• Asian Pacific Journal of Cancer Prevention
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• 제14권3호
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• pp.1643-1647
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• 2013

Background: To identify the risk factors and assess the role of survivin in predicting progessivity precancerous cervical lesions. Materials and Methods: This case-control study was conducted from October 2009 until May 2010. We obtained 74 samples, classified according to the degree of cervical intraepithelial neoplasia (CIN): 19 samples for CIN 1, 18 samples for CIN 2, 18 samples for CIN 3, and 19 samples as controls. Demographic profiles and risk factors assesment, histopathologic examination, HPV DNA tests, immunocytochemistry (ICC) and immunohistochemistry (IHC) staining for survivin expression were performed on all samples. Data was analyzed with bivariate and multivariate analysis. Results: Multivariate analysis revealed significant risk factors for developing precancerous cervical lesions are age <41 years, women with ${\geq}2$ sexual partners, course of education ${\geq}13$ years, use of oral contraceptives, positive high-risk HPV DNA, and high survivin expression by ICC or IHC staining. These factors were fit to a prediction model and we obtained a scoring system to predict the progressivity of CIN lesions. Conclusions: Determination of survivin expression by immunocytochemistry staining, along with other significant risk factors, can be used in a scoring system to predict the progressivity of CIN lesions. Application of this scoring system may be beneficial in determining the action of therapy towards the patient.

• ETRI Journal
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• 제46권1호
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• pp.82-95
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• 2024

As automated essay scoring (AES) has progressed from handcrafted techniques to deep learning, holistic scoring capabilities have merged. However, specific trait assessment remains a challenge because of the limited depth of earlier methods in modeling dual assessments for holistic and multi-trait tasks. To overcome this challenge, we explore providing comprehensive feedback while modeling the interconnections between holistic and trait representations. We introduce the DualBERT-Trans-CNN model, which combines transformer-based representations with a novel dual-scale bidirectional encoder representations from transformers (BERT) encoding approach at the document-level. By explicitly leveraging multi-trait representations in a multi-task learning (MTL) framework, our DualBERT-Trans-CNN emphasizes the interrelation between holistic and trait-based score predictions, aiming for improved accuracy. For validation, we conducted extensive tests on the ASAP++ and TOEFL11 datasets. Against models of the same MTL setting, ours showed a 2.0% increase in its holistic score. Additionally, compared with single-task learning (STL) models, ours demonstrated a 3.6% enhancement in average multi-trait performance on the ASAP++ dataset.

• 한국정보처리학회:학술대회논문집
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• 한국정보처리학회 2021년도 춘계학술발표대회
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• pp.408-410
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• 2021

In the NLP field, the pre-training model BERT launched by the Google team in 2018 has shown amazing results in various tasks in the NLP field. Subsequently, many variant models have been derived based on the original BERT, such as RoBERTa, ERNIEBERT and so on. In this paper, the WWMBERT (Whole Word Masking BERT) model suitable for Chinese text tasks was used as the baseline model of our experiment. The experiment is mainly for "Text-level Chinese text classification tasks" are improved, which mainly combines Tapt (Task-Adaptive Pretraining) and "Multi-Sample Dropout method" to improve the model, and compare the experimental results, experimental data sets and model scoring standards Both are consistent with the official WWMBERT model using Accuracy as the scoring standard. The official WWMBERT model uses the maximum and average values of multiple experimental results as the experimental scores. The development set was 97.70% (97.50%) on the "text-level Chinese text classification task". and 97.70% (97.50%) of the test set. After comparing the results of the experiments in this paper, the development set increased by 0.35% (0.5%) and the test set increased by 0.31% (0.48%). The original baseline model has been significantly improved.

• 응용통계연구
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• 제20권3호
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• pp.423-440
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• 2007

