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"Marques, Joao Alexandre Lobo"

Publication year

Between 2000 and 2025
- Between 2020 and 2025

Results 56 resources

Abstracts

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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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.
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: 4/4/25, 12:01 PM (UTC)