An Efficient Review Classification Of National Eligibility Cum Entrance Test Using Attention Mechanism With Recurrent Neural Network And Maximum Entropy
Medical entrance examination system for students in India is suffering from various shortcomings at conceptual as well as implementation level. Indian students who are writing National Eligibility cum Entrance Test suffering from dress code restrictions,etc., which leads to stress and pressure. To address these issues, a comprehensive analysis of various associated factors is essential. To achieve good standards of medical education, our objective should be to re-evaluate each and every aspect as create an efficient accreditation system, to promote an equal distribution of resources, redesign curricula with stricter implementation and improved assessment methodologies; all of which will generate efficient medical graduates and resulting in desired change within the system.
This thesis presents a NEET exam review classification is useful function to analyzing student reviews and predicting sentiments with different categories (positive, negative and neutral). The objective of NEET review prediction is to accurately classify the target class for all case in the data. This research presents an Attention Mechanism with Recurrent Neural Network (AMRNN) technique with GloVe word embedding algorithm, a capable way that has improve the classification accuracy, unless the large amount of information on these platforms make them viable for use as data sources, in applications based on NEET review analysis.
According to the experimental results, the proposed algorithms mainly focused on NEET exam related students review classification prediction results using R 3.4.0 simulation tool. Based on these results the Central Government must analyses these review classes (positive, negative and neutral