Professor Nikola Kasabov

Prof. Nikola Kasabov

Professor of Computer Science and Director KEDRI

Phone: +64 9 921 9506

Email: nkasabov@aut.ac.nz

Physical Address:

KEDRI
Auckland University of Technology
AUT Tower, Level 7
Corner Rutland and Wakefield Street
Auckland



Postal Address:
KEDRI
Auckland University of Technology
Private Bag 92006
Auckland 1142
New Zealand

Links to relevant web pages:

More information about Professor Nikola Kasabov

Knowledge Engineering & Discovery Research Institute (KEDRI)

Qualifications:

  • PhD (Math. Sciences), Technical University, Sofia, 1975
  • MSc (Applied Math.), Technical University, Sofia, 1972
  • MSc (Comp. Science and Eng.), Technical University, Sofia, 1971

Memberships and Affiliations:

  • IEEE (Institute of Electrical and Electronic Engineers), since 1994, Fellow 2010
  • RSNZ  (Royal Society of New Zealand), since 1996, Fellow, 2001
  • IITP (previously New Zealand Computer Society), since 1992, Fellow 2002
  • INNS (International Neural Network Society) since 1995, Senior Member, 2008
  • APNNA (Asia-Pacific Neural Network Assembly) – since 1993 as a co-founder.

Distinctions (e.g., prizes, scholarships, invited memberships, notable posts, honorary degrees):

  • The AUT Medal for 2015 – sustained and outstanding contribution to the academic success of AUT.
  • Distinguished Visiting Fellowship by the Royal Academy of Engineering (RAE), UK, 2013.
  • Recipient of the ‘Outstanding Achievements Award’ of the Asia Pacific Neural Network Assembly (APNNA), 2012.
  • Recipient of the INNS Gabor Award for 2012 (www.inns.org).
  • EU FP7 Marie Curie Fellowship, 2011 and 2012, INI/ETH and University of Zurich.
  • Distinguished Lecturer of the IEEE Distinguished Lectureship Program, CI Society (2011-2013).
  • Fellow of the IEEE (the Institute of Electrical and Electronic Engineers), since 2010.
  • President, International Neural Network Society (INNS, www.inns.org), 2009-2010.
  • Member of the Board of Governors, INNS, since 2005.
  • Honorary Guest Professor at Shanghai Jiao Tong University, China, (since 2010).
  • The AUT Vice Chancellor Award for Individual Research Excellence, 2010.
  • President, Asia-Pacific Neural Network Assembly, APNNA, www.apnna.net, 2008.
  • Best Paper Award, IEEE International Workshop on Data Mining & Artificial Intelligence, in conjunction with 11th IEEE Int. Conference on Computer and Information Technology (ICCIT2008), Bangladesh.
  • The Bayer Science Innovator Award, 2007.
  • The AUT Vice Chancellor’s Award for Postgraduate Research Supervision, 2007.
  • DAAD Visiting Professorship, 2005-2006, Germany.
  • APNNA Excellent Service Award for overall contribution to Neuro-information Processing, 2005.
  • President of the Asian Pacific Neural Network Assembly (APNNA), 1997 and 2008.
  • International Neural Network Society, Vice President, 2007 and 2008
  • Best Paper Award, IEEE 2003 Int. Conf. on Neural Networks & Signal Processing, Nanjing, China, December 2003.
  • Fellow of the Royal Society of New Zealand, since 2001.
  • The Royal Society of New Zealand Silver Medal for Contribution to Science and Technology,2001.
  • Member of the Top Achiever Doctoral Committee, Tertiary Education Committee, NZ (since 1999).
  • International Neural Network Society, Distinction, Washington DC, 1999.
  • New Zealand FRST Award for supervision of a PhD student (M. Laws), 1999.
  • Best paper award, The Fourteenth European Meeting on Cybern. and System Research, Vienna, 04/1998.
  • IFIP (International Federation for Information Processing), WG 12 for Artificial Intelligence, since 1997
  • NWO/SION (Dutch Organisation for Scient./Comp.Science) Research Grant, U. Maastricht, The Netherlands, 1998.
  • Research Fellowship Grant, University of Twente, The Netherlands, 1998.
  • Prize for Invention with High Practical Applicability, National Institute of Inventions, Bulgaria, 1992.
  • Leverhulme Trust Research Fellowship, University of Essex, United Kingdom, 1989/90.
  • Czechoslovakia, Research Fellowship, Institute of Cybernetics, Bratislava, 1987.
  • Research Fellowship, Research and Education Ministry, The Netherlands, 1984.

Biography:

Nikola K Kasabov is the Director of the Knowledge Engineering & Discovery Research Institute and Personal Chair of Knowledge Engineering in the School of Computer and Information Sciences, AUT.

He has published over 600 works, among them journal papers, text books, edited research books and monographs, conference papers, book chapters, edited conference proceedings, patents and authorship certificates in the area of intelligent systems, connectionist and hybrid connectionist systems, fuzzy systems, expert systems, speech recognition, bioinformatics, neurocomputing and neural networks. These works has been cited more than 10,000 times.

Prof. Kasabov is a Fellow of IEEE, Fellow of the Royal Society of New Zealand and the New Zealand Institute for IT Professionals. He is Past President and Board member of the International Neural Network Society (INNS) and the Asia Pacific Neural Network Assembly (APNNA). Prof. Kasabov is Advisory- Professor at Shanghai Jiao Tong University, China.
 
Prof. Kasabov is the General Chairman of a series of biannual international conferences on Neurocomputing in New Zealand.  He has been awarded several prestigious awards, such as : the INNS Gabor Award (2012); the APNNA Outstanding Achievement Award (2012); The Bayer Science Innovator Award (2007); The Royal Society of New Zealand Silver Medal (2001). He is a co-editor in chief of the Springer Evolving Systems journal an Associate Editor of numerous international journals.

Prof. Kasabov’s main areas of expertise are:
  • Information Sciences
  • Artificial Intelligence (Neural Networks, Fuzzy Systems, Evolutionary Computation)
  • Knowledge Engineering
  • Bioinformatics
  • Brain-like computing and neuroinformatics
  • Signal, Speech and Image Processing
  • Parallel Computer Systems

Teaching Areas:

  • Neuroinformatics
  • Data mining and knowledge engineering
  • Bioinformatics
  • Machine learning
  • Neural Networks

Research Areas:

  • Neurocomputation
  • Artificial Intelligence (Neural Networks, Fuzzy Systems, Evolutionary Computation)
  • Machine learning
  • Data Mining and Knowledge Engineering
  • Neuroinformatics
  • Bioinformatics
  • Signal, Speech and Image Processing

Current Research Projects:

  • A novel neurocomputing technology - NeuCube
  • Tripartite NZ-China agreement: SJTU-XU-AUT
  • Project related to European Union FP7
  • AUT SRIF project ‘Intellecte’

Publications:

Prof. N. Kasabov’s Publications and Citations on Google Scholar (29.10.2014): 9350 citations, h-ind=44; i10-ind=146:
http://scholar.google.com/citations?hl=en&user=YTa9Dz4AAAAJ&view_op=list_works

Books

Authored

1. Kasabov, N. Evolving Connectionist Systems: The Knowledge Engineering Approach (new edition), Springer Verlag, London, (2007) 458p
2. Benuskova, L. and N.Kasabov, Computational neuro-genetic modelling: Integrating bioinformatics and brain science data, information and knowledge via computational intelligence, Springer, New York, 2007, 290 pages
3. Kasabov, N. Evolving connectionist systems: Methods and applications in bioinformatics, brain study and intelligent machines, Springer Verlag, London, (2003) 308p
4. Kasabov, N. Foundations of Neural Networks, Fuzzy Systems and Knowledge Engineering. Cambridge, Massachussets, MIT Press (1996) 550p
5. Kasabov, N. and Romanski, R. Computer Architectures and Techniques Sofia, Technika (1992) 435p (in Bulgarian)
6. Stoichev, S. and Kasabov, N. Programming in PASCAL. Sofia, Technika (1989) 136p (in Bulgarian)
7. Stoichev, S. and Kasabov, N. Synthesis and Analysis of Algorithms. Sofia, Technika (1988) 84p (in Bulgarian)
8. Stoichev, S. and Kasabov, N. Computer Architectures and Techniques. Sofia, Technika (1986) 348p (in Bulgarian)
9. Stoichev, S. and Kasabov, N. Computers - Theory and Practice (Programming of Microprocessors). Sofia, Technika (1984) 120p (in Bulgarian)


Edited scientific, research books:

1. M. Hadjiski, N.Kasabov, D.Filev, V.Jotsov (eds) Novel Applications of Intelligent Systems, Springer, 2016. 2. Koprinkova-Hristova, P., Mladenov, V., & Kasabov, N. (2015). Artificial Neural Networks Methods and Applications in Bio-/Neuroinformatics (Vol. 4). Springer. doi:10.1007/978-3-319-09903-3
3. N.Kasabov (ed) The Springer Handbook of Bio- and Neuroinformatics, Springer (2014) 1230 p
4. P.Angelov, D.Filev, and N.Kasabov (eds) Evolving intelligent systems, IEEE Press and Wiley, 2010
5. Kasabov, N. (ed.) Future Directions for Intelligent Systems and Information Sciences, Heidelberg, Physica-Verlag (Springer Verlag) (2000), 420pp
6. Kasabov, N. and Kozma, R. (eds.) Neuro-Fuzzy Techniques for Intelligent Information Systems, Heidelberg, Physica-Verlag (Springer Verlag) (1999), 450pp
7. Amari, S. and Kasabov, N. (eds.) Brain-like Computing and Intelligent Information Systems, Singapore, Springer Verlag (1998), 533 p.


Edited Conference Proceedings:

1. Angelov, P., Atanassov, K.T., Doukovska, L., Hadjiski, M., Jotsov, V., Kacprzyk, J., Kasabov, N., Sotirov, S., Szmidt, E., Zadrożny, S. (Eds.) (2015) 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.
2. V.Mladenov, P.Koprinkova, B.Apoloni, N.Kasabov, Proc. of ICANN 2013, Sofia, 2013, Springer LNCS, 2013.
3. M.Koeppen, N.Kasabov and G.Coghill, Advancements in Neural Information Processing, Proc. off ICONIP 2008, Springer LNCS, vol. 5506/5507, 2009
4. J.Si, R.Sun, D.Brown, I.King and N.Kasabov (eds) Proceedings of the Int Joint Conference on Neural Networks – IJCNN, 12-16 August 2007, IEEE Press, 2007
5. 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
6. P.Angelov, D.Filev, N.Kasabov, O.Cordon (eds) Proc. 2006 Int. Symp. Evolving Fuzzy Systems, Lancaster, UK, IEEE Press, 2006
7. 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
8. M.Barley, N.Kasabov (eds) Intelligent Multi-agent Systems, LNAI, vol. , 2004
9. Kasabov N., Pang S., (eds) International Journal of Computers, Systems and Signals, Volume 5 No. 2, 2004
10. K. 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 (eds) Proceedings of the 7th Joint Conference on Information Sciences, JCIS 2003, 1780 pages
11. Kasabov, N, Zeke S.H. Chan (eds) Proceedings of the Conference on Neuro-Computing and Evolving Intelligence, November 2003, Auckland University of Technology, (2003) 122 pages
12. Kasabov, N. Proceedings of the Neurocomputing Colloquium and Workshop, October, AUT, (2002) 85 pages
13. Kasabov, N., B.Woodford (eds) Proceedings of the ANNES’2001, University of Otago (2001) 150 pages
14. Gedeon, T., P.Wong, S.Halgamuge, N.Kasabov, D.Nauck, and K.Fukushima (eds) ICONIP’99-Proceedings of the 6th Inter. Conf. on Neural Information Processing, 16-20.11.1999, Perth, IEEE Press (1999), Vol. I & II, 842 pages
15. 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)
16. Kasabov, N., Kozma, R., O’Shea, R., Ko, K., Coghill, G., and Gedeon, T., (eds) Advances in Connectionist-Based Information Systems. Proc. Int. Conf. Neural Information Processing ICONIP’97, Springer Verlag, (1998), 1550 pages
17. Kasabov, N. and Coghill, G. (eds) Proceedings of the Second New Zealand International Conference on Artificial Neural Networks and Expert Systems, ANNES'95, Dunedin, IEEE Computer Soc. Press, Los Alamitos (1995) 401 pages
18. 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


