Open Access

ARTICLE

Recognizing Ancient South Indian Language Using Opposition Based Grey Wolf Optimization

A. Naresh Kumar^1,*, G. Geetha²

1 Department of CSE, Anna University, Chennai, 600025, Tamilnadu, India
2 Department of CSE, Agni College of Technology, Chennai, 600130, Tamilnadu, India

* Corresponding Author: A. Naresh Kumar. Email:

Intelligent Automation & Soft Computing 2023, 35(3), 2619-2637. https://doi.org/10.32604/iasc.2023.028349

Received 08 February 2022; Accepted 15 March 2022; Issue published 17 August 2022

Abstract

Recognizing signs and fonts of prehistoric language is a fairly difficult job that requires special tools. This stipulation make the dispensation period overriding, difficult and tiresome to calculate. This paper present a technique for recognizing ancient south Indian languages by applying Artificial Neural Network (ANN) associated with Opposition based Grey Wolf Optimization Algorithm (OGWA). It identifies the prehistoric language, signs and fonts. It is an apparent from the ANN system that arbitrarily produced weights or neurons linking various layers play a significant role in its performance. For adaptively determining these weights, this paper applies various optimization algorithms such as Opposition based Grey Wolf Optimization, Particle Swarm Optimization and Grey Wolf Optimization to the ANN system. Performance results are illustrated that the proposed ANN-OGWO technique achieves superior accuracy over the other techniques. In test case 1, the accuracy value of OGWO is 94.89% and in test case 2, the accuracy value of OGWO is 92.34%, on average, the accuracy of OGWO achieves 5.8% greater accuracy than ANN-GWO, 10.1% greater accuracy than ANN-PSO and 22.1% greater accuracy over conventional ANN technique.

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Keywords

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