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Publications by topic of research plus other publications
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The main publications per research topic are grouped below (reference numbers are as per the list at the bottom of this web page; the symbol ‘*’ denotes a research topics introduced by Prof. Angelov; the symbol ‘†’ denotes a research topic for which Dr. Angelov is one of the first few who introduced it world-wide):
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  • * Interpretable deep rule-base classifiers [1]-[4]

  • * Empirical data analytics, [5]-[7], [59]

    • * Empirical fuzzy sets and systems [8], AnYa type fuzzy systems [56]

  • † Autonomous learning systems [9], [10], [58]

    • * Self-evolving fuzzy rule-based classifiers [11]-[13]

    • † Dynamically self-evolving predictive neuro-fuzzy models [14]-[20], [58], [63]

    • * Autonomous fault detection & identification/anomaly detection [21]-[23]

    • † Dynamically evolving clustering [24]-[26]

    • * Dynamically self-evolving controllers [27]-[29]

    • Drift detection, stability, correntropy analysis, [30]-[32]

  • * New direction aware distance metric [33]

  • Applications:

    • * Evolving human behaviour modelling [34]-[36]

    • Botnets detection [37]

    • Data fusion [38]

    • Image processing [1]-[4], [39]-[44], [54]

    • Bioinformatics/spectroscopy classification/biomarkers identification [45]-[49]

    • * eSensor (self-calibrating intelligent sensors) [50]-[51]

    • Autonomous vehicles [52]-[54]     

    • Rail (VideoAnalytics solution to Platform-Train Interfaces for faster, safer boarding) [55]

 

Earlier works (pre-1998):

​

  • Optimal control under uncertainties (of fuzzy type) [57]

  • Fuzzy mathematical programming under uncertainties (of fuzzy type) [60], [68]

  • Evolutionary synthesis of HVAC [61]-[62]

  • Evolutionary design of fuzzy rule-based systems [64]-[65]

  • Optimisation in an intuitionistic fuzzy environment [66]

  • Modelling biotechnological processes using neural networks [67]

 

References used above:

 

  1. X. Gu, P. Angelov, C. Zhang, P. Atkinson, A massively parallel deep rule-based ensemble classifier for remote sensing scenes, IEEE Geoscience and Remote Sensing Letters (IF 2.761), 15(3): 345-349, 2018

  2. P. Angelov, X. Gu, MICE: Multi-layer multi-model images classifier ensemble, 2017 IEEE International Conference on Cybernetics, CYBCONF2017, Exeter, UK, 2017, pp. 1-8, DOI: 10.1109/CYBConf.2017.7985788.

  3. P. Angelov, X. Gu, A Cascade of Deep Learning Fuzzy Rule-based Image Classifier and SVM, 2017 IEEE Intern. Conf. on Systems, Man, and Cybernetics (SMC2017), Banff, Canada, pp.746-751.

  4. P. Angelov, X. Gu, Deep rule-based classifier with human-level performance and characteristics, Information Sciences (IF 4.832), vol. 463-464, pp. 196-213, October 2018.

  5. P. Angelov, X. Gu, J. Principe, A generalized methodology for data analysis, IEEE Transactions on Cybernetics (IF 7.384), DOI: 10.1109/TCYB.2017.2753880, 2017.

  6. P. Angelov, X.  Gu, D. Kangin, Empirical data analytics, International Journal of Intelligent Systems (IF 2.929), 32(12): 1261-1284, Dec. 2017.

  7. P. Angelov, X. Gu, D. Kangin, J. Principe, Empirical data analysis: a new tool for data analytics, IEEE International Conference on Systems, Man, and Cybernetics (SMC2016), Budapest, Hungary 2016, pp. 000052 – 000059, DOI: 10.1109/SMC.2016.7844219.

  8. P. Angelov, X. Gu, Empirical Fuzzy Sets, International Journal of Intelligent Systems (IF 2.929), 33(2): 362-395, Feb.2018.

