scholarly journals Embedded FBG Sensor Based Impact Identification of CFRP Using Ensemble Learning

Sensors ◽  
2021 ◽  
Vol 21 (4) ◽  
pp. 1452
Author(s):  
Jun Li ◽  
Yinghong Yu ◽  
Xinlin Qing
Keyword(s):  
Neural Network ◽  
Ensemble Learning ◽  
Composite Structures ◽  
Back Propagation ◽  
Support Vector ◽  
Time Of Arrival ◽  
Neural Network Ensemble ◽  
Monitoring Method ◽  
Reinforced Plastics ◽  
Fbg Sensors

Impact brings great threat to the composite structures that are extensively used in an aircraft. Therefore, it is necessary to develop an accurate and reliable impact monitoring method. In this paper, fiber Bragg grating (FBG) sensors are embedded in unidirectional carbon fiber reinforced plastics (CFRPs) during the manufacturing process to monitor the strain that is related to the elastic modulus and the state of resin. After that, an advanced impact identification model is proposed. Support vector regression (SVR) and a back propagation (BP) neural network are combined appropriately in this stacking-based ensemble learning model. Then, the model is trained and tested through hundreds of impacts, and the corresponding strain responses are recorded by the embedded FBG sensors. Finally, the performances of different models are compared, and the influence of the time of arrival (ToA) on the neural network is also explored. The results show that compared with a single neural network, ensemble learning has a better capability in impact identification.

Bank Deposit Prediction Using Ensemble Learning

2021 ◽  
pp. 42-51
Author(s):  
Muhammed J. A. Patwary ◽  
S. Akter ◽  
M. S. Bin Alam ◽  
A. N. M. Rezaul Karim
Keyword(s):  
Neural Network ◽  
Ensemble Learning ◽  
Banking Sector ◽  
Performance Metrics ◽  
Financial Institution ◽  
Descriptive Analysis ◽  
Ensemble Methods ◽  
Support Vector ◽  
Classification Algorithms ◽  
Economic Depression

Bank deposit is one of the vital issues for any financial institution. It is very challenging to predict a customer if he/she can be a depositor by analyzing related information. Some recent reports demonstrate that economic depression and the continuous decline of the economy negatively impact business organizations and banking sectors. Due to such economic depression, banks cannot attract a customer's attention. Thus, marketing is preferred to be a handy tool for the banking sector to draw customers' attention for a term deposit. The purpose of this paper is to study the performance of ensemble learning algorithms which is a novel approach to predict whether a new customer will have a term deposit or not. A Portuguese retail bank data is used for our study, containing 45,211 phone contacts with 16 input attributes and one decision attribute. The data are preprocessed by using the Discretization technique. 40,690 samples are used for training the classifiers, and 4,521 samples are used for testing. In this work, the performance of the three mostly used classification algorithms named Support Vector Machine (SVM), Neural Network (NN), and Naive Bayes (NB) are analyzed. Then the ability of ensemble methods to improve the efficiency of basic classification algorithms is investigated and experimentally demonstrated. Experimental results exhibit that the performance metrics of Neural Network (Bagging) is higher than other ensemble methods. Its accuracy, sensitivity, and specificity are 96.62%, 97.14%, and 99.08%, respectively. Although all input attributes are considered in the classification method, in the end, a descriptive analysis has shown that some input attributes have more importance for this classification. Overall, it is shown that ensemble methods outperformed the traditional algorithms in this domain. We believe our contribution can be used as a depositor prediction system to provide additional support for bank deposit prediction.

A Confidence-Based RBF Neural Network Ensemble Learning Paradigm with Application to Delinquent Prediction for Credit Risk Management

2010 ◽  
pp. 105-117
Author(s):  
Lean Yu ◽  
Shouyang Wang
Keyword(s):  
Neural Network ◽  
Risk Management ◽  
Credit Risk ◽  
Ensemble Learning ◽  
Rbf Neural Network ◽  
Network Models ◽  
Neural Network Ensemble ◽  
Credit Risk Management ◽  
Learning Paradigm ◽  
Neural Network Models

In this study, a multistage confidence-based radial basis function (RBF) neural network ensemble learning model is proposed to design a reliable delinquent prediction system for credit risk management. In the first stage, a bagging sampling approach is used to generate different training datasets. In the second stage, the RBF neural network models are trained using various training datasets from the previous stage. In the third stage, the trained RBF neural network models are applied to the testing dataset and some prediction results and confidence values can be obtained. In the fourth stage, the confidence values are scaled into a unit interval by logistic transformation. In the final stage, the multiple different RBF neural network models are fused to obtain the final prediction results by means of confidence measure. For illustration purpose, two publicly available credit datasets are used to verify the effectiveness of the proposed confidence-based RBF neural network ensemble learning paradigm.

ARRHYTHMIA DISEASE DIAGNOSIS USING NEURAL NETWORK, SVM, AND GENETIC ALGORITHM-OPTIMIZED k-MEANS CLUSTERING

2011 ◽  
Vol 11 (04) ◽  
pp. 897-915 ◽  
Author(s):  
ROSHAN JOY MARTIS ◽  
CHANDAN CHAKRABORTY
Keyword(s):  
Neural Network ◽  
Genetic Algorithm ◽  
Normal Sinus Rhythm ◽  
Optimization Technique ◽  
Back Propagation ◽  
Principal Component ◽  
Disease Diagnosis ◽  
Back Propagation Neural Network ◽  
Polynomial Kernel ◽  
Support Vector

This work aims at presenting a methodology for electrocardiogram (ECG)-based arrhythmia disease detection using genetic algorithm (GA)-optimized k-means clustering. The open-source ECG data from MIT-BIH arrhythmia database and MIT-BIH normal sinus rhythm database are subjected to a sequence of steps including segmentation using R-point detection, extraction of features using principal component analysis (PCA), and pattern classification. Here, the classical classifiers viz., k-means clustering, error back propagation neural network (EBPNN), and support vector machine (SVM) have been initially attempted and subsequently m-fold (m = 3) cross validation is used to reduce the bias during training of the classifier. The average classification accuracy is computed as the average over all the three folds. It is observed that EBPNN and SVM with different order polynomial kernel provide significant accuracies in comparison with k-means one. In fact, the parameters (centroids) of k-means algorithm are locally optimized by minimizing its objective function. In order to overcome this limitation, a global optimization technique viz., GA is suggested here and implemented to find more robust parameters of k-means clustering. Finally, it is shown that GA-optimized k-means algorithm enhances its accuracy to those of other classifiers. The results are discussed and compared. It is concluded that the GA-optimized k-means algorithm is an alternate approach for classification whose accuracy will be near to that of supervised (viz., EBPNN and SVM) classifiers.

Multistage RBF neural network ensemble learning for exchange rates forecasting

2008 ◽  
Vol 71 (16-18) ◽  
pp. 3295-3302 ◽  
Author(s):  
Lean Yu ◽  
Kin Keung Lai ◽  
Shouyang Wang
Keyword(s):  
Neural Network ◽  
Exchange Rates ◽  
Ensemble Learning ◽  
Rbf Neural Network ◽  
Neural Network Ensemble
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