• Journal of East Asia Management
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• v.3 no.2
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• pp.21-41
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• 2022

This study aims to analyze the characteristics of the management situation of the Korea Railroad Corporation(KORAIL) through the management innovation process of the KORAIL and to suggest its implications for military application. Despite stable demand, the railway passenger industry had the limitation of not being able to abolish deficit routes due to public service obligations. In addition, the launch of the Suseo High-Speed Line has introduced a competitive system, posing a threat to corporate management. KORAIL wanted to overcome this crisis by innovating its management through the utilization of big data, improvement of the freight business, decentralization of demand, the introduction of tourism railroads, and development of station influence areas. By utilizing big data, KORAIL was able to optimize the railway fare system while reducing fixed costs spent on railway maintenance. It also drastically reduced the station of cargo and created a base station to pursue economies of scale. On the other hand, the existing exclusive station system was abolished to solve the chronic saturation of the downtown area, and the railway demand was moved to Gwangmyeong Station and Suwon Station to optimize the passenger supply. In particular, it developed a new business model called the tourism railway by developing the mountain Byeokjin Line, which was a chronic deficit line, and sought to improve liquidity through the development of the station influence area. Such a process of innovation at KORAIL suggests an appropriate direction in seeking ways to innovate the military medical institutions. First of all, the necessity of improving organizational immersion through the development of a personnel structure suitable for the compulsory organization, while expanding the facilities of the division and corps, and reducing the time required for medical treatment and waiting through the establishment of a data-based medical system was suggested. Next, it was also discussed to integrate the National Health Medical College, which received accreditation as a medical facility through the designation of advanced general hospitals and is ultimately under discussion with the Medical Institution. Through this, we hope that the military medical institutions, which are facing various challenges, will overcome existing limitations and be re-lighted as innovative institution that provides comprehensive public health services.

• Journal of Convergence Society for SMB
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• v.3 no.1
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• pp.15-22
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• 2013

Recently, in Korea lacks the innovation for small and medium enterprises the proportion of enterprises' capabilities are poor. In addition, sales of small business and medium scale venture are vulnerable because it is difficult to expect developments in the situation. thus the government 3.0 based small business and medium scale venture will present ways to take advantage of big data. Government 3.0 based big data infrastructure, small businesses and small and medium-sized ventures to build their autonomy is required so that you can take advantage of the platform advantage.

• The Journal of the Korea institute of electronic communication sciences
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• v.13 no.5
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• pp.1085-1092
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• 2018

Recently, Internet (IoT) and big data have been utilized in various fields such as medical, military and sports. Korea Nazarene University is a rehabilitation-welfare university with about 300 students with disabilities. This paper proposes a smart campus that provides the optimal path for the calculation of the route and risk avoidance using the BLE beacon and the 3-axis acceleration sensor when the students with disabilities move in the campus both indoors and outdoors. So we can manage the big data and sensor-based IoT technology for students with disabilities.

• Annual Conference on Human and Language Technology
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• 2021.10a
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• pp.629-632
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• 2021

기계 번역(machine translation)은 자연 언어로 된 텍스트를 다른 언어로 자동 번역 하는 기술로, 최근에는 주로 신경망 기계 번역(Neural Machine Translation) 모델에 대한 연구가 진행되었다. 신경망 기계 번역은 일반적으로 자기회귀(autoregressive) 모델을 이용하며 기계 번역에서 좋은 성능을 보이지만, 병렬화할 수 없어 디코딩 속도가 느린 문제가 있다. 비자기회귀(non-autoregressive) 모델은 단어를 독립적으로 생성하며 병렬 계산이 가능해 자기회귀 모델에 비해 디코딩 속도가 상당히 빠른 장점이 있지만, 멀티모달리티(multimodality) 문제가 발생할 수 있다. 본 논문에서는 단어 정렬(word alignment)을 이용한 비자기회귀 신경망 기계 번역 모델을 제안하고, 제안한 모델을 한국어-영어 기계 번역에 적용하여 단어 정렬 정보가 어순이 다른 언어 간의 번역 성능 개선과 멀티모달리티 문제를 완화하는 데 도움이 됨을 보인다.

• Korean Journal of Artificial Intelligence
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• v.11 no.3
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• pp.29-34
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• 2023

Recently, the rapid development of artificial intelligence technology, many studies are being conducted to predict the risk of heart disease in order to lower the mortality rate of cardiovascular diseases worldwide. This study presents exercise or dietary improvement contents in the form of a software app or web to patients with cardiovascular disease, and cardiovascular disease through digital devices such as mobile phones and PCs. LR, LDA, SVM, XGBoost for the purpose of developing "Life style Improvement Contents (Digital Therapy)" for cardiovascular disease care to help with management or treatment We compared and analyzed cardiovascular disease prediction models using machine learning algorithms. Research Results XGBoost. The algorithm model showed the best predictive model performance with overall accuracy of 80% before and after. Overall, accuracy was 80.0%, F1 Score was 0.77~0.79, and ROC-AUC was 80%~84%, resulting in predictive model performance. Therefore, it was found that the algorithm used in this study can be used as a reference model necessary to verify the validity and accuracy of cardiovascular disease prediction. A cardiovascular disease prediction analysis algorithm that can enter accurate biometric data collected in future clinical trials, add lifestyle management (exercise, eating habits, etc.) elements, and verify the effect and efficacy on cardiovascular-related bio-signals and disease risk. development, ultimately suggesting that it is possible to develop lifestyle improvement contents (Digital Therapy).

