• Journal of Radiation Industry
• /
• v.8 no.3
• /
• pp.155-159
• /
• 2014

RI-Biomics is a new concept that combines radioisotopes (RI) and Biomics. For efficient collection of information, establishment of database for technical information system and its application to the system, there is an increasing need for constructing the standard classification system of technical information by its systematical classification. In this paper, we have summarized the development process of the standard classification system of technical information in the field of RI-Biomics and its application to the system. Constructing the draft version for the standard classification system of technical information was based on that standard classification one in national science and technology in Korea. The final classification system was then derived through the reconstruction and the feedback process based on the consultation from the 7 experts. These results were applied to the database of technical information system after transforming as standard code. Thus, the standard classification system were composed of 5 large classifications and 20 small classifications, and those classification are expected to establish the foundation of information system by achieving the circular structure of collection-analysis-application of information.

• Journal of Korea Multimedia Society
• /
• v.21 no.12
• /
• pp.1417-1424
• /
• 2018

In order to improve the performance of image classifications using Convolutional Neural Networks (CNN), applying a category hierarchy to the classification can be a useful idea. However, the visual separation of object categories is very different according to the upper and lower category levels and highly uneven in image classifications. Therefore, it is doubtable whether the use of category hierarchies for classification is effective in CNN. In this paper, we have clarified whether the image classification using category hierarchies improves classification performance, and found at which level of hierarchy classification is more effective. For experiments we divided the image classification task according to the upper and lower category levels and assigned image data to each CNN model. We identified and compared the results of three classification models and analyzed them. Through the experiments, we could confirm that classification effectiveness was not improved by reduction of number of categories in a classification model. And we found that only with the re-training method in the last network layer, the performance of lower category classification was not improved although that of higher category classification was improved.

• Journal of Korea Multimedia Society
• /
• v.22 no.8
• /
• pp.832-843
• /
• 2019

Prostate cancer is a high-risk with a high incidence and is a disease that occurs only in men. Accurate diagnosis of cancer is necessary as the incidence of cancer patients is increasing. Prostate cancer is also a disease that is difficult to predict progress, so it is necessary to predict in advance through prognosis. Therefore, in this paper, grade classification is attempted based on texture feature extraction. There are two main methods of classification: Uses One-way Analysis of Variance (ANOVA) to determine whether texture features are significant values, compares them with all texture features and then uses only one classification i.e. Benign versus. The second method consisted of more detailed classifications without using ANOVA for better analysis between different grades. Results of both these methods are compared and analyzed through the machine learning models such as Support Vector Machine and K-Nearest Neighbor. The accuracy of Benign versus Grade 4&5 using the second method with the best results was 90.0 percentage.

• Journal of Gastric Cancer
• /
• v.22 no.4
• /
• pp.273-305
• /
• 2022

Gastric cancer (GC) is one of the most common lethal malignant neoplasms worldwide, with limited treatment options for both locally advanced and/or metastatic conditions, resulting in a dismal prognosis. Although the widely used morphological classifications may be helpful for endoscopic or surgical treatment choices, they are still insufficient to guide precise and/or personalized therapy for individual patients. Recent advances in genomic technology and high-throughput analysis may improve the understanding of molecular pathways associated with GC pathogenesis and aid in the classification of GC at the molecular level. Advances in next-generation sequencing have enabled the identification of several genetic alterations through single experiments. Thus, understanding the driver alterations involved in gastric carcinogenesis has become increasingly important because it can aid in the discovery of potential biomarkers and therapeutic targets. In this article, we review the molecular classifications of GC, focusing on The Cancer Genome Atlas (TCGA) classification. We further describe the currently available biomarker-targeted therapies and potential biomarker-guided therapies. This review will help clinicians by providing an inclusive understanding of the molecular pathology of GC and may assist in selecting the best treatment approaches for patients with GC.

