Empirical Approach—Introduction

Angelov, P. P. & Gu, X., 1/01/2019, Empirical Approach to Machine Learning. Angelov, P. & Gu, X. (eds.). Cham: Springer-Verlag, p. 103-133 31 p. (Studies in Computational Intelligence; vol. 800).

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Anomaly Detection—Empirical Approach

Angelov, P. P. & Gu, X., 2019, Empirical Approach to Machine Learning. Angelov, P. P. & Gu, X. (eds.). Cham: Springer, p. 157-173 17 p. (Studies in Computational Intelligence; vol. 800).

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Applications of Autonomous Anomaly Detection

Angelov, P. P. & Gu, X., 2019, Empirical Approach to Machine Learning. Angelov, P. & Gu, X. (eds.). Springer-Verlag, Vol. 800. p. 249-259 11 p. (Studies in Computational Intelligence; vol. 800).

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Applications of Autonomous Data Partitioning

Angelov, P. P. & Gu, X., 2019, Empirical Approach to Machine Learning. Angelov, P. & Gu, X. (eds.). Springer-Verlag, Vol. 800. p. 261-276 16 p. (Studies in Computational Intelligence; vol. 800).

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Applications of Autonomous Learning Multi-model Systems

Angelov, P. P. & Gu, X., 2019, Empirical Approach to Machine Learning. Angelov, P. & Gu, X. (eds.). Springer-Verlag, Vol. 800. p. 277-293 17 p. (Studies in Computational Intelligence; vol. 800).

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Applications of Deep Rule-Based Classifiers

Angelov, P. P. & Gu, X., 2019, Empirical Approach to Machine Learning. Angelov, P. & Gu, X. (eds.). Springer-Verlag, Vol. 800. p. 295-319 25 p. (Studies in Computational Intelligence; vol. 800).

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Applications of Semi-supervised Deep Rule-Based Classifiers

Angelov, P. P. & Gu, X., 2019, Empirical Approach to Machine Learning. Angelov, P. & Gu, X. (eds.). Springer-Verlag, Vol. 800. p. 321-340 20 p. (Studies in Computational Intelligence; vol. 800).

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Brief Introduction to Computational Intelligence

Angelov, P. P. & Gu, X., 2019, Empirical Approach to Machine Learning. Angelov, P. & Gu, X. (eds.). Springer-Verlag, Vol. 800. p. 69-99 31 p. (Studies in Computational Intelligence; vol. 800).

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Autonomous Learning Multi-model Systems

Angelov, P. P. & Gu, X., 2019, Empirical Approach to Machine Learning. Angelov, P. & Gu, X. (eds.). Springer-Verlag, Vol. 800. p. 199-222 24 p. (Studies in Computational Intelligence; vol. 800).

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Brief Introduction to Statistical Machine Learning

Angelov, P. P. & Gu, X., 2019, Empirical Approach to Machine Learning. Angelov, P. & Gu, X. (eds.). Springer-Verlag, Vol. 800. p. 17-67 51 p. (Studies in Computational Intelligence; vol. 800).

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Data Partitioning—Empirical Approach

Angelov, P. P. & Gu, X., 2019, Empirical Approach to Machine Learning. Angelov, P. & Gu, X. (eds.). Springer-Verlag, Vol. 800. p. 175-198 24 p. (Studies in Computational Intelligence; vol. 800).

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Empirical Fuzzy Sets and Systems

Angelov, P. P. & Gu, X., 2019, Empirical Approach to Machine Learning. Angelov, P. & Gu, X. (eds.). Springer-Verlag, Vol. 800. p. 135-155 21 p. (Studies in Computational Intelligence; vol. 800).

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Epilogue: Studies in Computational Intelligence

Angelov, P. P. & Gu, X., 2019, Empirical Approach to Machine Learning. Angelov, P. & Gu, X. (eds.). Springer-Verlag, Vol. 800. p. 341-346 6 p. (Studies in Computational Intelligence; vol. 800).

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Introduction

Angelov, P. P. & Gu, X., 2019, Empirical Approach to Machine Learning. Angelov, P. & Gu, X. (eds.). Springer-Verlag, Vol. 800. p. 1-14 14 p. (Studies in Computational Intelligence; vol. 800).

