Table 1 Articles related to monitoring and assessment
Author | Year | Type of study | The purpose of the study | Application intelligence tools | Outcome | Quality Assessment | Ref | |
|---|---|---|---|---|---|---|---|---|
Score | Quality | |||||||
Chew Lim Tan | 1989 | Experimental | Assisting the inspectorate in providing advice on radiation protection, licensing, and other regulatory matters | Expert system | An expert system was developed for use in radiation protection inspections under the supervision of the Singapore Ministry of Health | 0.7 | moderate | [16] |
Lee, S. Y | 2001 | Modeling study | A new personal dosimetry system using alpha-Al2O3:C exploiting its optical properties | Artificial neural networks | Spectral information of X-ray and γ-ray fields can be obtained by analyzing the response of a multi-element system | 0.9 | strong | [17] |
Mól, A. C. A | 2011 | Simulation study | Interpolation of radiation dose rate map in nuclear power plants | Neural networks and virtual reality | A simulation tool for evaluating radiation dose to minimize received dose | 0.9 | strong | [18] |
Militello, A | 2016 | Modeling study | Radiative transfer models by integrating satellite-based radiometric data | Wearable sensors | Â | 0.2 | weak | [19] |
Izadi-Moud, A | 2019 | Cross-sectional study | Evaluating the effect of absorbed dose by overtime on blood parameters | Machine learning | Â | 0.8 | strong | [20] |
Troville, J | 2021 | Modeling study | Creating a software system to keep the dose as low as acceptable | Neural network | Monitor operating room scattered radiation and dose to staff in real- time during fluoroscopy | 0.7 | moderate | [21] |
Psomas, C | 2022 | Analytical study | Presenting a complete framework for the design and analysis of far-field SWIPT under safety constraints | SWIPT | Providing insights regarding optimal design | 0.8 | strong | [22] |
Saifullah, Muhammad | 2022 | Case study | IoT-Enabled Intelligent System for the Radiation Monitoring and Warning Approach | IoT-Enabled Intelligent System | The proposed system warns humans about dangerous areas with audio/visual notifications or buzzing so that they can move to a safer place | 0.9 | strong | [23] |
Abdelhakim, A | 2023 | Modeling study | Identification and localization of radioactive sources | Machine learning | The proposed algorithm provides accurate source intensity estimation | 0.9 | strong | [24] |
Gu, Z. M | 2023 | Modeling study | Proposing an algorithm for optimizing the ground placement strategy, which plays an important role in reducing electromagnetic radiation from the edges of the printed circuit board (PCB) | Deep Learning | The final optimization strategy obtained by the proposed algorithm has a more effective electromagnetic interference reduction | 0.8 | strong | [25] |
Eghtesad, A | 2023 | Modeling study | Designed to evaluate the radiative properties of heterogeneous porous media, as well as the effects of conductive heat transfer | Artificial Neural Networks & Machine learning | The method used can improve the prediction accuracy compared to previous studies | 0.8 | strong | [26] |
Lagerquist, R | 2023 | Modeling study | Estimation of full longwave and shortwave radiative transfer with neural networks of different complexity | Neural Networks | Simulation of full shortwave and longwave RRTM with all predictor variables, using worldwide data | 0.9 | strong | [27] |
McFerran, N | 2023 | Modeling study | Context-Aware Roadside Radiation Measurement Testbed | Machine learning | Textual data can be combined with radiation measurement data to increase system sensitivity and accuracy in nuclear threat detection applications | 0.9 | strong | [28] |
Stomps, J. R | 2023 | Modeling study | Classification of SNM radiation signatures | Machine learning | Unlabeled data can be valuable in semi-supervised non-proliferation implementations | 0.8 | strong | [29] |
Garg, M | 2024 | Modeling study | Anticipating UV radiation and noise levels during welding and emphasizing worker safety | SVM and random forest | The first known application of machine learning techniques is to predict UV radiation and noise levels in arc welding processes | 0.7 | moderae | [30] |
Pathan, M. S | 2024 | Modeling study | Estimation of personal equivalent dose using thermoluminescence dosimeter | Machine learning | A new method to assess radiation exposure in workers | 0.8 | strong | [31] |