Fog-centric IoT based smart healthcare support service for monitoring and controlling an epidemic of Swine Flu virus
Informatics in Medicine Unlocked, ISSN: 2352-9148, Vol: 26, Page: 100636
2021
- 30Citations
- 78Captures
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Example: if you select the 1-year option for an article published in 2019 and a metric category shows 90%, that means that the article or review is performing better than 90% of the other articles/reviews published in that journal in 2019. If you select the 3-year option for the same article published in 2019 and the metric category shows 90%, that means that the article or review is performing better than 90% of the other articles/reviews published in that journal in 2019, 2018 and 2017.
Citation Benchmarking is provided by Scopus and SciVal and is different from the metrics context provided by PlumX Metrics.
Article Description
Disease detection is a time-consuming and essential task in the medical diagnosis system. Machine learning plays a vital role in predicting and identifying diseases at various stages. It is a very random and timely method for analyzing disease using clinical and laboratory signs and assists medical representatives in developing a more effective diagnostic strategy for such diseases. For example, swine flu, a contagious illness caused by influenza viruses, including the H1N1 virus, infects the respiratory tract of pigs, causing a barking cough, decreased appetite, nasal secretions, and uncontrollable behaviour. Cloud computing and the Internet of things help the medical sector by processing health information in ultra-low delay so that effective decisions can be taken timely. In this paper, a fog-centric IoT-based smart healthcare support service for monitoring and controlling the Swine Flu virus epidemic is proposed. The proposed framework utilizes the concept of fog computing for delay-sensitive applications. Furthermore, a hybrid classifier is used to classify the swine flu patient at an early stage and generate alerts to the health officials and patients' guardians. In the experimental setup, the iFogSim simulator is used to mimic the IoT devices and fog nodes for evaluating various parameters such as accuracy, energy, and Latency, whereas WEKA is used for developing a hybrid classifier. Results demonstrate the benefits of combining fog and cloud computing services to achieve higher network bandwidth reliability, a higher level of operation, and a shorter response time while generating real-time notifications, as compared to an existing cloud-only model.
Bibliographic Details
http://www.sciencedirect.com/science/article/pii/S2352914821001258; http://dx.doi.org/10.1016/j.imu.2021.100636; http://www.scopus.com/inward/record.url?partnerID=HzOxMe3b&scp=85120793162&origin=inward; https://linkinghub.elsevier.com/retrieve/pii/S2352914821001258; https://dx.doi.org/10.1016/j.imu.2021.100636
Elsevier BV
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