scholarly journals Predicting Vodka Adulteration: A Combination of Electronic Tongue and Artificial Neural Networks

2021 ◽  
Author(s):  
Leonardo Fabio León Marenco ◽  
Luiza Pereira Oliveira ◽  
Daniella Lopez Vale ◽  
Maiara Oliveira Salles
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Artificial Neural Network ◽  
Mean Squared Error ◽  
Tap Water ◽  
Electronic Tongue ◽  
Classification Model ◽  
Squared Error ◽  
Artificial Neural ◽  
Voltammetric Electronic Tongue

Abstract An artificial neural network was used to build models caple of predicting and quantifying vodka adulteration with methanol and/or tap water. A voltammetric electronic tongue based on gold and copper microelectrodes was used, and 310 analyses were performed. Vodkas were adulterated with tap water (5 to 50% (v/v)), methanol (1 to 13% (v/v)), and with a fixed addition of 5% methanol and tap water varying from 5 to 50% (v/v). The classification model showed 99.5% precision, and it correctly predicted the type of adulterant in all samples. Regarding the regression model, the root mean squared error was 3.464% and 0.535% for the water and methanol addition, respectively, and the prediction of the adulterant content presented an R2 0.9511 for methanol and 0.9831 for water adulteration.

Application of Artificial Neural Network Over Nickel-Based Catalyst for Combined Steam-Carbon Dioxide of Methane Reforming (CSDRM)

2020 ◽  
Vol 20 (9) ◽  
pp. 5716-5719 ◽  
Author(s):  
Cho Hwe Kim ◽  
Young Chul Kim
Keyword(s):  
Neural Network ◽  
Carbon Dioxide ◽  
Artificial Neural Network ◽  
Mean Squared Error ◽  
Coefficient Of Determination ◽  
Feed Ratio ◽  
Squared Error ◽  
Highly Nonlinear ◽  
Feed Forward Network ◽  
Artificial Neural

The application of artificial neural network (ANN) for modeling, combined steam-carbon dioxide reforming of methane over nickel-based catalysts, was investigated. The artificial neural network model consisted of a 3-layer feed forward network, with hyperbolic tangent function. The number of hidden neurons is optimized by minimization of mean square error and maximization of R2 (R square, coefficient of determination) and set of 8 neurons. With feed ratio, flow rate, and temperature as independent variables, methane, carbon dioxide conversion, and H2/CO ratio, were measured using artificial neural network. Coefficient of determination (R2) values of 0.9997, 0.9962, and 0.9985 obtained, and MAE (Mean Absolute Error), MSE (Mean Squared Error), RMSE (Root Mean Squared Error), and MAPE (Mean Absolute Percentage Error) showed low value. This study indicates ANN can successfully model a highly nonlinear process and function.

Predicting The Number of Tourists Based on Backpropagation Algorithm

2021 ◽  
Vol 5 (3) ◽  
pp. 439-445
Author(s):  
Dwi Marlina ◽  
Fatchul Arifin
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Network Architecture ◽  
Mean Squared Error ◽  
Predictive Accuracy ◽  
Training Process ◽  
Backpropagation Algorithm ◽  
Squared Error ◽  
Artificial Neural ◽  
Target Data

The number of tourists always fluctuates every month, as happened in Kaliadem Merapi, Sleman. The purpose of this research is to develop a prediction system for the number of tourists based on artificial neural networks. This study uses an artificial neural network for data processing methods with the backpropagation algorithm. This study carried out two processes, namely the training process and the testing process with stages consisting of: (1) Collecting input and target data, (2) Normalizing input and target data, (3) Creating artificial neural network architecture by utilizing GUI (Graphical User Interface) Matlab facilities. (4) Conducting training and testing processes, (5) Normalizing predictive data, (6) Analysis of predictive data. In the data analysis, the MSE (Mean Squared Error) value in the training process is 0.0091528 and in the testing process is 0.0051424. Besides, the validity value of predictive accuracy in the testing process is around 91.32%. The resulting MSE (Mean Squared Error) value is relatively small, and the validity value of prediction accuracy is relatively high, so this system can be used to predict the number of tourists in Kaliadem Merapi, Sleman.  

