{"id":30960,"date":"2025-07-01T22:16:11","date_gmt":"2025-07-01T22:16:11","guid":{"rendered":"https:\/\/www.europesays.com\/us\/30960\/"},"modified":"2025-07-01T22:16:11","modified_gmt":"2025-07-01T22:16:11","slug":"leveraging-federated-learning-and-edge-computing-for-pandemic-resilient-healthcare","status":"publish","type":"post","link":"https:\/\/www.europesays.com\/us\/30960\/","title":{"rendered":"Leveraging federated learning and edge computing for pandemic-resilient healthcare"},"content":{"rendered":"
  • \n

    Lefringhausen, C. T. Covid-19 vaccines-an australian review. J. Clin. Exp. Immunol. 7<\/b>(3), 491\u2013508 (2022).<\/p>\n


    \n Google Scholar<\/a>\u00a0\n <\/p>\n<\/li>\n

  • \n

    Haleem, A., Javaid, M. & Vaishya, R. Effects of covid-19 pandemic in daily life. Curr. Med. Res. Pract. 10<\/b>, 78 (2020).<\/p>\n

    Article<\/a>\u00a0
    \n     PubMed<\/a>\u00a0
    \n     PubMed Central<\/a>\u00a0
    \n
    \n Google Scholar<\/a>\u00a0\n <\/p>\n<\/li>\n

  • \n

    WHO. Bulletin of who at https:\/\/www.who.int\/publications\/m\/item\/covid-19-epidemiological-update-edition-167<\/a> (WHO, 2020).<\/p>\n<\/li>\n

  • \n

    Article. Reports of european centre for disease prevention and control at. https:\/\/www.ecdc.europa.eu\/en\/covid-19\/variants-concern<\/a> (2024).<\/p>\n<\/li>\n

  • \n

    https:\/\/www.unmc.edu\/healthsecurity\/transmission\/2023\/12\/19\/4-big-covid-predictions-for-2024<\/a> (2024).<\/p>\n<\/li>\n

  • \n

    Ellis, R. Who changes stance, says public should wear masks. webmd (2020).<\/p>\n<\/li>\n

  • \n

    Qin, B. & Li, D. Identifying facemask-wearing condition using image super-resolution with classification network to prevent covid-19. Sensors 20<\/b>, 5236 (2020).<\/p>\n

    Article<\/a>\u00a0
    \n     CAS<\/a>\u00a0
    \n     PubMed<\/a>\u00a0
    \n     PubMed Central<\/a>\u00a0
    \n
    \n Google Scholar<\/a>\u00a0\n <\/p>\n<\/li>\n

  • \n

    Feng, S. et al. Rational use of face masks in the covid-19 pandemic. Lancet Respir. Med. 8<\/b>, 434\u2013436 (2020).<\/p>\n

    Article<\/a>\u00a0
    \n     CAS<\/a>\u00a0
    \n     PubMed<\/a>\u00a0
    \n     PubMed Central<\/a>\u00a0
    \n
    \n Google Scholar<\/a>\u00a0\n <\/p>\n<\/li>\n

  • \n

    Ejaz, S. E.\u00a0A. & Islam, R. Implementation of principal component analysis on masked and non-masked face recognition. In 2019 1st International Conference on Advances in Science, Engineering and Robotics Technology (ICASERT) 1\u20135 (IEEE, 2019).<\/p>\n<\/li>\n

  • \n

    Prajapati, A. C. et al. A hospital based cross sectional study to find out factors associated with disease severity and length of hospital stay in covid-19 patients in tertiary care hospital of ahmedabad city. Indian J. Community Health 33<\/b>, 256\u2013259 (2021).<\/p>\n

    Article<\/a>\u00a0
    \n
    \n Google Scholar<\/a>\u00a0\n <\/p>\n<\/li>\n

  • \n

    Shadesh, N.\u00a0H. Enhancing public health: a better approach for face mask detection using transfer learning to prevent airborne disease. Ph.D. thesis, Sonargaon University (SU) (2023).<\/p>\n<\/li>\n

  • \n

    Lv, W. et al. Towards large-scale and privacy-preserving contact tracing in covid-19 pandemic: a blockchain perspective. IEEE Trans. Netw. Sci. Eng. 9<\/b>, 282\u2013298 (2020).<\/p>\n

