• Journal of Hospice and Palliative Care
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• v.14 no.4
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• pp.197-203
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• 2011

Purpose: Pain is the most common and influential symptom in cancer patients. Few studies concerning pain intensity in the terminally ill cancer patients have been done. This study aimed to identify factors related with more than moderate pain. Methods: This study used secondary data of 162 terminal cancer inpatients at the palliative ward of six training hospitals in Korea. Physician-assessed pain assessment was by 10 point numeric rating scale. Substantial pain was defined more than moderate intensity by the Korean National Guideline for cancer pain. The Korean version of the MD Anderson Symptom Inventory was self-administered to assess symptoms. Survival prediction was estimated by the attending physicians at the time of admission. Results: Less than six weeks of predicted survival and more than numeric rating of six for worst drowsiness in the previous 24 h were significantly related to substantial pain (P=0.012 and P=0.046, respectively). The dose of opioid analgesics was positively related to substantial pain (P=0.004). Conclusion: Factors positively related to substantial pain were less than six weeks of predicted survival and considerable drowsiness. Careful monitoring and active preparation for pain are required in terminal cancer patients having those factors.

• The Journal of Korean Academy of Prosthodontics
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• v.26 no.1
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• pp.31-49
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• 1988

This study was performed to investigate the sound distortion following the alternation of the palatal plate thickness, for this study, 3 subjects who were born in Seoul and spoke Seoul dialect were recruited from K university male student population. First, their sounds of /아(a)/, 어(e)/, 오(o)/, 우(u)/, 으($\.{+}$), 이(i)/,에(e)/ without inserting plate were recorded , and then the sounds with palatal plates of different thickness were recorded, respectively. The palatal plates was constructed to cover the alveolar & palatal surfaces of the maxilla with an approximate thickness of 1.0mm, 2.5mm, and thickness of 2.5mm over the alveolar ridge & 1.0mm elsewhere and, named B, C, D-type, in succession. Series of analysis were administered through Computer (16 bit IBM PC/AT) at analyze the sound distortions. These experiments were analyzed by the LPC, Log Area Ratio. The findings led to the following conclusions: 1. Sound distortions were relatively minute in each condition and informations, however, /이(i)/ was the most distorted vowel in all conditions. 2. By and large, sound distortion was large in C, D-types. However, there was no correlation of the distortion rate on the 3 informants, and all tested vowels. 3. It was similar to LPC, Log Area Ratio distortion rates. 4. It was found that the sound distortion wit]1 plate inserted was verified to the numeric value with LPC and Log Area Ratio method.

• Journal of the Korea Society of Computer and Information
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• v.26 no.5
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• pp.47-53
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• 2021

Sparse ratings data hinder reliable similarity computation between users, which degrades the performance of memory-based collaborative filtering techniques for recommender systems. Many works in the literature have been developed for solving this data sparsity problem, where the most simple and representative ones are the methods of utilizing Jaccard index. This index reflects the number of commonly rated items between two users and is mostly integrated into traditional similarity measures to compute similarity more accurately between the users. However, such integration is very straightforward with no consideration of the degree of data sparsity. This study suggests a novel idea of applying different similarity measures depending on the numeric value of Jaccard index between two users. Performance experiments are conducted to obtain optimal values of the parameters used by the proposed method and evaluate it in comparison with other relevant methods. As a result, the proposed demonstrates the best and comparable performance in prediction and recommendation accuracies.

• Journal of Intelligence and Information Systems
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• v.21 no.3
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• pp.37-52
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• 2015

The recent global trends display expansion and growing solidity in both cooperative collaboration between industry, education, and research and R&D network systems. A greater support for the network and cooperative research sector would open greater possibilities for the evolution of new scholar and industrial fields and the development of new theories evoked from synergized educational research. Similarly, the national need for a strategy that can most efficiently and effectively support R&D network that are established through the government's R&D project research is on the rise. Despite the growing urgency, due to the habitual dependency on simple individual personal information data regarding R&D industry participants and generalized statistical data references, the policies concerning network system are disappointing and inadequate. Accordingly, analyses of the relationships involved for each subject who is participating in the R&D industry was conducted and on the foundation of an educational-industrial-research network system, possible changes within and of the network that may arise were predicted. To predict the R&D network transitions, Common Neighbor and Jaccard's Coefficient models were designated as the basic foundational models, upon which a new prediction model was proposed to address the limitations of the two aforementioned former models and to increase the accuracy of Link Prediction, with which a comparative analysis was made between the two models. Through the effective predictions regarding R&D network changes and transitions, such study result serves as a stepping-stone for an establishment of a prospective strategy that supports a desirable educational-industrial-research network and proposes a measure to promote the national policy to one that can effectively and efficiently sponsor integrated R&D industries. Though both weighted applications of Common Neighbor and Jaccard's Coefficient models provided positive outcomes, improved accuracy was comparatively more prevalent in the weighted Common Neighbor. An un-weighted Common Neighbor model predicted 650 out of 4,136 whereas a weighted Common Neighbor model predicted 50 more results at a total of 700 predictions. While the Jaccard's model demonstrated slight performance improvements in numeric terms, the differences were found to be insignificant.

