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The full list of publications (continuation from page "All publications 1st part"):

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F. Other publications (24)

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  1. Z. Jiang, P. Angelov, D. Kangin, Z. Zhang, R. Jiang, On Neuron Activation Pattern and Applications, TechRxiv, 26 December 2023, DOI: 10.36227/techrxiv.170421894.45150592/v1.

  2. P. Angelov, D. Kangin, Z. Ziyang, IDEAL: Interpretable-by-design Deep Learning Algorithms, arXiv preprint arXiv:2311.11396

  3. Z. Zhang, P. Angelov, E. Soares, N. Longepe, P.-P. Mathieu, An Interpretable Deep Semantic Segmentation Method for Earth Observation, arXiv preprint arXiv:2210.12820

  4. E. Soares, P. Angelov, RADNN: Robust to imperceptible adversarial attacks Deep Neural Network, TechRxiv, 30 Sept. 2021, DOI: 10.36227/techrxiv.16709359

  5. E. Soares, P. Angelov, Z. Zhang, An Explainable approach to Dep Learning from CT-scans for Covid Idnetification, TechRxiv, Aug. 2021, DOI: 10.36227/techrxiv.15135846.v1

  6. N. L. Baisa, Z. Jiang, R. Vyas, B. Williams, H. Rahmani, P. Angelov, S. Black, Hand-Based Person Identification using Global and Part-Aware Deep Feature Representation Learning, arXiv preprint arXiv:2101.05260

  7. P. Angelov, E. Soares, Explainable-by-design approach for Covid-19 classification via CT-Scan, medRxiv, 24 April 2020, DOI: 10.1101/2020.04.24.20078584.

  8. P. Angelov, E. Soares, Towards Deep Machine Reasoning: a Prototype-based Deep Neural Network with Decision Tree Inference, arXiv preprint, arXiv:2002.03776, 2 Feb. 2020.

  9. P. Angelov, E. Soares, Towards Explainable Deep Neural Networks (xDNN), arXiv preprint, arXiv:1912.02523, 5 Dec. 2019.

  10. X. Gu, P. P. Angelov, E. A. Soares, A Self-Adaptive Synthetic Over-Sampling Technique for Imbalanced Classification, arXiv preprint arXiv:1911.11018, 25 Nov. 2019.

  11. E. Soares, P. Angelov, Novelty detection and learning from extremely weak supervision, arXiv preprint arXiv:1911.00616, 1 Nov 2019.

  12. E. Soares, P. Angelov, Fair-by-design explainable models for prediction of recidivism, arXiv preprint arXiv:1910.02043, 18 Sept. 2019.

  13. X Gu, MA Khan, P Angelov, B Tiwary, ES Yourdshah, ZX Yang, A Novel Self-Organizing PID Approach for Controlling Mobile Robot Locomotion, arXiv preprint arXiv:1912.08057.

  14. X. Gu, P. Angelov, M. Khan, An Odometer-Free Approach for Unmanned Ground-based Vehicle Simultaneous Localization and Mapping, 26 Oct 2019, IEEE Nuclear Science Symposium and Medical Imaging Conference, 26 Oct 2019, Manchester, UK.

  15. P. Angelov, C. Shang, F. Chao, The 16th Annual UK Workshop on Computational Intelligence, Editorial in Soft Computing, 22: 3123–3124, May 2018.

  16. N. Nedja, P. Angelov, O. Castillo, L. M. Mourelle, C. Wang, Editorial Soft Computing Applied to Swarm Robotics, Applied Soft Computing (IF 3.811), 57: 696-697, Aug. 2017.

  17. P. Angelov, J. Iglesias, Design and Tuning of Fuzzy Systems, In Encyclopaedia of Life Support Systems, Book on Computational Intelligence, (H. Ishibuchi Ed.), commissioned by UNESCO, 2015, available online at http://www.researchgate.net/publication/261728906_Design_and_Tuning_of_Fuzzy_Systems

  18. P. Angelov, D. Filev and N. Kasabov, Guest Editorial Evolving Fuzzy Systems, IEEE Transactions on Fuzzy Systems (IF 7.671), ISSN 1063-6706, 16(6): 1390-1392, 2008.

