Query: Automated Essay Grading using clustering Algorithm

title: Automated Essay Grading using Machine Learning Algorithm
abstract: Essays are paramount for of assessing the academic excellence along with linking the different ideas with the ability to recall but are notably time consuming when they are assessed manually. Manual grading takes significant amount of evaluator's time and hence it is an expensive process. Automated grading if proven effective will not only reduce the time for assessment but comparing it with human scores will also make the score realistic. The project aims to develop an automated essay assessment system by use of machine learning techniques by classifying a corpus of textual entities into small number of discrete categories, corresponding to possible grades. Linear regression technique will be utilized for training the model along with making the use of various other classifications and clustering techniques. We intend to train classifiers on the training set, make it go through the downloaded dataset, and then measure performance our dataset by comparing the obtained values with the dataset values. We have implemented our model using java.

title: AUTOMATED ESSAY GRADING SYSTEM USING DEEP LEARNING
abstract: 1Associate Professor, Dept. Of Computer Science and Engineering, Dhanekula Institute of Engineering and Technology, Andhra Pradesh, India 2, 3, 4, 5 Bachelor of Technology, Dept. Of Computer Science and Engineering, Dhanekula Institute of Engineering and Technology, Andhra Pradesh, India  In the view of educational institutions, assignments or essays play an important role in assessing the ability of students to understand and recall the topics explained to the students. The manual evaluation of these papers takes a lot of effort and time of the evaluators hence resulting in a time consuming process. The solution to grade a large number of papers effectively within a stipulated time is to let the machine do the grading. The automated grading system will not only reduce the time of evaluation but comparing it with human scores will also make the score more realistic. The project aim is to develop a system which grades an essay or a paper without any manual involvement. When an essay is loaded into the proposed grading system, the system accepts the essay given as the input and grades it using deep learning techniques and its layers such as LSTM and dense layers. This proposed system is useful while grading papers in smart schools and educational institutions.

title: An Automated Essay Grading Framework Based on Neural Networks (Dept.E)
abstract: This paper presents a proposed framework depending on Artificial Neural Networks (ANN) that enhances the process of assessing student's essays in order to save the line and cost of manual scoring of these essays. This framework consists of five phases and the UML model of that framework is defined. Also the activity diagrams of that system are explained. During the learning algorithm phase, the network trained on a set of pre graded student responses by teachers and then a set of essays not used in the training are introduced to a 10 tie network to judge the essay. The proposed framework was tested on case study and presents good results compared 10 a previously published work using the same case study with another technique.

title: Automated Essay Scoring (AES) A Semantic Analysis Inspired Machine Learning Approach: An automated essay scoring system using semantic analysis and machine learning is presented in this research
abstract: With the advancements in Artificial Intelligence (AI) 'Automated Essay Scoring' (AES) systems have become more and more prevalent in recent years. This research proposes an extension to the Coh Metrix algorithm AES, with a focus on feature lists. Technical features, such as, referential cohesion, lexical diversity, and syntactic complexity are evaluated. Furthermore, it proposes the use of four novel semantic measures, including estimating the topic overlap between an essay and its brief. A prototype implementation, using neural networks, is used to test the individual and comparative performance of the newly proposed AES system. The results show a considerable improvement on the results obtained in the existing research for the original Coh Metrix algorithm; from an adjacent accuracy of 91% to an adjacent accuracy of 97.5% (and a QWK of 0.822) This suggests that the new features and the proposed system have the potential to improve essay grading and would be a good area for further research

title: An Automated Tomato Quality Grading using Clustering based Support Vector Machine
abstract: This paper focus on representing the technique of fruit grade classification, automated machine vision based technology has become more potential and important to many areas like agricultural sector and food processing industry. The proposed system which calculates the grade of fruit based on its external features. Grading of fruits is one of the most important processes in post harvesting, but this procedure is mostly carried out manually which is not efficient as it requires enormous number of employment, and tends to human error. The grading process is carried out by capturing the fruit image using digital camera and this image is interpreted using image various processing techniques. Color is very prominent feature for recognizing defect and ripeness of the fruit. The major objective is to check the fruit quality with high speed for analyzing maximum number of fruits in least amount of time. The conventional process of fruit quality assessment needs new tools deciding the quality of fruit. This system performs color features and size of fruit and captures the fruit side view image. The Otsu thresholding and K Means clustering algorithms are used to extract the features of fruit. This system achieve the fruit quality sorting using Support Vector Machine and gives a few advantages over conventional techniques. This system will help in the advancement of an automated non destructive grading system with high speed, high accuracy and low cost. Implementation of this system will have applications in fruit quality grading in field like food science and trades where standardization is essential.

