• Journal of the Korea Society of Computer and Information
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• v.19 no.1
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• pp.111-118
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• 2014

Current friend recommendation system on Twitter primarily recommends the most influential twitter. However, this way of recommendation has drawbacks where it does not recommend the users of which attributes of interests are similar to theirs. Since users want other users of which attributes are similar, this study implements follower recommendation system based on the similarity of twitter node informations. The data in this study is from SNAP(Stanford Network Analysis Platform), and it consists of twitter node information of which number of followers is over 10,000 and twitter link information. We used the SNAP data as a training data, and generated a classifier which recommends and predicts the relation between followers. We evaluated the classifier by 10-Fold Cross validation. Once two twitter node informations are given, our model can recommend the relationship of the two twitters as one of following such as: FoFo(Follower Follower), FoFr(Follower Friend), NC(Not Connected).

• The Korean Journal of Emergency Medical Services
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• v.7 no.1
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• pp.179-198
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• 2003

If see disaster administration system of our country, start in terms of is coping by countermeasure after four immediately after disaster occurrence many problems blessing with a sons by tribe and so on of link nature between formation, disaster administration complete charge utensil's absence, disaster charge manpower and budget be indicated and join. If examine improvement way accordingly, is as following ; Necessity of synthetic disaster administration system, Fire fighting formation's independence guarantee, Integration of fire fighting connection similarity business, Disaster administration's permanent establishment complete charge utensil's necessity, Disaster administration midautumn complete charge utensil at a metropolitan autonomous fire fighting system reorganization, Role division of labor between center and local government, Disaster administration professional human strength positivity, Disaster administration information system construction practical use, Equipment and improvement of budget state, Education public information for safety culture consciousness fixing, Internationalization of fire fighting business, globalization propulsion, Structure, member of rescue confrontation system and efficiency. Fire fighting environment is changing greatly, and fire fighting must become center to correspond to do confrontation that do one thing troble when produce disaster.

• Proceedings of the Korean Information Science Society Conference
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• 2007.06c
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• pp.189-193
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• 2007

개념 간의 유사도 측정 방법은 의미망에서의 두 개념의 최단 경로의 수 노드의 깊이 관계의 종류 등의 정보를 이용하는 링크(Link) 기반 방법, 대용량의 말뭉치에서의 개념의 발생빈도를 확률로 계산한 정보량(Information Content) 기반 방법, 관련 단어들의 공기정보를 활용한 의미(Gloss) 기반 방법이 있으며, 이미 국외에서는 WordNet과 같은 의미적 언어자원을 활용하여 많은 연구가 진행되고 있다. 그러나 국내에서는 아직 한국어 의미망을 바탕으로 한 개념간의 유사성 측정 방법이나 이를 활용하는 방법에 대한 연구가 미흡하다. 본 논문에서는 이를 바탕으로 링크 타입 노드의 깊이 최단경로 정보량 등의 요소를 이용한 의미 유사도 측정방법을 제안하고 이를 활용하여 명사-용언간의 연계 정보를 확보함으로써, 효율적으로 명사-용언간의 네트워크를 구축하도록 한다.

• BMB Reports
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• v.41 no.9
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• pp.645-650
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• 2008

Nucleoside diphosphate kinase (NDPK) is involved in multiple signaling pathways in mammalian systems, including G-protein signaling. Arabidopsis NDPK2, like its mammalian counterparts, is multifunctional despite its initial discovery phytochrome-interacting protein. This similarity raises the possibility that NDPK2 may play a role in G-protein signaling in plants. In the present study, we explore the potential relationship between NDPK2 and the small G proteins, Pra2 and Pra3, as well as the heterotrimeric G protein, GPA1. We report a physical interaction between NDPK2 and these small G proteins, and demonstrate that NDPK2 can stimulate their GTPase activities. Our results suggest that NDPK2 acts as a GTPase-activating protein for small G proteins in plants. We propose that NDPK2 might be a missing link between the phytochrome-mediated light signaling and G protein-mediated signaling.

