• Korean Journal of Community Nutrition
• /
• v.21 no.5
• /
• pp.440-450
• /
• 2016

Objectives: We previously developed a dish-based semi-quantitative food frequency questionnaire (FFQ) for Korean adolescents and reported that it had reasonable reliability and validity. The objective of the current study was to construct a web-based dietary evaluation system applying the FFQ for Korean adolescents and examine its applicability in the context of reliability and validity. Methods: A web-based food frequency questionnaire system was designed using a comprehensive approach, incorporating not only dietary data survey but also up-to-date nutrition information and individualized eating behavior guidelines. A convenience sample of 50 boys and girls aged 12~18 years agreed to participate in the study and completed the FFQ twice and 3 days of dietary recall on the developed website during a two-month period. The FFQ's reliability and validity was examined using correlation and cross classification analysis. We also measured participants' subjective levels of the web site's usability, visual effect, understanding, and familiarity. Results: Spearman correlation coefficients for reliability ranged from 0.74 (for vitamin A) to 0.94 (for energy). From cross-classification analyses, the proportion of subjects in the same intake quartile was highest for energy (82.0%) and lowest for vitamin A (56.0%). With regard to validity analysis, Spearman correlation coefficients ranged from 0.34 (for fiber) to 0.79 (for energy). The proportions of subjects in the opposite categories between the first FFQ and 3-day diet recall data were generally low from 0.00% (for fat) to 36.2% (for sodium). Average subjective levels of the website's usability, visual effect, understanding, and familiarity were all found to be over 4 points out of 5 points. Conclusions: The web-based dietary evaluation system developed can serve as a valid and attractive tool for administering FFQ to Korean adolescents.

• Journal of Information Technology Services
• /
• v.11 no.4
• /
• pp.25-37
• /
• 2012

In recent year, the u-City construction projects which integrate IT technology into urban infrastructures are being pushed forward by many local governments. These projects contain various purposes in an aspect of regional economy : to reinforce a competitiveness of region by increasing efficiency of urban managements and to revitalize regional economy by stimulating the regional high-tech industries that related to u-City construction. In this context, regional economic impact assessment of u-City construction projects is particularly important because, it give us information about effectiveness of u-City construction policy as a stimulus of regional high-tech industries and the policy feasibility of u-City construction projects that can be a base of public projects. However, it is challenging to assess the impact of u-City projects on regional economy properly due to a lack of understanding about industrial classification, and specific industrial inputs related to u-City construction. In this study, we suggest u-City industrial classifications, and specific-industrial inputs induced by u-City construction projects based on associated legislations, business report for a u-City construction, and results from previous studies. Using these classification and industrial input, we also investigate the regional economic impacts of a u-City construction project in Wha-sung and Dong-tan cities employing Input-output analysis. The empirical results suggests that u-City industries have relatively high in production inducement, and value added inducement compared to input of other industrial sectors. These results indicate that regional economic impact of a Wha-sung and Dong-tan u-City construction project are relatively high, but economic impacts of u-City construction projects vary according to the regional industrial structure, and the specific expense accounts of u-City construction projects.

• KSII Transactions on Internet and Information Systems (TIIS)
• /
• v.13 no.8
• /
• pp.4191-4211
• /
• 2019

Software product lines (SPLs) are complex software systems by nature due to their common reference architecture and interdependencies. Therefore, any form of evolution can lead to a more complex situation than a single system. On the other hand, software product lines are developed keeping long-term perspectives in mind, which are expected to have a considerable lifespan and a long-term investment. SPL development organizations need to consider software evolution in a systematic way due to their complexity and size. Addressing new user requirements over time is one of the most crucial factors in the successful implementation SPL. Thus, the addition of new requirements or the rapid context change is common in SPL products. To cope with rapid change several researchers have discussed the evolution of software product lines. However, for the evolution of an SPL, the literature did not present a systematic process that would define activities in such a way that would lead to the rapid evolution of software. Our study aims to provide a requirements-driven process that speeds up the requirements engineering process using social network sites in order to achieve rapid software evolution. We used classification, topic modeling, and sentiment extraction to elicit user requirements. Lastly, we conducted a case study on the smartwatch domain to validate our proposed approach. Our results show that users' opinions can contain useful information which can be used by software SPL organizations to evolve their products. Furthermore, our investigation results demonstrate that machine learning algorithms have the capacity to identify relevant information automatically.

