Muhammad Arif, Mohamad and Muhammad Aliif, Ahmad (2024) Improved Artificial Neural Network Classification Model based Metaheuristic Optimization for Handwritten Character Recognition. International Journal of Advanced Research in Engineering Innovation (IJAREI), 6 (1). pp. 52-60. ISSN 2682-8499. (Published)
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Abstract
This study addresses the concerns regarding the performance of Handwritten Character Recognition (HCR) systems, focusing on the classification stage. It is widely acknowledged that the development of the classification model significantly impacts the overall performance of HCR. The problems identified specifically pertain to the classification model, particularly in the context of the Artificial Neural Network (ANN) learning problem, leading to low accuracy in recognizing handwritten characters. The objective of this study is to improve and refine the ANN classification model to achieve better HCR. To achieve this goal, this study proposed a hybrid Flower Pollination Algorithm with Artificial Neural Network (FPA-ANN) classification model for HCR. The FPA is one of the metaheuristic approaches is utilized as an optimization technique to enhance the performance of ANN, particularly by optimizing the network training process of ANN. The experimentation phase involves using the National Institute of Standards and Technology (NIST) handwritten character database. Finally, the proposed FPA-ANN classification model is analysed based on generated confusion matrix and evaluated performance of the classification model in terms of precision, sensitivity, specificity, F-score and accuracy.
| Item Type: | Article |
|---|---|
| Uncontrolled Keywords: | Metaheuristic, Machine Learning, Optimization, Flower Pollination Algorithm, Artificial Neural Network, Handwritten Character Recognition |
| Subjects: | Q Science > QA Mathematics > QA75 Electronic computers. Computer science T Technology > T Technology (General) |
| Faculty/Division: | Faculty of Computing |
| Depositing User: | Miss Amelia Binti Hasan |
| Date Deposited: | 13 May 2024 07:43 |
| Last Modified: | 13 May 2024 07:43 |
| URI: | http://umpir.ump.edu.my/id/eprint/41158 |
| Download Statistic: | View Download Statistics |
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