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).

Research output: Contribution in Book/Report/Proceedings - With ISBN/ISSN › Chapter

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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Federated Adversarial Learning for Robust Autonomous Landing Runway Detection

Li, Y., Angelov, P., Yu, Z., Lopez Pellicer, A. & Suri, N., 4/06/2024, (Accepted/In press) International Conference on Artificial Neural Networks (ICANN).

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UNICAD: A Unified Approach for Attack Detection, Noise Reduction and Novel Class Identification

Lopez Pellicer, A., Giatgong, K., Li, Y., Suri, N. & Angelov, P., 15/03/2024, (Accepted/In press) 2024 International Joint Conference on Neural Networks (IJCNN). IEEE, 8 p.

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Rethinking Self-Supervised Learning for Cross-Domain Adversarial Sample Recovery

Li, Y., Angelov, P. & Suri, N., 15/03/2024, (Accepted/In press) 2024 International Joint Conference on Neural Networks (IJCNN). IEEE

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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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On-line Estimators for Ad-hoc Task Execution: Learning Types and Parameters of Teammates for Effective Teamwork JAAMAS Track

do Carmo Alves, M. A., Yourdshahi, E. S., Varma, A., Marcolino, L. S., Ueyama, J. & Angelov, P., 30/05/2023, Proceedings of AAMAS-2023. New York: ACM, Vol. 2023-May. p. 140-142 3 p. (Proceedings of the International Joint Conference on Autonomous Agents and Multiagent Systems, AAMAS).

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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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Stochastic Computing co-processing elements for Evolving Autonomous Data Partitioning

Moran, A., Canals, V., Angelov, P. P., Frasser, C. F., Skibinsky-Gitlin, E. S., Font, J., Isern, E., Roca, M. & Rossello, J. L., 24/11/2021, 36th Conference on Design of Circuits and Integrated Systems, DCIS 2021. IEEE, p. 1-6 6 p. (36th Conference on Design of Circuits and Integrated Systems, DCIS 2021).

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Robust End-to-End Hand Identification via Holistic Multi-Unit Knuckle Recognition

Vyas, R., Rahmani, H., Boswell-Challand, R., Angelov, P., Black, S. & Williams, B., 4/08/2021, International Joint Conference on Biometrics (IJCB-2021). Shenzhen, China: IEEE, p. 1-8 8 p. 9484356. (2021 IEEE International Joint Conference on Biometrics, IJCB 2021).

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Keynote: Explainable-by-design Deep Learning.

Angelov, P., 24/05/2021, 2021 IEEE International Conference on Pervasive Computing and Communications Workshops and other Affiliated Events (PerCom Workshops). IEEE, p. 175 1 p.

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Towards Deep Machine Reasoning: A Prototype-based Deep Neural Network with Decision Tree Inference

Angelov, P. & Soares, E., 11/10/2020, 2020 IEEE International Conference on Systems, Man, and Cybernetics, SMC 2020. Institute of Electrical and Electronics Engineers Inc., Vol. October. p. 2092-2099 8 p. 9282812

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Concept Drift Detection Using Autoencoders in Data Streams Processing

Jaworski, M., Rutkowski, L. & Angelov, P., 7/10/2020, Artificial Intelligence and Soft Computing: 19th International Conference, ICAISC 2020, Zakopane, Poland, October 12-14, 2020, Proceedings, Part I. Rutkowski, L., Scherer, R., Korytkowski, M., Pedrycz, W., Tadeusiewicz, R. & Zurada, J. M. (eds.). Cham: Springer, p. 124-133 10 p. (Lecture Notes in Computer Science; vol. 12415 ).

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A Novel Self-Organizing PID Approach for Controlling Mobile Robot Locomotion

Gu, X., Khan, M., Angelov, P., Tiwary, B., Shafipour Yourdshahi, E. & Yang, Z., 26/08/2020, 2020 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE). IEEE, p. 1-10 10 p.

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Preface

Maglogiannis, I., Angelov, P., Macintyre, J., Iliadis, L., Kolias, S. & Pimenidis, E., 5/06/2020, Artificial Intelligence Applications and Innovations: 16th IFIP WG 12.5 International Conference, AIAI 2020, Neos Marmaras, Greece, June 5–7, 2020, Proceedings, Part I. Maglogiannis, I., Iliadis, L. & Pimenidis, E. (eds.). Cham: Springer, p. v-ix 5 p. (IFIP Advances in Information and Communication Technology; vol. 583).

