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Stream/Area:
-
Subject:
-
Paper ID:
IJSREM63601
Volume:
10 |
5 |
May 2026
Pages:
1-3
Publication Frequency:
Monthly (12 issues per year)
License:International Journal of Scientific Research in Engineering and Management follows the Creative Commons Attribution 4.0 (CC BY 4.0) License, allowing free use and distribution with proper attribution.
Computer
Matoshri College Of Engineering And Research Centre, Nashik
Computer
Matoshri College Of Engineering And Research Centre, Nashik
Computer
Matoshri College Of Engineering And Research Centre, Nashik
Computer
Matoshri College Of Engineering And Research Centre, Nashik
Traditional blood donation networks often lack the real-time communication and predictive planning necessary to respond effectively during disasters. This project proposes a framework that integrates geo-location alerts with machine learning-driven predictive modeling to improve emergency response. By analysing historical donation and usage trends, the system forecasts potential shortages to allow for proactive inventory management. Real-time geo-tracking and smart routing minimize delays and ensure the availability of blood in crisis scenarios.
KEYWORDS
The Disaster-Aware Blood Donation Network provides an intelligent solution for emergency blood management by centralising communication and using predictive analytics. The framework minimizes delivery delays and enhances healthcare resilience during crises. Future enhancements may include IoT-enabled monitoring, cloud scalability, and blockchain security to further protect data and improve response management.
[1] M. Patel and K. Rao, “Disaster-Aware Blood Supply Chain Optimization using IoT and Machine Learning,” 2024. [2] P. Roy and S. Banik, “Real-Time Blood Availability Prediction using Machine Learning Algorithms,” 2023. [3] D. Williams and M. Cooper, “Emergency Healthcare Communication System using Mobile Notification Services,” 2024. [4] World Health Organization (WHO), “Blood Safety and Availability Report,” 2024. [5] S. Lee, H. Kim, and Y. Park, “Artificial Intelligence in Healthcare Emergency Management: A Survey,” 2023. [6] H. Chen and X. Liu, “Deep Learning Approaches for Predicting Blood Demand and Inventory Management,” 2024. [7] Deshmukh and P. Kulkarni, “Geo-Location Based Emergency Blood Donor Tracking System,” 2023. [8] V. Sharma and A. Gupta, “Disaster Management using AI, IoT, and Cloud Computing Technologies,” 2023.
Publication Date
5/21/2026
Peer Review Type
Double-Blind Peer Review
Review Rounds: 2
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Conflict of Interest
The authors declare no conflict of interest related to this research work.
Funding Information
No funding information available for this research.
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