Book Chapters

1. Kasabov, N. (2015) Evolving connectionist systems: From neuro-fuzzy-, to spiking – and neurogenetic, in: Kacprzyk and Pedrycz (eds) Springer Handbook of Computational Intelligence, Springer, 771-782.
2. Kasabov N.K. Integrative Computational Neurogenetic Modeling. In: Arthur W. Toga, editor. Brain Mapping: An Encyclopedic Reference. Academic Press: Elsevier; 2015. pp. 667-674.
3. Kasabov, N. (2014). Understanding Nature Through the Symbiosis of Information Science, Bioinformatics and Neuroinformatics. In Springer Handbook of Bio-/Neuroinformatics.
4. Kasabov, N. (2014). Brain, Gene, and Quantum Inspired Computational Intelligence. In N. Kasabov (Ed.), Springer Handbook of Bio-/Neuroinformatics. Springer.
5. Georgieva, P., Silva, F., Milanova, M., & Kasabov, N. (2014). EEG Signal Processing for Brain-Computer Interfaces. In N. Kasabov (Ed.), Springer Handbook for Bio-/Neuroinformatics. Springer.
6. Schliebs, S., & Kasabov, N. (2014). Computational Modeling with Spiking Neural Networks. In N. Kasabov (Ed.), Springer Handbook of Bio-/Neuroinformatics.
7. Tegginmath, S., Pears, R., & Kasabov, N. (2014). Ontologies and Machine Learning Systems. Springer. In N. Kasabov (Ed.), Springer Handbook of Bio-/Neuroinformatics.
8. Liang, L., Krishnamurthi, R., Kasabov, N., & Feigin, V. (2014). Information methods for predicting risk and outcome of stroke. In N. Kasabov (Ed.), Springer Handbook of Bio-/Neuroinformatics.
9. Hu, Y., Kasabov, N., & Liang, W. (2014). Personalised Information Modelling Technologies for Personalised Medicine. In N. Kasabov (Ed.), Springer Handbook of Bio- and Neuroinformatics (pp. 1-32). Springer.
10. Kasabov, N. (2013). 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.
11. 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
12. 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, 2012.
13. 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
14. 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
15. 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
16. S Ozawa, S Pang and N Kasabov, On-line Feature Extraction for Evolving Intelligent Systems, in: P.Angelov, D.Filev, and N.Kasabov (eds) Evolving intelligent systems, IEEE Press and Wiley, 2010, (7) 151-172.
17. Wysoski SG, Benuskova L, Kasabov N, Brain-Like Evolving Spiking Neural Networks for Multimodal Information Processing. In Brain-Inspired Information Technology. Editors: Hanazawa A, Miki T, Horio K. 266: 15-27. Springer 2010.
18. 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.
19. Kasabov N, Integrative Probabilistic Evolving Spiking Neural Networks Utilising Quantum Inspired Evolutionary Algorithm: A Computational Framework. In Advances in Machine Learning II. Editors: Koronacki J, Ras ZW, Wierzchon ST, Kacprzyk J. 263: 415-425. Springer 2010
20. Kasabov, N. (2009). Soft computing methods for global, local and personalized modeling and applications in bioinformatics. In Soft ComputingBased Modeling in Intel. Systems (Vol. 196, pp. 1-18). doi:10.1007/978-3-642-00448-3.
21. 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, Chapter 4, 93-116 In: Smolin et al (eds) Computational Intelligence in Biomedicine and Bioinformatics, Springer, 2008
22. Pang, S., Havukkala, I., Hu, Yingjie, Kasabov, N.: Bootstrapping Consistency Method for Optimal Gene Selection from Microarray Gene Expression Data for Classification Problems. Chapter 4, In: Zhang, Y.-Q., Rajapakse, J.C. (eds.): Machine Learning for Bioinformatics. John Wiley & Sons, Inc., New Jersey (2008)
23. 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. Eds; Arpad Kelemen, Ajith Abraham, Yulan Liang, ISBN: 978-3-540-75766-5, 2008
24. Kasabov, N., Song, Q., & Ma, T. M. (2008). Fuzzy-neuro systems for local and personalized modelling. In Forging New Frontiers: Fuzzy Pioneers II (Vol. 218, pp. 175-197). Berlin / Heidelberg: Springer. doi:10.1007/978-3-540-73185-6_8
25. Pang, S., & Kasabov, N. (2008). SVMT-rule: Association rule mining over SVM classification trees. In Rule Extraction from Support Vector Machines (Vol. 80, pp. 135-162). doi:10.1007/978-3-540-75390-2_6
26. 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, 2007, pp. 243-260
27. N.Kasabov, Brain-, Gene-, and Quantum Inspired Computational Intelligence: Challenges and Opportunities, in: W. Duch and J. Manzduk (eds) Challenges in Computational Intelligence, ISBN: 978-3-540-71983-0, 193-219, Springer 2007.
28. 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
29. N.Kasabov, Brain-, Gene-, and Quantum Inspired Computational Intelligence: Challenges and Opportunities, in: Reusch. B (eds) Computational Intelligence, Theory and Applications, ISBN: 978-3-540-34780-4, 521-544, Springer 2006.
30. Kasabov, N., Liang Goh and Mike Sullivan, Integrated Prognostic Profiles: Combining Clinical and Gene Expression Information through Evolving Connectionist Approach, Chapter 10, in: Bajic. V and Tan Tin Wee (eds), Information Processing and Living Systems, Imperial College Press, Singapore, 2005, 695-706.
31. Kasabov, N. , Zeke Chan, Vishal Jain, Igor Sidorov and Dimiter Dimitrov, Computational Modelling of Gene Regulatory Networks, Ch 8, in: Bajic., V and Tan Tin Wee (eds), Information Processing and Living Systems, Imperial College Press, Singapore, 2005, 673-686.
32. Kasabov, N., Z.Chan, Q.Song and D.Greer, Evolving neuro-fuzzy systems with evolutionary parameter self-optimisation, chapter in: Do Adaptive Smart Systems exist? Springer, Series Study in Fuzziness, vol.173, 2005
33. Kasabov N., and L. Benuskova, Theoretical and Computational Models for Neuro-, Genetic-, and Neuro-Genetic Information Processing, in: M. Rieth and W. Sommers (eds) Handbook of Theoretical and Computational Nanotechnology, Vol. X pp 1-38, American Scientific Publisher, 2005
34. Dimitrov, D. S., Igor A. Sidorov and Nikola Kasabov Computational Biology, in: M. Rieth and W. Sommers (eds) Handbook of Theoret. and Computational Nanotechnology, Vol. 1 (1) American Scientific Publisher, Chapter 21, 2004
35. Kasabov, N. and D. Dimitrov, Discovering gene regulatory networks from gene expression data with the use of evolving connectionist systems, chapter in: L.Wang and Rajapakse (eds) Neural Inform. Processing, Vol. 152, Springer Verlag, 2004
36. 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).
37. Kasabov, N. Decision support systems and expert systems, in: M. Arbib (ed) Handbook of brain study and neural networks, MIT Press (2003).
38. 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 Functions, FLSI Soft Computing Series, Volume 3, World Scientific (2001), 77-113.
39. 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.
40. 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
41. 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
42. 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
43. 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
44. Taylor, J., Kasabov, N, Modelling the Emergence of Speech and Language through Evolving Connectionist Systems, in: N. Kasabov (ed) Future directions for intelligent inform. systems and information sciences, Springer Verlag, 2000, 102-126
45. 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
46. 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) Comput.Finance 1997, Kluwer Academic (1999).
47. 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
48. 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
49. 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
50. 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
51. 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
52. 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)
53. Kasabov, N., Advanced Neuro-Fuzzy Engin. for Building Intelligent Adaptive Inform. Systems. in: Fuzzy Systems Design: Social and Engineering Applications. L.Reznik, V.Dimitrov and J.Kacprzyk (eds) Heidelberg, Physica-Verlag (1998) 249-262
54. 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) Springer Verlag (1998) 106-126
55. 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) Springer Verlag (1998) 213-237
56. 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
57. 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 Publ.Company (1995) 137-156
58. 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
59. 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
60. 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
61. 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)
62. 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)


