Abstract-The study of medical various institutions in Thailand found that the success for teaching and learning by means of Problem Based Learning (PBL) depends on several factors including correct attitudes and cognitive learning. Problem of teaching a small group of PBL is consistent with the results of the audit were to evaluate the group's facilitator, diverse and inaccurate. Especially, the grading of a medical case study essay reports (Clinical case summaries). To solve such problems, we proposed automatic medical case study essay scoring for PBL of medical students. SVM with Genetic Algorithms (GA-SVM) was used to assess the quality medical case study essays written by medical students in the subject matter of muscular systems and movement. The medical case study essays written in response to a question were each evaluated by facilitators and assigned a human score. In the experiment, we used raw term frequency vectors of the essays and their corresponding human scores to train the SVM while GA was used for choosing the kernel function type and its parameter values to find a proper solution to an optimization and obtain the machine scores. The experimental results show that the addition of GA-SVM technique improves scoring performance.Index Terms-Essay scoring, medical case study, SVM, genetic algorithms (GA).
Today many businesses have adopted Twitter as a new marketing channel to promote their products and services. One of the potentially useful applications is to recommend users to follow businesses which match their interests. One possible solution is to apply classification algorithm to predict user's Twitter posts into some predefined business categories. Due to the short length characteristic, classifying Twitter posts is very difficult and challenging. In this paper, we propose a feature processing framework for constructing text categorization models. A topic model is constructed from a set of terms based on the Latent Dirichlet Allocation (LDA) algorithm. We apply the topic model for two different feature processing approaches:(1) feature transformation, i.e., using a set of topics as features and (2) feature expansion, i.e., appending a set of topics to a set of terms.Experimental results show that the highest accuracy of 95.7% is obtained with feature expansion technique, an improvement of 18.7% over the Bag of Words (BO W) model.
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