Aniza Mohamed Din

A subject identification method based on term frequency technique

The analyzing and extracting important information from a text document is crucial and has produced interest in the area of text mining and information retrieval. This process is used in order to notice particularly in the text. Furthermore, on view of the readers that people tend to read almost everything in text documents to find some specific information. However, reading a text document consumes time to complete and additional time to extract information. Thus, classifying text to a subject can guide a person to find relevant information. In this paper, a subject identification method which is based on term frequency to categorize groups of text into a particular subject is proposed. Since term frequency tends to ignore the semantics of a document, the term extraction algorithm is introduced for improving the result of the extracted relevant terms from the text. The evaluation of the extracted terms has shown that the proposed method is exceeded other extraction techniques.

Neural network forecasting model using smoothed data

Forecasting model has been applied in many areas of study. Neural Network (NN) is the most popular forecasting model among the intelligent methods. However, NN had some limitations in learning patterns which have terrific noise and nonlinear characteristic. This paper aims to analyze the performances of NN forecasting model using data that have been smoothed. The actual data were smoothed by three exponential smoothing techniques and normalized before the experiment. One NN model was developed and tested with ten different hidden units. The percentage of correctness and mean absolute error gathered from NN training were used to evaluate the NN performance. The findings show that the NN model using smoothed data gives better performance compared to actual data.