Performance improvement and complexity reduction in the classification of EMG signals with mRMR-based CNN-KNN combined model

X. Little Flower, S. Poonguzhali

DOI: 10.3233/jifs-220811
Published: 2023-01-30
Container: Journal of Intelligent & Fuzzy Systems
Publisher: SAGE Publications
Open access: unknown

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Cite this work

BibTeX

@article{allodium:10.3233/jifs-220811,
  title = {Performance improvement and complexity reduction in the classification of EMG signals with mRMR-based CNN-KNN combined model},
  author = {X. Little Flower and S. Poonguzhali},
  year = {2023},
  journal = {Journal of Intelligent \&amp; Fuzzy Systems},
  doi = {10.3233/jifs-220811},
  url = {https://doi.org/10.3233/jifs-220811}
}

RIS

TY  - JOUR
TI  - Performance improvement and complexity reduction in the classification of EMG signals with mRMR-based CNN-KNN combined model
AU  - X. Little Flower
AU  - S. Poonguzhali
PY  - 2023
JO  - Journal of Intelligent &amp; Fuzzy Systems
DO  - 10.3233/jifs-220811
UR  - https://doi.org/10.3233/jifs-220811
ER  -

APA

Flower, X. L., & Poonguzhali, S. (2023). Performance improvement and complexity reduction in the classification of EMG signals with mRMR-based CNN-KNN combined model. Journal of Intelligent & Fuzzy Systems. https://doi.org/10.3233/jifs-220811

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crossref · retrieved 2026-09-25T17:53:16.115Z