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IoT Based Machine Learning and Deep Learning Platform for COVID-19 Prevention and Control: A Systematic Review

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AI and IoT for Sustainable Development in Emerging Countries

Part of the book series: Lecture Notes on Data Engineering and Communications Technologies ((LNDECT,volume 105))

Abstract

The world has been facing a challenging issue of COVID-19, which has infected 223 countries and 223 billion deaths worldwide. It is believed that the world will need to take preventive measures against the COVID-19 pandemic until a vaccine is developed. Early detection of COVID19 infections is a major challenge for healthcare professionals, governments, and organizations to combat against the virus. Therefore, its need an intelligent monitoring system that detects COVID19 and tracks the infected person may improve clinical decision-making and stop spreading the virus among people. The Internet of things (IoT), machine learning, and the Deep learning approach changed our lives in the healthcare sector. This survey presents how IoT, machine learning, and deep learning are incorporated into the pandemic prevention and control system by detection, diagnosis, monitoring, tracing, and social distance finding. We examine and review the most recent literature and present the role of IoT, machine learning, and deep learning in combating the current COVID-19 pandemic. Further, we have also identified a few issues and research directions while using IoT during the COVID-19 pandemic.

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References

  1. What is IoT (Internet of Things) and How Does it Work? IoT Agenda, 2021. Available: https://internetofthingsagenda.techtarget.com/definition/Internet-of-Things-IoT

  2. Ndiaye M, Oyewobi SS, Abu-Mahfouz AM, Hancke GP, Kurien AM, Djouani K (2020) IoT in the wake of COVID-19: a survey on contributions, challenges and evolution. IEEE Access 8:186821–186839

    Google Scholar 

  3. Sharma N et al (2021) A smart ontology-based IoT framework for remote patient monitoring. Biomed Sig Process Control 68:102717

    Google Scholar 

  4. Kallel A, Rekik M, Khemakhem M (2021) Hybrid-based Framework for COVID-19 prediction via federated machine learning models

    Google Scholar 

  5. Coronavirus, Who.int, 2021. Available: https://www.who.int/health-topics/coronavirus#tab=tab_2

  6. Alqahtani M (2021) IOT within the Saudi Healthcare Industry during Covid-19. EasyChair 2516–2314

    Google Scholar 

  7. What is the Internet of Things (IoT)?. (2021). Retrieved 5 November 2021, from https://www.oracle.com/internet-ofthings/what-is-iot/

  8. Haddad Pajouh H, Dehghan Tanha A, Parizi RM, Aledhari M, Karimipour H (2019) A survey on internet of things security: requirements, challenges, and solutions. Internet of Things 100129

    Google Scholar 

  9. Phelan AL, Katz R, Gostin LO (2020) The novel coronavirus originating in Wuhan, China: challenges for global health governance. JAMA 323(8):709–710

    Google Scholar 

  10. Rahman MS, Peeri NC, Shrestha N, Zaki R, Haque U, Ab Hamid SH (2020) Defending against the Novel Coronavirus (COVID-19) outbreak: how can the Internet of Things (IoT) help to save the world? Health Policy Technol 9(2):136

    Google Scholar 

  11. S. Kurkute, N. Ahirao, R. Ankad, and V. Khatal, “IOT based smart system for the Helmet detection. In: Proceedings of international conference on sustainable computing in science, technology and management (SUSCOM), Amity University Rajasthan, Jaipur-India

    Google Scholar 

  12. Mohammed M et al (2019) Novel coronavirus disease (Covid-19): detection and diagnosis system using IoT based smart glasses. Pesquisa.bvsalud.org. Available: https://pesquisa.bvsalud.org/global-literature-on-novel-coronavirus-2019-ncov/resource/pt/covidwho-828827

  13. Chamberlain SD, Singh I, Ariza CA, Daitch AL, Philips PB, Dalziel BD (2020) Real-time detection of COVID-19 epicenters within the United States using a network of smart thermometers. MedRxiv

    Google Scholar 

  14. How drones are being used to combat COVID-19. Geospatial World, 2021. Available: https://www.geospatialworld.net/blogs/how-drones-are-being-used-to-combat-covid-19

  15. Sharma K (2021) Cyient provides drone-based surveillance technology to support Telangana state police in implementing COVID-19 lockdown. Cyient.com. (2021). Available: https://www.cyient.com/prlisting/corporate/cyient-provides-drone-basedsurveillance-technology-to-support-telangana-state-police-in-implementing-covid-19-lockdown

