• KSII Transactions on Internet and Information Systems (TIIS)
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• 제16권8호
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• pp.2587-2605
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• 2022

In order to efficiently detect community structure in complex networks, community detection algorithms can be designed from the perspective of node similarity. However, the appropriate parameters should be chosen to achieve community division, furthermore, these existing algorithms based on the similarity of common neighbors have low discrimination between node pairs. To solve the above problems, a noval community detection algorithm using closeness similarity based on common neighbor node clustering entropy is proposed, shorted as CSCDA. Firstly, to improve detection accuracy, common neighbors and clustering coefficient are combined in the form of entropy, then a new closeness similarity measure is proposed. Through the designed similarity measure, the closeness similar node set of each node can be further accurately identified. Secondly, to reduce the randomness of the community detection result, based on the closeness similar node set, the node leadership is used to determine the most closeness similar first-order neighbor node for merging to create the initial communities. Thirdly, for the difficult problem of parameter selection in existing algorithms, the merging of two levels is used to iteratively detect the final communities with the idea of modularity optimization. Finally, experiments show that the normalized mutual information values are increased by an average of 8.06% and 5.94% on two scales of synthetic networks and real-world networks with real communities, and modularity is increased by an average of 0.80% on the real-world networks without real communities.

• 소프트웨어공학소사이어티 논문지
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• 제25권4호
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• pp.69-74
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• 2012

본 논문은 소프트웨어 클러스터링 기법과 커뮤니티 검출 기법의 비교를 통하여 아키텍쳐 모듈 복원 프로세스에 커뮤니티 검출 알고리즘의 적용가능성을 제시한다. 또한, 대표적인 클러스터링 알고리즘과 커뮤니티 검출 알고리즘의 값과 나눠진 모듈간의 상관관계와 차이점을 분석한다. 이를 통하여 커뮤니티 검출 알고리즘이 소프트웨어 아키텍쳐 모듈 뷰 복원에 활용되어질 수 있다는 몇 가지 근거를 제시하였고, 기존의 클러스터링 결과와 커뮤니티 알고리즘의 결과치를 비교함으로써, 서로의 결과 데이터가 어떠한 연관성을 가지는지 제시하였다.

• 한국건강관리협회지
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• 제3권2호
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• pp.137-146
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• 2005

The Community based cancer screening program passed in 1960 was a milestone for initiating a national and local health program in Japan. And since then local governments and Cancer Society have been developing and providing cancer screening programs of Stomach, Cervix, Breast and Colorectum for population. To apply the effectiveness of community based cancer screening program, it is important to understand the key issue related to cancer screening participation of population and technology of cancer detection. The purpose of this study was to understand the community based cancer screening program in Japan, and to apply the information for establishment of community based cancer screening program in Korea. The characteristics of community based cancer screening program in Japan were as follows. The first, community based cancer screening program was implemented by the National Health and Medical Services Law for the Aged since 1983. The second, Cancer Society and Cancer Detection Center were core for cancer screening program. The third, the budget for cancer screening program was established by the National Health and Hygiene. The fourth, the continuous quality control for medical staff was provided by Cancer Society and Cancer Detection Center The fifth, the efforts for the promotion of cancer screening rate.

• KSII Transactions on Internet and Information Systems (TIIS)
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• 제12권5호
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• pp.2142-2161
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• 2018

Distance dynamics model is an excellent model for uncovering the community structure of a complex network. However, the model has poor robustness. To improve the robustness, we design an enhanced distance dynamics model based on Ego-Leader and propose a corresponding community detection algorithm, called E-Attractor. The main contributions of E-Attractor are as follows. First, to get rid of sensitive parameter ${\lambda}$, Ego-Leader is introduced into the distance dynamics model to determine the influence of an exclusive neighbor on the distance. Second, based on top-k Ego-Leader, we design an enhanced distance dynamics model. In contrast to the traditional model, enhanced model has better robustness for all networks. Extensive experiments show that E-Attractor has good performance relative to several state-of-the-art algorithms.

• 인터넷정보학회논문지
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• 제24권6호
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• pp.1-11
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• 2023

Federated learning (FL) is a ground breaking machine learning paradigm that allow smultiple participants to collaboratively train models in a cloud environment, all while maintaining the privacy of their raw data. This approach is in valuable in applications involving sensitive or geographically distributed data. However, one of the challenges in FL is dealing with heterogeneous and non-independent and identically distributed (non-IID) data across participants, which can result in suboptimal model performance compared to traditionalmachine learning methods. To tackle this, we introduce FedGCD, a novel FL algorithm that employs Graph Neural Network (GNN)-based community detection to enhance model convergence in federated settings. In our experiments, FedGCD consistently outperformed existing FL algorithms in various scenarios: for instance, in a non-IID environment, it achieved an accuracy of 0.9113, a precision of 0.8798,and an F1-Score of 0.8972. In a semi-IID setting, it demonstrated the highest accuracy at 0.9315 and an impressive F1-Score of 0.9312. We also introduce a new metric, nonIIDness, to quantitatively measure the degree of data heterogeneity. Our results indicate that FedGCD not only addresses the challenges of data heterogeneity and non-IIDness but also sets new benchmarks for FL algorithms. The community detection approach adopted in FedGCD has broader implications, suggesting that it could be adapted for other distributed machine learning scenarios, thereby improving model performance and convergence across a range of applications.

