Skip to main content
SpringerLink
Log in

Impact analysis of recovery cases due to COVID-19 outbreak using deep learning model

  • Published:
Multimedia Tools and Applications Aims and scope Submit manuscript

Abstract

The present world is badly affected by novel coronavirus (COVID-19). Using medical kits to identify the coronavirus affected persons are very slow. What happens in the next, nobody knows. The world is facing erratic problem and don’t know what will happen in near future. This paper is trying to make prognosis of the coronavirus recovery cases using LSTM(Long Short Term Memory). This work exploited data of 258 regions, their latitude and longitude and the number of death of 403 days ranging from 22-01-2020 to 27-02-2021. Specifically, advanced deep learning-based algorithms known as the LSTM, play a great effect on extracting highly essential features for time series data (TSD) analysis.There are lots of methods which already use to analyze propagation prediction. The main task of this paper culminates in analyzing the spreading of Coronavirus across worldwide recovery cases using LSTM deep learning-based architectures.

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

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8
Fig. 9

Similar content being viewed by others

Data Availability

Not Applicable.

Code Availability

The code is not available in the cloud.

References

  1. Acosta-Quiroz J, Iglesias-Osores S (2020) Salud mental en trabajadores expuestos a covid-19. Revista de Neuro-Psiquiatría 83(3):212–213

    Article  Google Scholar 

  2. Alok N, Krishan K, Chauhan P (2021) Deep learning-based image classifier for malaria cell detection. Mach Learn Healthcare Appl:187–197

  3. Ansari H, Vijayvergia A, Kumar K (2018) Dcr-hmm: depression detection based on content rating using hidden markov model. In: 2018 Conference on information and communication technology (CICT). IEEE, pp 1–6

  4. COVID G (2021) Database. Retrieved from https://github.com/cssegisanddata. COVID-19 (19)

  5. Dabral I, Singh M, Kumar K (2019) Cancer detection using convolutional neural network. In: International conference on deep learning, artificial intelligence and robotics. Springer, pp 290–298

  6. Darbari A, Kumar K, Darbari S, Patil PL (2021) Requirement of artificial intelligence technology awareness for thoracic surgeons. The Cardiothoracic Surgeon 29(1):1–10

    Article  Google Scholar 

  7. Fouladi S, Ebadi M, Safaei AA, Bajuri MY, Ahmadian A (2021) Efficient deep neural networks for classification of covid-19 based on ct images: virtualization via software defined radio. Comput Commun

  8. Géron A (2019) Hands-on machine learning with scikit-learn, keras, and tensorflow: concepts, tools, and techniques to build intelligent systems

  9. Gupta V, Jain N, Katariya P, Kumar A, Mohan S, Ahmadian A, Ferrara M (2021) An emotion care model using multimodal textual analysis on covid-19. Chaos Solitons Fractals 144:110708

    Article  Google Scholar 

  10. Hao Y, Xu T, Hu H, Wang P, Bai Y (2020) Prediction and analysis of corona virus disease 2019. PloS One 15(10):0239960

    Article  Google Scholar 

  11. Hassan SA, Sheikh FN, Jamal S, Ezeh JK, Akhtar A (2020) Coronavirus (covid-19): a review of clinical features, diagnosis, and treatment. Cureus, vol 12(3)

  12. Hochreiter S, Schmidhuber J (1997) Long short-term memory. Neural Computat 9(8):1735–1780

    Article  Google Scholar 

  13. Jiang X, Coffee M, Bari A, Wang J, Jiang X, Huang J, Shi J, Dai J, Cai J, Zhang T et al (2020) Towards an artificial intelligence framework for data-driven prediction of coronavirus clinical severity. Comput Materials Continua 63(1):537–551

    Article  Google Scholar 

  14. Khan AA, Amin R, Ullah S, Sumelka W, Altanji M (2022) Numerical simulation of a caputo fractional epidemic model for the novel coronavirus with the impact of environmental transmission. Alexandria Eng J 61(7):5083–5095

    Article  Google Scholar 

  15. Khan AA, Ullah S, Amin R (2022) Optimal control analysis of covid-19 vaccine epidemic model: a case study. European Phys J Plus 137(1):1–25

    Article  Google Scholar 

  16. Kumar K, Shrimankar DD (2017) F-des: fast and deep event summarization. IEEE Trans Multimed 20(2):323–334

    Article  Google Scholar 

  17. Kumari S, Singh M, Kumar K (2019) Prediction of liver disease using grouping of machine learning classifiers. In: International conference on deep learning, artificial intelligence and robotics. Springer, pp 339–349