  16. Delhi Civic Body Begins Thermal Screening People on Balconies With Drones. NDTV.com, 2021. Available: https://www.ndtv.com/delhi-news/coronavirus-delhi-civic-body-using-drones-to-check-temperature-of-people-on-balconies-2209832

  17. Kamal M, Aljohani A, Alanazi E (2020) IoT meets COVID-19: status, challenges, and opportunities. arXiv preprint arXiv:2007.12268, 2020

  18. Swayamsiddha S, Mohanty C (2020) Application of cognitive Internet of Medical Things for COVID-19 pandemic. Diab Metabol Syndr Clin Res Rev 14(5):911–915

    Google Scholar 

  19. Singh V, Chandna H, Kumar A, Kumar S, Upadhyay N, Utkarsh K (2020) IoT-Q-band: a low cost internet of things based wearable band to detect and track absconding COVID-19 quarantine subjects. EAI Endorsed Trans Internet of Things 6(21)

    Google Scholar 

  20. Kumar K, Kumar N, Shah R (2020) Role of IoT to avoid spreading of COVID-19. Int J Intell Netw 1:32–35

    Google Scholar 

  21. Otoom M, Otoum N, Alzubaidi MA, Etoom Y, Banihani R (2020) An IoT-based framework for early identification and monitoring of COVID-19 cases. Biomed Signal Process Control 62:102149

    Google Scholar 

  22. Autonomous robots are helping kill coronavirus in hospitals. IEEE Spectrum. Available: https://spectrum.ieee.org/autonomous-robots-are-helping-kill-coronavirus-inhospitals#toggle-gdpr

  23. GIS cloud offers technology and support for free to projects fighting to contain coronavirus|GIS cloud. GIS Cloud. Available: https://www.giscloud.com/blog/gis-cloud-offers-technology-and-support-for-free-to-projects-fighting-to-contain-coronavirus/

  24. Foresman B (2021) This wristband vibrates if you break social distancing rules|EdScoop. EdScoop. Available: https://edscoop.com/university-florida-social-distancing-wristband/

  25. Davenport T, Kalakota R (2019) The potential for artificial intelligence in healthcare. Fut Healthcare J 6(2):94

    Google Scholar 

  26. Ardakani AA, Kanafi AR, Acharya UR, Khadem N, Mohammadi A (2020) Application of deep learning technique to manage COVID-19 in routine clinical practice using CT images: results of 10 convolutional neural networks. Comput Biol Med 121:103795

    Google Scholar 

  27. Hemdan EE-D, Shouman MA, Karar ME (2020) Covidx-net: a framework of deep learning classifiers to diagnose covid-19 in x-ray images. arXiv preprint arXiv:2003.11055

  28. Narin A, Kaya C, Pamuk Z (2021) Automatic detection of coronavirus disease (covid-19) using X-ray images and deep convolutional neural networks. Pattern Anal Appl 1–14

    Google Scholar 

  29. Barstugan M, Ozkaya U, Ozturk S (2020) Coronavirus (covid-19) classification using CT images by machine learning methods. arXiv preprint arXiv:2003.09424

  30. Minaee S, Kafieh R, Sonka M, Yazdani S, Soufi GJ (2020) Deep-covid: predicting covid-19 from chest x-ray images using deep transfer learning. Med Image Anal 65:101794

    Google Scholar 

  31. Rehman A, Naz S, Khan A, Zaib A, Razzak I (2020) Improving coronavirus (COVID-19) diagnosis using deep transfer learning. MedRxiv

    Google Scholar 

  32. Zheng C et al (2020) Deep learning-based detection for COVID-19 from chest CT using weak label. MedRxiv

    Google Scholar 

  33. Farooq M, Hafeez A (2020) Covid-resnet: a deep learning framework for screening of covid19 from radiographs. arXiv preprint arXiv:2003.14395

  34. Afshar P, Heidarian S, Naderkhani F, Oikonomou A, Plataniotis KN, Mohammadi A (2020) Covid-caps: a capsule network-based framework for identification of covid-19 cases from x-ray images. Pattern Recogn Lett 138:638–643

    Google Scholar 

  35. Ibrahim DM, Elshennawy NM, Sarhan AM (2021) Deep-chest: multi-classification deep learning model for diagnosing COVID-19, pneumonia, and lung cancer chest diseases. Comput Biol Med 132:104348

