On artificial neural networks approach with new cost functions

Jafarian, Ahmad; Nia, S. Measoomy; Golmankhaneh, Alireza Khalili; Băleanu, Dumitru

doi:10.1016/j.amc.2018.07.053

Cited by 28 publications

(16 citation statements)

References 14 publications

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“…The neurons in each layer receive input data that are passed through a weighted connection. The total input value received by the neuron would be compared with the threshold and then processed by activation function to generate the output of the neuron [37,38]. after centralization of data set, we can proceed the eigenvalue decomposition of covariance matrices , calculating the eigenvector corresponding the large contributing eigenvalue ( ≤ ≤ ) to build projection matrix:…”

Section: Classification Of Reconstructed Xasmentioning

confidence: 99%

“…The neurons in each layer receive input data that are passed through a weighted connection. The total input value received by the neuron would be compared with the threshold and then processed by activation function to generate the output of the neuron [37,38]. The threshold can be replaced by bias input , is the connection weight [39], therefore the mathematical model of ANN is given by:…”

Section: Classification Of Reconstructed Xasmentioning

confidence: 99%

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Effect of Reconstruction Algorithm on the Identification of 3D Printing Polymers Based on Hyperspectral CT Technology Combined with Artificial Neural Network

Zheng

Wang

et al. 2020

Materials

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Hyperspectral X-ray CT (HXCT) technology provides not only structural imaging but also the information of material components therein. The main purpose of this study is to investigate the effect of various reconstruction algorithms on reconstructed X-ray absorption spectra (XAS) of components shown in the CT image by means of HXCT. In this paper, taking 3D printing polymer as an example, seven kinds of commonly used polymers such as thermoplastic elastomer (TPE), carbon fiber reinforced polyamide (PA-CF), acrylonitrile butadiene styrene (ABS), polylactic acid (PLA), ultraviolet photosensitive resin (UV9400), polyethylene terephthalate glycol (PETG), and polyvinyl alcohol (PVA) were selected as samples for hyperspectral CT reconstruction experiments. Seven kinds of 3D printing polymer and two interfering samples were divided into a training set and test sets. First, structural images of specimens were reconstructed by Filtered Back-Projection (FBP), Algebra Reconstruction Technique (ART) and Maximum-Likelihood Expectation-Maximization (ML-EM). Secondly, reconstructed XAS were extracted from the pixels of region of interest (ROI) compartmentalized in the images. Thirdly, the results of principal component analysis (PCA) demonstrated that the first four principal components contain the main features of reconstructed XAS, so we adopted Artificial Neural Network (ANN) trained by the reconstructed XAS expressed by the first four principal components in the training set to identify that the XAS of corresponding polymers exist in both of test sets from the training set. The result of ANN displays that FBP has the best performance of classification, whose ten-fold cross-validation accuracy reached 99%. It suggests that hyperspectral CT reconstruction is a promising way of getting image features and material features at the same time, which can be used in medical imaging and nondestructive testing.

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Section: Classification Of Reconstructed Xasmentioning

confidence: 99%

“…The neurons in each layer receive input data that are passed through a weighted connection. The total input value received by the neuron would be compared with the threshold and then processed by activation function to generate the output of the neuron [37,38]. The threshold can be replaced by bias input , is the connection weight [39], therefore the mathematical model of ANN is given by:…”

Section: Classification Of Reconstructed Xasmentioning

confidence: 99%

Effect of Reconstruction Algorithm on the Identification of 3D Printing Polymers Based on Hyperspectral CT Technology Combined with Artificial Neural Network

Zheng

Wang

et al. 2020

Materials

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“…The structure of the ANN is similar to the human brain. The neural network system has two computational processes [24,25]. The number of features selected by the Relief-F, random forest and RFBTRF-GWO method in Table 1.…”

Section: Classifiersmentioning

confidence: 99%

Classification and Prediction of Student Academic Performance using Gray Wolf Optimization Based Relief-F Budget Random Forest

Deepika*¹,

Sathyanarayana²

2019

IJRTE

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The student academic prediction model helps to predict the student performance that helps the university to provide necessary care to the particular students. Efficient prediction model helps to encourage the student for better performance in the academic. In this research, the Relief-F Budget Tree Random Forest with Gray Wolf Optimization (RFBTRF-GWO) method is proposed for the feature selection. The Gray Wolf Optimization (GWO) helps to scale the relevant feature with ranking order from the features selected by the Relief-F Budget Tree Random Forest (RFBTRF). The selected features are given as input to the classifier for the effective prediction. The k-Nearest Neighbor (kNN) and Artificial Neural Network (ANN) are used for the classification. The proposed RFBTRF-GWO method is evaluated on the three datasets such as two UCI datasets and one collected dataset. The RFBTRF-GWO has a higher performance accuracy of 96.2 % while the existing method RFBTRF has an accuracy of 70.88 %.

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“…Hosaka has developed a model for predicting bankruptcy by looking at the financial ratios of companies with an artificial neural network (Hosaka, 2019). Jafarian and colleagues studied neural networks with new cost functions and developed a general neural network approach for a fractional order problem in their study (Jafarian et. al., 2018).…”

Section: Data Mining and Machine Learningmentioning

confidence: 99%

A research on the comparison of classification algorithm in finance

Etı

İnel

2019

CyA

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Nowadays, data is developing with technology. With developments in the Internet and technology, it makes a warehouse that changes the data more rapidly and in volume. This situation increases the importance of data mining every day. The aim of this study is to examine classification algorithms which are the sub-subject of data mining. In the analysis part of the study, it is aimed to compare the algorithms with the accuracy ratio by using the financial data. As a result of the study conducted on stock marketing, the classification of enterprises of decision tree models with artificial neural networks has also been found to be more successful than other methods.

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On artificial neural networks approach with new cost functions

Cited by 28 publications

References 14 publications

Effect of Reconstruction Algorithm on the Identification of 3D Printing Polymers Based on Hyperspectral CT Technology Combined with Artificial Neural Network

Effect of Reconstruction Algorithm on the Identification of 3D Printing Polymers Based on Hyperspectral CT Technology Combined with Artificial Neural Network

Classification and Prediction of Student Academic Performance using Gray Wolf Optimization Based Relief-F Budget Random Forest

A research on the comparison of classification algorithm in finance

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