    Article<\/a>\u00a0
    \n     MathSciNet<\/a>\u00a0
    \n     PubMed<\/a>\u00a0
    \n
    \n Google Scholar<\/a>\u00a0\n <\/p>\n<\/li>\n

  • \n

    Desai, F. et al. Health cloud: a system for monitoring health status of heart patients using machine learning and cloud computing. IEEE Internet Things J. 17<\/b>, 1\u201335 (2021).<\/p>\n


    \n Google Scholar<\/a>\u00a0\n <\/p>\n<\/li>\n

  • \n

    Wu, T., Wu, F., Qiu, C., Redout\u00e9, J.-M. & Yuce, M. R. A rigid-flex wearable health monitoring sensor patch for iot-connected healthcare applications. IEEE Internet Things J. 7<\/b>, 6932\u20136945 (2020).<\/p>\n

    Article<\/a>\u00a0
    \n
    \n Google Scholar<\/a>\u00a0\n <\/p>\n<\/li>\n

  • \n

    Leung, N. H. et al. Respiratory virus shedding in exhaled breath and efficacy of face masks. Nat. Med. 26<\/b>, 676\u2013680 (2020).<\/p>\n

    Article<\/a>\u00a0
    \n     CAS<\/a>\u00a0
    \n     PubMed<\/a>\u00a0
    \n     PubMed Central<\/a>\u00a0
    \n
    \n Google Scholar<\/a>\u00a0\n <\/p>\n<\/li>\n

  • \n

    Christa, G.\u00a0H., Jesica, J., Anisha, K. & Sagayam, K.\u00a0M. Cnn-based mask detection system using opencv and mobilenetv2. In 2021 3rd International Conference on Signal Processing and Communication (ICPSC) 115\u2013119 (IEEE, 2021).<\/p>\n<\/li>\n

  • \n

    Rocha, A. et al. Edge ai for internet of medical things: a literature review. Comput. Electr. Eng. 116<\/b>, 109202 (2024).<\/p>\n

    Article<\/a>\u00a0
    \n
    \n Google Scholar<\/a>\u00a0\n <\/p>\n<\/li>\n

  • \n

    Vinuesa, R. et al. The role of artificial intelligence in achieving the sustainable development goals. Nat. Commun. 11<\/b>, 1\u201310 (2020).<\/p>\n

    Article<\/a>\u00a0
    \n
    \n Google Scholar<\/a>\u00a0\n <\/p>\n<\/li>\n

  • \n

    Joshi, M., P, A. & S, M. Federated learning for healthcare domain – pipeline, applications and challenges. ACM Trans. Computi. Healthcare3<\/b>, 1\u201336 (2022).<\/p>\n<\/li>\n

  • \n

    Peiyuan, J. et al. A review of yolo algorithm developments. Procedia Comput. Sci. 199<\/b>, 1066\u20131073. https:\/\/doi.org\/10.1016\/j.procs.2022.01.135<\/a> (2022).<\/p>\n

    Article<\/a>\u00a0
    \n
    \n Google Scholar<\/a>\u00a0\n <\/p>\n<\/li>\n

  • \n

    Li, Li, et al. Deepcovid-xr: An artificial intelligence algorithm to detect covid-19 on chest radiographs trained and tested on a large us clinical dataset. Radiology. https:\/\/pubs.rsna.org\/doi\/10.1148\/radiol.2020203511<\/a> (2021).<\/p>\n<\/li>\n

  • \n

    Habibzadeh, H. et al. A survey of healthcare internet of things (hiot): a clinical perspective. IEEE Internet Things J. 7<\/b>, 53\u201371 (2019).<\/p>\n

    Article<\/a>\u00a0
    \n     PubMed<\/a>\u00a0
    \n     PubMed Central<\/a>\u00a0
    \n
    \n Google Scholar<\/a>\u00a0\n <\/p>\n<\/li>\n

  • \n

    Chigada, J. & Madzinga, R. Cyberattacks and threats during covid-19: a systematic literature review. South Afr. J. Inf. Manage. 23<\/b>, 1\u201311 (2021).<\/p>\n


    \n Google Scholar<\/a>\u00a0\n <\/p>\n<\/li>\n

  • \n

    Park Jisu, E. S., Jinman, Jung & Sun, Y. Y. Ui elements identification for mobile applications based on deep learning using symbol marker. J. Inst. Internet Broadcast. Commun. 20<\/b>, 89\u201395 (2020).<\/p>\n


