• Journal of Digital Convergence
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• v.19 no.11
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• pp.517-523
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• 2021

The outcome of the consultation is important to both the counselor and the researcher. Analyzing the trends of research on the results of counseling that have been carried out so far will help to comprehensively structure the results of consultations. The purpose of this research is to analyze research trends in Korea, focusing on research related to the outcomes of counseling published in 「Korean Journal of Counseling」 from 2011 to 2021, which is one of the well-known academic journals in the field of counseling in Korea. This is to explore the direction of future research by navigating the knowledge structure of research. There were 197 studies used for analysis, and the final 339 keyword were extracted during the node extraction process and used for analysis. As a result of extracting potential topics using the LDA algorithm, "Measurement and evaluation of counseling outcomes", "emotions and mediate factors affecting interpersonal relationships", and "career stress and coping strategies" are the main topics. Identifying major topics through trend analysis of counseling performance research contributed to structuring counseling performance. In-depth research on these topics needs to continue thereafter.

• Journal of Periodontal and Implant Science
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• v.37 no.4
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• pp.825-838
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• 2007

Endosseous implants are used in the treatment of various types of tooth loss, and numerous long-term studies have demonstrated the excellent reliability of this method of treatment. However, the increase of implant failure are associated with inadequate quality and/or height of bone. At the end of the 1980s, Wide(>3.75mm) implants were initially used for managing these difficult bone situations. The recommended indications for its use included poor bone quality, inadequate bone height. immediate placement in fresh extraction sockets, and immediate replacement of failed implants. At the 2000s, wider implants(6.0mm and 6.5mm) were used in a few studies. Although good clinical outcomes have been reported in recent years, there is still a controversy on this topic. Therefore, the purpose of this study was to estimate the survival rate of wide implants($6.0{\sim}8.0mm$) in molar regions, evaluating the clinical outcome. In this study, 1135 RBM surfaced wide implants($Rescue^{TM}$, MEGAZEN Co., Korea/595 maxillary, 540 mandibular) were placed in 650 patients(403 male, 247 female/age mean: $51.2{\pm}11.1$ years, range 20 to 83 years). Of the total, 68.3% were used to treat fully or partially edentulous situations, including single-tooth losses and 31.7% were placed immediately after teeth extraction or removal of failed implants, of which all were in the molar regions. Implant diameter and length ranged from 6.0 to 8.0mm and from 5.0 to 10.0mm respectively. The implants were followed for up to 42 months (mean: $14.6{\pm}9.5$ months). Of 1135 placed implants, 58 implants were lost. Among them, 53 implants were lost within 12 months after implant placement. The survival rate was 93.6% in the maxilla and 96.3% in the mandible, yielding an overall survival rate of 94.9%, for up to 42 months. As the result of Cox regression model, prosthetic type, sinus graft, and patient gender have an statistical significance on the implant survival rate in this study. This study suggests that the use of wide implants($6.0{\sim}8.0mm$) would provide a predictable treatment alternative in posterior areas.

• Journal of the Korean Society for information Management
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• v.35 no.1
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• pp.231-250
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• 2018

In this paper, we propose a new methodology for extracting and formalizing subjective topics at a specific time using a set of keywords extracted automatically from online news articles. To do this, we first extracted a set of keywords by applying TF-IDF methods selected by a series of comparative experiments on various statistical weighting schemes that can measure the importance of individual words in a large set of texts. In order to effectively calculate the semantic relation between extracted keywords, a set of word embedding vectors was constructed by using about 1,000,000 news articles collected separately. Individual keywords extracted were quantified in the form of numerical vectors and clustered by K-means algorithm. As a result of qualitative in-depth analysis of each keyword cluster finally obtained, we witnessed that most of the clusters were evaluated as appropriate topics with sufficient semantic concentration for us to easily assign labels to them.

• Journal of Digital Convergence
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• v.15 no.8
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• pp.45-58
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• 2017

The international freight forwarders in South Korea currently have fierce competition. However, there are still a very small number of studies Korea locally and globally on the service quality of international freight forwarding industry. This study aims to extract international freight forwarders' service quality factors reflecting the characteristics of freight forwarding industry. Measures of service quality have been selected after literature review and interviews, and then surveys have been conducted on exporters and importers in Korea. The collected data has been analyzed using the exploratory factor analysis. As a result, two service quality factors of international freight forwarders have been extracted: operation characteristics factor defined as accuracy, speediness timeliness and stability, and customer orientation factor defined as professionalism and empathy. An important contribution of this study is that it presents the service quality factors reflecting the characteristics of freight forwarding industry unlike precedent studies. A future research topic is to find out which of the two service quality factors influences more on customer loyalty.

