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Artificial Intelligence and an Edge-IoMT-Based System for Combating COVID-19 Pandemic

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Intelligent Interactive Multimedia Systems for e-Healthcare Applications

Abstract

The novel severe contagious respiratory syndrome coronavirus called (COVID-19) has caused the greatest global challenge and public health, after the pandemic of influenza outbreak of 1918. The adoption of the Internet of Medical Things (IoMT) and wearable sensor in managing patients during any infectious disease outbreak have brought several opportunities. Until now there has been a rapid increase in diverts research works to find a lasting solution to this worldwide threat. The edge and IoMT smart healthcare based is gaining impact to deal with COVID-19 in this digital technology era. The popularity of wearable devices enables a new perspective for the precaution of infectious diseases. Hence, for the continuous monitoring of patients, wearable and implantable body area network systems are very useful during the COVID-19 outbreak. Therefore, this paper presents the applicability of edge-IoMT-based systems in medicine to minimize the works load of medical practitioners, caregivers and help people live an independent life during the COVID-19 pandemic, and besides providing people with quality care. Also, an intelligent Edge-IoMT-based architecture was proposing for monitoring patients during the COVID-19 outbreak. The edge computing was used to secure the capture data from the patients for proper decision making. The proposed system can be used in real-time by the medical personnel to advises patients about their health condition and to suggest preventive measures in saving lives. The significant contributions of this chapter are (a) Designed an architecture for MIoT-based Big Data Analytics. (b) presented several applicability prospects compelled by edge-IoMT-based system for fighting COVID-19 pandemic (c) lastly, the applicability of the proposed system was presented, and future directions of the system were discussed.

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References

  1. Singh, R. P., Javaid, M., Haleem, A., & Suman, R. (2020). Internet of things (IoT) applications to fight against COVID-19 pandemic. Diabetes & Metabolic Syndrome: Clinical Research & Reviews.

    Google Scholar 

  2. Folorunso, S. O., Awotunde, J. B., Adeboye, N. O., & Matiluko, O. E. (2022). Data classification model for COVID-19 pandemic. In Studies in systems, decision and control (Vol. 378, pp. 93–118).

    Google Scholar 

  3. Fung, S. Y., Yuen, K. S., Ye, Z. W., Chan, C. P., & Jin, D. Y. (2020). A tug-of-war between severe acute respiratory syndrome coronavirus 2 and host antiviral defence: Lessons from other pathogenic viruses. Emerging Microbes & Infections, 9(1), 558–570.

    Article  Google Scholar 

  4. Ogundokun, R. O., Lukman, A. F., Kibria, G. B., Awotunde, J. B., & Aladeitan, B. B. (2020). Predictive modeling of COVID-19 confirmed cases in Nigeria. Infectious Disease Modelling, 5, 543–548.

    Article  Google Scholar 

  5. Awotunde, J. B., Jimoh, R. G., AbdulRaheem, M., Oladipo, I. D., Folorunso, S. O., & Ajamu, G. J. (2022). IoT-Based wearable body sensor network for COVID-19 pandemic. In Studies in systems, decision and control (Vol. 378, pp. 253–275).

    Google Scholar 

  6. Allam, Z., & Jones, D. S. (2020). Pandemic stricken cities on lockdown. Where are our planning and design professionals [now, then, and into the future]? Land Use Policy, 104805.

    Google Scholar 

  7. Pullano, G., Pinotti, F., Valdano, E., Boëlle, P. Y., Poletto, C., & Colizza, V. (2020). Novel coronavirus (2019-nCoV) early-stage importation risk to Europe, January 2020. Eurosurveillance, 25(4), 2000057.

    Article  Google Scholar 

  8. Zhao, S., Lin, Q., Ran, J., Musa, S. S., Yang, G., Wang, W., Lou, Y., Gao, D., Yang, L., He, D., & Wang, M. H. (2020). Preliminary estimation of the basic reproduction number of novel coronavirus (2019-nCoV) in China, from 2019 to 2020: A data-driven analysis in the early phase of the outbreak. International Journal of Infectious Diseases, 92, 214–217.

    Article  Google Scholar 

  9. Bai, L., Yang, D., Wang, X., Tong, L., Zhu, X., Zhong, N., Bai, C., Powell, C. A., Chen, R., Zhou, J., & Song, Y. (2020). Chinese experts’ consensus on the internet of things-aided diagnosis and treatment of coronavirus disease 2019 (COVID-19). Clinical eHealth, 3, 7–15.

