 1999 - Kim, J., Mowat, A., Poole, P., and Kasabov, N., Applications of Connectionism to the Classification of Kiwifruit Berries from Visible-near Infrared Spectral Data, in: Emerging Knowledge Engineering and Connectionist-based Systems, Proceedings of the ICONIP/ANZIIS/ANNES’99 Workshop "Future directions for intelligent systems and information sciences, Dunedin, 22-23 Nov.1999, N.Kasabov and K.Ko (eds)213
- 1999 - Tuck, D., Watts, M., Song, Q. and Kasabov, N. (1999). A practical and flexible environment for adaptive knowledge and data fusion applications, Intern. Conference on Applications of Intelligent Systems, Melbourne, Australia, September.
- 1999 - Watts, M., Woodford, B., and Kasabov N., FuzzyCOPE - A Software Environment for Building Intelligent Systems - the Past, the Present and the Future, in: Emerging Knowledge Engineering and Connectionist-based Systems, Proceedings of the ICONIP/ANZIIS/ANNES’99 Workshop "Future directions for intelligent systems and information sciences, Dunedin, 22-23 Nov.1999, N.Kasabov and K.Ko (eds) 188-192
- 1999 - Woodford, B., Kasabov, N., and Wearing, H., Fruit Image Analysis using Wavelets, In: Emerging Knowledge Engineering and Connectionist-based Systems, Proceedings of the ICONIP/ANZIIS/ANNES’99 Workshop "Future directions for intelligent systems and information sciences, Dunedin, 22-23 Nov.1999, N.Kasabov and K.Ko (eds), 88-92
- 1999 - Woodford, B., Wearing, C.H., Walker, J.T.S and Kasabov, N. (1999). An adaptive agent-based distributed system for pest management,ICONIP/ANZIIS/ANNES'99 Workshop, Dunedin, New Zealand, November 22-24, 207-212.
- 1999 - Futschik, M; Schreiber, M; Brown, C, and Kasabov, N. (1999) "Comparative Studies of Neural Network Models for mRNA Analysis", in: Proceedings of the International Conference on Intelligent Systems for Molecular biology, Heidelberg, August 6-10 (1999)
- 1999 - Iliev, G., and Kasabov, N. Adaptive noise cancellation for speech applications, Proceedings of ICONIP’99, November 1999, Perth, Australia, IEEE Press (1999) 192-197
- 1999 - Ghobakhlou, A., Song, Q., and Kasabov, N., ROKEL: The Interactive learning and Navigating Robot of the Knowledge Engineering laboratory at Otago, in: Emerging Knowledge Engineering and Connectionist-based Systems, Proceedings of the ICONIP/ANZIIS/ANNES’99 Workshop "Future directions for intelligent systems and information sciences, Dunedin, 22-23 Nov.1999, N.Kasabov and K.Ko (eds) 57-59
- 1999 - Tuck, D., Watts, M., Song, Q., and Kasabov, N., A Practical and Flexible Environment for Adaptive Knowledge and Data Fusion Applications. in: Proceedings of International Conference On Applications of Intelligent Systems, Melbourne, Sept. 1999 (1999)
- 1998 - Kasabov, N. ECOS - A framework for evolving connectionist systems and the 'eco' training method, in: S.Usui and T.Omori (eds) Proceedings of ICONIP'98 - The Fifth International Conference on Neural Information Processing, Kitakyushu, Japan, 21-23 October 1998, IOS Press, vol.3, 1232-1235
- 1998 - Kasabov, N., Postma, E., and van den Herik, J., AVIS - An Integrated Connectionist Framework for Audio and Visual Information Processing Systems, in: T. Yamakawa and G. Matsumoto (eds) Methodologies for the Conception, Design and Application of Soft Computing, World Scientific, 1998, 422-425
- 1998 - Kasabov, N. Evolving fuzzy neural networks - algorithms, applications and biological motivation, in: T. Yamakawa and G. Matsumoto (eds) Methodologies for the Conception, Design and Application of Soft Computing, World Scientific, 1998, 271-274
- 1998 - Kasabov, N. Theory and applications of evolving connectionist agents and systems, Proceedings of the 1998 international conference on Neural Networks and Brain (NN&B), Beijing, October 27-30 (1998), Publishing House of Electronics Industry, China, 668-671
- 1998 - Kasabov, N. Adaptation in intelligent multi-modular systems: A case study on adaptive speech recognition, R.Trappl (ed), Proceedings of the European Meeting on Cybernetics and Systems Research - EMCSR'98, Austrian Society for Cybernetic Studies, Vienna, 14-17 April (1998) 622-627.