    \n Google Scholar<\/a>\u00a0\n <\/p>\n<\/li>\n

  • \n

    Li Chong, L.\u00a0J., Wang Rong & Linyu, F. Face detection based on yolov3. In Recent Trends in Intelligent Computing, Communication and Devices: Proceedings of ICCD 2018 277\u2013284 (Springer, 2020).<\/p>\n<\/li>\n

  • \n

    Ray, P. P., Thapa, N., Dash, D. & De, D. Novel implementation of iot based non-invasive sensor system for real-time monitoring of intravenous fluid level for assistive e-healthcare. Circ. World 45<\/b>, 109\u2013123 (2019).<\/p>\n

    Article<\/a>\u00a0
    \n
    \n Google Scholar<\/a>\u00a0\n <\/p>\n<\/li>\n

  • \n

    Habibzadeh, H. et al. A survey of healthcare internet of things (hiot): a clinical perspective. IEEE Internet Things J. 7<\/b>, 53\u201371 (2019).<\/p>\n

    Article<\/a>\u00a0
    \n     PubMed<\/a>\u00a0
    \n     PubMed Central<\/a>\u00a0
    \n
    \n Google Scholar<\/a>\u00a0\n <\/p>\n<\/li>\n

  • \n

    Yang Q, Liu Y, et al. Federated machine learning: concept and applications. arXiv (Cornell University). arXiv:1902.04885v1<\/a> (2019).<\/p>\n<\/li>\n

  • \n

    Ahmed, I., J, G., & Ahmad, I. M. Social distance monitoring framework using deep learning architecture to control infection transmission of covid-19 pandemic. Sustain. Cities Soc.69<\/b>, 102777 (2021).<\/p>\n<\/li>\n

  • \n

    Dubey, P., Dubey, P., Iwendi, C., Biamba, C.\u00a0N. & Rao, D.\u00a0D. Enhanced iot-based face mask detection framework using optimized deep learning models: a hybrid approach with adaptive algorithms. IEEE Access (2025).<\/p>\n<\/li>\n

  • \n

    Chen, S., Xue, D., Chuai, G., Yang, Q. & Liu, Q. Fl-qsar: a federated learning-based qsar prototype for collaborative drug discovery. Bioinformatics 36<\/b>, 5492\u20135498 (2020).<\/p>\n

    Article<\/a>\u00a0
    \n     CAS<\/a>\u00a0
    \n
    \n Google Scholar<\/a>\u00a0\n <\/p>\n<\/li>\n

  • \n

    Nishio, T. & Yonetani, R. Client selection for federated learning with heterogeneous resources in mobile edge. In ICC 2019-2019 IEEE International Conference on Communications (ICC) 1\u20137 (IEEE, 2019).<\/p>\n<\/li>\n

  • \n

    Ammad-Ud-Din, M. et al. Federated collaborative filtering for privacy-preserving personalized recommendation system. arXiv preprint arXiv:1901.09888<\/a> (2019).<\/p>\n<\/li>\n

  • \n

    Solanki, T., Rai, B.\u00a0K. & Sharma, S. Federated learning using tensor flow. In Federated Learning for IoT Applications 157\u2013167 (Springer, 2022).<\/p>\n<\/li>\n

  • \n

    Alawida, M., Omolara, A. E., Abiodun, O. I. & Al-Rajab, M. A deeper look into cybersecurity issues in the wake of covid-19: a survey. J. King Saud Univ.-Comput. Inf. Sci. 34<\/b>, 8176\u20138206 (2022).<\/p>\n

    Article<\/a>\u00a0
    \n     PubMed<\/a>\u00a0
    \n     PubMed Central<\/a>\u00a0
    \n
    \n Google Scholar<\/a>\u00a0\n <\/p>\n<\/li>\n

  • \n

    Huang, L. et al. Patient clustering improves efficiency of federated machine learning to predict mortality and hospital stay time using distributed electronic medical records. J. Biomed. Inform. 99<\/b>, 103291 (2019).<\/p>\n

    Article<\/a>\u00a0
    \n     PubMed<\/a>\u00a0
    \n
    \n Google Scholar<\/a>\u00a0\n <\/p>\n<\/li>\n

  • \n

    Ge, S. et al. Fedner: Privacy-preserving medical named entity recognition with federated learning. arXiv preprint arXiv:2003.09288<\/a> (2020).<\/p>\n<\/li>\n