    Article  Google Scholar 

  10. Wan, J., Al-awlaqi, M. A., Li, M., O’Grady, M., Gu, X., Wang, J., & Cao, N. (2018). Wearable IoT enabled real-time health monitoring system. EURASIP Journal on Wireless Communications and Networking, 2018(1), 298.

    Article  Google Scholar 

  11. Christaki, E. (2015). New technologies in predicting, preventing, and controlling emerging infectious diseases. Virulence, 6(6), 558–565.

    Article  Google Scholar 

  12. Sust, P. P., Solans, O., Fajardo, J. C., Peralta, M. M., Rodenas, P., Gabaldà, J., Eroles, L. G., Comella, A., Muñoz, C. V., Ribes, J. S., & Monfa, R. R. (2020). Turning the crisis into an opportunity: Digital health strategies deployed during the COVID-19 outbreak. JMIR Public Health and Surveillance, 6(2), e19106.

    Article  Google Scholar 

  13. Meskó, B., Drobni, Z., Bényei, É., Gergely, B., & Győrffy, Z. (2017). Digital health is a cultural transformation of traditional healthcare. Mhealth, 3.

    Google Scholar 

  14. Awotunde, J. B., Ogundokun, R. O., & Misra, S. (2021). Cloud and IoMT-based big data analytics system during COVID-19 pandemic. In Internet of things (pp. 181–201).

    Google Scholar 

  15. Marques, G., & Pitarma, R. (2018, November). Smartwatch-based application for an enhanced healthy lifestyle in indoor environments. In International conference on computational intelligence in information system (pp. 168–177). Springer, Cham.

    Google Scholar 

  16. Manogaran, G., Varatharajan, R., Lopez, D., Kumar, P. M., Sundarasekar, R., & Thota, C. (2018). A new architecture of internet of things and big data ecosystem for secured smart healthcare monitoring and alerting system. Future Generation Computer Systems, 82, 375–387.

    Article  Google Scholar 

  17. Özdemir, V., & Hekim, N. (2018). Birth of industry: Making sense of big data with artificial intelligence, “the internet of things” and next-generation technology policy. Omics: A Journal of Integrative Biology, 22(1), 65–76.

    Article  Google Scholar 

  18. Allam, Z., & Dhunny, Z. A. (2019). On big data, artificial intelligence and smart cities. Cities, 89, 80–91.

    Article  Google Scholar 

  19. Marques, M. S. (2016). G., Pitarma, R. (2016) Smartphone application for enhanced indoor health environments. Journal of Information Systems Engineering & Management, 1, 4.

    Google Scholar 

  20. Dimitrov, D. V. (2016). Medical internet of things and big data in healthcare. Healthcare Informatics Research, 22(3), 156–163.

    Article  Google Scholar 

  21. Marques, G., Ferreira, C. R., & Pitarma, R. (2019). Indoor air quality assessment using a CO2 monitoring system based on internet of things. Journal of Medical Systems, 43(3), 1–10.

    Article  Google Scholar 

  22. Kaur, P., Kumar, R., & Kumar, M. (2019). A healthcare monitoring system using random forest and internet of things (IoT). Multimedia Tools and Applications, 78(14), 19905–19916.

    Article  Google Scholar 

  23. Ayo, F. E., Awotunde, J. B., Ogundokun, R. O., Folorunso, S. O., & Adekunle, A. O. (2020). A decision support system for multi-target disease diagnosis: A bioinformatics approach. Heliyon, 6(3), e03657.

    Article  Google Scholar 

  24. Oladele, T. O., Ogundokun, R. O., Awotunde, J. B., Adebiyi, M. O., & Adeniyi, J. K. (2020, July). Diagmal: A malaria coactive neuro-fuzzy expert system. Lecture notes in computer science (including subseries lecture notes in artificial intelligence and lecture notes in bioinformatics) (Vol. 12254, pp. 428–441). LNCS.

    Google Scholar 

  25. Ayo, F. E., Ogundokun, R. O., Awotunde, J. B., Adebiyi, M. O., & Adeniyi, A. E. (2020, July). Severe acne skin disease: A fuzzy-based method for diagnosis. Lecture notes in computer science (including subseries lecture notes in artificial intelligence and lecture notes in bioinformatics) (Vol. 12254, pp. 320–334). LNCS.

    Google Scholar 

  26. Darkins, A., Ryan, P., Kobb, R., Foster, L., Edmonson, E., Wakefield, B., & Lancaster, A. E. (2008). Care Coordination/home telehealth: The systematic implementation of health informatics, home telehealth, and disease management to support the care of veteran patients with chronic conditions. Telemedicine and e-Health, 14(10), 1118–1126.