  19. P. Angelov, N. Kasabov, Evolving Intelligent Systems, eIS, IEEE SMC eNewsLetter, June 2006, pp.1-13.

  20. P. Angelov, Book review of 'Construction Scheduling, Cost Optimization and Management: A New Model Based on Neuro-Computing and Object technologies', by H. Adeli, A. Karim, In Engineering, Construction & Architectural Management Journal, 8 (3): 233-234, 2001.

  21. P. P. Angelov, V. I. Hanby and J. A. Wright, HVAC Systems Simulation: A Self-Structuring Fuzzy Rule-Based Approach, International Journal of Architectural Sciences, 1 (1) 30-39, 2000.

  22. P. Angelov, Crispification: Defuzzification over Intuitionistic Fuzzy Sets, Bulletin for Studies and Exchanges on Fuzziness And its AppLications, BUSEFAL, ISSN 0296-3698, 64, 51-55, 1995.

  23. P. Angelov, N. Zamdjiev, An Approach to Fuzzy Optimal Control via Parameterized Conjunction and Defuzzification, Fuzzy Systems and Artificial Intelligence, 2 (1) 53-57, 1993.

  24. P. Angelov, S. Tzonkov, Fuzzy Optimal Control of Ethanol Synthesis, Fuzzy Systems and Artificial Intelligence, 2(1): 45-51, 1993.

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G. Edited volumes/Conference Proceedings (34)

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  1. L. Iliadis, A. Papaleonidas, P. Angelov, C. Jayne, Artificial Neural Networks and Machine Learning – ICANN 2023: 32nd International Conference on Artificial Neural Networks, Heraklion, Crete, Greece, 26-29 Sept. 2023, Proceedings, Part VI, v. 14259. Springer Nature, 2023. DOI: 10.1007/978-3-031-44223-0.

  2. L. Iliadis, A. Papaleonidas, P. Angelov, C. Jayne, Artificial Neural Networks and Machine Learning – ICANN 2023: 32nd International Conference on Artificial Neural Networks, Heraklion, Crete, Greece, 26-29 Sept. 2023, Proceedings, Part IX. Vol. 14262. Springer Nature, 2023. DOI: 10.1007/978-3-031-44201-8.

  3. L. Iliadis, A. Papaleonidas, P. Angelov, C. Jayne, Artificial Neural Networks and Machine Learning – ICANN 2023: 32nd International Conference on Artificial Neural Networks, Heraklion, Crete, Greece, 26-29 Sept. 2023, Proceedings, Part VII. Vol. 14260. Springer Nature, 2023. DOI: 10.1007/978-3-031-44195-0.

  4. L. Iliadis, A. Papaleonidas, P. Angelov, C. Jayne, Artificial Neural Networks and Machine Learning – ICANN 2023: 32nd International Conference on Artificial Neural Networks, Heraklion, Crete, Greece, 26-29 Sept. 2023, Proceedings, Part VIII. Vol. 14261. Springer Nature, 2023. DOI: 10.1007/978-3-031-44198-1.

  5. L. Iliadis, A. Papaleonidas, P. Angelov, C. Jayne, Artificial Neural Networks and Machine Learning – ICANN 2023: 32nd International Conference on Artificial Neural Networks, Heraklion, Crete, Greece, 26-29 Sept. 2023, Proceedings, Part I. Vol. 14254. Springer Nature, 2023. DOI: 10.1007/978-3-031-44207-0.

  6. L. Iliadis, A. Papaleonidas, P. Angelov, C. Jayne, Artificial Neural Networks and Machine Learning – ICANN 2023: 32nd International Conference on Artificial Neural Networks, Heraklion, Crete, Greece, 26-29 Sept. 2023, Proceedings, Part III. Vol. 14256. Springer Nature, 2023.

  7. L. Iliadis, A. Papaleonidas, P. Angelov, C. Jayne, Artificial Neural Networks and Machine Learning – ICANN 2023: 32nd International Conference on Artificial Neural Networks, Heraklion, Crete, Greece, 26-29 Sept. 2023, Proceedings, Part X. Vol. 14263. Springer Nature, 2023.

