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( Hyunju Noh ),( Jiyoung Kim ),( Jiwon Park ) 대한물리치료학회 2020 대한물리치료학회지 Vol.32 No.5
Purpose: This study aimed to investigate the evidence that therapeutic horseback riding can improve balance, muscle, ADL, equivalenc, GMFM, gait, emotion with developmental disabilities and neural patients. Methods: To conduct meta-analysis, the search focused on studies that employed therapeutic horseback riding for developmental disabilities and neural patients for which eight databases (KIS, RISS, DBpia, National Assembly Library, Pubmed, Embase, Google scholar and Cochrane Library) were used to extract literature published from 2002 to September 2019. The data were analyzed the RevMan 3.5.3 program. Results: As a result of meta-analysis, therapeutic horseback riding total effect size is 0.552 for children with developmental disabilities and neural patients. And effect size result of according to assessment type variable first, balance effect size is 0.594. Second, muscle activities effect size is 0.425. Third, ADL effect size is 0.430. Fourth, equivalance effect size is 0.640. Fifth, GMFM effect size is 0.482. Sixth, gait effect size is 0.400 and seventh emotion effect size is 0.876. Conclusion: These findings is horseback riding is effective The effect size by outcome was observed to be the effective for children with developmental disabilities and neural patients. and also the horseback riding provided the positive effects of balance, muscle activities, ADL, equivalance, GMFM, gait, emotion for children with developmental disabilities and neural patients. It is hoped that this study will contribute to the development of effective treatments for children with developmental disabilities and neural patients therapeutic horseback riding and the development of study.
Handling Incomplete Data Problem in Collaborative Filtering System
Hyunju Noh,Minjung Kwak,Ingoo Han 한국지능정보시스템학회 2003 지능정보연구 Vol.9 No.2
Collaborative filtering is one of the methodologies that are most widely used for recommendation system. It is based on a data matrix of each customer's preferences of products. There could be a lot of missing values in such preference data matrix. This incomplete data is one of the reasons to deteriorate the accuracy of recommendation system. There are several treatments to deal with the incomplete data problem such as case deletion and single imputation. Those approaches are simple and easy to implement but they may provide biased results. Multiple imputation method imputes m values for each missing value. It overcomes flaws of single imputation approaches through considering the uncertainty of missing values. The objective of this paper is to suggest multiple imputation-based collaborative filtering approach for recommendation system to improve the accuracy in prediction performance. The experimental works show that the proposed approach provides better performance than the traditional Collaborative filtering approach, especially in case that there are a lot of missing values in dataset used for recommendation system.<br/> <br/>