MACHINE LEARNING ALGORITHMS FOR IDENTIFICATION OF ABNORMAL GLOW CURVES AND ASSOCIATED ABNORMALITY IN CaSO4:DY-BASED PERSONNEL MONITORING DOSIMETERS

Abstract In the present study, machine learning (ML) methods for the identification of abnormal glow curves (GC) of CaSO4:Dy-based thermoluminescence dosimeters in individual monitoring are presented. The classifier algorithms, random forest (RF), artificial neural network (ANN) and support vector m...

Full description

Saved in:
Bibliographic Details
Published in:Radiation protection dosimetry 2020-09, Vol.190 (3), p.342-351
Main Authors: Pathan, Munir S, Pradhan, S M, Selvam, T Palani
Format: Article
Language:English
Citations: Items that this one cites
Items that cite this one
Online Access:Get full text
Tags: Add Tag
No Tags, Be the first to tag this record!
Description
Summary:Abstract In the present study, machine learning (ML) methods for the identification of abnormal glow curves (GC) of CaSO4:Dy-based thermoluminescence dosimeters in individual monitoring are presented. The classifier algorithms, random forest (RF), artificial neural network (ANN) and support vector machine (SVM) are employed for identifying not only the abnormal glow curve but also the type of abnormality. For the first time, the simplest and computationally efficient algorithm based on RF is presented for GC classifications. About 4000 GCs are used for the training and validation of ML algorithms. The performance of all algorithms is compared by using various parameters. Results show a fairly good accuracy of 99.05% for the classification of GCs by RF algorithm. Whereas 96.7% and 96.1% accuracy is achieved using ANN and SVM, respectively. The RF-based classifier is recommended for GC classification as well as in assisting the fault determination of the TLD reader system.
ISSN:0144-8420
1742-3406
DOI:10.1093/rpd/ncaa108