본 논문의 목적은 기업 신용점수에 영향을 미치는 기업 인적자원 요소들을 찾아서 기업 신용점수 모형을 구축하는 것이다. 모형 구축을 위해 사용된 자료는 2005년 한국직업능력개발원의 인적자본 기업패널 (Human Capital Corporate Panel, HCCP) 설문조사 자료와 한국신용평가(주)의 KIS-신용평점모델에서 생성된 기업 신용점수이다. 모형 구축을 위한 독립변수는 McLagan (1989)의 '인적자원 바퀴모델'을 토대로 인적자본 기업패널 설문조사 문항을 선택하여 사용하였으며, 종속변수로는 기업 신용평가점수를 사용하였다. 또한 기업 인적자원 관련 변수를 이용한 기업 신용점수 모형 구축을 위해 로지스틱 회귀모형을 사용하였다. 모형 구축 결과 최종적으로 선택된 변수는 22개였다 영역별로 세분화해서 살펴보면 대분류 기준으로 HRD 영역은 6개, HRM 영역은 15개, 기타 1개이고, 중분류 기준으로 개인개발 2개, 경력개발 2개, 조직개발 2개, 조직직무설계 1개, 인적자원계획 4개, 정보체계 2개, 보상 및 장려 6개, 복지후생 1개, 노사관계 1개, 기업규모 1개가 선택되었다. 구축된 모형을 평가하기 위하여 10등급 교차타당성 분석을 통한 오분류율, G-mean은 각각 30.81, 68.27이었다. 그리고 반응율은 가장 좋은 십분위가 가장 나쁜 십분위보다 약 6.08배가 크고 점차 감소하는 경향을 보이고 있다. 그러므로 구축된 모형은 기업 인적자원 관련 변수를 이용해 기업 신용점수를 측정하는데 적당한 모형이라는 결론을 내릴 수 있다

• 실천공학교육논문지
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• 제15권1호
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• pp.193-198
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• 2023

본 논문은 파이썬 프로그래밍 교육에서 단원별 연습문제의 타당성을 분석하였다. 단원별로 제시되는 연습문제는 온라인 학습 시스템을 통해 제시되고 학생 각자가 답안 코드를 업로드하여 자동으로 채점된다. 한학기 동안 진행되는 파이썬 교육을 통해, 학생들의 중간시험점수, 기말시험 점수 그리고 각 단원별 연습문제 점수 등 데이터가 수집된다. 수집된 데이터들을 통해, 자동채점 연습문제들의 타당도를 분석하여 단원별 연습문제들을 개선할 수 있다. 본 논문에서는 자동 채점 연습문제들의 타당도를 분석하기 위해, Orange 머신러닝 도구를 사용하였다. 파이썬 과목에서 수집된 데이터를 전체, 상위권 그리고 하위권 그룹별로 4가지 분석을 실시하고 종합적으로 비교한다. 파이썬 단원별 연습문제 점수들로부터 학생의 최종 성적을 예측하는 머신러닝 모델의 예측 정확도로부터 단원별 자동채점 연습문제의 출제 타당도를 분석하였다.

• 한국지리정보학회지
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• 제8권4호
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• pp.81-90
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• 2005

본 연구에서는 산림청에서 산사태 위험지를 판정하는데 이용하고 있는 판정표를 기반으로 산사태 발생 위험도를 예측할 수 있는 GIS 응용모델을 개발하였다. 이 모델에서 산사태 위험지는 지형, 임상 및 지질과 같은 산림입지환경 인자들 중 7개 인자를 선별적으로 이용하여 분석하도록 설계되었다. 이러한 입지환경 인자들 중 경사길이, 경사위치, 사변형태의 분석은 DEM 자료를 이용하여 산지사면 분석이 가능하도록 개발된 '산사태 예측을 위한 산지사면 입지해석 모듈' 을 이용하였다. 산사태 위험지 판정 모델의 구조는 원자료를 입력받아 가공, 변환하는 입력모듈과, 산사태 위험지 판정인자를 분석하여 해당지역의 산사태 위험지를 분석하는 모듈, 분석된 산사태 위험도 판정 결과를 제시하는 출력모듈 등으로 구성되어 있다. 경기도 용인 안성 지역에서 발생한 산사태를 대상으로 모델을 적용한 결과 약 72%에 해당하는 산사태가 산사태 위험도 2등급 이상으로 판정된 곳에서 발생하였다.

• Journal of Gastric Cancer
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• 제22권1호
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• pp.24-34
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• 2022

Purpose: Total gastrectomy (TG) with lymph node (LN) dissection is recommended for early gastric cancer (EGC) but is not indicated for endoscopic resection (ER). We aimed to identify patients who could avoid TG by establishing a scoring system for predicting lymph node metastasis (LNM) in proximal EGCs. Materials and Methods: Between January 2003 and December 2017, a total of 1,025 proximal EGC patients who underwent TG with LN dissection were enrolled. Patients who met the absolute ER criteria based on pathological examination were excluded. The pathological risk factors for LNM were determined using univariate and multivariate logistic regression analyses. A scoring system for predicting LNM was developed and applied to the validation group. Results: Of the 1,025 cases, 100 (9.8%) showed positive LNM. Multivariate analysis confirmed the following independent risk factors for LNM: tumor size >2 cm, submucosal invasion, lymphovascular invasion (LVI), and perineural invasion (PNI). A scoring system was created using the four aforementioned variables, and the areas under the receiver operating characteristic curves in both the training (0.85) and validation (0.84) groups indicated excellent discrimination. The probability of LNM in mucosal cancers without LVI or PNI, regardless of size, was <2.9%. Conclusions: Our scoring system involving four variables can predict the probability of LNM in proximal EGC and might be helpful in determining additional treatment plans after ER, functioning as a good indicator of the adequacy of treatments other than TG in high surgical risk patients.

