Effect of training algorithms on neural networks aided pavement diagnosis

Gopalakrishnan, Kasthurirangan

doi:10.4314/ijest.v2i2.59147

Cited by 27 publications

(11 citation statements)

References 6 publications

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“…Scaled conjugate gradient backpropagation (SCG-BP) algorithm is a fully automated method, which was designed to avoid the time consuming line search often used in CGB and quasi-Newton BP algorithms [ 56 ]. We adopted SCG-BP to train the designed MLP-ANN in this work so as to take advantage of its well acclaimed speed of convergence [ 30 , 57 ]. The number of neurons in the hidden layer of our MLP was determined experimentally because there is currently no precise rule of thumb for selecting the number of hidden layer neurons [ 58 ].…”

Section: Methodsmentioning

confidence: 99%

Lung Cancer Prediction Using Neural Network Ensemble with Histogram of Oriented Gradient Genomic Features

Adetiba

Olugbara

2015

The Scientific World Journal

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This paper reports an experimental comparison of artificial neural network (ANN) and support vector machine (SVM) ensembles and their “nonensemble” variants for lung cancer prediction. These machine learning classifiers were trained to predict lung cancer using samples of patient nucleotides with mutations in the epidermal growth factor receptor, Kirsten rat sarcoma viral oncogene, and tumor suppressor p53 genomes collected as biomarkers from the IGDB.NSCLC corpus. The Voss DNA encoding was used to map the nucleotide sequences of mutated and normal genomes to obtain the equivalent numerical genomic sequences for training the selected classifiers. The histogram of oriented gradient (HOG) and local binary pattern (LBP) state-of-the-art feature extraction schemes were applied to extract representative genomic features from the encoded sequences of nucleotides. The ANN ensemble and HOG best fit the training dataset of this study with an accuracy of 95.90% and mean square error of 0.0159. The result of the ANN ensemble and HOG genomic features is promising for automated screening and early detection of lung cancer. This will hopefully assist pathologists in administering targeted molecular therapy and offering counsel to early stage lung cancer patients and persons in at risk populations.

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Section: Methodsmentioning

confidence: 99%

Lung Cancer Prediction Using Neural Network Ensemble with Histogram of Oriented Gradient Genomic Features

Adetiba

Olugbara

2015

The Scientific World Journal

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“…Training an ANN involves the minimization of an error function that depends on the network’s synaptic weights (the w’s in Figure 2 ), and these weights are iteratively updated by a learning algorithm, to approximate the target variable. The updating is usually accomplished by back-propagating the error, which is essentially a non-linear least-squares problem [ 19 ]. Back-propagation is a supervised learning algorithm based on a suitable error function, the values of which are determined by the target (i.e., marbling EPD) and the mapped (predicted) outputs of the network (i.e., fitted values of marbling EPD).…”

Section: Methodsmentioning

confidence: 99%

Predicting expected progeny difference for marbling score in Angus cattle using artificial neural networks and Bayesian regression models

Okut

Rosa

et al. 2013

Genet Sel Evol

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BackgroundArtificial neural networks (ANN) mimic the function of the human brain and are capable of performing massively parallel computations for data processing and knowledge representation. ANN can capture nonlinear relationships between predictors and responses and can adaptively learn complex functional forms, in particular, for situations where conventional regression models are ineffective. In a previous study, ANN with Bayesian regularization outperformed a benchmark linear model when predicting milk yield in dairy cattle or grain yield of wheat. Although breeding values rely on the assumption of additive inheritance, the predictive capabilities of ANN are of interest from the perspective of their potential to increase the accuracy of prediction of molecular breeding values used for genomic selection. This motivated the present study, in which the aim was to investigate the accuracy of ANN when predicting the expected progeny difference (EPD) of marbling score in Angus cattle. Various ANN architectures were explored, which involved two training algorithms, two types of activation functions, and from 1 to 4 neurons in hidden layers. For comparison, BayesCπ models were used to select a subset of optimal markers (referred to as feature selection), under the assumption of additive inheritance, and then the marker effects were estimated using BayesCπ with π set equal to zero. This procedure is referred to as BayesCpC and was implemented on a high-throughput computing cluster.ResultsThe ANN with Bayesian regularization method performed equally well for prediction of EPD as BayesCpC, based on prediction accuracy and sum of squared errors. With the 3K-SNP panel, for example, prediction accuracy was 0.776 using BayesCpC, and ranged from 0.776 to 0.807 using BRANN. With the selected 700-SNP panel, prediction accuracy was 0.863 for BayesCpC and ranged from 0.842 to 0.858 for BRANN. However, prediction accuracy for the ANN with scaled conjugate gradient back-propagation was lower, ranging from 0.653 to 0.689 with the 3K-SNP panel, and from 0.743 to 0.793 with the selected 700-SNP panel.ConclusionsANN with Bayesian regularization performed as well as linear Bayesian regression models in predicting additive genetic values, supporting the idea that ANN are useful as universal approximators of functions of interest in breeding contexts.

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“…This technique is fast and requires less memory use in analysis. Also, Gopalakrishnan () showed this algorithm performs better in developing prediction model in comparison with other error minimization techniques.…”

Section: Selection Of Nn Algorithmmentioning

confidence: 97%

Neural network modelling of asphalt adhesion determined by AFM

Tarefder

Ahsan

2014

Journal of Microscopy

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SummaryThis study constructs a neural network (NN) model to quantify adhesion from atomic force microscopy (AFM) data. AFM data contain five-point force-distance values. A total of 760 observations are used to build NN model. To train the network, AFM tip-sample distance data, percentage of lime, type and percentage of polymer and asphalt chemical functional groups are given as inputs and AFM force as an output. To select the NN architecture, one and two hidden layers with varying neurons are tried with 10 input nodes in the input layer and 5 output nodes in the output layer. Two hidden layers with 9 and 17 nodes in the first and second layer, respectively, show the best performance. A 10-9-17-5 NN is selected as the final structure of the NN model. Test results for the trained model show good prediction ability. The model is further applied to evaluate the effect of five different percentages of lime on the adhesion of asphalt. Results show that increase in the percentage of lime is very effective at reducing moisture damage in a styrene butadiene polymer modified asphalt sample. However, increase in lime percentage above 1.5% does not help reduce moisture damage in the styrene butadiene styrene polymer modified sample.

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Effect of training algorithms on neural networks aided pavement diagnosis

Cited by 27 publications

References 6 publications

Lung Cancer Prediction Using Neural Network Ensemble with Histogram of Oriented Gradient Genomic Features

Lung Cancer Prediction Using Neural Network Ensemble with Histogram of Oriented Gradient Genomic Features

Predicting expected progeny difference for marbling score in Angus cattle using artificial neural networks and Bayesian regression models

Neural network modelling of asphalt adhesion determined by AFM

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