• Smart Media Journal
• /
• v.10 no.1
• /
• pp.39-46
• /
• 2021

This study is about a method of extracting a summary from a news article in consideration of the importance of each sentence constituting the article. We propose a method of calculating sentence importance by extracting the probabilities of topic sentence, similarity with article title and other sentences, and sentence position as characteristics that affect sentence importance. At this time, a hypothesis is established that the Topic Sentence will have a characteristic distinct from the general sentence, and a deep learning-based classification model is trained to obtain a topic sentence probability value for the input sentence. Also, using the pre-learned ELMo language model, the similarity between sentences is calculated based on the sentence vector value reflecting the context information and extracted as sentence characteristics. The topic sentence classification performance of the LSTM and BERT models was 93% accurate, 96.22% recall, and 89.5% precision, resulting in high analysis results. As a result of calculating the importance of each sentence by combining the extracted sentence characteristics, it was confirmed that the performance of extracting the topic sentence was improved by about 10% compared to the existing TextRank algorithm.

• International Journal of Computer Science & Network Security
• /
• v.22 no.8
• /
• pp.260-268
• /
• 2022

The article considers the training, competitiveness of specialists, professional mobility, professionalism and competence of specialists in the context of distance learning. The advantages of distance learning are shown. The characteristic features of distance learning in the preparation of students and in the implementation of these technologies in the educational process of higher educational institutions are determined. Competitiveness, professional mobility, professionalism and competence of a specialist are qualities that determine a person's life and work success. Professional mobility is interpreted as a systemic quality of a specialist's personality, which includes a whole range of knowledge, skills, abilities, personal qualities, value orientations, and so on. The vision of mobility of specialists by foreign scientists is presented. It is noted that the classification of professional mobility presented in the article makes it possible to organize various movements from a single position, to present them as separate manifestations of the general process of professional and pedagogical mobility, to determine which type of mobility ensures the performance of certain social functions. It was found that mobility can be differentiated into differentiated and intergeneration. According to the subject, individual and group mobility are distinguished; according to the direction - internal and external. The classification of employees according to their attitude to mobility is shown, which can be divided into the following groups: actually mobile; potentially mobile; actually stable; potentially stable.

• Journal of the Korean BIBLIA Society for library and Information Science
• /
• v.18 no.2
• /
• pp.141-159
• /
• 2007

The purpose of this study was to try to conceptualize the Folksonomy, so called collaborative taggng or bookmarking and suggest its application on the Web information services. This paper explores how folksonomies could be used in web information services to enable end users to manage personal information spaces, get helped existing controlled vocabularies, and create and share their interests in online communities. Traditional classification system and philosophical issues on Folksonomy were reviewed in this paper in the context of internet based information and its services. The benefits and shortcomings of folksonomies are discussed. Some of the customizable features in existing library catalogue systems are reviewed to suggest other applicable features for web information services.

• 재활복지
• /
• v.14 no.3
• /
• pp.95-119
• /
• 2010

This study aims to develope participation scale of people with disabilities according to the International classification of Functioning, Disability, and Health(ICF). ICF includes a component for classifying and qualifying participation of individuals in the context of their environments. The participation scale were developed using 7 times with different focus groups using the ICIDH-2 as a contextual framework. Candidate 41 items were developed based on the 8 participation components and put into a survey format. Finally, purposeful sample of 363 people with mobility limitations was conducted survey. As a result of survey, participation scale is composed of 38 items that are placed in 7 domains used in the activity/participation component of the ICF: holisitc health; communication; mobility; domestic life; interpersonal interactions and relationships; social and economic life; civic life. This scale does not include the domains of learning and applying knowledge, general tasks and demands, recreation and leisure but more focuses on social and civic life.

