Bi-directional Long Short-Term Memory with Gated Recurrent Unit Approach for Next Word Prediction in Bodo Language

Next word prediction, an essential task in neural language processing, holds significant importance across various applications like text completion, suggestion system, and machine translation. Within this domain, Recurrent Neural Network (RNN), particularly the Bi-LSTM (Bidirectional Long Short-Term Memory) and GRU (Gated Recurrent Unit).In this study, we introduce a hybrid model that combines Bi-LSTM and GRU for next word prediction specifically in English. Our objective is to leverage the strengths of both architectures to enhance the model’s capability to capture contextual dependencies, thereby improving the accuracy of predictions. Word prediction is a commitment to prognosticate what word will come incontinently. It’s one of the main tasks of NLP and has numerous operations. Our thing is to make this model prognosticate the coming word as snappily as possible in the minimal quantum of time. Since RNN is a long-term short-term memory, it’ll understand the once textbook and prognosticate words, which can be helpful for druggies to make rulings, and this fashion uses letter-by-letter vaticination, which means it predicts letter-by-letter to form a word. Next-word vaticination is helpful for druggies and helps them type more directly and briskly.