scholarly journals Prediction of in vitro release of nanoencapsulated phenolic compounds using Artificial Neural Networks

DYNA ◽  
2020 ◽  
Vol 87 (212) ◽  
pp. 244-250
Author(s):  
Luz América Espinosa-Sandoval ◽  
Claudia Isabel Ochoa-Martínez ◽  
Alfredo Adolfo Ayala-Aponte
Keyword(s):  
Neural Networks ◽  
Artificial Neural Networks ◽  
Phenolic Compounds ◽  
In Vitro Release ◽  
Mathematical Correlation ◽  
Optimal Network ◽  
Artificial Neural ◽  
Hidden Layer ◽  
Vitro Release

In Vitro Release modeling (IVR) of nanoencapsulated phenolic compounds (PC) is complex, due to the number of factors involved in the process. Artificial Neural Networks (ANN) are useful tools for its prediction because they consider the effect of all factors on the response. The release at 5h is crucial in kinetics because, in most cases, it is an equilibrium point leading to a constant phase. The objective of this investigation was to predict the IVR of nanoencapsulated PC at 5h using ANN. A database with information from the scientific literature was used. This model permits mathematical correlation of the IVR at 5h with eleven factors. The optimal network configuration consisted of one hidden layer with one neuron. A mathematical model was obtained with a Mean Square Error (MSE) of 0.0516 and a correlation coefficient (r) of 0.8413.

Optimization Artificial Neural Network Using Artificial Bee Colony in Letter Recognition Classification

2020 ◽  
Vol 8 (4) ◽  
pp. 469
Author(s):  
I Gusti Ngurah Alit Indrawan ◽  
I Made Widiartha
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Artificial Neural Networks ◽  
Classification Accuracy ◽  
Artificial Bee Colony Algorithm ◽  
Artificial Bee Colony ◽  
Letter Recognition ◽  
Bee Colony ◽  
Artificial Neural ◽  
Hidden Layer

Artificial Neural Networks or commonly abbreviated as ANN is one branch of science from the field of artificial intelligence which is often used to solve various problems in fields that involve grouping and pattern recognition. This research aims to classify Letter Recognition datasets using Artificial Neural Networks which are weighted optimally using the Artificial Bee Colony algorithm. The best classification accuracy results from this study were 92.85% using a combination of 4 hidden layers with each hidden layer containing 10 neurons.

scholarly journals Prediction of Horizontal Daily Global Solar Irradiation Using Artificial Neural Networks (ANNs) in the Castile and León Region, Spain

Agronomy ◽  
2020 ◽  
Vol 10 (1) ◽  
pp. 96
Author(s):  
Francisco J. Diez ◽  
Luis M. Navas-Gracia ◽  
Leticia Chico-Santamarta ◽  
Adriana Correa-Guimaraes ◽  
Andrés Martínez-Rodríguez
Keyword(s):  
Neural Networks ◽  
Artificial Neural Networks ◽  
Predictive Modelling ◽  
Solar Irradiation ◽  
Data Series ◽  
Markov Analysis ◽  
Global Solar Irradiation ◽  
Ann Models ◽  
Artificial Neural ◽  
Hidden Layer

This article evaluates horizontal daily global solar irradiation predictive modelling using artificial neural networks (ANNs) for its application in agricultural sciences and technologies. An eight year data series (i.e., training networks period between 2004–2010, with 2011 as the validation year) was measured at an agrometeorological station located in Castile and León, Spain, owned by the irrigation advisory system SIAR. ANN models were designed and evaluated with different neuron numbers in the input and hidden layers. The only neuron used in the outlet layer was the global solar irradiation simulated the day after. Evaluated values of the input data were the horizontal daily global irradiation of the current day [H(t)] and two days before [H(t−1), H(t−2)], the day of the year [J(t)], and the daily clearness index [Kt(t)]. Validated results showed that best adjustment models are the ANN 7 model (RMSE = 3.76 MJ/(m2·d), with two inputs ([H(t), Kt(t)]) and four neurons in the hidden layer) and the ANN 4 model (RMSE = 3.75 MJ/(m2·d), with two inputs ([H(t), J(t)]) and two neurons in the hidden layer). Thus, the studied ANN models had better results compared to classic methods (CENSOLAR typical year, weighted moving mean, linear regression, Fourier and Markov analysis) and are practically easier as they need less input variables.

scholarly journals Application of Artificial Neural Networks to Assess the Mycological State of Bulk Stored Rapeseeds

