Application of Adaptive Neuro-Fuzzy Inference System for Prediction of Neutron Yield of IR-IECF Facility in High Voltages

Adineh-Vand, A.; Torabi, Mohammadamin; Roshani, Gholam Hossein; Taghipour, Mostafa; Feghhi, S.A.H.; Rezaei, Masoud; Sadati, S. M.

doi:10.1007/s10894-013-9631-z

Cited by 4 publications

(3 citation statements)

References 13 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…ANN eliminates the limitations of the classical approaches by extracting the desired information using the input data. Applying ANN to a system needs sufficient input and output data instead of a mathematical equation [24][25][26][27]. Multilayer perceptron (MLP) networks are the most widely used neural networks that consist of a great number of processing elements called neurons.…”

Section: Artificial Neural Networkmentioning

confidence: 99%

“…The second advantage is its high predictive accuracy, i.e., the predictive capability for ''new'' data (untrained data) [19][20][21][22][23]. The high predictive accuracy is an assured outcome of the ability of ANN to apprehend the data [21, 24,25]. On recognizing and application these advantages of ANN in MTT assays, in the current study, we report the design, training and validation of a feed-forward ANN to predict the inhibitory concentration (IC) data such that the designed ANN would (A) make sufficient use of the existing ICs data table of an available set of experimental data about chrysin enhances doxorubicin-induced cytotoxicity in human lung epithelial cancer cell lines by Brechbuhl et al [13], (B) predict the ICs evaluated with a MTT assay in human lung epithelial cancer cell lines treated with chrysin before exposure to DOX.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Application of Artificial Neural Network for Modeling and Prediction of MTT Assay on Human Lung Epithelial Cancer Cell Lines

AA¹

2015

J Biosens Bioelectron

View full text Add to dashboard Cite

Section: Artificial Neural Networkmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Application of Artificial Neural Network for Modeling and Prediction of MTT Assay on Human Lung Epithelial Cancer Cell Lines

AA¹

2015

J Biosens Bioelectron

View full text Add to dashboard Cite

“…[1]. Several conventional image processing and artificial intelligence-based approaches have been developed to overcome these limitations and reducing side effects in other medical imaging systems [2][3][4].…”

Section: Introductionmentioning

confidence: 99%

Automatic fetal biometry prediction using a novel deep convolutional network architecture

Oghli

Shabanzadeh²,

Moradi³

et al. 2021

Physica Medica

View full text Add to dashboard Cite

Combined application of neutron activation analysis using IECF device and neural network for prediction of cement elements

Roshani

Eftekhari-Zadeh

Shama

et al. 2017

Radiat Detect Technol Methods

Self Cite

View full text Add to dashboard Cite

Purpose Gamma spectrum of a cement sample is not straightforward to analyze as a result of peak overlapping produced by Compton effect of gamma rays radiated from activated elements in the neutron activation process and also because of the change into the neutron energy spectrum in the target sample during activation. Methods Artificial neural network (ANN) is an excellent solution for complex and nonlinear systems. Consequently, the use of ANN for quantitative analysis of major cement elements including Ca, Si, Al, and Fe would be very advantageous. Iranian inertial electrostatic confinement fusion device is a fast, monoenergetic and steady neutron generator. It was simulated as a high-energy neutron source for performing neutron activation analysis. In the present study, a library of 29 members of delayed gamma-ray spectra of knowing cement samples were generated via MCNPX version 2.7. Specific photo-peaks related to Ca, Si, Al and Fe obtained from these spectra were used as inputs for ANN (21 of them for training and 8 for testing). Then, using MLP architecture, an ANN model has been presented to model the system to predict the percentages of the elements in cement. The ANN model is optimized to have only a hidden layer with five neurons. Results The comparison between modeling data and results of proposed MLP network shows that there is a good consistency between them. The MAE of training set for Ca, Si and Fe outputs were 0.0351, 0.0656 and 0.0660, respectively, and the MAE of testing set for mentioned outputs were 0.0997, 0.3046 and 0.8699, respectively. Conclusion Overall, it can be concluded that the defined dispensable errors show that this modeling is a good one as a prediction tool for the mentioned purpose.

show abstract

Application of Adaptive Neuro-Fuzzy Inference System for Prediction of Neutron Yield of IR-IECF Facility in High Voltages

Cited by 4 publications

References 13 publications

Application of Artificial Neural Network for Modeling and Prediction of MTT Assay on Human Lung Epithelial Cancer Cell Lines

Application of Artificial Neural Network for Modeling and Prediction of MTT Assay on Human Lung Epithelial Cancer Cell Lines

Automatic fetal biometry prediction using a novel deep convolutional network architecture

Combined application of neutron activation analysis using IECF device and neural network for prediction of cement elements

Contact Info

Product

Resources

About