scholarly journals Deep Learning-Based Residual Control Chart for Binary Response

Symmetry ◽  
2021 ◽  
Vol 13 (8) ◽  
pp. 1389
Author(s):  
Jong-Min Kim ◽  
Il-Do Ha
Keyword(s):  
Neural Network ◽  
Deep Learning ◽  
Regression Model ◽  
Control Chart ◽  
Principal Component ◽  
Bayesian Variable Selection ◽  
Binary Response ◽  
Explanatory Variables ◽  
Nonlinear Pca ◽  
Highly Correlated

A residual (r) control chart of asymmetrical and non-normal binary response variable with highly correlated explanatory variables is proposed in this research. To avoid multicollinearity between multiple explanatory variables, we employ and compare a neural network regression model and deep learning regression model using Bayesian variable selection (BVS), principal component analysis (PCA), nonlinear PCA (NLPCA) or whole multiple explanatory variables. The advantage of our r control chart is able to process both non-normal and correlated multivariate explanatory variables by employing a neural network model and deep learning model. We prove that the deep learning r control chart is relatively efficient to monitor the simulated and real binary response asymmetric data compared with r control chart of the generalized linear model (GLM) with probit and logit link functions and neural network r control chart.

scholarly journals Residual Control Chart for Binary Response with Multicollinearity Covariates by Neural Network Model

Symmetry ◽  
2020 ◽  
Vol 12 (3) ◽  
pp. 381 ◽  
Author(s):  
Jong-Min Kim ◽  
Ning Wang ◽  
Yumin Liu ◽  
Kayoung Park
Keyword(s):  
Neural Network ◽  
Control Chart ◽  
Network Model ◽  
Neural Network Model ◽  
Multivariate Data ◽  
Principal Component ◽  
Binary Response ◽  
Link Functions ◽  
The Neural Network ◽  
Input Variables

Quality control studies have dealt with symmetrical data having the same shape with respect to left and right. In this research, we propose the residual (r) control chart for binary asymmetrical (non-symmetric) data with multicollinearity between input variables via combining principal component analysis (PCA), functional PCA (FPCA) and the generalized linear model with probit and logit link functions, and neural network regression model. The motivation in this research is that the proposed control chart method can deal with both high-dimensional correlated multivariate data and high frequency functional multivariate data by neural network model and FPCA. We show that the neural network r control chart is relatively efficient to monitor the simulated and real binary response data with the narrow length of control limits.

scholarly journals Hyperspectral Image Classification Based on Multi-Scale Residual Network with Attention Mechanism

2021 ◽  
Vol 13 (3) ◽  
pp. 335
Author(s):  
Yuhao Qing ◽  
Wenyi Liu
Keyword(s):  
Neural Network ◽  
Deep Learning ◽  
Convolutional Neural Network ◽  
Image Classification ◽  
Classification Accuracy ◽  
Hyperspectral Image ◽  
Principal Component ◽  
Hyperspectral Image Classification ◽  
Deep Network ◽  
Multi Scale

In recent years, image classification on hyperspectral imagery utilizing deep learning algorithms has attained good results. Thus, spurred by that finding and to further improve the deep learning classification accuracy, we propose a multi-scale residual convolutional neural network model fused with an efficient channel attention network (MRA-NET) that is appropriate for hyperspectral image classification. The suggested technique comprises a multi-staged architecture, where initially the spectral information of the hyperspectral image is reduced into a two-dimensional tensor, utilizing a principal component analysis (PCA) scheme. Then, the constructed low-dimensional image is input to our proposed ECA-NET deep network, which exploits the advantages of its core components, i.e., multi-scale residual structure and attention mechanisms. We evaluate the performance of the proposed MRA-NET on three public available hyperspectral datasets and demonstrate that, overall, the classification accuracy of our method is 99.82 %, 99.81%, and 99.37, respectively, which is higher compared to the corresponding accuracy of current networks such as 3D convolutional neural network (CNN), three-dimensional residual convolution structure (RES-3D-CNN), and space–spectrum joint deep network (SSRN).