scholarly journals Using Artificial Neural Network with Prey Predator Algorithm for Prediction of the COVID-19: The Case of Brazil and Mexico

Mathematics ◽  
2021 ◽  
Vol 9 (2) ◽  
pp. 180
Author(s):  
Nawaf N. Hamadneh ◽  
Muhammad Tahir ◽  
Waqar A. Khan
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Mean Squared Error ◽  
Prediction Errors ◽  
Metaheuristic Algorithm ◽  
Squared Error ◽  
Ann Models ◽  
Simulation Results ◽  
Artificial Neural ◽  
Predicted Values

The spread of the COVID-19 epidemic worldwide has led to investigations in various aspects, including the estimation of expected cases. As it helps in identifying the need to deal with cases caused by the pandemic. In this study, we have used artificial neural networks (ANNs) to predict the number of cases of COVID-19 in Brazil and Mexico in the upcoming days. Prey predator algorithm (PPA), as a type of metaheuristic algorithm, is used to train the models. The proposed ANN models’ performance has been analyzed by the root mean squared error (RMSE) function and correlation coefficient (R). It is demonstrated that the ANN models have the highest performance in predicting the number of infections (active cases), recoveries, and deaths in Brazil and Mexico. The simulation results of the ANN models show very well predicted values. Percentages of the ANN’s prediction errors with metaheuristic algorithms are significantly lower than traditional monolithic neural networks. The study shows the expected numbers of infections, recoveries, and deaths that Brazil and Mexico will reach daily at the beginning of 2021.

Analyzing the Impact of Learning Rate in development of the Prediction Model using Artificial Neural Network

2021 ◽  
Vol 23 (07) ◽  
pp. 121-135
Author(s):  
Anil Kumar Bisht ◽  
◽  
Ravendra Singh ◽  
Rakesh Bhutiani ◽  
Ashutosh Bhatt ◽  
...  
Keyword(s):  
Neural Network ◽  
Water Quality ◽  
Artificial Neural Network ◽  
Mean Squared Error ◽  
Learning Rate ◽  
Ganges River ◽  
Squared Error ◽  
Artificial Neural ◽  
The Ganges ◽  
The Impact

Water Quality (WQ) modeling and forecasting are very challenging for water management bodies due to the complex and nonlinear relationship between the parameters responsible for determining water quality. The main focus of this paper is the water quality prediction of the Ganges River by analyzing the impact of one of the critical configuration parameters of a neural network known as the learning rate. The proposed prediction model based on an artificial neural network (ANN) consists of different sets of experiments performed by comparing twelve different training functions against the variation in learning rates. A total of 360 experiments have been conducted on the dataset collected over the period 2001 to 2015 with five stations along the Ganges River in the state of Uttarakhand, India. All experiments have been conducted in MATLAB software. The ANN-based program is written in Matlab’s NN-Toolbox. As input parameters, we have used temperature, pH, dissolved oxygen (DO), biochemical oxygen demand (BOD), and total coliform. The water quality standard set by the Central Pollution Control Board of India has been used. The performance of the developed model has been calculated based on Mean Squared Error (MSE), Root Mean Squared Error (RMSE), Mean Absolute Percentage Error (MAPE). Trail training function-based artificial neural network models indicate higher predictive accuracy when compared to other models developed using the remaining eleven training functions when the learning rate is set to 0.04. In conclusion, ANN has the ability to efficiently predict the water quality of rivers and the learning rate has a greater impact on the development of such predictive models. So, it is required to be tuned very carefully.

scholarly journals Prediksi Inflasi Indonesia Memakai Model ARIMA dan Artificial Neural Network

