• Nuclear Engineering and Technology
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• 제49권5호
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• pp.914-919
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• 2017

While industry understands the importance of keeping equipment operational and well maintained, the importance of tracking maintenance information in reliability models is often overlooked. Prognostic models can be used to predict the failure times of critical equipment, but more often than not, these models assume that all maintenance actions are the same or do not consider maintenance at all. This study investigates the influence of integrating maintenance information on prognostic model prediction accuracy. By incorporating maintenance information to develop maintenance-dependent prognostic models, prediction accuracy was improved by more than 40% compared with traditional maintenance-independent models. This study acts as a proof of concept, showing the importance of utilizing maintenance information in modern prognostics for industrial equipment.

• Journal of information and communication convergence engineering
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• 제19권3호
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• pp.166-174
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• 2021

A large volume of patient data is generated from various devices used in healthcare applications. With increase in the volume of data generated in the healthcare industry, more wellness monitoring is required. A cloud-enabled analysis of healthcare data that predicts patient risk factors is required. Machine learning techniques have been developed to address these medical care problems. A novel technique called the radix-trie-based Tanimoto kernel regressive infomax boost classification (RT-TKRIBC) technique is introduced to analyze the heterogeneous health data in the cloud to predict the health risks and send alerts. The infomax boost ensemble technique improves the prediction accuracy by finding the maximum mutual information, thereby minimizing the mean square error. The performance evaluation of the proposed RT-TKRIBC technique is realized through extensive simulations in the cloud environment, which provides better prediction accuracy and less prediction time than those provided by the state-of-the-art methods.

• ETRI Journal
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• 제43권1호
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• pp.82-94
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• 2021

In this work, six voiced/unvoiced speech classifiers based on the autocorrelation function (ACF), average magnitude difference function (AMDF), cepstrum, weighted ACF (WACF), zero crossing rate and energy of the signal (ZCR-E), and neural networks (NNs) have been simulated and implemented in real time using the TMS320C6713 DSP starter kit. These speech classifiers have been integrated into a linear-predictive-coding-based speech analysis-synthesis system and their performance has been compared in terms of the percentage of the voiced/unvoiced classification accuracy, speech quality, and computation time. The results of the percentage of the voiced/unvoiced classification accuracy and speech quality show that the NN-based speech classifier performs better than the ACF-, AMDF-, cepstrum-, WACF- and ZCR-E-based speech classifiers for both clean and noisy environments. The computation time results show that the AMDF-based speech classifier is computationally simple, and thus its computation time is less than that of other speech classifiers, while that of the NN-based speech classifier is greater compared with other classifiers.

• 한국멀티미디어학회논문지
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• 제25권2호
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• pp.287-296
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• 2022

Today, air pollution is becoming a severe issue worldwide and various policies are being implemented to solve environmental pollution. In major cities, public bicycles are installed and operated to reduce pollution and solve transportation problems, and operational information is collected in real time. However, research using public bicycle operation information data has not been processed. This study uses the daily weather data of Korea Meteorological Agency and real-time air pollution data of Korea Environment Corporation to predict the amount of daily rental bicycles. Cross- validation, principal component analysis and multiple regression analysis were used to determine the independent variables of the predictive model. Then, the study selected the elements that satisfy the significance level, constructed a model, predicted the amount of daily rental bicycles, and measured the accuracy.

• International Journal of Computer Science & Network Security
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• 제22권10호
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• pp.11-16
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• 2022

Applying predictive analytics to predict software defects has improved the overall quality and decreased maintenance costs. Many supervised and unsupervised learning algorithms have been used for defect prediction on publicly available datasets. Most of these datasets suffer from an imbalance in the output classes. We study the impact of class imbalance in the defect datasets on the efficiency of the defect prediction model and propose a CPP method for handling imbalances in the dataset. The performance of the methods is evaluated using measures like Matthew's Correlation Coefficient (MCC), Recall, and Accuracy measures. The proposed sampling technique shows significant improvement in the efficiency of the classifier in predicting defects.

