scholarly journals Estimation Of Mechanical Properties Of Sandstones From Petrographic Characteristics Using Artificial Neural Networks (ANNs)

2021 ◽  
Vol 71 ◽  
pp. 13-22
Author(s):  
Yasin Abdi ◽  
◽  
Bijan Yusefi-Yegane ◽  
Amin Jamshidi
Keyword(s):  
Mean Squared Error ◽  
Accurate Determination ◽  
Dry Density ◽  
Ann Model ◽  
Laboratory Methods ◽  
Squared Error ◽  
Artificial Neural ◽  
Artificial Neural Network Ann ◽  
Mlr Model

The accurate determination of strength parameters of rocks such as uniaxial compressive strength (UCS) and elastic modulus (E) using direct and laboratory methods require substantial time and cost. Therefore, the production of predictive relationships and models to forecast the UCS and E is of critical necessity in rock engineering. This study deals with the estimation of UCS and E of sandstones from petrographic characteristics by an artificial neural network (ANN) and multiple regression. For this purpose, 130 core specimens were prepared from sandstones in different locations in Iran. The specimens were tested to determine UCS, E, dry density, and porosity. Also, the petrographic studies including the determination of 11 textural and mineralogy parameters were performed on selected samples. The performance of the ANN model and regression analysis was evaluated using the criteria such as correlation coefficient (R), root mean squared error (RMSE), and variance account for (VAF). According to the ANN results, values of R, RMSE, and VAF were obtained to be 0.925, 0.089, and 97% for UCS and 0.876, 0.094, and 96% for E, respectively. In comparison, for the MLR model, the obtained R, RMSE, and VAF were 0.845, 0.101, and 95% for UCS and 0.797, 0.116, and 93% for E, respectively. A comparison between the findings illustrated that the ANN model was more suitable for forecasting the UCS and E compared with the MLR method.

scholarly journals Spatial Variability of Aroma Profiles of Cocoa Trees Obtained through Computer Vision and Machine Learning Modelling: A Cover Photography and High Spatial Remote Sensing Application

Sensors ◽  
2019 ◽  
Vol 19 (14) ◽  
pp. 3054 ◽  
Author(s):  
Sigfredo Fuentes ◽  
Gabriela Chacon ◽  
Damir D. Torrico ◽  
Andrea Zarate ◽  
Claudia Gonzalez Viejo
Keyword(s):  
Management Practices ◽  
Mean Squared Error ◽  
Computer Application ◽  
Aerial Image ◽  
Canopy Architecture ◽  
Ann Model ◽  
Aroma Profile ◽  
Squared Error ◽  
Artificial Neural Network Ann ◽  
The Tropics

Cocoa is an important commodity crop, not only to produce chocolate, one of the most complex products from the sensory perspective, but one that commonly grows in developing countries close to the tropics. This paper presents novel techniques applied using cover photography and a novel computer application (VitiCanopy) to assess the canopy architecture of cocoa trees in a commercial plantation in Queensland, Australia. From the cocoa trees monitored, pod samples were collected, fermented, dried, and ground to obtain the aroma profile per tree using gas chromatography. The canopy architecture data were used as inputs in an artificial neural network (ANN) algorithm, with the aroma profile, considering six main aromas, as targets. The ANN model rendered high accuracy (correlation coefficient (R) = 0.82; mean squared error (MSE) = 0.09) with no overfitting. The model was then applied to an aerial image of the whole cocoa field studied to produce canopy vigor, and aroma profile maps up to the tree-by-tree scale. The tool developed could significantly aid the canopy management practices in cocoa trees, which have a direct effect on cocoa quality.

Determination of number of check dams by artificial neural networks in arid regions of Iran

2015 ◽  
Vol 72 (6) ◽  
pp. 952-959 ◽  
Author(s):  
Seyed Ali Asghar Hashemi ◽  
Hamed Kashi
Keyword(s):  
Curve Number ◽  
Absolute Error ◽  
Coefficient Of Determination ◽  
Ann Model ◽  
Effective Parameters ◽  
Check Dams ◽  
Artificial Neural ◽  
Artificial Neural Network Ann ◽  
Mlp Model

An artificial neural network (ANN) model with six hydrological factors including time of concentration (TC), curve number, slope, imperviousness, area and input discharge as input parameters and number of check dams (NCD) as output parameters was developed and created using GIS and field surveys. The performance of this model was assessed by the coefficient of determination R2, root mean square error (RMSE), values account and mean absolute error (MAE). The results showed that the computed values of NCD using ANN with a multi-layer perceptron (MLP) model regarding RMSE, MAE, values adjustment factor (VAF), and R2 (1.75, 1.25, 90.74, and 0.97) for training, (1.34, 0.89, 97.52, and 0.99) for validation and (0.53, 0.8, 98.32, and 0.99) for test stage, respectively, were in close agreement with their respective values in the watershed. Finally, the sensitivity analysis showed that the area, TC and curve number were the most effective parameters in estimating the number of check dams.