2019 ◽  
Vol 5 (1) ◽  
Author(s):  
Wahyudin S
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Analysis Of Variance ◽  
Mean Squared Error ◽  
Statistics Analysis ◽  
Root Mean Squared Error ◽  
Squared Error ◽  
Artificial Neural ◽  
Artificial Neural Network Ann ◽  
Hidden Layer

Inflasi merupakan indikator makro ekonomi yang sangat penting. Berbagai macam metoda prediksi inflasi Indonesia telah dipublikasikan. Namun pencarian metoda prediksi inflasi yang lebih akurat masih menjadi topik menarik. Pada penulisan ini diusulkan sebuah metoda baru untuk prediksi inflasi memakai model ARIMA dan Artificial Neural Network (ANN). Data inflasi yang digunakan adalah data inflasi bulanan year-on-year dari tahun 2010 sampai dengan tahun 2018 yang diterbitkan oleh Badan Pusat Statistik (BPS). Pertama dibuat 2 model ARIMA yaitu model ARIMA tanpa siklus tahunan dan dengan siklus tahunan. Prosedur standar dan diagostics test telah dilakukan antara lain: summary of statistics, analysis of variance (ANOVA), significance of coefficients test, residuals normality, heterocesdacity, dan stability. Dari hasil perbandingan kinerja memakai Root Mean Squared Error (RMSE) diperoleh bahwa model ARIMA dengan siklus tahunan lebih baik. Model tersebut berupa model ARIMA (2,1,0) (2,0,0) [12]. Kemudian, untuk meningkatkan kinerja prediksi inflasi, ANN telah dibuat berbasis model ARIMA tersebut. Model ANN memakai satu hidden layer dan dua neuron. Hasil pengujian menunjukkan bahwa model ANN menghasilkan RMSE yang lebih kecil daripada model ARIMA (2,1,0) (2,0,0) [12]. Hal ini kemungkinan disebabkan oleh kemampuan mengolah hubungan nonlinear antara variabel target dan variabel penjelas.

scholarly journals Simulation and Optimization of Artificial Neural Network Based Air Quality Estimator

2019 ◽  
Vol 8 (3) ◽  
pp. 5477-5482
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Network Model ◽  
Neural Network Model ◽  
Artificial Neural Network Model ◽  
Mean Squared Error ◽  
Research Work ◽  
Simulation And Optimization ◽  
Squared Error ◽  
Artificial Neural

E-sensor which are generally based on concept of E-nose are specially made to distinguish odours .In the present research work. E-sensor is developed using artificial intelligence technique to identify the concentration of carbon monoxide in a polluted environment. Data record access using Metal oxide sensor. The available data is broken into the number of segments .The length of data segment and the neurons in hidden layer is varied in number to find the optimized model of artificial neural network model using Mat Lab Coding. The artificial neural network model is optimized by verification in terms of mean squared error and regression. The regression is verified for training ,testing, validation and all. The mean squared error and regression are the artificial neural network model performance parameter

scholarly journals Artificial neural network model for predicting load capacity of driven piles

2021 ◽  
Vol 10 (1) ◽  
pp. e12210111526
Author(s):  
Alcineide Dutra Pessoa ◽  
Gean Carlos Lopes de Sousa ◽  
Rodrigo da Cruz de Araujo ◽  
Gérson Jacques Miranda dos Anjos
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Artificial Neural Network ◽  
Artificial Neural Networks ◽  
Mean Squared Error ◽  
Load Capacity ◽  
Pearson Correlation ◽  
Driven Piles ◽  
Load Tests ◽  
Artificial Neural