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Preface

Maglogiannis, I., Angelov, P., Macintyre, J., Iliadis, L., Kolias, S. & Pimenidis, E., 5/06/2020, Artificial Intelligence Applications and Innovations: 16th IFIP WG 12.5 International Conference, AIAI 2020, Neos Marmaras, Greece, June 5–7, 2020, Proceedings, Part II. Maglogiannis, I., Iliadis, L. & Pimenidis, E. (eds.). Cham: Springer, p. v-ix 5 p. (IFIP Advances in Information and Communication Technology; vol. 584).

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Preface

Maglogiannis, I., Angelov, P., Macintyre, J., Iliadis, L., Kolias, S. & Pimenidis, E., 5/06/2020, Artificial Intelligence Applications and Innovations. AIAI 2020 IFIP WG 12.5 International Workshops: MHDW 2020 and 5G-PINE 2020, Neos Marmaras, Greece, June 5–7, 2020, Proceedings. Maglogiannis, I., Iliadis, L. & Pimenidis, E. (eds.). Cham: Springer, p. v-vi 2 p. (IFIP Advances in Information and Communication Technology; vol. 585).

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On-line Estimators for Ad-hoc Task Allocation: Extended Abstract

Shafipour Yourdshahi, E., Do Carmo Alves, M., Soriano Marcolino, L. & Angelov, P., 9/05/2020, Proceedings of the 19th International Conference on Autonomous Agents and Multiagent Systems (AAMAS 2020). ACM, p. 1999–2001 3 p.

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On-Line Estimators for Ad-Hoc Task Allocation

Shafipour Yourdshahi, E., Aparecido do Carmo Alves, M., Soriano Marcolino, L. & Angelov, P., 2020, Proceedings of the 19th International Conference on Autonomous Agents and Multiagent Systems, AAMAS 2020. Richland, SC: International Foundation for Autonomous Agents and Multiagent Systems, p. 1999–2001 3 p. (Proceedings of the International Joint Conference on Autonomous Agents and Multiagent Systems, AAMAS; vol. 2020-May).

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ICDS 2019 Preface

Angelov, P., Boumhidi, J., Hagras, H., Nfaoui, E. H., Oubenaalla, Y., Loqman, C., Mestari, M. & Mousannif, H., 26/12/2019, 2019 Third International Conference on Intelligent Computing in Data Sciences (ICDS). IEEE, 1 p.

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Self-Organising and Self-Learning Model for Soybean Yield Prediction

Alghamdi, M., Angelov, P., Rufino, M., Gimenez, R. & Almeida Soares, E., 16/12/2019, 2019 6th International Conference on Social Networks Analysis, Management and Security, SNAMS 2019. Alsmirat, M. & Jararweh, Y. (eds.). IEEE, p. 441-446 6 p.

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Deep Rule-Based Aerial Scene Classifier using High-Level Ensemble Feature Descriptor

Gu, X. & Angelov, P. P., 30/09/2019, 2019 International Joint Conference on Neural Networks (IJCNN). IEEE, 7 p. 8851838

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Actively Semi-Supervised Deep Rule-based Classifier Applied to Adverse Driving Scenarios

Almeida Soares, E., Angelov, P. P., Costa, B. S. J. & Castro, M., 30/09/2019, 2019 International Joint Conference on Neural Networks (IJCNN). IEEE, p. 1-8 8 p.

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A Semi-Supervised Deep Rule-Based Approach for Remote Sensing Scene Classication

Gu, X. & Angelov, P. P., 3/04/2019, The 2019 INNS Big Data and Deep Learning (INNSBDDL 2019) conference. Springer, p. 257-266 10 p.

Research output: Contribution in Book/Report/Proceedings - With ISBN/ISSN › Conference contribution/Paper › peer-review

How best to Design Fuzzy Sets and Systems: In memory of Prof. Lotfi A. Zadeh

Angelov, P. P., 23/02/2019, Fuzzy Logic and Applications: 12th International Workshop, WILF 2018, Genoa, Italy, September 6–7, 2018, Revised Selected Papers. Fullér, R., Giove, S. & Massulli, F. (eds.). Cham: Springer, p. 236-239 4 p. (Lecture Notes in Artificial Intelligence; vol. 11291).

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Towards Evolving Cooperative Mapping for Large-Scale UAV Teams

Shafipour Yourdshahi, E., Angelov, P. P., Soriano Marcolino, L. & Tsianakas, G., 31/01/2019, 2018 IEEE Symposium Series on Computational Intelligence (SSCI). IEEE, p. 2262-2269 8 p.

