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Word Prediction Using Fuzzy Sets and Computational Intelligence
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Author(s): D. Rajalakshmi (SASTRA University, India), G. Revathy (SASTRA University, India), V. Prakash (SASTRA University, India)and R. Bhavani (SASTRA University, India)
Copyright: 2025
Pages: 28
Source title:
Enhancing Communication and Decision-Making With AI
Source Author(s)/Editor(s): Arul Kumar Natarajan (Samarkand International University of Technology, Uzbekistan), Mohammad Gouse Galety (Samarkand International University of Technology, Uzbekistan), Celestine Iwendi (University of Bolton, UK), Deepthi Das (Christ University, India)and Achyut Shankar (University of Warwick, UK)
DOI: 10.4018/979-8-3693-9246-1.ch011
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Abstract
Fuzzy soft sets find an extensive assortment of applications in many decision-making problems. Fuzzy sets are applied to overcome uncertainty, and soft sets came into existence as an extension. It is called soft because the boundary depends on the parameters. Soft sets are the generalization of fuzzy sets. Parameter reduction in soft sets shows an energetic role in reducing the complexity of any decision-making problems. Fuzzy soft sets are applied to identify a word. The set of alphabets is taken as the universal set, and the words formed using these alphabets give the parameter set. The length of the word is first calculated. The first three letters of the word to be identified are given as the input. The parameter set consists of words of the same length, and the first three letters are generated using NLG. The word is identified using machine learning of word classification and prediction. The results are compared, and the final accuracy is calculated.
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