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"Marques, Joao Alexandre Lobo"

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Marques, J. A. L., Gois, F. N. B., Xavier-Neto, J., & Fong, S. J. (2020). Predicting the Geographic Spread of the COVID-19 Pandemic: A Case Study from Brazil. In Predictive Models for Decision Support in the COVID-19 Crisis (pp. 89–98). Springer International Publishing. https://doi.org/10.1007%2F978-3-030-61913-8_6

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Marques, J. A. L., Gois, F. N. B., Xavier-Neto, J., & Fong, S. J. (2020). Nonlinear Prediction for the COVID-19 Data Based on Quadratic Kalman Filtering. In Predictive Models for Decision Support in the COVID-19 Crisis (pp. 55–68). Springer International Publishing. https://doi.org/10.1007%2F978-3-030-61913-8_4

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Marques, J. A. L., Gois, F. N. B., Xavier-Neto, J., & Fong, S. J. (2020). Forecasting COVID-19 Time Series Based on an Autoregressive Model. In Predictive Models for Decision Support in the COVID-19 Crisis (pp. 41–54). Springer International Publishing. https://doi.org/10.1007%2F978-3-030-61913-8_3

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Zacarias, H., Marques, J. A. L., Felizardo, V., Pourvahab, M., & Garcia, N. M. (2024). ECG Forecasting System Based on Long Short-Term Memory. Bioengineering, 11(1), 89. https://doi.org/10.3390/bioengineering11010089

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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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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 Application of Supervised and Unsupervised Computational Predictive Models to Simulate the COVID19 Pandemic. In J. A. L. Marques & S. J. Fong (Eds.), Epidemic Analytics for Decision Supports in COVID19 Crisis (pp. 103–139). Springer International Publishing. https://doi.org/10.1007/978-3-030-95281-5_5

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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). Analysis of the COVID19 Pandemic Behaviour Based on the Compartmental SEAIRD and Adaptive SVEAIRD Epidemiologic Models. In J. A. L. Marques & S. J. Fong (Eds.), Epidemic Analytics for Decision Supports in COVID19 Crisis (pp. 17–64). Springer International Publishing. https://doi.org/10.1007/978-3-030-95281-5_2

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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). Probabilistic Forecasting Model for the COVID-19 Pandemic Based on the Composite Monte Carlo Model Integrated with Deep Learning and Fuzzy System. In J. A. L. Marques & S. J. Fong (Eds.), Epidemic Analytics for Decision Supports in COVID19 Crisis (pp. 83–102). Springer International Publishing. https://doi.org/10.1007/978-3-030-95281-5_4

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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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Goncalves, M. V., Marques, J. A. L., Silva, B. R. S., Luther, V., & Hayes, S. (2022). Neuromarketing and Global Branding Reaction Analysis Based on Real-Time Monitoring of Multiple Consumer’s Biosignals and Emotions (SSRN Scholarly Paper No. 4071297). https://doi.org/10.2139/ssrn.4071297

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Silva, S. C. e, Machado, J. C., Martins, C., Duarte, P., & Marques, J. A. L. (2021). Modelling the barriers that are preventing e-commerce to thrive – a case study from Portugal, international journal of business excellence. International Journal of Business Excellence.
de Aguiar, A. W. O., Bezerra, J. D. H., Marques, J. A. L., & de Alexandria, A. R. (2019). Lead: An iOS Application to Help in the Construction of New Habits. International Journal of Information and Electronics Engineering, 9(4). http://www.ijiee.org/index.php?m=content&c=index&a=show&catid=87&id=820

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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., 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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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
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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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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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: 4/11/25, 4:01 AM (UTC)