High performance concrete compressive strength forecasting using ensemble models based on discrete wavelet transform

Erdal, Halil İbrahim; Karakurt, Onur; Namlı, Ersin

doi:10.1016/j.engappai.2012.10.014

Cited by 150 publications

(44 citation statements)

References 43 publications

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“…After training the Boosted-NNE components, the CLMS algorithm is applied to obtain the component's optimal weights in the model's combination module. In many NNE model implementations, the components are multi-layer perceptron neural networks (MLP) (Chao et al, 2014;Erdal et al, 2013). Thus we use MLP networks as the components of the Boosted-NNE.…”

Section: Methodology Formulationmentioning

confidence: 99%

A novel Boosted-neural network ensemble for modeling multi-target regression problems

Hadavandi

Shahrabi

Shamshirband

2015

Engineering Applications of Artificial Intelligence

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Section: Methodology Formulationmentioning

confidence: 99%

A novel Boosted-neural network ensemble for modeling multi-target regression problems

Hadavandi

Shahrabi

Shamshirband

2015

Engineering Applications of Artificial Intelligence

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“…A common ANN is the multilayer perceptron (MLP) algorithm which is made up from three layers as shown in Figure 7. The ANN is trained by entering information from the input layer through the hidden and output layers of the network [40]. The ANN is performed by using the back-propagation algorithm based on the Levenberg-Marquardt rule [41].…”

Section: Methodsmentioning

confidence: 99%

Classification of Pulmonary Nodules by Using Hybrid Features

Tartar

Kılıç

Akan

2013

Computational and Mathematical Methods in Medicine

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Early detection of pulmonary nodules is extremely important for the diagnosis and treatment of lung cancer. In this study, a new classification approach for pulmonary nodules from CT imagery is presented by using hybrid features. Four different methods are introduced for the proposed system. The overall detection performance is evaluated using various classifiers. The results are compared to similar techniques in the literature by using standard measures. The proposed approach with the hybrid features results in 90.7% classification accuracy (89.6% sensitivity and 87.5% specificity).

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“…The accuracy of a predictive model can be measured by various methods including R-squared, mean squared error (MSE), mean absolute error (MAE), and root mean squared error (RMSE) [39]- [41]. In this work, many measurements are used for different models.…”

Section: Training and Verification Of The Modelmentioning

confidence: 99%

Material Cost Prediction for Jewelry Production Using Deep Learning Technique

Phitthayanon¹,

Rungreunganun²

2019

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Production cost management is a key factor to increase industrial competitiveness. The precious metals and the gemstones comprise 65% of the jewelry material cost. Managing raw material cost is a challenging task especially when the price highly fluctuates. In this article, deep learning models were proposed to predict the prices of main raw materials of jewelry which are silver, gold, and diamond. These models are designed for Thai jewelry manufactures which are mostly small businesses. Therefore, our models only consider historical price data. This is because small businesses usually do not have access to other relevant data, i.e. oil prices and other economic data.The proposed precious metal price model can provide prediction with RMSE of 0.00765 which is comparable to other models in literature while requires less data and offers a simpler model. Also, the proposed diamond price model can provide RMSE of 0.0181 which is 42.41% improvement from the model normally used by jewelry manufacturers.In addition to the raw material price prediction model, a quantization method of diamond 4C grade is proposed and validated statistically and visually. This quantization method could be used in a diamond analysis.

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High performance concrete compressive strength forecasting using ensemble models based on discrete wavelet transform

Cited by 150 publications

References 43 publications

A novel Boosted-neural network ensemble for modeling multi-target regression problems

A novel Boosted-neural network ensemble for modeling multi-target regression problems

Classification of Pulmonary Nodules by Using Hybrid Features

Material Cost Prediction for Jewelry Production Using Deep Learning Technique

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