Review of Disaster Defect Management in Machine Learning

Authors

  • J. T. Thirukrishna Associate Professor, Department of Information Science and Engineering, Dayananda Sagar Academy of Technology and Management, Bangalore, Karnataka, India
  • M. R. Supriya Student, Department of Information Science and Engineering, Dayananda Sagar Academy of Technology and Management, Bangalore, Karnataka, India
  • Mayakuntla Rohini Student, Department of Information Science and Engineering, Dayananda Sagar Academy of Technology and Management, Bangalore, Karnataka, India
  • Nishmitha Student, Department of Information Science and Engineering, Dayananda Sagar Academy of Technology and Management, Bangalore, Karnataka, India
  • M. Nithya Shree Student, Department of Information Science and Engineering, Dayananda Sagar Academy of Technology and Management, Bangalore, Karnataka, India

DOI:

https://doi.org/10.51983/ajes-2023.12.2.3946

Keywords:

Disaster Management, Machine Learning, Internet Data

Abstract

Internet data performs a crucial part in calamity response during disaster events. The disaster might be natural or man-made, Internet community have proven to be the most productive transmission and combine among afflicted communities. Research has manifested utilizing methods for machine learning to recognize the applicable posts on web, net and other sources for disaster reciprocation. Communication breakdowns and association failures are common issues during disaster response, leading to delayed or inefficient aid distribution. In this paper, we are going to evaluate the effect caused due to calamities. This research is categorized into two proportions: Response after the disaster, identifying the damage caused, and allowing for faster response. The findings of this study may help experimenters in the future identify relevant areas for disaster assessment.

References

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Published

25-10-2023

How to Cite

Thirukrishna, J. T., Supriya, M. R., Rohini, M., Nishmitha, & Nithya Shree, M. (2023). Review of Disaster Defect Management in Machine Learning. Asian Journal of Electrical Sciences, 12(2), 10–14. https://doi.org/10.51983/ajes-2023.12.2.3946