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"Marques, João"

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Faculty of Business and Law

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Abstracts

Santos, A. S. K., & Marques, J. A. L. (2023). Sustainable Practices in Hotel Chains—A Comparative Analysis of Official Annual Hospitality Sustainable Reports from Listed Companies in Macau SAR, China. In W. C. Gartner (Ed.), New Perspectives and Paradigms in Applied Economics and Business (pp. 319–338). Springer International Publishing. https://doi.org/10.1007/978-3-031-23844-4_23
Fernandes, J. V. M. R., Alexandria, A. R. de, Lobo Marques, J. A., Assis, D. F. de, Motta, P. C., & Silva, B. R. dos S. (2024). Emotion Detection from EEG Signals Using Machine Deep Learning Models. Bioengineering, 11(8), 782. https://doi.org/10.3390/bioengineering11080782

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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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Luo, R., Marques, J. A. L., Ong, K.-L., & Fong, S. (2020). Crowdsensing-Based Gamification for Collective Assistance for Post-Era of Coronavirus Epidemic in Community Living. International Journal of Extreme Automation and Connectivity in Healthcare (IJEACH), 2(2), 54–64. https://doi.org/10.4018/IJEACH.2020070106

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Li, T., Fong, S., Lobo Marques, J. A., & Wong, R. K. (2018). Fast Cluster-learning with Prior Probability from Big Dataset. 2018 5th International Conference on Soft Computing & Machine Intelligence (ISCMI), 60–66. https://doi.org/10.1109/ISCMI.2018.8703219
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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Motta, P. C., Cesar Cortez, P., & Lobo Marques, J. A. (2023). COVID-19 Classification Using CT Scans with Convolutional Neural Networks. In J. A. Lobo Marques & S. J. Fong (Eds.), Computerized Systems for Diagnosis and Treatment of COVID-19 (pp. 99–116). Springer International Publishing. https://doi.org/10.1007/978-3-031-30788-1_7

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Arraut, I., Au, A., Tse, A. C., & Marques, J. A. L. (2020). On the probability flow in the Stock market I: The Black-Scholes case. ArXiv.Org, 1, 1–10. https://search.proquest.com/docview/2332255379?pq-origsite=primo

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Marques, J. A. L., Neto, A. C., Silva, S. C., & Bigne, E. (2025). Predicting consumer ad preferences: Leveraging a machine learning approach for EDA and FEA neurophysiological metrics. Psychology & Marketing, 42(1), 175–192. https://doi.org/10.1002/mar.22118

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Lobo Marques, J. A., Neto, A. C., Silva, S. C., & Bigne, E. (2024). Predicting consumer ad preferences: Leveraging a machinelearning approach for EDA and FEA neurophysiologicalmetrics. Psychology & Marketing, 42(1). https://doi.org/10.1002/mar.22118

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Yan, K., Li, T., Marques, J. A. L., Gao, J., Fong, S. J., Yan, K., Li, T., Marques, J. A. L., Gao, J., & Fong, S. J. (2023). A review on multimodal machine learning in medical diagnostics. Mathematical Biosciences and Engineering, 20(5), 8708–8726. https://doi.org/10.3934/mbe.2023382

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Qian, C., Lobo Marques, J. A., Alexandria, A. R. de, & Fong, S. J. (2025). Application of Multiple Deep Learning Architectures for Emotion Classification Based on Facial Expressions. Sensors, 25(5), 1–29. https://doi.org/10.3390/s25051478

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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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Marques, J. A. L., Gois, F. N. B., Xavier-Neto, J., & Fong, S. J. (2021). Predictive Models for Decision Support in the COVID-19 Crisis. Springer International Publishing. https://doi.org/10.1007/978-3-030-61913-8

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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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Last update from database: 4/24/25, 3:01 AM (UTC)