• Journal of the Korean Society of Radiology
• /
• v.81 no.2
• /
• pp.379-394
• /
• 2020

Purpose To design a scoring system to predict malignancy of additional MRI-detected lesions in breast cancer patients. Materials and Methods Eighty-six lesions (64 benign and 22 malignant) detected on preoperative MRI of 68 breast cancer patients were retrospectively included. The clinico-radiologic features were correlated with the histopathologic results using the Student's t-test, Fisher's exact test, and logistic regression analysis. The scoring system was designed based on the significant predictive features of malignancy, and its diagnostic performance was compared with that of the Breast Imaging-Reporting and Data System (BI-RADS) category. Results Lesion size ≥ 8 mm (p < 0.001), location in the same quadrant as the primary cancer (p = 0.005), delayed plateau kinetics (p = 0.010), T2 isointense (p = 0.034) and hypointense (p = 0.024) signals, and irregular mass shape (p = 0.028) were associated with malignancy. In comparison with the BI-RADS category, the scoring system based on these features with suspicious non-mass internal enhancement increased the diagnostic performance (area under the receiver operating characteristic curve: 0.918 vs. 0.727) and detected three false-negative cases. With this scoring system, 22 second-look ultrasound examinations (22/66, 33.3%) could have been avoided. Conclusion The scoring system based on the lesion size, location relative to the primary cancer, delayed kinetic features, T2 signal intensity, mass shape, and non-mass internal enhancement can provide a more accurate approach to evaluate MRI-detected lesions in breast cancer patients.

• Asia-Pacific Journal of Business Venturing and Entrepreneurship
• /
• v.11 no.1
• /
• pp.75-84
• /
• 2016

This study is to analyze the efficiency of oriental hospitals using DEA. The input variables are the numbers of doctors, nurses, medical technicians, and beds. The output variables are the numbers of annual inpatients and outpatients. The statistical analysis tools used are EnPas and IBM SPSS Statistics 19. The result in efficiency analysis by establishment type showed that the national and public hospitals had the most efficiency. In the case of location, the efficiency of the oriental hospitals in Seoul was the highest but those in the Metropolitan areas had a relatively low efficiency. If the number of the beds was generally less than 50 beds, the hospitals were highly efficient, but the hospitals in the medium category of 51-100 beds were low in efficiency. The Logistic regression analysis conducted to analyze the variables that have affected the efficiency of oriental hospitals resulted that the efficiency increased by 1.045 everytime the number of nurses increased by 1.

• Journal of Korea Water Resources Association
• /
• v.51 no.11
• /
• pp.959-969
• /
• 2018

The aging water pipes induce various problems, such as water supply suspension due to breakage, insufficient water pressure, deterioration of water quality, damage by sink holes, and economic losses due to water leaks. However, it is impractical and almost impossible to repair and/or replace all deteriorated water pipes simultaneously. Hence, it is required to quantitatively evaluate the deterioration rate of individual pipes indirect way to determine the rehabilitation order of priority. In this study, ANN(Artificial Neural Network)-Clustering method is suggested as a new approach to assess and assort the water pipes. The proposed method has been applied to a water supply network of YG-county in Jeollanam-do. To assess the applicability of the model, the evaluation results were compared with the results of the Numerical Weighting Method (NWM), which is being currently utilized in practice. The assessment results are depicted in a water pipe map to intuitively grasp the degree of deterioration of the entire pipelines. The application results revealed that the proposed ANN-Clustering models can successfully assess the water pipe deterioration along with the conventional approach of NWM.

• Journal of KIISE:Computing Practices and Letters
• /
• v.9 no.5
• /
• pp.521-529
• /
• 2003

In this paper we present an improved method by using demographic information for overcoming the similarity miss-calculation from the sparsity problem in collaborative filtering recommendation systems. The similarity between a pair of users is only determined by the ratings given to co-rated items, so items that have not been rated by both users are ignored. To solve this problem, we add virtual neighbor's rating using demographic information of neighbors for improving prediction accuracy. It is one kind of extentions of traditional collaborative filtering methods using the peason correlation coefficient. We used the Grouplens movie rating data in experiment and we have compared the proposed method with the collaborative filtering methods by the mean absolute error and receive operating characteristic values. The results show that the proposed method is more efficient than the collaborative filtering methods using the pearson correlation coefficient about 9% in MAE and 13% in sensitivity of ROC.

