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In the current study, utilizing HU treatment we have optimized and carefully characterized a cellular NSC aging model that could recapture many of the aging attributes.
Kespichayawattana and Wiwatvanich 12 explored active aging attributes in elite Thai elderly adults, and Nantsupawat et al 13 focused on active aging in older Thai adults living in one rural village in the northeastern region of the country.
Little is known about active aging in older Thai adults, and there has been no development of scales to measure the expression of active aging attributes.
So the semi-equivalency is measured by grouping Sex, and Edu attributes, while Age attribute is anonymized as per interval.
In case of the "age" attribute it is practically impossible to perform a similar procedure, as a human cannot estimate the age value from a face image.
The age attribute is used through the information provided in the data set, such as "20, early" (age range from 20 to 25 years old) and "20, late" (age range from 26 to 30).
The basic idea of cumulative features is as follows: using support vector regression (SVR) [29]; using the idea of cumulative features [4] as the source data for the SVR; using a two-stage scheme of determining the "age" attribute [8].
Figure 6 b shows changes within the age attribute.
Action: attribute removal; Age: age attribute is very important for our analysis and must be kept.
The two-step cluster algorithm was also applied to age attribute.
Action: attribute removal; (ii) Age: age attribute is very important for our analysis and must be kept.
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