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"Marques, João Alexandre Lôbo"

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Faculty of Business and Law

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Results 15 resources

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Arraut, I., Marques, J. A. L., Fong, S. J., Li, G., Gois, F. N. B., & Neto, J. X. (2022). A Quantum Field Formulation for a Pandemic Propagation. In J. A. L. Marques & S. J. Fong (Eds.), Epidemic Analytics for Decision Supports in COVID19 Crisis (pp. 141–158). Springer International Publishing. https://doi.org/10.1007/978-3-030-95281-5_6

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Marques, J. A. L., Fong, S. J., Li, G., Arraut, I., Gois, F. N. B., & Neto, J. X. (2022). Research and Technology Development Achievements During the COVID-19 Pandemic—An Overview. In J. A. L. Marques & S. J. Fong (Eds.), Epidemic Analytics for Decision Supports in COVID19 Crisis (pp. 1–15). Springer International Publishing. https://doi.org/10.1007/978-3-030-95281-5_1

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Marques, J. A. L., Gois, F. N. B., Xavier-Neto, J., & Fong, S. J. (2020). Prediction for Decision Support During the COVID-19 Pandemic. In Predictive Models for Decision Support in the COVID-19 Crisis (pp. 1–13). Springer International Publishing. https://doi.org/10.1007%2F978-3-030-61913-8_1

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Marques, J. A. L., Gois, F. N. B., Xavier-Neto, J., & Fong, S. J. (2020). Epidemiology Compartmental Models—SIR, SEIR, and SEIR with Intervention. In Predictive Models for Decision Support in the COVID-19 Crisis (pp. 15–39). Springer International Publishing. https://doi.org/10.1007%2F978-3-030-61913-8_2

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Marques, J. A. L., Gois, F. N. B., Xavier-Neto, J., & Fong, S. J. (2020). Artificial Intelligence Prediction for the COVID-19 Data Based on LSTM Neural Networks and H2O AutoML. In Predictive Models for Decision Support in the COVID-19 Crisis (pp. 69–87). Springer International Publishing. https://doi.org/10.1007%2F978-3-030-61913-8_5

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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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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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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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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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Last update from database: 6/14/25, 4:01 PM (UTC)