  • \n

    Stuart, R.\u00a0M., Abeysuriya, R.\u00a0G. et al. Role of masks, testing and contact tracing in preventing covid-19 resurgences: a case study from new south wales, australia. In BMJ Open 1\u20138 (IEEE, 2021).<\/p>\n<\/li>\n

  • \n

    Hussain, S. et al. Iot and deep learning based approach for rapid screening and face mask detection for infection spread control of covid-19. Appl. Sci. 2021<\/b>, 3495 (2021).<\/p>\n<\/li>\n

  • \n

    Sanyal S, W.\u00a0D. & B, N. A federated filtering framework for internet of medical things. In ICC 2019\u20132019 IEEE International Conference on Communications (ICC) 1\u20136. https:\/\/doi.org\/10.1109\/ICC.2019.8761381<\/a> (2019).<\/p>\n<\/li>\n

  • \n

    Teo, Z.\u00a0L. et al. Federated machine learning in healthcare: a systematic review on clinical applications and technical architecture. Cell Rep. Med. 5<\/b>, 56 (2024).<\/p>\n<\/li>\n

  • \n

    P, B.\u00a0D. & G, B.\u00a0B. Implementation of least mean square adaptive algorithm on covid-19 prediction. In JUITA: JurnalInformatika, e-ISSN: 2579-8901 1\u201311. https:\/\/doi.org\/10.1007\/s13748-012-0035-5<\/a> (2022).<\/p>\n<\/li>\n

  • \n

    Li Chong, L.\u00a0J., Wang Rong & Linyu, F. Face detection based on yolov3. In Recent Trends in Intelligent Computing, Communication and Devices: Proceedings of ICCD 2018 277\u2013284 (Springer, 2020).<\/p>\n<\/li>\n

  • \n

    Heidari, A. et al. A new lung cancer detection method based on the chest ct images using federated learning and blockchain systems. Artif. Intell. Med. 141<\/b>, 102572 (2023).<\/p>\n

    Article<\/a>\u00a0
    \n     PubMed<\/a>\u00a0
    \n
    \n Google Scholar<\/a>\u00a0\n <\/p>\n<\/li>\n

  • \n

    Amiri, Z., Heidari, A., Navimipour, N. J., Esmaeilpour, M. & Yazdani, Y. The deep learning applications in iot-based bio-and medical informatics: a systematic literature review. Neural Comput. Appl. 36<\/b>, 5757\u20135797 (2024).<\/p>\n

    Article<\/a>\u00a0
    \n
    \n Google Scholar<\/a>\u00a0\n <\/p>\n<\/li>\n

  • \n

    Amiri, Z., Heidari, A., Navimipour, N. J. & Unal, M. Resilient and dependability management in distributed environments: a systematic and comprehensive literature review. Clust. Comput. 26<\/b>, 1565\u20131600 (2023).<\/p>\n

    Article<\/a>\u00a0
    \n
    \n Google Scholar<\/a>\u00a0\n <\/p>\n<\/li>\n

  • \n

    Z, W. Use of supervised machine learning to detect abuse of covid-19 related domain names. Comput. Electr. Eng. 100<\/b>, 107864 (2022).<\/p>\n<\/li>\n

  • \n

    Sadeghi,\u00a0Z., Alizadehsani,\u00a0R., et al. A review of explainable artificial intelligence in healthcare. Comput. Electri. Engi. 118<\/b>, 109370 (2024).<\/p>\n<\/li>\n

  • \n

    Din Nizam Ud, B. S., Kamran, Javed & Juneho, Y. A novel gan-based network for unmasking of masked face. IEEE Access 8<\/b>, 44276\u201344287 (2020).<\/p>\n

    Article<\/a>\u00a0
    \n
    \n Google Scholar<\/a>\u00a0\n <\/p>\n<\/li>\n

  • \n

    Goodfellow, I. Deep learning (2016).<\/p>\n<\/li>\n

  • \n

    Heidari, A., Jamali, M. A. J. & Navimipour, N. J. Fuzzy logic multicriteria decision-making for broadcast storm resolution in vehicular ad hoc networks. Int. J. Commun. Syst. 38<\/b>, e6034 (2025).<\/p>\n

    Article<\/a>\u00a0
    \n
    \n Google Scholar<\/a>\u00a0\n <\/p>\n<\/li>\n

  • \n

    Wang,\u00a0R., Lei,\u00a0T, et al. Medical image segmentation using deep learning: a survey. IET image Process. 16<\/b>, 1243\u20131267 (2022).<\/p>\n<\/li>\n

