Assessment in the Education system plays a significant role in judging student performance. The present evaluation system is through human assessment. As the number of teachers' student ratio is gradually increasing, the manual evaluation process becomes complicated. The drawback of manual evaluation is that it is time-consuming, lacks reliability, and many more. This connection online examination system evolved as an alternative tool for pen and paper-based methods. Present Computer-based evaluation system works only for multiple-choice questions, but there is no proper evaluation system for grading essays and short answers. Many researchers are working on automated essay grading and short answer scoring for the last few decades, but assessing an essay by considering all parameters like the relevance of the content to the prompt, development of ideas, Cohesion, and Coherence is a big challenge till now. Few researchers focused on Content-based evaluation, while many of them addressed style-based assessment. This paper provides a systematic literature review on automated essay scoring systems. We studied the Artificial Intelligence and Machine Learning techniques used to evaluate automatic essay scoring and analyzed the limitations of the current studies and research trends. We observed that the essay evaluation is not done based on the relevance of the content and coherence.

Cardiovascular Disease or coronary illness is one of the significant dangerous infections in India as well as in the entire world. It is estimated that 28.1 % of deaths occur due to heart diseases. It is also the major cause for significant number of deaths which as more than 17.6 million in the year 2016. So proper and timely diagnosis, treatment of such diseases require a system that can predict with precise accuracy and reliability. Intensive research is carried out by various researchers using diverse machine learning algorithms to forecast the heart disease taking different datasets which consists of different attributes that result in heart attack. In this paper we analyzed the dataset collected from kaggle which consists of attributes related to heart disease such as age, gender, blood pressure, cholesterol and so on. We have also investigated the accuracy levels of various machine learning techniques such as Support Vector Machines (SVM), K-Nearest Neighbor (KNN), Decision Trees (DT). The performance and accuracy of above algorithms is not so well when executed using large dataset, so here we tried to improving the prediction accuracy using Artificial Neural Network(ANN), Tensor Flow Keras.

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Assessment is considered to play an essential function inside the Educational System. The interest of using automatic tools by human beings has been increased. So in the same way student response evaluation in education system with automatic assessment systems has grown exponentially in the last couple of years. Due to the increasing number of students and the use of online MOOC courses and lack of time and lack of consistency, assessment is shifted to automatic assessment. In this regard many of the researchers worked on it to make the assessment process easy. And they succeeded in assessment objective-type questions: i.e. multiple choices. Now here comes an interesting part is to assess the essays with automated tools. In this area, more number of researches worked and invented some tools for grading the essays but not up to the mark. Most of the assessment tools were assigning grades based on the style that is the number of sentences, the number of words, parts of speech, length of an essay, and grammar but not on the content of the essay. But few of the tools grading the essays based on the content by using traditional methods, and very few tools are using natural language processing methods.

Automatic essay scoring is an essential educational application in natural language processing (NLP). This automated process will alleviate the burden and increase the reliability and consistency of the assessment. With the advance in text embedding libraries and neural network models, AES systems achieved good results in terms of accuracy. However, the actual goals are not attained, like embedding essays into vectors with cohesion and coherence, and providing feedback to students is still challenging. In this paper, we proposed coherence-based embedding of an essay into vectors using sentence-BERT (Bidirectional Encoder Representations from Transformers). We trained these vectors on Long Short-Term Memory (LSTM) and Bi-LSTM (Bidirectional Long Short-Term Memory) to capture sentence connectivity with other sentences' semantics. We used two different datasets; one is standard ASAP Kaggle, and another is a domain-specific dataset with almost 2500 responses from 650 students. Our model performed well on both datasets, with an average QWK (Quadratic Weighted Kappa ) score of 0.76. Furthermore, we achieved good results compared to other prescribed models, and we also tested our model on adversarial responses of both datasets and observed decent outcomes.