Refereed Journal Articles


1. N.Kasabov, et al, Design methodology and selected applications of evolving spatio- temporal data machines in the NeuCube neuromorphic framework, Neural Networks, http://dx.doi.org/10.1016/j.neunet.2015.09.011.
2. Tao Gao and N. Kasabov, Adaptive Cow Movement Detection using Evolving Spiking Neural Network Models, Evolving Systems, Springer, 2016.
3. Elisa Capecci, Grace Y. Wang , Nikola Kasabov (2015), Analysis of connectivity in a NeuCube spiking neural network trained on EEG data for the understanding and prediction of functional changes in the brain: A case study on opiate dependence treatment, Neural Networks, vol.68, 62-77, 2015.
4. N.Kasabov. Brain-Like Spatio-Temporal Data Machines, Chemistry: Bulgarian Journal of Science Education, 24, 103-106 (2015).
5. H.Wu, N.Kasabov, Network-based method for inferring cancer progression at the pathway level from cross-sectional mutation data, IEEE/ACM Transactions on Computational Biology and Bioinformatics, to be published in 2016.
6. Wu H, Gao L, Li F, Song F, Yang X, Kasabov N. Identifying overlapping mutated driver pathways by constructing gene networks in cancer. BMC Bioinformatics 2015, doi:10.1186/1471-2105-16-S5-S3
7. Tao Gao and Nikola Kasabov, A method used for Dotted Data Matrix image processing Journal of Computational Methods in Sciences and Engineering 15 (2015) 685–693 685 DOI 10.3233/JCM-150581, IOS Press 8. Zhang Y-C, Jia Z-H, Qin X-Z, Yang J, Kasabov N. Unsupervised detection of different SAR images based on improved NSCT domain image fusion algorithm. Guangdianzi Jiguang/Journal of Optoelectronics Laser 26(10):2023-2030 15 Oct 2015.
9. Wang J, Li Q, Jia Z, Kasabov N, Yang J. A novel multi-focus image fusion method using PCNN in nonsubsampled contourlet transform domain. Optik 126(20):2508-2511 01 Oct 2015
10. Liu L, Jia Z, Yang J, Kasabov N. 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 01 Sep 2015
11. Wang J-J, Jia Z-H, Qin X-Z, Yang J, Kasabov N. Medical image enhancement algorithm based on NSCT and the improved fuzzy contrast. International Journal of Imaging Systems and Technology 25(1):7-14 01 Mar 2015
12. Wu H, Gao L, Li F, Song F, Yang X, Kasabov N. Identifying overlapping mutated driver pathways by constructing gene networks in cancer. BMC Bioinformatics 2015
13. Kasabov, N. Evolving connectionist systems for adaptive learning and knowledge discovery: Trends and Directions, Knowledge Based Systems, 2015, (2015), http://dx.doi.org/10.1016/j.knosys.2014.12.032.
14. E.Tu, J.Yang, N.Kasabov, Y.Zhang, Posterior Distribution Learning (PDL): A novel supervised learning framework using unlabeled samples to improve classification performance, Neurocomputing (2015), http://dx.doi.org/10.1016/j.neucom.2015.01.020i
15. Doborjeh, M., Wang, G., Kasabov, N. A Neucube Spiking Neural Network Model for the Study of Dynamic Brain Activities during a GO/NO_GO Task: A Case Study on Using EEG Data of Healthy Vs Addiction vs Treated Subjects, IEEE Trans. MBE, 2015-2016.
16. Nikola Kasabov, Maryam Gholami Doborjeh, Spatio-Temporal Brain Data Mining with a NeuCube Evolving Spiking Neural Network Model on the fMRI Case study, IEEE Transactions of Neural Networks and Learning Systems, accepted, 2016. 17. Enmei Tu, Nikola Kasabov, and Jie Yang, Mapping Temporal Variables into the NeuCube Spiking Neural Network Architecture for Improved Pattern Recognition, Predictive Modelling and Understanding of Stream Data, IEEE Transactions of Neural Networks and Learning Systems, 2016. 19. 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. American Scientific Publishers.4: 814-818. 01 Oct 2014. 20. 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 Computation 26(18):2893-2906 25 Dec 21. 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. American Scientific Publishers. 4: 953-956. 01 Dec 2014 23. Hu X-M, Jia Z-H, Qin X-Z, Yang J, Kasabov N. Remote sensing image change detection based on minimum spanning tree clustering. Guangdianzi Jiguang/Journal of Optoelectronics Laser 25(12):2417-2422 15 Dec 2014 24. Feigin, V., P.Parmar, S. Barker-Collo, D.A Bennett, C.S Anderson, A. G Thrift, B. Stegmayr, P. M Rothwell, M.Giroud, Y. Bejot, P. Carvil, R.Krishnamurthi, N.Kasabov, Geomagnetic Storms Can Trigger Stroke: Evidence From 6 Large Population-Based Studies in Europe and Australasia, Stroke, 45(6), 1639-1645, 2014.
25. Kasabov, N., E.Capecci, Spiking neural network methodology for modelling, classification and understanding of EEG spatio-temporal data measuring cognitive processes, Information Sciences, 294, 565-575, 2015, DOI: 10.1016/j.ins.2014.06.028, 2014..
26. Kasabov, N. Evolving Connectionist Systems for Adaptive Learning and Knowledge Discovery: The Past, The Present and the Future, Journal of Policy Science, vol. 8, 1-11, Ritsumeikan University Press, Japan, 2014.
27. Kasabov, N. NeuCube: A Spiking Neural Network Architecture for Mapping, Learning and Understanding of Spatio-Temporal Brain Data, Neural Networks vol.52 (2014), pp. 62-76, http://dx.doi.org/10.1016/j.neunet.2014.01.006
28. Tu, E., Cao, L., Yang, J., & Kasabov, N. (2014). A novel graph-based k-means for nonlinear manifold clustering and representative selection. Neurocomputing. doi:10.1016/j.neucom.2014.05.067
29. Kasabov, N., Feigin, V., Hou, Z. -G., Chen, Y., Liang, L., Krishnamurthi, R., Parmar, P. (2014). 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
30. Erogbogbo, F., May, J., Swihart, M., Prasad, P., Smart, K., Jack, S., et al. Gladding, P. (2013). Bioengineering Silicon Quantum Dot Theranostics using a Network Analysis of Metabolomic and Proteomic Data in Cardiac Ischemia. Theranostics, 3(9), 719-728, doi:10.7150/thno.5010, 2013
31. Kageyama, Y., Momose, A., Takahashi, T., Ishii, M., Nishida, M., Mohemmed, A., . Kasabov, N. (2013). 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
32. Pears, R., Widiputra, H., & Kasabov, N. (2013). 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.
33. Liang., Hu., & Kasabov, N. (2013). Evolving Personalized Modeling System for Integrated Feature, Neighborhood and Parameter Optimization utilizing Gravitational Search Algorithm. Evolving Systems. May, 2013, doi:10.1007/s12530-013-9081-x
34. Schliebs, S., & Kasabov, N. (2013). Evolving spiking neural network-a survey. Evolving Systems, 4(2), 87-98. doi:10.1007/s12530-013-9074-9, 2013.
35. Kasabov, N., Dhoble, K., Nuntalid, N., & Indiveri, G. (2013). Dynamic evolving spiking neural networks for on-line spatio- and spectro-temporal pattern recognition. Neural Networks, 41, 188-201.
36. Tu, E., Yang, J., Fang, J., Jia, Z., & Kasabov, N. (2013). An experimental comparison of semi-supervised learning algorithms for multispectral image classification. Photogrammetric Engineering and Remote Sensing, 79(4), 347-357.
37. Mohemmed, A., Schliebs, S., Matsuda, S., & Kasabov, N. (2013). Training spiking neural networks to associate spatio-temporal input-output spike patterns. Neurocomputing, 107, 3-10. doi:10.1016/j.neucom.2012.08.034
38. 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
39. Pears, R., 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.
40. 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.
41. 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 Trans. Electrical and Electr Engineering, Vol. 8, No. 5, 2012.
42. 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
43. 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, 2012. 44. Kasabov, N., Schliebs, R., Kojima, H., Probabilistic Computational Neurogenetic Framework: From Modelling Cognitive Systems to Alzheimer’s Disease. IEEE Trans. Autonomous Mental Development, 3(4):300-3011, 2011
45. 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
46. 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
47. 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
48. Fang, J. X., Yang, J., Tu, E. M., Jia, Z. H., & Kasabov, N. (2011, June). Efficient multiresolution level set image segmentation with multiple regions. OPT ENG, 50(6). doi:10.1117/1.3582863.
49. Fang, J. X., Yang, J., Tu, E. M., Jia, Z. H., & Kasabov, N. (2011, June). Multilayer level set method for multiregion image segmentation. OPT ENG, 50(6). doi:10.1117/1.3593159.
50. Pang, S. N., Ban, T., Kadobayashi, Y., & Kasabov, N. (2011, June 1). Personalized mode transductive spanning SVM classification tree. Information Sciences, 181(11), 2071-2085. doi:10.1016/j.ins.2011.01.008
51. Seed, P. T., Chappell, L. C., Black, M. A., Poppe, K. K., Hwang, Y. C., Kasabov, N., North, R. A. (2011). 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. doi:10.3109/10641955.2010.486460
52. M. Fiasché, A. Verma, M. Cuzzola, P. Iacopino, N. Kasabov and F. C. Morabito. Discovering Diagnostic Gene Targets for Early Diagnosis of Acute GVHD Using Methods of Computational Intelligence on Gene Expression Data. Journal of Artificial Intelligence and Soft Computing Research, 2011, Volume 1, Number 1, pp. 81- 89.
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53. M. Fiasché, M. Cuzzola, P. Iacopino, N. Kasabov, F.C. Morabito. Personalized Modeling based Gene Selection for acute GvHD Gene Expression Data Analysis: a Computational Framework Proposed. In: Australian Journal of Intelligent Information Processing Systems, Vol 12, No 4 (2010): Machine Learning Applications (Part II), pp. 13 - 18 ISSN: 1321-2133.
54. P. Iacopino, M.F. Lombardo, M. Cuzzola, G. Irrera, E. Spiniello, C. Garreffa, R. Saccardi, R. Piro, G. Grossi, M. Fiasché, D. Mannino, A. Verma, C. Morabito, N Kasabov. Hematopoietic stem cells for neovascularization and wound repair. Journal: BMC Geriatrics - BMC Geriatr , vol. 10, no. Suppl 1, pp. A109-1, 2010. DOI: 10.1186/1471-2318-10-S1-A109
55. Kasabov, N., & Hu, Y. (2010, December). Integrated optimisation method for personalised modelling and case studies for medical decision support. International Journal of Functional Informatics and Personalised Medicine, 3(3), 236-256. doi:10.1504/IJFIPM.2010.039123
56. Siddique, N. H., McDaid, L. J., Kasabov, N., & Widrow, B. (2010, December). Special Issue: Spiking Neural Networks, Introduction, Int. J. Neural Systems, 20(6), V-VII. doi:10.1142/S0129065710002590.
57. Hamed, H. N. A., Kasabov, N., & Shamsuddin, S. M. (2010). Probabilistic Evolving Spiking Neural Network Optimization Using Dynamic Quantum-inspired Particle Swarm Optimization. Australian Journal of Intelligent Information Processing Systems, 11(1). Retrieved from http://cs.anu.edu.au/ojs/index.php/ajiips/article/viewArticle/1074
58. Pang, S., Song, L., & Kasabov, N. (2010). Correlation-aided support vector regression for forex time series prediction. Neural Computing & Applications, 1-11.
59. N.Kasabov, To spike or not to spike: A probabilistic spiking neural model, Neural Networks, Vol 23, 1, 2010, 16-19
60. S. Schlebs, M.Defoin-Platel, N.Kasabov, On The Probabilistic Optimization Of Spiking Neural Networks, Int. J. of Neural Systems, Vol. 20, No. 6 (2010) 481–500, World Scientific Publ.Comp.
61. S.Soltic, N.Kasabov, Knowledge extraction from evolving spiking neural networks with a rank order population coding, Int.J.Neural Systems, Vol. 20, No. 6 (2010) 437-445, World Scientific Publ.
62. 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.
63. Hisada, M., Ozawa, S., Zhang, K., & Kasabov, N. (2010). Incremental linear discriminant analysis for evolving feature spaces in multitask pattern recognition problems. Evolving Systems, 1(1), 17-27. doi:10.1007/s12530-010-9000-3.
64. S.Wysoski, L.Benuskova, N.Kasabov, Evolving Spiking Neural Networks for Audio-Visual Information Processing, Neural Networks, vol 23, issue 7, pp 819-835, September 2010.
65. N.Kasabov, Integrative Connectionist Learning Systems Inspired by Nature: Current Models, Future Trends and Challenges, Natural Computing, Int. Journal, Springer, Vol. 8, Issue 2, pp. 199-218, 2009
66. 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, v.16, 189-205, 2009
67. M. Defoin-Platel, S.Schliebs, N.Kasabov, Quantum-inspired Evolutionary Algorithm: A multi-model EDA, IEEE Trans. Evolutionary Computation, vol.13, No.6, Dec.2009, 1218-1232
68. Schliebs, Michael Defoin Platel, Susan Worner and Nikola Kasabov, Integrated Feature and Parameter Optimization for Evolving Spiking Neural Networks: Exploring Heterogeneous Probabilistic Models, Neural Networks, 22, 623-632, 2009.
69. Atkinson KR, Blumenstein M, Black MA, Wu SH, Kasabov N, Taylor RS, Cooper GJS, North RA (2009) An altered pattern of circulating apolipoprotein E3 isoforms is implicated in preeclampsia. J Lipid Res, 50:71-80.
70. N.Kasabov, Evolving Intelligence in Humans and Machines: Integrative Connectionist Systems Approach, Feature article, IEEE CIS Magazine, August, 2008, vol.3, Num.3, pp. 23-37
71. N.Kasabov, Adaptive Modelling and Discovery in Bioinformatics: The Evolving Connectionist Approach, International Journal of Intelligent Systems, vol.23 (2008) 545-555
72. S.Pang, N.Kasabov, Encoding and Decoding the Knowledge of Association Rules over SVM Classification Trees, Knowledge and Information Systems, Springer, London, vol. 19, no. 1, pp. 79-105, June 2008
73. Shimo, N., Pang, S., Kasabov, N., & Yamakawa, T. (2008). Curiosity-Driven Multi-Agent Competitive and Cooperative LDA Learning. International Journal of Innovative Computing, Information and Control, 4(7), 1537-1552. Retrieved from http://www.ijicic.org/07-186-1.pdf
74. S.Wysoski, L.Benuskova, N.Kasabov, Fast and Adaptive Network of Spiking Neurons for Multi-view Visual Pattern Recognition, Neurocomputing, Elsevier,vol.71, no.13-15, pp. 2563-2575, 2008.
75. Kasabov, N., V.Jain, L.Benuskova, Integrating brain-gene ontology with evolving connectionist system for modelling and knowledge discovery, Neural Networks, 21 (2008), 266-275
76. Zeke S. H. Chan, I.Havukkala, V.Jain, Y. Hu and N.Kasabov, Soft Computing Methods to predict Gene Regulatory Networks: An Integrative approach on Time-Series Gene Expression Data, Applied Soft Computing, V. 8,3, 2008,1189-1199.
77. 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, June 2008, 1061-1074,
78. L.Benuskova and N.Kasabov, Modelling Brain Dynamics Using Computational Neurogenetic Approach, Cognitive Neurodynamics, Springer, vol.2, Num.4, 319-334, December,2008
79. 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
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80. N.Kasabov, Global, local and personalised modelling and profile discovery in Bioinformatics: An integrated approach, Pattern Recognition Letters, Vol. 28, Issue 6 , April 2007, 673-685
81. Chan Zeke S.H., Lesley Collins and N. Kasabov Bayesian learning of sparse gene regulatory networks, Biosystems, Volume 87, Issues 2-3, February 2007, Pages 299-306
82. N.Kasabov, V. Jain, P. Gottgtroy, L. Benuskova, and F.Joseph, 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
83. Benuskova L, Kasabov N, 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
84. 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
85. 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, Jan. 15, 2007
86. 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, Vol. 70, Issues 1-3, December 2006, Pages 409-419
87. 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
88. Benuskova L, Jain V, Wysoski SG and Kasabov N, Computational neurogenetic modeling: a pathway to new discoveries in genetic neuroscience. Intl. Journal of Neural Systems, 16(3): 215-227, 2006.
89. 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
90. 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.
91. Song, Q., N. Kasabov, T. Ma, M. 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, 2006, Vol.36, pp 235-244.
92. 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
93. Ozawa, S., Shaoning Pang and Nikola Kasabov, Incremental learning of feature space and classifier for on-line pattern recognition, Int. J. of Knowledge based and Intelligent Engineering Systems, Volume 10, 2006, pp 57-65.
94. Chan, Z., Lesley Collins, N.Kasabov, An Efficient Greedy K-means Algorithm for Global Gene Trajectory clustering, Expert Systems with Applications: An Int.Journal. Volume 30, Issue 1, January 2006, Pages 137-141.
95. 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 Int. J., Vol 30, Issue 1, 2006, 59-63.
96. Tsankova, D., Georgieva, V., Kasabov, N., Artificial Immune Networks as a Paradigm for Classification and Profiling of Gene Expression Data, Journal of Comput. and Theoretical Nanoscience, Vol 2, N.4, 2005, 543-550(8)
97. Kasabov, N , I.A. Sidorov, D S Dimitrov, Computational Intelligence, Bioinformatics and Computational Biology: A Brief Overview of Methods, Problems and Perspectives, J. of Comput. and Theoretical Nanoscience, Vol.2, No 4, pp 473-491, 2005
98. Kasabov N and Boeva V (2005) Bioinformatics: Challenges and opportunities for information science and knowledge engineering, Information Technologies and Control, No.4, 11-18
99. Havukkala I, Pang S, Jain V, Kasabov N, Classifying MicroRNAs by Gabor Filter Features from 2D Structure Bitmap Images on a Case Study of Human microRNAs, Journal of Theoretical and Computational Nanoscience, Volume 2, No. 4, ppp 506-513, 2005
100. Kasabov, N., L.Benuskova, and S. 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
101. Chan S.H., Kasabov N., Fast Neural Network Ensemble Learning via Negative-Correlation Data Correction, IEEE Transaction on Neural Networks 2005, Volume 16, Issue 6, pp 1707-1710
102. Pang, S., S. Ozawa and N. Kasabov, Incremental Linear Discriminant Analysis for Classification of Data Streams, IEEE Trans. SMC-B, vol. 35, No. 5, 2005, 905 – 914
103. Goh, L, N.Kasabov, An integrated feature selection and classification method to select minimum number of variables on the case study of gene expression data, J. of Bioinformatics and Computational Biology, Imperial College Press and World Sci. Publ., vol.3, N. 5, pp 1107-1136, 2005
104. Song Q. and N. Kasabov, NFI: A Neuro-Fuzzy Inference Method for Transductive Reasoning, IEEE Transactions on Fuzzy Systems, Volume 13, Issue 6, pp 799-808, 2005.
105. Chan, Z. and N.Kasabov, A Preliminary Study on Negative Correlation Learning via Correlation-Corrected Data (NCCD), Neural Processing Letters, Springer, Volume 21, Issue 3, pp, 207-214, 2005
106. Ozawa, S., S.Too, S.Abe, S. Pang and N. Kasabov, Incremental Learning of Feature Space and Classifier for Online Face Recognition, Neural Networks, August, 2005, pp 575-584
107. Marshall, M.R. , Q. Song, T.M. Ma, S. MacDonell, N.Kasabov, Evolving Connectionist System versus Algebraic Formulae for Prediction of Renal Function from Serum Creatinine, Kidney International, vol. 67 (2005), 1944 – 1954
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108. Chan, S. Z. , 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 and World Scie. Publ., vol.3 No.5, pp 1227-1242, 2005
109. Kasabov, N. 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.
110. Chan S. and N.Kasabov, Efficient global clustering using the greedy elimination method, Electronic Letters, vol. 40, No. 25, 1611 - 1612, 2004,
111. Kasabov N. and S. Pang, Transductive Support Vector Machines and Applications in Bioinformatics for Promoter Recognition, Neural Information Processing - Letters and Reviews 3(2), KAIST Press, pp.31-38., 2004
112. Kasabov N. and L. Benuskova, Computational Neurogenetics, International Journal of Theoretical and Computational Nanoscience, Vol. 1 (1) American Scientific Publisher, 2004, 47-61.
113. Chan Z.and N.Kasabov, Evolutionary computation for on-line and off-line parameter tuning of evolving fuzzy neural networks, Int. J. of Computational Intelligence and Applications, Imperial College Press, vol. 4, N.3, 2004, 309-319
114. Futschik, M. , M. Sullivan, A. Reeve, N. Kasabov, Prediction of clinical behaviour and treatment of cancers, Applied Bioinformatics, vol.3, 2003, 553-558
115. Cohen, T., 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
116. Futschik, M., 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
117. Kasabov, N., Spoken Language Analysis, Modeling And Recognition – Statistical And Adaptive Connectionist Approaches, Preface to a Special Issue of Inform. Sciences 2003, Volume 156 Numbers 1-2
118. Laws, M., R. Kilgour and N. Kasabov, Modelling the emergence of bilingual acoustic clusters: a preliminary case study, Information Sciences, 156 (2003) 85-107
119. Abdulla W., and N. Kasabov, Reduced feature-set based parallel CHMM speech recognition systems, Information Sciences, 156 (2003) 23-38
120. Ghobakhlou A., M. Watts and N. Kasabov, Adaptive speech recognition with evolving connectionist systems, Information Sciences, 156 (2003) 71-83
121. Rizzi., L. , Flavio Bazzana, Nikola Kasabov, Mario Fedrizzi and Luca Erzegovesi (2003). Simulation of ECB decisions and forecast of short term Euro rate with an adaptive fuzzy expert system. European Journal of Operational Research. 145 (2003) 363-381
122. Deng, D., N. Kasabov, On-line pattern analysis by evolving self-organising maps, Neurocomputing , 51, 2003, 87-103.
123. 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.
124. 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, December (2001) 902-918.
125. Kasabov, N., Artificial Neural Networks for Intelligent Information Processing, Transactions of Chemical Engineering, London, June 2001, 27-28.
126. 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, 2, April, (2002) 144-154
127. Kasabov, N. On-line learning, reasoning, rule extraction and aggregation in locally optimised evolving fuzzy neural networks, Neurocomputing, 41 (2001) 25-41
128. 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
129. Kasabov, N., Israel, S., and Woodford, B.J., Hybrid evolving connectionist systems for image classification, Journal of Advanced Computational Intelligence, vol.4, 1, (2000) 57-65
130. 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, (2000) 127-148
131. Kasabov, N., and Kozma, R., Methods and systems for intelligent human computer interaction – Editorial, Information Sciences, vol. 123 (2000) 1-2
132. Brown, C., Jacobs, G., M.Schreiber, J.Magnum, J.McNaughton, M.Cambray, M.Futschik, L.Major, O.Rackham, W.tate, P.Stockwell, C.Thompson, and N.Kasabov, Using bioinformatics to investigate post-trascriptional control of gene expression, NZ Bio Science, vol. 7, 4 (2000)11-12
133. Kim, J.S. and Kasabov, N. HyFIS: adaptive neuro-fuzzy systems and their application to non-linear dynamical systems, Neural Networks, vol. 12, 9 (1999) 1301-1319
134. 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, vol.130, 2 (1999) 349-367
135. Purvis, M., Kasabov, N., Benwell, G., Zhou, Q., and Zhang, F. Neuro-fuzzy methods for Environmental Modelling, System Research and Information Systems, vol.8, 4 (1999) 221-239
136. 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, vol.5, 3 (1998) 154-160
137. Kasabov, N. Connectionist-based information systems: Methods and applications (Guest editorial), Australian Journal of Intelligent Information Processing Systems, vol.5, 3 (1998) 153
138. 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, vol.4, 27 (1998) 594-611
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139. Kasabov, N. The ECOS framework and the 'eco' training method for evolving connectionist systems. Journal of Advanced Computational Intelligence vol.2, No.6, (1998) 195-202
140. Kasabov, N. and Kozma, R. Self-organisation and adaptation in intelligent systems – preface, Journal of Advanced Computational Intelligence vol.2, No.6, (1998) 177
141. Kasabov, N. and Kozma, R. Hybrid intelligent adaptive systems: a framework and a case study on speech recognition, International Journal of Intelligent Systems vol.13, 6 (1998) 455-466
142. Kasabov, N. and Kozma, R. Introduction: Hybrid intelligent adaptive systems. International Journal of Intelligent Systems vol.13, 6 (1998) 453-454
143. Kozma, R., Kasabov, N., Kim, J. and Cohen, T. Integration of connectionist methods and chaotic time series analysis for the prediction of environmental process data. Int. Journal of Intelligent Systems vol.13, 6 (1998) 520-538
144. Kasabov, N. Fuzzy neural networks, rules extraction and fuzzy synergistic reasoning. Systems Research and Information Systems 8, 45-59 (1998)
145. Israel, S. and Kasabov, N. Statistical, connectionist and fuzzy inference techniques for image classification. Journal of Electronic Imaging 6 (3):1-11 (1997)
146. 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)
147. Kasabov, N. and Hirota, K. Special issue on advanced neuro-fuzzy techniques and their applications: introduction. Information Sciences 101(3-4): 153-154 (1997)
148. 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)
149. 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)
150. Kasabov, N. Adaptable connectionist production systems. Neurocomputing 13(2-4):95-117 (1996)
151. 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)
152. Kasabov, N. Learning fuzzy rules and approximate reasoning in fuzzy neural networks and hybrid systems. Fuzzy Sets and Systems 82(2):2-20 (1996)
153. 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)
154. 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)
155. 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)
156. Kasabov, N. Hybrid connectionist fuzzy systems for speech recognition. Lecture Notes in Computer Science/ Artificial Intelligence 1011:19-33 (1995)
157. Kasabov, N. Hybrid Connectionist Fuzzy Production Systems - Towards Building Comprehensive AI. Intelligent Automation and Soft Computing 1(4): 351-360 (1995)
158. Kasabov, N. Connectionist fuzzy production systems. Lecture Notes in Computer Science/ Artificial Intelligence 847:114-128 (1994)
159. Kasabov, N. Hybrid connectionist production systems. Journal of Systems Engineering 3(1): 15-21 (1993)
160. 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)
161. 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)
162. Kasabov, N. Neural networks and genetic algorithms. Avtomatika i Informatika, 8/9:51-60 (1990) (in Bulgarian)
163. Kasabov, N. and Nikolaev, N. Parallel production systems. Avtomatika i Informatika, 7:37-45 (1990) (in Bulgarian)
164. Kasabov, N. Functionally reconfigurable general purpose parallel machines and some image processing and pattern recognition applications. Pattern Recognition Letters, 3:215-223 (1985)
165. Kasabov, N. A method for SIMD/MIMD functionally reconfigurable multi-microprocessor system design and parallel data exchange algorithms. Parallel Computing, 2:73-78 (1985)
166. 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)
167. Kasabov, N. A multi- microprocessor system with a functional reconfiguration and parallel computations. Avtomatika i Ischislitelna Technika, 1:38-46 (1983) (in Bulgarian)
168. 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)
169. Kasabov, N. Register commutation structures and algorithms for data exchange in multi-microprocessor systems. Avtomatika i Ischilitelna Technika, 5:17-24 (1983) (in Bulgarian)
170. 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)
171. 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)
172. 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)
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173. Kasabov, N., Method and algorithm for permutation of data records, Systemi i Upravlenie, Bulgaria, 1: 39 – 43 (1981) (in Bulgarian)
174. Kasabov, N. and Bidjev, G. Minimal representation of the symmetrical group close to the compact one. Cybernetika, (translated in English as “Cybernetics”), 3:135-136 (1980) (in Russian)
175. Kasabov, N., and Dakovski, L. Program and algorithm for the generation of algebraic transformations. Systemi i Upravlenie, 4: 25-28 (1979) (in Bulgarian)
176. Kasabov, N. Generating the symmetrical semi-group and the symmetrical group by using generating systems with excess, University Annual Applied Mathematics XIV(1):35-42 (1978) Sofia (in Bulgarian)
177. Bijev, G. and Kasabov, N. On effective representations of classes of transformations and their finite automata interpretation. University Annual Applied Mathematics XIV(1):57-62 (1978) Sofia (in Bulgarian)
178. Kasabov, N. On the problem of generating the symmetrical group. University Annual Applied Mathematics X (3): 55-59 (1974) Sofia (in Bulgarian)


