A Bayesian regularization-backpropagation neural network model for peeling computations

Gouravaraju, Saipraneeth; Narayan, Jyotindra; Sauer, Roger A.; Gautam, Sachin S.

doi:10.1080/00218464.2021.2001335

Cited by 48 publications

(15 citation statements)

References 37 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…As compared with some algorithms to improve ANN efficiency, Bayesian regularization shows superiority over Levenberg-Marquardt algorithm [26,52] and gradient descent with momentum and adaptive learning rate backpropagation -GDX [53], Scaled Conjugate Gradient (SCG) [54]. Besides, Gouravarajua [27] concludes that the artificial neural network-based Bayesian regularization combined with the K-fold cross-section technique which can greatly reduce computation time with high accuracy can be used to successfully study gecko adhesion problems. Thus, it is shown that utilizing the ANN-based Bayesian regularization model to estimate the compressive strength of concrete at high temperatures is achievable, hence saving time and money on trials.…”

Section: Resultsmentioning

confidence: 99%

“…Like other ML methods, ANN can suffer from an overfitting problem (especially with too small data but too high model complexity) [26]. The regularization in the ANN allows reducing error for obtaining the highest coefficient of correlation and lowest total square errors [27]. A tuning regularization technique in ANN that has been used very effectively is Bayesian regularization which has been used to successfully study various problems such as stock price prediction, data mining, etc [28][29][30][31][32].…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Prediction of compressive strength of concrete at high heating conditions by using artificial neural network-based Bayesian regularization

Hadzima-Nyarko¹,

Trinh²

2022

JSTT

View full text Add to dashboard Cite

Cement concrete is the most commonly used material today for constructing residential or commercial buildings, industrial parks, or particular components such as tunnel slabs where there is a high risk of fire. This structure requires concrete to be subjected to high temperatures generated by fires. However, concrete under the influence of high temperature has very complex behavior states with deformations, physical and chemical changes as the temperature rises dramatically. In this study, an artificial neural network-based Bayesian regularization (ANN) model is proposed to predict the compressive strength of concrete. The database in this study includes 208 experimental results synthesized from laboratory experiments with 9 input variables related to temperature change and design material composition. The performance of the ANN model was evaluated using K-fold cross-validation and statistical criteria, including mean absolute error (MAE), root mean square error (RMSE), and coefficient of determination (R2). The results show that the proposed ANN model is a reasonable, highly accurate, and useful prediction tool for saving time and minimizing costly experiments.

show abstract

Section: Resultsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Prediction of compressive strength of concrete at high heating conditions by using artificial neural network-based Bayesian regularization

Hadzima-Nyarko¹,

Trinh²

2022

JSTT

View full text Add to dashboard Cite

show abstract

“…For simplicity, we use The ID_BB and ID_ker variables in lines [1,2] to assign new in order IDs for the BBs and the kernels. In lines [5][6][7][8][9][10][11][12][13][14], we go over all the kernels and the basic blocks in the application and inject a function after each basic block to collect the IDs of the BB and the kernels. In lines [15][16][17][18][19][20][21], we use another function to receive the IDs data dynamically and calculate the BB counts.…”

Section: Bb Trace Extractionmentioning

confidence: 99%

“…Although the trained networks showed good accuracy for some of the applications, they could not fit some of the benchmarks of Table 1. Therefore, we employ the Bayesian Regularization Backpropagation Neural Network(BR-BPNN) that uses the Bayesian Regularization algorithm and minimizes a linear combination of squared errors and weights [14] to compare PNN to a competitive regression model.…”

Section: Deep Neural Network Model Architecturementioning

confidence: 99%

BB-ML: Basic Block Performance Prediction using Machine Learning Techniques

Aktar¹,

Abdelkhalik²,

Turja³

et al. 2022

Preprint

View full text Add to dashboard Cite

Recent years have seen the adoption of Machine Learning (ML) techniques to predict the performance of large-scale applications, mostly at a coarse level. In contrast, we propose to use ML techniques for performance prediction at much finer granularity, namely at the levels of Basic Block (BB), which are the single entry-single exit code blocks that are used as analysis tools by all compilers to break down a large code into manageable pieces. Utilizing ML and BB analysis together can enable scalable hardware-software co-design beyond the current state of the art. In this work, we extrapolate the basic block execution counts of GPU applications for large inputs sizes from the counts of smaller input sizes of the same application.We employ two ML models, a Poisson Neural Network (PNN) and a Bayesian Regularization Backpropagation Neural Network (BR-BPNN). We train both models using the lowest input values of the application and random input values to predict basic block counts. Results show that our models accurately predict the basic block execution counts of 16 benchmark applications. For PNN and BR-BPNN models, we achieve an average accuracy of 93.5% and 95.6%, respectively, while extrapolating the basic block counts for large input sets when the model is trained using smaller input sets. Additionally, the models show an average accuracy of 97.7% and 98.1%, respectively, while predicting basic block counts on random instances.

show abstract

“…It is a linear hybrid of Bayesian techniques and ANN to automatically choose the values of the appropriate weights while training the ANN model (Okut, 2016). A more detailed discussion can be found in Gouravaraju et al (2021). The second was called ANN-2 and trained with the Levenberg-Marquardt backpropagation, known as the TRAINLM function in MATLAB.…”

Section: 𝑓(𝑥mentioning

confidence: 99%

Machine Learning Model for Estimation of Local Scour Depth around Cylindrical Bridge Piers

Ahmed¹,

Waheed²,

Ashiq³

et al. 2022

IJCE

View full text Add to dashboard Cite

Scour around bridge piers is a well-known threat to bridge stability worldwide. It can cause losses in lives and the economy, especially during floods. Therefore, an artificial intelligence approach called artificial neural network (ANN) was used to predict the scour depth around bridge piers. The ANN model was trained with laboratory data, including pier width, flow velocity, particle diameter, sediment critical velocity, flow depth, and scour depth. The data was divided into 70% for training, 15 for validation, and 15% for testing. Besides, the ANN model was trained using various training algrthins and a single hidden layer with 20 neurons in the hidden layer. The results showed that the ANN model with Bayesian regularization backpropagation training algorithm provides a better predicted scour depth with a correlation coefficient (R) equal to 0. 9692 and 0.926 for training and test stages, respectively. Besides, it showed a low mean squared error (MSE), which was 0.0034 for training and 0.0066 for the test. These results were slightly better than the ANN with Levenberg-Marquardt backpropagation with R training equals 0.9552 (MSE training = 0.0047), and R test equals 0.838 (MSE test = 0.007).On the other hand, the ANN model with a scaled conjugate gradient backpropagation training algorithm showed worse predictions (R training = 0.7407 and R test = 0.6409). Besides, the ANN model shows better outcomes than the linear regression model. Finally, the sensitivity analysis has shown that the pier width is the most crucial parameter for estimating scour depth using the ANN model.

show abstract

A Bayesian regularization-backpropagation neural network model for peeling computations

Cited by 48 publications

References 37 publications

Prediction of compressive strength of concrete at high heating conditions by using artificial neural network-based Bayesian regularization

Prediction of compressive strength of concrete at high heating conditions by using artificial neural network-based Bayesian regularization

BB-ML: Basic Block Performance Prediction using Machine Learning Techniques

Machine Learning Model for Estimation of Local Scour Depth around Cylindrical Bridge Piers

Contact Info

Product

Resources

About