## 2 Literature Review
Experiment 2, which demonstrated 35.7% keystroke reserves for a crossbreed of n-gram categorizations with a back-off flattening model, produced the best results. Future research and other natural language processing (NLP) applications, like texting on a mobile device and helping individuals with disabilities, can benefit from this technology. Work [23] suggests that natural language processing (NLP) applications, like helping individuals with disabilities and texting on a mobile device, can be supported by this technology and used for future research. In the field of language modeling, this study investigates the use of deep learning methods, particularly temporal convolutional networks and recurrent neural networks, to predict the next word. The researchers obtained an accuracy of 71.51% for the RNN model and 65.20% for the TCN model using three databases: Coursera Swiftkey, the book _Writings of [PERSON]: Volume 1_ by [PERSON], and the News category from the [PERSON] corpus in the NLTK library. The findings imply that in language modeling, TCN architecture and RNN architecture compete. They advise looking into more recent deep learning mobile platforms. The study does not examine NWP for Arabic datasets due to resource constraints. The study evaluates current autoregressive language models (LMs) for their accuracy in human incremental processing and reading time prediction. They propose Cloze Distillation, a method for distilling linguistic information into pre-trained LMs, which improves analysis period forecasting and generalizes to finite cloze data [24]. The purpose of paper [25] is to examine the utility of deep learning models for Urdu's next-word calculation and proposition model. It suggests two word prediction models: BERT, which is specially made for natural language modeling, and LSTM, which is used for neural language modeling. The study trained these prototypes on a corpus of 1.1 million sentences written in Urdu. The BERT model performed better than the other two pre-trained models, whereas the LSTM model had an accuracy of 52.4%. Additionally, the study demonstrates how changing the input context's window size can impact performance. With the usage of next-word forecast technology, typing can be made easier by providing the next word. However, the absence of n-grams and a Kurdish text corpus presents difficulties for the language. Paper [26] presents a novel investigative work on next-word guessing for Kurdish Sorani and Kurmanji, with an accuracy rate of 96.3% and a reduction in typing time using an n-gram model. The aim of research study [27] is to create a new word predictor for Tigrigna text entry that will cut down on keystrokes by 55.06%. A probabilistic language model that combines unigram, bigram, trigram, and quadgram predictors is used by the predictor model. This helps people with limited vocabulary avoid spelling errors and saves over 50% of the time spent writing a document in Tigrigna, according to the study. Still, there are difficulties in Tigrigna word prediction, like figuring out affixes that might be either words or letters. The study intends to support applications such as texting on a mobile phone or PDA, handwriting recognition, and helping individuals with disabilities. Deep learning methods are presented in work [28]. Long Short-Term Memory and Bidirectional Long Short-Term Memory are the core neural network architectures used in this work. The model developed in this work had the highest accuracy among neural network-based methods and outperformed previous approaches using the IITB English-Hindi parallel corpus. Next-word prediction, compared with other algorithms, is mentioned in [29]. The paper aims to achieve state-of-the-art performance on most Arabic natural language processing (NLP) tasks by presenting a pre-trained BERT model for the Arabic language. The model achieved state-of-the-art results when compared to other approaches and Google's multilingual BERT.