scholarly journals A Novel Design of Classification of Coronary Artery Disease Using Deep Learning and Data Mining Algorithms

2021 ◽  
Vol 35 (3) ◽  
pp. 209-215
Author(s):  
Pratibha Verma ◽  
Vineet Kumar Awasthi ◽  
Sanat Kumar Sahu
Keyword(s):  
Neural Network ◽  
Data Mining ◽  
Deep Learning ◽  
Regression Tree ◽  
Principal Component ◽  
Classification And Regression Tree ◽  
Support Vector ◽  
Data Mining Algorithms ◽  
R Programming ◽  
Hidden Layer

Data mining techniques are included with Ensemble learning and deep learning for the classification. The methods used for classification are, Single C5.0 Tree (C5.0), Classification and Regression Tree (CART), kernel-based Support Vector Machine (SVM) with linear kernel, ensemble (CART, SVM, C5.0), Neural Network-based Fit single-hidden-layer neural network (NN), Neural Networks with Principal Component Analysis (PCA-NN), deep learning-based H2OBinomialModel-Deeplearning (HBM-DNN) and Enhanced H2OBinomialModel-Deeplearning (EHBM-DNN). In this study, experiments were conducted on pre-processed datasets using R programming and 10-fold cross-validation technique. The findings show that the ensemble model (CART, SVM and C5.0) and EHBM-DNN are more accurate for classification, compared with other methods.

scholarly journals Research on Quality Anomaly Recognition Method Based on Optimized Probabilistic Neural Network

2020 ◽  
Vol 2020 ◽  
pp. 1-11 ◽  
Author(s):  
Li-li Li ◽  
Kun Chen ◽  
Jian-min Gao ◽  
Hui Li
Keyword(s):  
Neural Network ◽  
Genetic Algorithm ◽  
Control Chart ◽  
Control Charts ◽  
Probabilistic Neural Network ◽  
Principal Component ◽  
Original Data ◽  
Improved Genetic Algorithm ◽  
Training Time ◽  
Mixed Pattern

Aiming at the problems of the lack of abnormal instances and the lag of quality anomaly discovery in quality database, this paper proposed the method of recognizing quality anomaly from the quality control chart data by probabilistic neural network (PNN) optimized by improved genetic algorithm, which made up deficiencies of SPC control charts in practical application. Principal component analysis (PCA) reduced the dimension and extracted the feature of the original data of a control chart, which reduced the training time of PNN. PNN recognized successfully both single pattern and mixed pattern of control charts because of its simple network structure and excellent recognition effect. In order to eliminate the defect of experience value, the key parameter of PNN was optimized by the improved (SGA) single-target optimization genetic algorithm, which made PNN achieve a higher rate of recognition accuracy than PNN optimized by standard genetic algorithm. Finally, the above method was validated by a simulation experiment and proved to be the most effective method compared with traditional BP neural network, single PNN, PCA-PNN without parameters optimized, and SVM optimized by particle swarm optimization algorithm.

Implementation of Reduce AI-NN Model for Highly Accurate Blood Pressure Measurement

2020 ◽  
Author(s):  
Jerry Lin ◽  
Rajeev Kumar Pandey ◽  
Paul C.-P. Chao
Keyword(s):  
Neural Network ◽  
Blood Pressure ◽  
Measurement Accuracy ◽  
Blood Pressure Measurement ◽  
Principal Component ◽  
Data Sets ◽  
British Hypertension Society ◽  
The Neural Network ◽  
Pca Method ◽  
Highly Correlated

