• The Transactions of the Korea Information Processing Society
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• v.13 no.6
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• pp.260-268
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• 2024

Cervical cancer is the fourth most common cancer in women worldwide, and more than 604,000 new cases were reported in 2020 alone, resulting in approximately 341,831 deaths. The Cox regression model is a major model widely adopted in cancer research, but considering the existence of nonlinear associations, it faces limitations due to linear assumptions. To address this problem, this paper proposes ResSurvNet, a new model that improves the accuracy of cervical cancer mortality prediction using ResNet's residual learning framework. This model showed accuracy that outperforms the DNN, CPH, CoxLasso, Cox Gradient Boost, and RSF models compared in this study. As this model showed accuracy that outperformed the DNN, CPH, CoxLasso, Cox Gradient Boost, and RSF models compared in this study, this excellent predictive performance demonstrates great value in early diagnosis and treatment strategy establishment in the management of cervical cancer patients and represents significant progress in the field of survival analysis.

• Journal of forensic and investigative science
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• v.1 no.2
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• pp.5-19
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• 2006

Data mining is an information extraction activity to discover hidden facts contained in databases. Using a combination of machine learning, statistical analysis, modeling techniques and database technology, data mining finds patterns and subtle relationships in data and infers rules that allow the prediction of future results. Typical applications include market segmentation, customer profiling, fraud detection, evaluation of retail promotions, and credit risk analysis. Law enforcement agencies deal with mass data to investigate the crime and its amount is increasing due to the development of processing the data by using computer. Now new challenge to discover knowledge in that data is confronted to us. It can be applied in criminal investigation to find offenders by analysis of complex and relational data structures and free texts using their criminal records or statement texts. This study was aimed to evaluate possibile application of data mining and its limitation in practical criminal investigation. Clustering of the criminal cases will be possible in habitual crimes such as fraud and burglary when using data mining to identify the crime pattern. Neural network modelling, one of tools in data mining, can be applied to differentiating suspect's photograph or handwriting with that of convict or criminal profiling. A case study of in practical insurance fraud showed that data mining was useful in organized crimes such as gang, terrorism and money laundering. But the products of data mining in criminal investigation should be cautious for evaluating because data mining just offer a clue instead of conclusion. The legal regulation is needed to control the abuse of law enforcement agencies and to protect personal privacy or human rights.

• Journal of Bio-Environment Control
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• v.10 no.3
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• pp.172-180
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• 2001

A grape fruit is required for a lot of labor to harvest in time in Korea, since the fruit is cut and grabbed currently by hand. In foreign country, especially France, a grape harvester has been developed for processing to make wine out of a grape, not to eat a fresh grape fruit. However, a harvester which harvests to eat a fresh grape fruit has not been developed yet. Therefore, this study was designed and constructed to develope a image processing system for a fresh grape harvester. Its development involved the integration of a vision system along with an personal computer and two cameras. Grape recognition, which was able to found the accurate cutting position in three dimension by the end-effector, needed to find out the object from the background by using two different images from two cameras. Based on the results of this research the following conclusions were made: The model grape was located and measured within less than 1,100 mm from camera center, which means center between two cameras. The distance error of the calculated distance had the distance error within 5mm by using model image in the laboratory. The image processing system proved to be a reliable system for measuring the accurate distance between the camera center and the grape fruit. Also, difference between actual distance and calculated distance was found within 5 mm using stereo vision system in the field. Therefore, the image processing system would be mounted on a grape harvester to be founded to the position of the a grape fruit.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.18 no.2
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• pp.405-414
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• 2017

In this study, a survey for a satisfaction evaluation of the Test Report Information Service (TRIS) was conducted. A survey questionnaire on modified Information System Success Model(ISSM) of Delone and Mclean was carried out by 183 users in three groups, such as munition quality assurance agency, munition corporation, and test institute. As a survey result, training on the TRIS was in strong demand in all three groups. An understanding and proficiency of the overall system were different from the work process of each user group. In addition, the munition quality assurance agency needs to enhance the system function with its characteristics. Test institute has necessity of the linkage method with the TRIS depending on the authentication system. User groups are different in the operational method of TRIS between the contractor and cooperation. Accordingly, cooperation needs to be educated continually. This study can help in the construction of a Military Quality Integration Information System to secure the reliability of munitions.

• Journal of Intelligence and Information Systems
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• v.29 no.2
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• pp.241-265
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• 2023

A corporate insolvency prediction model serves as a vital tool for objectively monitoring the financial condition of companies. It enables timely warnings, facilitates responsive actions, and supports the formulation of effective management strategies to mitigate bankruptcy risks and enhance performance. Investors and financial institutions utilize default prediction models to minimize financial losses. As the interest in utilizing artificial intelligence (AI) technology for corporate insolvency prediction grows, extensive research has been conducted in this domain. However, there is an increasing demand for explainable AI models in corporate insolvency prediction, emphasizing interpretability and reliability. The SHAP (SHapley Additive exPlanations) technique has gained significant popularity and has demonstrated strong performance in various applications. Nonetheless, it has limitations such as computational cost, processing time, and scalability concerns based on the number of variables. This study introduces a novel approach to variable selection that reduces the number of variables by averaging SHAP values from bootstrapped data subsets instead of using the entire dataset. This technique aims to improve computational efficiency while maintaining excellent predictive performance. To obtain classification results, we aim to train random forest, XGBoost, and C5.0 models using carefully selected variables with high interpretability. The classification accuracy of the ensemble model, generated through soft voting as the goal of high-performance model design, is compared with the individual models. The study leverages data from 1,698 Korean light industrial companies and employs bootstrapping to create distinct data groups. Logistic Regression is employed to calculate SHAP values for each data group, and their averages are computed to derive the final SHAP values. The proposed model enhances interpretability and aims to achieve superior predictive performance.

