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A Feature Selection Technique in the Neural Network for Demand Forecasting of Mobile Payment System
Ho-Joon Kim, Yun-Seok Cho, Kyungmi Kim
http://doi.org/10.5626/JOK.2018.45.4.370
In this paper, we present a time series prediction technique based on neural network as a methodology for forecasting service demand of mobile payment system. We propose a two-stage neural network model for the feature selection process and the prediction process. Three types of fuzzy membership functions were adopted for the representation of feature data, and a hyperbox-based neural network model is used for the evaluation of feature relevance factor. The proposed feature selection technique reduces the amount of computation and eliminates erroneous feature data in the learning data set. We evaluated the usefulness of the proposed method through experiments using two years of data obtained form actual smart campus systems.
Dynamic Parameter Visualization and Noise Suppression Techniques for Contrast-Enhanced Ultrasonography
This paper presents a parameter visualization technique to overcome the limitation of the naked eye in contrast-enhanced ultrasonography. A method is also proposed to compensate for the distortion and noise in ultrasound image sequences. Meaningful parameters for diagnosing liver disease can be extracted from the dynamic patterns of the contrast enhancement in ultrasound images. The visualization technique can provide more accurate information by generating a parametric image from the dynamic data. Respiratory motions and noise from micro-bubble in ultrasound data may cause a degradation of the reliability of the diagnostic parameters. A multi-stage algorithm for respiratory motion tracking and an image enhancement technique based on the Markov Random Field are proposed. The usefulness of the proposed methods is empirically discussed through experiments by using a set of clinical data.
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