COVID-19 detection using federated machine learning
Author
Abstract
Suggested Citation
DOI: 10.1371/journal.pone.0252573
Download full text from publisher
References listed on IDEAS
- Wieczorek, Michał & Siłka, Jakub & Woźniak, Marcin, 2020. "Neural network powered COVID-19 spread forecasting model," Chaos, Solitons & Fractals, Elsevier, vol. 140(C).
- Panwar, Harsh & Gupta, P.K. & Siddiqui, Mohammad Khubeb & Morales-Menendez, Ruben & Singh, Vaishnavi, 2020. "Application of deep learning for fast detection of COVID-19 in X-Rays using nCOVnet," Chaos, Solitons & Fractals, Elsevier, vol. 138(C).
Citations
Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
Cited by:
- Ali, Furqan & Ullah, Farman & Khan, Junaid Iqbal & Khan, Jebran & Sardar, Abdul Wasay & Lee, Sungchang, 2023. "COVID-19 spread control policies based early dynamics forecasting using deep learning algorithm," Chaos, Solitons & Fractals, Elsevier, vol. 167(C).
Most related items
These are the items that most often cite the same works as this one and are cited by the same works as this one.- Tang, Yugui & Yang, Kuo & Zhang, Shujing & Zhang, Zhen, 2024. "Wind power forecasting: A temporal domain generalization approach incorporating hybrid model and adversarial relationship-based training," Applied Energy, Elsevier, vol. 355(C).
- Yas Al-Hadeethi & Intesar F. El Ramley & Hiba Mohammed & Nada M. Bedaiwi & Abeer Z. Barasheed, 2024. "A Novel Computational Instrument Based on a Universal Mixture Density Network with a Gaussian Mixture Model as a Backbone for Predicting COVID-19 Variants’ Distributions," Mathematics, MDPI, vol. 12(8), pages 1-24, April.
- Srinka Basu & Sugata Sen, 2023. "COVID 19 Pandemic, Socio-Economic Behaviour and Infection Characteristics: An Inter-Country Predictive Study Using Deep Learning," Computational Economics, Springer;Society for Computational Economics, vol. 61(2), pages 645-676, February.
- Iloanusi, Ogechukwu & Ross, Arun, 2021. "Leveraging weather data for forecasting cases-to-mortality rates due to COVID-19," Chaos, Solitons & Fractals, Elsevier, vol. 152(C).
- Olga Krivorotko & Nikolay Zyatkov, 2024. "The Forecasting of the Spread of Infectious Diseases Based on Conditional Generative Adversarial Networks," Mathematics, MDPI, vol. 12(19), pages 1-22, September.
- Çaparoğlu, Ömer Faruk & Ok, Yeşim & Tutam, Mahmut, 2021. "To restrict or not to restrict? Use of artificial neural network to evaluate the effectiveness of mitigation policies: A case study of Turkey," Chaos, Solitons & Fractals, Elsevier, vol. 151(C).
- Perone, G., 2020. "Comparison of ARIMA, ETS, NNAR and hybrid models to forecast the second wave of COVID-19 hospitalizations in Italy," Health, Econometrics and Data Group (HEDG) Working Papers 20/18, HEDG, c/o Department of Economics, University of York.
- Dingding Wang & Jiaqing Mo & Gang Zhou & Liang Xu & Yajun Liu, 2020. "An efficient mixture of deep and machine learning models for COVID-19 diagnosis in chest X-ray images," PLOS ONE, Public Library of Science, vol. 15(11), pages 1-15, November.
- Yan, Tao & Wong, Pak Kin & Ren, Hao & Wang, Huaqiao & Wang, Jiangtao & Li, Yang, 2020. "Automatic distinction between COVID-19 and common pneumonia using multi-scale convolutional neural network on chest CT scans," Chaos, Solitons & Fractals, Elsevier, vol. 140(C).
- Prabhjot Kaur & Shilpi Harnal & Rajeev Tiwari & Fahd S. Alharithi & Ahmed H. Almulihi & Irene Delgado Noya & Nitin Goyal, 2021. "A Hybrid Convolutional Neural Network Model for Diagnosis of COVID-19 Using Chest X-ray Images," IJERPH, MDPI, vol. 18(22), pages 1-17, November.
- Toraman, Suat & Alakus, Talha Burak & Turkoglu, Ibrahim, 2020. "Convolutional capsnet: A novel artificial neural network approach to detect COVID-19 disease from X-ray images using capsule networks," Chaos, Solitons & Fractals, Elsevier, vol. 140(C).
- Abdelrahman E. E. Eltoukhy & Ibrahim Abdelfadeel Shaban & Felix T. S. Chan & Mohammad A. M. Abdel-Aal, 2020. "Data Analytics for Predicting COVID-19 Cases in Top Affected Countries: Observations and Recommendations," IJERPH, MDPI, vol. 17(19), pages 1-25, September.
- Mohammad Khishe & Fabio Caraffini & Stefan Kuhn, 2021. "Evolving Deep Learning Convolutional Neural Networks for Early COVID-19 Detection in Chest X-ray Images," Mathematics, MDPI, vol. 9(9), pages 1-18, April.
- Ballı, Serkan, 2021. "Data analysis of Covid-19 pandemic and short-term cumulative case forecasting using machine learning time series methods," Chaos, Solitons & Fractals, Elsevier, vol. 142(C).
- Panwar, Harsh & Gupta, P.K. & Siddiqui, Mohammad Khubeb & Morales-Menendez, Ruben & Bhardwaj, Prakhar & Singh, Vaishnavi, 2020. "A deep learning and grad-CAM based color visualization approach for fast detection of COVID-19 cases using chest X-ray and CT-Scan images," Chaos, Solitons & Fractals, Elsevier, vol. 140(C).
- Jonathan S. Talahua & Jorge Buele & P. Calvopiña & José Varela-Aldás, 2021. "Facial Recognition System for People with and without Face Mask in Times of the COVID-19 Pandemic," Sustainability, MDPI, vol. 13(12), pages 1-19, June.
- Paul, Ayan & Reja, Selim & Kundu, Sayani & Bhattacharya, Sabyasachi, 2021. "COVID-19 pandemic models revisited with a new proposal: Plenty of epidemiological models outcast the simple population dynamics solution," Chaos, Solitons & Fractals, Elsevier, vol. 144(C).
- Ben-Loghfyry, Anouar & Charkaoui, Abderrahim, 2023. "Regularized Perona & Malik model involving Caputo time-fractional derivative with application to image denoising," Chaos, Solitons & Fractals, Elsevier, vol. 175(P1).
- Jayles, Bertrand & Cheong, Siew Ann & Herrmann, Hans J., 2022. "Modeling the resilience of social networks to lockdowns regarding the dynamics of meetings," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 602(C).
- Tayarani N., Mohammad-H., 2021. "Applications of artificial intelligence in battling against covid-19: A literature review," Chaos, Solitons & Fractals, Elsevier, vol. 142(C).
Corrections
All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:plo:pone00:0252573. See general information about how to correct material in RePEc.
If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.
If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .
If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.
For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: plosone (email available below). General contact details of provider: https://journals.plos.org/plosone/ .
Please note that corrections may take a couple of weeks to filter through the various RePEc services.