Research monographs:

• P. Angelov, X. Gu, Empirical Approach to Machine Learning, Springer International Publishing, Dec. 2018, ISBN 978-3-030-02384-3.

• 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.

Edited Books:

• P. Angelov (Ed.), Handbook in Computational Intelligence, World Scientific, 2 volumes, 870pp., 2016, ISBN: 978-0-470-28719-4.

• P. Angelov (Ed.), Sense and Avoid in UAS: Research and Applications, 385pp., John Willey and Sons, May 2012, ISBN: 978-0-470-97975-4; (in addition to the English edition also in Chinese).

• P. Angelov, D. Filev and N. Kasabov (Eds.), Evolving Intelligent Systems: Methodology and Applications, 484 pp., John Willey and Sons, April 2010, ISBN: 978-0-470-28719-4.

Granted patents:

• P. Angelov, Machine Learning (Collaborative Systems), priority date 23 Oct 2007, granted 21 August 2012, USA patent 8250004.

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

Peer reviewed journal articles:

• E. A. Soares, P. Angelov, Detecting and Learning from Unknown by Extremely Weak Supervision: eXploratory Classifier (xClass), Neural Computing and Applications (IF 5.61), published online 6 June 2021, to appear.

• X. Gu, P. P. Angelov, C. Zhang, P. M. Atkinson, An Active Semi-Supervised Deep Rule-Based Approach for Complex Satellite Sensor Image Analysis, IEEE Transactions on Pattern Analysis and Machine Intelligence, TPAMI (IF 16.39), published online 30 December 2020, DOI: 10.1109/TPAMI.2020.3048268, to appear.

• E. A. Soares, P. Angelov, Towards Explainable Deep Neural Networks (xDNN), Neural Networks (IF 8.05), 130: 185-194, Oct 2020.

• E. Soares, P. Angelov, M. P. G. Castro, S. Nageshrao, B. Costa, D. FIlev, Explaining Deep Learning Models Through Rule-Based Approximation and Visualization, IEEE Transactions on Fuzzy Systems (IF 12.03), DOI: 10.1109/TFUZZ.2020.2999776, published online 3 June 2020.

• X. Gu, Q. Shen, P. Angelov, Particle Swarm Optimized Autonomous Learning Fuzzy System, IEEE Transactions on Cybernetics (IF 11.45), DOI: 10.1109/TCYB.2020.2967462, published online 20 Feb. 2020.

• X. Gu, P. P. Angelov, E. A. Soares, A Self-adaptive Synthetic over-sampling technique for imbalanced classification, International Journal on Intelligent Systems (IF 8.71), 35: 923-943, 23 Feb. 2020, DOI: 10.1002/int.22230.

• X. Gu, P. Angelov, H. J. Rong, Local Optimality of Zero-Order Autonomous Learning Neuro-Fuzzy Systems, Information Sciences (IF 6.795), 503: 351-380, 2019.

• P. Angelov, X. Gu, J. Principe, A generalized methodology for data analysis, IEEE Transactions on Cybernetics (IF 11.45), 48(10): 2981-2993, Oct. 2018.

• H.-J. Rong, P. Angelov, X. Gu, J. Bai, Stability of Evolving Fuzzy Systems based on Data Clouds, IEEE Transactions on Fuzzy Systems (IF 12.03), 26(5): 2774-2784, Oct. 2018.

• X. Gu, P. Angelov, J. Principe, A method for autonomous data partitioning into data clouds, Information Sciences, vol. 460-461, pp. 65-82 (IF 6.795), Sept. 2018.

• P. Angelov, X. Gu, Towards Anthropomorphic Machine Learning, IEEE Computer (IF 3.564), 51 (9): 18-27, Sept. 2018.

• P. Angelov, X. Gu, J. Principe, Autonomous learning multi-model systems from data streams, IEEE Transactions on Fuzzy Systems, (IF 12.03), 26(4): 2213-2224, Aug. 2018.

• P. Angelov, X. Gu, Deep rule-based classifier with human-level performance and characteristics, Information Sciences (IF 6.795), 463-464: 196-213, October 2018.

• † R. Bao, H. Rong, P. Angelov, B. Chen, P. Wong, Correntropy-Based Evolving Fuzzy Neural System, IEEE Transactions on Fuzzy Systems (IF 12.03), 26(3): 1324-1338, 23 June 2017.

• X. Gu, P. Angelov, Self-organising fuzzy logic classifier, Information Sciences (IF 6.795), 447, 36-51, 2018.

• 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 4.89), 15 (3), 345-349, 2018.

• P. Angelov, X. Gu, Empirical Fuzzy Sets, International Journal of Intelligent Systems (IF 8.71), 33(2): 362-395, Feb.2018; top20 most downloadable article

• X. Gu, P. Angelov, D. Kangin, J. Principe, Self-organised direction aware data partitioning algorithm, Information Sciences (IF 6.795), 423: 80-95, Jan. 2018.

• X. Gu, P. Angelov, D. Kangin, J. Principe, A new type of distance metric and its use for clustering, Evolving Systems (IF 1.91), 8(3): 167-177, 2017.

• R. Hyde, P. Angelov, A. MacKenzie, Fully online clustering of evolving data streams into arbitrarily shaped clusters, Information Sciences (IF 6.795), 382: 96-114, 2017.

• P. Angelov, X.  Gu, D. Kangin, Empirical data analytics, International Journal of Intelligent Systems (IF 8.71), 32(12): 1261-1284, Dec. 2017; top20 most downloadable article

• A. Sargano, P. Angelov, Z. Habib, A comprehensive review on handcrafted and learning-based action representation approaches for human activity recognition, Applied Sciences (IF 2.68), 7(1): 110, 2017.

