Table 2 Articles related to detection and monitoring
Author | Year | Type of study | The purpose of the study | Application intelligence tools | Outcome | Quality Assessment | Ref | |
|---|---|---|---|---|---|---|---|---|
Score | Quality | |||||||
Latner, Norman | 2002 | Modeling study | An intelligent radiation detector system for real-time gamma radiation remote monitoring | Intelligent Radiation Detectors's | Very valuable for detecting radioactivity from moving vehicles | 0.8 | strong | [32] |
Varley, A | 2015 | Modeling study | Creating an optimal detector-algorithm combination | Neural Network & Support Vector Machines | Providing the best detection capability | 0.8 | strong | [33] |
Varley, A | 2016 | Modeling study | Identifying the optimal combination of the detector and spectral processing routine | Machine learning & Monte Carlo simulation | Development of a method for depth extraction and activity estimation for 226Ra contamination | 0.9 | strong | [34] |
Aminalragia | 2018 | Modeling study | Revealing artificial intelligence for space radiation monitor data | Artificial Intelligence & Machine Learning | A new detection method | 0.9 | strong | [35] |
Kang, H. H | 2019 | Modeling study | Prediction of radiation properties of metal substrates | Neural Network | Data-driven surrogate forecasting works precisely and comprehensively | 0.9 | strong | [36] |
Mortazavi, SMJ | 2020 | Modeling study | Prediction of annual dose in health care workers exposed to different levels of ionizing radiation | Artificial neural network | Improve/facilitate the dose estimation process | 0.8 | strong | [37] |
Sun, D. J | 2020 | Modeling study | Identification of representative sites among multiple environmental variables, such as elevation and land cover types | Gaussian mixture model | Capture the heterogeneity of air dose rates in a systematic manner by a minimum number of monitoring sites | 0.8 | strong | [38] |
Bilton, K. J | 2021 | Modeling study | Mobile spectroscopy gamma ray source detection | Neural Network | Using simulated data from a detector | 0.9 | strong | [39] |
Dam, R. S. D | 2021 | Modeling study | The method of tracking radioactive particles and optimizing the number of detectors in single-phase flow | Neural Network | The proposal is a single-phase flow RPT system | 0.8 | strong | [40] |
Durbin, M | 2021 | Modeling study | Differentiation of gamma rays and neutrons in organic scintillators | K-Nearest Neighbors | This light output limit can be reduced in all six tested detector assembly combinations with the proposed method compared to conventional performance improvement methods | 0.9 | strong | [41] |
Hashima, S | 2021 | Modeling study | Efficient wireless sensor network for radiation detection at nuclear sites | Machine learning | Development of wireless sensor networks that precisely monitor irregular radioactivity | 0.7 | moderate | [42] |
Jain, S. K | 2021 | Modeling study | Real-time solar radiation forecast for Indore region | Machine learning | It provides solar radiation prediction with 96.9% accuracy | 0.8 | strong | [43] |
Shao, Hong | 2021 | Modeling study | An improved genetic detector system to avoid"blind spots"in detector monitoring | Machine learning | Monitor the operation status of the photoelectric detection system instantly and ensure the safe and stable operation of the detector system | 0.8 | strong | [44] |
Song, H. B | 2021 | Modeling study | Design of joint active and passive beams based on learning | Machine learning & Wireless Networks | Deployment of a neural network for instantaneous prediction | 0.7 | moderate | [45] |
Wang, J. F | 2021 | Modeling study | Estimation of nuclear medicine exposure measures based on intelligent computer processing | The analysis module is designed in MATLAB | Use dose estimation to analyze internal exposure data in radiation management | 0.8 | strong | [46] |
Angelone, M | 2022 | Modeling study | Accurate simulation of the proposed detectors by Monte Carlo technique | Monte Carlo technique | Help choose the best detector | 0.7 | moderate | [47] |
Balanya, S. A | 2022 | Modeling study | Prediction of radiation dose in nuclear power plant reactors | Machine learning | These models can be used to estimate future radiation dose values in a data-driven manner | 0.9 | strong | [48] |
Cárdenas-Montes, M | 2022 | Modeling study | Uncertainty estimation in predicting time series of Rn-222 radiation level | Deep learning | To predict periods of low radiation, enabling the correct planning of unshielded periods for maintenance operations | 0.8 | strong | [49] |
Durbin, M | 2022 | Modeling study | Gamma-ray localization capabilities to predict real-time source locations | Machine learning | Experimental tests of gamma-ray localization | 0.7 | moderate | [50] |
Fobar, D | 2022 | Modeling study | Direction estimation between a detector array and a stationary Cs-137 source | Machine learning | Multidetector array response performance for nuclear search | 0.8 | strong | [51] |
Garankin, J | 2022 | Modeling study | Detection of ionizing radiation particles by the response of a plastic scintillation detector | Machine learning | The model is able to separate particles in low-energy and high-energy transfer domains | 0.8 | strong | [52] |
Ghawaly, J. M | 2022 | Modeling study | Identification of unusual radioactive signatures in gamma-ray spectra collected by NaI (Tl) detectors | Deep convolutional | Automatic encoder radiation anomaly detection (ARAD) | 0.8 | strong | [53] |
Hwang, J | 2022 | Modeling study | Spectrum dose prediction for plastic scintillation detector and environmental dose equivalent prediction | Deep learning | Validation of ensemble model performance for representative radioisotopes | 0.8 | strong | [54] |
Ildefonso, A | 2022 | Modeling study | Reduction of single-event disturbances in RF circuits and systems | K-nearest neighbor & Machine learning | Demonstrate the potential benefits of using ML techniques in the field of radiation effects | 0.7 | moderate | [55] |
Kavuncuoglu, E | 2022 | Modeling study | Performance evaluation of fiber optic scintillation detector | Neural Network | Accurate, fast, and dynamic estimation of fiber optic scintillation detector performance | 0.7 | moderate | [56] |
Mendes, F | 2022 | Modeling study | Locating and identifying radioactive hotspots | Deep learning | Developing new techniques and new solutions to protect human lives | 0.8 | strong | [57] |
Bandstra, M. S | 2023 | Modeling study | Detection and identification of gamma rays | Machine-learning | Identify the modifications that should be applied to adapt the methods to gamma-ray spectral data | 0.8 | strong | [58] |
Brunet, R | 2023 | Modeling study | A synthetic database of infrared measurements for the inverse thermographic model | Deep learning | The proposed method can minimize the computational burden associated with models such as Monte Carlo to generate training data for real surfaces | 0.8 | strong | [59] |
Dey, R | 2023 | Modeling study | Estimation of an empirical formula for germanium type detector BEGe efficiency | Machine learning | Rapid estimation of full energy peak efficiency values and verification of nominal geometric parameters | 0.7 | moderate | [60] |
Hu, H | 2023 | Modeling study | An independent radiation source detection policy with generalized capability in unknown environments | Deep learning | Generalization of hierarchy control showed the best performance among independent decision policies as well as robustness and capability | 0.9 | strong | [61] |
Paleti, B | 2023 | Modeling study | Identification of gamma-emitting natural isotopes in environmental sample spectra: | Convolutional neural network | Among the options for automating gamma-ray spectroscopy, pattern recognition methods are the best | 0.8 | strong | [62] |
Pluzek, A | 2023 | Modeling study | Classification of partial discharges using scintillation phenomenon | Machine learning | It was possible to find out which classifier (algorithm) worked best for the task | 0.7 | moderate | [63] |