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"Joao, Alexandre Lobo Marques"

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Fong, S. J., Marques, J. A. L., Li, G., Dey, N., Crespo, R. G., Herrera-Viedma, E., Gois, F. N. B., & Neto, J. X. (2022). The Comparison of Different Linear and Nonlinear Models Using Preliminary Data to Efficiently Analyze the COVID-19 Outbreak. In J. A. L. Marques & S. J. Fong (Eds.), Epidemic Analytics for Decision Supports in COVID19 Crisis (pp. 65–81). Springer International Publishing. https://doi.org/10.1007/978-3-030-95281-5_3

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Pordeus, D., Ribeiro, P., Zacarias, L., Paulo Madeiro, J., Lobo Marques, J. A., Miguel Rodrigues, P., Leite, C., Alves Neto, M., Aires Peixoto Jr, A., & de Oliveira, A. (2023). Classification of Severity of COVID-19 Patients Based on the Heart Rate Variability. In J. A. Lobo Marques & S. J. Fong (Eds.), Computerized Systems for Diagnosis and Treatment of COVID-19 (pp. 155–177). Springer International Publishing. https://doi.org/10.1007/978-3-031-30788-1_10

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Ribeiro, P., Pordeus, D., Zacarias, L., Leite, C., Alves Neto, M., Aires Peixoto Jr, A., de Oliveira, A., Paulo Madeiro, J., Lobo Marques, J. A., & Miguel Rodrigues, P. (2023). Evaluation of ECG Non-linear Features in Time-Frequency Domain for the Discrimination of COVID-19 Severity Stages. In J. A. Lobo Marques & S. J. Fong (Eds.), Computerized Systems for Diagnosis and Treatment of COVID-19 (pp. 137–154). Springer International Publishing. https://doi.org/10.1007/978-3-031-30788-1_9

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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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Lobo Marques, J. A., Macedo, D. S., Motta, P., dos Santos Silva, B. R., Carvalho, F. H. C., Kehdi, R. C., Cavalcante, L. R. L., da Silva Viana, M., Lós, D., & Fiorenza, N. G. (2023). Exploratory Data Analysis on Clinical and Emotional Parameters of Pregnant Women with COVID-19 Symptoms. In J. A. Lobo Marques & S. J. Fong (Eds.), Computerized Systems for Diagnosis and Treatment of COVID-19 (pp. 179–209). Springer International Publishing. https://doi.org/10.1007/978-3-031-30788-1_11

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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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Lôbo Marques, J. A., Bernardo Gois, F. N., Nunes da Silveira, J. A., Li, T., & Fong, S. J. (2022). AI and deep learning for processing the huge amount of patient-centric data that assist in clinical decisions. 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. 101–121). Academic Press. https://doi.org/10.1016/B978-0-323-85751-2.00001-3

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Gois, F. N. B., Marques, J. A. L., de Oliveira Dantas, A. B., Santos, M. C., Neto, J. V. S., de Macêdo, J. A. F., Du, W., & Li, Y. (2023). Malaria Blood Smears Object Detection Based on Convolutional DCGAN and CNN Deep Learning Architectures. In R. Lee (Ed.), Computer and Information Science (pp. 197–212). Springer International Publishing. https://doi.org/10.1007/978-3-031-12127-2_14

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Gois, F. N. B., Marques, J. A. L., De Oliveira Dantas, A. B., Santos, M. C., Neto, J. V. S., De Macêdo, J. A. F., Du, W., & Li, Y. (2023). Malaria Blood Smears Object Detection Based on Convolutional DCGAN and CNN Deep Learning Architectures. In R. Lee (Ed.), Computer and Information Science (Vol. 1055, pp. 197–212). Springer International Publishing. https://doi.org/10.1007/978-3-031-12127-2_14

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Bernardo Gois, F. N., Lima, A., Santos, K., Oliveira, R., Santiago, V., Melo, S., Costa, R., Oliveira, M., Henrique, F. das C. D. M., Neto, J. X., Martins Rodrigues Sobrinho, C. R., & Lôbo Marques, J. A. (2021). Predictive models to the COVID-19. In U. Kose, D. Gupta, V. H. C. de Albuquerque, & A. Khanna (Eds.), Data Science for COVID-19 (pp. 1–24). Academic Press. https://doi.org/10.1016/B978-0-12-824536-1.00023-X

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Last update from database: 5/14/25, 5:01 AM (UTC)