  8. L. Iliadis, A. Papaleonidas, P. Angelov, C. Jayne, Artificial Neural Networks and Machine Learning – ICANN 2023: 32nd International Conference on Artificial Neural Networks, Heraklion, Crete, Greece, 26-29 Sept. 2023, Proceedings, Part IV. Vol. 14257. Springer Nature, 2023.

  9. L. Iliadis, A. Papaleonidas, P. Angelov, C. Jayne, Artificial Neural Networks and Machine Learning – ICANN 2023: 32nd International Conference on Artificial Neural Networks, Heraklion, Crete, Greece, 26-29 Sept. 2023, Proceedings, Part II. Vol. 14255. Springer Nature, 2023.

  10. L. Iliadis, A. Papaleonidas, P. Angelov, C. Jayne, Artificial Neural Networks and Machine Learning – ICANN 2023: 32nd International Conference on Artificial Neural Networks, Heraklion, Crete, Greece, 26-29 Sept. 2023, Proceedings, Part V. Vol. 14258. Springer Nature, 2023.

  11. R. Pecori, P. Angelov, L. Valerio, F. M. Nardini, M. L. Bernardi, PerConAI 2022: 1st Workshop on Pervasive and Resource-Constrained Artificial Intelligence, ISBN 978-1-6654-1647-4, DOI: 10.1109/PerComWorkshops53856.2022.9767268.

  12. L. Iliadis, P. P. Angelov, C. Jayne, A. Papaleonidas, M. Aydin, Artificial Neural Networks and Machine Learning – ICANN 2022: 31st International Conference on Artificial Neural Networks, Bristol, UK, 6-9 September 2022, Proceedings, Part I, In: Lecture Notes in Computer Science (LNCS) 13529, 761 pp., https://doi.org/10.1007/978-3-031-15919-0, Springer Cham.

  13. L. Iliadis, P. P. Angelov, C. Jayne, A. Papaleonidas, M. Aydin, Artificial Neural Networks and Machine Learning – ICANN 2022: 31st International Conference on Artificial Neural Networks, Bristol, UK, 6-9 September 2022, Proceedings, Part II, In: Lecture Notes in Computer Science (LNCS) 13530, 813 pp., https://doi.org/10.1007/978-3-031-15931-2, Springer Cham.

  14. L. Iliadis, P. P. Angelov, C. Jayne, A. Papaleonidas, M. Aydin, Artificial Neural Networks and Machine Learning – ICANN 2022: 31st International Conference on Artificial Neural Networks, Bristol, UK, 6-9 September 2022, Proceedings, Part III, In: Lecture Notes in Computer Science (LNCS) 13531, 813 pp., https://doi.org/10.1007/978-3-031-15934-3, Springer Cham.

  15. L. Iliadis, P. P. Angelov, C. Jayne, A. Papaleonidas, M. Aydin, Artificial Neural Networks and Machine Learning – ICANN 2022: 31st International Conference on Artificial Neural Networks, Bristol, UK, 6-9 September 2022, Proceedings, Part IV, In: Lecture Notes in Computer Science (LNCS) 13532,  pp.795, https://doi.org/10.1007/978-3-031-15937-4 , Springer Cham.

  16. P. Angelov, M. L. Bernardi, F. M. Nardini, R. Pecori, L. Valerio, PerConAI 2023: 2nd Workshop on Pervasive and Resource-Constrained Artificial Intelligence, ISBN 978-1-6654-5381-3, pp.105, DOI: 10.1109/PerComWorkshops56833.2023.10150273.

  17. L. Iliadis, P. P. Angelov, C. Jayne, E. Pimenides, Proceedings of the 21st EANN (Engineering Applications of Neural Networks) 2020 Conference, Springer, ISBN: 978-3-030-48790-4, DOI: 10.1007/978-3-030-48791-1, 619pp.​

  18. T. Yildirim, Y. Manolopoulos, P. Angelov, L. Iliadis, Proceedings of the 2018 Innovations in Intelligent Systems and Applications (INISTA 2018), IEEE Xplore, ISBN: 978-1-5386-5150-6, DOI: 10.1109/INISTA.2018.8466265.

