• Journal of the Korea Institute of Information and Communication Engineering
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• v.25 no.10
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• pp.1375-1380
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• 2021

Korea's security response to application service app is still insufficient due to the initial opening of the public safety network. Therefore, preemptive security measures are essential. In this study, we proposed to establish a 'public safety network app security system' to prevent potential vulnerabilities to the app store that distributes app in public safety network and android operating system that operate app on dedicated terminal devices. In order for an application service app to be listed on the public safety network mobile app store, a dataset of malicious and normal app is first established to extract characteristics and select the most effective AI model to perform static and dynamic analysis. According to the analysis results, 'Safety App Certificate' is certified for non-malicious app to secure reliability for listed apps. Ultimately, it minimizes the security blind spots of public safety network app. In addition, the safety of the network can be secured by supporting public safety application service of certified apps.

• Journal of Preventive Medicine and Public Health
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• v.56 no.1
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• pp.67-76
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• 2023

Objectives: Previous studies have reported that people with disabilities are more likely to be impoverished and affected by excessive medical costs than people without disabilities. Public transfer income (PTI) reduces financial strain in low-income households. This study examined the impact of PTI on catastrophic health expenditures (CHE), focusing on low-income households and households with Medical Aid beneficiaries that contained people with disabilities. Methods: We constructed a panel dataset by extracting data on registered households with disabilities from the Korea Welfare Panel Study 2012-2019. We then used a generalized estimating equation model to estimate the impacts of PTI on CHE. A subgroup analysis was carried out to assess the moderating effects of family income levels and health insurance types. Results: As PTI increased, the odds ratio (OR) of CHE in households that contained people with disabilities decreased significantly (OR, 0.92; 95% confidence interval [CI], 0.89 to 0.94; p<0.001). In particular, PTI effectively reduced the likelihood of CHE for low-income households (OR, 0.85; 95% CI, 0.81 to 0.89; p<0.001) and those who received medical benefits (OR, 0.78; 95% CI, 0.68 to 0.89; p<0.001). Conclusions: This study highlights the positive effect of PTI on decreasing CHE. Household income and the health insurance type were significant effect modifiers, but economic barriers seemed to persist among low-income households with non-Medical Aid beneficiaries. Federal policies or programs should consider increasing the total amount of PTI targeting low-income households with disabilities that are not covered by the Medical Aid program.

• Journal of Computing Science and Engineering
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• v.8 no.1
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• pp.57-63
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• 2014

User satisfaction has always been important in the success of software, regardless of whether it is closed and proprietary or open source software (OSS). OSS users are geographically distributed and include technical as well as novice users. However, it is generally believed that if OSS was more usable, its popularity would increase tremendously. Hence, users and their requirements need to be addressed in the priorities of an OSS environment. Online public forums are a major medium of communication for the OSS community. The research model of this work studies the relationship between user requirements in open source software and online public forums. To conduct this research, we used a dataset consisting of 100 open source software projects in different categories. The results show that online forums play a significant role in identifying user requirements and addressing their requests in open source software.

• International Journal of Advanced Culture Technology
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• v.11 no.3
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• pp.297-301
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• 2023

The study identified the various causes of odor problems, the discomfort they cause, and the importance of the public health and environmental issues associated with them. To solve the odor problem, you must identify the cause and perform an accurate analysis. Therefore, we proposed a CNN-Transformer hybrid model (CTHM) that combines CNN and Transformer and evaluated its performance. It was evaluated using a dataset consisting of 120,000 odor samples, and experimental results showed that CTHM achieved an accuracy of 93.000%, a precision of 92.553%, a recall of 94.167%, an F1 score of 92.880%, and an RMSE of 0.276. Our results showed that CTHM was suitable for odor analysis and had excellent prediction performance. Utilization of this model is expected to help address odor problems and alleviate public health and environmental concerns.

• Proceedings of the Korean Society of Computer Information Conference
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• 2020.07a
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• pp.301-302
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• 2020

The number of abandoned dogs were increasing every year in South Korea. However, many dogs are euthanized in the shelter because of the lack of budget. This project predicts euthanasia of abandoned dogs using machine learning algorithm. It collects data from the public data portal where Korea government provides a public dataset as a form of open API. This project uses recent three-year data 2017 to 2019 and 263371 cases were founded. This project implements random forest and logistic regression models. This project attained an average 72% of prediction accuracy.

• Asian Pacific Journal of Cancer Prevention
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• v.13 no.11
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• pp.5525-5528
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• 2012

Introduction: Breast cancer is the most common cancer in women. Improvements of early diagnosis modalities have led to longer survival rates. This study aimed to determine the 5, 10 and 15 year mortality rates of breast cancer patients compared to the normal female population. Materials and Methods: The follow up data of a cohort of 615 breast cancer patients referred to Iranian Breast Cancer Research Center (BCRC) from 1986 to 1996 was considered as reference breast cancer dataset. The dataset was divided into 5 year age groups and the 5, 10 and 15 year probability of death for each group was estimated. The annual mortality rate of Iranian women was obtained from the Death Registry system. Standardized mortality ratios (SMRs) of breast cancer patients were calculated using the ratio of the mortality rate in breast cancer patients over the general female population. Results: The mean age of breast cancer patients at diagnosis time was 45.9 (${\pm}10.5$) years ranging from 24-74. A total of 73, 32 and 2 deaths were recorded at 5, 10 and 15 years, respectively, after diagnosis. The SMRs for breast cancer patients at 5, 10 and 15 year intervals after diagnosis were 6.74 (95% CI, 5.5-8.2), 6.55 (95%CI, 5-8.1) and 1.26 (95%CI, 0.65-2.9), respectively. Conclusion: Results showed that the observed mortality rate of breast cancer patients after 15 years from diagnosis was very similar to expected rates in general female population. This finding would be useful for clinicians and health policy makers to adopt a beneficial strategy to improve breast cancer survival. Further follow-up time with larger sample size and a pooled analysis of survival rates of different centres may shed more light on mortality patterns of breast cancer.

