Skip to main content

Enhancing Social Divergence Using Genetic Algorithms and Linked Open Data for Improving Immunity System Against COVID-19

  • Conference paper
  • First Online:
International Conference on Computing, Communication, Electrical and Biomedical Systems

Part of the book series: EAI/Springer Innovations in Communication and Computing ((EAISICC))

Abstract

COVID-19 is one of the dangerous viruses that appears in 2020. The virus has gained popularity with its massive spread across the countries. The number of casualties has increased dramatically, which led many countries to declare a state of emergency as a result of the outbreak of this epidemic and their inability to control it. Several studies and researches have emerged to shed light on the mechanism of the virus and ways to prevent it, making it easier to control in the future. The World Health Organization (WHO) has begun to publish detailed numbers of injuries, deaths, and recovery cases and has given many advices, including the imposition of a total and partial curfew in many areas in addition to emphasizing the principle of social divergence in order to prevent the rapid spread of the virus among groups of society. The main goal of this paper is to design a system that used genetic algorithms (GAs) and the principles of linked open data (LOD) for improving the immunity system by enhancing social divergence. The system starts using GA for the purpose of finding the characteristics that must be present in a person who is dangerous to society in order to get away from him as much as possible. After taking these features, the system will take the values of these features and add it to the features for all persons in order to check it in the future and give alarm to all their friends or people around them. The RDF (Resource Description Framework) is a standard model for data interchange on the Web. The main idea for using RDF in this paper is finding a proper representation for user personal file and give the flexibility to connect many personal files in order to find a deep information and can reach an unknown person from known person using the FOAF (Friend Of A Friend) and vCard (virtual card) as a standard for vocabularies. The system takes the Statistics from the WHO which show the total infected cases in all countries arranged in decreasing order. The system gives a good result for analyzing the COVID-19 virus information and detecting the infected (possible infected) person and send warning to all nearest people and his friend and family, because sometimes the person has no coronavirus symptoms but he is infected so we need a technique for detecting that virus and take a proper action as soon as possible.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 229.00
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 299.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info
Hardcover Book
USD 299.99
Price excludes VAT (USA)
  • Durable hardcover edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

References

  1. Dhama, K., et al.: Coronavirus disease 2019 – COVID-19. Preprints. 2020, 2020030001. https://doi.org/10.20944/preprints202003.0001.v2

  2. World Health Organization: Coronavirus disease 2019 (COVID-19): situation report, 115, World Health Organization (2020)

    Google Scholar 

  3. Filho, J.R., Alippi, C., Treleaven, P.: Genetic algorithm programming environments. Computer. 27(6), 28–43 (1994)

    Article  Google Scholar 

  4. Chakkarwar, V.A., Joshi, A.A.: Semantic web mining using RDF data. Int. J. Comput. Appl. 133(10), 14–19 (2016)

    Google Scholar 

  5. Hojjat Adeli , Kamal C. Sarma , “Cost Optimization of Structures, Fuzzy Logic, Genetic Algorithms,and Parallel Computing”, john whiley & Son (2006)

    Google Scholar 

  6. Knjazew, D., Ome, G.A.: A Competent Genetic Algorithm for Solving Permutation and Scheduling Problems, Book. Kluwer Academic Publisher (2002)

    MATH  Google Scholar 

  7. Kramer, O.: Genetic Algorithm Essentials. Studies in Computational Intelligence, Springer International Publishing AG (2017)

    Book  Google Scholar 

  8. Sikos, L.F.: Mastering Structured Data on the Semantic Web: from HTML5 Microdata to Linked Open Data, Apress, Berkeley, CA,1st edn, (2015)

    Book  Google Scholar 

  9. Bizer, C., et al.: Linked data – the story so far. Int. J. Semant. Web Informat. Syst. 5(3), 1–22 (2009)

    Article  Google Scholar 

  10. Auer, S.: Introduction to LOD2, Linked Open Data. LNCS. 8661, 1–17 (2014)

    Google Scholar 

  11. Thuraisingham, B.: Building Trustworthy Semantic Webs, Book. Auerbach Publications, Taylor & Francis Group (2008)

    Google Scholar 

  12. Ding, L., et al.: How the semantic web is being used: an analysis of FOAF documents. In: Proceedings of the 38th Annual Hawaii International Conference on System Sciences, pp. 113c–113c, Big Island, HI (2005). https://doi.org/10.1109/HICSS.2005.299

  13. Hepp, M., et al.: Ontology Management Semantic Web, Semantic Web Services, and Business Applications, Book. Springer (2008)

    Google Scholar 

  14. Boncz, P., Erling, O., Pham, M.-D.: Advances in large-scale RDF data management, Linked Open Data. LNCS. 8661, 21–44 (2014)

    Google Scholar 

  15. Ristoski, P., Paulheim, H.: Semantic web in data mining and knowledge discovery: a comprehensive survey. Web Semant. Sci. Serv. Agents World Wide Web. 36, 1–22 (2016)

    Article  Google Scholar 

  16. Leuf, B.: The Semantic Web Crafting Infrastructure for Agency, Book. John Wiley (2006)

    Google Scholar 

  17. Antoniou, G., van Harmelen, F.: Semantic Web Primer, Book. The MIT Press (2004)

    Google Scholar 

  18. Daconta, M.C., Obrst, L.J., Smith, K.T.: The Semantic Web: A Guide to the Future of XML, Web Services, and Knowledge Management. Wiley Publishing Inc. (2003)

    Google Scholar 

Download references

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Asaad Sabah Hadi .

Editor information

Editors and Affiliations

Rights and permissions

Reprints and permissions

Copyright information

© 2022 The Author(s), under exclusive license to Springer Nature Switzerland AG

About this paper

Check for updates. Verify currency and authenticity via CrossMark

Cite this paper

Hadi, A.S. (2022). Enhancing Social Divergence Using Genetic Algorithms and Linked Open Data for Improving Immunity System Against COVID-19. In: Ramu, A., Chee Onn, C., Sumithra, M. (eds) International Conference on Computing, Communication, Electrical and Biomedical Systems. EAI/Springer Innovations in Communication and Computing. Springer, Cham. https://doi.org/10.1007/978-3-030-86165-0_29

Download citation

  • DOI: https://doi.org/10.1007/978-3-030-86165-0_29

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-86164-3

  • Online ISBN: 978-3-030-86165-0

  • eBook Packages: EngineeringEngineering (R0)

Publish with us

Policies and ethics