  9. P. Angelov, Autonomous Learning Systems: From Data Streams to Knowledge in Real time, John Willey and Sons, Dec.2012, ISBN: 978-1-1199-5152-0.

  10. P. Angelov, Machine Learning (Collaborative Systems), USA patent 8250004, granted 21 August 2012; priority date: 1 Nov. 2006; international filing date 23 Oct. 2007.

  11. *P. Angelov, X. Zhou, Evolving Fuzzy-Rule-based Classifiers from Data Streams, IEEE Transactions on Fuzzy Systems (IF 7.671), ISSN 1063-6706, 16(6): 1462-1475, 2008.

  12. P. Angelov, E. Lughofer and X. Zhou, Evolving Fuzzy Classifiers with Different Architectures, Fuzzy Sets and Systems (IF 2.718), 159, 3160-3182, 2008.

  13. P. Angelov, X. Zhou, F. Klawonn, Evolving Fuzzy Rule-based Classifiers, Proc. 2007 IEEE International Conference on Computational Intelligence Applications for Signal and Image Processing, April 1-5, 2007, Hawaii, USA, pp.220-225, IEEE Xplore.

  14. P. Angelov, R. Buswell, Evolving Rule-based Models: A Tool for Intelligent Adaptation, Proc. 9th IFSA World Congress, Vancouver, BC, Canada, 25-28 July 2001, pp.1062-1067.

  15. P. P. Angelov, Evolving Rule-based Models: A Tool for Design of Flexible Adaptive Systems, Springer-Verlag, Heidelberg, Germany, 2002, 215 pp., ISBN 3-7908-1457-1

  16. P. Angelov, R. Buswell, Identification of Evolving Rule-based Models, IEEE Transaction on Fuzzy Systems (IF 7.671), 10 (5): 667-677, 2002.

  17. P. Angelov, D. Filev, An Approach to On-line Identification of Takagi-Sugeno Fuzzy Models, IEEE Transactions on System, Man, and Cybernetics, part B – Cybernetics (IF 7.384), 34 (1): 484-498, 2004.

  18. P. Angelov, D. Filev, Simpl_eTS: A Simplified Method for Learning Evolving Takagi-Sugeno Fuzzy Models, Proc. The 2005 IEEE International Conference on Fuzzy Systems, FUZZ-IEEE 2015, Reno, USA, 22-25 May 2005, ISSN 0-7803-9158-6/05, pp.1068-1073.

  19. *P. Angelov, X. Zhou, Evolving Fuzzy Systems from Data Streams in Real-Time, Proc. 2006 International Symposium on Evolving Fuzzy Systems, 7-9 September, 2006, Ambleside, UK, IEEE Press, ISBN 0-7803-9719-3, pp.29-35.

  20. P. Angelov, Evolving Takagi-Sugeno Fuzzy Systems from Data Streams (eTS+), In Evolving Intelligent Systems: Methodology and Applications (Angelov P., D. Filev, N. Kasabov Eds.), John Willey and Sons, pp. 21-50, ISBN: 978-0-470-28719-4, Feb. 2010.

  21. P. Angelov, Anomalous System State Identification, USA patent 9390265, granted 12 July 2016; priority date 15 May 2012, GB1208542.9.

  22. B. S. J. Costa, P. Angelov, L. A. Guedes, Fully unsupervised fault detection and identification based on recursive density estimation and self-evolving cloud-based classifier, Neurocomputing (IF 2.0), 150A: 289-303, 2015.

  23. B. S. J. Costa, P. P. Angelov, L. A. Guedes, Real-Time Fault Detection using Recursive Density Estimation, Journal of Control, Automation and Electrical Systems, ISSN: 2195-3880, 25 (4): 428-437, 2014.

  24. P. Angelov, An approach for fuzzy rule-base adaptation using on-line clustering, International Journal of Approximate Reasoning (IF 2.845), 35 (3): 275-289, 2004.