• Health Policy and Management
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• v.25 no.3
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• pp.197-206
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• 2015

Background: The purpose of this study was to propose a method for developing a measure of hospital-wide all-cause risk-standardized readmissions using administrative claims data in Korea and to discuss further considerations in the refinement and implementation of the readmission measure. Methods: By adapting the methodology of the United States Center for Medicare & Medicaid Services for creating a 30-day readmission measure, we developed a 6-step approach for generating a comparable measure using Korean datasets. Using the 2010 Korean National Health Insurance (NHI) claims data as the development dataset, hierarchical regression models were fitted to calculate a hospital-wide all-cause risk-standardized readmission measure. Six regression models were fitted to calculate the readmission rates of six clinical condition groups, respectively and a single, weighted, overall readmission rate was calculated from the readmission rates of these subgroups. Lastly, the case mix differences among hospitals were risk-adjusted using patient-level comorbidity variables. The model was validated using the 2009 NHI claims data as the validation dataset. Results: The unadjusted, hospital-wide all-cause readmission rate was 13.37%, and the adjusted risk-standardized rate was 10.90%, varying by hospital type. The highest risk-standardized readmission rate was in hospitals (11.43%), followed by general hospitals (9.40%) and tertiary hospitals (7.04%). Conclusion: The newly developed, hospital-wide all-cause readmission measure can be used in quality and performance evaluations of hospitals in Korea. Needed are further methodological refinements of the readmission measures and also strategies to implement the measure as a hospital performance indicator.

• Journal of Biomedical Engineering Research
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• v.44 no.6
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• pp.384-391
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• 2023

Among the elderly, fatal injuries and deaths are significantly attributed to falls. Therefore, a pre-impact fall detection system is necessary for injury prevention. In this study, a robust threshold-based algorithm was proposed for pre-impact fall detection, reducing false positives in highly dynamic daily-living movements. The algorithm was validated using public datasets (KFall and FARSEEING) that include the real-world elderly fall. A 6-axis IMU sensor (Movella Dot, Movella, Netherlands) was attached to S2 of 20 healthy adults (aged 22.0±1.9years, height 164.9±5.9cm, weight 61.4±17.1kg) to measure 14 activities of daily living and 11 fall movements at a sampling frequency of 60Hz. A 5Hz low-pass filter was applied to the IMU data to remove high-frequency noise. Sum vector magnitude of acceleration and angular velocity, roll, pitch, and vertical velocity were extracted as feature vector. The proposed algorithm showed an accuracy 98.3%, a sensitivity 100%, a specificity 97.0%, and an average lead-time 311±99ms with our experimental data. When evaluated using the KFall public dataset, an accuracy in adult data improved to 99.5% compared to recent studies, and for the elderly data, a specificity of 100% was achieved. When evaluated using FARSEEING real-world elderly fall data without separate segmentation, it showed a sensitivity of 71.4% (5/7).

• Journal of the Semiconductor & Display Technology
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• v.22 no.1
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• pp.11-17
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• 2023

This paper proposes a lightweight deep learning network for detecting an image splicing forgery. The research on image forgery detection using CNN, a deep learning network, and research on detecting and localizing forgery in pixel units are in progress. Among them, CAT-Net, which learns the discrete cosine transform coefficients of images together with images, was released in 2022. The DCT coefficients presented by CAT-Net are combined with the JPEG artifact learning module and the backbone model as pre-learning, and the weights are fixed. The dataset used for pre-training is not included in the public dataset, and the backbone model has a relatively large number of network parameters, which causes overfitting in a small dataset, hindering generalization performance. In this paper, this learning module is designed to learn the characterization depending on the DCT domain in real-time during network training without pre-training. The DCT RRU-Net proposed in this paper is a network that combines RRU-Net which detects forgery by learning only images and JPEG artifact learning module. It is confirmed that the network parameters are less than those of CAT-Net, the detection performance of forgery is better than that of RRU-Net, and the generalization performance for various datasets improves through the network architecture and training method of DCT RRU-Net.

• International Journal of Computer Science & Network Security
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• v.24 no.5
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• pp.1-10
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• 2024

Individuals with visual impairments face numerous challenges in their daily lives, with navigating streets and public spaces being particularly daunting. The inability to identify safe crossing locations and assess the feasibility of crossing significantly restricts their mobility and independence. Globally, an estimated 285 million people suffer from visual impairment, with 39 million categorized as blind and 246 million as visually impaired, according to the World Health Organization. In Saudi Arabia alone, there are approximately 159 thousand blind individuals, as per unofficial statistics. The profound impact of visual impairments on daily activities underscores the urgent need for solutions to improve mobility and enhance safety. This study aims to address this pressing issue by leveraging computer vision and deep learning techniques to enhance object detection capabilities. Two models were trained to detect objects: one focused on street crossing obstacles, and the other aimed to search for objects. The first model was trained on a dataset comprising 5283 images of road obstacles and traffic signals, annotated to create a labeled dataset. Subsequently, it was trained using the YOLOv8 and YOLOv5 models, with YOLOv5 achieving a satisfactory accuracy of 84%. The second model was trained on the COCO dataset using YOLOv5, yielding an impressive accuracy of 94%. By improving object detection capabilities through advanced technology, this research seeks to empower individuals with visual impairments, enhancing their mobility, independence, and overall quality of life.