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Improved Artificial Neural Network Classification Model based Metaheuristic Optimization for Handwritten Character Recognition

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 identifi...

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Main Authors: Muhammad Arif, Mohamad, Muhammad Aliif, Ahmad
Format: Article
Language:English
Published: Asian Scholars Network 2024
Subjects:
QA75 Electronic computers. Computer science
T Technology (General)
Online Access:http://umpir.ump.edu.my/id/eprint/41158/1/Improved%20Artificial%20Neural%20Network%20Classification%20Model%20based%20Metaheuristic%20Optimization%20for%20Handwritten%20Character%20Recognition.pdf
http://umpir.ump.edu.my/id/eprint/41158/
https://myjms.mohe.gov.my/index.php/ijarei/article/view/26293
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spelling my.ump.umpir.411582024-05-13T07:43:12Z http://umpir.ump.edu.my/id/eprint/41158/ Improved Artificial Neural Network Classification Model based Metaheuristic Optimization for Handwritten Character Recognition Muhammad Arif, Mohamad Muhammad Aliif, Ahmad QA75 Electronic computers. Computer science T Technology (General) 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. Asian Scholars Network 2024-03 Article PeerReviewed pdf en cc_by_4 http://umpir.ump.edu.my/id/eprint/41158/1/Improved%20Artificial%20Neural%20Network%20Classification%20Model%20based%20Metaheuristic%20Optimization%20for%20Handwritten%20Character%20Recognition.pdf 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) https://myjms.mohe.gov.my/index.php/ijarei/article/view/26293
institution Universiti Malaysia Pahang Al-Sultan Abdullah
building UMPSA Library
collection Institutional Repository
continent Asia
country Malaysia
content_provider Universiti Malaysia Pahang Al-Sultan Abdullah
content_source UMPSA Institutional Repository
url_provider http://umpir.ump.edu.my/
language English
topic QA75 Electronic computers. Computer science
T Technology (General)
spellingShingle QA75 Electronic computers. Computer science
T Technology (General)
Muhammad Arif, Mohamad
Muhammad Aliif, Ahmad
Improved Artificial Neural Network Classification Model based Metaheuristic Optimization for Handwritten Character Recognition
description 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.
format Article
author Muhammad Arif, Mohamad
Muhammad Aliif, Ahmad
author_facet Muhammad Arif, Mohamad
Muhammad Aliif, Ahmad
author_sort Muhammad Arif, Mohamad
title Improved Artificial Neural Network Classification Model based Metaheuristic Optimization for Handwritten Character Recognition
title_short Improved Artificial Neural Network Classification Model based Metaheuristic Optimization for Handwritten Character Recognition
title_full Improved Artificial Neural Network Classification Model based Metaheuristic Optimization for Handwritten Character Recognition
title_fullStr Improved Artificial Neural Network Classification Model based Metaheuristic Optimization for Handwritten Character Recognition
title_full_unstemmed Improved Artificial Neural Network Classification Model based Metaheuristic Optimization for Handwritten Character Recognition
title_sort improved artificial neural network classification model based metaheuristic optimization for handwritten character recognition
publisher Asian Scholars Network
publishDate 2024
url http://umpir.ump.edu.my/id/eprint/41158/1/Improved%20Artificial%20Neural%20Network%20Classification%20Model%20based%20Metaheuristic%20Optimization%20for%20Handwritten%20Character%20Recognition.pdf
http://umpir.ump.edu.my/id/eprint/41158/
https://myjms.mohe.gov.my/index.php/ijarei/article/view/26293
_version_ 1822924312222367744
score 13.235362

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