- 1998 - Kasabov, N., Kozma, R. and Duch, W. Rule extraction from linguistic rule networks and from fuzzy neural networks: propositional versus fuzzy rules, in: Proceedings of the Conference on Neural Networks and Their Applications NEURAP'98, Marseilles, France, 11-13 March (1998) 403-406
- 1998 - Kozma, R. and Kasabov, N. Rules of Chaotic Behaviour Extracted from the Fuzzy-Neural Network FuNN, in: Proceedings of World Congress on Computational Intelligence WCCI'98, International Conference on Fuzzy Systems, IEEE Press, Ancorage, Alaska, May (1998) 1159-1163
- 1998 - Postma, E., Kasabov, N. and van den Herik, J. Enhancing recognition systems through an integrated processing of visual and audio information, Proc. 1998 IEEE International Conference on Systems, Man and Cybernetics, San Diego, California, USA, 11-14 October (1998) IEEE Press
- 1998 - Postma, E.O., Kasabov, N., and Herik, H.J. van. Dynamic Audio-Visual Features for Person Identification Proceedings of the 10th Netherlands/Belgium Conference on Artificial Intelligence, BNAIC'99 (eds) H. La Poutré and H. J. van den Herik) (1998) 107-116.
- 1998 - Watts, M. and Kasabov, N. Genetic algorithms for the design of fuzzy neural networks, in: Proceedings of ICONIP'98 - The Fifth International Conference on Neural Information Processing, Kitakyushu, Japan, 21-23 October 1998,
Forthcoming October 1998
- 1997 - Gray, A., Kilgour, R. and Kasabov, N. An agent based framework for modular speech recognition and language processing systems, In Proceedings of the International Conference on Neural Information Processing ICONIP'97, Dunedin, Springer Verlag Singapore (1997) 1076-1079
- 1997 - Kasabov, N. Fuzzy rule extraction, reasoning and rule adaptation in fuzzy neural networks, In Proceedings of the International Conference on Neural Networks ICNN'97. Houston, May 1997, IEEE Press (1997) 102-107
- 1997 - Kasabov, N. and Kozma, R. Chaotic adaptive fuzzy neural networks and their applications for phoneme-based spoken language recognition, Proc. Int.Conf. Vision, Recognition, Action: Neural Models of Mind and Machines, Boston University, May 28-31, p.109, 1997.
- 1997 - Kasabov, N. and Watts, M. Genetic algorithms for structural optimisation, dynamic adaptation and automated design of fuzzy neural networks. In Proceedings of the International Conference on Neural Networks ICNN'97, Houston, May 1997, IEEE Press (1997) 97-101
- 1997 - Kasabov, N., R. Kozma, R. Kilgour, M. laws, J. Taylor, M. Watts, A. Gray. A methodology for speech data analysis and a framework for adaptive speech recognition, Progress in Connectionist based Information Systems, Proc. ICONIP97, Dunedin, November 24-28, 1997, Vol. 2, pp. 1055-1060.
- 1997 - Kim, J.S., Mowatt, A., Kasabov, N., Connectionist systems for fruit growth prediction based on infrared spectra processing, In Proceedings of the International Conference on Neural Information Processing ICONIP'97, Dunedin, Springer Verlag Singapore (1997) 780 - 784
- 1997 - Kozma, R., J.A. Swope, M.J.A. Williams, Kasabov, N. A multi-agent realisation of fractal analysis by fuzzy neural networks, Progress in Connectionist based Information Systems, Proc. ICONIP97, Dunedin, November 24-28, 1997, Vol. 1, pp. 162-165.