Simultaneous Quantitative Analysis of Clorsulon and Ivermectin in a Veterinary Formulation by Artificial Neural Network

2008 ◽  
Vol 59 (10) ◽  
Author(s):  
Gozde Pektas ◽  
Erdal Dinc ◽  
Dumitru Baleanu
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Transfer Function ◽  
Back Propagation ◽  
Ann Model ◽  
Back Propagation Algorithm ◽  
Artificial Neural ◽  
Artificial Neural Network Ann ◽  
Hidden Layer

Simultaneaous spectrophotometric determination of clorsulon (CLO) and invermectin (IVE) in commercial veterinary formulation was performed by using the artificial neural network (ANN) based on the back propagation algorithm. In order to find the optimal ANN model various topogical networks were tested by using different hidden layers. A logsig input layer, a hidden layer of neurons using the logsig transfer function and an output layer of two neurons with purelin transfer function was found suitable for basic configuration for ANN model. A calibration set consisting of CLO and IVE in calibration set was prepared in the concentration range of 1-23 �g/mL and 1-14 �g/mL, repectively. This calibration set contains 36 different synthetic mixtures. A prediction set was prepared in order to evaluate the recovery of the investigated approach ANN chemometric calibration was applied to the simultaneous analysis of CLO and IVE in compounds in a commercial veterinary formulation. The experimental results indicate that the proposed method is appropriate for the routine quality control of the above mentioned active compounds.

scholarly journals Prediction of rubber vulcanization using an artificial neural network

2021 ◽  
Vol 75 (5) ◽  
pp. 277-283
Author(s):  
Jelena Lubura ◽  
Predrag Kojic ◽  
Jelena Pavlicevic ◽  
Bojana Ikonic ◽  
Radovan Omorjan ◽  
...  
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Large Scale ◽  
Mean Squared Error ◽  
Percentage Error ◽  
Dense Layer ◽  
Ann Model ◽  
Different Temperatures ◽  
Artificial Neural ◽  
Artificial Neural Network Ann

Determination of rubber rheological properties is indispensable in order to conduct efficient vulcanization process in rubber industry. The main goal of this study was development of an advanced artificial neural network (ANN) for quick and accurate vulcanization data prediction of commercially available rubber gum for tire production. The ANN was developed by using the platform for large-scale machine learning TensorFlow with the Sequential Keras-Dense layer model, in a Python framework. The ANN was trained and validated on previously determined experimental data of torque on time at five different temperatures, in the range from 140 to 180 oC, with a step of 10 oC. The activation functions, ReLU, Sigmoid and Softplus, were used to minimize error, where the ANN model with Softplus showed the most accurate predictions. Numbers of neurons and layers were varied, where the ANN with two layers and 20 neurons in each layer showed the most valid results. The proposed ANN was trained at temperatures of 140, 160 and 180 oC and used to predict the torque dependence on time for two test temperatures (150 and 170 oC). The obtained solutions were confirmed as accurate predictions, showing the mean absolute percentage error (MAPE) and mean squared error (MSE) values were less than 1.99 % and 0.032 dN2 m2, respectively.

scholarly journals Prediction of Concrete Compressive Strength by Evolutionary Artificial Neural Networks

2015 ◽  
Vol 2015 ◽  
pp. 1-8 ◽  
Author(s):  
Mehdi Nikoo ◽  
Farshid Torabian Moghadam ◽  
Łukasz Sadowski
Keyword(s):  
Neural Networks ◽  
Compressive Strength ◽  
Artificial Neural Networks ◽  
Ann Model ◽  
Ann Models ◽  
Compressive Strength Of Concrete ◽  
Artificial Neural ◽  
Artificial Neural Network Ann ◽  
Different Characteristics ◽  
Mlr Model

Compressive strength of concrete has been predicted using evolutionary artificial neural networks (EANNs) as a combination of artificial neural network (ANN) and evolutionary search procedures, such as genetic algorithms (GA). In this paper for purpose of constructing models samples of cylindrical concrete parts with different characteristics have been used with 173 experimental data patterns. Water-cement ratio, maximum sand size, amount of gravel, cement, 3/4 sand, 3/8 sand, and coefficient of soft sand parameters were considered as inputs; and using the ANN models, the compressive strength of concrete is calculated. Moreover, using GA, the number of layers and nodes and weights are optimized in ANN models. In order to evaluate the accuracy of the model, the optimized ANN model is compared with the multiple linear regression (MLR) model. The results of simulation verify that the recommended ANN model enjoys more flexibility, capability, and accuracy in predicting the compressive strength of concrete.