• Journal of Intelligence and Information Systems
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• v.23 no.2
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• pp.19-38
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• 2017

Collaborative filtering (CF) algorithm has been popularly used for implementing recommender systems. Until now, there have been many prior studies to improve the accuracy of CF. Among them, some recent studies adopt 'hybrid recommendation approach', which enhances the performance of conventional CF by using additional information. In this research, we propose a new hybrid recommender system which fuses CF and the results from the social network analysis on trust and distrust relationship networks among users to enhance prediction accuracy. The proposed algorithm of our study is based on memory-based CF. But, when calculating the similarity between users in CF, our proposed algorithm considers not only the correlation of the users' numeric rating patterns, but also the users' in-degree centrality values derived from trust and distrust relationship networks. In specific, it is designed to amplify the similarity between a target user and his or her neighbor when the neighbor has higher in-degree centrality in the trust relationship network. Also, it attenuates the similarity between a target user and his or her neighbor when the neighbor has higher in-degree centrality in the distrust relationship network. Our proposed algorithm considers four (4) types of user relationships - direct trust, indirect trust, direct distrust, and indirect distrust - in total. And, it uses four adjusting coefficients, which adjusts the level of amplification / attenuation for in-degree centrality values derived from direct / indirect trust and distrust relationship networks. To determine optimal adjusting coefficients, genetic algorithms (GA) has been adopted. Under this background, we named our proposed algorithm as SNACF-GA (Social Network Analysis - based CF using GA). To validate the performance of the SNACF-GA, we used a real-world data set which is called 'Extended Epinions dataset' provided by 'trustlet.org'. It is the data set contains user responses (rating scores and reviews) after purchasing specific items (e.g. car, movie, music, book) as well as trust / distrust relationship information indicating whom to trust or distrust between users. The experimental system was basically developed using Microsoft Visual Basic for Applications (VBA), but we also used UCINET 6 for calculating the in-degree centrality of trust / distrust relationship networks. In addition, we used Palisade Software's Evolver, which is a commercial software implements genetic algorithm. To examine the effectiveness of our proposed system more precisely, we adopted two comparison models. The first comparison model is conventional CF. It only uses users' explicit numeric ratings when calculating the similarities between users. That is, it does not consider trust / distrust relationship between users at all. The second comparison model is SNACF (Social Network Analysis - based CF). SNACF differs from the proposed algorithm SNACF-GA in that it considers only direct trust / distrust relationships. It also does not use GA optimization. The performances of the proposed algorithm and comparison models were evaluated by using average MAE (mean absolute error). Experimental result showed that the optimal adjusting coefficients for direct trust, indirect trust, direct distrust, indirect distrust were 0, 1.4287, 1.5, 0.4615 each. This implies that distrust relationships between users are more important than trust ones in recommender systems. From the perspective of recommendation accuracy, SNACF-GA (Avg. MAE = 0.111943), the proposed algorithm which reflects both direct and indirect trust / distrust relationships information, was found to greatly outperform a conventional CF (Avg. MAE = 0.112638). Also, the algorithm showed better recommendation accuracy than the SNACF (Avg. MAE = 0.112209). To confirm whether these differences are statistically significant or not, we applied paired samples t-test. The results from the paired samples t-test presented that the difference between SNACF-GA and conventional CF was statistical significant at the 1% significance level, and the difference between SNACF-GA and SNACF was statistical significant at the 5%. Our study found that the trust/distrust relationship can be important information for improving performance of recommendation algorithms. Especially, distrust relationship information was found to have a greater impact on the performance improvement of CF. This implies that we need to have more attention on distrust (negative) relationships rather than trust (positive) ones when tracking and managing social relationships between users.