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Transparent Deep Rule-Based Classifiers

Angelov, P. P. & Gu, X., 2019, Empirical Approach to Machine Learning. Angelov, P. & Gu, X. (eds.). Springer-Verlag, Vol. 800. p. 223-245 23 p. (Studies in Computational Intelligence; vol. 800).

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Preface

Corrales, J. C., Iglesias, J. A. & Angelov, P., 1/01/2018, Advances in Information and Communication Technologies for Adapting Agriculture to Climate Change: Proceedings of the International Conference of ICT for Adapting Agriculture to Climate Change (AACC'17), November 22-24, 2017, Popayán, Colombia. Angelov, P., Iglesias, J. A. & Corrales, J. C. (eds.). Cham: Springer, p. v-vi 2 p. (Advances in Intelligent Systems and Computing; vol. 687).

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A robust evolving cloud-based controller

Angelov, P., Skrjanc, I. & Blazic, S., 2015, Springer Handbook of Computational Intelligence . Kacprzyk, J. & Pedrycz, W. (eds.). Berlin: Springer, Vol. G. p. 1435-1449 15 p.

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Autonomous Machine Learning (ALMA): generating rules from data streams

Angelov, P., 19/09/2011, Proceedings of the Special International Conference on Complex Systems, COSY-2011: 16-19 September 2011. Ohrid, FYR of Macedonia, p. 249-256 8 p.

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A multistream deep rule-based ensemble system for aerial image scene classification

Gu, X. & Angelov, P. P., 29/06/2022, Deep Learning, Intelligent Control and Evolutionary Computation. Angelov, P. (ed.). World Scientific Publishing Co., Vol. 2. p. 661-695 35 p.

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Handbook on computer learning and intelligence

Angelov, P. P., 29/06/2022, Explainable AI and Supervised Learning; Deep Learning, Intelligent Control and Evolutionary Computation. Angelov, P. (ed.). Singapore: World Scientific Publishing Co., Vol. 1-2. p. 1-1056 1056 p.

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Applying computational intelligence to community policing and forensic investigations

Ali, A. M. & Angelov, P., 1/01/2017, Advanced Sciences and Technologies for Security Applications. Springer, p. 231-246 16 p. (Advanced Sciences and Technologies for Security Applications).

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Handbook on Computational Intelligence

Angelov, P. P., 31/05/2016, Handbook On Computational Intelligence (In 2 Volumes). World Scientific Publishing Co., p. 1-964 964 p.

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Prediction of the attention area in ambient intelligence tasks

Shafi, J., Angelov, P. P. & Umair, M., 3/02/2016, Innovative issues in intelligent systems. Sgurev, V., Yager, R., Kacprzyk, J. & Jotsov, V. (eds.). Berlin: Springer, p. 33-56 24 p. (Studies in Computational Intelligence; vol. 623).

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ARTOD: Autonomous Real Time Objects Detection by a moving camera using recursive density estimation

Sadeghi Tehran, P. & Angelov, P., 28/01/2016, Novel applications of intelligent systems. Hadjiski, M., Kasabov, N., Filev, D. & Jotsov, V. (eds.). Cham: Springer Verlag, p. 123-138 16 p. (Studies in Computational Intelligence; vol. 586).

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Evolving fuzzy systems

Angelov, P., 1/11/2012, Computational Complexity: Theory, Techniques, and Applications. Springer New York, Vol. 9781461418009. p. 1053-1065 13 p.

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Evolving Takagi-Sugeno Fuzzy Systems from Streaming Data (eTS+)

Angelov, P., 14/04/2010, Evolving Intelligent Systems: Methodology and Applications. Angelov, P., Angelov, D. & Kasabov, N. (eds.). John Wiley and Sons, p. 21-50 30 p.

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Evolving Inferential Sensors in the Chemical Process Industry

Angelov, P. & Kordon, A., 14/04/2010, Evolving Intelligent Systems: Methodology and Applications. Angelov, P., Filev, D. P. & Kasabov, N. (eds.). John Wiley and Sons, p. 313-336 24 p.

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Evolving Takagi-Sugeno fuzzy systems from data streams (eTS+).