• Journal of Korean Society of Industrial and Systems Engineering
• /
• v.46 no.4
• /
• pp.1-14
• /
• 2023

As markets and industries continue to evolve rapidly, technology opportunity discovery (TOD) has become critical to a firm's survival. From a common consensus that TOD based on a firm's capabilities is a valuable method for small and medium-sized enterprises (SMEs) and reduces the risk of failure in technology development, studies for TOD based on a firm's capabilities have been actively conducted. However, previous studies mainly focused on a firm's technological capabilities and rarely on business capabilities. Since discovered technologies can create market value when utilized in a firm's business, a firm's current business capabilities should be considered in discovering technology opportunities. In this context, this study proposes a TOD method that considers both a firm's business and technological capabilities. To this end, this study uses patent data, which represents the firm's technological capabilities, and trademark data, which represents the firm's business capabilities. The proposed method comprises four steps: 1) Constructing firm technology and business capability matrices using patent classification codes and trademark similarity group codes; 2) Transforming the capability matrices to preference matrices using the fuzzy function; 3) Identifying a target firm's candidate technology opportunities using the collaborative filtering algorithm; 4) Recommending technology opportunities using a portfolio map constructed based on technology similarity and applicability indices. A case study is conducted on a security firm to determine the validity of the proposed method. The proposed method can assist SMEs that face resource constraints in identifying technology opportunities. Further, it can be used by firms that do not possess patents since the proposed method uncovers technology opportunities based on business capabilities.

• Imaging Science in Dentistry
• /
• v.53 no.4
• /
• pp.271-281
• /
• 2023

Purpose: The objective of this scoping review was to investigate the applicability and performance of various convolutional neural network (CNN) models in tooth numbering on panoramic radiographs, achieved through classification, detection, and segmentation tasks. Materials and Methods: An online search was performed of the PubMed, Science Direct, and Scopus databases. Based on the selection process, 12 studies were included in this review. Results: Eleven studies utilized a CNN model for detection tasks, 5 for classification tasks, and 3 for segmentation tasks in the context of tooth numbering on panoramic radiographs. Most of these studies revealed high performance of various CNN models in automating tooth numbering. However, several studies also highlighted limitations of CNNs, such as the presence of false positives and false negatives in identifying decayed teeth, teeth with crown prosthetics, teeth adjacent to edentulous areas, dental implants, root remnants, wisdom teeth, and root canal-treated teeth. These limitations can be overcome by ensuring both the quality and quantity of datasets, as well as optimizing the CNN architecture. Conclusion: CNNs have demonstrated high performance in automated tooth numbering on panoramic radiographs. Future development of CNN-based models for this purpose should also consider different stages of dentition, such as the primary and mixed dentition stages, as well as the presence of various tooth conditions. Ultimately, an optimized CNN architecture can serve as the foundation for an automated tooth numbering system and for further artificial intelligence research on panoramic radiographs for a variety of purposes.

• International conference on construction engineering and project management
• /
• 2024.07a
• /
• pp.1284-1284
• /
• 2024

Leveraging large language models and safety accident report data has unique potential for analyzing construction accidents, including the classification of accident types, injured parts, and work processes, using unstructured free text accident scenarios. We previously proposed a novel approach that harnesses the power of fine-tuned Generative Pre-trained Transformer to classify 6 types of construction accidents (caught-in-between, cuts, falls, struck-by, trips, and other) with an accuracy of 82.33%. Furthermore, we proposed a novel methodology, saliency visualization, to discern which words are deemed important by black box models within a sentence associated with construction accidents. It helps understand how individual words in an input sentence affect the final output and seeks to make the model's prediction accuracy more understandable and interpretable for users. This involves deliberately altering the position of words within a sentence to reveal their specific roles in shaping the overall output. However, the validation of saliency visualization results remains insufficient and needs further analysis. In this context, this study aims to qualitatively validate the effectiveness of saliency visualization methods. In the exploration of saliency visualization, the elements with the highest importance scores were qualitatively validated against the construction accident risk factors (e.g., "the 4m pipe," "ear," "to extract staircase") emerging from Construction Safety Management's Integrated Information data scenarios provided by the Ministry of Land, Infrastructure, and Transport, Republic of Korea. Additionally, construction accident precursors (e.g., "grinding," "pipe," "slippery floor") identified from existing literature, which are early indicators or warning signs of potential accidents, were compared with the words with the highest importance scores of saliency visualization. We observed that the words from the saliency visualization are included in the pre-identified accident precursors and risk factors. This study highlights how employing saliency visualization enhances the interpretability of models based on large language processing, providing valuable insights into the underlying causes driving accident predictions.