  • \n

    Wang Yi, G. G., Xiao, Song & Ni, L. A multi-scale feature extraction-based normalized attention neural network for image denoising. Electronics 10<\/b>, 319 (2021).<\/p>\n

    Article<\/a>\u00a0
    \n
    \n Google Scholar<\/a>\u00a0\n <\/p>\n<\/li>\n

  • \n

    Tedeschi, S., Emmanouilidis, C., Mehnen, J. & Roy, R. A design approach to iot endpoint security for production machinery monitoring. Sensors 19<\/b>, 2355 (2019).<\/p>\n

    Article<\/a>\u00a0
    \n     PubMed<\/a>\u00a0
    \n     PubMed Central<\/a>\u00a0
    \n
    \n Google Scholar<\/a>\u00a0\n <\/p>\n<\/li>\n

  • \n

    Mondal, S. & Mitra, P. The role of emerging technologies to fight against covid-19 pandemic: an exploratory review. Trans. Indian Natl. Acad. Eng. 7<\/b>, 157\u2013174 (2022).<\/p>\n<\/li>\n

  • \n

    Heidari, A., Amiri, Z., Jamali, M. A. J. & Navimipour, N. J. Enhancing solar convection analysis with multi-core processors and gpus. Eng. Rep. 7<\/b>, e13050 (2025).<\/p>\n

    Article<\/a>\u00a0
    \n
    \n Google Scholar<\/a>\u00a0\n <\/p>\n<\/li>\n

  • \n

    Jain,\u00a0R., T, S., Gupta, M. & J, H. D. Deep learning based detection and analysis of covid-19 on chest x-ray images. Appl. Intell.51<\/b>, 1690\u20131700 (2021).<\/p>\n<\/li>\n

  • \n

    Toumaj, S., Heidari, A., Shahhosseini, R. & Jafari Navimipour, N. Applications of deep learning in alzheimer\u2019s disease: a systematic literature review of current trends, methodologies, challenges, innovations, and future directions. Artif. Intell. Rev. 58<\/b>, 44 (2024).<\/p>\n

    Article<\/a>\u00a0
    \n
    \n Google Scholar<\/a>\u00a0\n <\/p>\n<\/li>\n

  • \n

    M, B. The risk to population health equity posed by automated decision systems-a narrative review. ArXiv200106615 Cs Jan. 2020. Accessed Nov 21 2020. (2020).<\/p>\n<\/li>\n

  • \n

    M Seif, R.\u00a0T. & Li, M. Wireless federated learning with local differential privacy,. ArXiv200205151 Cs Math, Feb. 2020, Accessed: Nov. 21, 2020. (2020).<\/p>\n<\/li>\n

  • \n

    Lyu, L, Yu, H, et al. Threats to federated learning-a survey,. ArXiv200302133 Cs Stat, Mar. 2020, Accessed: Nov. 21, 2020. (2020).<\/p>\n<\/li>\n

  • \n

    Liu, Y, Ma, Z, et al. Boosting privately-privacy-preserving federated extreme boosting for mobile crowdsensing,. ArXiv190710218 Cs, Apr. 2020, Accessed: Nov. 21, 2020. (2020).<\/p>\n<\/li>\n

  • \n

    Yao, X., Huang, T., Wu, C., Zhang, R. & Sun, L. Towards faster and better federated learning: a feature fusion approach. 2019 IEEE International Conference on Image Processing (ICIP) 175\u2013179 (2019).<\/p>\n<\/li>\n

  • \n

    Mansour, Y., Mohri, M., Ro, J. & Suresh, A.\u00a0T. Three approaches for personalization with applications to federated learning. arXiv preprint arXiv:2002.10619<\/a> (2020).<\/p>\n<\/li>\n

  • \n

    Abdul Salam, M., Taha, S. & Ramadan, M. Covid-19 detection using federated machine learning. PLoS ONE 16<\/b>, e0252573 (2021).<\/p>\n

    Article<\/a>\u00a0
    \n     CAS<\/a>\u00a0
    \n     PubMed<\/a>\u00a0
    \n     PubMed Central<\/a>\u00a0
    \n
    \n Google Scholar<\/a>\u00a0\n <\/p>\n<\/li>\n

  • \n

    Kandati, D. R. & Gadekallu, T. R. Genetic clustered federated learning for covid-19 detection. Electronics 11<\/b>, 2714 (2022).<\/p>\n

