• Journal of the Korean Society for information Management
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• v.26 no.1
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• pp.185-214
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• 2009

The purpose of this study is aimed to clarify the usefulness of meta search system using Open API of library online catalog by constructing OPAC-based search system using Open API of library online catalog and meta search system using Open API of library online catalog, and comparing the usability of the two experimental search systems. As for usability, on the whole, it was higher in meta search system using Open API of library online catalog than OPAC-based search system using Open API of library online catalog, and there was statistically significant difference. Therefore, if libraries share and use enriched content which is provided through Open API for book search, which is opened by Internet bookstores, search engines and Web portals, it is expected that it will be helpful in enhancing bibliographic data, expanding subject access point, empowering subject search ability, extending meta search service, improving book availability, and reducing catalog cost.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.17 no.7
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• pp.1773-1793
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• 2023

Automated machine learning, often referred to as "AutoML," is the process of automating the time-consuming and iterative procedures that are associated with the building of machine learning models. There have been significant contributions in this area across a number of different stages of accomplishing a data-mining task, including model selection, hyper-parameter optimization, and preprocessing method selection. Among them, preprocessing method selection is a relatively new and fast growing research area. The current work is focused on the recommendation of preprocessing methods, i.e., feature subset selection (FSS) algorithms. One limitation in the existing studies regarding FSS algorithm recommendation is the use of a single learner for meta-modeling, which restricts its capabilities in the metamodeling. Moreover, the meta-modeling in the existing studies is typically based on a single group of data characterization measures (DCMs). Nonetheless, there are a number of complementary DCM groups, and their combination will allow them to leverage their diversity, resulting in improved meta-modeling. This study aims to address these limitations by proposing an architecture for preprocess method selection that uses ensemble learning for meta-modeling, namely AutoFE-Sel. To evaluate the proposed method, we performed an extensive experimental evaluation involving 8 FSS algorithms, 3 groups of DCMs, and 125 datasets. Results show that the proposed method achieves better performance compared to three baseline methods. The proposed architecture can also be easily extended to other preprocessing method selections, e.g., noise-filter selection and imbalance handling method selection.

• Journal of Dental Rehabilitation and Applied Science
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• v.38 no.4
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• pp.196-203
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• 2022

Purpose: This study aimed to evaluate the accuracy and clinical usability of an identification model using deep learning for 79 dental implant types. Materials and Methods: A total of 45396 implant fixture images were collected through panoramic radiographs of patients who received implant treatment from 2001 to 2020 at 30 dental clinics. The collected implant images were 79 types from 18 manufacturers. EfficientNet and Meta Pseudo Labels algorithms were used. For EfficientNet, EfficientNet-B0 and EfficientNet-B4 were used as submodels. For Meta Pseudo Labels, two models were applied according to the widen factor. Top 1 accuracy was measured for EfficientNet and top 1 and top 5 accuracy for Meta Pseudo Labels were measured. Results: EfficientNet-B0 and EfficientNet-B4 showed top 1 accuracy of 89.4. Meta Pseudo Labels 1 showed top 1 accuracy of 87.96, and Meta pseudo labels 2 with increased widen factor showed 88.35. In Top5 Accuracy, the score of Meta Pseudo Labels 1 was 97.90, which was 0.11% higher than 97.79 of Meta Pseudo Labels 2. Conclusion: All four deep learning algorithms used for implant identification in this study showed close to 90% accuracy. In order to increase the clinical applicability of deep learning for implant identification, it will be necessary to collect a wider amount of data and develop a fine-tuned algorithm for implant identification.

• The KIPS Transactions:PartB
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• v.13B no.5 s.108
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• pp.533-540
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• 2006

The semantic web environment promise semantic search of heterogeneous data from distributed web page. Semantic search would resuit in an overwhelming number of results for users is increased, therefore elevating the need for appropriate personalized ranking schemes. Culture Finder helps semantic web agents obtain personalized culture information. It extracts meta data for each web page(culture news, culture performance, culture exhibition), perform semantic search and compute result ranking point to base user profile. In order to work efficient, Culture Finder uses five major technique: Machine learning technique for generating user profile from user search behavior and meta data repository, an efficient semantic search system for semantic web agent, query analysis for representing query and query result, personalized ranking method to provide suitable search result to user, upper ontology for generating meta data. In this paper, we also present the structure used in the Culture Finder to support personalized search service.

• Journal of Broadcast Engineering
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• v.10 no.3
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• pp.436-450
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• 2005

In this paper we propose an integrated media format for a photo album including media resources and corresponding meta-data The main purpose of the integrated media is to be more reusable meta-data and to facilitate constructing a photo album from a large number of photo images as well. The proposed media format is based on MAF(multimedia Application Format) which is recently going on progress in MPEG standards. In this paper, we propose the integrated media consisting of JPEG data and content-based meta-data based on MPEG-7 MDS. We verified the usefulness of the proposed media through experiments with implementation of encoder and photo MAF player for the MAF-based media format.

