Neutron spectrum unfolding using radial basis function neural networks

Alvar, Amin Asgharzadeh; Deevband, Mohammad Reza; Ashtiyani, Meghdad

doi:10.1016/j.apradiso.2017.07.048

Cited by 19 publications

(9 citation statements)

References 30 publications

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“…When there is a necessity to use prediction methods with the minimum error and maximum reliability, artificial neural networks are used. The advantages of ANNs over statistical methods include no limitation in the number of inputs and outputs, insensitivity to sudden changes in the data, the potential of modeling highly nonlinear behaviors, fast training, and a process to prevent overfitting [14,15].…”

Section: Methodsmentioning

confidence: 99%

A New Method on Kerma Estimation in Mammography Screenings

et al. 2021

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Background: Given the extensive use and preferred diagnostic method in common mammography tests for screening and diagnosis of breast cancer, there is concern about the increased dose absorbed by the patient due to the sensitivity of the breast tissue.Objective: This study aims to evaluate the entrance surface air kerma (ESAK) before irradiation to the patient through its estimation.Material and Methods: In this descriptive paper, firstly, a phantom was used to measure some data, including ESAK, Kvp, mAs, HVL, and type of filter/target. Secondly, the MultiLayer Perceptron (MLP) neural network model was trained with Levenberg-Marquardt (LM) backpropagation training algorithm and finally, ESAK was estimated.Results: Based on results obtained from the program in different neuron numbers, it was found that the number of 35 neurons is the most optimal value, offering a regression coefficient of 95.7%. The Mean Squared Error (MSE) for all data was 0.437 mGy and accounting for 4.8% of the output range changes, predicting 95.2% accuracy in the present research. Conclusion:Using neural networks in ESAK prediction, the method proposed in the present research leads to the possible ESAK estimation of patients before X-Ray. The results suggested that the regression coefficient represented 4.3% difference between the kerma measured by solid-state dosimeter in the radiation field and the value predicted in the research. In comparison with the Monte-Carlo simulation method, this method has better accuracy.

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Section: Methodsmentioning

confidence: 99%

A New Method on Kerma Estimation in Mammography Screenings

et al. 2021

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“…However, scientific and technological tools have not been developed that allow applying this type of technology with efficiency in real experimental places [64,65].…”

Section: Neutron Spectrometry With Artificial Neural Networkmentioning

confidence: 99%

Synapse. A Neutron Spectrum Unfolding Code Based on Generalized Regression Artificial Neural Networks

Martínez-Blanco¹,

Serrano-Muñoz²,

Vega-Carrillo³

et al. 2020

EAI Endorsed Transactions on Industrial Networks and Intelligen

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In its broadest sense, the term artificial intelligence indicates the ability of an artifact to perform the same types of functions that characterize human thought. The goal of AI is to use algorithms, heuristics and methodologies based on the ways in which the human brain solves problems. Artificial neural networks recreate the structure of the human brain imitating the learning process. The Artificial neural networks theory has provided an alternative to classical computing for those problems in which traditional methods have delivered results that are not very convincing or not very convenient such as in the case of the neutron spectrometry and dosimetry problem for radiation protection purposes, using the Bonner spheres spectrometer as measurement system, mainly because many problems are encountered when trying to determine the neutron energy spectrum of a measured data. The most delicate part of the spectrometry based on this system is the unfolding process, for which several neutron spectrum unfolding codes have being developed. However, these codes require an initial guess spectrum in order to initiate the unfolding process. Their poor availability and their not easy management for the end user are other associated problems. Artificial Intelligence technology, is an alternative technique that is gaining popularity among researchers in neutron spectrometry research area, since it offers better results compared with the traditional solution methods. In this work, "Synapse", a neutron spectrum unfolding code based on Generalized Regression Artificial Neural Networks technology is presented. The Synapse code is capable to unfold the neutron spectrum and to calculate 15 dosimetric quantities using the count rates, coming from a BSS as the only entrance information. The results obtained show that the Synapse code, based on GRANN technology, is a promising and innovative technological alternative for solving the neutron spectrometry and dosimetry problems.

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“…In this case-control study, eight participants with tinnitus (two females; mean age: 48.2 years; range: 28-59) and eight healthy individuals (three females; mean age: 30.8 years; range: [25][26][27][28][29][30][31][32][33][34][35][36][37][38][39][40][41][42] were included in the study. All individuals with tinnitus suffered from chronic tinnitus for duration of at least 12 months (range: 1-30 years).…”

Section: Participantsmentioning

confidence: 99%

“…To assess the severity of a perceived tinnitus handicap, the standardized Persian version of Tinnitus Handicap Inventory (THI) questionnaire was presented to the participants with tinnitus. A tinnitus handicap is clustered in five categories, including slight (0-16), mild (18)(19)(20)(21)(22)(23)(24)(25)(26)(27)(28)(29)(30)(31)(32)(33)(34)(35)(36), moderate (38)(39)(40)(41)(42)(43)(44)(45)(46)(47)(48)(49)(50)(51)(52)(53)(54)(55)(56), severe (58-76) and catastrophic (78-100) [20]. In our study, one participant was characterized by a slight, mild moderate, five ones and one catastrophic handicap.…”

Section: Participantsmentioning

confidence: 99%

“…SVM works vigorously with a small number of training samples and a large number of features [32]. As the labeled samples are used for training, SVM is a supervised classifier, which can be applied with several kernels www.jbpe.ir Mohagheghian F. et al such as linear, polynomial, Multilayer Perceptron (MLP) and radial basis function (RBF) kernel [33][34][35]. The specified network measures were extracted from connectivity matrices as classification features.…”

Section: Weighted Phase Lag Index (Wpli) Connectivity Analysismentioning

confidence: 99%

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Computer-Aided Tinnitus Detection based on Brain Network Analysis of EEG Functional Connectivity

et al. 2019

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Background:Tinnitus known as a central nervous system disorder is correlated with specific oscillatory activities within auditory and non-auditory brain areas. Several studies in the past few years have revealed that in the most tinnitus cases, the response pattern of neurons in auditory system is changed due to auditory deafferentation, which leads to variation and disruption of the brain networks.Objective: In this paper, we introduce an approach to automatically distinguish tinnitus individuals from healthy controls based on whole-brain functional connectivity and network analysis. Material and Methods:The functional connectivity analysis was applied to the resting state electroencephalographic (EEG) data of both groups using Weighted Phase Lag Index (WPLI) for various frequency bands in 2-44 Hz frequency range. In this case-control study, the classification was performed on graph theoretical measures using support vector machine (SVM) as a robust classification method.Results: Experimental results showed promising classification performance with a high accuracy, sensitivity, and specificity in all frequency bands, specifically in the beta2 frequency band. Conclusion:The current study provides substantial evidence that tinnitus network can be successfully detected by consistent measures of the brain networks based on EEG functional connectivity.

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Neutron spectrum unfolding using radial basis function neural networks

Cited by 19 publications

References 30 publications

A New Method on Kerma Estimation in Mammography Screenings

A New Method on Kerma Estimation in Mammography Screenings

Synapse. A Neutron Spectrum Unfolding Code Based on Generalized Regression Artificial Neural Networks

Computer-Aided Tinnitus Detection based on Brain Network Analysis of EEG Functional Connectivity

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