    Article<\/a>\u00a0
    \n
    \n Google Scholar<\/a>\u00a0\n <\/p>\n<\/li>\n

  • \n

    Rahman, A. et al. Federated learning-based ai approaches in smart healthcare: concepts, taxonomies, challenges and open issues. Clust. Comput. 26<\/b>, 2271\u20132311 (2023).<\/p>\n

    Article<\/a>\u00a0
    \n
    \n Google Scholar<\/a>\u00a0\n <\/p>\n<\/li>\n

  • \n

    Shahin Ali, M. et al. Federated learning in healthcare: model misconducts, security, challenges, applications, and future research directions\u2013a systematic review. arXiv e-prints arXiv\u20132405 (2024).<\/p>\n<\/li>\n

  • \n

    Keniya, R. & Mehendale, N. Real-time social distancing detector using socialdistancingnet-19 deep learning network. Available at SSRN 3669311 (2020).<\/p>\n<\/li>\n

  • \n

    Wang, L., Lin, Z. Q. & Wong, A. Covid-net: a tailored deep convolutional neural network design for detection of covid-19 cases from chest x-ray images. Sci. Rep. 10<\/b>, 19549 (2020).<\/p>\n

    Article<\/a>\u00a0
    \n     CAS<\/a>\u00a0
    \n     PubMed<\/a>\u00a0
    \n     PubMed Central<\/a>\u00a0
    \n
    \n Google Scholar<\/a>\u00a0\n <\/p>\n<\/li>\n

  • \n

    Loke, C. H. et al. Physical distancing device with edge computing for covid-19 (paddie-c19). Sensors 22<\/b>, 279 (2021).<\/p>\n

    Article<\/a>\u00a0
    \n     PubMed<\/a>\u00a0
    \n     PubMed Central<\/a>\u00a0
    \n
    \n Google Scholar<\/a>\u00a0\n <\/p>\n<\/li>\n

  • \n

    Li, J. & Wu, Z. The application of yolov4 and a new pedestrian clustering algorithm to implement social distance monitoring during the covid-19 pandemic. In Journal of Physics: Conference Series, vol. 1865 042019 (IOP Publishing, 2021).<\/p>\n<\/li>\n

  • \n

    Shareef, A. A.\u00a0A., Yannawar, P.\u00a0L., Abdul-Qawy, A. S.\u00a0H. & Ahmed, Z.\u00a0A. Yolov4-based monitoring model for covid-19 social distancing control. In Smart Systems: Innovations in Computing: Proceedings of SSIC 2021 333\u2013346 (Springer, 2022).<\/p>\n<\/li>\n

  • \n

    Elbachir, Y.\u00a0M., Makhlouf, D., Mohamed, G., Bouhamed, M.\u00a0M. & Abdellah, K. Federated learning for multi-institutional on 3d brain tumor segmentation. In 2024 6th International Conference on Pattern Analysis and Intelligent Systems (PAIS) 1\u20138 (IEEE, 2024).<\/p>\n<\/li>\n

  • \n

    Rahman, A. et al. Federated learning-based ai approaches in smart healthcare: concepts, taxonomies, challenges and open issues. Clust. Comput. 26<\/b>, 2271\u20132311 (2023).<\/p>\n

    Article<\/a>\u00a0
    \n
    \n Google Scholar<\/a>\u00a0\n <\/p>\n<\/li>\n

  • \n

    Rauniyar, A. et al. Federated learning for medical applications: a taxonomy, current trends, challenges, and future research directions. IEEE Internet Things J. 11<\/b>, 7374\u20137398 (2023).<\/p>\n

    Article<\/a>\u00a0
    \n
    \n Google Scholar<\/a>\u00a0\n <\/p>\n<\/li>\n

  • \n

    Dhade, P. & Shirke, P. Federated learning for healthcare: a comprehensive review. Eng. Proc. 59<\/b>, 230 (2024).<\/p>\n


    \n Google Scholar<\/a>\u00a0\n <\/p>\n<\/li>\n

  • \n

    Mahmoud, M., Kasem, M.\u00a0S. & Kang, H.-S. A comprehensive survey of masked faces: recognition, detection, and unmasking. arXiv preprint arXiv:2405.05900<\/a> (2024).<\/p>\n<\/li>\n

  • \n

    Seresirikachorn, K. et al. Investigating public behavior with artificial intelligence-assisted detection of face mask wearing during the covid-19 pandemic. PLoS ONE 18<\/b>, e0281841 (2023).<\/p>\n

