Application of artificial neural network for estimating the qualitative characteristics of cantaloupe melon and comparison with the regression model

AbstractRegression models are often used to predict age of an individual based on methylation patterns. Artificial neural network (ANN) however was recently shown to be more accurate for age prediction. Additionally, the impact of ethnicity and sex on our previous regression model have not been studied. Furthermore, there is currently no age prediction study investigating the lower limit of input DNA at the bisulfite treatment stage prior to pyrosequencing. Herein, we evaluated both regression and ANN models, and the impact of ethnicity and sex on age prediction for 333 local blood samples using three loci on the pyrosequencing platform. Subsequently, we trained a one locus-based ANN model to reduce the amount of DNA used. We demonstrated that the ANN model has a higher accuracy of age prediction than the regression model. Additionally, we showed that ethnicity did not affect age prediction among local Chinese, Malays and Indians. Although the predicted age of males were marginally overestimated, sex did not impact the accuracy of age prediction. Lastly, we present a one locus, dual CpG model using 25 ng of input DNA that is sufficient for forensic age prediction. In conclusion, the two ANN models validated would be useful for age prediction to provide forensic intelligence leads.

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Performance comparison of artificial neural network and logistic regression model for differentiating lung nodules on CT scans

Expert Systems with Applications ◽

10.1016/j.eswa.2012.04.001 ◽

2012 ◽

Vol 39 (13) ◽

pp. 11503-11509 ◽

Cited By ~ 32

Author(s):

Hui Chen ◽

Jing Zhang ◽

Yan Xu ◽

Budong Chen ◽

Kuan Zhang

Keyword(s):

Neural Network ◽

Artificial Neural Network ◽

Logistic Regression ◽

Regression Model ◽

Logistic Regression Model ◽

Performance Comparison ◽

Ct Scans ◽

Lung Nodules ◽

Artificial Neural

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The Artificial Neural Network and Regression Analysis Approach to Prediction of the Fiber Diameter of Spunbonding Fabrics

Applied Mechanics and Materials ◽

10.4028/www.scientific.net/amm.33.74 ◽

2010 ◽

Vol 33 ◽

pp. 74-78

Author(s):

B. Zhao

Keyword(s):

Neural Network ◽

Artificial Neural Network ◽

Regression Model ◽

Network Model ◽

Process Parameters ◽

Neural Network Model ◽

Artificial Neural Network Model ◽

Fiber Diameter ◽

Statistical Regression ◽

Artificial Neural

In this work, the artificial neural network model and statistical regression model are established and utilized for predicting the fiber diameter of spunbonding nonwovens from the process parameters. The artificial neural network model has good approximation capability and fast convergence rate, which is used in this research. The results show the artificial neural network model can provide quantitative predictions of fiber diameter and yield more accurate and stable predictions than the statistical regression model, which reveals that the artificial neural network model is based on the inherent principles, and it can yield reasonably good prediction results and provide insight into the relationship between process parameters and fiber diameter.

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Artificial neural network and regression models for prediction of sewing thread consumption for multilayered fabric assembly at lockstitch 301 seam

Research Journal of Textile and Apparel ◽

10.1108/rjta-09-2020-0103 ◽

2021 ◽

Vol ahead-of-print (ahead-of-print) ◽

Author(s):

Md Vaseem Chavhan ◽

M. Ramesh Naidu ◽

Hayavadana Jamakhandi

Keyword(s):

Neural Network ◽

Artificial Neural Network ◽

Regression Model ◽

Regression Models ◽

Network Models ◽

Quadratic Regression ◽

Neural Network Models ◽

Content Type ◽

Sewing Thread ◽

Artificial Neural

Purpose This paper aims to propose the artificial neural network (ANN) and regression models for the estimation of the thread consumption at multilayered seam assembly stitched with lock stitch 301. Design/methodology/approach In the present study, the generalized regression and neural network models are developed by considering the fabric types: woven, nonwoven and multilayer combination thereof, with basic sewing parameters: sewing thread linear density, stitch density, needle count and fabric assembly thickness. The network with feed-forward backpropagation is considered to build the ANN, and the training function trainlm of MATLAB software is used to adjust weight and basic values according to the optimization of Levenberg Marquardt. The performance of networks measured in terms of the mean squared error and the layer output is set according to the sigmoid transfer function. Findings The proposed ANN and regression model are able to predict the thread consumption with more accuracy for multilayered seam assembly. The predictability of thread consumption from available geometrical models, regression models and industrial empirical techniques are compared with proposed linear regression, quadratic regression and neural network models. The proposed quadratic regression model showed a good correlation with practical thread consumption value and more accuracy in prediction with an overall 4.3% error, as compared to other techniques for given multilayer substrates. Further, the developed ANN network showed good accuracy in the prediction of thread consumption. Originality/value The estimation of thread consumed while stitching is the prerequisite of the garment industry for inventory management especially with the introduction of the costly high-performance sewing thread. In practice, different types of fabrics are stitched at multilayer combinations at different locations of the stitched product. The ANN and regression models are developed for multilayered seam assembly of woven and nonwoven fabric blend composition for better prediction of thread consumption.

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Reliability Assessment of Wafer Level Package using Artificial Neural Network Regression Model

Journal of Mechanics ◽

10.1017/jmech.2019.20 ◽

2019 ◽

Vol 35 (6) ◽

pp. 829-837 ◽

Cited By ~ 5

Author(s):

P. H. Chou ◽

K.N. Chiang ◽

Steven Y. Liang

Keyword(s):

Neural Network ◽

Artificial Neural Network ◽

Regression Model ◽

Electronic Packaging ◽

Design Parameters ◽

Simulation Technology ◽

Wafer Level ◽

Structure Reliability ◽

Artificial Neural ◽

Packaging Structure

ABSTRACTFor electronic packaging structure, there are many design parameters that will affect its reliability performance, using experimental way to obtain the reliability result will take a considerable amount of time. Therefore, how to shorten the design time becomes a critical issue for new electronic packaging structure development. This research will combine artificial intelligence (AI) and simulation technology to assess the long-term reliability of wafer level packaging (WLP). A simulation technology using finite element method (FEM) with appropriate mechanics theories has been validated by multiple experiments will replace the experiment to create reliability results for different WLP structures. After a big WLP structure-reliability database created, this study will apply artificial neural network (ANN) theory to analyze this database and obtains a regression model for structure-reliability relationship of WLP. Once the regression model is established and validated, the WLP geometry, such as pad size, die and buffer layer thickness, and solder volume, etc. can be simply entered, and then the WLP reliability results can be immediately obtained through the ANN regression model.

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