Major Reviews

1. Kasabov, N., Connectionist-based information systems, Report on a FRST funded project UOO606, University of Otago (1998) 600pages
2. Kasabov, N. and Watson, C. Automatic Speech Recognition: methods, Tools and Their Application for Communication and Intelligent Information Systems, Report for TELECOM NZ, Department of Information Science, University of Otago, 1994, 100 p
3. Kasabov, N. Connectionist knowledge based expert systems. in: Connectionism & AI. P.Braspenning, J.Taylor, P.Gallinary and N.Kasabov (eds) Lecture Notes of the Summer School "ISAI'90", Albena, Bulgaria (1990) 364-402
4. Andriesen, H. and Kasabov, N. Interconnection Strategies for Tightly Coupled Multi-processor Systems, Technical Report 85-10, Depart. of Mathematics and Informatics, Delft University of Technology, The Netherlands (1984) 20p


Publications in Refereed Conference Proceedings

1. Wu H, Gao L, Kasabov N. Inference of cancer progression from somatic mutation data [J]. SYSID2015, IFAC-PapersOnLine, 2015, 48(28): 234-238
2. 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.
3. 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.
4. 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.
5. 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.
6. 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
7. E. Capecci, F. Carlo Morabito, M. Campolo, N. Mammone, D. Labate, and Nikola Kasabov, A Feasibility Study of Using the NeuCube Spiking Neural Network Architecture for Modelling Alzheimer’s Disease EEG Data, Springer International Publishing Switzerland 2015 159 S. Bassis et al. (eds.), Recent Advances of Neural Networks Models and Applications, Smart Innovation, Systems and Technologies 37, DOI: 10.1007/978-3-319-18164-6_16.
8. E. Tu, J. Yang, and N. Kasabov, Posterior Distribution Learning (PDL): A Novel Supervised Learning Framework Including Unlabeled Samples Distribution into Decision, Proc. ICONIP 2014 Kuching, November, Springer LNCS, 2014.
9. N. Murli, N. Kasabov, and B. Handaga, Classification of fMRI Data in the NeuCube Evolving Spiking Neural Network Architecture, Proc. ICONIP 2014, Springer LNCS, 2014..
10. M. G. Doborjeh, E. Capecci and N. Kasabov, Classification and Segmentation of fMRI Spatio-Temporal Brain Data with a NeuCube Evolving Spiking Neural Network Model, Proc. SSCI, IEEE Press, 2014.
11. E. Tu, N. Kasabov, M.Othman, Y. Li, S.Worner, J.Yang and Z. Jia, NeuCube(ST) for Spatio-Temporal Data Predictive Modelling with a Case Study on Ecological Data, Proc. WCCI 2014, Beijing, 7-13 July 2014, IEEE Press.
12. D. Taylor, N.Scott, N. Kasabov, E.Capecci, E. Tu, N. Saywell, Y. Chen, J.Hu and Z.Hou, Feasibility of NeuCube SNN architecture for detecting motor execution and motor intention for use in BCI applications, Proc. WCCI 2014, Beijing, 7-13 July 2014, IEEE Press.
13. R. Hartono, R. Pears, N. Kasabov and S. Worner, Extracting Temporal Knowledge from Time Series: A Case Study in Ecological Data, Proc. WCCI 2014, Beijing, 7-13 July 2014, IEEE Press.
14. M. Othman, N.Kasabov, E.Tu, V. Feigin, R.Krishnamurthi, Z.Hou, Y. Chen and J.Hu, Improved Predictive Personalized Modelling with the use of Spiking Neural Network System and a Case Study on Stroke Occurrences Data, Proc. WCCI 2014, Beijing, 7-13 July 2014, IEEE Press.
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15. Hu, J., Hou, Z., Chen, Y., Kasabov, N., & Scott, N. (2014). 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
16. N. Kasabov, J.Hu, Y. Chen, N.Scott, and Y. Turkova, Spatio-temporal EEG data classification in the NeuCube 3D SNN Environment: Methodology and Examples, Proc. ICONIP 2013, Springer LNCS, vol.8228, pp.63-69.
17. Y.Chen, J.Hu, N.Kasabov, Z. Hou and L.Cheng, NeuroCubeRehab: A Pilot Study for EEG Classification in Rehabilitation Practice Based on Spiking Neural Networks, Proc. ICONIP 2013, Springer LNCS, vol.8228, pp.70-77.
18. N. Scott, N. Kasabov, and G.Indiveri, NeuCube Neuromorphic Framework for Spatio-Temporal Brain Data and Its Python Implementation, Proc. ICONIP 2013, Springer LNCS, vol.8228, pp.78-84..
19. S.Schliebs, E.Capecci, and N.Kasabov, A spiking neural network reservoir model for on-line cognitive activity classification based on EEG data, Proc. ICONIP 2013, Springer LNCS, vol.8228, pp.55-62.
20. Zhou, L., Gong, C., Li, Y., Qiao, Y., Yang, J., & Kasabov, N. (2013). Salient Object Segmentation Based on Automatic Labeling. In ICONIP 2013, Daegu, Korea, Springer LNCS, vol.8228, 584-590,.
21. Kasabov, N., NeuCube EvoSpike Architecture for Spatio-Temporal Modelling and Pattern Recognition of Brain Signals, in: Mana, Schwenker and Trentin (Eds) ANNPR, Springer LNAI 7477, 2012, 225-243.
22. Mohemmed, A., Guoyu Lu, N. Kasabov, Evaluating SPAN incremental Learning for Handwritten Digit Recognition, T. Huang et al. (Eds.): ICONIP 2012, Part III, Springer LNCS 7665, pp. 670–677, 2012.
23. Kasabov, N. and Schliebs, S. and Mohemmed, A. Modeling the Effect of Genes on the Dynamics of Probabilistic Spiking Neural Networks for Computational Neurogenetic Modeling, Proc. 8th International Meeting on Computational Intelligence Methods for Bioinformatics and Biostatistics, Gargnano-Lago di Garda, Italy, 30 June, 2011, Springer LNBI 7548, pp.1-9, 2012.
24. Schliebs, S. and M. Fiasch´e and N. Kasabov, Constructing robust Liquid State Machines to process highly variable data streams, Proc. ICANN 2012, Lausanne, 11-14 September, 2012, Springer LNCS 7552, 604-611, 2012.
25. 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
26. 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
27. 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
28. 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
29. 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
30. 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
31. 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.
32. 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.
33. 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.
34. 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
35. 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
36. 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
37. 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
38. 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
39. Mohemmed, A., Schliebs, S., Matsuda, S., & Kasabov, N. (2011, September 15). Method for training a spiking neuron to associate input-output spike trains. In EANN/AIAI 2011, Part I, IFIP AICT 363, pp. 219--228. IFIP International
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Federation for Information Processing (2011) (pp. 219-228). Greece. Retrieved from http://delab.csd.auth.gr/eann2011/index.html
40. Schliebs,S., Nuntalid, N., & Kasabov, N. (2010). Towards spatio-temporal pattern recognition using evolving spiking neural networks. Proc. ICONIP 2010, Part I, Lecture Notes in Computer Science (LNCS), 6443, 163-170.
41. Nuzly, N.Kasabov, S.Shamsuddin (2010) Probabilistic Evolving Spiking Neural Network Optimization Using Dynamic Quantum Inspired Particle Swarm Optimization, Proc. ICONIP 2010, Part I, LNCS, vol.6443.
42. Hamed, H., Kasabov, N., & Shamsuddin, S. (2010). Dynamic Quantum-inspired Particle Swarm Optimization as Feature and Parameter Optimizer for Evolving Spiking Neural Networks. In Proc. ICCSM 2010. Manila.
43. Stefan Schliebs, Michael Defoin-Platel and Nikola Kasabov, Analyzing the Dynamics of the Simultaneous Feature and Parameter Optimization of an Evolving Spiking Neural Network, Proc. IJCNN, Barcelona, July 2010, IEEE Press, 933-940). doi:10.1109/IJCNN.2010.5596727
44. Shaoning Pang, Tao Ban, Youki Kadobayashi and Nikola Kasabov, Incremental and Decremental LDA Learning with Applications, Proc. IJCNN, Barcelona, July 2010, IEEE Press, 1-8
45. N. Gunasekara, S. Pang, N. Kasabov, “Tuning N-gram String Kernel SVMs via Meta Learning,” Proc. of ICONIP2010, Springer, Nov. 2010
46. Y. Chen, S. Pang, N. Kasabov, “Factorizing Class Characteristics via Group MEBs Construction,” Proc. of ICONIP2010, Springer, Nov. 2010, 283-290
47. 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
48. Pang, S. Ban, T. Kadobayashi Y. and Kasabov, N., Spanning SVM Tree for Personalized Transductive Learning, Proc. of ICANN 2009, Part I, LNCS 5768, pp. 913-922, 2009, Springer.
49. Chen, Y. Pang, S. Kasabov, N. Ban, T. and Kadobayashi, Y Hierarchical Core Vector Machines for Network Intrusion Detection, Proc. of ICONIP 2009, Part II, LNCS 5864, pp. 520-529, 2009.
50. Pang, S. Dhoble , K. Chen, Y. Kasabov, N. Ban, T. and Kadobayashi, Y. (2009) Active Mode Incremental Nonparametric Discriminant Analysis Learning. Proc. of the Eighth International Conference on Information and Management Sciences, 407-412 July 2009 Kunming, China.
51. Ozawa, S., Kawashima, Y., Pang, S., & Kasabov, N. (2009). Adaptive incremental principal component analysis in nonstationary online learning environments.. In IJCNN (pp. 2394-2400). Atlanta, Georgia: IEEE. doi:10.1109/IJCNN.2009.5178997
52. 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.
53. N. Kasabov, Integrative Probabilistic Evolving Spiking Neural Networks Utilising Quantum Inspired Evolutionary Algorithm: A Computational Framework, in: M. Koeppen, N. Kasabov, G. Goghill (eds) Advances in neural information processing, Proc. of ICONIP 2008, Auckland, Springer LNCS-5506, 3-13, 2009
54. Hu, Y., & Kasabov, N. (2009). Coevolutionary Method for Gene Selection and Parameter Optimization in Microarray Data Analysis. In C. S. Leung, M. Lee, & J. H. Chan (Eds.), Neural Information Processing Lecture Notes in Computer Science (pp. 483-492). Heidelberg, Germany: Springer. doi:10.1007/978-3-642-10684-2_54
55. Fiasché, M., Verma, A., Cuzzola, M., Iacopino, P., Kasabov, N., & Morabito, F. C. (2009). 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) Vol. 5769 LNCS (pp. 10-19). doi:10.1007/978-3-642-04277-5_2
56. Hamed, H. N. A., Kasabov, N., & Shamsuddin, S. M. (2009). Integrated feature selection and parameter optimization for evolving spiking neural networks using quantum inspired particle swarm optimization. In SoCPaR 2009 - Soft Computing and Pattern Recognition (pp. 695-698). Malacca, Malaysia. doi:10.1109/SoCPaR.2009.139
57. Verma, A., Fiasche, M., Cuzzola, M., Iacopino, P., Morabito, F., & Kasabov, N. (2009). Ontology Based Personalized Modeling for Type 2 Diabetes Risk Analysis: An Integrated Approach. In Proc. of ICONIP 2009, Part II, LNCS Vol. 5864 (pp. 360-366). Bangkok, Thailand.
58. 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 (eds) Advances in neural information processing, Proc. of ICONIP 2008, Auckland, Springer LNCS-5506, 962-969, 2009
59. 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 (eds) Advances in neural information processing, Proc. of ICONIP 2008, Auckland, Springer LNCS-5506, 1163-1171, 2009
60. 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
61. Widiputra H, Pears R, and Kasabov N (2009) “A Novel Evolving Clustering Algorithm with Polynomial Regression for Chaotic Time-Series Prediction”. Proceedings of the 16th International Conference on Neural Information Processing of the Asia-Pacific Neural Network Assembly, 114-121.
62. 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 (eds) Advances in neural information processing, Proc. of ICONIP 2008, Auckland, Springer LNCS-5506, 1204-1210, 2009
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63. Y. Hu, Q. Song, K. Kasabov, Personalized Modeling Based Gene Selection for Microarray Data Analysis, in: M. Koeppen, N. Kasabov, G. Goghill (eds) Advances in neural information processing, Proc. of ICONIP 2008, Auckland, Springer LNCS-5506, 1221-1228, 2009
64. 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 (eds) Advances in neural information processing, Proc. of ICONIP 2008, Auckland, Springer LNCS-5506, 1229-1236, 2009
65. Widiputra H, Pears R, Kasbov N, "Personalised Modelling for Multiple Time-Series Data Prediction", 15th Int. Conference on Neural Information Processing ICONIP, 2008, 1237-1244.
66. S.Soltic, S.Wysoski and N.Kasabov, Evolving spiking neural networks for taste recognition, Proc.WCCI 2008, Hong Kong, IEEE Press, 2008
67. Ozawa, S., Matsumoto, K., Pang, S., & Kasabov, N. (2008). An incremental principal component analysis based on dynamic accumulation ratio. In Proceedings of the SICE Annual Conference (pp. 2471-2475).
68. Kasabov, N. (2008). 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. doi:10.1007/978-3-540-69139-6
69. Kasabov, N. (2008). 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. doi:10.1007/978-3-540-69139-6
70. Kasabov, N., & Benuskova, L. (2008). 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. doi:10.1007/978-1-4020-8387-7_1
71. 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). Retrieved from http://www.wseas.us/e-library/conferences/2008/sofia/NN/nn11.pdf
72. Pang, S., Ban, T., Kadobayashi, Y., & Kasabov, N. (2008). 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. Retrieved from http://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=04803112
73. Kasabov, N., Jain, V., & Benuskova, L. (2008). Integrating evolving brain-gene ontology and connectionist-based system for modeling and knowledge discovery. In Neural Networks Vol. 21 (pp. 266-275). doi:10.1016/j.neunet.2007.12.041
74. P.Hwang, Q.Song, N.Kasabov, Multifunctional neuro-fuzzy inference systems, Proc. WCCI 2008, Hong Kong, IEEE Press, 2008
75. Y Hu, N Kasabov, Ontology-Based Framework for Personalized Diagnosis and Prognosis of Cancer Based on Gene Expression Data, ICONIP2007, Japan, 13-16.11. 2007, LNCS, Part II, 4985, pp. 846-855, Springer, 2008
76. 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
77. Seiichi Ozawa, Shaoning Pang, Nikola Kasabov, Adaptive Face Recognition System Using Fast Incremental Principal Component Analysis, ICONIP2007, Japan, 13-16.11.2007, LNCS, Part II, 4985, 396-405, Springer, 2008
78. Wysoski, S., L Benuskova and N. Kasabov, Adaptive Spiking Neural Networks for Audiovisual Pattern Recognition, ICONIP2007, Japan, 13-16 November 2007, LNCS, , Part II, pp.406-415 Springer, 2007
79. 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. doi:10.1109/ICCIMA.2007.111
80. Pang, S., & Kasabov, N. (2008). 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. doi:10.1109/IJCNN.2008.4634145
81. Kasabov, N. (2007). Evolving Connectionist and Hybrid Systems: Methods, Tools, Applications.. In HIS (pp. 3). Germany: IEEE Computer Society.
82. S Wysoski, L Benuskova, N Kasabov, Text-independent Speaker Authentication with Spiking Neural Networks, Proc. ICANN 2007, Porto, LNCS, Springer, 2007
83. M.Defoin-Platel, S.Schliebs, N.Kasabov, A versatile quantum inspired evolutionary algorithm, Proc. IEEE Congress on Evolutionary Computation, IEEE Press, 2007.
84. N Kasabov, VJain, P Gottgtroy, L Benuskova, S Wysoski, Frances Joseph, Evolving Brain-Gene Ontology System (EBGOS): towards Integrating Bioinformatics and Neuroinformatics Data to facilitate Discoveries, F International Joint Conference on Neural Networks, IJCNN, 2007, Orlando, IEEE Press, 2007