    Article<\/a>\u00a0
    \n     CAS<\/a>\u00a0
    \n     PubMed<\/a>\u00a0
    \n     PubMed Central<\/a>\u00a0
    \n
    \n Google Scholar<\/a>\u00a0\n <\/p>\n<\/li>\n

  • \n

    Al-Rakhami, M. S. & Al-Amri, A. M. Lies kill, facts save: detecting covid-19 misinformation in twitter. Ieee Access 8<\/b>, 155961\u2013155970 (2020).<\/p>\n

    Article<\/a>\u00a0
    \n     PubMed<\/a>\u00a0
    \n
    \n Google Scholar<\/a>\u00a0\n <\/p>\n<\/li>\n

  • \n

    Verma, S. et al. An automated face mask detection system using transfer learning based neural network to preventing viral infection. Expert. Syst. 41<\/b>, e13507 (2024).<\/p>\n

    Article<\/a>\u00a0
    \n
    \n Google Scholar<\/a>\u00a0\n <\/p>\n<\/li>\n

  • \n

    AlZubi, A. A., Al-Maitah, M. & Alarifi, A. Cyber-attack detection in healthcare using cyber-physical system and machine learning techniques. Soft. Comput. 25<\/b>, 12319\u201312332 (2021).<\/p>\n

    Article<\/a>\u00a0
    \n
    \n Google Scholar<\/a>\u00a0\n <\/p>\n<\/li>\n

  • \n

    M, S. & S, K. A.\u00a0K. The power of deep learning for intelligent tumor classification systems: a review. Comput. Electri. Eng.106<\/b>, 107586 (2023).<\/p>\n<\/li>\n

  • \n

    Nguyen, T. et al. A novel decentralized federated learning approach to train on globally distributed, poor quality, and protected private medical data. Sci. Rep. 12<\/b>, 8888 (2022).<\/p>\n

    Article<\/a>\u00a0
    \n     CAS<\/a>\u00a0
    \n     PubMed<\/a>\u00a0
    \n     PubMed Central<\/a>\u00a0
    \n
    \n Google Scholar<\/a>\u00a0\n <\/p>\n<\/li>\n

  • \n

    Amiri, Z., Heidari, A. & Navimipour, N.\u00a0J. Comprehensive survey of artificial intelligence techniques and strategies for climate change mitigation. Energy 132827 (2024).<\/p>\n<\/li>\n

  • \n

    Asadi, M., Jamali, M. A. J., Heidari, A. & Navimipour, N. J. Botnets unveiled: a comprehensive survey on evolving threats and defense strategies. Trans. Emerg. Telecommun. Technol. 35<\/b>, e5056 (2024).<\/p>\n

    Article<\/a>\u00a0
    \n
    \n Google Scholar<\/a>\u00a0\n <\/p>\n<\/li>\n

  • \n

    Heidari, A., Amiri, Z., Jamali, M. A. J. & Jafari, N. Assessment of reliability and availability of wireless sensor networks in industrial applications by considering permanent faults. Concurr. Comput. Pract. Exp. 36<\/b>, e8252 (2024).<\/p>\n

    Article<\/a>\u00a0
    \n
    \n Google Scholar<\/a>\u00a0\n <\/p>\n<\/li>\n

  • \n

    Amiri, Z., Heidari, A., Zavvar, M., Navimipour, N. J. & Esmaeilpour, M. The applications of nature-inspired algorithms in internet of things-based healthcare service: a systematic literature review. Trans. Emerg. Telecommun. Technol. 35<\/b>, e4969 (2024).<\/p>\n

    Article<\/a>\u00a0
    \n
    \n Google Scholar<\/a>\u00a0\n <\/p>\n<\/li>\n

  • \n

    Zanbouri, K. et al. A gso-based multi-objective technique for performance optimization of blockchain-based industrial internet of things. Int. J. Commun. Syst. 37<\/b>, e5886 (2024).<\/p>\n

    Article<\/a>\u00a0
    \n
    \n Google Scholar<\/a>\u00a0\n <\/p>\n<\/li>\n

  • \n

    Heidari, A., Navimipour, N. J., Zeadally, S. & Chamola, V. Everything you wanted to know about chatgpt: components, capabilities, applications, and opportunities. Internet Technol. Lett. 7<\/b>, e530 (2024).<\/p>\n