  25. R. D. Baruah, P. Angelov, DEC: Dynamically Evolving Clustering Autonomous and its Application to Structure Identification of Evolving Fuzzy Models, IEEE Transactions on Cybernetics (IF 7.384), 44(9): 1619-1631, 2013.

  26. X. Gu, P. Angelov, D. Kangin, J. Principe, Self-organised direction aware data partitioning algorithm, Information Sciences (IF 4.832), 423: 80-95, Jan. 2018.

  27. P. P. Angelov, A Fuzzy Controller with Evolving Structure, Information Sciences (IF 4.832), 161: 21-35, 2004.

  28. P. Sadeghi-Tehran, A. B. Cara, P. Angelov, H. Pomares, I. Rojas, A. Prieto, Self-Evolving Parameter-free Rule-based Controller, 2012 World Congress on Computational Intelligence, WCCI2012, 10-15 June 2012, Brisbane, Australia, pp.754-761, ISBN 978-1-4673-1489-3.

  29. P. Angelov, I Skrjanc, S. Blazic, A Robust Evolving Cloud-based Controller, In Springer Hand-book on Computational Intelligence, (J. Kacprzyk and W. Pedrzyc eds.), part G, chapter 75, pp. 1435-1449, 2015, ISBN 978-3-662-43504-5, DOI: 10.1007/978-3-662-43505-2_75.

  30. E. Lughofer, P. Angelov, Handling Drifts and Shifts in On-line Data Streams with Evolving Fuzzy Systems, Applied Soft Computing (IF 3.811), 11(2): 2057-2068, 2011.

  31. H.-J. Rong, P. Angelov, X. Gu, J. Bai, Stability of Evolving Fuzzy Systems based on Data Clouds, IEEE Transactions on Fuzzy Systems (IF 7.671), DOI: 10.1109/TFUZZ.2018.2793258, published online 15 Jan. 2018.

  32. † R. Bao, H. Rong, P. Angelov, B. Chen, P. Wong, Correntropy-Based Evolving Fuzzy Neural System, IEEE Transactions on Fuzzy Systems (IF 7.671), DOI:10.1109/TFUZZ.2017.2719619, June 2017.

  33. X. Gu, P. Angelov, D. Kangin, J. Principe, A new type of distance metric and its use for clustering, Evolving Systems, 8(3): 167-177, 2017.

  34. J. Iglesias, P. Angelov, A. Ledezma, A. Sanchis, Creating evolving user behavior profiles automatically, IEEE Transactions on Knowledge Data Engineering (IF 3.438), 24(5): 854-867, 2012.

  35. J. A. Iglesias, P. Angelov, A. Ledezma, A. Sanchis, Human Activity Recognition based on Evolving Fuzzy Systems, International Journal of Neural Systems (IF 6.507), 20(5): 355-364, 2010.

  36. J. A. Iglesias, P. Angelov, A. Ledezma, A. Sanchis, Evolving Classification of Agents' Behaviours: A General Approach, Evolving Systems, ISSN 1868-6478, 1(3): 161-171, 2010.

  37. † P. Angelov, R. Bruncak, D Hutchison, S Simpson, P Smith, System for identifying illegitimate communications between computers by comparing evolution of data flows, USA patent 9847924 (B2), granted 19 December 2017, priority date 2 April 2015.

  38. P. Angelov, R. Yager, Density-based Averaging - a new Operator for Data Fusion, Information Sciences (IF 4.832), 222: 163-174, 2013.

  39. P. Angelov, P. Sadeghi‐Tehran, Look‐a‐Like: A Fast Content‐Based Image Retrieval Approach Using a Hierarchically Nested Dynamically Evolving Image Clouds and Recursive Local Data Density, International Journal of Intelligent Systems (IF 2.929), 32(1): 82-103, 2017.