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.

Predicting the Air Permeability of Ultrafine Glass Fiber Felts: A Comparison of Artificial Neural Network, Linear Fitting, and Polynomial Fitting

2021 ◽  
Vol 8 (2) ◽  
pp. 9-17
Author(s):  
Fei Wang ◽  
Zhaofeng Chen ◽  
Cao Wu
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Glass Fiber ◽  
Mean Squared Error ◽  
Air Permeability ◽  
Ann Model ◽  
Polynomial Fitting ◽  
Linear Fitting ◽  
Artificial Neural ◽  
Artificial Neural Network Ann

In this study, the air permeability of ultrafine glass fiber felts (UGFFs) as a function of bulk density and thickness was predicted by three analysis methods including linear fitting, polynomial fitting, and an artificial neural network (ANN). A 36-set database was obtained by the measurements of samples produced by the flame blowing process. It was shown that the ANN structure with six neurons in the hidden layer was optimal. The ANN model showed much better quality of predicting the permeation rate compared with linear fitting and polynomial fitting, which was evaluated by three important parameters, namely mean relative error (MRE), mean squared error (MSE), and correlation coefficient (R). The prediction diagrams applying the ANN model also matched the theoretical analysis very well, which verified the advantages and practicability of ANN.

scholarly journals Metaheuristic Approaches Integrated with ANN in Forecasting Daily Emergency Department Visits

2021 ◽  
Vol 2021 ◽  
pp. 1-14
Author(s):  
Engin Pekel ◽  
Muhammet Gul ◽  
Erkan Celik ◽  
Samuel Yousefi
Keyword(s):  
Mean Squared Error ◽  
Absolute Error ◽  
Emergency Department Visits ◽  
Training Dataset ◽  
Percentage Error ◽  
Quality Level ◽  
Ann Model ◽  
Squared Error ◽  
Ed Visits ◽  
Artificial Neural Network Ann

The overall service quality level of Emergency Departments (EDs) can be improved by accurate forecasting of patient visits. Accordingly, this study aims to evaluate the use of three metaheuristic approaches integrated with Artificial Neural Network (ANN) in forecasting daily ED visits. To do this, five performance measures are used for evaluating the accuracy of the proposed approaches, including Bayesian ANN, Genetic Algorithm-based ANN (GA-ANN), and Particle Swarm Optimization algorithm-based ANN (PSO-ANN). The outputs of this study show that the PSO-ANN model provides the most dominant performance in both the training and testing process. The lowest error is obtained with a mean absolute percentage error (MAPE) of 6.3%, Mean Absolute Error (MAE) of 42.797, Mean Squared Error (MSE) of 2499.340, Root Mean Square Error (RMSE) of 49.933, and R-squared (R2) of 0.824 on the training dataset. The lowest error with an MAPE of 6.0%, MAE of 40.888, MSE of 2839.998, RMSE of 53.292, and R2 of 0.791 is also obtained on the testing process.

scholarly journals Prediction of electric conductivity for ionic liquids by two chemometrics methods

2013 ◽  
Vol 78 (5) ◽  
pp. 653-667 ◽  
Author(s):  
Yu Cao ◽  
Jia Yu ◽  
Hang Song ◽  
Xianlong Wang ◽  
Shun Yao
Keyword(s):  
Ionic Liquids ◽  
Electric Conductivity ◽  
Mean Squared Error ◽  
Back Propagation ◽  
Ionic Conductivities ◽  
Ann Model ◽  
Artificial Neural Network Ann ◽  
Set Up ◽  
Effective Models ◽  
Mlr Model

In recent years, the study of properties of ionic liquids (ILs) and their structures has developed to a great extent. Among the common physicochemical properties of pure ILs, electric conductivity (EC) is of crucial importance for both practical and fundamental levels. In order to develop effective models for predicting EC value of various ILs, the relationship between the structural descriptors and the EC of thirty-five ionic liquids at different temperatures has been investigated by multi linear regression (MLR) and back propagation artificial neural network (ANN), respectively. As a result, a three layer ANN with four variables selected by MLR model as input node was set up successfully. The descriptors selected by MLR were suitable and significant to be the input nodes of the ANN model in this study. And the calculated ionic conductivities by ANN model with high correlation coefficient and low root mean squared error were quantitative in good agreement with the experimental values, and it was proved better than the MLR model.

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