• The Korean Journal of Cytopathology
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• v.10 no.1
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• pp.43-53
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• 1999

We retrospectively reviewed the results of 1,850 fine needle aspiration cytology(FNAC) of thyroid nodules performed from 1990 to 1991 in the Department of Pathology, Seoul National University Hospital. Among 1,528 cases and 322 cases aspirated by clinicians and a pathologist, 465 cases(30.4%) and 13 cases(4.0%) of the aspirates were inadequate, respectively. In 227 cases, correlation of the FNAC diagnosis and histologic diagnosis was done. Excluding the inadequate cases, the sensitivity nor the detection of neoplasm(malignancy together with follicular adenoma) was 86.4% and the specificity was 70.7%. The overall diagnostic accuracy was 79.0%. There were 16 false-positive cases(7.0%), and 19 false-negative cases(8.4%). The predictive value of each cytologic diagnosis was 92% in papillary carcinoma, and 100%, in Hashimoto's thyroiditis. The expectancy of malignancy was 52.8% in "suspicious malignancy" and 26.7% in "atypical lesion".

• Environmental and Resource Economics Review
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• v.14 no.1
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• pp.201-220
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• 2005

Recently, we have been witnessing new records of crude oil price hikes. One question which naturally arises would be the possibility and accuracy of forecasting crude oil prices. This study tries to answer the relative predictability of futures prices compared to the forecasts based on experts system. Using WTI crude oil spot and futures prices, this study performs simple statistical comparisons in forecasting accuracy and a formal test of differences in forecasting errors. According to statistical results, WTI crude oil futures market turns out to be equally efficient relative to EIA experts system. Consequently, WTI crude oil futures market could be utilized as a market-based tool for price forecasting and/or resource allocation for both of petroleum producers and consumers.

• Computers and Concrete
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• v.16 no.5
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• pp.741-757
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• 2015

In the last decade, several modeling approaches have been proposed and applied to estimate the high-performance concrete (HPC) slump flow. While HPC is a highly complex material, modeling its behavior is a very difficult issue. Thus, the selection and application of proper modeling methods remain therefore a crucial task. Like many other applications, HPC slump flow prediction suffers from noise which negatively affects the prediction accuracy and increases the variance. In the recent years, ensemble learning methods have introduced to optimize the prediction accuracy and reduce the prediction error. This study investigates the potential usage of bagging (Bag), which is among the most popular ensemble learning methods, in building ensemble models. Four well-known artificial intelligence models (i.e., classification and regression trees CART, support vector machines SVM, multilayer perceptron MLP and radial basis function neural networks RBF) are deployed as base learner. As a result of this study, bagging ensemble models (i.e., Bag-SVM, Bag-RT, Bag-MLP and Bag-RBF) are found superior to their base learners (i.e., SVM, CART, MLP and RBF) and bagging could noticeable optimize prediction accuracy and reduce the prediction error of proposed predictive models.

• Advances in nano research
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• v.16 no.6
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• pp.531-548
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• 2024

The study focuses on using remote sensing to gather data about the Earth's surface, particularly in urban environments, using satellites and aircraft-mounted sensors. It aims to develop a classification framework for road targets using multi-spectral imagery. By integrating Convolutional Neural Networks (CNNs) with XGBoost, the study seeks to enhance the accuracy and efficiency of road target identification, aiding urban infrastructure management and transportation planning. A novel aspect of the research is the incorporation of quantum sensors, which improve the resolution and sensitivity of the data. The model achieved high predictive accuracy with an MSE of 0.025, R-squared of 0.85, RMSE of 0.158, and MAE of 0.12. The CNN model showed excellent performance in road detection with 92% accuracy, 88% precision, 90% recall, and an f1-score of 89%. These results demonstrate the model's robustness and applicability in real-world urban planning scenarios, further enhanced by data augmentation and early stopping techniques.

• Nuclear Engineering and Technology
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• v.55 no.11
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• pp.4102-4111
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• 2023

In order to better perform thermal hydraulic calculation and analysis of supercritical water reactor, based on the experimental data of supercritical water, the model training and predictive analysis of the heat transfer coefficient of supercritical water were carried out by using the support vector machine (SVM) algorithm. The changes in the prediction accuracy of the supercritical water heat transfer coefficient are analyzed by the changes of the regularization penalty parameter C, the slack variable epsilon and the Gaussian kernel function parameter gamma. The predicted value of the SVM model obtained after parameter optimization and the actual experimental test data are analyzed for data verification. The research results show that: the normalization of the data has a great influence on the prediction results. The slack variable has a relatively small influence on the accuracy change range of the predicted heat transfer coefficient. The change of gamma has the greatest impact on the accuracy of the heat transfer coefficient. Compared with the calculation results of traditional empirical formula methods, the trained algorithm model using SVM has smaller average error and standard deviations. Using the SVM trained algorithm model, the heat transfer coefficient of supercritical water can be effectively predicted and analyzed.

