Performance and Improvement of Linguistic Data Analysis on Different Languages Using Deep Learnedness Techniques

Venkateswaran Radhakrishnan; Asadi Srinivasulu; Suresh Palarimath; Rogelio Gutierrez; Praveen Kumar C

doi:10.55524/ijircst.2023.11.6.6

Abstract

India is an assorted country with various kinds of societies in each edge of the country. This is an organization of 30 states and 8 association regions with various dialects and particular social legacy. Thus, there have been many discussions on the beginning of the constitution, and the worry of the public language. It is said about the language of India, "The language expressed in India changes very much like the flavour of water changes in India each couple of kilometres". Nonetheless, India does not have a public language as there is an impressive contrast between an authority language and a public language. infusion synopsis of message archives generally comprises of positioning the record string of words and separating the highest-level string of words subject to the rundown dimension requirements. We investigate and commitment of different directed acquisition calculations to the string of words positioning assignment. That is the reason, we present an original string of words positioning philosophy in view of the similitude score between an up-and-comer string of words and touchstone rundowns. The famous direct relapse framework accomplished the high-grade outcomes in undeniably assessed datasets. Moreover, the direct relapse framework, which included Part-of-Speech supported highlights, beat the same with factual elements as it were. The proposed framework beat than the current framework with boundaries/measurements as precision (81.43%), blunder rate (0.13), val_loss (0.41), val_accuracy (0.50), size of dataset utilized in research (1.30 GB), No. of ages (50), Time-intricacy (O(n2)) and execution time (1012 ms).

Keywords

Linguistics Data, Autoencoders, Deep learnedness, Prediction, CNN, RNN, ECNN, ERNN, Data minelaying, Feature Selection, Data Pre-processing.

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Cites this article as

V. Radhakrishnan, A. Srinivasulu, S. Palarimath, R. Gutierrez, P. K. C, "Performance and Improvement of Linguistic Data Analysis on Different Languages Using Deep Learnedness Techniques", International Journal of Innovative Research in Computer Science and Technology (IJIRCST), Vol-11, no.6, pp.32-38, 2023. Available from: https://doi.org/10.55524/ijircst.2023.11.6.6

Corresponding Author

Venkateswaran Radhakrishnan

Faculty- Information Technology Department, College of Computing and Information Sciences, University of Technology and Applied Sciences-Salalah, Oman

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