• Asian Pacific Journal of Cancer Prevention
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• v.17 no.2
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• pp.791-797
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• 2016

Background: Punjab state is particularly reporting a rising burden of cancer. A 'door to door cancer awareness and early detection campaign' was therefore launched in the Punjab covering about 2.67 million population, wherein after initial training accredited social health activists (ASHAs) and other health staff conducted a survey for early detection of cancer cases based on a twelve point clinical algorithm. Objective: To ascertain unit cost for undertaking a population-based cancer awareness and early detection campaign. Materials and Methods: Data were collected using bottom-up costing methods. Full economic costs of implementing the campaign from the health system perspective were calculated. Options to meet the likely demand for project activities were further evaluated to examine their worth from the point of view of long-term sustainability. Results: The campaign covered 97% of the state population. A total of 24,659 cases were suspected to have cancer and were referred to health facilities. At the state level, incidence and prevalence of cancer were found to be 90 and 216 per 100,000, respectively. Full economic cost of implementing the campaign in pilot district was USD 117,524. However, the financial cost was approximately USD 6,301. Start-up phase of campaign was more resource intensive (63% of total) than the implementation phase. The economic cost per person contacted and suspected by clinical algorithm was found to be USD 0.20 and USD 40 respectively. Cost per confirmed case under the campaign was 7,043 USD. Conclusions: The campaign was able to screen a reasonably large population. High to high economic cost points towards the fact that the opportunity cost of campaign put a significant burden on health system and other programs. However, generating awareness and early detection strategy adopted in this campaign seems promising in light of fact that organized screening is not in place in India and in many developing countries.

• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.17 no.2
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• pp.75-82
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• 2017

In this paper, we consider social network analysis that focuses on community detection. Social networks embed community structure characteristics, i.e., a society can be partitioned into many social groups of individuals, with dense intra-group connections and much sparser inter-group connections. Exploring the community structure allows predicting as well as understanding individual's behaviors and interactions between people. In this paper, based on the interaction information extracted from a real-life Bluetooth contacts, we aim to reveal the social groups in a society of mobile carriers. Focusing on estimating the closeness of relationships between network entities through different similarity measurement methods, we introduce the clustering scheme to determine the underlying social structure. To evaluate our community detection method, we present the evaluation mechanism based on the basic properties of friendship.

• Proceedings of the Korea Information Processing Society Conference
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• 2022.05a
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• pp.366-369
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• 2022

The glut of information aggravated the process of data analysis and other procedures including data mining. Many algorithms were devised in Big Data and Data Mining to solve such an intricate problem. In this paper, we conducted research about the comparison of several similarity measures and community detection algorithms in collaborative filtering for movie recommendation systems. Movielense data set was used to do an empirical experiment. We applied three different similarity measures: Cosine, Euclidean, and Pearson. Moreover, betweenness and eigenvector centrality were used to detect communities from the network. As a result, we elucidated which algorithm is more suitable than its counterpart in terms of recommendation accuracy.

• Journal of KIISE:Computing Practices and Letters
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• v.16 no.4
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• pp.432-436
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• 2010

Finding communities from network data including social networks can be done by clustering the nodes of the network as densely interconnected groups, where keeping interconnection between groups sparse. To exploit a clustering algorithm for community detection task, we need a well-defined similarity measure between network nodes. In this paper, we propose a new similarity measure named "Common Neighborhood Sub-graph density" and combine the similarity with affinity propagation, which is a recently devised clustering algorithm.

• Structural Monitoring and Maintenance
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• v.2 no.4
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• pp.399-418
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• 2015

Automated damage detection through Structural Health Monitoring (SHM) techniques has become an active area of research in the bridge engineering community but widespread implementation on in-service infrastructure still presents some challenges. In the meantime, visual inspection remains as the most common method for condition assessment even though collected information is highly subjective and certain types of damage can be overlooked by the inspector. In this article, a Frequency Response Functions-based model updating algorithm is evaluated using experimentally collected data from the University of Central Florida (UCF)-Benchmark Structure. A protocol for measurement selection and a regularization technique are presented in this work in order to provide the most well-conditioned model updating scenario for the target structure. The proposed technique is composed of two main stages. First, the initial finite element model (FEM) is calibrated through model updating so that it captures the dynamic signature of the UCF Benchmark Structure in its healthy condition. Second, based upon collected data from the damaged condition, the updating process is repeated on the baseline (healthy) FEM. The difference between the updated parameters from subsequent stages revealed both location and extent of damage in a "blind" scenario, without any previous information about type and location of damage.