- 1997 - Kozma, R., Kasabov, N.K., J.A. Swope, M.J.A. Williams, Combining Neuro-Fuzzy and Chaos Methods for Intelligent Time Series Analysis - Case Study of Heart Rate Variability, Proc. of the 1997 IEEE Int. Conf. on Systems, Man and Cybernetics, Orlando, October 12-15, 1997, Vol. 4, pp. 3025-3029, 1997
- 1997 - Kozma, R., Kasabov, N., Swope, J. and Williams, M. Neuro-fuzzy- chaos analysis for building hybrid connectionist systems, in: Proceedings of the 1997 International Conference on Systems, Man and Cybernetics, Orlando, IEEE Press (1997) 3025 - 3029
- 1997 - Purvis, M., Kasabov, N., Benwell, G., Zhou, Q., and Zhang, F. Neuro-fuzzy methods for Environmental Modelling, in: Proceedings of the Second International Symposium on Environmental Software Systems. Whistler, Canada (1997) 30 - 37
- 1997 - Topchy, A., Lebedko, O., Miagkikh, V., Kasabov, N. An Approach to Radial Basis Function Networks Training based on Cooperative Evolution and Evolutionary Programming. In Proceedings of the International Conference on Neural Information Processing ICONIP'97, Dunedin, 24- 28 November, 1997, Springer Verlag Singapore (1997) 253-258
- 1997 - Zhou, Q., Purvis, M. and Kasabov, N. Membership function selection for fuzzy neural networks, in Proceedings of the International Conference on Neural Information Processing ICONIP'97, Dunedin, 24- 28 November, 1997, Springer Verlag Singapore (1997) 785 - 788
- 1996 - Kasabov, N. Connectionist methods for fuzzy rules extraction, reasoning and adaptation. In Proceedings of the International Conference on Fuzzy Systems, Neural Networks and Soft Computing Iizuka'96, Iizuka, Japan, World Scientific, (1996) 74-77
- 1996 - Kasabov, N. Investigating neuro-fuzzy approach to building adaptive intelligent information systems. In Proceedings of the First International Panel Conference on Soft and Intelligent Computing, SIC'96, Budapest, Technical University of Budapest (1996) 83 - 88
- 1996 - Kasabov, N. Investigating the adaptation and forgetting in fuzzy neural networks through a method of training and zeroing. In Proceedings of the International Conference on Neural Networks ICNN'96: Plenary, Panel and Special Sessions, Washington DC, IEEE Press (1996) 118-123
- 1996 - Kasabov, N. Learning strategies for adaptive fuzzy neural networks. In Proceedings of the International Conference on Fuzzy Systems, Neural Networks and Soft Computing Iizuka'96, Iizuka, Japan, World Scientific (1996) 578-581
- 1996 - Kasabov, N. Learning strategies for modular connectionist hybrid systems: a case study on phoneme-based speech recognition. In Proceedings of the World Congress of Neural Networks WCNN'96, San Diego, Lawrence Erlbaum (1996)
- 1996 - Kasabov, N. Adaptive learning in modular fuzzy neural networks. In Lecture Notes in Computer Science/Artificial Intelligence: Proceedings of the International Conference on Neural Information Processing ICONIP'96, Hong Kong, Springer Verlag Singapore (1996) 1096-1102
- 1996 - Kasabov, N. Advanced Neuro-Fuzzy Engineering: Adaptation and Forgetting in Fuzzy Neural Networks. In Proceedings of the International Discourse on Fuzzy Logic and the Management of Complexity FLAMOC'96, Sydney, Sydney University of Technology (1996) 213-222
- 1996 - Yeap, W.K., Sun, J., Sallis, P.J., Kasabov, N.K. From Generative Lexicon to Interpretation, Proc. of the European International Conference on Speech and Language, October 1996, St Petersburg, Russia (1996) 40 - 44
- 1996 - Purvis, M., Kasabov, N., Zhang, F. and Benwell, G. Connectionist-based methods for knowledge acquisition from spatial data in Proceedings of the IASTED International Conference, Gold Coast, Australia, IASTED-ACTA Press (1996) 151-154
- 1995 - Kasabov, N., Cohen, A., Bailey, M., and Mason, P. Using AI in pollution control - case studies of Neural Network and Fuzzy Control Applications, in Proceedings of New Zealand Biotechnology Association Annual Scientific Meeting, Dunedin (1995)
- 1995 - Kasabov, N. Building comprehensive AI and the task of speech recognition in Proceedings of the International Workshop on Applications of Neural Networks to Telecommunications, J.Alspector, R.Goodman and T.Brown eds. Stockholm, Lawrence Erlbaum Ass. Publ. (1995) 178-187 (see also the reference in the chapters of books section)
