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United Nations SDGs

03 - Good Health and Well-being

Publication year

Between 2000 and 2024

Results 33 resources

Abstracts

dos Santos Silva, B. R., Cortez, P. C., da Silva Neto, M. G., & Lobo Marques, J. A. (2023). X-Ray Machine Learning Classification with VGG-16 for Feature Extraction. In J. A. Lobo Marques & S. J. Fong (Eds.), Computerized Systems for Diagnosis and Treatment of COVID-19 (pp. 65–78). Springer International Publishing. https://doi.org/10.1007/978-3-031-30788-1_5
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
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
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
Cavalcante, C. H. L., Primo, P. E. O., Sales, C. A. F., Caldas, W. L., Silva, J. H. M., Souza, A. H., Marinho, E. S., Pedrosa, R. C., Marques, J. A. L., Santos, H. S., Madeiro, J. P. V., Cavalcante, C. H. L., Primo, P. E. O., Sales, C. A. F., Caldas, W. L., Silva, J. H. M., Souza, A. H., Marinho, E. S., Pedrosa, R. C., … Madeiro, J. P. V. (2023). Sudden cardiac death multiparametric classification system for Chagas heart disease’s patients based on clinical data and 24-hours ECG monitoring. Mathematical Biosciences and Engineering, 20(5), 9159–9178. https://doi.org/10.3934/mbe.2023402
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
Khatoon, F., Kumar, M., Khalid, A. A., Alshammari, A. D., Khan, F., Alshammari, R. D., Balouch, Z., Verma, D., Mishra, P., Abotaleb, M., Makarovskikh, T., El-kenawy, E. M., Dutta, P. K., & Marques, J. A. (2022). Quality of life during the pandemic: a cross sectional study about attitude, individual perspective and behavior change affecting general population in daily life. 379–383. https://doi.org/10.1049/icp.2023.0596
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
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
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
Singh, P., Chaudhary, G., & Lobo Marques, J. A. (2023). Medical Information Extraction of Clinical Notes and Pictorial Visualisation of Electronic Medical Records Summary Interface. In Smart Distributed Embedded Systems for Healthcare Applications (pp. 29–40). CRC Press. https://www.routledge.com/Smart-Distributed-Embedded-Systems-for-Healthcare-Applications/Nagrath-Alzubi-Singla-Rodrigues-Verma/p/book/9781032183473
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
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
dos Santos Silva, B. R., Cesar Cortez, P., Gomes Aguiar, R., Rodrigues Ribeiro, T., Pereira Teixeira, A., Bernardo Gois, F. N., & Lobo Marques, J. A. (2023). Lung Segmentation of Chest X-Rays Using Unet Convolutional Networks. In J. A. Lobo Marques & S. J. Fong (Eds.), Computerized Systems for Diagnosis and Treatment of COVID-19 (pp. 15–28). Springer International Publishing. https://doi.org/10.1007/978-3-031-30788-1_2
Lobo Marques, J. A. (2022, August 19). IoT-based smart health system for ambulatory maternal and fetal monitoring [Symposium]. Symposium on Intelligent Manufacturing and Artificial Intelligence Technologies (ISIMAIT 2022), Zhejiang, China.
Lobo Marques, J. A. (2023, May 26). Intelligent Data Fusion System for Assessing and Classifying the Long-Term Effects of Exposure to COVID-19 in Pregnancy (Long Covid): Associated Neurophysiological and Epigenetic Mechanisms and Consequences for Infant Development [Conference]. 2023 7th International Conference on Data Mining, Communications and Information Technology (DMCIT & CSMO 2023), Chongqin, China.
Guilherme Cunha Santos, A., Alexandre Lobo Marques, J., Rigo Jr., L., & Paulo Madeiro, J. (2023, November 26). Exploring EEG Signal Features for Predicting Post Cardiac Arrest Prognosis. 2023 Computing in Cardiology Conference. https://doi.org/10.22489/CinC.2023.312
Lobo Marques, J. A., Macedo, D. S., Motta, P., dos Santos Silva, B. R., Carvalho, F. H. C., Kehdi, R. C., Cavalcante, L. R. L., da Silva Viana, M., Lós, D., & Fiorenza, N. G. (2023). Exploratory Data Analysis on Clinical and Emotional Parameters of Pregnant Women with COVID-19 Symptoms. In J. A. Lobo Marques & S. J. Fong (Eds.), Computerized Systems for Diagnosis and Treatment of COVID-19 (pp. 179–209). Springer International Publishing. https://doi.org/10.1007/978-3-031-30788-1_11
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
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

Last update from database: 4/24/24, 6:34 AM (UTC)

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