  19. P. Angelov, Y. Manolopoulos, E. Lughofer, L. Iliadis, Proceedings of the 2018 Evolving and Adaptive Intelligent Systems (EAIS), IEEE Xplore, ISBN: 978-1-5386-1376-4, ISSN: 2473-4691 DOI: 10.1109/EAIS.2018.8397197.

  20. P. Angelov, J. A. Iglesias, J. C. Corrales (Eds.), Advances in Information and Communication Technologies for Adapting Agriculture to Climate Change, In: Advances in Intelligent Systems and Computing, v. 687, Springer, 265pp., 1st ed. 2018, ISBN-13: 978-3319701868.

  21. P. Angelov, A. Gegov, C. Jayene, Q. Shen (Eds.), Advances in Computational Intelligence Systems, In Advances in Intelligent Systems and Computing series, ISSN 2194-5357, v.513, 2017, Springer, ISBN 978-3-319-46561-6, DOI 10.1007/978-3-319-46562-3, 508pp.

  22. P. Angelov, Y. Manolopoulos, L. Iliadis, A. Roy, M. Vellasco, Advances in Big Data, In Advances, In Intelligent Systems and Computing series, ISSN 2194-5357, v.529, Springer Inter-national Publishing, DOI 10.1007/978-3-319-47898-2, ISBN 978-3-319-47897-5, 2017, 368 pp.

  23. M. Sayed-Mouchaweh, A. Fleury, P. P. Angelov, E. Lughofer, J. A. Iglesias (Eds.), Proc. 2015 IEEE International Conference on Evolving and Adaptive Intelligent Systems (EAIS2015), 26pp.

  24. P. Angelov, K. Atanassov, L. Doukovska, M. Hadjiski, V. Jotsov, J. Kacprzyk, N. Kasabov, S. Sotirov, E. Szmidt, S. Zadrozny (Eds.), Intelligent Systems' 2014, v.1: Mathematical Foundations, Theory, Analyses, v.322, Springer, ISBN 978-3-319-11312-8.

  25. P. Angelov, D. Filev, N. Kasabov, E. Lughofer, E.P. Klement, S. Saminger-Platz (Eds.), Proc. 2014 IEEE Conference on Evolving and Adaptive Intelligent Systems, Piscataway, N.J.: IEEE. 150 pp.

  26. P. Angelov, D. Levine, P. Erdi, M. Vellasco, E. del M Hernandez, B. Apolloni (Eds.), Proc. 2013 International Joint Conference on Neural Networks, IJCNN 2013, 3000pp., IEEE Press, Aug. 2013, ISBN: 978-1-4673-6129-3.

  27. J del R. Millán, D. Filev, P. Angelov, A. Abraham (Eds.), Proc. IEEE Conference on Cybernetics 2013, 300 pp., IEEE Press, June 2013, ISBN: 978-1-4673-6469-0.

  28. P. Angelov, D. Filev and N. Kasabov (Eds.), Proc. IEEE Symposium on Evolving and Adaptive Intelligent Systems, 120 pp., IEEE Press, April 2013, ISBN: 978-1-4673-5855-2.

  29. P. Angelov, D. Filev and N. Kasabov, J. Iglesias (Eds.), Evolving and Adaptive Intelligent Systems, 206 pp., IEEE Press, May 2012, ISBN: 978-1-4673-1726-9.

  30. P. Angelov, D. Filev and N. Kasabov (Eds.), Proc. IEEE Workshop on Evolving and Adaptive Intelligent Systems 2011, 193pp., IEEE Press, April 2011, ISBN: 978-1-4244-9977-9.

  31. P. Angelov, D. Filev and N. Kasabov (Eds.), Proc. Symposium on Evolving Intelligent Systems, 72pp., AISB Publication, April 2010, ISBN: 978-1902956947.

  32. Hoffmann, O. Cordon, P. Angelov, F. Klawonn (Eds.) Genetic and Fuzzy Systems, IEEE Press, 2008, ISBN 0-7803-9718-5.

  33. P. Angelov, D. Filev, N. Kasabov, O. Cordon (Eds.), Proc. IEEE Workshop on Evolving Fuzzy Systems, IEEE Press, 2006, 325pp., ISBN 0-7803-9719-3.

