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Academic Units

Faculty of Business and Law
- Alexandre Lobo

Resource type

Book Section

Publication year

Between 2000 and 2024

Results 31 resources

Abstracts

Lobo-Marques, J. Al. (2018). Artificial intelligence and forensic computing. In Forensic Computing.
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
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
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
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., 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
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
Bernardo Gois, F. N., Lima, A., Santos, K., Oliveira, R., Santiago, V., Melo, S., Costa, R., Oliveira, M., Henrique, F. das C. D. M., Neto, J. X., Martins Rodrigues Sobrinho, C. R., & Lôbo Marques, J. A. (2021). Predictive models to the COVID-19. In U. Kose, D. Gupta, V. H. C. de Albuquerque, & A. Khanna (Eds.), Data Science for COVID-19 (pp. 1–24). Academic Press. https://doi.org/10.1016/B978-0-12-824536-1.00023-X
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
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
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
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
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
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
Lôbo Marques, J. A., Bernardo Gois, F. N., Nunes da Silveira, J. A., Li, T., & Fong, S. J. (2022). AI and deep learning for processing the huge amount of patient-centric data that assist in clinical decisions. 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. 101–121). Academic Press. https://doi.org/10.1016/B978-0-323-85751-2.00001-3
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
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
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
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
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

Last update from database: 4/26/24, 2:06 PM (UTC)

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