World Scientific
  • Search
    Quick Search in Journals
    Access type:
    Quick Search anywhere
    Access type:
    Advanced Search
  • My Cart
  •   
Skip main navigation
Close Drawer MenuOpen Drawer Menu

Our site uses Javascript to enchance its usability.You can disable your ad blocker or whitelist our website www.worldscientific.com to view the full content.

Select your blocker:

Adblock Plus Instructions

  1. Click the AdBlock Plus icon in the extension bar
  2. Click the blue power button
  3. Click refresh
Our website is made possible by displaying certain online content using javascript.
In order to view the full content, please disable your ad blocker or whitelist our website www.worldscientific.com.

System Upgrade on Tue, Oct 25th, 2022 at 2am (EDT)

Existing users will be able to log into the site and access content. However, E-commerce and registration of new users may not be available for up to 12 hours.
For online purchase, please visit us again. Contact us at [email protected] for any enquiries.
Research PaperNo Access

Analysing and predicting COVID-19 AI tracking using artificial intelligence

    https://doi.org/10.1142/S1793962321410051Cited by:2 (Source: Crossref)
    Previous
    Next
    This article is part of the issue:
    Special Issue: Modeling and Simulation Addressing the COVID-19 Crisis
    Featured Editors: Agostino Bruzzone and Joe Rosen

    Abstract

    In this paper, we will discuss prediction methods to restrict the spread of the disease by tracking contact individuals via mobile application to individuals infected with the COVID-19 virus. We will track individuals using bluetooth technology and then we will save information in the central database when they are in touch. Monitoring cases and avoiding the infected person help with social distance. We also propose that sensors used by people to obtain blood oxygen saturation level and their body temperature will be used besides bluetooth monitoring. The estimation of the frequency of the disease is based on the data entered by the patient and also on the data gathered from the users who entered the program on the state of the disease. In this study, we will also propose the best way to restrict the spread of COVID-19 by using methods of artificial intelligence to predict the disease in Jordan using Tensorflow.

    References

    • 1. Chen N. et al., Epidemiological and clinical characteristics of 99 cases of 2019 novel coronavirus pneumonia in Wuhan, China: A descriptive study, Lancet 395(10223) :507–513, 2020, https://doi.org/10.1016/S0140-6736(20)30211-7. Crossref, ISI, Google Scholar
    • 2. Wang D. et al., Clinical characteristics of 138 hospitalized patients with 2019 novel coronavirus-infected pneumonia in Wuhan, China, JAMA 323(11) :1061–1069, 2020. Crossref, ISI, Google Scholar
    • 3. Li Q. et al., Early transmission dynamics in Wuhan, China, of novel coronavirus-infected pneumonia, N. Engl. J. Med. 382 :1199–1207, 2020. Crossref, ISI, Google Scholar
    • 4. Holshue M. L. et al., First case of 2019 novel coronavirus in the United States, N. Engl. J. Med. 382 :929–936, 2020, https://doi.org/10.1056/NEJMoa2001191. Crossref, ISI, Google Scholar
    • 5. Ai T. et al., Correlation of chest CT and RT-PCR testing in coronavirus disease 2019 (COVID-19) in China: A report of 1014 cases, Radiology 296(2) :E32–E40, 2020, https://doi.org/10.1148/radiol.2020200642. Crossref, ISI, Google Scholar
    • 6. Fang Y. et al., Sensitivity of chest CT for COVID-19: Comparison to RT-PCR, Radiology 296(2) :E115–E117, 2020, https://doi.org/10.1148/radiol.2020200432. Crossref, ISI, Google Scholar
    • 7. Chung M. et al., CT imaging features of 2019 novel coronavirus (2019-nCoV), Radiology 295(1) :202–207, 2020. Crossref, ISI, Google Scholar
    • 8. Huang C. et al., Clinical features of patients infected with 2019 novel coronavirus in Wuhan, China, Lancet 395(10223) :P497–P506, 2020, https://doi.org/10.1016/S0140-6736(20)30183-5. Crossref, ISI, Google Scholar
    • 9. Xia C. et al., A multi-modality network for cardiomyopathy death risk prediction with CMR images and clinical information, Int. Conf. Medical Image Computing and Computer-Assisted Intervention, Springer, Cham, 2019. Crossref, Google Scholar
    • 10. Kong B. et al., Learning tree-structured representation for 3D coronary artery segmentation, Comput. Med. Imaging Graph. 80 :101688, 2020. Crossref, ISI, Google Scholar
    • 11. Ye H. et al., Precise diagnosis of intracranial hemorrhage and subtypes using a three-dimensional joint convolutional and recurrent neural network, Eur. Radiol. 29(11) :6191–6201, 2019. Crossref, ISI, Google Scholar
    • 12. Kermany D. S. et al., Identifying medical diagnoses and treatable diseases by image-based deep learning. Cell 172(5) :1122.e9–1131.e9, 2018. Crossref, ISI, Google Scholar
    • 13. Rajaraman S. et al., Visualization and interpretation of convolutional neural network predictions in detecting pneumonia in pediatric chest radiographs. Appl. Sci. 8(10) :1715, 2018. Crossref, Google Scholar
    • 14. Depeursinge A. et al., Automated classification of usual interstitial pneumonia using regional volumetric texture analysis in high-resolution CT, Invest. Radiol. 50(4) :261, 2015. Crossref, ISI, Google Scholar
    • 15. Anthimopoulos M. et al., Lung pattern classification for interstitial lung diseases using a deep convolutional neural network, IEEE Trans. Med. Imag. 35(5) :1207–1216, 2016. Crossref, ISI, Google Scholar
    • 16. Hossain M. S., Muhammad G. , Emotion recognition using deep learning approach from audio–visual emotional big data, Inf Fusion 49 :69–78, 2019. Crossref, ISI, Google Scholar
    • 17. Ardabili S., Mosavi A., Dehghani M., Vrkonyi-Kczy A. R. , Deep learning and machine learning in hydrological processes climate change and earth systems a systematic review. Int. Conf. Global Research and Education, Springer, Cham, pp. 52–62, 2019. Google Scholar
    • 18. Banerjee A. K., Arora N. , Machine learning techniques in biological data classification and clustering: Initiation of a scientific voyage, J. PeerScientist 2(1) :e1000011, 2020. Google Scholar
    • 19. Bienvenido-Huertas D., Rubio-Bellido C., Prez-Ordez J. L., Moyano J. , Optimizing the evaluation of thermal transmittance with the thermometric method using multilayer perceptrons, Energy Build. 198 :395–411, 2019. Crossref, ISI, Google Scholar
    • 20. Tan M., Le, Q. V., EfficientNet: Rethinking model scaling for convolutional neural networks. preprint, 2019, arXiv:1905.11946. Google Scholar
    • 21. Liu C., Li Z., Hu W., Xing L., Peng, H. , Range and dose verification in proton therapy using proton-induced positron emitters and recurrent neural networks (RNNs), Phys. Med. Biol. 64(17) :175009, 2019. Crossref, ISI, Google Scholar
    • 22. Russell S. J., Norvig P. , Artificial Intelligence: A Modern Approach, 3rd edn., Prentice Hall, 2010. Google Scholar