85. Wysoski, S. G., Benuskova, L., & Kasabov, N. (2006). Adaptive learning procedure for a network of spiking neurons and visual pattern recognition. In Lecture Notes in Computer Science Vol. 4179 (pp. 1133-1142). Antwerp, Belgium.
86. Ozawa, S., Pang, S., & Kasabov, N. (2006). An incremental principal component analysis for chunk data. In IEEE International Conference on Fuzzy Systems (pp. 2278-2285). Vancouver. Retrieved from http://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=01682016
87. Kasabov, N. (2006). 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. doi:10.1007/978-1-84628-226-3_1
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88. Kasabov, N., Brain-, gene-, and quantum inspired computational intelligence:, in: B.Reusch (ed) Challenges and opportunities in Computational Intelligence, Theory and Practice, Advances in Soft Computing, Springer, 521-544, 2006
89. Kasabov, N., Neuro-, genetic-, and quantum inspired evolving intelligent systems, Proc. 2006 Int. Symposium on Evolving Fuzzy Systems, September 2006, UK, IEEE Press, 63-73, 2006
90. Kasabov, N., Filev, D., Evolving intelligent systems, Proc. 2006 Int. Symposium on Evolving Fuzzy Systems, September 2006, Lake District, UK, IEEE Press, 8-18, 2006
91. L. Benuskova, S. Wysoski, and N. Kasabov, Computational neuro-genetic modelling: A methodology to study gene interactions underlying neural oscillations, Proc. IJCNN 2006, IEEE Press, 2006, 4638,4644
92. Pang, Shaoning, Ilkka Havukkala, Nikola Kasabov, Two-Class SVM Trees (2-SVMT) for Biomarker Data Analysis, Lecture Notes in Computer Science, Volume 3973/2006, pp 629-634
93. Pang, Shaoning, Nikola Kasabov, Investigating LLE Eigenface on Pose and Face Identification, Lecture Notes in Computer Science, Volume 3972/2006 pp 134-139
94. Wysoski, S. L. Benuskova and N. Kasabov (2006) On-line learning with structural adaptation in a network of spiking neurons for visual pattern recognition, in: Artificial Neural Networks - ICANN 2006, LNCS 4131, 61-70
95. 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, Pages 197-204, 2006
96. Ilkka Havukkala, Lubica Benuskova, Shaoning Pang, Vishal Jain, Rene Kroon and Nikola Kasabov, Image and Fractal Information Processing for Large-Scale Chemoinformatics, Genomics Analyses and Pattern Discovery, Pattern Recognition in Bioinformatics, Lecture Notes in Computer Science, Volume 4146/2006, Pages 163-173, 2006
97. Song Q, Ma T, Kasabov N, Transductive Knowledge Based Fuzzy Inference System For Personalised Modeling, IFSA 2005, Beijing, pp 1097-1100
98. 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
99. Pang, S. , Seiichi Ozawa, Nikola Kasabov, Chunk Incremental LDA Computing on Data Streams, Lecture Notes in Computer Science, Volume 3497, Jan 2005, pp.51-56
100. Chan, S. H. , Collins, L. , Kasabov, N. Global K Means 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 pp 405-415
101. 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
102. 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
103. Kasabov, N. , L. Benuskova L and Wysoski SG (2005) Computational neurogenetic modeling: integration of spiking neural networks, gene networks, and signal processing techniques. In: ICANN 2005, LNCS 3697, W. Duch et al (Eds), Springer-Verlag, Berlin Heidelberg, pp. 509-514.
104. T. Ma, Q Song, M.R. Marshall, N Kasabov, TWNFC-Transductive Neural-Fuzzy Classifier with Weighted Data Normalization and Its Application in Medicine, CIMCA 2005, Austria
105. Q. Song, T.M. Ma, N.Kasabov, Transductive Knowledge Based Fuzzy Inference System for Personalized Modelling, L.Wang and Y.Lin (eds): FSKD 2005, LNAI 3614, Springer-Verlag, Berlin- Heidelberg, 2005, 528 – 535.
106. N. Kasabov, Global, Local and Personalised Modeling and Pattern Discovery in Bioinformatics: An Integrated Approach, Proc. IEEE Int. Workshop on Soft Computing Applications - SOFA, 2005, Szeged-Arad, 2005, 56-67
107. Kasabov, N., L.Benuskova, S.Wysoski, A Computational Neurogenetic Model of a Spiking Neuron, IJCNN 2005 Conf. Proc., IEEE Press, 2005, Vol. 1, 446-451
108. Mohan, N. and N. Kasabov, Transductive Modelling with GA parameter optimisation, IJCNN 2005 Conf. Proceed., IEEE Press, 2005, Volume 2, pp 839-844
109. Huang, L., Song, Q., Kasabov, N., Evolving Connectionist Systems Based Role Allocation of Robots for Soccer Playing, Joint 2005 International Symposium on Intelligent Control & 13th Mediterranean Conference on Control and Automation (2005 ISIC-MED), June 27-29, 2005, Limassol, Cyprus
110. Angelov, P., N. Kasabov, Evolving Computational Intelligence Systems, In: (R. Alcala et al Eds.) Proc. of the I Workshop on Genetic Fuzzy Systems, Granada, March 17-19, 2005, pp.76-82, ISBN 84-689-1117-8
111. Kasabov, N. D Zhang, P S Pang, Incremental Learning in Autonomous Systems: Evolving Connectionist Systems for On-line Image and Speech Recognition, 2005 IEEE Workshop on Advanced Robotics and Social Impacts, 120-125
112. Pang, S., N Kasabov, Inductive vs. Transductive Inference, Global vs. Local Models: SVM, TSVM and SVMT for Gene Expression , Proc. IEEE , IJCNN 2005
113. Kasabov, N., L.Benuskova, S.Wysoski, Computational Neurogenetic Modelling: Integration of spiking neural networks, gene networks, and signal processing techniques, Proc. IEEE Workshop on Biomedical Applications of Circuits and Systems, Singapore, 1-3 December 2004, IEEE Press
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114. Kasabov, N. , Z. S.H. Chan, Igor Sidorov and Dimiter Dimitrov, Gene Regulatory Network Discovery for Time Series Gene Expression Data – A Computational Intelligence Approach, Lecture Notes in Computer Science, Vol.3316, 2004, Springer Verlag, 1344-1353.
115. Chan, Z.S., N.Kasabov, and L. Collins, A two-stage methodology for gene regulatory network extraction from time-course gene expression data, Proc. IEEE Workshop on Biomedical Applications of Circuits and Systems, Singapore, 1-3 December 2004, IEEE Press
116. Ozawa, S., Shaoning Pang and Nikola Kasabov On-line Feature Selection for Adaptive Evolving Connectionist Systems, Fuzzy Systems & Innovation Computing, Kitakyushu Japan, 2004
117. Zhang, D. N. Kasabov, A. Ghobakhlou An Adaptive Model of Person Identification Combining Speech and Image Information, in ICARCV 2004, Kunming, China
118. Gottgtroy P., Kasabov N. and MacDonell S., An ontology driven approach for knowledge discovery in Biomedicine, in: Proceedings of the Third Brazilian Symposium on Mathematical and Computational Biology Volume 1, R.Modaini (ed), Brazil, 2004
119. Gottgtroy P., Kasabov N. and MacDonell S., Building Evolving Ontology Maps for Data Mining and Knowledge Discovery, in: Proc. Pacific Rim International Conference on Artificial Intelligence, PRICAI, Auckland, August, 2004
120. Song, Q., 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.
121. Chan Z.S., and N. Kasabov, Gene Trajectory Clustering with a Hybrid Genetic Algorithm and Expectation Maximization Method, in: Proc. International Joint Conference on Neural Networks, IJCNN 2004, Budapest, 16-30 June 2004, IEEE Press
122. Pang S. and N. Kasabov, Inductive vs Transductive Inference, Global vs Local Models: SVM, TSVM, and SVMT for Gene Expression Classification Problems, in Proc. International Joint Conference on Neural Networks, IJCNN 2004, Budapest, 16-30 June 2004, IEEE Press
123. Q. Song and N. Kasabov, WDN-RBF: Weighted Data Normalization for Radial Basic Function Type Neural Networks, in: Proc. International Joint Conference on Neural Networks, IJCNN 2004, Budapest, 16-30 June 2004, IEEE Press.
124. N. Kasabov, L. Benuskova and S. G. Wysoski, Computational Neurogenetic Modelling: Gene Networks within Neural Networks, in: Proc. International Joint Conference on Neural Networks, IJCNN 2004, Budapest, 16-30 June 2004, IEEE Press
125. L.Goh, Q. Song and N. Kasabov, A Novel Feature Selection Method to Improve Classification of Gene Expression Data, in: Proc. Second Asia-Pacific Bioinformatics Conference (APBC 2004), Dunedin, 18-22nd January 2004, Australian Computer Science Communications, Volume 26, Number 4 (161-166)
126. Soltic, S. , S.Pang, N.Kasabov, S. Worner and L.Peacock, Dynamic Neuro-fuzzy Inference and Statistical Models for Risk Analysis of Pest Insect Establishment, Lect. Notes of Computer Science, vol. 3316, Springer, 2004, 971-976.
127. Ghobakhlou, A., D. Zhang and N. Kasabov An Evolving Neural Network Model for Person Verification Combining Speech and Image, Lecture Notes of Computer Science, vol. 3316, Springer, 2004, 381-386.
128. Song Q. , and N. Kasabov, TWRBF – transductive RBF Neural Network with Weighted Data Normalization, Lecture Notes in Computer Science, Vol.3316, Springer Verlag, 2004, 633-640.
129. 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 ANZIIS, Sydney, Australia Dec. 2003, 285-290.
130. 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 ANZIIS, Sydney, Australia, Dec. 2003, 241-245
131. N. Kasabov, S. Pang, Transductive Support Vector Machines And Applications In Bioinformatics For Promoter Recognition, in: Proc. IEEE International Conference on Neural Networks and Signal Processing, Nanjing, China, Dec. 2003 (1-6), IEEE Press.
132. 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.
133. 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.
134. 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)
135. 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)
136. 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
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137. N.Kasabov, Q.Song, I.Nishikawa, Evolutionary Computation for Dynamic Parameter Optimisation of Evolving Connectionist Systems for On-line Prediction of Time Series with Changing Dynamics, Proc. of the International Joint Conference on Neural Networks, IJCNN 03, Portland, Oregon, July 2003 (438-443)
138. L.Goh, N. Kasabov, Integrated Gene Expression Analysis of Multiple Microarray Data Sets Based on a Normalization Technique and on Adaptive Connectionist Model , Proc. of the International Joint Conference on Neural Networks, IJCNN 03, Portland, Oregon, July 2003 (1724-1728)
139. N.Kasabov, G.Venkov, S.Minchev, Neural Systems for Solving the Inverse Problem of Recovering the Primary Signal Waveform in Potential Transformers, Proc. of the International Joint Conference on Neural Networks, IJCNN 03, Portland, Oregon, July 2003 (2124-2129)
140. 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)
141. 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)
142. N. Kasabov and Song, Q. GA-Optimisation of evolving connectionist systems for classification with a case study from bio-informatics, Proc. of ICONIP’2002, Singapore, November, IEEE Press (2002)
143. Kasabov, N. 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)
144. N. Kasabov, Evolving connectionist systems for dynamic modelling and knowledge discovery: methods, tools, applications, IEEE Int. Symposium on Intelligent Systems, St Konstantin, Bulgaria, Sept. 2002, IEEE Press.
145. Futschik, M. and N. Kasabov, Fuzzy clustering of gene expression data, Proc. of World Congress of Computational Intelligence WCCI’2002, Hawaii, 12-17 May, IEEE Press (2002)
146. Watts, M. and N. Kasabov, Evolutionary optimisation of evolving connectionist systems, Proc. of World Congress of Computational Intelligence WCCI’2002, Hawaii, 12-17 May, IEEE Press (2002)
147. 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 (2001) 175-178
148. 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, Proc. of the Atlantic Symposium on Computational Biology, Genome Information Systems and Technology, eds. C.Wu, P.Wang, and J.Wang, 15-17 March 2001, Durham, North Carolina, USA (2001) 240-244
149. D. Deng and N. Kasabov, An evolving localised learning model for on-line image colour quantisation, Proc. Inter. Conf. on Image Processing 2001, Thessaloniki, Greece, Oct. 2001, 906-909
150. Woodford, B.J. and Kasabov, N.K. Ensembles of EFuNNs: An architecture for a multi module classifier. Proceedings of FUZZ-IEEE 2001 - The 10th IEEE International Conference on Fuzzy Systems. IEEE Press, Melbourne, 2-5 December (2001) 441-445
151. Deng, D., and Kasabov, N., Evolving Localised Learning for On-Line Colour Image Quantisation, Proceedings of the International Conference on Vision Computing, November 2000, Hamilton, New Zealand (2000) 186-191
152. Kasabov, N., Evolving Connectionist Systems – a Symbiosis of Learning and Evolution, Proceedings of ICONIP’2000, November 14-18, 2000, Taejon, Korea, 676-680
153. Ghobakhlou, A., Watts, M., and Kasabov, N., On-Line Expansion of Output Space in Evolving Fuzzy Neural Networks Proceedings of ICONIP’2000, November 14-18, 2000, Taejon, Korea, 891-896
154. 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
155. 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, (2000) 696-702.
156. Koprinska, I., and Kasabov, N., Evolving Fuzzy Neural Network for Camera Operations Recognition Proceedings of the International Conference on Pattern Recognition, Sept. 2000, ICPR, Barcelona Vol. II, 523-526.
157. Deng, D., and Kasabov, N., ESOM: An Algorithm to Evolve Self-Organizing Maps from On-Line Data Streams. In: Shun-Ichi Amari, C. Lee Giles, Marco Gori, Vincenzo Piuri (eds) Proceedings of the IJCNN’2000: New Challenges and Perspectives for the New Millennium, Como, Italy, July 24-27, 2000 Vol. VI, 3-8
158. 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.
159. 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), (2000) 301-307.
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160. 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., (2000) 844-848.
161. 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)
162. Abdulla, W. and Kasabov, N., Parallel CHMM speech recognition systems, Proceedings of Joint Conference of Information Sciences (JCIS), Atlantic City, New Jersey, February (2000)
163. Abdulla, W., and Kasabov, N., Speech Recognition Enhancement via Robust CHMM Speech Background Discrimination, 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) (1999) 65-70
164. Iliev, G., and Kasabov, N., Adaptive Filtering with Averaging in Noise Cancellation for Voice and Speech Recognition, 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) (1999) 71-75
165. Deng, D. and Kasabov, N., Evolving Self-orginizing Map and its Application in Generating a World Macroeconomic Map, 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), (1999) 7:12
166. 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, Nov.1999, N.Kasabov and K.Ko (eds), 88-92.
167. 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), (1999) 96-102
168. 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
169. 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
170. 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