  18. Kuniya T (2020) Prediction of the epidemic peak of coronavirus disease in japan, 2020. J Clinical Med 9(3):789

    Article  Google Scholar 

  19. Mahmoudi MR, Baleanu D, Mansor Z, Tuan BA, Pho K-H (2020) Fuzzy clustering method to compare the spread rate of covid-19 in the high risks countries. Chaos Solitons Fractals 140:110230

    Article  MathSciNet  Google Scholar 

  20. Negi A, Chauhan P, Kumar K, Rajput R (2020) Face mask detection classifier and model pruning with keras-surgeon. In: 2020 5th IEEE international conference on recent advances and innovations in engineering (ICRAIE). IEEE, pp 1–6

  21. Negi A, Kumar K (2021) Classification and detection of citrus diseases using deep learning. In: Data science and its applications. Chapman and Hall/CRC, pp 63–85

  22. Negi A, Kumar K (2021) Face mask detection in real-time video stream using deep learning. Computat Intell Healthcare Inf:255–268

  23. Negi A, Kumar K, Chaudhari NS, Singh N, Chauhan P (2021) Predictive analytics for recognizing human activities using residual network and fine-tuning. In: International conference on big data analytics. Springer, pp 296–310

  24. Negi A, Kumar K, Chauhan P (2021) Deep neural network-based multi-class image classification for plant diseases. Agri Inf Autom Using IoT Mach Learn:117–129

  25. Negi A, Kumar K, Chauhan P, Rajput R (2021) Deep neural architecture for face mask detection on simulated masked face dataset against covid-19 pandemic. In: 2021 International conference on computing, communication, and intelligent systems (ICCCIS). IEEE, pp 595–600

  26. Pal R, Sekh AA, Kar S, Prasad DK (2020) Neural network based country wise risk prediction of covid-19. Appl Sci 10(18):6448

    Article  Google Scholar 

  27. Petropoulos F, Makridakis S (2020) Forecasting the novel coronavirus covid-19. PloS One 15(3):0231236

    Article  Google Scholar 

  28. Rahman T, Khandakar A, Qiblawey Y, Tahir A, Kiranyaz S, Kashem SBA, Islam MT, Al Maadeed S, Zughaier SM, Khan MS et al (2021) Exploring the effect of image enhancement techniques on covid-19 detection using chest x-ray images. Comput Bio Med 132:104319

    Article  Google Scholar 

  29. Rath S, Tripathy A, Tripathy AR (2020) Prediction of new active cases of coronavirus disease (covid-19) pandemic using multiple linear regression model. Diabetes Metabolic Syndrome Clinical Res Rev 14(5):1467–1474

    Article  Google Scholar 

  30. Ray EL, Wattanachit N, Niemi J, Kanji AH, House K, Cramer EY, Bracher J, Zheng A, Yamana TK, Xiong X et al (2020) Ensemble forecasts of coronavirus disease 2019 (covid-19) in the us. medRXiv

  31. Salehi AW, Baglat P, Gupta G (2020) Review on machine and deep learning models for the detection and prediction of coronavirus. Materials Today Proc 33:3896–3901

    Article  Google Scholar 

  32. Shah K, Khan ZA, Ali A, Amin R, Khan H, Khan A (2020) Haar wavelet collocation approach for the solution of fractional order covid-19 model using caputo derivative. Alexandria Engi J 59(5):3221–3231

    Article  Google Scholar 

  33. Singhal T (2020) A review of coronavirus disease-2019 (covid-19). Indian J Pediatrics 87( 4):281–286

    Article  Google Scholar 

  34. Tiwari S, Kumar S, Guleria K (2020) Outbreak trends of coronavirus disease–2019 in india: a prediction. Disaster Med Public Health Preparedness 14(5):33–38

    Article  Google Scholar 

  35. Waibler Z, Anzaghe M, Ludwig H, Akira S, Weiss S, Sutter G, Kalinke U (2007) Modified vaccinia virus ankara induces toll-like receptor-independent type i interferon responses. J Virology 81(22):12102–12110

    Article  Google Scholar 

  36. Wan H, Cui JA, Yang G-J (2020) Risk estimation and prediction by modeling the transmission of the novel coronavirus (covid-19) in mainland China excluding hubei province. medRxiv

