• Smart Structures and Systems
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• 제30권2호
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• pp.195-207
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• 2022

Computational drawbacks associated with regular predictive models have motivated engineers to use hybrid techniques in dealing with complex engineering tasks like simulating the compressive strength of concrete (CSC). This study evaluates the efficiency of tree potential metaheuristic schemes, namely shuffled complex evolution (SCE), multi-verse optimizer (MVO), and beetle antennae search (BAS) for optimizing the performance of a multi-layer perceptron (MLP) system. The models are fed by the information of 1030 concrete specimens (where the amount of cement, blast furnace slag (BFS), fly ash (FA1), water, superplasticizer (SP), coarse aggregate (CA), and fine aggregate (FA2) are taken as independent factors). The results of the ensembles are compared to unreinforced MLP to examine improvements resulted from the incorporation of the SCE, MVO, and BAS. It was shown that these algorithms can considerably enhance the training and prediction accuracy of the MLP. Overall, the proposed models are capable of presenting an early, inexpensive, and reliable prediction of the CSC. Due to the higher accuracy of the BAS-based model, a predictive formula is extracted from this algorithm.

• 한국빅데이터학회지
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• 제6권2호
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• pp.161-168
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• 2021

본 연구에서는 기계 학습을 사용하여 심혈관 질환 예측 모델을 제안한다. 먼저 두 집단간에 다양한 차이를 다차원분석하고 그 결과를 시각화한다. 특히, 질환과 같이 정상집단과 환자집단 간에 높은 클래스 불균형이 존재하는 경우에 대하여 민감도를 향상시킬 수 있는 비용 민감 학습을 사용하는 예측 모델을 제안한다. 본 연구에서는 대표적인 머신러닝 기술인 CART와 XGBoost를 사용하여 예측모델을 개발하고, 심혈관 질환 환자 데이터를 대상으로 예측하고 성능을 비교한다. 연구결과에 따르면 CART가 XGBoost 보다 더 높은 정확도와 특이도를 보였으며, 정확도는 약 70%~74%로 나타났다.

• Journal of Medicine and Life Science
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• 제20권4호
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• pp.141-157
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• 2023

Stroke is a leading cause of disability and death. The condition requires prompt diagnosis and treatment. The quality of care provided to patients with stroke can vary depending on the availability of medical resources, which in turn, can affect prognosis. Recently, there has been growing interest in using machine learning (ML) to support stroke diagnosis and treatment decisions based on large medical data sets. Current ML applications in stroke care can be divided into two categories: analysis of neuroimaging data and clinical information-based predictive models. Using ML to analyze neuroimaging data can increase the efficiency and accuracy of diagnoses. Commercial software that uses ML algorithms is already being used in the medical field. Additionally, the accuracy of predictive ML models is improving with the integration of radiomics and clinical data. is expected to be important for improving the quality of care for patients with stroke.

• Communications for Statistical Applications and Methods
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• 제31권2호
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• pp.213-234
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• 2024

In recent decades, increasing research attention has been directed toward predicting the price of stocks in financial markets using deep learning methods. For instance, recurrent neural network (RNN) is known to be competitive for datasets with time-series data. Long short term memory (LSTM) further improves RNN by providing an alternative approach to the gradient loss problem. LSTM has its own advantage in predictive accuracy by retaining memory for a longer time. In this paper, we combine both supervised and unsupervised dimension reduction methods with LSTM to enhance the forecasting performance and refer to this as a dimension reduction based LSTM (DR-LSTM) approach. For a supervised dimension reduction method, we use methods such as sliced inverse regression (SIR), sparse SIR, and kernel SIR. Furthermore, principal component analysis (PCA), sparse PCA, and kernel PCA are used as unsupervised dimension reduction methods. Using datasets of real stock market index (S&P 500, STOXX Europe 600, and KOSPI), we present a comparative study on predictive accuracy between six DR-LSTM methods and time series modeling.

• 문화기술의 융합
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• 제10권3호
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• pp.145-154
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• 2024

행정 빅데이터를 활용하는 예측 모형을 운영하기 위해서는 정책의 변화 및 변동성 심한 데이터의 특성이 고려가 되어야만 한다. 이런 상황을 고려하여 본 연구에서는 Cut-off Voting Classifier(CVC) 알고리즘을 제안한다. 제안하는 알고리즘은 여러개의 약 분류기를 활용하여 적중률이 급격하게 하락하는 것을 방지하는 알고리즘이다. 본 연구에서는 제안하는 알고리즘을 실험을 통해 성능을 검증한다. 성능검증 결과 급격하게 예측모형 적중률이 하락하는 상황에서도 안정적으로 예측률을 유지한다는 것을 입증할 수 있었다.

• Structural Engineering and Mechanics
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• 제91권3호
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• pp.279-289
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• 2024

The purpose of this study is to represent a useful alternative for the preliminary seismic vulnerability assessment of existing reinforced concrete buildings by introducing a statistical approach employing the binary logistic regression technique. Two different predictive statistical models, namely full and reduced models, were generated utilizing building characteristics obtained from the damage database compiled after 1999 Düzce earthquake. Among the inspected building parameters, number of stories, overhang ratio, priority index, soft story index, normalized redundancy ratio and normalized lateral stiffness index were specifically selected as the predictor variables for vulnerability classification. As a result, normalized redundancy ratio and soft story index were identified as the most significant predictors affecting seismic vulnerability in terms of life safety performance level. In conclusion, it is revealed that both models are capable of classifying the set of buildings being severely damaged or collapsed with a balanced accuracy of 73%, hence, both are able to filter out high-priority buildings for life safety performance assessment. Thus, in this study, having the same high accuracy as the full model, the reduced model using fewer predictors is proposed as a simple and viable classifier for determining life safety levels of reinforced concrete buildings in the preliminary seismic risk assessment.