Angelov, P., 04/2010, Evolving intelligent systems : methodology and applications. Angelov, P., Filev, D. & Kasabov, N. (eds.). New York, USA: John Wiley and Sons and IEEE Press, p. 21-50 30 p. (IEEE Press series in Computational Intelligence).

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Evolving intelligent sensors in chemical industry.

Angelov, P. & Kordon, A., 04/2010, Evolving intelligent systems : methodology and applications. Angelov, P., Filev, D. & Kasabov, N. (eds.). New York, USA: John Wiley and Sons and IEEE Press, p. 313-336 24 p. (IEEE Press series in Computational Intelligence).

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Applications of evolving intelligent systems to oil and gas industry.

Macias Hernandez, J. & Angelov, P., 04/2010, Evolving intelligent systems : methodology and applications. Angelov, P., Filev, D. & Kasabov, N. (eds.). New York, USA: John Wiley and Sons and IEEE Press, p. 401-422 22 p. (IEEE Press series in Computational Intelligence).

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Evolving Fuzzy Systems.

Angelov, P., 06/2009, Encyclopedia of Complexity and System Science. Mayers, R. (ed.). Berlin/Heidelberg: Springer, Vol. Articl. 10370 p.

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A retrospective comparative study of three data modelling techniques in anticoagulation therapy.

McDonald, S., Angelov, P. & Xydeas, C., 28/05/2008, BMEI 2008. International Conference on BioMedical Engineering and Informatics, 2008.. Sanya, China: IEEE, p. 219-225 7 p.

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Evolving fuzzy classifier for novelty detection and landmark recognition by mobile robots

Angelov, P. & Zhou, X., 2/05/2007, Mobile Roots: The Evolutionary Approach. Nedjah, N., Macedo Mourelle, L. & Santos Coelho, L. (eds.). p. 89-118 30 p. (Studies in Computational Intelligence; vol. 50).

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Evolving Fuzzy Classifier for Real-time Novelty Detection and Landmark Recognition by a Mobile Robot

Angelov, P. & Zhou, X., 2007, Mobile Robots: The Evolutionary Approach. Nedja, N., Coelho, L. & Mourelle, L. (eds.). Berlin/Heidelberg: Springer, Vol. 50. p. 95-124 30 p. (Studies in Computational Intelligence).

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Algorithms for Real-Time Clustering and Generation of Rules from Data

Filev, D. & Angelov, P., 2007, Advances in Fuzzy Clustering and Its Applications. de Oliveira, J. V. & Pedrycz, W. (eds.). Chichester: John Willey and Sons, p. 353-370 18 p.

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An approach to on-line design of fuzzy controllers with evolving structure

Angelov, P., 10/2004, Applications and Science in Soft Computing. Ahmad, L. & Garibaldi, J. M. (eds.). Berlin/Heidelberg: Springer, p. 63-68 6 p. (Advances in Soft Computing).

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Optimal design synthesis of component-based systems using intelligent techniques

Angelov, P., Zhang, Y. & Wright, J. A., 2004, Do Smart Adaptive Systems Exist?. Gabrys, B., Leviska, K. & Strackelijan, J. (eds.). 173 ed. Berlin/Heidelberg: Springer, Vol. 173. p. 267-284 18 p. (Studies in Fuzziness and Soft Computing).

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Automatic design synthesis and optimization of component-based systems by evolutionary algorithms

Angelov, P. P., Zhang, Y., Wright, J. A., Hanby, V. I. & Buswell, R. A., 07/2003, Genetic and Evolutionary Computation. Cantu-Paz, E., Foster, J. A., Deb, K., Davis, L. D., Roy, R., O'Reilly, U-M., Beyer, H. G., Standish, R., Kendall, G., Willson, S., Harman, M., Dasgupta, D., Potter, M. A., Schultz, A. C., Jonoska, N. & Miller, J. (eds.). 2724 ed. Berlin/Heidelberg: Springer, Vol. 2. p. 1938-1950 13 p. (Lecture Notes in Computer Science).

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On-line Design of Takagi-Sugeno Models.