171. 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
172. 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
173. 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)
174. Abdulla, W. and Kasabov, N., Two pass Hidden Markov Model for speech recognition systems, in: Proceedings of International Conference of Information and Communication Systems (ICICS-99), Singapore, 1999.
175. Kasabov, N., Deng, D., Erzegovezi, L., Fedrizzi, M., and Beber, A., Hybrid Intelligent Decision Support Systems and Applications for Risk Analysis and Prediction, International conference on intelligent systems for investment decision making, Bond University, Gold Cost, December (1999)
176. Iliev, G., and Kasabov, N. Adaptive noise cancellation for speech applications, Proceedings of ICONIP’99, November 1999, Perth, Australia, IEEE Press (1999) 192-197
177. Kasabov, N. and Fedrizzi, M. Fuzzy neural networks and evolving connectionist systems for intelligent decision making, Proc. of the Eight International Fuzzy Systems Association World Congress, Taiwan, August 17-20 (1999)
178. Kasabov, N. Evolving connectionist systems and applications for adaptive speech recognition, Proceedings of IJCNN'99, Washington DC, July 1999, IEEE Press,
179. Kasabov, N and Woodford, B., Rule insertion and rule extraction from evolving fuzzy neural networks: algorithms and applications for building adaptive, intelligent expert systems, 1999 IEEE International Fuzzy Systems Conference Proceedings, Seoul, August 1999, v.III (1999)1406-1409
180. Kasabov, N., Tuck, D., and Watts, M., Implementing Knowledge and Data Fusion in a Versatile Software Environment for Adaptive Learning and Decision-Making, in: Proceedings of the International Conference on Data Fusion, San Hose, July 1999 (1999)
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181. 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)
182. Kasabov, N. Evolving fuzzy neural networks for adaptive, on-line intelligent agents and systems, in: O. Kaynak, S. Tosunoglu and M. Ang (eds) Recent Advances in Mechatronics, Springer Verlag , Singapore (1999): Proceedings of the international conference, Istanbul, Turkey, 24-26 May 1999, 27-41.
183. 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
184. Watts, M. and Kasabov, N. Genetic algorithms for the design of fuzzy neural networks, 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.2, 793-796
185. 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
186. Kasabov, N. Evolving fuzzy neural networks - algorithms, applications and biological motivation, in: T. Yamakawa and G. Matsumoto (eds) Methodologies for the Conception, Design and Appl. of Soft Computing, World Scientific, 1998, 271-274
187. 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
188. 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, IEEE Press (1998)
189. Postma, E.O., Kasabov, N., and Herik, H.J. van. Dynamic Audio-Visual Features for Person Identification, Proc. 10th Netherlands/Belgium Conference on Artificial Intelligence, BNAIC'99 (eds) H. La Poutré and H. J. van den Herik) (1998) 107-116.
190. 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
191. 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.
192. 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
193. 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
194. 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
195. Kasabov, N. and Kozma, R. Chaotic adaptive fuzzy neural networks and their applications to phoneme-based spoken language recognition, in: Proceedings of International Conference Vision, Recognition, Action: Neural Models of Mind and Machines, Boston, May 1997, Boston University (1997) 109
196. Kozma, R., Kasabov, N., Swope, J. and Williams, M. Neuro-fuzzy- chaos analysis for building hybrid connectionist systems, in: Proc. 1997 Int. Conf. on Systems, Man and Cybernetics, Orlando, IEEE Press (1997) 3025 - 3029
197. Kozma, R., Kasabov, N., Swope, J, and Williams, M. Combining neuro-fuzzy and chaos techniques for intelligent systems: heart rate variability case study. in: Proceedings of the International Conference on Neural Information Processing ICONIP’97, Dunedin, Springer Verlag, Singapore (1997) 162-165
198. Kasabov, N., Kozma, R., Kilgour, R., Laws, M., Taylor, J., Watts, M., and Gray, A. Hybrid connectionist-based systems for speech recognition – HySpeech/2. in: Proceedings of the International Conference on Neural Information Processing ICONIP’97, Dunedin, Springer Verlag Singapore (1997) 1055-1060
199. 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
200. Kim, J.S., Mowatt, A., and 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
201. Topchy, A., Lebedko, O., Miagkikh, V., and Kasabov, N. An Approach to Radial Basis Function Networks Training based on Cooperative Evolution and Evolutionary Programming, in: Proc.of the International Conference on Neural Information Processing ICONIP’97, Dunedin, 24- 28 November, 1997, Springer Verlag Singapore (1997) 253-258
202. 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
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203. Purvis, M., Kasabov, N., Benwell, G., Zhou, Q., and Zhang, F. Neuro-fuzzy methods for Environmental Modelling, in: Proc. of the Second International Symposium on Environmental Software Systems. Whistler, Canada (1997) 30 - 37
204. 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
205. 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
206. 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
207. 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
208. 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
209. Kasabov, N. Learning strategies for modular connectionist hybrid systems: a case study on phoneme-based speech recognition, in Proc. World Congress of Neural Networks WCNN’96, San Diego, Lawrence Erlbaum (1996)
210. 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
211. Purvis, M., Kasabov, N., Zhang, F. and Benwell, G. Connectionist-based methods for knowledge acquisition from spatial data in Proceedings of the IASTED Int. Conf., Gold Coast, Australia, IASTED-ACTA Press (1996) 151-154
212. Yeap, W.K., Sun, J., Sallis, P.J., and Kasabov, N.K. From Generative Lexicon to Interpretation, Proceedings of the European International Conference on Speech and Language, October 1996, St Petersburg, Russia (1996) 40 - 44
213. Kasabov, N., Cohen, A., Bailey, M., and Mason, P. Using AI in pollution control – case studies of Neural Network and Fuzzy Control Applications, in Proc. NZ Biotechnology Association Annual Scientific Meeting, Dunedin (1995)
214. 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
215. 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
216. 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 Int. Conf. on Artificial Neural Networks and Expert Systems, Dunedin, IEEE Comp. Soc. Press, Los Alamitos (1995) 265-268
217. 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 NZ Int. Conf. on Artificial Neural Networks and Expert Systems. Dunedin, IEEE Computer Society Press, Los Alamitos (1995) 261-264
218. 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 Int. Conf. on Artificial Neural Networks and Expert Systems. Dunedin, IEEE Computer Society Press, Los Alamitos (1995) 294-297
219. 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
220. 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
221. Benwell, G., Kasabov, N., Purvis, M., Zhang, F., McLennan, B., and Mann, S., Spatial Analysis with Artificial Neural Networks. in Proceedings of the Eight Australian Joint Artificial Intelligence Conference, Workshop on AI and the Environment, Canberra, Australian Defence Force Academy (1995) 43-52
222. 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
223. 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
224. 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
225. 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
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226. 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
227. 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
228. 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
229. Kasabov, N. and Trifonov, R. 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
230. 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
231. 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
232. 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
233. 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
234. 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
235. 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
236. Kasabov, N. Neural networks and fuzzy systems for knowledge engineering, in Proceedings of the 13th New Zealand Computer Society Conference. Auckland (1993) 338-352
237. 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 Publ. North-Holland (1992) 749-752
238. 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 Sc. Publ.North-Holland (1992) 699- 702
239. 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
240. Kasabov, N. and Petkov, S. Neural networks and logic programming - a hybrid model and its applicability to building expert systems, in Proc. 10th European Conf.on Artificial Intelligence Vienna, John Wiley & Sons (1992) 287-288
241. 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)
242. 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
Publications in conference proceedings in Bulgarian or Russian (if not specified otherwise)
243. Kasabov, N., Trishina, E. A knowledge based production system for parallel processing: a model and its implementation on transputers, in: Proc. Int. Conf. on Artificial Intelligence ’89, Sozopol, Bulgaria (1989) 41-47
244. Kasabov, N., Pavlova, R., Some analytical representations for multiprocessor computing systems in Proceedings of the Radio and Communication Annual Symposium, Sofia, Bulgaria (1986) 83-87
245. Groen, A., van den Herik, H.J., Hofland, A., Kasabov, N., Kerckhoffs, E. and Stoop, J. Linking knowledge-based systems to conventional simulation models - current and planned research projects in Proceedings of the Working Conference on Artificial Intelligence in Simulation, Ghent, Belgium, University of Ghent (1985) 58-62 (in English)
246. Kasabov, N., Ianev, K., Gradinarski, J., Trampov, P., Atanassov, I., Dimitrov, H., Topalov P., and Stefanova, N. Eight-microprocessor module for parallel processing of CAMAC-data of and building modular extendable multiprocessor systems, in: Proc. Symposium – 40 years of the Higher Inst. Machines and Electrotechnics, Bulgaria (1985) 57-62
247. Kasabov, N. and Trampov, P. Parallel computations in SIMD/MIMD multi-microprocessor systems with functional reconfiguration in Abstracts of the Proc. of Parallel Computing'83. Berlin, Springer Verlag (1983) 40 (in English)
248. Kasabov, N., Bijev, G., and Jechev, B. Hierarchical discrete Systems and the realisation of Parallel Algorithms, in Proc. Conf. Problems and Programming for Parallel computing Berlin, Springer-Verlag (1983) 415-422 (in English)
249. Kasabov, N., Dakovski, L., and Daskalov, P. Applications of stack memory devices in microprocessor systems, in Proc. 6th Bulgarian Int.Conf. on Computer Science – Microprocessor Systems, Plovdiv, Bulgaria (1983) 16-20.
250. Kasabov, N. Design and applications of multimicroprocessor systems with functional reconfiguration in Proceedings of 6thBulgarian Int. Conf. on Computer Science – Microprocessor Systems, Plovdiv, Bulgaria (1983) 56-59
251. Kasabov, N., and Trampov, P. On some applications of a multi-microprocessor system with a functional reconfiguration, in Proc. 6th Bulgarian Int. Conf. Computer Science – Microprocessor Systems, Plovdiv, Bulgaria (1983) 35-39
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252. Kasabov, N. The structure and organisation of multi-microprocessor systems for control of technological processes. A multi-microprocessor system – operational modes and algorithms, in Proc. Radio Commun. Ann.Symp., Sofia (1983) 65-69
253. Kasabov, N., Parallel computation in multi-microprocessor systems. Microprocessor control of a technological process for cutting metal without a remainder, in Proc. Radio and Commun. Annual Symposium, Sofia, Bulgaria (1983) 70-73
254. Kasabov, N., and Bijev, G. Computer analysis of geometric transformations in Proc. of the international symposium on Automata, languages, systems ’82, Bulgarian Academy of Sciences, Bulgaria (1982) 54-59
255. Kasabov, N. On a basis of the symmetrical group of transformations and its automatic realisation, in Proc. Intern. Symposium on Automata, languages and systems ’82, Bulgarian Academy of Sciences, Varna, Bulgaria (1982) 93-99
256. Kasabov, N. Parallel systems with a direct access to data – a comparative analysis in Proceedings of the Radio and Communication Annual Symposium, Sofia, Bulgaria (1981)
257. Kasabov, N., and Kassabova, D. A probabilistic simulation model of operations and processes in digital computers regarding input streams in Proc. of the Radio and Communication Annual, Sofia, Bulgaria (1980) 57-62
258. Kasabov, N., Structural realisation of homogenous probability automata in Proceedings of the 5th International Symposium on Applied Aspects of Automata Theory, Bulgarian Academy of Sciences, Varna, Bulgaria (1979) 49-54
259. Kasabov, N., and Pavlova, R., Methods of factor analysis for evaluation of multiprocessor systems in Proceedings of the 5th Bulgarian International Conference on Computer Science, Sofia, Bulgaria (1979) 11-18
260. Kasabov, N. Structural representation of basic algebraic transformations in a finite automata in Proceedings of the Radio and Communication Annual Symposium, Sofia, Bulgaria (1979) 49-54
261. Dakovski, L., and Kasabov, N. Non-minimal generating sets of PN and SN and their finite automata realisation in Proceedings of the Radio and Communication Annual Symposium, Sofia, Bulgaria (1978) 23-29
262. Dakovski, L., and Kasabov, N. About implementation of sequential circuits in computational modules in Proceedings of the Radio and Communication Annual Symposium, Sofia, Bulgaria (1978) 57-61
263. Dakovski, L., and Kasabov, N. Logical-, register- and system design in homogenous cellular structures in Proceedings of the Radio and Communication Annual Symposium, Sofia, Bulgaria (1978) 91-95
264. Borovski, B., Egorov, A., and Kasabov, N. Probabilistic models for evaluating the performance of computer systems in Proceedings of the Radio and Communication Annual Symposium, Sofia, Bulgaria (1978) 78-83
265. Kasabov, N. On the generation of algebraic transformations and the design of discrete systems – possibilities and problems in Proceedings of the Radio and Communication Annual Symposium, Sofia, Bulgaria (1977) 75 - 79
266. Dakovski, L., and Kasabov, N. Structural synthesis of random number generators in Proceedings of the 2nd Bulgarian Conference on Computer Science, Sofia, Bulgaria (1973) 86 – 91