• The Journal of Korean Association of Computer Education
• /
• v.15 no.2
• /
• pp.75-81
• /
• 2012

As the number of web users is increasing, the leakage of personal information is increasing. If some personal information is leaked, the victim can suffer from material damage or mental damage at the same time. Most of the leakages are result from the people who works for the personal information by accident or design. Hence, the Ministry of Public Administration and Security proposeed the measuring index and enumerates the details. The index is used in a system to check protection of a personal information. However, because this system is used to evaluate after the leakage, it cannot be used to construct some security system or programming a security system. To solve this problem, it needs to express the diversity of items and be able to count what assessors want to count. Thus, a summary sheet which displays the result of the tool will be presented in a radial form graph. Details will be presented as a bar graph. Therefore, it will be proposed that the tool can grasp the weak point and propose the direction of security.

• KIPS Transactions on Software and Data Engineering
• /
• v.10 no.11
• /
• pp.529-534
• /
• 2021

Computing thinking is regarded as one of the important skills required in the 21st century, and many countries have introduced and implemented computing thinking training courses. Among computational thinking education methods, educational game-based methods increase student participation and motivation, and increase access to computational thinking. Autothinking is an educational game developed for the purpose of providing computational thinking education to learners. It is an adaptive system that dynamically provides feedback to learners and automatically adjusts the difficulty according to the learner's computational thinking ability. However, because the game was designed based on rules, it cannot intelligently consider the computational thinking of learners or give feedback. In this study, game data collected through Autothikning is introduced, and game score prediction that reflects computational thinking is performed in order to increase the adaptability of the game by using it. To solve this problem, a comparative study was conducted on linear regression, decision tree, random forest, and support vector machine algorithms, which are most commonly used in regression problems. As a result of the study, the linear regression method showed the best performance in predicting game scores.

• Journal of the Society of Disaster Information
• /
• v.19 no.2
• /
• pp.241-253
• /
• 2023

Purpose: The purpose of this study is to introduce the structure and functions of an application developed for enhancing risk knowledge/perception, called KNOWRISK, and to identify and determine problems and requirements based on the usability evaluation results of the application. Method: The evaluation was conducted using the Mobile App Rating Scale (MARS) with a sample of 43 application users and related experts. Result: The application received a satisfactory evaluation score with an overall average of 4.074 points, and there was no significant difference in evaluation scores between experts and users. The highest score was for ease of use at 4.47, while the lowest score was for cost payment usage at 2.88. Conclusion: The results of this study suggest that efforts to increase risk knowledge and promote safe behavior using a mobile application can be an effective and efficient strategy for preventing industrial accidents and enhancing safety management.

• Korean Journal of Food Science and Technology
• /
• v.36 no.1
• /
• pp.75-80
• /
• 2004

This research was conducted to predict taste of barley kanjang using multiple regression analysis between taste components and sensory score. In the analysis of single correlation, the correlation coefficient of proline, alanine, Methionine, lysine, histidine, lavulinic acid, ${\alpha}$-ketogutaric acid was significant in 5% level. On the other hand, the taste of barley kanjang was not significantly effected by threonine, serine, cystein, phenylalanine, succinic acid, arabinose, xylose, and sucrose. It was impossible to measure taste of kanjang with barley bran to use simple correlation analysis. The 93% of barley kanjang taste was predicted using multiple regression analysis with taste components and sensory evaluation scores.

• Journal of the Korean Society for information Management
• /
• v.22 no.3 s.57
• /
• pp.213-232
• /
• 2005

The purpose of this study is to determine factors affecting text difficulty and to model objective formulas which measure readability scores. Some readability-related factors such as total number of letters, total number of syllables, total number of unique syllables, total number of sentences and total number of paragraphs were found through correlation analysis. Some regression equations with these factors as their variables were produced through regression analysis. A model estimating readability score from total number of unique syllables was a good formula, while a model with two factors, total number of unique syllables and new syllable occurrence ratio, was a better enhanced one. The readability score represents detailed level so we can recommend students read texts corresponding to their reading levels.

• The Journal of the Institute of Internet, Broadcasting and Communication
• /
• v.10 no.3
• /
• pp.51-59
• /
• 2010

There have been proposed several movie recommendation algorithms based on Collaborative Filtering(CF). CF decides neighbors whose ratings are the most similar to each other and it predicts how well users will like new movies, based on ratings from neighbors. This paper proposes a new method to improve the result predicted by CF based on genres of the movies seen by users. The proposed method can be combined to the most of all existing CF algorithms. In this paper, a performance evaluation has been conducted between an existing simple CF algorithm and CF-Genre that is the proposed genre-based method added to the CF algorithm. The result shows that CF-Genre improves 3.3% in prediction performance over existing CF algorithms.