- 1995 - Kasabov, N. Hybrid fuzzy connectionist rule-based systems and the role of fuzzy rules extraction in Proceedings of FUZZ-IEEE/IFS'95 - Fourth IEEE International Conference on Fuzzy Systems. Yokohama, IEEE Press (1995) 49-56
- 1995 - Bailey, M., Solomon, C., Kasabov, N. and Greig, S. Hybrid Systems for Medical Data Analysis and Decision Making - A Case study on Varicose Vein Disorders in Proceedings of ANNES'95 - the Second New Zealand International Conference on Artificial Neural Networks and Expert Systems, Dunedin, IEEE Computer Society Press, Los Alamitos (1995) 265-268
- 1995 - Bailey, M., Kasabov, N., Cohen, T., Mason, P. and A. Grey. Hybrid Systems for Prediction - A Case Study of Predicting Effluent Flow to a Sewage Plant in Proceedings of ANNES'95 - the Second New Zealand International Conference on Artificial Neural Networks and Expert Systems. Dunedin, IEEE Computer Society Press, Los Alamitos (1995) 261-264
- 1995 - Kasabov, N., Sinclair, S., Kilgour, R., Watson, C., Laws, M. and Kassabova, D. Intelligent Human Computer Interfaces and the Case Study of Building English-to-Maori Talking Dictionary in Proceedings of ANNES'95 - the Second New Zealand International Conference on Artificial Neural Networks and Expert Systems. Dunedin, IEEE Computer Society Press, Los Alamitos (1995) 294-297
- 1995 - Solomon, C., Kasabov, N., Bailey,M., Greig,S. and van Rij, A. Artificial computer neural networks for the assessment of the results of venous calf air plethysmography, in Proceedings of the XII World congress on Plethysmology. London, Royal Society of Medicine- Phlebology (1995) Supplementary. 1:172-174
- 1995 - Kasabov, N. Learning, Generalisation, Adaptation and Forgetting in Fuzzy Neural Networks and Hybrid Systems in Proceedings of the International Conference on Neural Information Processing ICONIP'95, Beijing, Publishing House of Electronics Industry, Beijing (1995) 973-976
- 1995 - Benwell, G., Kasabov, N., Purvis, M., Zhang, F., McLennan, B., and Mann, S., Spatial Analysis with Artificial Neural Networks. in Conference proceedings of the Eight Australian Joint Artificial Intelligence Conference, Workshop on AI and the Environment, Canberra, Australian Defence Force Academy (1995) 43-52
- 1994 - Kasabov, N. Towards using hybrid connectionist fuzzy production systems for speech recognition. in Proceedings of the IEEE/Nagoya University World Wise men/women Workshop on Fuzzy Logic and Neural Networks/Genetic Algorithms. Nagoya, Nagoya University (1994) 9-13
- 1994 - Kasabov, N. and Peev, E. Phoneme recognition with hierarchical self organised neural networks and fuzzy systems - a case study in: Proceedings of the International Conference on Artificial Neural Networks. M.Marinaro and P.Moraso eds, Sorento, Italy, Springer Verlag (1994) 201-204
- 1994 - Kasabov, N. Connectionist Fuzzy Production Systems as Universal Machines for Approximate Reasoning in Proceedings of the International Conference on Fuzzy Systems, Neural Networks and Soft Computing Iizuka'94, Iizuka, Japan, Kyushu Institute of Technology (1994) 151-152
- 1994 - Kasabov, N. A filtering neuron and its application for building connectionist production systems in Proceedings of the International Conference on Neuro Information processing ICONIP'94. Seoul, IEEE Press (1994) 53-58
- 1994 - Kasabov, N. Connectionist models for analogy-based prediction and learning fuzzy analogy rules in Proceedings of the 7th International Conference on Systems Research, Informatics and Cybernetics (ICSRIC'94), Baden-Baden, Germany, International Institute for Advanced Studies in Systems Research and Cybernetics (1994) 105-110
- 1994 - Kasabov, N., Watson, C., Sinclair, S. and Kilgour, R. Integrating neural networks and fuzzy systems for speech recognition in Proceedings of the Speech Science and Technology Conference SST-94. Perth, University of South Australia (1994) 462-467
- 1994 - Mann, S., Holland, P., Kasabov, N. and Morgan, R. The integration of ecological modelling, remote sensing and GIS for monitoring and prediction in tussock grasslands in Proceedings of the Sixth Annual Colloquium of the Spatial Information Research Centre. Dunedin, University of Otago Press (1994) 31-44