• 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 8.71), 32(1): 82-103, 2017.

• 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 5.72), 150A: 289-303, 2015.

• † M. Pratama, S. Anavatti, P. Angelov, E. Lughofer, PANFIS: A Novel Incremental Learning Machine, IEEE Trans. on Neural Networks and Learning Systems (IF 8.793), 25 (1): 55-68, 2014.

• P. Angelov, Outside the box: An Alternative Data Analytics Framework, Journal of Automation, Mobile Robotics and Intelligent Systems (IF 0.54), 8(2):29-35, 2014.

• 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 11.45), 44(9): 1619-1631, 2013.

• P. Angelov, R. Yager, Density-based Averaging - a new Operator for Data Fusion, Information Sciences (IF 6.795), 222: 163-174, 2013.

• J. Iglesias, P. Angelov, A. Ledezma, A. Sanchis, Creating evolving user behavior profiles automatically, IEEE Transactions on Knowledge Data Engineering (IF 6.98), 24(5): 854-867, 2012.

• P. Angelov, R. Yager, A New Type of Simplified Fuzzy Rule-based Systems, International Journal of General Systems (IF 2.259), 41(2): 163-185, 2012.

• P. Angelov, Fuzzily Connected Multi-Model Systems Evolving Autonomously from Data Streams, IEEE Transactions on Systems, Man, and Cybernetics - part B, Cybernetics (IF 11.45), 41(4): 898-910, 2011.

• 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 8.71), 26(3): 189-205, 2011.

• † J. de Jesús Rubio, P. Angelov, E. García, An uniformly stable backpropagation algorithm to train a feedforward neural network , IEEE Transactions on Neural Networks (IF 8.793), 22(3): 356-366, 2011.

• E. Lughofer, P. Angelov, Handling Drifts and Shifts in On-line Data Streams with Evolving Fuzzy Systems, Applied Soft Computing (IF 6.73), 11(2): 2057-2068, 2011.

• 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 17.24) Exchange, May 2010, DOI: 10.1038/nprot.2010.97.

• † 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 3.3), 398(5): 2191-2201, 2010.

• 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 11.47), 40(2): 529-539, 2010.

• *P. Angelov, X. Zhou, Evolving Fuzzy-Rule-based Classifiers from Data Streams, IEEE Transactions on Fuzzy Systems (IF 12.03), ISSN 1063-6706, 16(6): 1462-1475, 2008, nominated for Outstanding Transactions paper.

• P. Angelov, E. Lughofer and X. Zhou, Evolving Fuzzy Classifiers with Different Architectures, Fuzzy Sets and Systems (IF 3.34), 159, 3160-3182, 2008.

• 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 11.45), 34 (1): 484-498, 2004.

• P. Angelov, R. Buswell, Identification of Evolving Rule-based Models, IEEE Transactions on Fuzzy Systems (IF 12.03), 10 (5): 667-677, 2002.

Papers in peer reviewed conference proceedings:

• P. Angelov, E. Soares, Towards Deep Machine Reasoning: a Prototype-based Deep Neural Network with Decision Tree Inference, IEEE Intern. Conf. on Systems, Man and Cybernetics, IEEE SMC2020, 11-14 Oct 2020, Toronto, Canada, pp. 2092-2099, DOI: 10.1109/SMC42975.2020. 9282812.

• E. Soares, P. Angelov, B. Costa, M. Castro, Actively Semi-Supervised Deep Rule-based Classifier Applied to Adverse Driving Scenarios, 2019 International Joint Conference on Neural Networks (IJCNN2019), Budapest, Hungary, 14-19 July 2019, DOI: 10.1109/IJCNN.2019.8851842.

• 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.

• P. P. Angelov, Typicality Distribution Function: A New Density based Data Analytics Tool, 2015 IEEE International Joint Conference on Neural Networks, IJCNN 2015, pp.1-6, DOI 10.1109/IJCNN.2015.7280438.

• P. Angelov, Anomaly Detection based on Eccentricity Analysis, Proc. 2014 IEEE Symposium on Evolving and Autonomous Learning Systems, EALS within SSCI2014, Orlando, USA, 9-12 Dec. 2014, pp.1-8, ISBN 978-1-4799-4495-8.

• R. D. Baruah, P. Angelov, Evolving Local Means Method for Clustering of Streaming Data, In Proc. 2012 World Congress on Computational Intelligence, WCCI-2012, 10-15 June 2012, Brisbane, Australia, pp.2161-2168 (IEEE Press ISBN 978-1-4673-1489-3).

• R. Dutta-Baruah, P. Angelov, J. Andreu, Simpl_eClass: Simple Potential-free Evolving Fuzzy Rule-Based On-line Classifiers, Proc. 2011 IEEE International Conference on Systems, Man and Cybernetics, SMC 2011, Anchorage, Alaska, USA, 7-9 Oct, 2011, pp.2249-2254.

• P. Angelov, R. Ramezani, X. Zhou, Autonomous Novelty Detection and Object Tracking in Video Streams using Evolving Clustering and Takagi-Sugeno type Neuro-Fuzzy System, Proc. 2008 IEEE World Congress on Computational Intelligence, Hong Kong, June 1-6, 2008, ISBN 978-1-4244-1821-3/08, pp.1457-1464, IEEE Xplore.

• 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.

• 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.

• *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.

• 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.

• 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.

• P. Angelov, Evolving Fuzzy Rule-Based Models, Proc. 8th IFSA World Congress, Taipei, Taiwan, August 17-20, 1999, v.1, pp.19-23.

Other (open access) publications:

• 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

• 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.

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

Book chapters:

• 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.

• 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.

• 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