• Communications for Statistical Applications and Methods
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• 제30권6호
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• pp.577-587
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• 2023

Conventional categorical data imputation techniques, such as mode imputation, often encounter issues related to overestimation. If the variable has too many categories, multinomial logistic regression imputation method may be impossible due to computational limitations. To rectify these limitations, we propose a two-stage imputation method. During the first stage, we utilize the Boruta variable selection method on the complete dataset to identify significant variables for the target categorical variable. Then, in the second stage, we use the important variables for the target categorical variable for logistic regression to impute missing data in binary variables, polytomous regression to impute missing data in categorical variables, and predictive mean matching to impute missing data in quantitative variables. Through analysis of both asymmetric and non-normal simulated and real data, we demonstrate that the two-stage imputation method outperforms imputation methods lacking variable selection, as evidenced by accuracy measures. During the analysis of real survey data, we also demonstrate that our suggested two-stage imputation method surpasses the current imputation approach in terms of accuracy.

• 대한세포병리학회지
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• 제8권2호
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• pp.120-128
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• 1997

Fine needle aspiration cytology of breast lesion is well known as a simple, economic and effective diagnostic modality. For the evaluation of cytohistologic correlation, 256 cases of cytologic smears and subsequent histologic sections during 2-year period from Jan. 1995 to Dec. 1996 were reviewed. 1. Fifteen cases(5.9%) were proven as insufficient for evaluation, and 13 of them were fibrocystic change histologically. One case of carcinoma exhibiting sufficient amount of aspirates with no malignant cells on smear was regarded as inadequate. 2. Cytohistologic correlation of 240 cases revealed sensitivity 87.0%, specificity 100.0%, positive predictive value 100.0%, negative predictive value 97.0%, false positive rate 0.0% and false negative rate 13.0%. Total diagnostic accuracy is 95.7%. 3. Total 6 cases of negative were due to small amount of aspirates containing scantiness of malignant cells in two and underestimation in four. 4. Diagnostic concordance rates of fibrocystic change and fibroadenoma were 95.5% and 80.0%, respectively. Diagnostic discrepancies were noted in 7 cases of fibrocystic change and 6 cases of fibroadenoma, however, cytologic discrimination of two entities was not easy in seven of them. 5. In a case of phyllodes tumor and a case of duct ectasia, the discrepancy was due to targeting error. Other three cases(lymphoma, adenomyoepithelioma and granulomatous mastitis) were misinterpreted because of poor acquaintance with those entities. Diagnostic accuracy of fine needle aspiration cytology of breast lesions are relatively high. However, good technique on aspiration and adequate interpretation are necessary to reduce the false negative rate and the discrepancy between cytologic and histologic diagnoses.

• Asian Pacific Journal of Cancer Prevention
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• 제16권6호
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• pp.2327-2331
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• 2015

Purpose: To determine the accuracy of visual inspection with acetic acid (VIA) in detecting high-grade cervical intraepithelial neoplasia (CIN) in pre- and post-menopausal women with atypical squamous cells of undetermined significance (ASC-US) and low grade squamous intraepithelial lesion (LSIL) Papanicolaou (Pap) smears. Materials and Methods: Two hundred women (150 pre-menopausal and 50 post-menopausal) with ASC-US and LSIL cytology who attended the colposcopy clinic, Thammasat University Hospital, between March 2013 and August 2014 were included. All women underwent VIA testing and colposcopy by gynecologic oncologists. Diagnostic values of VIA testing including sensitivity, specificity, positive predictive value (PPV) and negative predictive value (NPV) for detecting high-grade CIN were determined using the histopathology obtained from colposcopic-directed biopsy as a gold standard. Results: VIA testing was positive in 54/150 (36%) pre-menopausal women and 5/50 (10%) post-menopausal women. Out of 54 pre-menopausal women with positive VIA testing, 15 (27.8%) had high-grade CIN and 39 (72.2%) had either CIN 1 or insignificant pathology. Ten (10.4%), 43 (44.8%) and 43 (44.8%) out of the remaining 96 pre-menopausal women with negative VIA testing had high-grade CIN, CIN 1 and insignificant pathology, respectively. Out of 5 post-menopausal women with positive VIA testing, there were 4 (80%) women with high-grade CIN, and 1 (20%) women with insignificant pathology. Out of 45 VIA-negative post-menopausal women, 42 (93.3%) women had CIN 1 and insignificant pathology, and 3 (6.7%) had high-grade CIN. Sensitivity, specificity, PPV and NPV of the VIA testing were 59.4%, 76.2%, 32.2% and 90.8%, respectively (60%, 68.8%, 27.8% and 89.6% in pre-menopausal women and 57.1%, 97.7%, 80% and 93.3% in post-menopausal women). Conclusions: VIA testing may be used as a screening tool for detecting high-grade CIN in women with minor cervical cytological abnormalities in a low-resource setting in order to lower the rate of colposcopy referral.