Agriculture ◽  
2020 ◽  
Vol 10 (11) ◽  
pp. 567
Author(s):  
Jolanta Wawrzyniak
Keyword(s):  
Neural Networks ◽  
Artificial Neural Networks ◽  
Control Systems ◽  
Multilayer Perceptron ◽  
Activation Function ◽  
Fungal Population ◽  
Support Tool ◽  
Artificial Neural ◽  
Hidden Layer ◽  
And Storage

Artificial neural networks (ANNs) constitute a promising modeling approach that may be used in control systems for postharvest preservation and storage processes. The study investigated the ability of multilayer perceptron and radial-basis function ANNs to predict fungal population levels in bulk stored rapeseeds with various temperatures (T = 12–30 °C) and water activity in seeds (aw = 0.75–0.90). The neural network model input included aw, temperature, and time, whilst the fungal population level was the model output. During the model construction, networks with a different number of hidden layer neurons and different configurations of activation functions in neurons of the hidden and output layers were examined. The best architecture was the multilayer perceptron ANN, in which the hyperbolic tangent function acted as an activation function in the hidden layer neurons, while the linear function was the activation function in the output layer neuron. The developed structure exhibits high prediction accuracy and high generalization capability. The model provided in the research may be readily incorporated into control systems for postharvest rapeseed preservation and storage as a support tool, which based on easily measurable on-line parameters can estimate the risk of fungal development and thus mycotoxin accumulation.

Optimal Design for Micromixer Using Macromodel Based on Artificial Neural Networks

2013 ◽  
Vol 339 ◽  
pp. 55-58
Author(s):  
Xue Ye Chen ◽  
Hui Xu
Keyword(s):  
Neural Networks ◽  
Artificial Neural Networks ◽  
Navier Stokes ◽  
Model Design ◽  
Design Variables ◽  
Input Layer ◽  
The Neural Networks ◽  
Physical Variables ◽  
Artificial Neural ◽  
Hidden Layer

The micromixer device is modeled using artificial neural networks trained with finite element simulations of the underlying incompressible Navier-Stokes and mass transport PDEs. The neural networks design is based on a three layers perceptron with one input layer, one nonlinear hidden layer and one linear output layer. The neural networks can map the micromixer behavior into a set of analytical performance functions parameterized by the systems physical variables. The macromodel has been extracted from training output of the artificial neural networks. Three design variables, i.e., the flow velocity, the channel width, and the numbers of the mixing unit are selected for model design. The mixing index at the end of the serpentine channels is employed as the objective function. The macromodel has been validated with numerical simulations. It can be demonstrated that this macromodel should facilitate the design of microfluidic device with sophisticated channels networks.

scholarly journals Heat Transfer Coefficients Analysis in a Helical Double-Pipe Evaporator: Nusselt Number Correlations through Artificial Neural Networks

Entropy ◽  
2019 ◽  
Vol 21 (7) ◽  
pp. 689 ◽  
Author(s):  
Arianna Parrales ◽  
José Hernández-Pérez ◽  
Oliver Flores ◽  
Horacio Hernandez ◽  
José Gómez-Aguilar ◽  
...  
Keyword(s):  
Heat Transfer ◽  
Neural Networks ◽  
Nusselt Number ◽  
Artificial Neural Networks ◽  
Transfer Coefficients ◽  
Heat Transfer Coefficients ◽  
Artificial Neural ◽  
Input Variables ◽  
Hidden Layer ◽  
Double Pipe

In this study, two empirical correlations of the Nusselt number, based on two artificial neural networks (ANN), were developed to determine the heat transfer coefficients for each section of a vertical helical double-pipe evaporator with water as the working fluid. Each ANN was obtained using an experimental database of 1109 values obtained from an evaporator coupled to an absorption heat transformer with energy recycling. The Nusselt number in the annular section was estimated based on the modified Wilson plot method solved by an ANN. This model included the Reynolds and Prandtl numbers as input variables and three neurons in their hidden layer. The Nusselt number in the inner section was estimated based on the Rohsenow equation, solved by an ANN. This ANN model included the numbers of the Prandtl and Jackob liquids as input variables and one neuron in their hidden layer. The coefficients of determination were R 2 > 0.99 for both models. Both ANN models satisfied the dimensionless condition of the Nusselt number. The Levenberg–Marquardt algorithm was chosen to determine the optimum values of the weights and biases. The transfer functions used for the learning process were the hyperbolic tangent sigmoid in the hidden layer and the linear function in the output layer. The Nusselt numbers, determined by the ANNs, proved adequate to predict the values of the heat transfer coefficients of a vertical helical double-pipe evaporator that considered biphasic flow with an accuracy of ±0.2 for the annular Nusselt and ±4 for the inner Nusselt.

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