- 1993 - Kasabov, N. and Trifonov, R.,i> Using hybrid connectionist systems for spatial information processing in Proceedings of the Fifth Colloquium of the Spatial Information Research Centre. Dunedin, University of Otago Press (1993) 85-95
- 1993 - Kasabov, N. Learning fuzzy production rules for approximate reasoning with connectionist production systems in Proceedings of the International Conference on Artificial Neural Networks ICANN'93. S. Gielen and B. Kappen, (eds) Amsterdam, Springer Verlag (1993) 337-345
- 1993 - Kasabov, N., and Shishkov, S. Approximate reasoning with parallel connectionist production systems in Proceedings of the International Joint Conference on Neural Networks IJCNN'93. Nagoya, Japan, IEEE (1993) 2963-2966
- 1993 - Kasabov, N., Towards connectionist realisation of fuzzy production systems in Proceedings of ACNN'93 - the Fourth Australian Conference on Neural Networks. Sydney University Electrical Engineering (1993) 134-137
- 1993 - Kasabov, N., Learning fuzzy rules through neural networks in Proceedings of the Artificial Neural Networks and Expert Systems Conference - ANNES'93. Dunedin, IEEE Computer Society Press (1993) 137-140
- 1993 - Kasabov, N. and Jain, L.C., Connectionist expert systems in Proceedings of Artificial Neural Networks and Expert Systems Conference - ANNES'93. Dunedin, IEEE Computer Society Press (1993) 220-221
- 1993 - Kasabov, N., Nikovski, D. and Peev, E. Speech recognition with Kohonen's self organised neural networks and hybrid systems in Proceedings of Artificial Neural Networks and Expert Systems Conference - ANNES'93. Dunedin, IEEE Computer Society Press (1993) 113-118
- 1993 - Kasabov, N. Neural networks and fuzzy systems for knowledge engineering in Proceedings of the 13th New Zealand Computer Society Conference. Auckland (1993) 338-352
- 1992 - Kasabov, N. and Petkov, S. Approximate Reasoning with Hybrid Connectionist Logic Programming Systems in Artificial Neural Networks 2. I.Aleksander and J.Taylor (eds) Elsevier Science Publishers B.V. North-Holland (1992) 749-752
- 1992 - Kasabov, N. and Shishkov, S. On the problem of connectionist production systems - models and their implementation in Artificial Neural Networks 2. I.Aleksander and J.Taylor (eds) Elsevier Science Publishers B.V.(North-Holland) (1992) 699- 702
- 1992 - Kasabov, N. COPE-a hybrid connectionist production system environment in Proceedings of the Third Australian Conference on Neural Networks (ACNN'92). Sydney, Sydney University Electrical Engineering (1992) 135-138
- 1992 - Kasabov, N. and Petkov, S. Neural networks and logic programming - a hybrid model and its applicability to building expert systems in Proceedings of the 10th European Conference on Artificial Intelligence Vienna, John Wiley & Sons (1992) 287-288
- 1992 - Lavington, S., Wang, C., Kasabov, N. and Lin, S. Hardware support for data parallelism in production systems in Proceedings of the International Workshop of VLSI for AI and Neural Networks Oxford, Oxford University (1992)
- 1991 - Kasabov, N. and Clarke, G. Towards a template-based implementation of supervised and unsupervised learning in connectionist knowledge based systems in Artificial Neural Networks 1. Kohonen, T. et al (eds), Elsevier Science Publishers B.V. North-Holland (1991) 477-481
- 2015 - Gholami Doborjeh, Z., Kasabov, N., & Gholami, M. (2015). ERP Evidence for Predicting Consumers’ Preferences to Beverage Logos. In 13 International Conference on Neuro-Computing and Evolving Intelligence 2015 (NCEI ‘15). Auckland, New Zealand.
- 2006 - Nikola Kasabov, Vishal Jain, Paulo C.M. Gottgtroy, Lubica Benuskova, Frances Joseph, "Brain-Gene Ontology: Integrating Bioinformatics and Neuroinformatics Data, Information and Knowledge to Enable Discoveries," his , p. 13, 2006. ISBN: 0-7695-2662-4
- 2005 - Benuskova L, Kasabov N and Wysoski SG (2005) Computational neurogenetic modelling: methodology and preliminary results. In: Proc. 23rd Australasian Winter Conference on Brain Research, AWCBR'05, p. 44. ISSN: 1176-3183.
