An artificial neural network model for predicting compression strength of heat treated woods and comparison with a multiple linear regression model

Tiryaki, Sebahattin; Aydın, Aytaç

doi:10.1016/j.conbuildmat.2014.03.041

Cited by 132 publications

(90 citation statements)

References 34 publications

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“…According to Tiryaki and Aydin (2014), the correlation coefficient (R) between the ANNs actual and estimated values is another important indicator that can be used to verify the validity of the model. These authors have noted that when the R value is close to 1, the forecasting accuracy increases (Tiryaki and Aydın, 2014).…”

Section: Fig 4 the Final Multi-layer Perceptron Artificial Neural Nmentioning

confidence: 98%

“…and f (. ) are the activation functions of output and hidden neurons respectively (Tiryaki and Aydın, 2014).…”

Section: Fig 1 the Study's Artificial Neural Network Structurementioning

confidence: 99%

“…The neurons are organized into several layers and interconnected with each other through synaptic weights. Synaptic weights represent the intensity of the interaction between every pair of neurons, and the activation functions calculate the potential of every neuron (Dell´Olio et al, 2017;Garrido et al, 2014;Tiryaki and Aydın, 2014).…”

Section: Introductionmentioning

confidence: 99%

“…The second layer is the hidden layer. This layer performs the operations that are designed to achieve the model's output (Tiryaki and Aydın, 2014). The hidden layer is also used to capture non-linear relationships among variables (Kar et al, 2015;Lahmiri and Gagnon, 2015).…”

Section: Introductionmentioning

confidence: 99%

“…The ANNs output layer provides the model's predicted values (Kar et al, 2015;Lahmiri and Gagnon, 2015). The number of neurons in the input layer is equal to the number of input variables or independent variables, and the number of output neurons is equal to the number of output variable(s) or dependent variable(s) (Srisaeng et al, 2015;Tiryaki and Aydın, 2014).…”

Section: Introductionmentioning

confidence: 99%

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The Use of an Artificial Neural Network to Predict Australia’s Export Air Cargo Demand

2018

IJTTE

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Abstract:In this paper an Artificial Neural Network (ANN) is proposed for predicting Australia's annual export air cargo demand. The modelling in the study was based on annual data from 1993 to 2016. The ANN model was developed using the input parameters of world real merchandise exports, world population growth, world jet fuel prices, world air cargo yields (proxy for air cargo costs), outbound flights from Australia, and Australian/ United States dollar exchange rate and two dummy variables, which controlled for the strong cyclical fluctuations in air cargo demand which occurred in 2003 and 2015. The artificial neural network (ANN) used multi-layer perceptron (MLP) architecture that compromised a multi-layer feed-forward network and the sigmoid and linear functions were used as activation functions with the feed forward-back propagation algorithm. The ANN was applied during training, testing and validation and had 8 inputs, 1 neuron in the hidden layer and 1 neuron in the output layer. The data was randomly divided into three data sets; training, testing and model validation. The best-fit model was selected according to four goodness-of-fit measures: mean absolute error (MAE), mean square error (MSE), root mean square errors (RMSE), and mean absolute percentage errors (MAPE). The highest R-value obtained from the ANN model is 0.97844. The results suggest that the ANN model is an efficient tool for predicting Australia's annual export air cargo demand.

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Section: Fig 4 the Final Multi-layer Perceptron Artificial Neural Nmentioning

confidence: 98%

“…and f (. ) are the activation functions of output and hidden neurons respectively (Tiryaki and Aydın, 2014).…”

Section: Fig 1 the Study's Artificial Neural Network Structurementioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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The Use of an Artificial Neural Network to Predict Australia’s Export Air Cargo Demand

2018

IJTTE

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Accurate Prediction of Microstructure of Composites using Machine Learning

Sang

Chen

Fan

et al. 2022

Advcd Theory and Sims

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In this prospective work, a machine learning (ML) model based on multiple independent random forest models to predict the configuration of binary composite bars is developed. The input variables to the ML model are elastic wave signals collected at one end of the composite bar, while the targets of the ML model are binary vectors representing the configuration of the bars.This study results indicate: First, a short period of elastic wave propagated through a composite bar can collect and carry the detailed information of the entire bar; second, the patterns hidden in the collected signals can be detected, extracted, and used by the ML model; finally, the ML model can be well trained using a relatively small dataset pool (less than 0.1% of all possible samples), and make accurate predictions. For the 30 sections of bars used in this study, the average prediction accuracy for each section of this bar can reach 95% and even higher. This ML guided technique can be modified and used in different functionalities and applications such as composites characterization, structure health monitoring, limestone determination, and archaeological detection.

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Machine Learning assisted design of tailor‐made nanocellulose films: A combination of experimental and computational studies

et al. 2019

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Nowadays, modern nanomaterial research is complemented by machine learning methods to reduce experimental costs and process time. With this motivation, here, we implemented artificial neural network (ANN), random forest (RF), and multiple linear regression (MLR) methods to predict the mechanical properties of threecomponent nanocomposite films consisting of polyvinyl alcohol (PVA) crosslinked 2,2,6,6-tetramethylpiperidine-1-oxyl (TEMPO) oxidized cellulose nanofibers (TOCNFs) and either ammonium zirconium carbonate (AZC) or glyoxal (Gx) using the mechanical properties of mono-component TOCNF films and two-component nanocomposites containing PVA, AZC, or Gx-crosslinked TOCNF as the input of prediction system. Prediction methods were evaluated with performance indicators and experimental data. Overall, MLR performed with least accuracy, whereas ANN prediction displayed the lowest error followed closely by RF. Additionally, the physically or/and chemically crosslinked hybrid films with optimized amount of crosslinkers resulted in structures with a strength to rupture that was significantly higher than that of the pure nanocellulose films (increases of up to~90% in tensile strength and~70% in Young's modulus). POLYM. COM-POS., 40:4013-4022, 2019.

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An artificial neural network model for predicting compression strength of heat treated woods and comparison with a multiple linear regression model

Cited by 132 publications

References 34 publications

The Use of an Artificial Neural Network to Predict Australia’s Export Air Cargo Demand

The Use of an Artificial Neural Network to Predict Australia’s Export Air Cargo Demand

Accurate Prediction of Microstructure of Composites using Machine Learning

Machine Learning assisted design of tailor‐made nanocellulose films: A combination of experimental and computational studies

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