EDP Sciences logo
Web of Conferences logo
Search
Menu
All issues Volume 430 (2023) E3S Web Conf., 430 (2023) 01153 References
Open Access
Issue
E3S Web Conf.
Volume 430, 2023
15th International Conference on Materials Processing and Characterization (ICMPC 2023)
Article Number 01153
Number of page(s) 11
DOI https://doi.org/10.1051/e3sconf/202343001153
Published online 06 October 2023
  1. C. Drosten et al., “Identification of a novel corona virus associated with severe acute respiratory syndrome”. N EnglJ Med. 348, (2003)1967-1976. [Google Scholar]
  2. https://www.ecdc.europa.eu/ [Google Scholar]
  3. Y. Chen, Q. Liu, D. Guo,”Emerging corona virus es: genome structure, replication, and pathogenesis”. J Med Virol. 92, (2020) 418-423. [CrossRef] [PubMed] [Google Scholar]
  4. N. Zhu et al.,”A novel corona virus from patientswith pneumonia in China, 2019”. N Engl J Med. 382, (2020) 727-733. [CrossRef] [PubMed] [Google Scholar]
  5. J. F. W. Chanet al.,”A familial cluster of pneumonia associated with the 2019 novel corona virus indicating person-to-person transmission: a study of a family cluster”. Lancet. 395, (2020)514–523. [CrossRef] [PubMed] [Google Scholar]
  6. https://covid19.who.int/ [online dataaccessed on 5 September 2020 (2:45 PM CEST)]. [Google Scholar]
  7. Madhu, Bhukya, and M. Venu Gopalachari. “Classification of the Severity of Attacks on Internet of Things Networks.” In Sentiment Analysis and Deep Learning: Proceedings of ICSADL 2022, pp. 411-424. Singapore: Springer Nature Singapore, 2023. [Google Scholar]
  8. https://www.mohfw.gov.in/[online data accessed on 5 September 2020 (8:00AM IST)]. [Google Scholar]
  9. S. K. Dey, M. M. Rahman, U. R. Siddiqi, A. Howlader, “Analyzing the epidemiological outbreak of COVID-19: Avisual exploratory data analysis approach”. J Med Virol. 92, (2020) 632-638. [CrossRef] [PubMed] [Google Scholar]
  10. V. Volpert,M. Banerjee, S. Petrovskii,” On a quarantine model of corona virus infection and data analysis”. Math Model Nat Pheno.1, (2020) 24. [Google Scholar]
  11. Madhu, Bhukya, M. Venu Gopala Chari, Ramdas Vankdothu, Arun Kumar Silivery, and Veerender Aerranagula. “Intrusion detection models for IOT networks via deep learning approaches.” Measurement: Sensors 25 (2023): 100641. [CrossRef] [Google Scholar]
  12. Z. Liu,P. Magal, O. Seydi, G. Webb, “Predicting the cumulative number of cases for the COVID-19 epidemic in China from early data”.Math Biosci Eng. 17, (2020) 3040-3051. [CrossRef] [Google Scholar]
  13. M. Chen,Y. Hao,K. Hwang,L. Wang, “Disease prediction by machine learning over big data from healthcare communities”. IEEE Access. 5, (2017) 8869-8879. [CrossRef] [Google Scholar]
  14. P. Kumar et al., “Forecasting the dynamics of COVID-19 Pandemic in Top 15 countries in April 2020: ARIMA Model with Machine Learning Approach, medRxiv. (2020). [Google Scholar]
  15. Sivakumar, S. A., Tegil J. John, Thamarai G. Selvi, Bhukya Madhu, C. Udhaya Shankar, and K. P. Arjun. “IoT based Intelligent Attendance Monitoring with Face Recognition Scheme.” In 2021 5th International Conference on Computing Methodologies and Communication (ICCMC), pp. 349-353. IEEE, 2021. [Google Scholar]
  16. L. Jia, K. Li, Y. Jiang, X. Guo, T. Zhao, “Prediction and analysis of corona virus disease” arXiv:2003, (2019) 05447. [Google Scholar]
  17. https://www.mohfw.gov.in/ [Google Scholar]
  18. https://www.covid19india.org/ [Google Scholar]
  19. https://www.kaggle.com/datasets ? [Google Scholar]
  20. https://mlcourse.ai/articles/topic9-part2-prophet/ [Google Scholar]
  21. https://machinelearningmastery.com/time-series-forecasting-with-prophet-in-python/ [Google Scholar]
  22. https://facebook.github.io/prophet/docs/quick_start.html [Google Scholar]
  23. https://www.analyticsvidhya.com/blog/2018/05/generate-accurate-forecasts-facebook-prophet-python-r/ [Google Scholar]

Current usage metrics show cumulative count of Article Views (full-text article views including HTML views, PDF and ePub downloads, according to the available data) and Abstracts Views on Vision4Press platform.

Data correspond to usage on the plateform after 2015. The current usage metrics is available 48-96 hours after online publication and is updated daily on week days.

Initial download of the metrics may take a while.