• Asian Pacific Journal of Cancer Prevention
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• 제15권11호
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• pp.4705-4709
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• 2014

Objective: To determine the accuracy of magnetic resonance imaging (MRI) in detection of metastasis in pelvic and para-aortic lymph nodes from different gynecological malignancies. Materials and Methods: This retrospective cross sectional analytic study was conducted at the Department of Diagnostic Radiology, Aga Khan University Hospital Karachi Pakistan from January 2011 to December 2012. A sample of 48 women, age range between 20-79 years, fulfilling inclusion criteria were included. All patients had histopathologically proven gynecological malignancies in the cervix, endometrium or ovary and presented for a pretreatment MRI to our radiology department. Results: MRI was 100% sensitive and had a 100% positive predictive value to detect lymph node metastasis in lymph nodes with spiculated margins and 100% sensitive with a 75% positive predictive value to detect lymph node metastasis in a lymph node with lobulated margins. The sensitivity and positive predictive value of MRI to detect heterogeneous nodal enhancement were 100% and 75% respectively. Conclusions: Our study results reinforce that MRI should be used as a modality of choice in the pretreatment assessment of lymph nodes in proven gynaecological malignancies in order to determine the line of patientmanagement, distinguishing surgical from non-surgical cases.

• 한국전문물리치료학회지
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• 제27권2호
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• pp.140-148
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• 2020

Background: Scapular winging (SW) could be caused by tightness or weakness of the periscapular muscles. Although data mining techniques are useful in classifying or predicting risk of musculoskeletal disorder, predictive models for risk of musculoskeletal disorder using the results of clinical test or quantitative data are scarce. Objects: This study aimed to (1) investigate the difference between young women with and without SW, (2) establish a predictive model for presence of SW, and (3) determine the cutoff value of each variable for predicting the risk of SW using the decision tree method. Methods: Fifty young female subjects participated in this study. To classify the presence of SW as the outcome variable, scapular protractor strength, elbow flexor strength, shoulder internal rotation, and whether the scapula is in the dominant or nondominant side were determined. Results: The classification tree selected scapular protractor strength, shoulder internal rotation range of motion, and whether the scapula is in the dominant or nondominant side as predictor variables. The classification tree model correctly classified 78.79% (p = 0.02) of the training data set. The accuracy obtained by the classification tree on the test data set was 82.35% (p = 0.04). Conclusion: The classification tree showed acceptable accuracy (82.35%) and high specificity (95.65%) but low sensitivity (54.55%). Based on the predictive model in this study, we suggested that 20% of body weight in scapular protractor strength is a meaningful cutoff value for presence of SW.

• 한국환경복원기술학회지
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• 제19권6호
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• pp.19-30
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• 2016

The projection of climate-related range shift is critical information for conservation planning of Korean fir (Abies koreana E. H. Wilson). We first modeled the distribution of Korean fir under current climate condition using five single-model species distribution models (SDMs) and the pre-evaluation weighted ensemble method and then predicted the distributions under future climate conditions projected with HadGEM2-AO under four $CO_2$ emission scenarios, the Representative Concentration Pathways (RCP) 2.6, 4.5, 6.0 and 8.5. We also investigated the predictive uncertainty stemming from five individual algorithms and four $CO_2$ emission scenarios for better interpretation of SDM projections. Five individual algorithms were Generalized linear model (GLM), Generalized additive model (GAM), Multivariate adaptive regression splines (MARS), Generalized boosted model (GBM) and Random forest (RF). The results showed high variations of model performances among individual SDMs and the wide range of diverging predictions of future distributions of Korean fir in response to RCPs. The ensemble model presented the highest predictive accuracy (TSS = 0.97, AUC = 0.99) and predicted that the climate habitat suitability of Korean fir would increase under climate changes. Accordingly, the fir distribution could expand under future climate conditions. Increasing precipitation may account for increases in the distribution of Korean fir. Increasing precipitation compensates the negative effects of increasing temperature. However, the future distribution of Korean fir is also affected by other ecological processes, such as interactions with co-existing species, adaptation and dispersal limitation, and other environmental factors, such as extreme weather events and land-use changes. Therefore, we need further ecological research and to develop mechanistic and process-based distribution models for improving the predictive accuracy.

• 전력전자학회논문지
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• 제9권6호
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• pp.592-601
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• 2004

능동전력필터의 성능은 인버터의 특성, 제어 방법, 그리고 기준신호발생기의 정확도에 좌우된다. 이중에서 기준신호발생기의 정확도는 능동전력필터의 성능을 결정짓는 가장 중요한 요소이다. 본 논문은 개선된 적응예측필터로 구성한 새로운 기준신호발생기에 대해 소개하고 그 성능을 MATLAB을 이용한 시뮬레이션으로 검증하였다. 그리고 실제 하드웨어의 적용타당성을 평가하기 위해 개선된 기준신호발생기로 구성한 알고리즘을 TMS320C31 DSP(digital signal processor)에 구현하고 이 기준신호발생기를 기반으로 하는 단상능동전력필터의 축소모형을 제작하여 실험을 실시하였다. 시뮬레이션과 실험결과로 제안하는 기준신호발생기가 위상지연이 없는 기준신호를 추출하여 능동전력필터에 활용가능하며 그 성능도 대단히 우수함을 알 수 있었다.