• Journal of Global Scholars of Marketing Science
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• v.13
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• pp.113-133
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• 2004

The present study examines the the role of subjectively perceived factors of the attitude toward job-seeking activities in forming an intention to use a web. An integrative research model is presented and tested empirically. It includes the following two aspects of belief in Davis' TAM: perceived usefulness, perceived ease of use. Specially, internet job-seeking efficacy, or the belief in one's capabilities to organize and execute courses of Internet actions required to achieve given goals, is a potentially important factor in efforts to gain more favorable attitude toward Internet uses. Survey data were collected to develop a reliable operational measure of Internet job-seeking efficacy and to examine its construct validity. An four-item Internet job-seeking efficacy scale developed for the present study was found to be reliable and internally consistent. Also, many previous studies have established that perceived usefulness is an important factor influencing user acceptance and usage behavior of information technologies. However, very little research has been conducted to understand how that perception forms and changes over time. The current work presents and tests the determinants of perceived usefulness. The present study found that higher internet job-seeking efficacy is an important concept which is significantly related to job-seeking activities by positively influencing intention and performance as well as usefulness on the Internet.

• Management & Information Systems Review
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• v.28 no.1
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• pp.71-97
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• 2009

In this study, I compare the ability of alternative accounting method for employee stock option to reflect firm value using the Ohlson's(1995) valuation model for 200 firms. The each methods, I compare are employee stock option expense recognition based on the K-GAAP disclosures, and asset recognition at the grant date based on the SFAS No. 123 Exposure Draft: Accounting for stock-based compensation. The model include: (1) a model that uses reported earnings, equity book value, and compensation expense based on the K-GAAP disclosures; (2) a model that uses pro-forma earnings, equity book value and adds a measure of the unrecognized asset arising form granting of employee stock options. Finding form estimating equations that the K-GAAP method for calculating compensation has no explanatory power, and the SFAS No.123 Draft Exposure method for arising asset and fair value compensation better captures than market's perception of the economic impact of stock options on firm values. However, the correlation of employee stock option compensation expense is positive. These results suggest that incentive benefits derived from employee stock option plans outweigh the cost associated with plan. In addition, I couldn't find evidence that company in KOSDAQ that have high growth potential benefit more from employee stock option plan compared to lager, more mature firm in SEC.

• Journal of Internet of Things and Convergence
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• v.6 no.4
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• pp.7-14
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• 2020

This study analyzed the effect of the number of deaths of circulatory system diseases according to 12-day short-term exposure of carbon monoxide from January 2010 to December 2018, and predicted the future treatment cost of circulatory system diseases according to increased carbon monoxide concentration. Data were extracted from Air Korea of Korea Environment Corporation and Korea Statistical Office, and analyzed using Poisson regression analysis and ARIMA intervention model. For statistical processing, SPSS Ver. 21.0 program was used. The results of the study are as follows. First, as a result of analyzing the relationship between the impact of short-term carbon monoxide exposure on death of circulatory system diseases from the day to the previous 11 days, it was found that the previous 11 days had the highest impact. Second, with the increase in carbon monoxide concentration, the future circulatory system disease treatment cost was estimated at 10,123 billion won in 2019, higher than the observed value of 9,443 billion won at the end of December 2018. In addition, when summarized by month, it can be seen that the cost of treatment for circulatory diseases increases from January to December, reflecting seasonal fluctuations. Through such research, the future for a healthy life for all citizens can be realized by distributing various devices and equipment utilizing IoT to preemptively respond to the increase in air pollutants such as carbon monoxide.

• The Journal of the Korea institute of electronic communication sciences
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• v.9 no.10
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• pp.1071-1078
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• 2014

Recently, information providing service using Big Data is being expanded. Big Data processing technology is actively being academic research to an important issue in the IT industry. In this paper, we analyze a skilled pattern of welder through Big Data analysis or extraction of welding based on R programming. We are going to reduce cost on welding work including weld quality, weld operation time by providing analyzed results non-skilled welder. Welding has a problem that should be invested long time to be a skilled welder. For solving these issues, we apply connection rules algorithms and regression method to much pattern variable for welding pattern analysis of skilled welder. We analyze a pattern of skilled welder according to variable of analyzed rules by analyzing top N rules. In this paper, we confirmed the pattern structure of power consumption rate and wire consumption length through experimental results of analyzed welding pattern analysis.

• Transactions of the Korean Society of Mechanical Engineers A
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• v.36 no.6
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• pp.645-651
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• 2012

In this paper, we propose a new method for stabilizing the image captured from a camera mounted on a buoy robot. In this study, in order to solve the problem of cumulative errors and noise produced by a general gyro sensor measuring the orientation angle of the buoy robot, we propose new method for stabilizing the image. In this method, image processing techniques are combined with a newly designed target mounting mechanism that adapts to wave fluctuations. New target extraction and angle estimation techniques are introduced, along with the new mounting mechanism used for the camera and the target, which produce a stabilized image even if the buoy robot is on fluctuating waves.