  40. P. Angelov, P. Sadeghi-Tehran, Data Structuring and Searching Method and Apparatus, GB1417807.3, priority date 8 October 2014.

  41. A. Antoniou, P. Angelov, A general purpose intelligent surveillance system for mobile devices using deep learning, International Joint Conference on Neural Networks (IJCNN-2016), Vancouver Canada, 2016, pp.2879-2886.

  42. G. Morris, P. P. Angelov, Edge flow, 2015 IEEE International Conference on Systems, Man and Cybernetics, SMC 2015, Hong Kong, pp.1942-1948, DOI 10.1109/SMC.2015.339.

  43. G. Morris, P. Angelov, Real-time novelty detection in video using background subtraction techniques: State of the art a practical review, Proc. IEEE International Conference on Systems, Man and Cybernetics, SMC2014, 5-8 October 2014, San Diego, USA, pp.537-543.

  44. P. Angelov, P. Sadeghi-Tehran, R. Ramezani, An Approach to Autonomous Novelty Detection and Object Tracking in Video Stream, International Journal of Intelligent Systems (IF 2.929), 26(3): 189-205, 2011.

  45. J. Trevisan, P. P. Angelov, A. D. Scott, P. L. Carmichael, F. L. Martin, IRootLab: a free and open-source MATLAB toolbox for vibrational biospectroscopy data analysis, Bioinformatics (IF 7.307),  29 (8): 1095-1097, 2013.

  46. J. Trevisan, P. P. Angelov, P. L. Carmichael, A. D. Scott and F. L. Martin, A computational protocol and software implementation (as a MATLAB application) for biomarker identification in infrared spectroscopy datasets, Nature Protocols (IF 10.03) Exchange, May 2010, DOI: 10.1038/nprot.2010.97.

  47. † J. Trevisan, P. P. Angelov, P. L. Carmichael, A. D. Scott and F. L. Martin, Extracting biological information with computational analysis of Fourier transform infrared (FTIR) bio-spectroscopy datasets: current practices to future perspectives, Analyst (IF 4.107), 137: 3202-3215, 2012.

  48. † J. G. Kelly, P. Angelov, J. Trevisan, N. Vlachopoulou, E. Paraskevaidis, P.L. Martin-Hirsch, and M.L. Martin, Robust classification of low-grade cervical cytology following analysis with ATR-FTIR spectroscopy and subsequent application of self-learning classifier eClass, Journal of Analytical and Bio-analytical Chemistry (IF 3.578), 398(5): 2191-2201, 2010.

  49. J. Kelly, P. Angelov, M. J. Walsh, H. M. Pollock, M. A. Pitt, P. L. Martin-Hirsch and F. Martin, A Self-Learning Fuzzy Classifier with Feature Selection for Intelligent Interrogation of mid-IR Spectroscopy Data Derived from Different Categories of Exfoliative Cervical Cytology, Inter-national Journal on Computational Intelligence Research, ISSN 0974-1259, 4(4): 392–401, 2008.

  50. P. Angelov, A. Kordon, Adaptive Inferential Sensors based on Evolving Fuzzy Models: An Industrial Case Study, IEEE Transactions on Systems, Man and Cybernetics-B (IF 7.384), 40(2): 529-539, 2010.

  51. J. J. Macias-Hernandez, P. Angelov, X. Zhou, Soft Sensor for Predicting Crude Oil Distillation Side Streams using Takagi Sugeno Evolving Fuzzy Models, Proc. 2007 IEEE International Conference on Systems, Man, and Cybernetics, SMC2007, 7-10 October, 2007, Montreal, Canada, ISBN 1-4244-0991-8/07, pp.3305-3310, IEEE Xplore.

  52. X. Zhou, P. Angelov, An Approach to Autonomous Self-localization of a Mobile Robot in Completely Unknown Environment using Evolving Fuzzy Rule-based Classifier, Proc. 2007 IEEE International Conference on Computational Intelligence Applications for Defense and Security, April 1-5, 2007, Honolulu, Hawaii, USA, pp.131-138, IEEE Xplore.

