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"Madeiro, João Paulo"

Publication year

Between 2000 and 2025
- Between 2020 and 2025

Results 9 resources

Abstracts

Rodrigues, P. M., Madeiro, J. P., & Marques, J. A. L. (2023). Enhancing Health and Public Health through Machine Learning: Decision Support for Smarter Choices. Bioengineering, 10(7), 792. https://doi.org/10.3390/bioengineering10070792

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do Vale Madeiro, J. P., Lobo Marques, J. A., Han, T., & Coury Pedrosa, R. (2020). Evaluation of mathematical models for QRS feature extraction and QRS morphology classification in ECG signals. Measurement, 156, 107580. https://doi.org/10.1016/j.measurement.2020.107580

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Silva Neto, M. G. da, Madeiro, J. P. do V., Marques, J. A. L., & Gomes, D. G. (2021). Towards an efficient prognostic model for fetal state assessment. Measurement, 185, 110034. https://doi.org/10.1016/j.measurement.2021.110034

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Caldas, W. L., Do Vale Madeiro, J. P., Pedrosa, R. C., Gomes, J. P. P., Du, W., & Marques, J. A. L. (2023). Noise Detection and Classification in Chagasic ECG Signals Based on One-Dimensional Convolutional Neural Networks. In R. Lee (Ed.), Computer and Information Science (Vol. 1055, pp. 117–129). Springer International Publishing. https://doi.org/10.1007/978-3-031-12127-2_8

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Caldas, W. L., do Vale Madeiro, J. P., Pedrosa, R. C., Gomes, J. P. P., Du, W., & Marques, J. A. L. (2023). Noise Detection and Classification in Chagasic ECG Signals Based on One-Dimensional Convolutional Neural Networks. In R. Lee (Ed.), Computer and Information Science (pp. 117–129). Springer International Publishing. https://doi.org/10.1007/978-3-031-12127-2_8

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Marques, J. A. L., Han, T., Wu, W., Madeiro, J. P. do V., Neto, A. V. L., Gravina, R., Fortino, G., & de Albuquerque, V. H. C. (2021). IoT-Based Smart Health System for Ambulatory Maternal and Fetal Monitoring. IEEE Internet of Things Journal, 8(23), 16814–16824. https://doi.org/10.1109/JIOT.2020.3037759
Marques, J. A. L., Gois, F. N. B., Madeiro, J. P. do V., Li, T., & Fong, S. J. (2022). Artificial neural network-based approaches for computer-aided disease diagnosis and treatment. In A. K. Bhoi, V. H. C. de Albuquerque, P. N. Srinivasu, & G. Marques (Eds.), Cognitive and Soft Computing Techniques for the Analysis of Healthcare Data (pp. 79–99). Academic Press. https://doi.org/10.1016/B978-0-323-85751-2.00008-6

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Ribeiro, P., Marques, J. A. L., Pordeus, D., Zacarias, L., Leite, C. F., Sobreira-Neto, M. A., Peixoto, A. A., de Oliveira, A., Madeiro, J. P. do V., & Rodrigues, P. M. (2024). Machine learning-based cardiac activity non-linear analysis for discriminating COVID-19 patients with different degrees of severity. Biomedical Signal Processing and Control, 87, 105558. https://doi.org/10.1016/j.bspc.2023.105558

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Pordeus, D., Ribeiro, P., Zacarias, L., de Oliveira, A., Marques, J. A. L., Rodrigues, P. M., Leite, C., Neto, M. A., Peixoto, A. A., & do Vale Madeiro, J. P. (2023). Training Strategies for Covid-19 Severity Classification. In I. Rojas, O. Valenzuela, F. Rojas Ruiz, L. J. Herrera, & F. Ortuño (Eds.), Bioinformatics and Biomedical Engineering (pp. 514–527). Springer Nature Switzerland. https://doi.org/10.1007/978-3-031-34953-9_40

Last update from database: 4/5/25, 9:01 AM (UTC)