Other Significant Conference Presentations and Publications


Invitational Addresses, Keynote Speeches

1. ICONIP 2015, Istanbul, November 2015.
2. Invited talk, C-TRIC Translational Medicine, Londonderry, September 2015.
3. Keynote, EANN 2015, Rhodos, Greece, September 2015
4. Keynote, SIREN/ Italy, May, 2015
5. Keynote, ACIIDS, Bali, March, 2015
6. Keynote, EANN, Sofia, September 2014
7. Keynote, SCDM 2014, Soft Computing and Data Mining, Malaysia, June 2014
8. Invited, ICONIP 2014, Kuching, Malaysia
9. Keynote, ICANN 2013, Sofia, 10- 13.09.2013
10. Keynote, EANN 2013, Halkidiki, Greece, 13-16.09.2013
11. Keynote, 6th Balkan Conf. on Informatics, 17th Panhellenic Conf. in Informatics, Thessalonica,Greece, 19-21.09.2013
12. Keynote, ICIC 2013, 28-31 July 2013, Nanning, China
13. Plenary, ICONIP 2012, 25-27 November, Qatar
14. Keynote, EANN 2012, 23-25 September, London
15. Keynote, ANNPR, September 17-19, Trento, Italy
16. Keynote, IEEE IS 2012, 7-9 September, Sofia
17. Invited, WCCI 2012, 10-15 June, Brisbane, Australia
18. Plenary, ICONIP 2011, Shanghai
19. Keynote, EANN 2011, Greece, September 2011.
20. Keynote, CIBB 2011, Italy.
21. Keynote, the Irish AICS (Artificial Intelligence and Cognitive Systems) conference ( September 2011), Londonderry.
22. Keynote, ICANNGA (Int. Conf. ANN and GA), Ljubljana, April 2011: Neurogenetic modelling.
23. Keynote, INNS Education Symposium on Neural Networks, Lima, Peru, February, 2011: New Directions for NN.
24. Keynote, First INNS Indian Symp. on New Directions in Neural Networks, December, 2010
25. Keynote, ICANN 2010, Thessaloniki, Evolving spiking neural networks,
26. Keynote, KES 2010, Cardiff, Brain-, Gene- and Quantum Inspired Connectionist Systems for Computational Intelligence and Knowledge Engineering.
27. Keynote, ICSI, Beijing, Peking University, June 2010
28. Keynote, ICDI, Qinghuangdao, China, June 2010
29. 2009, Plenary talk, ICONIP 2009, Bangkok
30. 2009, Keynote Talk, EANN 2009, London, August 2009
31. 2009, Keynote Talk, ICONS 2009, Istanbul, September, 2009
32. 2009, Keynote Talk, ICAIS 2009, Klagenfurt, Austria, September 2009
33. 2009, Invited talk, IJCNN, Atlanta, June, 2009
34. 2009, Invited talk, Dynamic Brain Forum, 1-4 March, Atami, Japan, 2009
35. 2008, Invited talk, INNS NNN’2008 Symposia, Auckland, NZ, 2008
36. 2008, Plenary Talk, Brazilian Congress on NN and AI, October 2008
37. 2008, Plenary Talk, World Computer Congress WCC2008, Milano, 7-10.09.2008
38. 2007, Plenary talk, Automatics and Informatics 2007, Sofia, Bulgaria, October, 2007
39. 2007, Plenary talk, HIS 2007, Germany, September, 2007
40. 2007, Invited talk, Dynamic Brain Forum, Hakuba, Japan, March, 2007
41. 2006, Keynote speech, KES’2006, Bournemoth, UK, October 2006
42. 2006, Invited talk, ICONIP’2006, Hong Kong, October 2006
43. 2006, Keynote speech, Int. Conference 9th Fuzzy Days, Dortmund, Germany, September 2006
44. 2005, Keynote talk, BCS AI 2005, Cambridge, UK, December 2005
45. 2005, Invited talk, BISCSE, UC Berkeley, 3-5, November, 2005
46. 2005, Keynote speech, SOFA Int. conference, Szeged-Arad, Hungary, August 2005
47. 2005, Keynote speech, SAER, Varna, 2005, Bulgaria
48. 2005, Keynote speech, BioInfo, Plovdiv, Bulgaria
49. 2005, Invited talk, ARSO’2005 – Advanced Robotics and their Social Impact, Nagoya, June 2005
50. 2005, Keynote speech, Intern. Symposium on Computational Intelligence, Korea, 1-2 Febr.2005
51. 2004, Keynote speech, Int. Conf. on Hybrid Intelligent Systems, HIS’04, Kitakushu, Japan, December 2004
52. 2004, Keynote speech, ICONIP’2004 – Int. Conf. Neuro Information Processing, Calcutta, 2004
53. 2004, Keynote speech, The Founding meeting for the German chapter of the IEEE Comp. Intell. Society, Keiserslautern.
54. 2004, Open lecture, Bioinformatics: The knowledge engine.approach, Techn.Univ. Sofia -Plovdiv, Bulgaria, July 2004
55. 2004, Plenary talk, IEEE Symposium on Intelligent Systems, Varna, Bulgaria, June
56. 2002, Invited talk, ICONIP’2002, November, Singapore
57. 2002, Keynote speech, Int. Conf.on Industrial Applications of Intelligent and Expert Systems, IAE, Cairns, June 2002
58. 2001, Plenary Chair and invited talk, CEC’2001, Seoul, Korea
59. 2000, Invited talk, ICONIP’2000, Taijon, Korea
60. 2000, Closing Speech, Iizuka’2000, Fukuoka, Japan, 1-4 October 2000.
61. 1999, Invited lecture, Innovation in wastewater treatment, national seminar, Auckland, 30/04/99
62. 1998, Keynote presentation, 3rd On-line World Conference on Soft Computing in Engineering Design and Manufacturing, 21-30 June 1998, World Wide Web
63. 1998, Keynote speech, Neuro-Fuzzy Day, 11 June 1998, University of Twente, The Netherlands
64. 1998, Invited talk, Fuzzy neural networks and speech recognition, International workshop on Future Devices for Human-Computer Interaction, Japanese Ministry for Science and Technology, Beppu, Japan, 16-24 January, 1998
65. 1997, Opening lecture, Connectionist-based systems in the age of technology, ICONIP’97, Dunedin, 24-28 November.
66. 1996, Keynote speech, International Discourse on Fuzzy Logic and the Management of Complexity FLAMOC’96, Sydney University of Technology, 15-18 January (1996),
67. 1996, Invited talk, Int.Conf. on Neural Information Processing ICONIP’96, Hong Kong, 14-18 September, 1996,
68. 1996, Invited talk, Int.Conf.on Fuzzy Systems, Neural Networks and Soft Computing, Iizuka’96, Japan, KIT.
69. 1996, Invited talk, International Panel Conference on Soft and Intelligent Computing, SIC’96, Budapest
70. 1995, Invited talk (with T.Cohen, M.Bailey, P.Mason), Annual Conference of the New Zealand Biotechnology Association, Dunedin, 30 August,
71. 1994, Keynote speech, New Zealand Computer Society, ANNES SIG national seminar, Auckland
72. 1994, Keynote speech, New Zealand Computer Society, ANNES SIG national seminar, University of Otago,
73. 1982, Invited talk, Stack Memory Devices. International conference on Memory Devices ’82. Veliko Turnovo, Bulgaria
74. 1982, Invited talk, Utilisation of the semigroup theory for exchange operations in magnetic domain memory. International conference on Memory Devices, Veliko Turnovo, Bulgaria