  34. M. Hadjiski, P. Angelov (Eds), Intelligent Adaptive Systems, IEEE Press, 2002, 55 pp., ISBN 0-7803-7602-1.

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H. Book chapters – peer reviewed (27)

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  1. X. Gu, P. Angelov, A Multi-Stream Deep Rule-based Ensemble System for Aerial Image Scene Classification, In: Handbook on Computer Learning and Intelligence (P. Angelov Ed., 2nd edition), World Scientific, to appear, 2022.

  2. A. Ali, P. Angelov, Applying Computational Intelligence to Community Policing and Forensic Investigations, In Community Policing - A European Perspective, Springer International Publishing, pp.231-246, 2017, ISBN 978-3-319-53395-7, DOI 10.1007/978-3-319-53396-4_16.

  3. A. Bux, P. Angelov, Z. Habib, Vision based human activity recognition: a review, In: Advances in Computational Intelligence Systems, Springer International Publishing, pp.341-371, 2017.

  4. A. Ali, P. Angelov, X. Gu, Detecting anomalous behaviour using heterogeneous data, In: Advances in Computational Intelligence Systems, Springer International Publ., pp. 253-273, 2017.

  5. P. Sadeghi-Tehran, P. Angelov, ARTOD: Autonomous Real Time Objects Detection by a Moving Camera using Recursive Density Estimation, In: Novel Applications of Intelligent Systems, M. Hadjiski, N. Kasabov, D. Filev, V. Jotsov (Eds.), Springer, p. 123-138, 2016. ISBN: 9783319141930.

  6. S. Blažič, P. Angelov, I. Škrjanc, Comparison of approaches for identification of all-data cloud-based evolving systems, IFAC-PapersOnLine, vol.48 (10), pp.129-134, 2015.

  7. P. Angelov, P. Sadeghi-Tehran, A Nested Hierarchy of Dynamically Evolving Clouds for Big Data Structuring and Searching, INNS Conference on Big Data, San Francisco, CA, USA, 8-10 August, 2015, In Procedia Computer Science, Elsevier, v. 53, pp. 1-8.

  8. D. Kangin, P. Angelov, J. A. Iglesias, A. Sanchis, Evolving Classifier TEDAClass for Big Data, INNS Conference on Big Data, San Francisco, CA, USA, 8-10 August, 2015, In Procedia Computer Science, Elsevier, v. 53, p. 9-18.

  9. 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.

  10. C. Bezerra, B. Costa, L. A. Guedes, P. Angelov, RDE with Forgetting: an Approximate Solution for Large Values of k with an Application to Fault Detection Problems, In A. Gammerman, V. Vovk, H. Papadopoulos (Eds.), Statistical Learning and Data Sciences, pp. 169-178, ISBN 978-3-319-17090-9, Lecture Notes in Computer Science, v.9047, DOI10.1007/978-3-319-17091-6_12

  11. D. Kangin. P. Angelov, Recursive SVM based on TEDA, In A. Gammerman, V. Vovk, H. Papadopoulos (Eds.), Statistical Learning and Data Sciences, pp. 156-168, ISBN 978-3-319-17090-9, Lecture Notes in Computer Science, vol. 9047  DOI: 10.1007/978-3-319-17091-6_11.

  12. P. Sadeghi Tehran, P. Angelov, ATDT: Autonomous Template-based Detection and Tracking of objects from airborne camera, 2015 In: Advances in Intelligent Systems and Computing; v. 323 Intelligent Systems v.2 Tools, Architectures, Systems, Applications. (Filev, D., Jabłkowski, J., Kacprzyk, J., Krawczak, M., Popchev, I., Rutkowski, L., Sgurev, V., Sotirova, E., Szynkarczyk, P. and Zadrozny, S. eds.), Springer, pp. 555-565.

  13. D. Kolev, M. Suvorov, E. Morozov, G. Markarian, P. Angelov, Incremental anomaly identification in flight data analysis by adapted one-class SVM method, In: 2015 Artificial neural networks: methods and applications in bio-/neuroinformatics. (P. Koprinkova-Hristova, V. Mladenov and N K Kasabov Eds.), Springer Series in Bio-/Neuroinformatics; v.4, pp.373-391, ISBN 978-3-319-09902-6, DOI: 10.1007/978-3-319-09903-3_18.

