Analytical modeling and simulation of I–V characteristics in carbon nanotube based gas sensors using ANN and SVR methods

Akbari, Elnaz; Buntat, Zolkafle; Enzevaee, Aria; Ebrahimi, Monireh; Yazdavar, Amir Hossein; Yusof, Rubiyah

doi:10.1016/j.chemolab.2014.07.001

Cited by 22 publications

(17 citation statements)

References 36 publications

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“…The diameter of silver nanoparticles synthesized through Tollens process also predicted, ammonia concentration and the type of reducing agents were used as model predictors [16]. Other related applications of ANN includes, prediction of antibacterial peptides [17], current-voltage characteristics of carbon nanotubes (CNTs) [18], prediction of fluorquinolone antibacterial activity from topological indices of molecules [19], prediction of antibacterial activity using physiochemical characteristics of antimicrobial peptides [20], antibacterial activity of imidazole derivatives used as cationic 3-D structure against Streptococcus pyogenes [21].…”

Section: Introductionmentioning

confidence: 99%

Neural network modeling and principal component analysis of antibacterial activity of chitosan/AgCl-TiO2 colloid treated cotton fabric

et al. 2015

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The present work was undertaken to predict the antibacterial activity of chitosan/AgCl-TiO 2 colloid treated cotton fabric with artificial neural network (ANN) using chitosan/AgCl-TiO 2 concentration and curing time as predictors. Cotton fabric samples were prepared by treating with different blends of chitosan/AgCl-TiO 2 colloid and varying curing time. The antibacterial activity against Staphylococcus aureus (gram positive) and Escherichia coli (gram negative) was measured in terms of % bacterial reduction (% BR). Feedforward neural network models were trained with combination of LevenbergMarqaurdt algorithm and Bayesian regularization support incorporated in backpropagation. The 10 % cross-validation technique was also carried out to rule out any chance of over-fitting of the trained networks. Furthermore trend analysis was also performed with developed models to understand the effect of input parameters. The promising results realized with excellent coefficient of determination and acceptable mean absolute error during network training and their testing on the novel data patterns. The developed models will benefit the design and development of clean and hygienic textiles. Principal component analysis (PCA) was also performed to visualize and analyze the correlation between the variables and the trend of individual observation on two-dimensional space.

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Section: Introductionmentioning

confidence: 99%

Neural network modeling and principal component analysis of antibacterial activity of chitosan/AgCl-TiO2 colloid treated cotton fabric

et al. 2015

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“…Ethylene glycol (EG) is an organic solvent which is used as an antifreeze, a heat transfer fluid, a precursor of polymers and which is envisaged for CO 2 absorption [1][2] . Besides many other applications, this solvent is used as a trainer for the separation of water-ethanol mixtures using extractive distillation [3][4][5][6][7][8][9] . To design or simulate this process, sound knowledge of the physical and transport properties of this ternary mixture is important.…”

Section: Introductionmentioning

confidence: 99%

Simultaneous Prediction of the Density and Viscosity of the Ternary System Water-Ethanol-Ethylene Glycol Using Support Vector Machine

Kianfar

2020

Fine Chemical Engineering

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Ethylene glycol is an organic solvent used in extractive distillation to separate water-ethanol mixtures. An appropriate process description requires accurate physical property data. In this paper, experimental liquid densities and dynamic viscosities of pure ethylene glycol as well as the ternary system water-ethanol-ethylene glycol are presented over a wide temperature range (298.15 to 328.15 K) at atmospheric pressure. Density and viscosity data of the ternary system of water-ethanol-ethylene glycol solutions were obtained from the literature and a support vector machine model was used to predict the density and viscosity of this system. The determination coefficients for density and viscosity of the ternary system of water-ethanol-ethylene glycol are 0.9854 and 0.9892, respectively. The mean square errors of density and viscosity are obtained 4.6572×10-4 and 3.4920×10-4, respectively. The results confirmed that the proposed method can predict the density and viscosity of the ternary system of water-ethanol-ethylene glycol as a function of temperature, using a support vector machine.

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“…The gas sensors [1][2][3][4][5][6][7] and biosensors [8][9][10][11][12][13][14][15][16] models have been studied by differential equations in the literature. To improve the sustainability of our society, we use the gas sensors to detect, monitor and control the presence of dangerous gases in the atmosphere.…”

Section: Introductionmentioning

confidence: 99%

“…To improve the sustainability of our society, we use the gas sensors to detect, monitor and control the presence of dangerous gases in the atmosphere. Many metal oxide semiconductor-based gas sensors are used to detect the different kinds of gas molecules namely H 2 , CO,NH 3 , H 2 S, SO 2 , odours,other applications are reported in the field such as the detection of explosive gases, fire detection, and air quality [1][2][3][4][5][6][7]. This semiconductor-based gas sensor consists of sintered block, thick film and thin film.…”

Section: Introductionmentioning

confidence: 99%

“…Symmetries and transformations methods are also used to find the exact solutions for the same without considering their initial conditions. So the researchers keep on trying the new methods to overcome the limitations stated in the nonlinear differential equations Many researchers have investigated the substrate reaction-diffusion process through differential equations in nonlinear form, and they have also discussed the various methods to solve, and it can be found in [1][2][3][4][5][6][7][8][9][10][11][12][13][14][15][16]. The dynamic behaviour of such a process is encountered in various physical and chemical phenomena and very helpful in modeling certain parameters like reaction-diffusion equations.…”

Section: Introductionmentioning

confidence: 99%

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Wavelet Solution for Biosensor Response at Mixed Enzyme Kinetics

Venkatesh*,

Balasubramanian,

Balachandar

2019

IJEAT

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This paper presents the approximate solution of the reaction diffusion equation based on the hybridization of classical polynomials and Legendre wavelets. The systems of equations are generated first for the differential equations by the properties of Legendre wavelets. Theoretical analysis for the proposed scheme is discussed and computed solutions are also compared with other numerical solutions available in the literature.

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Analytical modeling and simulation of I–V characteristics in carbon nanotube based gas sensors using ANN and SVR methods

Cited by 22 publications

References 36 publications

Neural network modeling and principal component analysis of antibacterial activity of chitosan/AgCl-TiO2 colloid treated cotton fabric

Neural network modeling and principal component analysis of antibacterial activity of chitosan/AgCl-TiO2 colloid treated cotton fabric

Simultaneous Prediction of the Density and Viscosity of the Ternary System Water-Ethanol-Ethylene Glycol Using Support Vector Machine

Wavelet Solution for Biosensor Response at Mixed Enzyme Kinetics

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