Abstract This study proposes a reduce AI model for the accurate measurement of the blood pressure (BP). In this study varied temporal periods of photoplethysmography (PPG) waveforms is used as the features for the artificial neural networks to estimate blood pressure. A nonlinear Principal component analysis (PCA) method is used herein to remove the redundant features and determine a set of dominant features which is highly correlated to the Blood pressure (BP). The reduce features-set not only helps to minimize the size of the neural network but also improve the measurement accuracy of the systolic blood pressure (SBP) and diastolic blood pressure (DBP). The designed Neural Network has the 5-input layer, 2 hidden layers (32 nodes each) and 2 output nodes for SBP and DBP, respectively. The NN model is trained by the PPG data sets, acquired from the 96 subjects. The testing regression for the SBP and DBP estimation is obtained as 0.81. The resultant errors for the SBP and DBP measurement are 2.00±6.08 mmHg and 1.87±4.09 mmHg, respectively. According to the Advancement of Medical Instrumentation (AAMI) and British Hypertension Society (BHS) standard, the measured error of ±6.08 mmHg is less than 8 mmHg, which shows that the device performance is in grade “A”.

scholarly journals Neural Network Application to Support Regression Model in Forecasting Single-Sectional Demand for Telecommunications Services

2018 ◽  
Vol 18 (2) ◽  
pp. 159-177
Author(s):  
Paweł Kaczmarczyk
Keyword(s):  
Neural Network ◽  
Regression Model ◽  
Research Work ◽  
Neural Model ◽  
Integrated Model ◽  
Short Term ◽  
Explanatory Variables ◽  
Network Application ◽  
Telecommunications Services ◽  
Short Term Forecasting

Abstract The aim of this research study is to test the effectiveness of the single-sectional integrated model, in which a neural network is applied to support a regression, as a consistent tool for short-term forecasting of hourly demand (in sec.) for telecommunications services. The theoretical part of the paper involves the idea of the single-sectional integrated model and differences between this model and a multi-sectional integrated model. Moreover, the research methodology is described, i.e. the elements used in the constructed model (the feedforward neural model and the regression with dichotomous explanatory variables), and the manner of their integration are discussed. In the empirical part of this work, the results of the carried out experiments are included. The comparison of the obtained effectiveness (in terms of approximation and prediction) of the explored single-sectional integrated model with the effectiveness of the non-supported regression model and the multi-sectional integrated model are conducted. In this research work, it is proved that the single-sectional integrated model enables better results in comparison to the non-integrated regression and the mutli-sectional integrated model. The originality of this paper is based on: the created single-sectional integrated model in terms of the analysed phenomenon, the verification of the model effectiveness, and the comparison of the constructed model with other models and assessment.

scholarly journals A new modified ridge-type estimator for the beta regression model: simulation and application

2021 ◽  
Vol 7 (1) ◽  
pp. 1035-1057
Author(s):  
Muhammad Nauman Akram ◽  
◽  
Muhammad Amin ◽  
Ahmed Elhassanein ◽  
Muhammad Aman Ullah ◽  
...  
Keyword(s):  
Regression Model ◽  
Mean Squared Error ◽  
Model Simulation ◽  
Beta Regression ◽  
Explanatory Variables ◽  
Squared Error ◽  
The Matrix ◽  
Biased Estimators ◽  
Highly Correlated ◽  
Beta Regression Model

<abstract> <p>The beta regression model has become a popular tool for assessing the relationships among chemical characteristics. In the BRM, when the explanatory variables are highly correlated, then the maximum likelihood estimator (MLE) does not provide reliable results. So, in this study, we propose a new modified beta ridge-type (MBRT) estimator for the BRM to reduce the effect of multicollinearity and improve the estimation. Initially, we show analytically that the new estimator outperforms the MLE as well as the other two well-known biased estimators i.e., beta ridge regression estimator (BRRE) and beta Liu estimator (BLE) using the matrix mean squared error (MMSE) and mean squared error (MSE) criteria. The performance of the MBRT estimator is assessed using a simulation study and an empirical application. Findings demonstrate that our proposed MBRT estimator outperforms the MLE, BRRE and BLE in fitting the BRM with correlated explanatory variables.</p> </abstract>

scholarly journals PREDICTING LAND PRICES AND MEASURING UNCERTAINTY BY COMBINING SUPERVISED AND UNSUPERVISED LEARNING

2021 ◽  
Vol 25 (2) ◽  
pp. 169-178
Author(s):  
Changro Lee
Keyword(s):  
Neural Network ◽  
Principal Component Analysis ◽  
Deep Learning ◽  
Unsupervised Learning ◽  
Supervised Learning ◽  
Hybrid Approach ◽  
Principal Component ◽  
Component Analysis ◽  
Land Prices ◽  
Property Valuation