title: Increasing accuracy of automated essay grading by grouping similar graders
abstract: Automated essay evaluation is a widely used practical solution for replacing time consuming manual grading of student essays. Automated systems are used in combination with human graders in different high stake assessments, where grading models are learned on essays datasets scored by different graders. Despite the unified grading rules, human graders can unintentionally introduce subjective bias into scores. Consequently, a grading model has to learn from a data that represents a noisy relationship between essay attributes and its grade. We propose an approach for separating a set of essays into subsets that represent similar graders, which uses an explanation methodology and clustering. The results confirm our assumption that learning from the ensemble of separated models can significantly improve the average prediction accuracy on artificial and real world datasets.

title: Optimized Neural Network Based Improved Multiverse Optimizer Algorithm For Automated Arabic Essay Scoring
abstract: The automated essay scoring is recognized as an automatic evaluation of essays or automated essay grading. Such methods are very helpful for assessing human graders and experts when evaluating a large volume of essays. In this paper, a new method is presented to score essays automatically. It uses particle swarm optimization to generate the initial population for the multiverse optimizer algorithm to train the classic Neural Network. It is called pMVO NN. The proposed method is evaluated using 200 student's essays. These essays are scored by two human experts then they are passed to a pre processing phase to be prepared and converted to a digit's matrix. The results are evaluated using a set of measures and it is compared with well known optimization algorithms. The pMVO NN outperformed all compared algorithms and obtained a correlation equals to 0.987 with the scores of the human experts.

title: Entropy based Recognition of Anomalous Answers for Efficient Grading of Short Answers with an Evolutionary Clustering Algorithm
abstract: Short answer question is a common assessment tool eliciting a specific textual response for knowledge assessment. The divide and grade is an automated grading approach that uses clustering to assign answers into sets each of which is supposed to be sufficiently similar to receive the same grade. This approach can potentially produce accurate grading with significantly reduced manual grading effort. Many current clustering methods are known to suffer from the presence of anomalies, answers that are deviated from the modes of model answers or common misconceptions. The freedom afforded to the composition of answers often lead to these anomalies, and in particular, contextual anomalous answers that are marginally correct or wrong. This paper proposes an evolutionary clustering method for the divide and conquer approach. The method is coupled with anomalous answer recognition based on an entropy formulation of the cluster membership of answers for rectifying misclustered answers. The method has been evaluated with an open short answer grading dataset and the accuracy compares favorably with existing algorithms. Result analysis has suggested that the method can effectively leverage small manual grading effort on issues of great impact on short answer grading accuracy.

title: Automated Vehicle Number Plate Recognition System using Stability Score and K Means Clustering Algorithm
abstract: Due to the increase in vehicle usage, itis a challenging task to monitor, analyze the vehicles by a human for security purposes. There is a need for an automatic vehicle recognition system since various places nowadays have checkpoints for vehicles, to track the stolen vehicles, and to monitor traffic violations. The problem exists when the vehicle number plate is encountered in different formats, different scales, and illumination to number plates. In the case of an indeterminate situation, identifying vehicle number plates in poor lighting conditions and worse traffic situations can be analyzed using an automatic vehicle number plate recognition system. The vehicle name board edge finding techniques are used to easily identify the vehicle number in the name board. A dataset with 200 license plates has been collected as training datasets for recognition, estimation, and identification, thus improving system accuracy of recognition when compared to existing works. The training input samples include images of vehicle number plates taken from the traffic department. The automated vehicle number recognition system is improvised in terms of accuracy by estimating stability score and using the k means clustering algorithm.

title: Robust Neural Automated Essay Scoring Using Item Response Theory
abstract: Automated essay scoring (AES) is the task of automatically assigning scores to essays as an alternative to human grading. Conventional AES methods typically rely on manually tuned features, which are laborious to effectively develop. To obviate the need for feature engineering, many deep neural network (DNN) based AES models have been proposed and have achieved state of the art accuracy. DNN AES models require training on a large dataset of graded essays. However, assigned grades in such datasets are known to be strongly biased due to effects of rater bias when grading is conducted by assigning a few raters in a rater set to each essay. Performance of DNN models rapidly drops when such biased data are used for model training. In the fields of educational and psychological measurement, item response theory (IRT) models that can estimate essay scores while considering effects of rater characteristics have recently been proposed. This study therefore proposes a new DNN AES framework that integrates IRT models to deal with rater bias within training data. To our knowledge, this is a first attempt at addressing rating bias effects in training data, which is a crucial but overlooked problem.