    Article<\/a>\u00a0
    \n
    \n Google Scholar<\/a>\u00a0\n <\/p>\n<\/li>\n

  • \n

    Vakili, A. et al. A new service composition method in the cloud-based internet of things environment using a grey wolf optimization algorithm and mapreduce framework. Concurr. Comput. Pract. Exp. 36<\/b>, e8091 (2024).<\/p>\n

    Article<\/a>\u00a0
    \n
    \n Google Scholar<\/a>\u00a0\n <\/p>\n<\/li>\n

  • \n

    Heidari, A., Shishehlou, H., Darbandi, M., Navimipour, N. J. & Yalcin, S. A reliable method for data aggregation on the industrial internet of things using a hybrid optimization algorithm and density correlation degree. Clust. Comput. 27<\/b>, 7521\u20137539 (2024).<\/p>\n

    Article<\/a>\u00a0
    \n
    \n Google Scholar<\/a>\u00a0\n <\/p>\n<\/li>\n

  • \n

    Heidari, A., Navimipour, N. J. & Unal, M. A secure intrusion detection platform using blockchain and radial basis function neural networks for internet of drones. IEEE Internet Things J. 10<\/b>, 8445\u20138454 (2023).<\/p>\n

    Article<\/a>\u00a0
    \n
    \n Google Scholar<\/a>\u00a0\n <\/p>\n<\/li>\n

  • \n

    Aggarwal, D., Zhou, J. & Jain, A.\u00a0K. Fedface: collaborative learning of face recognition model. In 2021 IEEE International Joint Conference on Biometrics (IJCB) 1\u20138 (IEEE, 2021).<\/p>\n<\/li>\n

  • \n

    Zhang, J. et al. A review on face mask recognition. Sensors25<\/b>, https:\/\/doi.org\/10.3390\/s25020387<\/a> (2025).<\/p>\n<\/li>\n","protected":false},"excerpt":{"rendered":"Lefringhausen, C. T. Covid-19 vaccines-an australian review. J. Clin. Exp. Immunol. 7(3), 491\u2013508 (2022). Google Scholar\u00a0 Haleem, A.,…\n","protected":false},"author":3,"featured_media":30961,"comment_status":"","ping_status":"","sticky":false,"template":"","format":"standard","meta":{"footnotes":""},"categories":[22],"tags":[26264,26265,745,26268,26271,16622,26267,12991,26266,26269,26272,10046,10047,159,26270,158,67,132,68],"class_list":{"0":"post-30960","1":"post","2":"type-post","3":"status-publish","4":"format-standard","5":"has-post-thumbnail","7":"category-computing","8":"tag-computational-science","9":"tag-computer-science","10":"tag-computing","11":"tag-contact-tracing","12":"tag-cyber-attack","13":"tag-deep-learning","14":"tag-deep-transfer-learning","15":"tag-edge-computing","16":"tag-epidemiology","17":"tag-facemask","18":"tag-federated-learning","19":"tag-humanities-and-social-sciences","20":"tag-multidisciplinary","21":"tag-science","22":"tag-social-distancing","23":"tag-technology","24":"tag-united-states","25":"tag-unitedstates","26":"tag-us"},"share_on_mastodon":{"url":"https:\/\/pubeurope.com\/@us\/114780293199055823","error":""},"_links":{"self":[{"href":"https:\/\/www.europesays.com\/us\/wp-json\/wp\/v2\/posts\/30960","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/www.europesays.com\/us\/wp-json\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/www.europesays.com\/us\/wp-json\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/www.europesays.com\/us\/wp-json\/wp\/v2\/users\/3"}],"replies":[{"embeddable":true,"href":"https:\/\/www.europesays.com\/us\/wp-json\/wp\/v2\/comments?post=30960"}],"version-history":[{"count":0,"href":"https:\/\/www.europesays.com\/us\/wp-json\/wp\/v2\/posts\/30960\/revisions"}],"wp:featuredmedia":[{"embeddable":true,"href":"https:\/\/www.europesays.com\/us\/wp-json\/wp\/v2\/media\/30961"}],"wp:attachment":[{"href":"https:\/\/www.europesays.com\/us\/wp-json\/wp\/v2\/media?parent=30960"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/www.europesays.com\/us\/wp-json\/wp\/v2\/categories?post=30960"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/www.europesays.com\/us\/wp-json\/wp\/v2\/tags?post=30960"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}