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Environmental science and pollution research international
Feb, 2022;29 (10): 14036-14045.

Introducing artificial intelligence to the radiation early warning system.

Mohammed Al Saleh, Béatrice Finance, Yehia Taher, Rafiqul Haque, Ali Jaber, Nourhan Bachir
Author Information
  1. Mohammed Al Saleh: David Laboratory, University of Versailles (UVSQ), 45 avenue des Etats-Unis, 78035, Versailles, France. mhdalsaleh@gmail.com. ORCID
  2. Béatrice Finance: David Laboratory, University of Versailles (UVSQ), 45 avenue des Etats-Unis, 78035, Versailles, France.
  3. Yehia Taher: David Laboratory, University of Versailles (UVSQ), 45 avenue des Etats-Unis, 78035, Versailles, France.
  4. Rafiqul Haque: Intelligencia R&D, Paris, France.
  5. Ali Jaber: Lebanese University, Rafic Hariri University Campus, Al Hadath, Beirut, Lebanon.
  6. Nourhan Bachir: Lebanese University, Rafic Hariri University Campus, Al Hadath, Beirut, Lebanon.
PMID: 34601676 DOI: 10.1007/s11356-021-16771-5

Abstract

Although radiation level is a serious concern which requires continuous monitoring, many existing systems are designed to perform this task. Radiation early warning system (REWS) is one of these systems which monitor the gamma radiation level in air. Such system requires high manual intervention, depends totally on experts' analysis, and has some shortcomings that can be risky sometimes. In this paper, the approach called RIMI (refining incoming monitored incidents) will be introduced which aims to improve this system while becoming more autonomous with keeping the final decision to the experts. A new method is presented which will help in changing this system to become more intelligent while learning from past incidents of each specific system.

Keywords

Anomaly detection Clustering Data analytics Early warning system Prediction Radiation Refining incoming monitored incidents

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MeSH Term

Artificial Intelligence
Journal Article

Word Cloud

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