## 2 Literature Review
Reducing the output look-up table size also improved the model's performance. In research study [16] for smartphone users, a hybrid language model called RNN-LM is suggested to enhance Japanese text prediction. To predict subsequent words, this model combines an n-gram model with a recurrent neural network (RNN). Long Short-Term Memory networks (LSTMs) are used to connect the input, output, and hidden layers of the model, which works best in a client-server architecture. The model is 10% less confusing than traditional models and is compact. It is now part of IME, Flick, which, in a Japanese text input experiment, beats Mozc by 16% in time and 34% in keystrokes. In article [17], many language tools are available in Ukrainian, and next-word prediction is essential for users. Unlike T9, LSTM and Markov chains are selected for this task because of their sequential nature and capacity to produce multiple words in conjunction with their hybrid model. The goal of work [18], on the Long Short-Term Memory (LSTM) network model for instantaneous messaging, is to predict the word or words that will come after a collection of modern terms. Research study [19] uses a transcript of the Assamese language based on the International Phonetic Association (IPA) chart; the model achieves an accuracy of 72.10% for a phonetic transcript of the Assamese language and 88.20% for Assamese text, especially during phonetic typing. Next-word prediction is crucial for typing subsystems and digital document writing. Researchers are exploring ways to improve this capability using natural language processing (NLP) practices like word embedding and successive contextual modeling. This paper compares embedding methods for Bengali next-word prediction, comparing word2 vec skip-gram, word2 vec CBOW, fastText skip-gram, and fastText CBOW. The results provide insights into contextual and sequential information gathering, enabling the implementation of a context-based Bengali next-word prediction system. Next-word prediction, also known as language modeling, is a machine learning application that uses Long Short-Term Memory (LSTM) and other models to predict the next word. LSTMs have a built-in mechanism to prevent overfitting and use activation functions like ReLU and softmax to introduce non-linearity. Combining techniques like pre-training, advanced architectures, and large datasets can improve performance. The paper discusses different approaches to achieve the best results. In reference [20], a large-scale language model (LM) study on 50 different languages is presented, with an emphasis on sub-word-level data. In order to incorporate sub-word-level information into semantic word vectors and train neural language models, the researchers offer a novel approach. The study offers new benchmarks for LM and demonstrates significant gains in perplexity across all 50 languages, particularly morphologically rich ones. The datasets and code are openly accessible. This paper discusses recurrent neural networks, introducing LSTM as an effective model and recommending a language model based on LSTM for character prediction. Comparing LSTM with standard RNN models reveals significant potential in character prediction. Paper [21] discusses the development of recurrent neural networks (RNNs) and introduces LSTM, a more effective model. It recommends a language model based on RNNs for character prediction. Paper [22] compares LSTM and standard RNN models, highlighting their potential in character prediction. The program runs on TensorFlow, demonstrating RNNs' sequential processing ability. The goal of the project is to use the n-gram language model, a statistical machine learning technique, to create a Ge'ez word prediction model. The model predicts likely stem/root words and morphological features using affixes and morphological datasets. The keystroke savings are used as a metric to assess the model. Three experiments were carried out as part of the study, using smaller and larger datasets in experiments 1, 2, and 3.