Selected Publications in top journals​

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• IEEE Transactions on Pattern Analysis and Machine Intelligence, TPAMI (IF 20.8)

X. Gu, P. P. Angelov, C. Zhang, P. M. Atkinson, A Semi-Supervised Deep Rule-Based Approach for Complex Satellite Sensor Image Analysis, vol. 44(5): 2281-2292, DOI: 10.1109/TPAMI.2020.3048268, May 2022

• Nature Protocols (17.5)

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, May 2010, DOI: 10.1038/nprot.2010.97.

• Information Fusion (IF 14.8)

X. Gu, C. Zhang, Q. Shen, J. Han, P.P. Angelov, P.M. Atkinson, A Self-Training Hierarchical Prototype-based Ensem-ble Framework for Remote Sensing Scene Classification, vol.80, 179-204, April 2022.

• AI Review (IF 11.7)

X. Gu, J. Han, Q. Shen, P. Angelov, Autonomous Learning for Fuzzy Systems: a review, vol. 56: 7549-7595, 2023, 

DOI 10.1007/s10462-022-10355-6.

• IEEE Transactions on Fuzzy Systems (IF 10.7)

P. Angelov, R. Buswell, Identification of Evolving Rule-based Models, vol. 10 (5): 667-677, 2002.

P. Angelov, X. Zhou, Evolving Fuzzy-Rule-based Classifiers from Data Streams, vol. 16(6): 1462-1475, 2008, highly cited, nominated for Outstanding Transactions paper (2008)

P. Angelov, X. Gu, J. Principe, Autonomous learning multi-model systems from data streams, vol. 26(4): 2213-2224, Aug. 2018.

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

E. Soares, P. Angelov, M. P. G. Castro, S. Nageshrao, B. Costa, D. Filev, Explaining Deep Learning Models Through Rule-Based Approximation and Visualization, vol.29 (8): 2399-2407, DOI: 10.1109/TFUZZ.2020.2999776, Aug. 2021.

• IEEE Transactions on Neural Networks (IF 10.2)

J. de Jesús Rubio, P. Angelov, E. García, An uniformly stable backpropagation algorithm to train a feedforward neural network, vol. 22(3): 356-366, 2011.

• IEEE Transactions on Cybernetics (IF 9.4)

P. Angelov, X. Gu, J. Principe, A generalized methodology for data analysis, vol. 48(10): 2981-2993, Oct. 2018.

P. Angelov, Fuzzily Connected Multi-Model Systems Evolving Autonomously from Data Streams, vol. 41(4): 898-910, 2011.

• IEEE Transactions on Knowledge Data Engineering (IF 8.9)

J. Iglesias, P. Angelov, A. Ledezma, A. Sanchis, Creating evolving user behavior profiles automatically, vol. 24(5): 854-867, 2012.

• International Journal on Computers in Biology and Medicine (IF 7)

C. Xydeas, P. Angelov, S. Chiao and M. Reoullas, Advances in EEG Signals Classification via Dependant HMM models and Evolving Fuzzy Classifiers, vol. 36 (10): 1064-1083, 2006.

• International Journal of Neural Systems (IF 6.6)

J. A. Iglesias, P. Angelov, A. Ledezma, A. Sanchis, Human Activity Recognition based on Evolving Fuzzy Systems, vol. 20(5): 355-364, 2010.

• Wiley Interdisciplinary Reviews (WIRE) on Data Mining and Knowledge Discovery (IF 6.3)

P. P. Angelov, E. A. Soares, R. Jiang, N. I. Arnold, P. M. Atkinson, Explainable artificial intelligence: an analytical review, DOI:10.1002/widm.1424, published online 12 July 2021.

• Neural Networks (IF 6.0)

E. A. Soares, P. Angelov, Towards Explainable Deep Neural Networks (xDNN), vol. 130: 185-194, Oct. 2020, 350+ citations

• Information Sciences (IF 5.91)

P. Angelov, X. Gu, Deep rule-based classifier with human-level performance and characteristics, vol. 463-464: 196-213, October 2018.

P. Angelov, R. Yager, Density-based Averaging - a new Operator for Data Fusion, vol. 222: 163-174, 2013.

• Neurocomputing (IF 5.5)

D. Kangin, P. Angelov, Z. Zhang, IDEAL: Interpretable-by-Design ALgorithms for learning from foundation feature spaces, vol. 626, 14 April 2025, DOI: 10.1016/j.neucom.2025.129464

• Control Engineering Practice (IF 5.4)

L. Chen, O. Bernard, G. Bastin and P. Angelov, Hybrid Modelling of Biotechnological Processes using Neural Networks, vol. 8(7):821-827, 2000.

• International Journal on Intelligent Systems (IF 5)

P. Angelov, X.  Gu, D. Kangin, Empirical data analytics, vol. 32(12): 1261-1284, Dec. 2017; top20 most downloadable article

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, vol. 32(1): 82-103, 2017.

• Neural Computation and Applications (IF 4.8)

P. Angelov, E. A. Soares, Detecting and Learning from Unknown by Extremely Weak Supervision: eXploratory Classifier (xClass), vol.33 (22), 15145-15157, November, 2021.

• Bioinformatics (IF 4.4)

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

• Analyst (IF 4.2)

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 4.62), 137: 3202-3215, 2012.

• IEEE Geoscience and Remote Sensing Letters (IF 4)

X. Gu, P. Angelov, C. Zhang, P. Atkinson, A massively parallel deep rule-based ensemble classifier for remote sensing scenes, vol. 15 (3), 345-349, 2018.

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and others. Also, some selected

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. 2nd edition: P. Angelov (Ed.), Handbook in Computer Learning and Intelligence, World Scientific, 2 volumes, 1056pp., DOI: 10.1142/12498, Sept. 2022

• 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 Wiley 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.

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