• Journal of Animal Science and Technology
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• v.62 no.4
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• pp.429-437
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• 2020

This study was conducted to construct basic data for the selection of elite cows by analyzing the estimated breeding value (EBV) and accuracy using the pedigree of Hanwoo cows in Gyeongnam. The phenotype trait used in the analysis are the carcass weight (CWT), eye muscle area (EMA), backfat thickness (BFT) and marbling score (MS). The pedigree of the test group and reference group was collected to build a pedigree structure and a numeric relationship matrix (NRM). The EBV, genetic parameters and accuracy were estimated by applying NRM to the best linear unbiased prediction (BLUP) multiple-trait animal model of the BLUPF90 program. Looking at the pedigree structure of the test group, there were a total of 2,371 cows born between 2003 to 2009, of these 603 cows had basic registration (25%), 562 cows had pedigree registration (24%) and 1,206 cows had advanced registration (51%). The proportion of pedigree registered cows was relatively low but it gradually increased and reached a point of 20,847 cows (68%) between 2010 to 2017. Looking at the change in the EBV, the CWT improved from 4.992 kg to 9.885 kg, the EMA from 0.970 ㎠ to 2.466 ㎠, the BFT from -0.186 mm to -0.357 mm, and the MS from 0.328 to 0.559 points. As a result of genetic parameter estimation, the heritability of CWT, EMA, BFT, and MS were 0.587, 0.416, 0.476, and 0.571, respectively, and the accuracy of those were estimated to be 0.559, 0.551, 0.554, and 0.558, respectively. Selection of superior genetic breed and efficient improvement could be possible if cow ability verification is implemented by using the accurate pedigree of each individual in the farms.

• Journal of muscle and joint health
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• v.8 no.1
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• pp.27-50
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• 2001

Rheumatoid arthritis is a chronic systemic autoimmune disease. Although the joints are the major loci of the disease activity, fatigue is a common extraarticular symptom that exists in all gradations of rheumatoid arthritis. Fatigue is defined as a subjective sense of generalized tiredness or exhaustion and has multiple dimensions. Therefore fatigue is a common and frequent problem for those with rheumatoid arthritis. In fact, 88-100% of individuals with rheumatoid arthritis experience fatigue. Especially the degree of fatigue is higher in women than men with rheumatoid arthritis. Despite the importance of fatigue among the patients with rheumatoid arthritis, the mechanism that leads to fatigue in rheumatoid arthritis is not completely understood. This study was intended to test and validate a model to predict fatigue in women with rheumatoid arthritis. Especially it was intended to identify the direct and indirect effects of the variables of pain, disability, depression, sleep disturbance, morning stiffness, and symptom duration to fatigue. Data were collected by questionnaires including Multidimensional Assesment of Fatigue(Tack, 1991), numeric scale of pain, graphic scale of joints, Ritchie Articular Index, Korean Health Assessment Questionnaire(Bae, et al., 1998), Inventory of Function Status(Tulman, et al., 1991), Center for Epidemiologic Studies-Depression, and Korean Sleep Scale(Oh, et al 1998). The sample consisted of 345 women with a mean duration of rheumatoid arthritis for 10.06 years and a mean age of 49.64 years. SPSS win and Win LISREL were used for the data analysis. Structural equation modeling revealed the overall fit of the model. Pain predicted fatigue directly and indirectly through disability, depression, and sleep disturbance. Disability, sleep disturbance predicted fatigue only directly, while depression only indirectly through disability and sleep disturbance. Also morning stiffness and symptom duration predicted fatigue through disability and depression. All predictors accounted for 65% of the variance of fatigue. Depression, pain, and disability predicted sleep disturbance. Depression had reciprocal relationship with disability and they both were predicted by pain directly and indirectly. In summary, pain, depression, disability, sleep disturbance, morning stiffness, and symptom duration contributed to the fatigue of patients with rheumatoid arthritis. The best predictor of fatigue was pain. This finding indicates that the modification of pain, depression, disability, sleep disturbance, morning stiffness could be nursing intervention for relief or prevention of fatigue.

• The Journal of the Korea Contents Association
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• v.21 no.11
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• pp.135-144
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• 2021

Recently, data-driven decision-making technology has become a key technology leading the data industry, and machine learning technology for this requires high-quality training datasets. However, real-world data contains missing values for various reasons, which degrades the performance of prediction models learned from the poor training data. Therefore, in order to build a high-performance model from real-world datasets, many studies on automatically imputing missing values in initial training data have been actively conducted. Many of conventional machine learning-based imputation techniques for handling missing data involve very time-consuming and cumbersome work because they are applied only to numeric type of columns or create individual predictive models for each columns. Therefore, this paper proposes a new data imputation technique called 'Denoising Self-Attention Network (DSAN)', which can be applied to mixed-type dataset containing both numerical and categorical columns. DSAN can learn robust feature expression vectors by combining self-attention and denoising techniques, and can automatically interpolate multiple missing variables in parallel through multi-task learning. To verify the validity of the proposed technique, data imputation experiments has been performed after arbitrarily generating missing values for several mixed-type training data. Then we show the validity of the proposed technique by comparing the performance of the binary classification models trained on imputed data together with the errors between the original and imputed values.