In geotechnics, several models, empirical or not, have been proposed for the calculation of load capacity in deep foundations. These models run mainly through physical assumptions and construction of approximations by mathematical models. Artificial Neural Networks (ANN), in addition to other applications, are excellent computational mechanisms that, based on biological neural learning, can perform predictions and approximations of functions. In this work, an application of artificial neural networks is presented. The objective here is to propose a mathematical model based on artificial intelligence focused on Artificial Neural Network (ANN) learning capable of predicting the load capacity for driven piles. The results obtained through the neural network were compared with actual values of load capacities obtained in the field through load tests. For this quantitative comparison, the following metrics have been chosen: Pearson correlation coefficient and mean squared error. The database used to carry out the project consisted of 233 load tests, carried out in diverse cities and different countries, for which load capacity, hammer weight, hammer drop height, pile length, pile diameter and pile penetration per blow values ​​were available. These values have been used as input values in a multilayer perceptron neural network to estimate the load capacities of the respective piles. It has been found that the proposed neural model presented, in general, correlation with field values above 90%, reaching 96% in the best result.

scholarly journals Optimal Artificial Neural Network Type Selection Method for Usage in Smart House Systems

Sensors ◽  
2020 ◽  
Vol 21 (1) ◽  
pp. 47
Author(s):  
Vasyl Teslyuk ◽  
Artem Kazarian ◽  
Natalia Kryvinska ◽  
Ivan Tsmots
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Artificial Neural Network ◽  
Artificial Neural Networks ◽  
Input Data ◽  
Optimization Criterion ◽  
Fuzzy Input ◽  
Different Types ◽  
Smart House ◽  
Artificial Neural

In the process of the “smart” house systems work, there is a need to process fuzzy input data. The models based on the artificial neural networks are used to process fuzzy input data from the sensors. However, each artificial neural network has a certain advantage and, with a different accuracy, allows one to process different types of data and generate control signals. To solve this problem, a method of choosing the optimal type of artificial neural network has been proposed. It is based on solving an optimization problem, where the optimization criterion is an error of a certain type of artificial neural network determined to control the corresponding subsystem of a “smart” house. In the process of learning different types of artificial neural networks, the same historical input data are used. The research presents the dependencies between the types of neural networks, the number of inner layers of the artificial neural network, the number of neurons on each inner layer, the error of the settings parameters calculation of the relative expected results.

scholarly journals Application of artificial neural networks to predict the deflections of reinforced concrete beams

2016 ◽  
Vol 38 (2) ◽  
pp. 37-46 ◽  
Author(s):  
Mateusz Kaczmarek ◽  
Agnieszka Szymańska
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Artificial Neural Network ◽  
Reinforced Concrete ◽  
Concrete Beams ◽  
Reinforced Concrete Beams ◽  
The Neural Network ◽  
Eurocode 2 ◽  
Artificial Neural ◽  
Concrete Elements

Abstract Nonlinear structural mechanics should be taken into account in the practical design of reinforced concrete structures. Cracking is one of the major sources of nonlinearity. Description of deflection of reinforced concrete elements is a computational problem, mainly because of the difficulties in modelling the nonlinear stress-strain relationship of concrete and steel. In design practise, in accordance with technical rules (e.g., Eurocode 2), a simplified approach for reinforced concrete is used, but the results of simplified calculations differ from the results of experimental studies. Artificial neural network is a versatile modelling tool capable of making predictions of values that are difficult to obtain in numerical analysis. This paper describes the creation and operation of a neural network for making predictions of deflections of reinforced concrete beams at different load levels. In order to obtain a database of results, that is necessary for training and testing the neural network, a research on measurement of deflections in reinforced concrete beams was conducted by the authors in the Certified Research Laboratory of the Building Engineering Institute at Wrocław University of Science and Technology. The use of artificial neural networks is an innovation and an alternative to traditional methods of solving the problem of calculating the deflections of reinforced concrete elements. The results show the effectiveness of using artificial neural network for predicting the deflection of reinforced concrete beams, compared with the results of calculations conducted in accordance with Eurocode 2. The neural network model presented in this paper can acquire new data and be used for further analysis, with availability of more research results.

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