scholarly journals High-efficiency phenotyping for vitamin A in banana using artificial neural networks and colorimetric data

Bragantia ◽  
2016 ◽  
Vol 75 (3) ◽  
pp. 268-274 ◽  
Author(s):  
César Fernandes Aquino ◽  
Luiz Carlos Chamhum Salomão ◽  
Alcinei Mistico Azevedo
Keyword(s):  
Neural Networks ◽  
Artificial Neural Networks ◽  
Vitamin A ◽  
High Efficiency ◽  
Least Mean Square ◽  
Hue Angle ◽  
Colorimetric Data ◽  
Improvement Programs ◽  
Artificial Neural ◽  
Hidden Layer

ABSTRACT Banana is one of the most consumed fruits in Brazil and an important source of minerals, vitamins and carbohydrates for human diet. The characterization of banana superior genotypes allows identifying those with nutritional quality for cultivation and to integrate genetic improvement programs. However, identification and quantification of the provitamin carotenoids are hampered by the instruments and reagents cost for chemical analyzes, and it may become unworkable if the number of samples to be analyzed is high. Thus, the objective was to verify the potential of indirect phenotyping of the vitamin A content in banana through artificial neural networks (ANNs) using colorimetric data. Fifteen banana cultivars with four replications were evaluated, totaling 60 samples. For each sample, colorimetric data were obtained and the vitamin A content was estimated in the ripe banana pulp. For the prediction of the vitamin A content by colorimetric data, multilayer perceptron ANNs were used. Ten network architectures were tested with a single hidden layer. The network selected by the best fit (least mean square error) had four neurons in the hidden layer, enabling high efficiency in prediction of vitamin A (r2 = 0.98). The colorimetric parameters a* and Hue angle were the most important in this study. High-scale indirect phenotyping of vitamin A by ANNs on banana pulp is possible and feasible.

scholarly journals Adaptive Detection of Direct-Sequence Spread-Spectrum Signals Based on Knowledge-Enhanced Compressive Measurements and Artificial Neural Networks

Sensors ◽  
2021 ◽  
Vol 21 (7) ◽  
pp. 2538
Author(s):  
Shuang Zhang ◽  
Feng Liu ◽  
Yuang Huang ◽  
Xuedong Meng
Keyword(s):  
Neural Networks ◽  
Artificial Neural Networks ◽  
Spread Spectrum ◽  
High Efficiency ◽  
Detection System ◽  
Direct Sequence Spread Spectrum ◽  
Detection Performance ◽  
Detection Methods ◽  
Direct Sequence ◽  
Artificial Neural

The direct-sequence spread-spectrum (DSSS) technique has been widely used in wireless secure communications. In this technique, the baseband signal is spread over a wider bandwidth using pseudo-random sequences to avoid interference or interception. In this paper, the authors propose methods to adaptively detect the DSSS signals based on knowledge-enhanced compressive measurements and artificial neural networks. Compared with the conventional non-compressive detection system, the compressive detection framework can achieve a reasonable balance between detection performance and sampling hardware cost. In contrast to the existing compressive sampling techniques, the proposed methods are shown to enable adaptive measurement kernel design with high efficiency. Through the theoretical analysis and the simulation results, the proposed adaptive compressive detection methods are also demonstrated to provide significantly enhanced detection performance efficiently, compared to their counterpart with the conventional random measurement kernels.

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.

scholarly journals The Use of Artificial Neural Networks for Forecasting of Air Temperature inside a Heated Foil Tunnel

Sensors ◽  
2020 ◽  
Vol 20 (3) ◽  
pp. 652 ◽  
Author(s):  
Sławomir Francik ◽  
Sławomir Kurpaska
Keyword(s):  
Neural Networks ◽  
Artificial Neural Networks ◽  
Mean Square ◽  
Internal Temperature ◽  
Data Set ◽  
External Temperature ◽  
Factors Influencing ◽  
Testing Data ◽  
Artificial Neural ◽  
Hidden Layer

