Neuro-FGA Based Machine Translation System for Sanskrit to Hindi Language

Abstract

Today is the era of technology and in this various automated systems are used for different purposes. Machine translation systems (MTSs) are also very popular now-a-days and it is used for both personal and professional works. As it is on high demand so these systems needs to be very effective in terms of their performance. For this, a number of MTSs were proposed for different languages. Here in this paper, a new hybrid MTS is proposed to achieve optimal performance based on Neural and Genetic Algorithm (GA) based Fuzzy inference System. In this hybrid system, fuzzy is used for rule generation where GA helps to optimize the rules generated by fuzzy and collaboratively it is known as GA based Fuzzy inference System (FGA) then neural is used to train the system and then it will be matched with the rules generated by FGA for translation. The translation of the Sanskrit to Hindi language is done by using this proposed MTS and its performance is analyzed on the basis of different parameters.