  14. J. Shafi, P.P. Angelov, M. Umair, Prediction of the attention area in ambient intelligence tasks, In: Innovative Issues in Intelligent Systems. Berlin: Springer pp.33-56, ISBN: 9783319272665, 2016.

  15. D. Kangin, G. Kolev, P. Angelov, Vehicle Plate Recognition using Improved Neocognitron Neural Network, In: Lecture Notes in Computer Sciences LNCS (V. Mladenov et al. Eds.), vol. 8131, pp.628-640, Springer, Heidelberg, 2013.

  16. M. Suvorov, S. Ivliev, G. Markarian, D. Kolev, D. Zvikhachevskiy, P. Angelov, OSA: One-class Re-cursive SVM Algorithm with Negative Samples for Fault Detection, In: Lecture Notes in Computer Sciences LNCS (V. Mladenov et al. Eds.), vol. 8131, pp.194-207, Springer, Heidelberg, 2013.

  17. 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.

  18. P. Angelov, A. Kordon, Evolving Intelligent Sensors in Chemical Industry, In Evolving Intelligent Systems: Methodology and Applications (Angelov P., D. Filev, N. Kasabov Eds.), John Willey and Sons, pp.313-336, ISBN: 978-0-470-28719-4, Feb. 2010.

  19. J. M. Hernandez, P. Angelov, Applications of Evolving Intelligent Systems to Oil and Gas Industry, In Evolving Intelligent Systems: Methodology and Applications (Angelov P., D. Filev, N. Kasabov Eds.), John Willey and Sons, pp.399-420, ISBN: 978-0-470-28719-4, Feb. 2010.

  20. P. Angelov, Evolving Fuzzy Systems, In Encyclopedia on Complexity and System Science (Bob Meyers Editor-in-Chief), 10398 pp., ISBN: 978-0-387-75888-6, article 194, Springer, June 2009.

  21. P. Angelov, X.-W. Zhou, Evolving Fuzzy Classifier for Real-time Novelty Detection and Landmark Recognition by a Mobile Robot, In: Mobile Robots: The Evolutionary Approach (N. Nedja, L. Coelho, L. Mourelle Eds.), Studies in Comp. Intelligence, Springer, 2007, pp.95-124, ISBN 978-3-540-49719-6.

  22. D. Filev, P. Angelov, Algorithms for Real-Time Clustering and Generation of Rules from Data, In: Advances in Fuzzy Clustering and its Applications (J. Oliveira, W. Pedrycz Eds.), Wiley, NY, US, 2007, pp.353-370, ISBN 978-0-470-02760-8.

  23. P. P. Angelov, Y. Zhang, and J. A. Wright. Optimal Design Synthesis of Component-based Systems using Intelligent Techniques, 2004, pp. 267-284. In Do Smart Adaptive Systems Exist? (B. Gabrys, K. Leiviskä, and J. Strackeljan Eds), Springer, ISBN 3-540-24077-2.

  24. P.P. Angelov, An Approach to On-line Design of Fuzzy Controllers with Evolving Structure, In: Applications and Science in Soft Computing Series: Advances in Soft Computing, Lotfi A., J. Garibaldi (Eds.), 2004, v. X, pp.63-68, ISBN: 3-540-40856-8.

  25. 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.

  26. P. Angelov, D. Filev, On-line Design of Takagi-Sugeno Models, In: 10th International Fuzzy Systems Association World Congress, IFSA2003 (T. Bilgiç, B. De Baets, O. Kaynak Eds.), Lecture Notes in Artificial Intelligence, 2715, pp. 576-584, 2003.

  27. P. P. Angelov, V. I. Hanby, R. A. Buswell, J.A. Wright, Automatic Generation of Fuzzy Rule-based Models from Data by Genetic Algorithms, In Developments in Soft Computing (R. John, R. Birkenhead Eds.), Springer: Heidelberg, Germany, 2001, pp.31-40.