It is important to correctly predict the microclimate of a greenhouse for control and crop management purposes. Accurately forecasting temperatures in greenhouses has been a focus of research because internal temperature is one of the most important factors influencing crop growth. Artificial Neural Networks (ANNs) are a powerful tool for making forecasts. The purpose of our research was elaboration of a model that would allow to forecast changes in temperatures inside the heated foil tunnel using ANNs. Experimental research has been carried out in a heated foil tunnel situated on the property of the Agricultural University of Krakow. Obtained results have served as data for ANNs. Conducted research confirmed the usefulness of ANNs as tools for making internal temperature forecasts. From all tested networks, the best is the three-layer Perceptron type network with 10 neurons in the hidden layer. This network has 40 inputs and one output (the forecasted internal temperature). As the networks input previous historical internal temperature, external temperature, sun radiation intensity, wind speed and the hour of making a forecast were used. These ANNs had the lowest Root Mean Square Error (RMSE) value for the testing data set (RMSE value = 3.7 °C).

scholarly journals Predicting Estimated Blood Loss and Transfusions in Gynecologic Surgery Using Artificial Neural Networks

2021 ◽  
Vol 16 (1) ◽  
pp. 1-15
Author(s):  
Steven Walczak ◽  
Emad Mikhail
Keyword(s):  
Neural Networks ◽  
Artificial Neural Networks ◽  
Blood Loss ◽  
Gynecologic Surgery ◽  
Estimated Blood Loss ◽  
Transfusion Requirements ◽  
Ann Models ◽  
Artificial Neural ◽  
Hidden Layer ◽  
Prediction Problems

This chapter explores valuating the efficacy of using artificial neural networks (ANNs) for predicting the estimated blood loss (EBL) and also transfusion requirements of myomectomy patients. All 146 myomectomy surgeries performed over a 6-year period from a single site are captured. Records were removed for various reasons, leaving 96 cases. Backpropagation and radial basis function ANN models were developed to predict EBL and perioperative transfusion needs along with a regression model. The single hidden layer backpropagation ANN models performed the best for both prediction problems. EBL was predicted on average within 127.33 ml of measured blood loss, and transfusions were predicted with 71.4% sensitivity and 85.4% specificity. A combined ANN ensemble model using the output of the EBL ANN as an input variable to the transfusion prediction ANN was developed and resulted in 100% sensitivity and 62.9% specificity. The preoperative identification of large EBL or transfusion need can assist caregivers in better planning for possible post-operative morbidity and mortality.

scholarly journals Radyal Tabanlı Yapay Sinir Ağlarının Kurutma Kinetiğinin Modellenmesinde Kullanımı

2020 ◽  
Vol 8 (2) ◽  
pp. 511
Author(s):  
Adil Koray Yıldız ◽  
Muhammed Taşova ◽  
Hakan Polatcı
Keyword(s):  
Neural Networks ◽  
Artificial Neural Networks ◽  
Network Models ◽  
Drying Kinetics ◽  
Hot Pepper ◽  
Neural Network Models ◽  
Drying Time ◽  
Drying Behavior ◽  
Artificial Neural ◽  
Hidden Layer

Drying method is preferred in agricultural products since it provides advantages in many processes such as increasing the strength of products, transporting and storing. It is necessary to estimate the drying behavior of the products in order to achieve the best drying without reducing the product quality. For this reason, many numerical drying models have been developed to estimate the drying kinetics of the products. Recently, artificial neural networks have been widely used for the development of these models. Artificial neural networks are mathematical models that work in a similar way to natural neuron cells. Radial based artificial neural networks are radial based activation functions in the transition to the hidden layer unlike other networks. In this study, modeling of drying kinetics with radial based networks was investigated. For the experiment, red hot pepper (Capsicum annuum L.) was dipped in boiled water and microwave pretreatments and, then dried in the oven at 65°C. The absorbable moisture values were calculated during the drying period. The radial based artificial neural network models were trained with the drying time values as input and the absorbable moisture values as output. The study was carried out with two data sets including all data and only the average. In trainings with all data, R value of the best model was calculated as 0.9566. R was calculated as 0.9998 with average data.

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