Your search

In authors or contributors

"Fong, Simon"

Results 34 resources

Abstracts

Lobo Marques, J. A., & Fong, S. J. (2023). Technology Developments to Face the COVID-19 Pandemic: Advances, Challenges, and Trends. In J. A. Lobo Marques & S. J. Fong (Eds.), Computerized Systems for Diagnosis and Treatment of COVID-19 (pp. 1–13). Springer International Publishing. https://doi.org/10.1007/978-3-031-30788-1_1

View on doi.org
Lobo Marques, J. A., & Fong, S. (Eds.). (2022). Epidemic analytics for decision supports in COVID19 crisis. Springer. https://link.springer.com/book/10.1007/978-3-030-95281-5#about-this-book

View on link.springer.com
Lobo Marques, J. A., & Fong, S. J. (Eds.). (2023). Computerized Systems for Diagnosis and Treatment of COVID-19. Springer International Publishing. https://doi.org/10.1007/978-3-031-30788-1

View on link.springer.com
Bernardo Gois, F. N., Lobo Marques, J. A., & Fong, S. J. (2023). Classification of COVID-19 CT Scans Using Convolutional Neural Networks and Transformers. In J. A. Lobo Marques & S. J. Fong (Eds.), Computerized Systems for Diagnosis and Treatment of COVID-19 (pp. 79–97). Springer International Publishing. https://doi.org/10.1007/978-3-031-30788-1_6

View on doi.org
Bernardo Gois, F. N., Lobo Marques, J. A., & Fong, S. J. (2023). TPOT Automated Machine Learning Approach for Multiple Diagnostic Classification of Lung Radiography and Feature Extraction. In J. A. Lobo Marques & S. J. Fong (Eds.), Computerized Systems for Diagnosis and Treatment of COVID-19 (pp. 117–135). Springer International Publishing. https://doi.org/10.1007/978-3-031-30788-1_8

View on doi.org
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

View on www.igi-global.com
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

View on www.igi-global.com
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

View on doi.org
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

View on doi.org
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

View on www.aimspress.com
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

View on www.springer.com
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

View on doi.org
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

View on doi.org
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

View on doi.org
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

View on doi.org
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

View on doi.org
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

View on doi.org
Bernardo Gois, F. N., & Lobo Marques, J. A. (2023). Segmentation of CT-Scan Images Using UNet Network for Patients Diagnosed with COVID-19. In J. A. Lobo Marques & S. J. Fong (Eds.), Computerized Systems for Diagnosis and Treatment of COVID-19 (pp. 29–44). Springer International Publishing. https://doi.org/10.1007/978-3-031-30788-1_3

View on doi.org
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

View on doi.org

Last update from database: 12/21/24, 1:01 PM (UTC)