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The unified Parkinson's disease rating scale (UPDRS) is the most widely employed scale for tracking Parkinson's disease (PD) symptom progression. However, conventional way to achieve UPDRS, mainly based on the physical examinations of clinic patients performed by the trained medical staffs, involves the disadvantages of inconvenience and high medical expense. Hence, in this study, we try to explore...
For the practical big time series data, it is the important step to eliminate the noise and get the dynamic information of the time series data. For the series data, the extraction model and the transform model are given in this paper, and the sampling interval of the models is discussed to guarantee the estimated system convergence. The dynamic information is analyzed according to the estimated dynamic...
Recently, key-phrase extraction from patent document has received considerable attention. However, the current statistical approaches of Chinese key-phrase extraction did not realize the semantic comprehension, thereby resulting in inaccurate and partial extraction. In this study, a Chinese patent mining approach based on sememe statistics and key-phrase extraction has been proposed to extract key-phrases...
Patent is one of the most important carriers of product innovation which provides richer technology information. Patent mining has significant effects for product innovation. Patent information can be act as higher-dimension time-series for it has the characteristics of time and higher-dimension. In this paper, we improved the locally linear embedding algorithm of manifold learning method. Then the...
The problem of similarity measure for time series has attracted considerable research interest. Most of the recently used algorithms utilize the Dynamic Time Warping (DTW) distance for measuring the similarity of time series, in various areas such as science, medicine, industry, and finance. DTW is a considerably more robust distance measure for time series, which allows similar shapes to match even...
In many life science applications, due to the requirement of real time data processing and/or the very large size of the available dataset, a classifier with high efficiency is usually more preferable, or even necessary, with the prerequisite of not deteriorating effectiveness too much. That means a more desirable classifier in this context should run faster but still retain high accuracy. In this...
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