• The Journal of the Korea Contents Association
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• v.11 no.5
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• pp.50-56
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• 2011

This paper proposes 3D visualization method of compound knowledge which will be able to identify and search easily compound knowledge objects based the multidimensional relationship. For this, we structurized a compound knowledge with link and node which become the semantic network. and we suggested 3D visualization method using SOM. Also, to arrange compound knowledge from 3D space and to provide the chance of realistic and intuitional information retrieval to the user, we proposed compound knowledge 3D clustering methods using object similarity. Compound knowledge 3D visualization and clustering using SOM will be the optimum method to appear context of compound knowledge and connectivity in space-time.

• Journal of Korea Society of Industrial Information Systems
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• v.10 no.2
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• pp.26-31
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• 2005

In this paper, we present the document summarizers which are simpler and more condense than the existing ones generally used in the web search engines. This method is a statistic-based summarization method using the concept of the complete graph. We suppose that each sentence as a vertex and the similarity between two sentences as a link of the graph. We compare this summarizer with those of Clustering and MMR techniques which are well-known as the good summarization methods. For the comparison, we use FScore using the summarization results generated by human subjects. Our experimental results verify the accuracy of this method, being about $30\%$ better than the others.

• Journal of Intelligence and Information Systems
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• v.27 no.1
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• pp.23-46
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• 2021

Collaborative filtering, which is often used in personalization recommendations, is recognized as a very useful technique to find similar customers and recommend products to them based on their purchase history. However, the traditional collaborative filtering technique has raised the question of having difficulty calculating the similarity for new customers or products due to the method of calculating similaritiesbased on direct connections and common features among customers. For this reason, a hybrid technique was designed to use content-based filtering techniques together. On the one hand, efforts have been made to solve these problems by applying the structural characteristics of social networks. This applies a method of indirectly calculating similarities through their similar customers placed between them. This means creating a customer's network based on purchasing data and calculating the similarity between the two based on the features of the network that indirectly connects the two customers within this network. Such similarity can be used as a measure to predict whether the target customer accepts recommendations. The centrality metrics of networks can be utilized for the calculation of these similarities. Different centrality metrics have important implications in that they may have different effects on recommended performance. In this study, furthermore, the effect of these centrality metrics on the performance of recommendation may vary depending on recommender algorithms. In addition, recommendation techniques using network analysis can be expected to contribute to increasing recommendation performance even if they apply not only to new customers or products but also to entire customers or products. By considering a customer's purchase of an item as a link generated between the customer and the item on the network, the prediction of user acceptance of recommendation is solved as a prediction of whether a new link will be created between them. As the classification models fit the purpose of solving the binary problem of whether the link is engaged or not, decision tree, k-nearest neighbors (KNN), logistic regression, artificial neural network, and support vector machine (SVM) are selected in the research. The data for performance evaluation used order data collected from an online shopping mall over four years and two months. Among them, the previous three years and eight months constitute social networks composed of and the experiment was conducted by organizing the data collected into the social network. The next four months' records were used to train and evaluate recommender models. Experiments with the centrality metrics applied to each model show that the recommendation acceptance rates of the centrality metrics are different for each algorithm at a meaningful level. In this work, we analyzed only four commonly used centrality metrics: degree centrality, betweenness centrality, closeness centrality, and eigenvector centrality. Eigenvector centrality records the lowest performance in all models except support vector machines. Closeness centrality and betweenness centrality show similar performance across all models. Degree centrality ranking moderate across overall models while betweenness centrality always ranking higher than degree centrality. Finally, closeness centrality is characterized by distinct differences in performance according to the model. It ranks first in logistic regression, artificial neural network, and decision tree withnumerically high performance. However, it only records very low rankings in support vector machine and K-neighborhood with low-performance levels. As the experiment results reveal, in a classification model, network centrality metrics over a subnetwork that connects the two nodes can effectively predict the connectivity between two nodes in a social network. Furthermore, each metric has a different performance depending on the classification model type. This result implies that choosing appropriate metrics for each algorithm can lead to achieving higher recommendation performance. In general, betweenness centrality can guarantee a high level of performance in any model. It would be possible to consider the introduction of proximity centrality to obtain higher performance for certain models.

