Autors: Iman Abouhassan., Nikola Kasabov., Popov, G. I., Trifonov, R. I.
Title: Why Use Evolving Neuro-Fuzzy and Spiking Neural Networks for incremental and explainable learning of time series? A case study on predictive modelling of trade imports and outlier detection
Keywords: Learning systems , Biological system modeling , Oils , Time

References

    Issue

    IEEE 11th International Conference on Intelligent Systems (IS), Warsaw, pp. 1-7, 2022, Poland, doi: 10.1109/IS571182022.10019673., ISBN:978-1-6654-5656-2

    Цитирания (Citation/s):
    1. Lysenko, S.; Bobrovnikova, K.; Kharchenko, V.; Savenko, O. IoT Multi-Vector Cyberattack Detection Based on Machine Learning Algorithms: Traffic Features Analysis, Experiments, and Efficiency. Algorithms 2022, 15, 239. https://doi.org/10.3390/a15070239 - 2022 - в издания, индексирани в Scopus и/или Web of Science
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    3. AbouHassan I., Kasabov N.K., Bankar T., Garg R., Sen Bhattacharya B., ePAMeT: evolving predictive associative memories for time series, 2025, Evolving Systems, issue 1, vol. 16, DOI 10.1007/s12530-024-09628-y, issn 18686478, eissn 18686486 - 2025 - в издания, индексирани в Scopus
    4. Hassan I.Y.A., Kasabov N.K., NeuDen: a framework for the integration of neuromorphic evolving spiking neural networks with dynamic evolving neuro-fuzzy systems for predictive and explainable modelling of streaming data, 2025, Evolving Systems, issue 1, vol. 16, DOI 10.1007/s12530-024-09630-4, issn 18686478, eissn 18686486 - 2025 - в издания, индексирани в Scopus

    Вид: публикация в международен форум, публикация в реферирано издание, индексирана в Scopus