Open Access
| Issue |
E3S Web Conf.
Volume 591, 2024
International Conference on Renewable Energy Resources and Applications (ICRERA-2024)
|
|
|---|---|---|
| Article Number | 08007 | |
| Number of page(s) | 5 | |
| Section | Communication and Signal Processing | |
| DOI | https://doi.org/10.1051/e3sconf/202459108007 | |
| Published online | 14 November 2024 | |
- Ayres-de Campos et al. SisPorto 2.0: a program for automated analysis of cardiotocograms. J Matern Fetal Med. 2000 Sep-Oct;9(5):311-8. [Google Scholar]
- Rongdan Zeng et al. Cardiotocography signal abnormality classification using time-frequency features and Ensemble Cost-sensitive SVM classifier, Computers in Biology and Medicine, Volume 130, 2021, 104218, ISSN 0010-4825. [PubMed] [Google Scholar]
- N. Chamidah et al. Fetal state classification from cardiotocography based on feature extraction using hybrid K-Means and support vector machine, 2015 International Conference on Advanced Computer Science and Information Systems (ICACSIS), Depok, Indonesia, 2015, pp. 37-41, doi: 10.1109/ICACSIS .2015.7415166. [Google Scholar]
- T. H. Dang et al. Classification of Fetal Status from Cardiotocogram Data by Using Machine Learning, 2023 12th International Conference on Control, Automation and Information Sciences (ICCAIS), Hanoi, Vietnam, 2023, pp. 352-357, doi: 10.1109/ICCAIS59597. 2023.10382381. [Google Scholar]
- J. Piri et al. Multi-objective Ant Lion Optimization Based Feature Retrieval Methodology for Investigation of Fetal Wellbeing, 2021 Third International Conference on Inventive Research in Computing Applications (ICIRCA), Coimbatore, India, 2021, pp. 1732-1737, doi: 10.1109/ICIRCA51532.2021.9544860. [Google Scholar]
- V. Shukla et al. Machine Learning-Based Fetal Assessment: A Non-Intrusive Approach, 2024 3rd International Conference on Artificial Intelligence For Internet of Things (AIIoT), Vellore, India, 2024, pp. 1-6, doi: 10.1109/AIIoT58432. 2024.10574555. [Google Scholar]
- Y. Salini et al. Cardiotocography Data Analysis for Fetal Health Classification Using Machine Learning Models, in IEEE Access, vol. 12, pp. 26005-26022, 2024, doi: 10.1109/ACCESS.2024.3364755. [CrossRef] [Google Scholar]
- R. Hephzibah et al. An ensemble technique of SmoteENN and random forest for classification of the cardiotocography data, International Conference on Computer Vision and Internet of Things 2023 (ICCVIoT'23), Coimbatore, India, 2023, pp. 152-157, doi: 10.1049/icp.2023.2869. [Google Scholar]
- Sai Prasad Potharaju et al. Data mining approach for accelerating the classification accuracy of cardiotocography, Clinical Epidemiology and Global Health, 7(2):160–164, 2019. [CrossRef] [Google Scholar]
- Suman, J. V et al. A Review of Machine Learning- Based Device Modeling and Performance Optimization for FinFETs, International Journal of Scientific Research in Engineering and Management (IJSREM) 7 (2023): 1-10. [Google Scholar]
- Alekhya, L. et al. A new approach to detect cardiovascular diseases using ECG scalograms and ML-based CNN algorithm, 2023. International Journal of Computational Vision and Robotics/ Inderscience publishers. DOI: 10.1504/IJCVR. 2022.10051429 [Google Scholar]
- Campos, D. et al. Cardiotocography. UCI Machine Learning Repository. (2010). https://doi.org/10.24432/C51S4N. [Google Scholar]
Current usage metrics show cumulative count of Article Views (full-text article views including HTML views, PDF and ePub downloads, according to the available data) and Abstracts Views on Vision4Press platform.
Data correspond to usage on the plateform after 2015. The current usage metrics is available 48-96 hours after online publication and is updated daily on week days.
Initial download of the metrics may take a while.