Abstract
Nowadays, special care of patient systems are required for predicting or detecting Covid-19 patients ubiquitously. Also, there is a requirement for quarantine centers to set up for treating Covid-19 patients ubiquitously in a real-world environment due to the highly infectious virus. In a pandemic situation, it is difficult to keep track of the health condition of every individual patient. Also, doctors face problems to monitor and controlling controls patients’ health conditions. In this regard, it is investigated the survey on cognitive Internet of Things based predicting Covid-19 patients using a machine learning algorithm. In this paper, it discusses a detailed survey on the proposed problem statement in terms of limitations, advantages and disadvantages, and performance parameters for various algorithms, and finally, it proposes system architecture for predicting and monitoring Covid-19 patients ubiquitously. Hence the proposed system is used to monitor the symptoms of patients like Temperature, SpO2, and Cough rate of Covid-19 patients ubiquitously using intelligent sensors. The proposed system transmits data to the web server using Wi-Fi connectivity.
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References
Bhardwaj, V., Joshi, R., & Gaur, A. M. (2022). IoT-based smart health monitoring system for COVID-19. Journal Computer Science, 3(137), 1–11.
Goncalo, M., Nuno, G., & Pombo, N. (2019) A survey on IoT: Architectures, elements, applications, QoS, platforms and security concepts. Journal Electronics, 8(10), 1–27.
Choudary, A., & Godara, S. (2017). Internet of things: A survey paper on architecture and challenges. Journal International Journal of Engineering Technology Science and Research IJETSR, 4(6), 442–447.
Hussein, A. H. (2019). Internet of things (IOT): Research challenges and future applications. Journal (IJACSA) International Journal of Advanced Computer Science and Applications, 10(6), 77–82 (2019).
Bhajantri, L. B., & Balugari, P. (2019). A survey on data perception in cognitive internet of things. Journal of Telecommunications and Information Technology, 3, 75–86.
Park, J. -H., Salim, M. M., Jo, J. H., Sicato, J. C. S., Rathore, S., & Park, J. H. (2019). CIoT-Net: a scalable cognitive IoT based smartcity network architecture. 9(29), 1–20.
Song, Y., Zheng, S., Li, L., Zhang, X., Zhang, X., Huang, Z., Chen, J., Wang, R., Zhao, H., Chong, Y., Shen, J., Zha, Y., & Yang, Y. (2020). Deep learning enables accurate diagnosis of novel-coronavirus with CT images. IEEE/ACM Transactions on Computational Biology and Bioinformatic, 18(6).
Rashidi, N. A. (2020). Covid-19 detection based on CT-scan. Journal Computer Science, 9(20)
Nooruddin. (2019–2020). Covid-19 prediction using real time data.
Palanisamy, R., Kartik, M., Rohit, H., Jay, S., Puranik, A., & Vaidya, A. (2019). IoT based patient monitoring system. International Journal of Recent Technology and Engineering (IJRTE), 8(2S11), 1–6.
Mukhtar, H., Rubaiee, S., Krichen, M., & Alroobaea, R. (2021). Screening of COVID-19 using real time data from wearable sensors. International Journal Environment Research Public Health, 18(8), 1–17.
Bassam, N. A., Hussain, S. A., Qaraghuli, A. A., Khan, J., Sumesh, E. P., & Lavanya, V. (2021). IoT based wearable device to monitor the signs of quarantined remote patients of Covid-19. Journal Elsevier Public Health Emergency Collection, 9, 1–16.
Petrovic, N., & Kocic, D. (2020). IoT based system for covid-19 indoor safety monitoring. In IcETRAN (pp. 1–7).
Fayez, Q., & Krishnan, S. (2018). Wearable hardware design for IoT medical things. Journal Department of Electrical, Computer and Biomedical Engineering, 18(11), 1–22.
Shah, M. A., Zhang, S., & Maple, C. (2013). Cognitive radio networks for internet of things; application, challenges and futures. In 19th International Conference on Automation and Computing (pp. 1–6).
Shreerang, J., Pranav, M. S., Jitendra, P., More, M., Prayag, S., Satish, P., & Marathe, S. (2020). IoT based patient health care for covid-19 center. International Journal Recent Technology and Engineering. 9(3), 258–263.
Acho, L., Vargas, A. N., & Pujol-Vazquez, G. (2020). Low cost open source Mechanical ventilator with pulmonary monitoring for Covid-19 patients. 9(3), 1–14.
Dagazany, A. R., Stegagno, P., & Mankodiya, K. (2018). Variable internet of things and deep learning for big data analytics. Journal Mobile Information System. 1–20.
Brodeur, A., Gray, D., Islam, A., & Bhuiyan, S. (2021). A literature review of the economics of covid-19. Journal Economics Survey, 35(2), 1007–1044.
Salehi, A. W., Baglet, P., & Gupta, G. (2020). Review on machine and deep learning models for detection and prediction of coronavirus. In Proceedings Conference on Nanotechnology (pp. 3896–3901).
Revar, D. S., Sevaniya, J. S., & Joshi, V. R. (2020). Pulse oximeter design to predict covid-19 possibilities on patients health using machine learning. GRD Journal,5(10), 9–14.
Abhadji, I. E., Awuzi, B. O., Ngowi, A. B., & Millham, R. C. (2020). Review of big data analytics, artificial intelligence and nature inspired computing models towards accurate detection of covid-19 pandemic cases and contract tracking. International Journal Environment Research Public Health, 17(15), 1–16.
Valanarasu, R., & Christy, A. (2019). Comprehensive survey of wireless cognitive and 5G networks. Journal of Ubiquitous Computing and Communication Technologies (UCCT), 23–32.
Smys, S., & Raj, J. S. (2022). Future challenges of the internet of things in the health care domain-an overview. Journal of Trends in Computer Science and Smart Technology, 3(4), 274–286.
Cavovean, D., Ioana, I., & Nitulescu, G. (2020). IoT system in diagnosis of covid-19 patients. In Informatic Economic (Vol. 24(2), pp. 75–89). Bucharest University of Economic Studies.
Gothai, R., Thamilselvan, E., & Sakthivel, R. (2020). Prediction of COVID-19 growth and trend using machine learning approach. In Proceeding of International Virtual Conference on Sustainable Materials (pp. 1–5).
Theerthagiri, P., Jacob, I. J., Ruby, A. U., & Yendapalli, V. (2020). Prediction of COVID-19 possibilities using KNN classification algorithm.
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Bhajantri, L.B., Kadadevar, N., Jeeragal, A., Jeeragal, V., Jamdar, I. (2023). A Survey on Cognitive Internet of Things Based Prediction of Covid-19 Patient. In: Shakya, S., Du, KL., Ntalianis, K. (eds) Sentiment Analysis and Deep Learning. Advances in Intelligent Systems and Computing, vol 1432. Springer, Singapore. https://doi.org/10.1007/978-981-19-5443-6_28
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