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Abstract: This paper proposes a web-based interactive speaking improvement system for EFL learners. Using fuzzy matching, the system automatically compares the learner’s and the system’s pronunciation and immediately provides corrective interaction whereby the student can improve speaking via continuous imitation and practice at the computer. The system, having been tested on Taiwanese students who needed to achieve a speaking proficiency equivalent to CEF Level A2, is found to be highly effective for the intended purpose. [Copyright &y& Elsevier]

Accounting frauds have continuously happened all over the world. This leads to the need of predicting business failures. Statistical methods and machine learning techniques have been widely used to deal with this issue. In general, financial ratios are one of the main inputs to develop the prediction models. This paper presents a hybrid financial analysis model including static and trend analysis models to construct and train a back-propagation neural network (BPN) model. Further, the experiments employ four datasets of Taiwan enterprises which support that the proposed model not only provides a high predication rate but also outperforms other models including discriminant analysis, decision trees, and the back-propagation neural network alone. [Copyright &y& Elsevier]

• Statistical and machine learning methods for imputing missing electric power data are studied. • Multiple missing feature values of each electric power data are considered. • The performance of different methods over the summer and non-summer season data is compared. • The imputation performances during the peak, off-peak, and semi-peak times are examined. Demand for electricity is gradually increasing in many countries. Efforts in related studies have been made for the application of data mining techniques over related electric power data for the development of more effective energy management systems. However, one major challenge is how to compensate for parts of the collected dataset, such as power consumption, voltage, or electric current that may be missing for a specific period of time. In the literature, several methods have been employed for imputation of the missing data, especially single feature value imputation. However, the performance of the different types of imputation methods, i.e. statistical and machine learning methods, for multiple missing features of electric power data has not been fully explored. Moreover, variations in their imputation performance during the summer/non-summer seasons and in the peak/off-peak/semi-peak times have not been investigated. In this paper, the performance of five well-known imputation methods for processing electric power data, two statistical methods, autoregressive integrated moving average (ARIMA) and linear interpolation (LI) models, and three machine learning methods, k-nearest neighbor (K-NN), multilayer perceptron (MLP), and support vector regression (SVR) is compared. The experimental results, based on electric power data for a two-year period in Taiwan, show that the machine learning methods generally perform better than the statistical ones, with K-NN and SVR performing the best. In particular, all of the imputation methods produced higher error rates during the summer season than the non-summer seasons. Moreover, the machine learning methods (especially K-NN) are better choices for the imputation of missing data during peak times, whereas the statistical methods (especially LI) are better for off-peak and semi-peak times. [ABSTRACT FROM AUTHOR]