A methodology building for multilayer feed-forward neural network (MLFFNN): an application in biometry modelling
This research aims to develop a hybrid method for Multi-Layer Feed-Forward Neural Network (MLFFNN) with two different approaches; (i) Multiple Logistic Regression (MLogisticR) for the first method, (ii) Multiple Linear Regression (MLinearR) for the second method. The developed hybrid method is ba...
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| Main Author: | |
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| Format: | Thesis |
| Language: | English |
| Published: |
2023
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| Subjects: | |
| Online Access: | http://eprints.usm.my/58898/1/MOHAMAD%20NASARUDIN%20BIN%20ADNAN-FINAL%20TESIS%20P-SGM000822%28R%29%20-24%20pages.pdf |
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| Summary: | This research aims to develop a hybrid method for Multi-Layer Feed-Forward
Neural Network (MLFFNN) with two different approaches; (i) Multiple Logistic
Regression (MLogisticR) for the first method, (ii) Multiple Linear Regression
(MLinearR) for the second method. The developed hybrid method is based on
bootstrap, regression, and MLFFNN. In the first method, the accuracy of the developed
method is measured based on the value of the Mean Squared Error Neural Network
(MSE.net), Mean Absolute Deviance (MAD), and the accuracy percentage. While for
the second method, Mean Squared Error Neural Network (MSE.net) and R2 will be
used to evaluate the performance of the proposed method. All those components serve
as a yardstick to determine the accuracy and efficiency of the developed model.
Existing software only produces limited results. The main focus of this study is the
need for better decision-making with solid evidence. The main goal of this research is
to build a hybrid method and generate a numerical result and visualization (graphical
representation). The results from both case studies show that the hybrid method has
successfully improved the accuracy, effectiveness, and efficiency of parameter
estimation in the final results of the analysis. The findings of this study contribute to the development of a comprehensive research methodology in future and suggest more
accurate results for the decision-making process. |
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