• The Journal of the Korea Contents Association
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• v.20 no.11
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• pp.354-367
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• 2020

This study is on the consideration of the scope of application of art therapy and fractal through the review of literature at home and abroad. The complex system is the opposite of the Euclidean system, a concept suitable for understanding the contemporaries with ambiguous boundaries and decentralized phenomena. The self-similarity and inventiveness of fractal, the geometry of nature, is used as fractal art in art as well as tree trunk, cloud and plant, especially in art therapy, fractal is considered to be available in the field of mandala and neuroscience. From brain-based research to mandala, exposure to natural patterns, clinical diagnosis through fractal analysis and software development, fractal has potential elements that can be developed in art therapy. Fractal, which is easy to link with computers due to its nature, is a necessary study at this point when non-face-to-face contact with the Corona virus is recommended. Currently, research on fractal art therapy is insufficient in Korea. Therefore, this research is intended to present as a basis for scientific and objective diagnostic tools and treatment at clinical sites using art therapy using fractal.

• The KIPS Transactions:PartB
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• v.19B no.1
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• pp.53-62
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• 2012

Clustering technology is used to deal efficiently with many searched documents in information retrieval system. But the accuracy of the clustering is satisfied to the requirement of only some domains. This paper proposes two methods to increase accuracy of the clustering. We define a common-word, that is frequently used but has low weight during clustering. We propose the method that automatically gathers the common-word and calculates its weight from the searched documents. From the experiments, the clustering error rates using the common-word is reduced to 34% compared with clustering using a stop-word. After generating first clusters using average link clustering from the searched documents, we propose the algorithm that reevaluates the similarity between document and clusters and reclassifies the document into more similar clusters. From the experiments using Naver JiSikIn category, the accuracy of reclassified clusters is increased to 1.81% compared with first clusters without reclassification.

• Asian-Australasian Journal of Animal Sciences
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• v.27 no.6
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• pp.898-906
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• 2014

The objective of this study was to investigate differences in the faecal microbial composition among Lantang, Bama, Erhualian, Meishan, Xiaomeishan, Duroc, Landrace, and Yorkshire sows and to explore the possible link of the pig breed with the gut microbial community. Among the sows, the Meishan, Landrace, Duroc, and Yorkshire sows were from the same breeding farm with the same feed. Fresh faeces were collected from three sows of each purebred breed for microbiota analysis and volatile fatty acid (VFA) determination. Denaturing gradient gel electrophoresis (DGGE) analysis revealed that samples from Bama, Erhualian, and Xiaomeishan sows, which from different farms, were generally grouped in one cluster, with similarity higher than 67.2%, and those from Duroc, Landrace, and Yorkshire sows were grouped in another cluster. Principal component analysis of the DGGE profile showed that samples from the foreign breeds and the samples from the Chinese indigenous breeds were scattered in two different groups, irrespective of the farm origin. Faecal VFA concentrations were significantly affected by the pig breed. The proportion of acetate was higher in the Bama sows than in the other breeds. The real-time PCR analysis showed that 16S rRNA gene copies of total bacteria, Firmicutes and Bacteroidetes were significantly higher in the Bama sows compared to Xiaomeishan and Duroc sows. Both Meishan and Erhualian sows had higher numbers of total bacteria, Firmicutes, Bacteroidetes and sulphate-reducing bacteria as compared to Duroc sows. The results suggest that the pig breed affects the composition of gut microbiota. The microbial composition is different with different breeds, especially between overseas breeds (lean type) and Chinese breeds (relatively obese type).