• Journal of Arbitration Studies
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• v.31 no.2
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• pp.119-135
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• 2021

South Korea has the most advanced technology in the Fourth Industrial Revolution era because of its high-speed Internet commercialization. However, the industry is shrinking due to its various regulations in building and its utilization of personal information as big data. Currently, South Korea's personal data utilization business is in its early stages. In the era of the 4th Industrial Revolution, it is difficult for startups to use data. There are various causes here. Above all, legal regulations to protect personal information are emphasized. This study confirms that transactions of personal medical records through My Data can be made. Moreover, it confirms that there is a need for a mediating role between stakeholders. This study lacks statistical access in the process of performing stakeholder roles. However, personal medical records will be traded safely in the future, and new subjects will enter the market. Furthermore, the domestic bio-industry will develop. Through this study, various problems were derived in establishing Medical MyData in Korea. Moreover, it looks forward to continuing various studies in the health care sector in the future.

• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.19 no.1
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• pp.187-195
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• 2019

Recently, it has become possible to collect, store, process, and analyze data generated in various fields by the development of the technology related to the big data. These big data technologies are used for clinical results analysis and the optimization of clinical trial design will reduce the costs associated with health care. Therefore, in this paper, we are going to analyze clinical results and present guidelines that can reduce the period and cost of clinical trials. First, we use Sqoop to collect clinical results data from relational databases and store in HDFS, and use Hive, a processing tool based on Hadoop, to process data. Finally we use R, a big data analysis tool that is widely used in various fields such as public sector or business, to analyze associations.

• Korean Journal of Artificial Intelligence
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• v.12 no.1
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• pp.25-29
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• 2024

In this study, research was conducted to predict the probability of cervical cancer occurrence associated with the use of hormonal contraceptives. Cervical cancer is influenced by various environmental factors; however, the human papillomavirus (HPV) is detected in 99% of cases, making it the primary attributed cause. Additionally, although cervical cancer ranks 10th in overall female cancer incidence, it is nearly 100% preventable among known cancers. Early-stage cervical cancer typically presents no symptoms but can be detected early through regular screening. Therefore, routine tests, including cytology, should be conducted annually, as early detection significantly improves the chances of successful treatment. Thus, we employed artificial intelligence technology to forecast the likelihood of developing cervical cancer. We utilized the logistic regression algorithm, a predictive model, through Microsoft Azure. The classification model yielded an accuracy of 80.8%, a precision of 80.2%, a recall rate of 99.0%, and an F1 score of 88.6%. These results indicate that the use of hormonal contraceptives is associated with an increased risk of cervical cancer. Further development of the artificial intelligence program, as studied here, holds promise for reducing mortality rates attributable to cervical cancer.

• Quality Improvement in Health Care
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• v.8 no.1
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• pp.56-72
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• 2001

Background : To shorten processing time for variety of medical affairs of the patient at the outpatient clinic of a big hospital is very important to qualify medical care of the patient. Therefore, patient's waiting time for medical examination is often utilized as a strong tool to evaluate patient satisfaction with a medical care provided. We performed this study to investigate factors delaying related with waiting time for medical examination. Methods : The data were collected from June 26 to July 30, 1999. A total 275 case of medical treatment and 5,634 patients who visited outpatient clinics of a tertiary hospital were subjected to evaluate the waiting time. The data were analyzed using frequency, t-test, ANOVA, $X^2$-test by SPSS Windows 7.5 program. Results : The mean patient's waiting time objectively evaluated ($30.9{\pm}33.9$ min) was longer than that subjectively by patient evaluated ($25.1{\pm}26.2$ min). Patient waiting time objectively evaluated was influenced by the starting time of medical examination, consultation hours, patients arriving time etc, as expected. The time discrepancy between two evaluations was influenced by several causative factors. Regarding the degree of patients accepted waiting time with the medical examination is 20 min. Conclusion : The results show that, besides the starting time of medical examination, consultation hours and patients arriving time, influence the patient's subjective evaluation of waiting time for medical examination and his satisfaction related with the service in the big hospital. In order to improve patient satisfaction related with waiting time for medical examination, it will be effective examination rather than to shorten the real processing time within the consultation room.

• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.20 no.3
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• pp.163-170
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• 2020

With the development of information and communication technologies, the growing volume of data is increasing exponentially, raising interest in big data. As technologies related to big data have developed, big data is being collected, stored, processed, analyzed, and utilized in many fields. Big data analytics in the health care sector, in particular, is receiving much attention because they can also have a huge social and economic impact. It is predicted that it will be able to use Big Data technology to analyze patients' diagnostic data and reduce the amount of money that is spent on simple hospital care. Therefore, in this thesis, patient data is analyzed to present to patients who are unable to go to the hospital or caregivers who do not have medical expertise with close care guidelines. First, the collected patient data is stored in HDFS and the data is processed and classified using R, a big data processing and analysis tool, in the Hadoop environment. Visualize to a web server using R Shiny, which is used to implement various functions of R on the web.