A Machine Learning-Driven Crisis Management System: Real-Time Incident Reporting and Response Optimization

A. R. Dhedeep Reddy, Shamil Saidu Mohamed, Hareni M., Harini C. J., Gayathri R.

2025

Abstract

The Crisis Management System seeks to improve the coordination of emergency management through real-time incident reporting and the categorization of crises. The two major end users of the system are the normal users who have the privileges to submit reports and undergo crisis management training, and the admins who review the reports, give follow- up on current crises, and coordinate with the responders. At the center of the system is a Convolutional Neural Network that may be employed for accurate predictions in the type of crisis at hand and, in its essence, hurries decision-making processes. The platform will be best complemented with modern machine learning techniques and cutting-edge web technologies to optimize crisis management and, by default, increase response times and coordination. It is fully developed using Next.js, with MongoDB Atlas for data storage security.

Paper Citation

in Harvard Style

Reddy A., Mohamed S., M. H., J. H. and R. G. (2025). A Machine Learning-Driven Crisis Management System: Real-Time Incident Reporting and Response Optimization. In Proceedings of the 1st International Conference on Research and Development in Information, Communication, and Computing Technologies - ICRDICCT`25; ISBN 978-989-758-777-1, SciTePress, pages 804-810. DOI: 10.5220/0013921000004919

in Bibtex Style

@conference{icrdicct`2525,
author={A. Reddy and Shamil Mohamed and Hareni M. and Harini J. and Gayathri R.},
title={A Machine Learning-Driven Crisis Management System: Real-Time Incident Reporting and Response Optimization},
booktitle={Proceedings of the 1st International Conference on Research and Development in Information, Communication, and Computing Technologies - ICRDICCT`25},
year={2025},
pages={804-810},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0013921000004919},
isbn={978-989-758-777-1},
}

in EndNote Style

TY - CONF

JO - Proceedings of the 1st International Conference on Research and Development in Information, Communication, and Computing Technologies - ICRDICCT`25
TI - A Machine Learning-Driven Crisis Management System: Real-Time Incident Reporting and Response Optimization
SN - 978-989-758-777-1
AU - Reddy A.
AU - Mohamed S.
AU - M. H.
AU - J. H.
AU - R. G.
PY - 2025
SP - 804
EP - 810
DO - 10.5220/0013921000004919
PB - SciTePress