Evidence map›Paper›PMID 34601676›Full record

ArticleEnvironmental science and pollution research international2022

Introducing artificial intelligence to the radiation early warning system.

Mohammed Al Saleh, Béatrice Finance, Yehia Taher, Rafiqul Haque, Ali Jaber, Nourhan Bachir

Abstract read
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In one paragraph

Article in Environmental science and pollution research international, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

0numbers the graph read from it
0cells of the map it votes in
0citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

0 citing papers in PubMed.

No citing paper in PubMed yet.

4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

6 authors.

Mohammed Al SalehDavid Laboratory, University of Versailles (UVSQ), 45 avenue des Etats-Unis, 78035, Versailles, France. mhdalsaleh@gmail.com.ORCID http://orcid.org/0000-0001-6703-940X
Béatrice FinanceDavid Laboratory, University of Versailles (UVSQ), 45 avenue des Etats-Unis, 78035, Versailles, France.
Yehia TaherDavid Laboratory, University of Versailles (UVSQ), 45 avenue des Etats-Unis, 78035, Versailles, France.
Rafiqul HaqueIntelligencia R&D, Paris, France.
Ali JaberLebanese University, Rafic Hariri University Campus, Al Hadath, Beirut, Lebanon.
Nourhan BachirLebanese University, Rafic Hariri University Campus, Al Hadath, Beirut, Lebanon.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

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.

Indexed as

Artificial IntelligenceAnomaly detectionClusteringData analyticsEarly warning systemPredictionRadiationRefining incoming monitored incidents

Identifiers

PMID34601676

What OpenQuestion holds

Textmetadata
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Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.