• Proceedings of the Korea Inteligent Information System Society Conference
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• 2001.01a
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• pp.97-108
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• 2001

Frauds detection is a difficult problem, requiring huge computer resources and complicated search activities Researchers have struggled with the problem. Even though a fee research approaches have claimed that their solution is much better than others, research community has not found 'the best solution'well fitting every fraud. Because of the evolving nature of the frauds. a novel and self-adapting method should be devised. In this research a new approach is suggested to solving frauds in insurance claims credit card transaction. Based on evolutionary computing approach, the method is itself self-adjusting and evolving enough to generate a new self of decision-makin rules. We believe that this new approach will provide a promising alternative to conventional ones, in terms of computation performance and classification accuracy.

• Journal of the Korea Society of Computer and Information
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• v.19 no.12
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• pp.239-246
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• 2014

In this paper, we propose a method to decide the additional edges in order to integrate two communitites A,B($${\mid}A{\mid}{\geq_-}{\mid}B{\mid}$$, ${\mid}{\cdot}{\mid}$ is the size of the set). The proposed algorithm uses a fitness function that shows the property of a community and the fitness function is defined by the number of edges which exist in the community and connect two nodes, one is in the community and the other is out of the community. The community has a strong property when the function has a large value. The proposed algorithm is a kind of greedy method and when a node of B is merged to A, the minimum number of additional edges is decided to increase the fitness function value of A. After determining the number of additional edges, we define the community connectivity measures using the node centrality to determine the edges locations. The connections of the new edges are fixed to maximize the connectivity measure of the combined community. The procedure is applied for all nodes in B to integrate A and B. The effectiveness of the proposed algorithm is shown by solving the Zachary Karate Club network.

• Journal of KIISE
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• v.41 no.12
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• pp.1058-1065
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• 2014

In order to assist user's who are in the process of executing a search, a query expansion method suggests keywords that are related to an input query. Recently, several studies have suggested keywords that are identified by finding domains using a clustering method over the documents that are retrieved. However, the clustering method is not relevant when presenting various domains because the number of clusters should be fixed. This paper proposes a method that suggests keywords by finding various domains related to the input queries by using a community detection algorithm. The proposed method extracts words from the top-30 documents of those that are retrieved and builds communities according to the word graph. Then, keywords representing each community are derived, and the represented keywords are used for the query expansion method. In order to evaluate the proposed method, we compared our results to those of two baseline searches performed by the Google search engine and keyword recommendation using TF-IDF in the search results. The results of the evaluation indicate that the proposed method outperforms the baseline with respect to diversity.

• Proceedings of the Korea Information Processing Society Conference
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• 2023.11a
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• pp.322-326
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• 2023

We propose a method of movie recommendation that involves an algorithm known as spectral bipartition. The Social Network is constructed manually by considering the similar movies viewed by users in MovieLens dataset. This kind of similarity establishes implicit ties between viewers. Because we assume that there is a possibility that there might be a connection between users who share the same set of viewed movies. We cluster users by applying a community detection algorithm based on the spectral bipartition. This study helps to uncover the hidden relationships between users and recommend movies by considering that feature.

• Journal of Korea Multimedia Society
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• v.13 no.7
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• pp.950-959
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• 2010

The Election paradigm can be used as a building block in many practical problems such as group communication, atomic commit and replicated data management where a protocol coordinator might be useful. The problem has been widely studied in the research community since one reason for this wide interest is that many distributed protocols need an election protocol. However, mobile ad hoc systems are more prone to failures than conventional distributed systems. Solving election in such an environment requires from a set of mobile nodes to choose a unique node as a leader based on its priority despite failures or disconnections of mobile nodes. In this paper, we describe a solution to the election problem from mobile ad hoc computing systems and it was proved by temporal logic. This solution is based on the Group Membership Detection algorithm.