    Article  Google Scholar 

  27. Pham, M., Mengistu, Y., Do, H., & Sheng, W. (2018). Delivering home healthcare through a cloud-based smart home environment (CoSHE). Future Generation Computer Systems, 81, 129–140.

    Google Scholar 

  28. Awotunde, J. B., Matiluko, O. E., & Fatai, O. W. (2014). Medical diagnosis system using fuzzy logic. African Journal of Computing & ICT, 7(2), 99–106.

    Google Scholar 

  29. Solanki, A., & Nayyar, A. (2019). Green internet of things (G-IoT): ICT technologies, principles, applications, projects, and challenges. In Handbook of research on big data and the IoT (pp. 379–405). IGI Global.

    Google Scholar 

  30. Yang, T., Gentile, M., Shen, C. F., & Cheng, C. M. (2020). Combining point-of-care diagnostics and the internet of medical things (IoMT) to combat the COVID-19 pandemic.

    Google Scholar 

  31. Koh, D. (2020). SPHCC employs IoT tech and wearable sensors to monitor COVID-19 patients. Mobi Health News. https://www.mobihealthnews.com/news/Asia-pacific/sphcc-employs-IoT-tech-and-wearable-sensors-monitor-covid-19-

  32. Baharudin, H., & Wong, L. Coronavirus: Singapore develops a smartphone app for efficient contact tracing. https://www.straitstimes.com/singapore/coronavirus-singapore-develops-smartphone-app-for-efficient-contact-tracing

  33. Lu, L., Zhang, J., Xie, Y., Gao, F., Xu, S., Wu, X., & Ye, Z. (2020). Wearable health devices in health care: Narrative systematic review. JMIR mHealth and uHealth, 8(11), e18907.

    Article  Google Scholar 

  34. Wen, F., He, T., Liu, H., Chen, H. Y., Zhang, T., & Lee, C. (2020). Advances in chemical sensing technology for enabling the next-generation self-sustainable integrated wearable system in the IoT era. Nano Energy, 105155.

    Google Scholar 

  35. Athavale, Y., & Krishnan, S. (2020). A telehealth system framework for assessing knee-joint conditions using vibroarthrographic signals. Biomedical Signal Processing and Control, 55, 101580.

    Article  Google Scholar 

  36. Pustokhina, I. V., Pustokhin, D. A., Gupta, D., Khanna, A., Shankar, K., & Nguyen, G. N. (2020). An effective training scheme for deep neural network in edge computing enabled Internet of medical things (IoMT) systems. IEEE Access, 8, 107112–107123.

    Article  Google Scholar 

  37. Janet, B., & Raj, P. (2019). Smart city applications: the smart leverage of the internet of things (IoT) paradigm. In Novel practices and trends in grid and cloud computing (pp. 274–305). IGI Global.

    Google Scholar 

  38. Raj, P., & Pushpa, J. (2018). Expounding the edge/fog computing infrastructures for data science. In Handbook of research on cloud and fog computing infrastructures for data science (pp. 1–32). IGI Global.

    Google Scholar 

  39. Rehman, H. U., Khan, A., & Habib, U. (2020). Fog computing for bioinformatics applications. Fog Computing: Theory and Practice, 529–546.

    Google Scholar 

  40. Devarajan, M., Subramaniyaswamy, V., Vijayakumar, V., & Ravi, L. (2019). Fog-assisted personalized healthcare-support system for remote patients with diabetes. Journal of Ambient Intelligence and Humanized Computing, 10(10), 3747–3760.

    Article  Google Scholar 

  41. Pan, J., & McElhannon, J. (2017). Future edge cloud and edge computing for internet of things applications. IEEE Internet of Things Journal, 5(1), 439–449.

    Article  Google Scholar 

  42. Xu, X., Liu, Q., Luo, Y., Peng, K., Zhang, X., Meng, S., & Qi, L. (2019). A computation offloading method over big data for IoT-enabled cloud-edge computing. Future Generation Computer Systems, 95, 522–533.

    Article  Google Scholar 

  43. Alzubi, J., Nayyar, A., & Kumar, A. (2018, November). Machine learning from theory to algorithms: An overview. In Journal of physics: Conference series (Vol. 1142, No. 1, p. 012012). IOP Publishing.