• Environmental Analysis Health and Toxicology
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• 제25권1호
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• pp.41-55
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• 2010

The aims of this study were to develop the suitable "system software" in chemical ranking and scoring (CRS) for the food hazardous chemicals associated with environmental emission and to suggest the priority lists of food contamination by environmental-origined pollutants. Study materials were selected with reference to the priority pollutants list for environment and food management from domestic and foreign research and the number of study materials is 103 pollutants (18 heavy metals, 10 PBTs, 10 EDs, and 65 organic compounds). The Food-CRS-Korea system consisted of the environmental fate model via multimedia, transfer environment to food model, and health risk assessment by contaminated food intake. We have established that health risks of excess cancer risks, hazard quotients (HQs) by chronic toxicity and HQs by reproductive toxicity convert to score, respectively. The creditable scoring system was designed to consider uncertainty of quantitative risk assessment based on VOI (Value-Of-Information). The predictability of the Food-CRS-Korea model was evaluated by comparing the presumable values and the measured ones of the environmental media and foodstuffs. The priority lists based on emissions with background-level-correction are 15 pollutants such as arsenic, cadmium, and etc. The priority lists based on environmental monitoring date are 17 pollutants including DEHP, TCDD, and so on. Consequently, we suggested the priority lists of 13 pollutants by considering the several emission and exposure scenarios. According to the Food-CRS-Korea system, arsenics, cadmium, chromes, DEHP, leads, and nickels have high health risk rates and reliable grades.

• Journal of the Korean Data and Information Science Society
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• 제21권6호
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• pp.1031-1039
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• 2010

10종경기는 4개의 트랙경기와 6개의 필드경기로 구성된 10종목을 2일 동안에 겨루어 각 종목의 성적을 채점표에 의해 점수로 환산, 합계점이 많은 선수가 상위가 된다. 10종경기는 불합리한 점을 여러 번 개선하여 현재 사용하는 채점표를 사용한다지만 10개의 종목을 모두 골고루 잘하는 만능육상선수를 뽑는다는 원칙에서 벗어나 특정 종목들을 잘하는 선수들이 유리한 경우가 많다. 본 연구에서는 10종경기의 원래 취지처럼 각 종목 점수의 평균 및 표준편차가 전체점수의 경우와 같고, 각 종목점수는 정규분포를 따르며, 각 종목별 난이도가 서로 같다는 가정을 만족하는 10종경기에 대한 새로운 점수모형을 제안하였다. 사용된 데이터는 1991년부터 2009년 사이의 세계육상선수권대회와 올림픽에서의 기록 중 상위 200명의 기록이며, 그 결과 제안된 점수모형은 현행 모형보다 훨씬 더 바람직한 득점체계를 제공한다.

• Tuberculosis and Respiratory Diseases
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• 제67권5호
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• pp.436-444
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• 2009

Background: Micro computed tomography (CT) is rapidly developing as an imaging tool, especially for mice, which have become the experimental animal of choice for many pulmonary disease studies. We evaluated the usefulness of micro CT for evaluating lung fibrosis in the murine model of bleomycin-induced lung inflammation and fibrosis. Methods: The control mice (n=10) were treated with saline. The murine model of lung fibrosis (n=60) was established by administering bleomycin intra-tracheally. Among the 70 mice, only 20 mice had successful imaging analyses. We analyzed the micro CT and pathological findings and examined the correlation between imaging scoring in micro CT and histological scoring of pulmonary inflammation or fibrosis. Results: The control group showed normal findings on micro CT. The abnormal findings on micro CT performed at 3 weeks after the administration of bleomycin were ground-glass opacity (GGO) and consolidation. At 6 weeks after bleomycin administration, micro CT showed various patterns such as GGO, consolidation, bronchiectasis, small nodules, and reticular opacity. GGO (r=0.84) and consolidation (r=0.69) on micro CT were significantly correlated with histological scoring that reflected pulmonary inflammation (p<0.05). In addition, bronchiectasis (r=0.63) and reticular opacity (r=0.83) on micro CT shown at 6 weeks after bleomycin administration correlated with histological scoring that reflected lung fibrosis (p<0.05). Conclusion: These results suggest that micro CT findings from a murine model of bleomycin-induced lung fibrosis reflect pathologic findings, and micro CT may be useful for predicting bleomycin-induced lung inflammation and fibrosis in mice.