• Nutrition Research and Practice
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• 제9권4호
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• pp.370-378
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• 2015

BACKGROUND/OBJECTIVES: Athletes generally desire changes in body composition in order to enhance their athletic performance. Often, athletes will practice chronic energy restrictions to attain body composition changes, altering their energy needs. Prediction of resting metabolic rates (RMR) is important in helping to determine an athlete's energy expenditure. This study compared measured RMR of athletic and non-athletic adolescents with predicted RMR from commonly used prediction equations to identify the most accurate equation applicable for adolescent athletes. SUBJECTS/METHODS: A total of 50 athletes (mean age of $16.6{\pm}1.0years$, 30 males and 20 females) and 50 non-athletes (mean age of $16.5{\pm}0.5years$, 30 males and 20 females) were enrolled in the study. The RMR of subjects was measured using indirect calorimetry. The accuracy of 11 RMR prediction equations was evaluated for bias, Pearson's correlation coefficient, and Bland-Altman analysis. RESULTS: Until more accurate prediction equations are developed, our findings recommend using the formulas by Cunningham (-29.8 kcal/day, limits of agreement -318.7 and +259.1 kcal/day) and Park (-0.842 kcal/day, limits of agreement -198.9 and +196.9 kcal/day) for prediction of RMR when studying male adolescent athletes. Among the new prediction formulas reviewed, the formula included in the fat-free mass as a variable [$RMR=730.4+15{\times}fat-free\;mass$] is paramount when examining athletes. CONCLUSIONS: The RMR prediction equation developed in this study is better in assessing the resting metabolic rate of Korean athletic adolescents.

• Asian Pacific Journal of Cancer Prevention
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• 제15권10호
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• pp.4169-4173
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• 2014

Background: Endobronchial ultrasound-guided transbronchial needle aspiration (EBUS-TBNA) is highly accurate in diagnosing mediastinal lymphadenopathies of lung cancer and benign disorders. However, the utility of EBUS-TBNA in the diagnosis of mediastinal lymphomas is unclear. The aim of this study was to determine the diagnostic value of EBUS-TBNA in patients with suspected lymphoma. Materials and Methods: Sixty-eight patients with isolated mediastinal lymphadenopathy and suspected of lymphoma were included in the study. EBUS-TBNA was performed on outpatients under moderate sedation. The sensitivity, specificity, negative predictive value and diagnostic accuracy of EBUS-TBNA were calculated. Results: Sixty-four patients were diagnosed by EBUS-TBNA, but four patients with non-diagnostic EBUS-TBNA required surgical procedures. Thirty-five (51.5%) patients had sarcoidosis, six (8.8%) had reactive lymphadenopathy, nine (13.3%) had tuberculosis, one (1.5%) had squamous cell carcinoma, two (2.9%) had sarcoma and fifteen (22%) had lymphoma (follicular center cell, large B-cell primary, and Hodgkin lymphomas in three, two, and ten, respectively). Of the 15 lymphoma patients, thirteen were diagnosed by EBUS and two by thoracotomy and mediastinoscopy. The sensitivity, specificity, negative predictive value, and diagnostic accuracy of EBUS-TBNA for the diagnosis of lymphoma were calculated as 86.7%, 100%, 96.4%, and 97%, respectively. Conclusions: EBUS-TBNA can be employed in the diagnosis of mediastinal lymphoma, instead of more invasive surgical procedures.