    Google Scholar 

  44. Zivkovic, M., Bacanin, N., Venkatachalam, K., Nayyar, A., Djordjevic, A., Strumberger, I., & Al-Turjman, F. (2021). COVID-19 cases prediction by using hybrid machine learning and beetle antennae search approach. Sustainable Cities and Society, 66, 102669.

    Article  Google Scholar 

  45. Lu, F. S., Hou, S., Baltrusaitis, K., Shah, M., Leskovec, J., Hawkins, J., Brownstein, J., Conidi, G., Gunn, J., Gray, J., & Santillana, M. (2018). Accurate influenza monitoring and forecasting using novel internet data streams: A case study in the Boston metropolis. JMIR Public Health and Surveillance, 4(1), e4.

    Article  Google Scholar 

  46. Naudé, W. (2020). Artificial intelligence vs COVID-19: Limitations, constraints and pitfalls. AI & Society, 35(3), 761–765.

    Article  Google Scholar 

  47. Ajagbe, S. A., & Adesina, A. O. Design and development of an access control based electronic medical record (Emr). CPJ, 2020008, 26108.

    Google Scholar 

  48. Kononenko, I. (2001). Machine learning for medical diagnosis: History, state of the art and perspective. Artificial Intelligence in Medicine, 23(1), 89–109.

    Article  Google Scholar 

  49. Foster, K. R., Koprowski, R., & Skufca, J. D. (2014). Machine learning, medical diagnosis, and biomedical engineering research-commentary. Biomedical Engineering Online, 13(1), 94.

    Article  Google Scholar 

  50. Wang, Y., Fan, Y., Bhatt, P., & Davatzikos, C. (2010). High-dimensional pattern regression using machine learning: From medical images to continuous clinical variables. NeuroImage, 50(4), 1519–1535.

    Article  Google Scholar 

  51. Ai, T., Yang, Z., Hou, H., Zhan, C., Chen, C., Lv, W., Tao, Q., Sun, Z., & Xia, L. (2020). Correlation of chest CT and RT-PCR testing in coronavirus disease 2019 (COVID-19) in China: A report of 1014 cases. Radiology, 200642.

    Google Scholar 

  52. Luo, H., Tang, Q. L., Shang, Y. X., Liang, S. B., Yang, M., Robinson, N., & Liu, J. P. (2020). Can Chinese medicine be used for prevention of corona virus disease 2019 (COVID-19)? A review of historical classics, research evidence and current prevention programs. Chinese Journal of Integrative Medicine, 1–8.

    Google Scholar 

  53. Haleem, A., Javaid, M., & Vaishya, R. (2020). Effects of COVID 19 pandemic in daily life. Current Medicine Research and Practice.

    Google Scholar 

  54. Biswas, K., & Sen, P. (2020). Space-time dependence of corona virus (COVID-19) outbreak. arXiv preprint arXiv:2003.03149

  55. Stebbing, J., Phelan, A., Griffin, I., Tucker, C., Oechsle, O., Smith, D., & Richardson, P. (2020). COVID-19: Combining antiviral and anti-inflammatory treatments. The Lancet Infectious Diseases, 20(4), 400–402.

    Article  Google Scholar 

  56. Sohrabi, C., Alsafi, Z., O’Neill, N., Khan, M., Kerwan, A., Al-Jabir, A., Iosifidis, C., & Agha, R. (2020). World health organization declares global emergency: A review of the 2019 novel coronavirus (COVID-19). International Journal of Surgery.

    Google Scholar 

  57. Chen, S., Yang, J., Yang, W., Wang, C., & Bärnighausen, T. (2020). COVID-19 control in China during mass population movements at New Year. The Lancet, 395(10226), 764–766.

    Article  Google Scholar 

  58. Bobdey, S., & Ray, S. (2020). Going viral–Covid-19 impact assessment: A perspective beyond clinical practice. Journal of Marine Medical Society, 22(1), 9.

    Article  Google Scholar 

  59. Gozes, O., Frid-Adar, M., Greenspan, H., Browning, P. D., Zhang, H., Ji, W., Bernheim, A., & Siegel, E. (2020). Rapid AI development cycle for the coronavirus (covid-19) pandemic: Initial results for automated detection & patient monitoring using deep learning CT image analysis. arXiv preprint arXiv:2003.05037

  60. Pirouz, B., Shaffiee Haghshenas, S., Shaffiee Haghshenas, S., & Piro, P. (2020). Investigating a serious challenge in the sustainable development process: Analysis of confirmed cases of COVID-19 (new type of coronavirus) through a binary classification using artificial intelligence and regression analysis. Sustainability, 12(6), 2427.