Tutorials and Workshops presented at International Conferences and published:

1. 2014, Tutorial on spiking neural networks, WCCI/IJCNN, Beijing July
2. 2013, Tutorial on evolving systems, Texas, IJCNN 2013 (with P.Angelov)
3. 2010, Workshop on evolving systems (with P.Angelov and D.Filev), WCCI, Barcelona, July 2010.
4. 2008, Tutorial on evolving systems, CBR Brazilian Symposium on NN, Salvador, Brazil, Oct., 2008
5. 2007, Tutorial on evolving intelligent systems, ICANN 2007, Porto, September 2007
6. 2007, Tutorial on evolving intelligent systems, IJCNN 2007, Orlando, August, 2007
7. 2007, Tutorial on evolving intelligent systems, IEEE Symposia, Hawaii, April 2007
8. 2006, Tutorial on evolving intelligent systems, ICANN, September 2006, Athens
9. 2006, Tutorial on evolving intelligent systems, WCCI 2006, Montreal, August, 2006
10. 2005, Tutorial on evolving connectionist systems, IJCNN,05, Montreal, July 2005
11. 2005, Tutorial on evolving connectionist systems, ICANN’05, Warsaw, Sept. 2005
35
12. 2004, Tutorial on evolving connectionist systems, ICONIP’04, Calcutta, Nov. 2004
13. 2004, Tutorial on data mining and knowledge discovery in bioinformatics, Int. Joint Conf. on Neural Networks – IJCNN, Budapest, 2004
14. 2003, Adaptive neural networks for data mining and knowledge discovery, Tutorial at the Int. Joint Conf. on Neural Networks (IJCNN’03), July 2003, Portland, Oregon, IEEE and INNS
15. 2003, Knowledge-based Neural Networks for Bioinformatics, University of California at Berkley, BISC Workshop on FLINT-CIBI (USA)
16. 2000, Evolving Connectionist Systems: Methods, Tools, Applications, Tutorial at ICONIP’2000 (Taejon, Korea)
17. 2000, Evolving connectionist systems, Tutorial , euroComputation conference NC’2000 (Berlin)
18. 1999, Workshop “Future directions for intelligent systems and information sciences”, Dunedin, November 1999
19. 1999, Evolving connectionist systems – methods, tools, applications, Tutorial, ICONIP’99, Nov. 1999, Perth
20. 1999, Speech and language recognition, Tutorial Track at IJCNN’99, Washington DC, July 1999
21. 1997, Connectionist-Based Intelligent Information Systems, Tutorial, ICONIP’97, Dunedin, 24-28 November 1997
22. 1997, Hybrid Connectionist-Based Intelligent Information Systems – Methodologies, Tools, Industrial Applications, Tutorial, World Manufacturing Congress, WMC’97, 18 November 1997, Auckland
23. 1997, AI/GIS systems and their applications, Tutorial, The 2nd Annual Conf. on Geo Computation, Dunedin, June 1997
24. 1997, Hybrid Intelligent Information Systems, Tutorial, ICNN’97 (The IEEE International Conference on Neural Networks), Houston, USA, May
25. 1996, Hybrid AI/GIS systems and their applications, Tutorial, Australian Urban and Resource Planning Information Society AURISA’96, Hobart, November 1996
26. 1996, Hybrid (neuro-fuzzy) intelligent information systems: methods, tools, industrial applications, Tutorial at Iizuka’96, (International Conference on fuzzy systems, neural networks and soft computing), Iizuka, Japan, September 1996
27. 1995, Intelligent Hybrid Systems for Problem Solving and Knowledge Acquisition, Workshop at the Second New Zealand International Conference on Artificial Neural Networks and Expert Systems ANNES’95, Dunedin, November 1995
28. 1995, Hybrid (Connectionist, Fuzzy, Symbolic) Environments and Their Applications for Building Complex Decision Making Systems, Tutorial, International Conference on Neural Information Processing (ICONIP’95- Beijing), Beijing, 1995
29. 1995, Fuzzy Data Analysis, Workshop, Eight Colloquium of the Spatial Information Research Centre of the University of Otago, Palmerston North.
30. 1994, Hybrid (Symbolic-, Connectionist-Fuzzy-, Chaotic) Systems, Tutorial, AI’94 – the Joint Australian Conference on Artificial Intelligence, Armidale
31. 1993, The Basics of Fuzzy Systems, Tutorial, ANNES’93 conference, Dunedin, U. Otago
32. 1993, Neural Networks for Problem Solving. Tutorial, ANNES’93 conference, Dunedin, U. Otago


Audio-Visual Recordings Published as CDs


2010, The science gets personal, AUT and YouTube
2008, The KEDRI Repository of Intelligent Connectionist Based Systems – RICBIS (www.kedri.info)
2000, Smart Voice Technologies, CD, Information Science Department, University of Otago.
1999, Speech and language Processing, Tutorial Track 8, CD, IJCNN’99, Washington DC,July.
1998, Connectionist-based Inform. Systems, CD UOO606 FRST project results (software, papers) (1998)
1996, Fuzzy system implementation on the Fisher & Paykel's PSC-III, Video film, in collaboration with the University of Otago Audio Visual Centre.


Computer Software - Developed and Published

1. N.Kasabov et al, NeuCube – a spiking neural network spatio-temporal machine, KEDRI, 2013-2015
2. N.Kasabov et al, EvoSpike - a spiking neural network software envrioment for modelling spatio-temporal data, 2013, http://ncs.ethz.ch/projects/evospike/
3. R.Hu, N.Kasabov et al, The KEDRI_Personalised Modelling Development System v1, March 2012
4. S.Schliebs, N.Kasabov et al, The KEDRI_EvoSpike Development System v1, February 2012
5. N.Kasabov et al, The KEDRI RICBIS Computational Intelligence Repository 2008 (www.kedri.info)
6. N.Kasabov, L.Benuskova, V.Jain, P.Gottgtroy, BGO – The KEDRI Brain-Gene Ontology (www.kedri.info)
7. Z.Chan, N.Kasabov, V.Jain, GenNetXP – A gene regulatory network modelling software (www.kedri.info), 2003-2005
8. D. Greer, N. Kasabov, Q. Song, L.Goh – Siftware: A Gene Expression Profiling Software, 2003-2005
9. P.Hwang, D. Greer, N. Kasabov, Q. Song, P. Pang, NeuCom – A Neurocomputing environment for intelligent decision support systems, (www.the neucom.com), 2003 - 2006
10. Richard Walton, Dougal Greer, Nik Kasabov, Qun Song – Cardio Vascular Disease Prediction System, 2003
11. Song, Q. and N.Kasabov, ECOS MATLAB Toolbox, (www.kedri.info), 2002-2003
12. Abdula, W. and N.Kasabov, Speech recognition development environment, Department of Information Science, University of Otago, 1999
13. Deng, D., Koprinska, I., Kasabov, N., et al, The NZ Repository of Intelligent Connectionist-Based Modules and Systems – NZ-RICBIS, http://divcom.otago.ac.nz/infosci/kel/CBIIS.html
36
14. Kilgour, R., Kasabov, N., Kozma, R., Laws, M., et al. HySPEECH/2 - An experimental software system for speech recognition and translation from English to Maori, Windows95, PGSF FRST NZ/University of Otago, 1998, http://kel.otago.ac.nz
15. Watts, M., Kasabov, N., and Pearson, S. FuzzyCOPE/3 - A hybrid fuzzy connectionist production systems environment, MS Windows/Windows95, June 1998, University of Otago, http://kel.otago.ac.nz
16. Purvis, M., Kasabov, N., Zhang, F., et al, AI/GIS hybrid intelligent information system for spatial information processing, UNIX/SUN-ArchInfo, FRST NZ/University of Otago, 1997
17. Kasabov, N., Garden, J., Jones, P., Kilgour, R., Gray, A., et al. FuzzyCOPE-1 & 2 - A hybrid fuzzy connectionist production systems environment, MS Windows/Windows95, 1995-1997; University of Otago
18. Watson, C., Kasabov, N., Sinclair, S., Laws, M., Kilgour, R., and Kassabova, D., Otago Speech Corpora on New Zealand English, CD, Windows95/UNIX, University of Otago, 1995, http://kel.otago.ac.nz
19. Kasabov, N. et al, COPE - Connectionist production systems environment, Technical University of Sofia, 1992
20. Kasabov, N. and Nikolaev, N. GESPAR - Generator of Expert Systems for Parallel Computers, TU Sofia, 1990
21. Kasabov, N., Besenshek, D.Georgiev and Svetlin. GESMI - Generator of Expert Systems, TU Sofia, 1987


Patents

1. N.Kasabov, V.Feigin, Z.Hou, Y.Chen, Improved method and system for predicting outcomes based on spatio/spectro-temporal data, PCT patent WO2015/030606 A2.
2. N.Kasabov, Data Analysis and Predictive Systems and Related Methodologies, US patent 9,002,682 B2, 7 April 2015.
3. R. North, M. Blumenstein, M. McMaster, N. Kasabov, M. Black, G. Cooper, L. McCowan, Biomarkers for prediction of preeclampsia and/or cardiovascular disease, PCT, 2008
4. N.Kasabov, M. Futschik, M.Sullivan, A.Reeve, Method and Medical Decision Support System Utilizing Gene Expression and Clinical Information, PCT/US03/25563, 15.08. 2003
5. N.Kasabov, A.Reeve, M. Futschik, M.Sullivan, and P. Guildford, Medical Applications of Adaptive Learning Systems using Gene Expression Data, Patent USA, PCT WO 03/079286
6. N. Kasabov, A. Ghobakhlou, Adaptive Sound and Image Learning System and Method, PCT WO 2005/038774 A1.
7. Kasabov, N., and Q. Song, Transductive Neuro-Fuzzy Inference Method for Personalised Modelling, PCT WO 2005/048185 A1.
8. Kasabov, N., Adaptive learning system and method, Patent USA, PCT WO 01/78003, 18.10.2001
9. Kasabov, N., and Abdulla, W., Speech recognition system and method, PCT patent, WO 02/23525 A1
10. Kasabov, N., Multi-microprocessor system, 258015 Czechoslovakia, 2/6/1989
11. Kasabov, N. and Dakovski, L. Stack Memory Device, 1026164 Russia, 1/3/1983 (in Russian)
12. Dakovski, L. and Kasabov, N. Bus-register device for information processing, 4 362 926 USA, 7/12/1982
13. Dakovski, L. and Kasabov, N. Numerical Control of Machines, 2037040 UK, 24/11/1982
14. Dakovski, L. and Kasabov, N. Stack Memory Device, 4 305 138 USA, 8/12/1981
15. Dakovski, L. and Kasabov, N. Arithmetische Registereinrichtung Offenlegunsshrift, DE3128816A1 Bundesrepublik Deutschland, 22/7/1980
16. Dakovski, L. and Kasabov, N. Dispositif memoire formant pile, 79 26385 France, 24/10/1979
17. Kasabov N. et al, A variable word- length computer memory,62135, Bulgaria, 19/08/1983
18. Kasabov, N. Multi-microprocessor system, 36605 Bulgaria, 22/9/1983
19. Kasabov, N. and Dakovski, L. Method for permutation of data records, 35714 Bulgaria, 1/4/1983
20. Kasabov, N. et al, An electronic device for a direct access to computer memory, 36902 Bulgaria, 18/11/1983
21. Kasabov, N. and Dakovski, L. Arithmetic register device, 33404 Bulgaria, 22/07/1980
22. Kasabov, N. n – switch element, 29707 Bulgaria, 05/02/1979
23. Kasabov, N. and Dakovski, L. Cyclic automata, 27684 Bulgaria, 3/5/1978
24. Dakovski, L. and Kasabov, N. 2n – Universal device for realisation of permutation automata, 26333 Bulgaria, 1/3/1978
25. Dakovski, L. and Kasabov, N. A method and a device for realisation of asynchronous automata, 26334 Bulgaria, 27/3/1978
26. Dakovski, L. and Kasabov, N. An electronic device for realisation of finite automata, 26335 Bulgaria, 27/3/1978
27. Kasabov, N. and Dakovski, L. A method and a bus register device for the realisation of sequential finite automata, 29106 Bulgaria, 08/11/1978
28. Dakovski, L. and Kasabov, N. Stack Memory Device, 29114 Bulgaria, 08/11/1978
29. Dakovski, L. and Kasabov, N. A method for discrete signal commutation and control of electronic commutation devices, 25630 Bulgaria, 31/5/1977


Other Creative Works

1. MBIE project proposal reviews, 2012-2014.
2. Project proposal reviews of Science Foundations of Singapore, Hong Kong, Qatar.
3. Kim, J., Kozma, R., Kasabov, N., Gols, B., Geerink, M. and Cohen, T. A fuzzy neural network model for the estimation of he feeding rate to an anaerobic waste water treatment process, Departmental Technical Report, Department of Information Science, University of Otago, August 1998
4. Kasabov, N, 20 entries in the Short Bulgarian Encyclopaedia for Mathematical and Physical Sciences, 1988-90 (in Bulgarian)


Last updated: 01-Apr-2016 3.22pm

The information on this page was correct at time of publication. For a comprehensive overview of AUT qualifications, please refer to the Academic Calendar.