• Asian Pacific Journal of Cancer Prevention
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• v.14 no.4
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• pp.2627-2630
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• 2013

Background: Ovarian cancer continues to pose a major challenge to physicians and radiologists. It is the third most common gynecologic malignancy and estimated to be fifth leading cancer cause of death in women, constituting 23% of all gynecological malignancies. Multi-detector computed tomography (MDCT) appears to offer an excellent modality in diagnosing ovarian cancer based on combination of its availability, meticulous technique, efficacy and familiarity of radiologists and physicians. The aim of this study was to compute sensitivity, specificity, positive and negative predictive values and diagnostic accuracy of 64-slice MDCT in classifying ovarian masses; 95% confidence intervals were reported. Materials and Methods: We prospectively designed a cross-sectional analytical study to collect data from July 2010 to August 2011 from a tertiary care hospital in Karachi, Pakistan. A sample of 105 women aged between 15-80 years referred for 64-MDCT of abdomen and pelvis with clinical suspicion of malignant ovarian cancer, irrespective of stage of disease, were enrolled by non-probability purposive sampling. All patients who were already known cases of histologically proven ovarian carcinoma and having some contraindication to radiation or iodinated contrast media were excluded. Results: Our prospective study reports sensitivity, specificity; positive and negative predictive values with 95%CI and accuracy were computed. Kappa was calculated to report agreement among the two radiologists. For reader A, MDCT was found to have 92% (0.83, 0.97) sensitivity and 86.7% (0.68, 0.96) specificity, while PPV and NPV were 94.5% (0.86, 0.98) and 86.7% (0.63, 0.92), respectively. Accuracy reported by reader A was 90.5%. For reader B, sensitivity, specificity, PPV and NPV were 94.6% (0.86, 0.98) 90% (0.72, 0.97) 96% (0.88, 0.99) and 87.1% (0.69, 0.95) respectively. Accuracy computed by reader B was 93.3%. Excellent agreement was found between the two radiologists with a significant kappa value of 0.887. Conclusion: Based on our study results, we conclude MDCT is a reliable imaging modality in diagnosis of ovarian masses accurately with insignificant interobserver variability.

• The Journal of Society for e-Business Studies
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• v.17 no.3
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• pp.85-103
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• 2012

This study focused on user evaluation for personalized recommender systems with the integrated view of performance of the system and user attitude of recommender systems. Since users' evaluations of recommender systems can be affected by recommendation outcomes and presentation methods, both system performances based on outcomes and user attitudes formed by the presentation methods should be considered when explaining users' evaluations. However, an integrated view of system performance and user attitudes has not been applied to explain users' evaluation of recommender systems. Thus, the goal of this study is to explain users' evaluations of recommender systems under the integrated view of predictive features and explanation features at the same time. Our findings suggest that social presence, both accuracy and noveltyhave impacts onuser satisfaction for recommender systems. Especially, predictive features including accuracy and novelty affected user satisfaction. Novelty as well as accuracy is one of the significant factors for user satisfaction while recommender systems provided usual items users have experienced when systems provide serendipitous items. Likewise, explanation features with social presence and self-reference were important for user evaluation of personalized recommender systems. For explanation features, while social presence appears as one of important factors to user satisfaction of evaluating personalized recommendations, self-reference has no significant effect on user's satisfaction for recommender systems when compared to the result of social presence. Self-referencing messages did not affect user satisfaction but the levels of self-referencing are different between low and high groups in the experiment.

• Asian Pacific Journal of Cancer Prevention
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• v.15 no.13
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• pp.5355-5358
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• 2014

Background: To assess the role of sonographic endometrial thickness and hysteroscopic polyp size in predicting premalignant and malignant polyps in postmenopausal women. Materials and Methods: A total of 328 postmenopausal women with abnormal uterine bleeding and thickened endometrium underwent operative hysteroscopy due to detection of endometrial polyps were included in this retrospective study. Preoperative endometrial thickness measured by transvaginal ultrasonography and polyp size on hysteroscopy were noted. Hysteroscopic resection with histology was performed for endometrial polyps. Endometrial thickness and polyp size were evaluated on the basis of final diagnosis established by histologic examination. Receiver operator characteristic curves were calculated to assess the sensitivity, specificity, positive predictive value, negative predictive value and diagnostic accuracy of endometrial thickness and polyp size for detecting pemalignant and malignant polyps. Results: Premalignant and malignant polyps were identified in 26 (7.9%) of cases. Sonographic measurement showed a greater endometrial thickness in cases of premalignant and malignant polyps when compared to benign polyps. On surgical hysteroscopy, premalignant and malignant polyps were also larger. Endometrial thickness demonstrated a sensitivity of 53.8%, specificity of 85.8%, PPV of 24.6% and NPV of 95.6% at a cut-off limit of 11.5 mm with diagnostic accuracy of 83.2%. Polyp size has a diagnostic accuracy of 94.8% with a sensitivity of 92.3%, specificity of 95.0%, PPV of 61.5% and NPV of 99.3% at a cut-off point of 19.5mm. Conclusions: Endometrial thickness measured by transvaginal ultrasonography is not sufficient in predicting premalignant and malignant endometrial polyps in postmenopausal women with abnormal uterine bleeding and thickened endometrium. Polyp size on hysteroscopy is a more accurate parameter, because of better sensitivity and specificity. However, while polyp size ${\geq}19.5mm$ seems to have a great accuracy for predicting premalignancy and malignancy, histologic evaluation is still necessary to exclude premalignant and malignant polyps.

