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Academic Units

Faculty of Business and Law

Publication year

Between 2000 and 2024
- Between 2020 and 2024
  - 2020

Results 15 resources

Abstracts

Lei, W. C. (2020). The Road to Service Export Diversification: Gambling and Convention in Macao. In E. Lau, B. Simonetti, I. Trinugroho, & L. M. Tan (Eds.), Economics and Finance Readings: Selected Papers from Asia-Pacific Conference on Economics & Finance, 2019. Springer Singapore. https://doi.org/10.1007/978-981-15-2906-1
Marques, J., Reis, J., Phillips, J. O. L., & Diakite, A. (2020). The Importance of Readiness for Change, a Leadership Perspective Based on a Case Study in Macau, SAR China. Journal of Advanced Management Science, 116–120. https://doi.org/10.18178/joams.8.4.116-120
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
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
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
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
Marques, J. A. L., Cortez, P. C., Madeiro, J. P. V., Albuquerque, V. H. C., Fong, S. J., & Schlindwein, F. S. (2020). Nonlinear characterization and complexity analysis of cardiotocographic examinations using entropy measures. Journal of Supercomputing, 76(2), 1305–1320. https://doi.org/10.1007/s11227-018-2570-8
Lan, K., Liu, L., Li, T., Chen, Y., Fong, S., Marques, J. A. L., Wong, R. K., & Tang, R. (2020). Multi-view convolutional neural network with leader and long-tail particle swarm optimizer for enhancing heart disease and breast cancer detection. Neural Computing and Applications. https://doi.org/10.1007/s00521-020-04769-y
Li, T., Marques, J. A. L., & Fong, S. (2020). Health and Well-Being Education: Extending the SCARF Learning Analytics Model for Identifying the Learner Happiness Indicators. International Journal of Extreme Automation and Connectivity in Healthcare (IJEACH), 2(2), 42–53. https://doi.org/10.4018/IJEACH.2020070105
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
do Vale Madeiro, J. P., Lobo Marques, J. A., Han, T., & Coury Pedrosa, R. (2020). Evaluation of mathematical models for QRS feature extraction and QRS morphology classification in ECG signals. Measurement, 156, 107580. https://doi.org/10.1016/j.measurement.2020.107580
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
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
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
Lampo, A. (2020). Adapting the Unified Theory of Acceptance and Use of Technology 2 (Utaut 2) to Explain Acceptance of Battery Electric Vehicles: Evidence From Macau [PhD in Business Administration, University of Saint Joseph]. http://library-opac.usj.edu.mo/cgi-bin/koha/opac-detail.pl?biblionumber=174665

Last update from database: 5/18/24, 4:17 PM (UTC)

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