• International Journal of Computer Science & Network Security
• /
• v.23 no.8
• /
• pp.190-198
• /
• 2023

To enhance customer satisfaction for higher profits, an e-commerce sector can establish a continuous relationship and acquire new customers. Utilize machine-learning models to analyse their customer's behavioural evidence to produce their competitive advantage to the e-commerce platform by helping to improve overall satisfaction. These models will forecast customers who will churn and churn causes. Forecasts are used to build unique business strategies and services offers. This work is intended to develop a machine-learning model that can accurately forecast retainable customers of the entire e-commerce customer data. Developing predictive models classifying different imbalanced data effectively is a major challenge in collected data and machine learning algorithms. Build a machine learning model for solving class imbalance and forecast customers. The satisfaction accuracy is used for this research as evaluation metrics. This paper aims to enable to evaluate the use of different machine learning models utilized to forecast satisfaction. For this research paper are selected three analytical methods come from various classifications of learning. Classifier Selection, the efficiency of various classifiers like Random Forest, Logistic Regression, SVM, and Gradient Boosting Algorithm. Models have been used for a dataset of 8000 records of e-commerce websites and apps. Results indicate the best accuracy in determining satisfaction class with both gradient-boosting algorithm classifications. The results showed maximum accuracy compared to other algorithms, including Gradient Boosting Algorithm, Support Vector Machine Algorithm, Random Forest Algorithm, and logistic regression Algorithm. The best model developed for this paper to forecast satisfaction customers and accuracy achieve 88 %.

• KSII Transactions on Internet and Information Systems (TIIS)
• /
• v.18 no.3
• /
• pp.591-609
• /
• 2024

In this study, preprocessings with all combinations were examined in terms of the effects on decreasing word number, shortening the duration of the process and the classification success in balanced and imbalanced datasets which were unbalanced in different ratios. The decreases in the word number and the processing time provided by preprocessings were interrelated. It was seen that more successful classifications were made with Turkish datasets and English datasets were affected more from the situation of whether the dataset is balanced or not. It was found out that the incorrect classifications, which are in the classes having few documents in highly imbalanced datasets, were made by assigning to the class close to the related class in terms of topic in Turkish datasets and to the class which have many documents in English datasets. In terms of average scores, the highest classification was obtained in Turkish datasets as follows: with not applying lowercase, applying stemming and removing stop words, and in English datasets as follows: with applying lowercase and stemming, removing stop words. Applying stemming was the most important preprocessing method which increases the success in Turkish datasets, whereas removing stop words in English datasets. The maximum scores revealed that feature selection, feature size and classifier are more effective than preprocessing in classification success. It was concluded that preprocessing is necessary for text classification because it shortens the processing time and can achieve high classification success, a preprocessing method does not have the same effect in all languages, and different preprocessing methods are more successful for different languages.

• The Journal of Korean Orthopaedic Ultrasound Society
• /
• v.7 no.2
• /
• pp.77-83
• /
• 2014

Purpose: The purpose of current study was to evaluate the validity of the existing radiological classifications as a diagnostic modality for predicting characteristics of calcific deposition in calcific tendinitis of the shoulder joint. For that purpose, we determined the inter-observer reliability for evaluating diagnostic precisions of the classification and also evaluated diagnostic accuracy of predicting the toothpaste type calcific deposition. Materials and Methods: We performed retrospective study with total 26 patients surgically treated with calcific tendinitis of the shoulder joint from March 2010 to October 2013. Two independent observers reviewed preoperative radiographs of shoulder joints, and classified the characteristics of calcific depositions according to the criteria of Gartner, DePalma and Patte. Cohen's kappa were calculated for each classifications to evaluate inter-observer reliability. Sensitivity, specificity, positive likelihood ratio, negative likelihood ratio and diagnostic odds ratio were determined for type of calcific depositions with Gartner type III, DePalma type I, and Patte type II for predicting toothpaste type calcific deposition. Results: The values of Cohen's kappa were the highest in the classification of Patte, 0.62, and the values for the classifications of DePalma and Gartner were 0.56 and 0.36, respectively. The sensitivities for predicting toothpaste type calcific deposition in Gartner Type III, DePalma type I and Patte type II were 83.3%, 91.7%, and 58.3%, respectively. Specificities were 85.7% 50.0% and 64%, positive likelihood ratios were 5.833, 1.833 and 1.633, negative likelihood ratios were 0.194, 0.167 and 0.648, and diagnostic odds ratios were 30.00, 11.00 and 2.52, respectively. Conclusion: There were no radiologic classifications of calcific tendinitis which has both high precision and accuracy. Further studies with other diagnostic modalities such as ultrasonography are needed for predicting characteristics of calcific deposition in calcific tendinitis of the shoulder joint.