• Asian Pacific Journal of Cancer Prevention
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• v.17 no.4
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• pp.1861-1864
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• 2016

Although early diagnosis of thyroid nodule type is very important, the diagnostic accuracy of standard tests is a challenging issue. We here aimed to find an optimal combination of factors to improve diagnostic accuracy for distinguishing malignant from benign thyroid nodules before surgery. In a prospective study from 2008 to 2012, 345 patients referred for thyroidectomy were enrolled. The sample size was split into a training set and testing set as a ratio of 7:3. The former was used for estimation and variable selection and obtaining a linear combination of factors. We utilized smoothly clipped absolute deviation (SCAD) logistic regression to achieve the sparse optimal combination of factors. To evaluate the performance of the estimated model in the testing set, a receiver operating characteristic (ROC) curve was utilized. The mean age of the examined patients (66 male and 279 female) was $40.9{\pm}13.4years$ (range 15- 90 years). Some 54.8% of the patients (24.3% male and 75.7% female) had benign and 45.2% (14% male and 86% female) malignant thyroid nodules. In addition to maximum diameters of nodules and lobes, their volumes were considered as related factors for malignancy prediction (a total of 16 factors). However, the SCAD method estimated the coefficients of 8 factors to be zero and eliminated them from the model. Hence a sparse model which combined the effects of 8 factors to distinguish malignant from benign thyroid nodules was generated. An optimal cut off point of the ROC curve for our estimated model was obtained (p=0.44) and the area under the curve (AUC) was equal to 77% (95% CI: 68%-85%). Sensitivity, specificity, positive predictive value and negative predictive values for this model were 70%, 72%, 71% and 76%, respectively. An increase of 10 percent and a greater accuracy rate in early diagnosis of thyroid nodule type by statistical methods (SCAD and ANN methods) compared with the results of FNA testing revealed that the statistical modeling methods are helpful in disease diagnosis. In addition, the factor ranking offered by these methods is valuable in the clinical context.

• Radiation Oncology Journal
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• v.35 no.4
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• pp.340-348
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• 2017

Purpose: To evaluate the diagnostic accuracy of computed tomography (CT), magnetic resonance imaging (MRI), and positron emission tomography/computed tomography (PET/CT) in predicting pelvic lymph node (LN) metastases in patients with cervical cancer. Materials and Methods: From January 2009 to March 2015, 114 patients with FIGO stage IA1-IIB uterine cervical cancer who underwent hysterectomy with pelvic lymphadenectomy and took CT, MRI, and PET/CT before surgery were enrolled in this study. The criteria for LN metastases were a LN diameter ${\geq}1.0cm$ and/or the presence of central necrosis on CT, a LN diameter ${\geq}1.0cm$ on MRI, and a focally increased FDG uptake on PET/CT. The sensitivity, specificity, positive predictive value (PPV), negative predictive value (NPV), and accuracy for pelvic LN metastases were estimated. Results: The sensitivity, specificity, PPV, NPV, and accuracy for detection of pelvic LN metastases were 51.4%, 85.9%, 41.3%, 90.1%, and 80.3% for CT; 24.3%, 96.3%, 56.3%, 86.8%, and 84.6% for MRI; and 48.6%, 89.5%, 47.4%, 90.0%, and 82.9% for PET/CT, respectively. The sensitivity of PET/CT and CT was higher than that of MRI (p=0.004 and p= 0.013, respectively). The specificity of MRI was higher than those of PET/CT and CT (p=0.002 and p=0.001, respectively). The difference of specificity between PET/CT and CT was not statistically significant (p=0.167). Conclusion: These results indicate that preoperative CT, MRI, and PET/CT showed low to moderate sensitivity and PPV, and moderate to high specificity, NPV, and accuracy. More efforts are necessary to improve sensitivity of imaging modalities in order to predict pelvic LN metastases.