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Foreign Currency Exchange Rate Prediction using Neuro-Fuzzy Systems

Yong, Y. L., Lee, Y., Gu, X., Angelov, P. P., Ling Ngo, D. C. & Shafipour Yourdshahi, E., 1/12/2018, Procedia Computer Science. Elsevier, Vol. 144. p. 232-238 7 p. (Procedia Computer Science; vol. 144).

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Automatic detection of computer network traffic anomalies based on eccentricity analysis

Martins, R. S., Angelov, P. & Costa, B. S. J., 15/10/2018, 2018 IEEE International Conference on Fuzzy Systems, FUZZ 2018 - Proceedings. Institute of Electrical and Electronics Engineers Inc., 8 p. 8491507

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A Deep Rule-based Approach for Satellite Scene Image Analysis

Gu, X. & Angelov, P. P., 7/10/2018, 2018 IEEE International Conference on Systems, Man, and Cybernetics (SMC). IEEE, p. 2778-2783 6 p. (2018 IEEE International Conference on Systems, Man, and Cybernetics (SMC)).

Research output: Contribution in Book/Report/Proceedings - With ISBN/ISSN › Conference contribution/Paper › peer-review

Towards Large Scale Ad-hoc Teamwork

Shafipour Yourdshahi, E., Pinder, T., Dhawan, G., Soriano Marcolino, L. & Angelov, P. P., 13/09/2018, 2018 IEEE International Conference on Agents (ICA). IEEE, 6 p.

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A cascade of deep learning fuzzy rule-based image classifier and SVM

Angelov, P. P. & Gu, X., 5/10/2017, Systems, Man, and Cybernetics (SMC), 2017 IEEE International Conference on: Human Intelligence for Systems and Cybernetics. IEEE, p. 746-751 6 p.

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Human action recognition using transfer learning with deep representations

Bux, A., Wang, X., Angelov, P. P. & Habib, Z., 3/07/2017, 2017 International Joint Conference on Neural Networks (IJCNN). IEEE

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MICE: Multi-layer multi-model images classifier ensemble

Angelov, P. P. & Gu, X., 22/06/2017, The 3rd IEEE International Conference on Cybernetics. IEEE, p. 436-443 8 p.

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Gender and Age Classification of Human Faces for Automatic Detection of Anomalous Human Behaviour

Wang, X., Mohd Ali, A. & Angelov, P. P., 21/06/2017, 2017 IEEE International Conference on Cybernetics, CYBCONF2017. IEEE, p. 1-6 6 p.

Research output: Contribution in Book/Report/Proceedings - With ISBN/ISSN › Conference contribution/Paper › peer-review

Robust Evolving Cloud-based Controller (ReCCo)

Andonovski, G., Angelov, P. P., Blazic, S. & Skrjanc, I., 2/06/2017, 2017 Evolving and Adaptive Intelligent Systems (EAIS). IEEE, p. 1-6 6 p.

Research output: Contribution in Book/Report/Proceedings - With ISBN/ISSN › Conference contribution/Paper › peer-review

Autonomous anomaly detection

Gu, X. & Angelov, P. P., 31/05/2017, IEEE Conference on Evolving and Adaptive Intelligent Systems. p. 1-8 8 p.

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Autonomous Learning Multi-Model Classifier of 0-Order (ALMMo-0)

Angelov, P. P. & Gu, X., 31/05/2017, IEEE Conference on Evolving and Adaptive Intelligent Systems 2017. p. 1-7 7 p.

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A randomized neural network for data streams

Pratama, M., Angelov, P. P., Lu, J., Lughofer, E., Seera, M. & Lim, C. P., 19/05/2017, Proceedings of the 2017 International Joint Conference on Neural Networks (IJCNN). USA: IEEE, p. 3423-3430 8 p.

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Local modes-based free-shape data partitioning

Angelov, P. P. & Gu, X., 9/12/2016, 2016 IEEE Symposium Series on Computational Intelligence (SSCI). IEEE, 8 p.

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Parallel computing TEDA for high frequency streaming data clustering

Gu, X., Angelov, P. P., Gutierrez, G., Iglesias, J. A. & Sanchi, A., 23/10/2016, Advances in Big Data: Proceedings of the 2nd INNS Conference on Big Data, October 23-25, 2016, Thessaloniki, Greece. Angelov, P., Manolopoulos, Y., Iliadis, L., Roy, A. & Vellasco, M. (eds.). Cham: Springer, p. 238-253 16 p.