  53. P. Sadeghi-Tehran, S. Behera, P. Angelov, J. Andreu, Autonomous Visual Self-Localization in Completely Unknown Environment, 2012 IEEE Conference on Evolving and Adaptive Intelligent Systems, EAIS-2012, 17-18 May 2012, Madrid, Spain, pp.90-95, ISBN978-1-4673-1727-6.

  54. P. Angelov, P. Sadeghi-Tehran, C. Clarke, AURORA: autonomous real-time on-board video analytics, Neural Computing and Applications (IF 2.505), 28(5): 855-865, 2017.

  55. P. Angelov, G. Morris, H. Parkinson, Transport Information Systems, PCT/GB2017/053586, filing date 28 November 2017; GB1620099.0, priority date 28 November 2016.

  56. P. Angelov, R. Yager, A New Type of Simplified Fuzzy Rule-based Systems, International Journal of General Systems (IF 1.637), 41(2): 163-185, 2012.

  57. D. Filev, P. Angelov, Fuzzy Optimal Control, Fuzzy Sets and Systems (IF 2.718), 47(2): 151-156, 1992.

  58. P. Angelov, X. Gu, J. Principe, Autonomous learning multi-model systems from data streams, IEEE Transactions on Fuzzy Systems (IF 7.671), DOI:10.1109/TFUZZ.2017.2769039, 2017.

  59. P. Angelov, Outside the box: An Alternative Analytics Framework, Journal of Automation, Mobile Robotics and Intelligent Systems, 8(2):29-35, 2014.

  60. P. Angelov, L. Kuncheva, About Analytical Solving Fuzzy Mathematical Programming Problem, Proc. International Conference on Mathematical Modelling and Computing'93 (S. Markov Ed.), Sozopol, Bulgaria, 14-17 September 1993, pp.109-112.

  61. P.    Angelov, Y. Zhang, J. Wright, R. Buswell, V. Hanby, Automatic Design Synthesis and Optimization of Component-based Systems by Evolutionary Algorithms, In: Lecture Notes in Computer Science 2724 Genetic and Evolutionary Computation (E. Cantu-Paz et. al Eds.): Springer-Verlag, 2003, pp.1938-1950.

  62. J. A. Wright, Y. Zhang, P. P. Angelov, R. A. Buswell and V. I. Hanby, Evolutionary Synthesis of HVAC System Configurations: Algorithm Development, International Journal of HVAC Research, 14 (1): 33-55, 2008.

  63. P. Angelov, Fuzzily Connected Multi-Model Systems Evolving Autonomously from Data Streams, IEEE Transactions on Systems, Man, and Cybernetics - part B, Cybernetics (IF 7.384), 41(4): 898-910, 2011.

  64. P. Angelov, R. Buswell, Automatic Generation of Fuzzy Rule-based Models from Data by Genetic Algorithms, Information Sciences (IF 4.832), 150 (1/2): 17-31, 2003.

  65. P. Angelov, An Evolutionary Approach to Fuzzy Rule-based Model Synthesis using Rules Indices, Fuzzy Sets and Systems (IF 2.718), 137 (3): 325-338, 2003.

  66. P. Angelov, Optimization in an Intuitionistic Fuzzy Environment, Fuzzy Sets and Systems (IF 2.718), 68: 301-306, 1997.

  67. † L. Chen, O. Bernard, G. Bastin and P. Angelov, Hybrid Modelling of Biotechnological Processes using Neural Networks, Control Engineering Practice (IF 2.602), 8(7):821-827, 2000.

  68. P. Angelov, A Parameterized Generalization of Fuzzy Mathematical Programming Problem, Proc. 5th IFSA World Congress, Seoul, Korea, July 4-9, 1993, v.1, pp.612-615.​

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