Despite the popularity deep learning has been gaining, measuring the uncertainty within the result has not met expectations in many deep learning applications and this includes property valuation. In real-world tasks, however, rather than simply requiring predictions, assurance of the certainty of the predictions is also demanded. In this study, supervised learning is combined with unsupervised learning to bridge this gap. A method based on principal component analysis, a popular tool of unsupervised learning, was developed and used to represent the uncertainty in property valuation. Then, a neural network, a representative algorithm to implement supervised learning, was constructed, and trained to predict land prices. Finally, the uncertainty that was measured using principal component analysis was incorporated into the price predicted by the neural network. This hybrid approach is shown to be likely to improve the credibility of the valuation work. The findings of this study are expected to generate interest in the integration of the two learning approaches, thereby promoting the rapid adoption of deep learning tools in the property valuation industry.

scholarly journals Comparison between normal weights and conditional Bays weights in Iterative principal component estimators

2018 ◽  
Vol 24 (109) ◽  
pp. 535
Author(s):  
اياد حبيب شمال
Keyword(s):  
Logistic Regression ◽  
Renal Disease ◽  
Regression Model ◽  
Nonlinear Regression ◽  
Logistic Regression Model ◽  
Principal Component ◽  
Binary Response ◽  
Nonlinear Regression Model ◽  
Qualitative Variable ◽  
Mean Error

Abstract: This paper discusses the problem of semi maulticollinearity in the nonlinear regression model (the multi-logistic regression model) When the dependent variable is a qualitative variable, the binary response is either equal to one for a response or zero for no response, Through the use of Iterative principal component estimatorsWhich are based on the normal weights and conditional Bays weights . If the appliede Estimates this model Through the use of two types of drugs concentrations thy concentration of ciprodar (variable X1) On a number of people with Patients with renal disease represent the dependent variable (The person heals from the disease  , The person has not recovered from the disease )from through Mean Error Squares (MSE) The results were indicative of Iterative principal component estemaite   Depending on the conditional Bays weights prefer the Iterative principal component estimators Depending on the the normal weights.

scholarly journals Multi-Granularity Neural Network Encoding Method for Land Cover and Land Use Image Classification

2021 ◽  
Author(s):  
Guoyin Wang ◽  
Musabe Jean Bosco ◽  
Hategekimana Yves
Keyword(s):  
Neural Network ◽  
Remote Sensing ◽  
Land Use ◽  
Deep Learning ◽  
Land Cover ◽  
Large Scale ◽  
Principal Component ◽  
Classification Problem ◽  
Video Data ◽  
Learning Tools

Deep learning classification is the state-of-the-art of machine learning approach. Earlier work proves that the deep convolutional neural network has successfully and brilliantly in different applications such as images or video data. Recognizing and clarifying the remote sensing aspect of the earth's surface and exploit land cover and land use (LCLU). First, this article summarized the remote sensing emerging application and challenges for deep learning methods. Second, we propose four approaches to learn efficient and effective CNNs to transfer image representation on the ImageNet dataset to recognize LCLU datasets. We use VGG16, Inception-ResNet-V2, Inception-V3, and DenseNet201 models to extract features from the EACC dataset. We use pre-trained CNNs on ImageNet to extract features. For feature selection we proposed principal component analysis (PCA) to improve accuracy and speed up the model. We train our model by multi-layer perceptron (MLP) as a classifier. Lastly, we apply the multi-granularity encoding ensemble model. We achieve an overall accuracy of 92.3% for the nine-class classification problem. This work will help remote sensing scientists understand deep learning tools and apply them in large-scale remote sensing challenges

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