    Article  Google Scholar 

  61. Whitelaw, S., Mamas, M. A., Topol, E., & Van Spall, H. G. (2020). Applications of digital technology in COVID-19 pandemic planning and response. The Lancet Digital Health.

    Google Scholar 

  62. Wan, K. H., Huang, S. S., Young, A. L., & Lam, D. S. C. (2020). Precautionary measures needed for ophthalmologists during pandemic of the coronavirus disease 2019 (COVID-19). Acta Ophthalmologica, 98(3), 221–222.

    Article  Google Scholar 

  63. Li, L., Qin, L., Xu, Z., Yin, Y., Wang, X., Kong, B., Bai, J., Lu, Y., Fang, Z., Song, Q., & Cao, K. (2020). Artificial intelligence distinguishes COVID-19 from community acquired pneumonia on chest CT. Radiology.

    Google Scholar 

  64. Smeulders, A. W., & Van Ginneken, A. M. (1989). An analysis of pathology knowledge and decision making for the development of artificial intelligence-based consulting systems. Analytical and Quantitative Cytology and Histology, 11(3), 154–165.

    Google Scholar 

  65. Gupta, R., & Misra, A. (2020). Contentious issues and evolving concepts in the clinical presentation and management of patients with COVID-19 infection with reference to use of therapeutic and other drugs used in co-morbid diseases (Hypertension, diabetes etc). Diabetes & Metabolic Syndrome: Clinical Research & Reviews.

    Google Scholar 

  66. Hussain, A., & do Vale Moreira, N. C. (2020). Clinical considerations for patients with diabetes in times of COVID-19 pandemic. Diabetes & Metabolic Syndrome, 14(4), 451

    Google Scholar 

  67. Gupta, R., Ghosh, A., Singh, A. K., & Misra, A. (2020). Clinical considerations for patients with diabetes in times of COVID-19 epidemic. Diabetes & metabolic syndrome, 14(3), 211.

    Article  Google Scholar 

  68. Nienhold, D., Dornberger, R., & Korkut, S. (2016, October). Sensor-based tracking and big data processing of patient activities in ambient assisted living. In 2016 IEEE international conference on healthcare informatics (ICHI) (pp. 473–482). IEEE.

    Google Scholar 

  69. Patel, S., Park, H., Bonato, P., Chan, L., & Rodgers, M. (2012). A review of wearable sensors and systems with application in rehabilitation. Journal of Neuroengineering and Rehabilitation, 9(1), 1–17.

    Article  Google Scholar 

  70. Shnayder, V., Chen, B. R., Lorincz, K., Fulford-Jones, T. R., & Welsh, M. (2005). Sensor networks for medical care.

    Google Scholar 

  71. Chauhan, J., & Bojewar, S. (2016, August). Sensor networks based healthcare monitoring system. In 2016 International conference on inventive computation technologies (ICICT) (Vol. 2, pp. 1–6). IEEE.

    Google Scholar 

  72. Pantelopoulos, A., & Bourbakis, N. G. (2009). A survey on wearable sensor-based systems for health monitoring and prognosis. IEEE Transactions on Systems, Man, and Cybernetics, Part C (Applications and Reviews), 40(1), 1–12.

    Google Scholar 

  73. Awotunde, J. B., Jimoh, R. G., Oladipo, I. D., Abdulraheem, M., Jimoh, T. B., & Ajamu, G. J. (2021). Big data and data analytics for an enhanced COVID-19 epidemic management. In Studies in systems, decision and control (Vol. 358, pp. 11–29).

    Google Scholar 

  74. Mohammed, M. N., Hazairin, N. A., Syamsudin, H., Al-Zubaidi, S., Sairah, A. K., Mustapha, S., & Yusuf, E. (2020). 2019 Novel coronavirus disease (Covid-19): Detection and diagnosis system using IoT based smart glasses. International Journal of Advanced Science and Technology, 29(7 Special Issue).

    Google Scholar 

  75. Tamura, T., Huang, M., & Togawa, T. (2018). Current developments in wearable thermometers. Advanced Biomedical Engineering, 7, 88–99.

    Article  Google Scholar 

  76. Mohammed, M. N., Hazairin, N. A., Al-Zubaidi, S., AK, S., Mustapha, S., & Yusuf, E. (2020). Toward a novel design for coronavirus detection and diagnosis system using IoT based drone technology. International Journal of Psychosocial Rehabilitation, 24(7), 2287–2295.