Angelov, P. & Filev, D., 2003, Fuzzy Sets and Systems – IFSA 2003. Biglic, T., de Baets, B. & Kaynak, O. (eds.). 2715/2 ed. Berlin/Heidelberg: Springer, Vol. 2715/2. p. 576-584 9 p. (Lecture Notes in Computer Science).

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Automatic generation of fuzzy rule-based models from data by genetic algorithms

Angelov, P., Hanby, V. I., Buswell, R. A. & Wright, J. A., 2001, Developments in Soft Computing. John, R. I. & Birkenhead, R. (eds.). Berlin/Heidelberg: Springer, p. 31-40 10 p. (Advances in Soft Computing).

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Interpretable-Through-Prototypes Deepfake Detection for Diffusion Models

Aghasanli, A., Kangin, D. & Angelov, P., 2/10/2023, Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV) Workshops, 2023. Computer Vision Foundation, p. 467-474 8 p.

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Fuzzy Detectors Against Adversarial Attacks

Li, Y., Angelov, P. & Suri, N., 15/09/2023, (Accepted/In press) IEEE Symposium Series on Computational Intelligence. Mexico, 5 p.

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Domain Generalization and Feature Fusion for Cross-domain Imperceptible Adversarial Attack Detection

Li, Y., Angelov, P. & Suri, N., 2/08/2023, 2023 International Joint Conference on Neural Networks (IJCNN). Gold Coast, Australia: IEEE, 8 p.

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PPFM: An Adaptive and Hierarchical Peer-to-Peer Federated Meta-Learning Framework

Yu, Z., Lu, Y., Angelov, P. & Suri, N., 29/03/2023, 2022 18th International Conference on Mobility, Sensing and Networking (MSN). IEEE, p. 502-509 8 p.

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Similarity-based Deep Neural Network to Detect Imperceptible Adversarial Attacks

Almeida Soares, E., Angelov, P. & Suri, N., 30/01/2023, Proceedings of the 2022 IEEE Symposium Series on Computational Intelligence, SSCI 2022. Ishibuchi, H., Kwoh, C-K., Tan, A-H., Srinivasan, D., Miao, C., Trivedi, A. & Crockett, K. (eds.). IEEE, p. 1028-1035 8 p. (Proceedings of the 2022 IEEE Symposium Series on Computational Intelligence, SSCI 2022).

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An Interpretable Deep Semantic Segmentation Method for Earth Observation

Zhang, Z., Angelov, P., Almeida Soares, E., Longepe, N. & Mathieu, P. P., 25/01/2023, 2022 IEEE 11th International Conference on Intelligent Systems (IS). Atanassov, K. T., Doukovska, L., Kacprzyk, J., Krawczak, M., Owsinski, J. W., Sgurev, V., Szmidt, E. & Zadrozny, S. (eds.). IEEE, p. 1-8 8 p. (2022 IEEE 11th International Conference on Intelligent Systems (IS)).

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Graph-context Attention Networks for Size-varied Deep Graph Matching

Jiang, Z., Rahmani, H., Angelov, P., Black, S. & Williams, B., 27/09/2022, Proceedings - 2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition, CVPR 2022. IEEE, p. 2333-2342 10 p. (Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition; vol. 2022-June).

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Preface

Angelov, P., Papadopoulos, G. A., Castellano, G., Casalino, G., Lughofer, E. & Leite, D., 6/06/2022, 2022 IEEE International Conference on Evolving and Adaptive Intelligent Systems (EAIS). IEEE, Vol. 2022-May. (IEEE Conference on Evolving and Adaptive Intelligent Systems).

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Hand-Based Person Identification using Global and Part-Aware Deep Feature Representation Learning

Baisa, N. L., Williams, B., Rahmani, H., Angelov, P. & Black, S., 2/06/2022, 2022 11th International Conference on Image Processing Theory, Tools and Applications, IPTA 2022. Institute of Electrical and Electronics Engineers Inc., p. 1-6 6 p. (2022 11th International Conference on Image Processing Theory, Tools and Applications, IPTA 2022).

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Automated Person Identification Framework Based on Fingernails and Dorsal Knuckle Patterns

Alghamdi, M., Angelov, P. & Williams, B., 24/01/2022, 2021 IEEE Symposium Series on Computational Intelligence (SSCI). IEEE, 8 p.

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