• Journal of Digital Convergence
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• v.6 no.2
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• pp.55-64
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• 2008

The Internet possesses not only features of mass media but also features of word of mouth communication. Communication channel is considered as one of most important variables in diffusion process. In this paper, we examined functionality of technology diffusion on the Internet through the use of meta tags. We have measured the coefficients of the Bass diffusion model which has been well-established in new product diffusion. This research shows that the Bass model is appropriate for describing technology diffusion on the Internet. The external influence as represented by the coefficient of innovation was found to be much smaller while the internal influence dominates in all meta tag diffusion. In meta tag diffusion, the internal influence as represented by the coefficient of imitation was increased at least twice bigger than that of consumer durables and information technology. Collecting necessary data in social sciences research can be a burden. This research shows that it can be alleviated through the use of software agents over the Internet. The research made use of software agents for collecting longitudinal data from publicly accessible archive such as Archive.org.

• Journal of Korean Library and Information Science Society
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• v.33 no.2
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• pp.109-137
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• 2002

A literature analysis for the planning and realization of meta database system was carried out to establish the ocean electronic resources, the first in Korea. The study targeted from web resources and to oceanographic survey data. The focus of the analysis lies in the providing practical information retrieval service for ocean electronic resources based on the framework of effective Dublin Core metadata with network resources description. The analyses included ocean electronic resources, metadata descriptive elements, metadata classification, system organization and retrieval for planning and implementation of meta database system.

• Asian Pacific Journal of Cancer Prevention
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• v.14 no.3
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• pp.2013-2017
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• 2013

Background: To compare the effects of two adjuvant chemotherapy regimens, anthracycline-based and cyclophosphamide, methotrexate, fluorourical (CMF) on disease free survival for breast cancer patients in the Eastern Mediterranean region and Asia. Methods: In a systematic review with a multivariate mixed model meta-analysis, the reported survival proportion at multiple time points in different studies were combined. Our data sources were studies linking the two chemotherapy regimens on an adjuvant basis with disease free survival published in English and Persian in the Eastern Mediterranean region and Asia. All survival curves were generated with Graphdigitizer software. Results: 14 retrospective cohort studies were located from electronic databases. We analyzed data for 1,086 patients who received anthracycline-based treatment and 1,109 given CMF treatment. For determination of survival proportions and time we usesb the transformation Ln (-Ln(S)) and Ln (time) to make precise estimations and then fit the model. All analyses were carried out with STATA software. Conclusions: Our findings showed a significant efficacy of anthracycline-based adjuvant therapy regarding disease free survival of breast cancer. As a limitation in this meta-analysis we used studies with different types of anthracycline-based regimens.

• Korean Journal of Clinical Pharmacy
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• v.25 no.3
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• pp.131-137
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• 2015

Background: The protective effect of metformin against breast cancer is inconclusive. Objective: To evaluate the effect of metformin on breast cancer risk and mortality in patients with type 2 diabetes. Method: A comprehensive literature search was performed for pertinent articles published prior to June 30, 2014, using PubMed and EMBASE. Study heterogeneity was estimated with $I^2$ statistic. The data from the included studies were pooled and weighted by random-effects model. The quality of each included study was assessed on the basis of the 9-star Newcastle-Ottawa Scale and publication bias was evaluated by visual inspection of a funnel plot. Results: Ten studies were included in the meta-analysis of the association of metformin and breast cancer risk. By synthesizing the data from the studies, the pooled odds ratio (OR) was 0.72 (95% CI: 0.59, 0.87) (p = 0.0005). Three cohort studies were included for meta-analysis of the association between metformin and breast cancer-related mortality. Metformin was associated with a significant decrease in mortality (Risk ratio: 0.68; 95% CI: 0.51, 0.90, p = 0.007). Conclusion: The present meta-analysis suggests that metformin appears to be associated with a lower risk of breast cancer incidence and mortality in patients with type 2 diabetes.

• Asian Pacific Journal of Cancer Prevention
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• v.15 no.8
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• pp.3503-3505
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• 2014

Background: Interleukin-33 (IL-33) has recently been implicated in tumor development. Methods: Data was obtained from PubMed, EMBASE, Clinical trial, Cochrane Library, Web of Science, CNKI and Wanfang databases. After quality assessment and data extraction, a meta-analysis was performed using Review Manager 5.2 software. Results: There were eight documents included in this meta-analysis. The results showed IL33 levels to be higher in tumor patients than that in health people, but no correlations tumor stage, metastasis and survival time of tumor patients were evident. Conclusion: IL33 may be useful as an alarm factor in tumor detection and prognosis.