    Google Scholar 

  77. Chamberlain, S. D., Singh, I., Ariza, C. A., Daitch, A. L., Philips, P. B., & Dalziel, B. D. (2020). Real-time detection of COVID-19 epicenters within the United States using a network of smart thermometers. medRxiv.

    Google Scholar 

  78. Dubov, A., & Shoptaw, S. (2020). The value and ethics of using technology to contain the COVID-19 epidemic. The American Journal of Bioethics, 1–5.

    Google Scholar 

  79. McNeil, D. G. (2020). Can smart thermometers track the spread of the coronavirus? The New York Times.

    Google Scholar 

  80. Mohammed, M. N., Syamsudin, H., Al-Zubaidi, S., AKS, R. R., & Yusuf, E. (2020). Novel COVID-19 detection and diagnosis system using IOT based smart helmet. International Journal of Psychosocial Rehabilitation, 24(7).

    Google Scholar 

  81. Ruktanonchai, N. W., Ruktanonchai, C. W., Floyd, J. R., & Tatem, A. J. (2018). Using Google location history data to quantify fine-scale human mobility. International Journal of Health Geographics, 17(1), 28.

    Article  Google Scholar 

  82. Ghosh, S. (2020). Police in China, Dubai, and Italy are using these surveillance helmets to scan people for COVID-19 fever as they walk past, and it may be our future regular. Business Insider.

    Google Scholar 

  83. Adeniyi, E. A., Ogundokun, R. O., & Awotunde, J. B. (2021). IoMT-based wearable body sensors network healthcare monitoring system. In IoT in healthcare and ambient assisted living (pp. 103–121). Springer, Singapore.

    Google Scholar 

  84. Awotunde, J. B., Folorunso, S. O., Bhoi, A. K., Adebayo, P. O., & Ijaz, M. F. (2021). Disease diagnosis system for IoT-based wearable body sensors with machine learning algorithm. In Intelligent systems reference library (Vol. 209, pp. 201–222).

    Google Scholar 

  85. Awotunde, J. B., Folorunso, S. O., Jimoh, R. G., Adeniyi, E. A., Abiodun, K. M., & Ajamu, G. J. (2021). Application of artificial intelligence for COVID-19 epidemic: An exploratory study, opportunities, challenges, and future prospects. In Studies in systems, decision and control (Vol. 358, pp. 47–61).

    Google Scholar 

  86. Awotunde, J. B., Bhoi, A. K., & Barsocchi, P. (2021). Hybrid cloud/fog environment for healthcare: An exploratory study, opportunities, challenges, and future prospects. In Intelligent systems reference library (Vol. 209, pp. 1–20).

    Google Scholar 

  87. Bright, J., & Liao, R. (2020). Chinese startup Rokid pitches COVID-19 detection glasses in the US.

    Google Scholar 

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Authors and Affiliations

  1. Department of Computer Science, University of Ilorin, Ilorin, Nigeria

    Joseph Bamidele Awotunde & Rasheed Gbenga Jimoh

  2. Center for System and Information Services, Landmark University, Omu Aran, Nigeria

    Opeyemi Emmanuel Matiluko

  3. Department of Computer Science, Landmark University, Omu-Aran, Nigeria

    Babatunde Gbadamosi

  4. Department of Agricultural Extension and Rural Development, Landmark University, Omu Aran, Nigeria

    Gbemisola Janet Ajamu

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  1. Joseph Bamidele Awotunde
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  2. Rasheed Gbenga Jimoh
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  3. Opeyemi Emmanuel Matiluko
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  4. Babatunde Gbadamosi
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  5. Gbemisola Janet Ajamu
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Correspondence to Joseph Bamidele Awotunde .

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Editors and Affiliations

  1. Vellore Institute of Technology, Chennai, Tamil Nadu, India

    Amit Kumar Tyagi

  2. Machine Intelligence Research Labs (MIR Labs), Auburn, WA, USA

    Ajith Abraham

  3. Department of Civil Engineering, Vilnius Gediminas Technical University, Vilnius, Lithuania

    Arturas Kaklauskas

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Awotunde, J.B., Jimoh, R.G., Matiluko, O.E., Gbadamosi, B., Ajamu, G.J. (2022). Artificial Intelligence and an Edge-IoMT-Based System for Combating COVID-19 Pandemic. In: Tyagi, A.K., Abraham, A., Kaklauskas, A. (eds) Intelligent Interactive Multimedia Systems for e-Healthcare Applications. Springer, Singapore. https://doi.org/10.1007/978-981-16-6542-4_11

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