The MOOC Discussion Forum is the place where students and teachers communicate, often plagued by information overload and confusion. Posts that students used to express confusion and demanded teachers' attention are most likely to be overwhelmed by the amount of noise in the forum. Therefore, how to pay attention to urgent posts in time has become a critical problem to be solved. In this paper, we present a new hybrid neural network for identifying ''urgent'' posts that require immediate attention from instructors. We proposed a semantic and structure extraction part including convolutional neural network (CNN) and gated recurrent unit (GRU), which can simultaneously learn the semantic information and structural information of sentences. In addition, Due to a lot of noise such as spelling mistakes and emoticons in the forum comment text, we propose to use Character-level Convolutional Networks (Char-CNN) to capture these special information. Finally, the semantic and structural information learned by the semantic and structural extraction part is merged with the character information learned by Char-CNN, and the attention mechanism to learn their weights, the final representation of the sentence can be obtained. In our experiments, we achieve urgent posts classification with a micro F-score of 91.8%, 91.3% and 88.4% on the Stanford MOOCPosts dataset, outperforming the state-of-the-art approach by 1.8%, 2.4% and 1.5% respectively. This work can help teachers prioritize their responses and better manage numerous posts. Teachers can answer learner questions in a timely manner and help reduce dropout rates and improve completion rates.INDEX TERMS MOOC, urgent posts, convolutional neural network, gated recurrent unit, semantic information and structural information, intervention learning.
Massive Open Online Courses (MOOCs) have boomed in recent years because learners can arrange learning at their own pace. High dropout rate is a universal but unsolved problem in MOOCs. Dropout prediction has received much attention recently. A previous study reported the problem of learning behavior discrepancy leading to a wide range of fluctuation of prediction results. Besides, previous methods require iterative training which is time intensive. To address these problems, we propose DT-ELM, a novel hybrid algorithm combining decision tree and extreme learning machine (ELM), which requires no iterative training. The decision tree selects features with good classification ability. Further, it determines enhanced weights of the selected features to strengthen their classification ability. To achieve accurate prediction results, we optimize ELM structure by mapping the decision tree to ELM based on the entropy theory. Experimental results on the benchmark KDD 2015 dataset demonstrate the effectiveness of DT-ELM, which is 12.78%, 22.19%, and 6.87% higher than baseline algorithms in terms of accuracy, AUC, and F1-score, respectively.
Drug-drug interactions (DDIs) may bring huge health risks and dangerous effects to a patient’s body when taking two or more drugs at the same time or within a certain period of time. Therefore, the automatic extraction of unknown DDIs has great potential for the development of pharmaceutical agents and the safety of drug use. In this article, we propose a novel recurrent hybrid convolutional neural network (RHCNN) for DDI extraction from biomedical literature. In the embedding layer, the texts mentioning two entities are represented as a sequence of semantic embeddings and position embeddings. In particular, the complete semantic embedding is obtained by the information fusion between a word embedding and its contextual information which is learnt by recurrent structure. After that, the hybrid convolutional neural network is employed to learn the sentence-level features which consist of the local context features from consecutive words and the dependency features between separated words for DDI extraction. Lastly but most significantly, in order to make up for the defects of the traditional cross-entropy loss function when dealing with class imbalanced data, we apply an improved focal loss function to mitigate against this problem when using the DDIExtraction 2013 dataset. In our experiments, we achieve DDI automatic extraction with a micro F-score of 75.48% on the DDIExtraction 2013 dataset, outperforming the state-of-the-art approach by 2.49%.
Abstract. Classification rule mining is one of the important problems in the emerging field of data mining which is aimed at finding a small set of rules from the training data set with predetermined targets. To efficiently mine the classification rule from databases, a novel classification rule mining algorithm based on particle swarm optimization (PSO) was proposed. The experimental results show that the proposed algorithm achieved higher predictive accuracy and much smaller rule list than other classification algorithm.
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