- 2005 - Gottgtroy, P., Jain, V., Kasabov N., and Macdonell, S., An Ontological Representation of Gene-brain Diseases. Proceedings of International Australasian Winter Research Conference on Brain Research, ISSN 1176-3183, Vol. 23, pp 28
- 2005 - Verma, A., Gottgtroy, P., Havukkala, I., & Kasabov, N. Understanding the Molecular Basis of Type-2 Diabetes by Means of Evolving Ontologies and Intelligent Modelling. In: 15th Annual Queenstown Molecular Biology Meeting 30th August – 2 September 2005.
Patents and reports
- 2003 - N.Kasabov, M. Futschik, M.Sullivan, A.Reeve, A method and system for integrating microarray gene expression data and clinical information, Patent No. NZ03/00045, New Zealand dated 17/03/2003
- 2003 - A.Ghobakglou and N.Kasabov, Adaptive speech recognition, Provisional patent application, submitted July 2003, New Zealand
- 2002 - N.Kasabov, M. Futschik, M.Sullivan, A.Reeve, A method and system for using microarray gene expression data and clinical information, Preliminary application, 2002, USA
- 2002 - A.Reeve, M. Futschik, M.Sullivan, N.Kasabov, P. Guildford, Medical Applications of Adaptive Learning Systems, Preliminary Patent Application, February 2002, New Zealand
- 2001 - Kasabov, N., Adaptive learning system and method, WO 01/78003, under PCT (publication date 20.04.2001)
- 2001- Kasabov, N., and Abdulla, W., Method and system for robust speech recognition, ref. No. 25675/25688, New Zealand, PCT
- 2000 - "System and Method for Video Production" Kellock P., Subramaniam S., Padmanabhan R., Goh L., US Application No. 09/509,280 filed 27 March 2000
- 1999 - "System and Method for Video Production", Kellock P., Subramaniam S., Padmanabhan R., Goh L., PCT/SG99/00142, filed 16 Dec 1999.
- 2015 - Doborjeh, Z.G, Doborjeh, M.G, Kasabov, N., Wang, G. (2015) ERP Evidence for Prediction, classification, and visualisation of the Consumers’ Preferences to Marketing Logos in Neuro- Marketing”, Abstract in NCEI, New Zealand.
- 2010 - Lei Song, Shaoning Pang, Nik Kasabov, NIDVS: A Network Intrusion Detection Visualization System, KEDRI-NICT Project Report, Auckland University of Technology
- 2010 - Nuwan Amila, Shaoning Pang, Nik Kasabov, Meta Learning on String Kernel SVMs for String Categorization, KEDRI-NICT Project Report, Auckland University of Technology
- 2010 - Shaoning Pang, Fan Liu, Nik Kasabov, Learner Independent Multi-task Machine Learning by Knowledge Transfer in Minimum Enclosing Balls, KEDRI-NICT Project Report and Patent Application, Auckland University of Technology
- 2009 - S. Pang, G. Chen, K. Dhoble, Z. Michlovsky, and N. Kasabov, High Speed Algorithms for Outlier Detection and Classification over Huge-size Network Data Streams, KEDRI-NICT Project Report, Auckland University of Technology
- 1999 - Abdulla, W. and Kasabov, N. (1999). The Concept of Hidden Markov Model in Speech Recognition, Technical Report TR99/09, Department of Information Science, University of Otago.
- 1999 - Kasabov, N. (1999). Evolving Connectionist Systems for On-line, Knowledge-based Learning: Principles and Applications, Technical Report TR99/02, Department of Information Science, University of Otago (revised version was accepted in IEEE Trans. on Man, Machine and Cybernetics, 2000).
- 1999 - Kasabov, N. and Watts, M. (1999). Spatial-Temporal Adaptation in Evolving Fuzzy Neural Networks for On-line Adaptive Phoneme Recognition, Technical Report TR99/03, Department of Information Science, University of Otago.
- 1999 - Kasabov, N. and Song, Q. (1999). Dynamic Evolving Fuzzy Neural Networks with 'm-out-of-n' Activation Nodes for On-line Adaptive Systems, Technical Report TR99/04, Department of Information Science, University of Otago.
Our research groups
Within our institute we have a number of research groups that explore the use cases of the NeuCube across a variety of areas.
Meet the team
Find out about the individual expertise of our team members at the AUT Knowledge Engineering and Discovery Research Institute.