• The Journal of the Korea Contents Association
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• v.20 no.2
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• pp.96-104
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• 2020

As consumers' consumption activities become more active due to the activation of online shopping malls, companies are conducting item trend analyses to boost sales. The existing item trend analysis methods are analyzed by considering only the activities of users in online shopping mall services, making it difficult to identify trends for new items without purchasing history. In this paper, we propose a trend analysis method that combines data in online shopping mall services and social network data to analyze item trends in users and potential customers in shopping malls. The proposed method uses the user's activity logs for in-service data and utilizes hot topics through word set extraction from social network data set to reflect potential users' interests. Finally, the item trend change is detected over time by utilizing the item index and the number of mentions in the social network. We show the superiority of the proposed method through performance evaluations using social network data.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.8 no.5
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• pp.1676-1689
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• 2014

At present, the Content-Based Image Retrieval (CBIR) system has become a hot research topic in the computer vision field. In the CBIR system, the accurate extractions of low-level features can reduce the gaps between high-level semantics and improve retrieval precision. This paper puts forward a new retrieval method aiming at the problems of high computational complexities and low precision of global feature extraction algorithms. The establishment of the new retrieval method is on the basis of the SIFT and Harris (APISH) algorithm, and the salient region of interest points (SRIP) algorithm to satisfy users' interests in the specific targets of images. In the first place, by using the IPDSH and SRIP algorithms, we tested stable interest points and found salient regions. The interest points in the salient region were named as salient interest points. Secondary, we extracted the pseudo-Zernike moments of the salient interest points' neighborhood as the feature vectors. Finally, we calculated the similarities between query and database images. Finally, We conducted this experiment based on the Caltech-101 database. By studying the experiment, the results have shown that this new retrieval method can decrease the interference of unstable interest points in the regions of non-interests and improve the ratios of accuracy and recall.

• Journal of KIISE:Software and Applications
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• v.33 no.2
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• pp.243-251
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• 2006

In multi-document summarization by means of salient sentence extraction, it is important to remove redundant information. In the removal process, the similarities and differences of sentences are considered. In this paper, we propose a method for multi-document summarization which extracts salient sentences without having redundant sentences by way of cohesive term clustering method that utilizes co-occurrence Information. In the cohesive term clustering method, we assume that each term does not exist independently, but rather it is related to each other in meanings. To find the relations between terms, we cluster sentences according to topics and use the co-occurrence information oi terms in the same topic. We conduct experimental tests with the DUC(Document Understanding Conferences) data. In the tests, our method shows better performance of summarization than other summarization methods which use term co-occurrence information based on term cohesion of document or sentence unit, and simple statistical information.

• Journal of KIISE
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• v.43 no.9
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• pp.983-997
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• 2016

Many available online product reviews for any given product makes it difficult for a consumer to locate the helpful reviews. The purpose of this study was to investigate automatic helpfulness evaluation of online product reviews according to review information types based on the target of information. The underlying assumption was that consumers find reviews containing specific information related to the product itself or the reliability of reviewers more helpful than peripheral information, such as shipping or customer service. Therefore, each sentence was categorized by given information types, which reduced the semantic space of review sentences. Subsequently, we extracted specific information from sentences by using a topic-based representation of the sentences and a clustering algorithm. Review ranking experiments indicated more effective results than other comparable approaches.

• The Journal of Korean Medicine Ophthalmology and Otolaryngology and Dermatology
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• v.33 no.4
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• pp.55-73
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• 2020

Objectives : We investigated the effectiveness and safety of intranasal phototherapy for allergic rhinitis (AR). Methods : We searched 8 electronic databases (PubMed, Cochrane Library, CNKI, CiNii Articles, OASIS, NDSL, KISS, KMbase) to identify randomized controlled trials (RCTs) that reported the use of intranasal phototherapy for AR from their inception until May 30, 2020. Two investigators independently searched, collected, and screened the RCTs. We performed data extraction and evaluation for risk of bias using the Cochrane risk-of-bias tool. Results : This study included 12 RCTs; six studies compared intranasal and sham phototherapy, of which four studies reported a significant inter group difference and two studies reported a significant difference partially. No significant changes in symptoms were observed between the phototherapy and conventional therapy groups. The phototherapy and concurrent acupuncture treatment group showed a significantly higher effectiveness rate compared with the group that received only acupuncture. Both the phototherapy and laser acupuncture group showed significant improvement in the symptom severity scale scores. Six studies reported mild adverse effects, such as dryness and nasal pain in the intranasal phototherapy group; however, no severe adverse effects were reported. Conclusions : This study confirmed the safety and effectiveness of intranasal phototherapy for symptom relief and improved quality of life in patients with AR. However, further studies are needed on this topic in order to demonstrate it clearly.

• Journal of Korean Society of Industrial and Systems Engineering
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• v.43 no.3
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• pp.112-121
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• 2020

Recently, as the internet usage is increasing, accordingly generated text data is also increasing. Because this text data on the internet includes users' comments, the text data on the Internet can help you get users' opinion more efficiently and effectively. The topic of text mining has been actively studied recently, but it primarily focuses on either the content analysis or various improving techniques mostly for the performance of target mining algorithms. The objective of this study is to propose a novel method of analyzing the user's requirements by utilizing the text-mining technique. To complement the existing survey techniques, this study seeks to present priorities together with efficient extraction of customer requirements from the text data. This study seeks to identify users' requirements, derive the priorities of requirements, and identify the detailed causes of high-priority requirements. The implications of this study are as follows. First, this study tried to overcome the limitations of traditional investigations such as surveys and VOCs through text mining of online text data. Second, decision makers can derive users' requirements and prioritize without having to analyze numerous text data manually. Third, user priorities can be derived on a quantitative basis.