Field-embedded database query system based on natural language processing

This research seeks to develop a paradigm that will improve user-database interaction. To convert the user's queries into structured query language (SQL), natural language processing (NLP) is needed, and then the SQL can be processed quickly by the query system in the embedded database. The primary goal of NLP is to facilitate human-computer interaction with little reliance on programming knowledge. To access the data efficiently, field embedded database query system (FEDQS) uses NLP to take in 2880 structured queries about train prices and seat availability from the train reservation database and turn them into a SQL query. Therefore, field embedded database query system (FEDQS) is suggested in this research to help the users access the data efficiently. The simulation findings show that the proposed method achieves a translation accuracy of 92%, precision of 91%, RMSE of 7%, and MAE of 9%.