## 3 Related Work
In [39], N-Gram was integrated with cosine similarity in an AES system. It weighs terms using term frequency-inverse document frequency (TF \(-\) IDF). The level of agreement between the teacher's responses and the students' responses is calculated. Exam scores will be determined by the cosine value of this procedure. N-Gram is used to overcome the default of cosine similarity, so word order is taken into consideration. When compared to other methods, such as LSA and similarity by cosine, server performance was shown to be favorable due to reduced CPU consumption and accelerated load times. Despite this, the system remained stuck with the drawbacks of N-gram and cosine similarity. N-gram has also been proven to be better for calculating similarity in short sentences only, and cosine similarity does not take word order into consideration. Using a fuzzy semantic optimal control retrieval approach to extract useful data from an English information table was proposed by the authors in [40] to improve the AES model's accuracy. This study uses a two-stage training method. First, standard English-to-English translation data were used to build the model. Then, English-to-English translation data, which include fuzzy semantic information, are used to adjust the model for the best control. The model is split into two parts: a sequence-generating network that can forecast the probabilities of individual words and an assessment network that may predict the final results of sentences. In order to increase the reliability of automatic writing scoring systems, the authors in [41] devised a methodology to use deep learning methods in a wireless network environment. The results of the experiments show that the technique is effective in evaluating the writing skills of English as a second language (ESL) students, opening the door to the development of an automatic composition grading system for use in massive online machine exams and self-study websites. The primary goals of the study reported in [42] are the proposal and training of a long-short-term memory (LSTM) model on a collection of hand evaluated essays with scores. The purpose of sentiment analysis is to establish whether an article is positive, negative, or neutral. A sentiment algorithm is constructed using the Twitter sample dataset, and the student's opinion towards a subject is used to analyze the sentiment. All essays also go through a plagiarism check and a check for grammatical mistakes to ensure originality. The essay's quality, number of syntactical mistakes, proportion of copying, and general tone all factor into the final score. Students receive constructive criticism in the form of a revised essay. For automatically scoring spoken Chinese-English translation, the authors of [43] propose using attention LSTM. Semantic terms, phrase drift, and naturalness of speech were prioritized as primary criteria for evaluation. In this article, we employ the synonym discrimination technique to isolate the terms that are essentially the same in all the exam takers' answers. The attention LSTM model is applied at the sentence level in order to analyze the examinees' ability to convey the main idea of the sentence in their translation. Lastly, a person's level of ease in speaking is evaluated according to their tempo, rate, and spread of speech. Each of these three factors contributes to the ultimate translation quality score, which is calculated by adding up their respective weights. The testing results demonstrate that, in comparison to other techniques, the suggested approach yields outcomes that are in excellent accord with those obtained through human scoring. In [44], researchers investigate human-machine joint grading of subjective assignments using a three-way decision-making process (TA-S3 WD). To begin, the authors suggested a TA-S3 WD model for human-machine task distribution to set up a formal structure for human-machine joint grading of subjective assignments.