• 한국정보처리학회:학술대회논문집
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• 한국정보처리학회 2019년도 춘계학술발표대회
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• pp.273-276
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• 2019

There is a lot of information in our world, quick access to the most accurate information or finding the information we need is more difficult and complicated. The recommendation system has become important for users to quickly find the product according to user's preference. A social recommendation system using community detection based on label propagation is proposed. In this paper, we applied community detection based on label propagation and collaborative filtering in the movie recommendation system. We implement with MovieLens dataset, the users will be clustering to the community by using label propagation algorithm, Our proposed algorithm will be recommended movie with finding the most similar community to the new user according to the personal propensity of users. Mean Absolute Error (MAE) is used to shown efficient of our proposed method.

• Asian Pacific Journal of Cancer Prevention
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• 제16권15호
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• pp.6249-6254
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• 2015

Targeting breast cancer awareness along with comprehensive cancer care is appropriate in low and middle income countries like India, where there are no organized and affordable screening services. It is essential to identify the existing awareness about breast cancer in the community prior to launching an organized effort. This study assessed the existing awareness about breast cancer amongst women and their health seeking practices in an urban community in Mumbai, India. A postal survey was undertaken with low or no cost options for returning the completed questionnaires. The majority of the women were aware about cancer but awareness about symptoms and signs was poor. Women were willing to accept more information about cancer and those with higher awareness scores were more likely to seek medical help. They were also more likely to have undergone breast examination in the past and less likely to use alternative medicines. High income was associated with better awareness but this did not translate into better health seeking behaviour. Organized programmes giving detailed information about breast cancer and its symptoms are needed and women from all income categories need to be encouraged for positive change towards health seeking. Further detailed studies regarding barriers to health seeking in India are necessary.

• 한국보건간호학회지
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• 제21권2호
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• pp.241-251
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• 2007

Purpose: To identify the effects on tuberculosis mortality of a tuberculosis control program conducted at 108 community health centers in terms of structure and process. Methods: The dependent variable was tuberculosis mortality, and the independent variables were the structure(type of centers, staff, nurses, doctors, budget) and process(chest X-ray checking, immunization, case detection, health education, patients registering & managing) of the tuberculosis control programs at the community health centers. Data were analyzed using descriptive analysis and stepwise regression analysis. Result: Tuberculosis morality was positively correlated with type of centers(rural area)(p<0.01), but negatively correlated with type of centers(large cities) (p<0.01), (middle cities)(p<0.05), staff FTE(p<0.05), and number of nurses(p<0.05). Regression analysis indicated that type of centers(rural area)($\beta$=0.457) and case detection($\beta$=0.234) had a significant effect on tuberculosis mortality. Conclusion: Ultimately, this study will provide information to improve the effectiveness of tuberculosis control programs in community health centers.

• Asian Pacific Journal of Cancer Prevention
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• 제16권2호
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• pp.559-564
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• 2015

Background: The participation of women in cervical cancer screening in Malaysia is low. Self-sampling might be able to overcome this problem. The aim of this study was to assess the reliability of self-sampling for cervical smear in our country. Materials and Methods: This cross-sectional study was conducted on 258 community dwelling women from urban and rural settings who participated in health campaigns. In order to reduce the sampling bias, half of the study population performed the self-sampling prior to the physician sampling while the other half performed the self-sampling after the physician sampling, randomly. Acquired samples were assessed for cytological changes as well as HPV DNA detection. Results: The mean age of the subjects was $40.4{\pm}11.3years$. The prevalence of abnormal cervical changes was 2.7%. High risk and low risk HPV genotypes were found in 4.0% and 2.7% of the subjects, respectively. A substantial agreement was observed between self-sampling and the physician obtained sampling in cytological diagnosis (k=0.62, 95%CI=0.50, 0.74), micro-organism detection (k=0.77, 95%CI=0.66, 0.88) and detection of hormonal status (k=0.75, 95%CI=0.65, 0.85) as well as detection of high risk (k=0.77, 95%CI=0.4, 0.98) and low risk (K=0.77, 95%CI=0.50, 0.92) HPV. Menopausal state was found to be related with 8.39 times more adequate cell specimens for cytology but 0.13 times less adequate cell specimens for virological assessment. Conclusions: This study revealed that self-sampling has a good agreement with physician sampling in detecting HPV genotypes. Self-sampling can serve as a tool in HPV screening while it may be useful in detecting cytological abnormalities in Malaysia.

• 한국인터넷방송통신학회논문지
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• 제17권2호
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• pp.75-82
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• 2017

본 논문에서는 사회관계구조에 초점을 맞춘 사회관계망 분석을 고려한다. 사회관계망은 많은 사회집단으로 구성되어 있으며, 사회관계 구조 특성으로 인하여 같은 사회집단 내의 노드들은 서로 강한 유대관계를 가지고 있으며 다른 사회집단에 속한 노드와는 상대적으로 약한 유대를 가지게 된다. 사회관계망에서의 사회관계구조 검출은 사람들의 행동 및 상호작용의 분석과 예측을 가능하게 한다. 본 논문에서는 사회관계구조와 사회집단을 검출하기 위하여 사람들이 소지하는 스마트기기의 실제 블루투스 접촉 데이터를 이용한다. 네트워크 노드 간 유대를 추정하기 위한 다양한 유사도 측정 방식과 클러스터링을 기반으로 하는 사회관계구조 검출 방안을 제시한다. 제안하는 방안을 검증하기 위하여 교유관계 특성을 이용하는 성능측정방안을 이용한다.