Modeling and prediction of retardance in citric acid coated ferrofluid using artificial neural network
Source abstract
Abstract
Abstract Citric acid coated (citrate-stabilized) magnetite (Fe 3 O 4 ) magnetic nanoparticles have been conducted and applied in the biomedical fields. Using Taguchi-based measured retardances as the training data, an artificial neural network (ANN) model was developed for the prediction of retardance in citric acid (CA) coated ferrofluid (FF). According to the ANN simulation results in the training stage, the correlation coefficient between predicted retardances and measured retardances was found to be as high as 0.9999998. Based on the well-trained ANN model, the predicted retardance at excellent program from Taguchi method showed less error of 2.17% compared with a multiple regression (MR) analysis of statistical significance. Meanwhile, the parameter analysis at excellent program by the ANN model had the guiding significance to find out a possible program for the maximum retardance. It was concluded that the proposed ANN model had high ability for the prediction of retardance in CA coated FF.
Source: openaire · Quality: candidate unverified · Source record ↗
Research context
OpenAlex topics: Power Transformer Diagnostics and Insulation · Characterization and Applications of Magnetic Nanoparticles · Spectroscopy and Chemometric Analyses
SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.
References (20)
Show source-provided references
1. Development of citrate-stabilized Fe3O4 nanoparticles: conjugation and release of doxorubicin for therapeutic applications10.1016/j.jmmm.2010.09.009 ↗
2. Innovative synthesis of citrate-coated superparamagnetic Fe3O4 nanoparticles and its preliminary applications10.1016/j.jcis.2011.03.059 ↗
3. Citric acid coated magnetic nanoparticles: synthesis, characterization and application in removal of Cd(II) ions from aqueous solution10.1016/j.jwpe.2014.10.005 ↗
4. Linear birefringence and dichroism in citric acid coated Fe3O4 magnetic nanoparticles10.1016/j.jmmm.2014.07.064 ↗
5. Concurrent measurement of linear birefringence and dichroism in ferrofluids using rotating-wave-plate Stokes polarimeter10.1016/j.optcom.2011.12.032 ↗
6. Investigation on the synthesis conditions at the interpore distance of nanoporous anodic aluminum oxide: a comparison of experimental study, artificial neural network, and multiple linear regression10.1016/j.commatsci.2013.05.048 ↗
7. Comparison of multiple linear regression and artificial neural network in developing the objective functions of the orthopaedic screws10.1016/j.cmpb.2010.11.004 ↗
8. Estimation of machining performances using MRA, GMDH and Artificial Neural Network in Wire EDM of EN-3110.1016/j.mspro.2014.07.209 ↗
9. Comparison of regression and artificial neural network models for estimation of global solar radiations10.1016/j.rser.2015.08.021 ↗
10. Modeling of retardance in ferrofluid with Taguchi-based multiple regression analysis
11. Modeling and prediction of surface roughness in turning operations using artificial neural network and multiple regression method10.1016/j.eswa.2010.11.041 ↗
12. A comprehensive review for industrial applicability of artificial neural networks10.1109/TIE.2003.812470 ↗
13. Comparison of response surface methodology and artificial neural network approach towards efficient ultrasound-assisted biodiesel production from muskmelon oil10.1016/j.ultsonch.2014.10.019 ↗
14. A modified back-propagation method to avoid false local minima10.1016/S0893-6080(98)00087-2 ↗
15. Ali Keçebas, Investigation on thermal performance calculation of two type solar air collectors using artificial neural network10.1016/j.eswa.2010.07.090 ↗
16. Clinical data analysis using artificial neural networks (ANN) and principal component analysis (PCA) of patients with breast cancer after mastectomy10.1016/S1507-1367(10)60036-3 ↗
17. Artificial neural networks in medical diagnosis10.2478/v10136-012-0031-x ↗
18. Advantages and disadvantages of using artificial neural networks versus logistic regression for predicting medical outcomes10.1016/S0895-4356(96)00002-9 ↗
19. Extrusion detoxification technique on flaxseed by uniform design optimization10.1016/j.seppur.2007.09.016 ↗
20. Effect of synthesis conditions on the properties of citric-acid coated iron oxide nanoparticles10.1016/j.mee.2013.02.045 ↗