Sentence examples for pattern identification for from inspiring English sources

The present study was conducted to examine the reliability and validity of the 'Korean Standard Pattern Identification for Stroke (K-SPI-Stroke)', which was developed and evaluated within the context of traditional Korean medicine (TKM).

Table 5 displays the Bayer CFA pattern identification performance for the proposed method and conventional algorithm.

Therefore, existing frequent pattern identification algorithms for certain graph cannot be applied to frequent pattern identification of biological probability network.

In this study, failure modes occurring during Filled hole compression (FHC) tests were analysed using post-mortem analysis for failure pattern identification and high-speed cameras for failure scenario (damage initiation and progress).

Although "pattern identification as the basis for determining treatment" is the key concept for treating patients in TCM which is different from that of Western medicine doctors, the TCM doctors in Taiwan still have to use ICD-9-CM codes to make a diagnosis for outpatient visits.

Three glycosylation sites were initially predicted by primary sequence analysis through a PROSITE search used for pattern identification (http://www.expasy.org/prosite)[23].

These final averaged values were subsequently annotated using the BEAR suite annotator and used for pattern identification and correlation with gene homologs.

OA patients were treated with the thought of "pattern identification as the basis for determining treatment" by TCM physicians, and the prescriptions were different among TCM doctors according to their individual personal experience and their knowledge of Traditional Chinese herbs.

TCM doctors in Taiwan frequently prescribe a combination of Chinese herbal formulae and Chinese single herb with the concepts of "pattern identification as the basis for determining treatment" and "sovereign, minister, assistant, and courier" [ 33, 35, 37].

We have developed a complementary approach for pattern identification in non-sequence data with a largely overlapping range of applications, called discovery of all significant substructures (DASS) (Hollunder et al., 2007a).

The results showed that the back propagation neural network model provided the best results as an estimation model for flow pattern identification whereas regression decision tree had the best performance for liquid holdup determination.