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Autonomous data-driven clustering for live data stream

Gu, X. & Angelov, P. P., 9/10/2016, 2016 IEEE International Conference on Systems, Man, and Cybernetics (SMC 2016). IEEE, p. 1128-1135 8 p. 1303

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Empirical data analysis: a new tool for data analytics

Angelov, P. P., Gu, X., Principe, J. & Kangin, D., 9/10/2016, 2016 IEEE International Conference on Systems, Man, and Cybernetics (SMC). IEEE, p. 52-59 8 p. 1008

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Detecting anomalous behaviour using heterogeneous data

Mohd Ali, A., Angelov, P. P. & Gu, X., 7/09/2016, Advances in Computational Intelligence Systems: Contributions Presented at the 16th UK Workshop on Computational Intelligence, September 7–9, 2016, Lancaster, UK. Angelov, P., Gegov, A., Jayne, C. & Shen, Q. (eds.). Springer, p. 253-276 24 p. (Advances in Intelligent Systems and Computing; vol. 513).

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Vision based human activity recognition: a review

Bux, A., Angelov, P. P. & Habib, Z., 7/09/2016, Advances in Computational Intelligence Systems: Contributions Presented at the 16th UK Workshop on Computational Intelligence, September 7–9, 2016, Lancaster, UK. Angelov, P., Gegov, A., Jayne, C. & Shen, Q. (eds.). Springer, p. 341-371 30 p. (Advances in Intelligent Systems and Computing; vol. 513).

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Unsupervised classification of data streams based on typicality and eccentricity data analytics

Costa, B. S. J., Bezerra, C. G., Guedes, L. A. & Angelov, P. P., 24/07/2016, 2016 IEEE International Conference on Fuzzy Systems (FUZZ). Vancouver Canada: IEEE, p. 58-63 6 p.

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A general purpose intelligent surveillance system for mobile devices using deep learning

Antoniou, A. & Angelov, P. P., 24/07/2016, 2016 International Joint Conference on Neural Networks (IJCNN). Vancouver Canada: IEEE, p. 2879-2886 8 p. (Neural Networks (IJCNN), 2016 International Joint Conference on).

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Online evolving fuzzy rule-based prediction model for high frequency trading financial data stream

Gu, X., Angelov, P. P., Mohd Ali, A., Gruver, W. A. & Gaydadjiev, G., 25/05/2016, Evolving and Adaptive Intelligent Systems (EAIS), 2016 IEEE Conference on. IEEE, p. 169-175 7 p.

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A new evolving clustering algorithm for online data streams

Bezerra, C. G., Costa, B. S. J., Guedes, L. A. & Angelov, P. P., 23/05/2016, 2016 IEEE International Conference on Evolving and Adaptive Intelligent Systems, EAIS2016. IEEE, p. 162-168 7 p.

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Challenges in deep learning

Angelov, P. & Sperduti, A., 27/04/2016, ESANN 2016 - 24th European Symposium on Artificial Neural Networks. i6doc.com publication, p. 489-496 8 p. (ESANN 2016 - 24th European Symposium on Artificial Neural Networks).

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Analysis of adaptation law of the robust evolving cloud-based controller

Andonovski, G., Blazic, S., Angelov, P. P. & Skrjanc, I., 3/12/2015, Proceedings 2015 IEEE International Conference on Evolving and Adaptive Intelligent Systems (EAIS). IEEE, p. 1-7 7 p.

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Edge flow

Morris, G. & Angelov, P. P., 9/10/2015, Proceedings of the 2015 IEEE International Conference on Systems, Man, and Cybernetics (SMC). IEEE, p. 1942-1948 7 p.

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Robust evolving cloud-based controller in normalized data space for heat-exchanger plant

Andonovski, G., Blazic, S., Angelov, P. & Skrjanc, I., 09/2015, Fuzzy Systems (FUZZ-IEEE), 2015 IEEE International Conference on. IEEE, p. 1-7 7 p.

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A comparative study of autonomous learning outlier detection methods applied to fault detection

Bezerra, C. G., Costa, B. S. J., Guedes, L. A. & Angelov, P. P., 08/2015, Proceedings of the 2015 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE) . IEEE, p. 1-7 7 p.

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Online fault detection based on typicality and eccentricity data analytics

Costa, B. S. J., Bezerra, C. G., Guedes, L. A. & Angelov, P. P., 12/07/2015, Proceedings of the 2015 International Joint Conference on Neural Networks (IJCNN). IEEE, p. 1-6 6 p.

Research output: Contribution in Book/Report/Proceedings - With ISBN/ISSN › Conference contribution/Paper › peer-review