• Journal of the Korean Geotechnical Society
• /
• v.21 no.3
• /
• pp.19-25
• /
• 2005

A tunnel that uses the RMR method or the Q-system is called a 'modem tunnel' because the New Austrian Tunneling Method (NATM) is not employed, even though shotcrete and rock bolts are used as support. It is known that the modem tunnel, which is supported by shotcrete, is basically different from the conventional tunnel, which is supported by steel ribs. In order to preserve the load-carrying capacity of the rock mass, loosening and excessive rock deformations must be minimized. Although it is known that this can be achieved by applying shotcrete in the case of the modem tunnel, this has not been clearly demonstrated. In order to inspect the distinctions between the conventional tunnel and the modern tunnel, their support characteristics and the rock loads of the rock mass classifications are compared. Terzaghi's rock load classification was used as the conventional tunnel's representative rock mass classification. The RMR method and the Q-system were adopted as the modem tunnel's representative rock mass classification. The study's results show that the load-carrying capacity of shotcrete, when used as the main support in the modern tunnel, is greater than the load-capacity of the steel ribs used in the conventional tunnel. Because it has been verified that the rock loads of their rock mass classifications are not different, then, according to the rock mass classifications, the load-carrying capacity of the rock mass of the modern tunnel, which uses shotcrete, is not greater than that of the conventional tunnel.

• The Journal of Internal Korean Medicine
• /
• v.30 no.4
• /
• pp.696-707
• /
• 2009

Objective : We designed this study to investigate differences between stroke reattack and stroke first attack group to establish fundamental data and prevent a secondary stroke. Methods : 826 subjects were recruited from the patients admitted to the department of internal medicine at Kyung Hee University Oriental Medical Center, Kyung Hee University East-West Neo Medical Center, Kyungwon University Incheon Oriental Medical Center, Kyungwon University Songpa Oriental Medical Center and Dongguk University Ilsan Oriental Medical Center from 1 April 2007 to 31 August 2009. We compared general characteristics, classification of diagnosis, subtypes of cerebral infarction, risk factors, Sasang constitution, diagnostic classifications between stroke reattck and stroke first attack groups. Results : 1. In general characteristics, age differed significantly between the reattck and first attack groups. 2. Classification of diagnosis differed significantly between reattck and first attack groups. 3. In risk factors, hypertension, diabetes mellitus, alcohol drinking, and stress were significantly different between reattck and first attack groups. 4. Diagnostic classifications were significantly different between reattck and first attack groups. Conclusion : To prevent recurrence of stroke, education on stroke risk factors associated with recurrence is needed. In addition, those who are diagnosed as Dampness-Phlegm need to be well-controlled.

• Health Policy and Management
• /
• v.23 no.1
• /
• pp.35-43
• /
• 2013

Background: Diagnosis procedure combination (DPC) has recently been introduced in Korea as a demonstration project and it has aimed the improvement of accuracy in bundled payment instead of Diagnosis related group (DRG). The purpose of this study is to investigate that the model of end-stage liver disease (MELD) score as the severity classification of liver diseases is adequate for improving reimbursement of DPC. Methods: The subjects of this study were 329 patients of liver disease (Korean DRG ver. 3.2 H603) who had discharged from National Health Insurance Corporation Ilsan Hospital which is target hospital of DPC demonstration project, between January 1, 2007 and July 31, 2010. We tested the cost differences by severity classifications which were DRG severity classification and clinical severity classification-MELD score. We used a multiple regression model to find the impacts of severity on total medical cost controlling for demographic factor and characteristics of medical services. The within group homogeneity of cost were measured by calculating the coefficient of variation and extremal quotient. Results: This study investigates the relationship between medical costs and other variables especially severity classifications of liver disease. Length of stay has strong effect on medical costs and other characteristics of patients or episode also effect on medical costs. MELD score for severity classification explained the variation of costs more than DRG severity classification. Conclusion: The accuracy of DRG based payment might be improved by using various clinical data collected by clinical situations but it should have objectivity with considering availability. Adequate compensation for severity should be considered mainly in DRG based payment. Disease specific severity classification would be an alternative like MELD score for liver diseases.