  37. Wang W, Enilov M (2020) The global impact of covid-19 on financial markets. Available at SSRN 3588021

  38. Xu Q (2013) A novel machine learning strategy based on two-dimensional numerical models in financial engineering. Math Probl Eng, vol 2013

  39. Xu Q, Huang G, Yu M, Guo Y (2020) Fall prediction based on key points of human bones. Phys Stat Mech Appl 540:123205

    Article  MathSciNet  Google Scholar 

  40. Xu Q, Wang F, Gong Y, Wang Z, Zeng K, Li Q, Luo X (2019) A novel edge-oriented framework for saliency detection enhancement. Image Vis Comput 87:1–12

    Article  Google Scholar 

  41. Xu Q, Wang Z, Wang F, Gong Y (2019) Multi-feature fusion cnns for drosophila embryo of interest detection. Phys Stat Mech Appl 531:121808

    Article  Google Scholar 

  42. Xu Q, Wu J, Chen Q (2014) A novel mobile personalized recommended method based on money flow model for stock exchange. Math Probl Eng, vol 2014

  43. Yuan X, Xu J, Hussain S, Wang H, Gao N, Zhang L (2020) Trends and prediction in daily new cases and deaths of covid-19 in the united states: an internet search-interest based model. Exploratory Res Hypo Med 5(2):1

    Google Scholar 

  44. Zamir M, Shah K, Nadeem F, Bajuri MY, Ahmadian A, Salahshour S, Ferrara M (2021) Threshold conditions for global stability of disease free state of covid-19. Results Phys 21:103784

    Article  Google Scholar 

  45. Zhou T, Liu Q, Yang Z, Liao J, Yang K, Bai W, Lu X, Zhang W (2020) Preliminary prediction of the basic reproduction number of the wuhan novel coronavirus 2019-ncov. J Evidence-Based Med 13(1):3–7

    Article  Google Scholar 

Download references

Funding

Not Applicable

Author information

Author notes

    Authors and Affiliations

    1. School of Computing, Mathematics and Engineering, Charles Sturt University, Bathurst, Australia

      Ershadul Haque & Manoranjan Paul

    2. Department of EEE, Feni University, Barahipur, Feni, 3900, Bangladesh

      Ershadul Haque & Sami Ul Hoque

    3. Institute of IR 4.0., University Kebangsaan Malaysia, 43600, UKM, Bnagi, Selangor, Malaysia

      Mahidur R Sarker

    4. Department of Clinical Medicine, Macquarie University, Sydney, Australia

      Abdullah Al Suman

    5. School of Computer Science, Queensland University of Technology, Brisbane, Australia

      Tanvir Ul Huque

    Authors
    1. Ershadul Haque
      View author publications

      You can also search for this author in PubMed Google Scholar

    2. Sami Ul Hoque
      View author publications

      You can also search for this author in PubMed Google Scholar

    3. Manoranjan Paul
      View author publications

      You can also search for this author in PubMed Google Scholar

    4. Mahidur R Sarker
      View author publications

      You can also search for this author in PubMed Google Scholar

    5. Abdullah Al Suman
      View author publications

      You can also search for this author in PubMed Google Scholar

    6. Tanvir Ul Huque
      View author publications

      You can also search for this author in PubMed Google Scholar

    Contributions

    All the authors have equal contribution in this article.

    Corresponding author

    Correspondence to Sami Ul Hoque.

    Ethics declarations

    The following sections contain the information regarding the declaration.

    Conflict of Interests

    The authors declare that there is no conflict of interest.

    Ethics approval

    This work has not been published anywhere yet.

    Consent to participate

    Everyone among us opined to participate in this study.

    Consent for Publication

    Everyone of us is agreed to publish the article in this journal.

    Additional information

    Publisher’s note

    Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

    Md Ershadul Haque, Sami Ul Hoque, Manoranjan Paul, Mahidur R Sarker, Abdullah Al Suman and Tanvir Ul Huque are contributed equally to this work.

    Rights and permissions

    Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.

    Reprints and permissions

    About this article

    Check for updates. Verify currency and authenticity via CrossMark

    Cite this article

    Haque, E., Hoque, S.U., Paul, M. et al. Impact analysis of recovery cases due to COVID-19 outbreak using deep learning model. Multimed Tools Appl 83, 11169–11185 (2024). https://doi.org/10.1007/s11042-023-14837-9

    Download citation

    • Received:

    • Revised:

    • Accepted:

    • Published:

    • Issue Date:

    • DOI: https://doi.org/10.1007/s11042-023-14837-9

    Keywords

    Search

    Navigation