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I. Published Abstracts in Conference Proceedings (not peer reviewed) (16)

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  1. P. Angelov, Keynote: Explainable-by-design Deep Learning, 2021 IEEE International Conference on Pervasive Computing and Communications Workshops and other Affiliated Events (PerCom Workshops), PerDL-2021, published online 25 May 2021, DOI: 10.1109/PerComWorkshops51409.2021.9431114

  2. E. Shafipour Yourdshahi, M. A. do C. Alves, L. S. Marcolino, P. Angelov, On-line Estimators for Ad-hoc Task Allocation, Proc. 19th International Conference on Autonomous Agents and Multiagent Systems (AAMAS 2020), B. An, N. Yorke-Smith, A. El Fallah Seghrouchni, G. Sukthankar (eds.), May 9–13, 2020, Auckland, New Zealand.

  3. A. Azman, D. Hutchison, P. Angelov, P Smith, Towards an Autonomous Resilience Strategy, The Implementation of a Self-Evolving Rate Limiter, In Proc. UKCI 2013 (Y. Jin and S. A. Thomas Eds), 9-11 September, 2013, Guildford, UK, p.35, ISBN 978-1-4799-1568-2.

  4. J. Trevisan, P. P. Angelov, P. L. Carmichael, A. D. Scott and F. L. Martin, Advances in Fourier-transform infrared spectroscopy analysis to characterise chemical-induced alterations in the Syrian hamster embryo assay-towards biomarkers stability, Mutagenesis, 27(6): 792, ISSN 0267-8357, 2012.

  5. J. Trevisan, P. P. Angelov, P. L. Carmichael, A. D. Scott and F. L. Martin, Designing open, multi-class computational strategies to classify infrared spectroscopy data derived from the Syrian hamster embryo (SHE) assay, Mutagenesis, 27(1): 111, ISSN 0267-8357, 2012.

  6. A. Azman, P. Angelov, D. Hutchison, Towards Adaptive, Self-learning Resilience Strategies, 6th International Workshop on Self-Organizing Systems, IWSOS-2012, Delft, The Netherlands, 15-16 March 2012.

  7. P. Angelov, ALMA for Evolving Systems, 12th NCEI, Auckland, New Zealand, 8 June 2012.

  8. J. Trevisan, P. P. Angelov, P. L. Carmichael, A. D. Scott, and F. L. Martin, A mathematical framework for spectroscopy data analysis to characterize chemical-induced alterations in the SHE assay, Mutagenesis, 25(6): 658, 2010.

  9. J. Trevisan, P.P. Angelov, F.L.A. Martin, Derivation of a computational approach to iteratively discriminate a transformation phenotype in Syrian hamster embryo cells, Mutagenesis, 24(6): 543, 2009.

  10. P. Angelov, C. Xydeas, C. D. Bocaniala, D. Ansell, C. Patchett and M. Everett, UAV collision avoi-dance- state of the art and possible solutions, VTOL UAV, Helitech, 3-5 Oct. 2007, Duxford, UK

  11. P. Angelov, F. von Eggeling, R. Guthke, Classification of Carcinoma Kidney Tissue Status based on the Data of Protein Expression using LS – SVM, International Workshop on Intelligent Technologies for Gene Expression-based Individualized Medicine, Jena, Germany, 9 May 2003, pp. 18-19, full paper on CD-ROM

  12. P. Angelov, Nature-inspired Techniques for Real-Time Knowledge Extraction form Data, NiSiS Conference, 8-9 June 2006, pp.1-3, ISBN 3-86130-926-2.

  13. P. Angelov, O. Bernard, G. Bastin, C. Stentelaire and M. Asther, Hybrid Modelling of Biotechnological Processes using Neural Networks, 2nd European Symposium on Bio-Engineering Systems ESBES-2, Porto, Portugal, Sept. 17-20, 1998, p.219

  14. P. Angelov, Fuzzy Mathematical Programming Problem Solving, 13th European Congress on Operations Research EURO-XIII, July 4-6, 1994, Glasgow, UK, p.374

  15. P. Angelov, N. Zamdzhiev, S. Tzonkov, Optimal Control of Biotechnological processes, 10th Control Conference, Wroczlaw, Poland, 1993, p.171-172

  16. D. Carline, P. Angelov, and R. Clifford, Agile Collaborative agents for classification of underwater targets, Undersea Defence Technology Conference, 21-23 June 2005, Amsterdam, the Netherlands.

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