    Google Scholar 

  36. Rahimzadeh M, Attar A (2020) A modified deep convolutional neural network for detecting COVID-19 and pneumonia from chest X-ray images based on the concatenation of Xception and ResNet50V2. Inf Med Unlocked 19:100360

    Google Scholar 

  37. Xu X et al (2020) A deep learning system to screen novel coronavirus disease 2019 pneumonia. Engineering 6(10):1122–1129

    Google Scholar 

  38. Yadav M, Perumal M, Srinivas M (2020) Analysis on novel coronavirus (COVID-19) using machine learning methods. Chaos Solit Fract 139:110050

    Google Scholar 

  39. Khanday AMUD, Rabani ST, Khan QR, Rouf N, Din MMU (2020) Machine learning based approaches for detecting COVID-19 using clinical text data. Int J Inf Technol 12(3):731–739

    Google Scholar 

  40. Punn NS, Sonbhadra SK, Agarwal S (2020) COVID-19 epidemic analysis using machine learning and deep learning algorithms. MedRxiv

    Google Scholar 

  41. Ferrari D et al (2020) Machine learning in predicting respiratory failure in patients with COVID-19 pneumonia—challenges, strengths, and opportunities in a global health emergency. PloS One 15(11):e0239172

    Google Scholar 

  42. Li WT et al (2020) Using machine learning of clinical data to diagnose COVID-19: a systematic review and meta-analysis. BMC Med Inform Decis Mak 20(1):1–13

    Google Scholar 

  43. Ahamad MM et al (2020) A machine learning model to identify early stage symptoms of SARS-Cov-2 infected patients. Exp Syst Appl 160:113661

    Google Scholar 

  44. Sujath R, Chatterjee JM, Hassanien AE (2020) A machine learning forecasting model for COVID-19 pandemic in India. Stoch Env Res Risk Assess 34:959–972

    Google Scholar 

  45. Khan MIN, Ullah F, Hassan MA, Hussain A (2020) COVID-19 classification based on Chest X-Ray images using machine learning techniques. J Comput Sci Technol Stud 2(2):1–11

    Google Scholar 

  46. Ullah SI, Salam A, Ullah W, Imad M (2021) COVID-19 lung image classification based on logistic regression and support vector machine. In: European, Asian, Middle Eastern, North African conference on management & information systems. Springer, Berlin, pp 13–23

    Google Scholar 

  47. Elaziz MA, Hosny KM, Salah A, Darwish MM, Lu S, Sahlol AT (2020) New machine learning method for image-based diagnosis of COVID-19. Plos One 15(6):e0235187

    Google Scholar 

  48. Abed M et al (2021) A comprehensive investigation of machine learning feature extraction and classification methods for automated diagnosis of Covid-19 based on X-ray images. Comput Mater Continua 3289–3310

    Google Scholar 

  49. Ardabili SF et al (2020) Covid-19 outbreak prediction with machine learning. Algorithms 13(10):249

    MathSciNet  Google Scholar 

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Author information

Authors and Affiliations

  1. Abasyn University, Peshawar, Pakistan

    Muhammad Imad, Muhammad Abul Hassan & Najm Ul Sahar

  2. Islamia College University, Peshawar, Pakistan

    Adnan Hussain

  3. Allama Iqbal Open University, Islamabad, Pakistan

    Zainab Butt

Authors
  1. Muhammad Imad
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  2. Adnan Hussain
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  3. Muhammad Abul Hassan
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  4. Zainab Butt
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  5. Najm Ul Sahar
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Editor information

Editors and Affiliations

  1. LIM, Hassan II University, Mohammedia, Morocco

    Zakaria Boulouard

  2. Moulay Ismail University, Meknes, Morocco

    Mariya Ouaissa

  3. Moulay Ismail University, Meknes, Morocco

    Mariyam Ouaissa

  4. Sidi Mohamed Ben Abdellah University, Fes, Morocco

    Sarah El Himer

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Imad, M., Hussain, A., Hassan, M.A., Butt, Z., Sahar, N.U. (2022). IoT Based Machine Learning and Deep Learning Platform for COVID-19 Prevention and Control: A Systematic Review. In: Boulouard, Z., Ouaissa, M., Ouaissa, M., El Himer, S. (eds) AI and IoT for Sustainable Development in Emerging Countries. Lecture Notes on Data Engineering and Communications Technologies, vol 105. Springer, Cham. https://doi.org/10.1007/978-3-030-90618-4_26

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