• Genomics & Informatics
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• v.17 no.4
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• pp.45.1-45.3
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• 2019

To identify pathways associated with survival phenotypes using gene expression data, we recently proposed the hierarchical structural component model for pathway analysis of gene expression data (HisCoM-PAGE) method. The HisCoM-PAGE software can consider hierarchical structural relationships between genes and pathways and analyze multiple pathways simultaneously. It can be applied to various types of gene expression data, such as microarray data or RNA sequencing data. We expect that the HisCoM-PAGE software will make our method more easily accessible to researchers who want to perform pathway analysis for survival times.

• The Korean Journal of Applied Statistics
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• v.29 no.4
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• pp.613-625
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• 2016

The development of technology has changed the world drastically. Patent data analysis helps to understand modern technology trends and predict prospective future technology. In this paper, we analyze the patent citation network using the USPTO data between 1985 and 2012 to identify technology trends. We use network centrality measures that include a PageRank algorithm to find core technologies and identify groups of technology with similar properties with statistical network models.

• The Korean Journal of Food & Health Convergence
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• v.9 no.3
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• pp.1-9
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• 2023

Nowadays, with the development of the internet and e-commerce, opening tea shops online has become an increasing choice for selling tea. However, the product page information of many online tea shops cannot effectively attract consumers, resulting in their profits being compromised. To investigate this, we conducted this paper and hope to provide effective suggestions. This paper is based on 229 questionnaires and selects the product page information of online tea shops as the research object. Using the four dimensions of perceived value theory as independent variables with consumer purchase intention as the dependent variable. A structural equation model was constructed to analyze the role of the perceived value of product page information in online tea shops how influencing consumers' purchase intentions. It was found that information on the perceived functional value of online tea shops did not have a significant positive effect on consumer purchase intentions. However, information on the perceived monetary value, perceived social value, and perceived emotional value of online tea shops had a significant positive impact on consumers' purchase intentions. Based on the above conclusions, online tea shops should focus on the expression of product page information to enhance the level of consumers' perceived value of tea products, thereby enhancing their intention to purchase tea products.

• Fashion & Textile Research Journal
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• v.13 no.2
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• pp.205-214
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• 2011

The purpose of this study is to develop a size information providing model which is easy recognition and utilization for customer. This study also implemented web page to apply the size-informational model. Web page implemented using Apache Web Server and JAVA client-side scripting. Research result on the actual condition of fashion electronic commerce, most of the firms are used the old named same with period of 1980. On the same named-code, they are used different sizing systems by firms or items. Size interval is used 2~5 cm, different by firms. In the size information, is provided only named-code(55, 66 etc.) or garment size, and is confusing whether the marked is body size or garment size. Many of the marked size information were wrong. The sizing system of KS K5001(2009) is not used well. These problems are increased a lose customer and firm by return, exchange, mending-cost, stock, etc. Therefore, the problems should be improved by providing correct and detailed information of size and garment, as well as standardization of sizing systems based on KS K5001.

• Journal of the Korea Society of Computer and Information
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• v.25 no.12
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• pp.165-172
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• 2020

Recently, due to exponential growth of access information on the web, the importance of predicting a user's next web page use has been increasing. One of the methods that can be used for predicting user's next web page is deep learning. To predict next web page, web logs are analyzed by data preprocessing and then a user's next web page is predicted on the output of the analyzed web logs using a deep learning algorithm. In this paper, we propose a framework for web page prediction that includes methods for web log preprocessing followed by deep learning techniques for web prediction. To increase the speed of preprocessing of large web log, a Hadoop based MapReduce programming model is used. In addition, we present a web prediction system that uses an efficient deep learning technique on the output of web log preprocessing for training and prediction. Through experiment, we show the performance improvement of our proposed method over traditional methods. We also show the accuracy of our prediction.

• The Journal of the Convergence on Culture Technology
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• v.10 no.2
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• pp.517-524
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• 2024

This study aims to train and implement a deep learning model for the fusion of website creation and artificial intelligence, in the era known as the AI revolution following the launch of the ChatGPT service. The deep learning model was trained using 3,000 collected web page images, processed based on a system of component and layout classification. This process was divided into three stages. First, prior research on AI models was reviewed to select the most appropriate algorithm for the model we intended to implement. Second, suitable web page and paragraph images were collected, categorized, and processed. Third, the deep learning model was trained, and a serving interface was integrated to verify the actual outcomes of the model. This implemented model will be used to detect multiple paragraphs on a web page, analyzing the number of lines, elements, and features in each paragraph, and deriving meaningful data based on the classification system. This process is expected to evolve, enabling more precise analysis of web pages. Furthermore, it is anticipated that the development of precise analysis techniques will lay the groundwork for research into AI's capability to automatically generate perfect web pages.

• Journal of Biosystems Engineering
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• v.27 no.1
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• pp.45-50
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• 2002

This study was performed to develop thin layer drying equations fur short grain rough rice using far-infrared ray. Thin layer drying tests was conducted at four far-infrared ray temperature levels of 30, 40, 50, 60$^{\circ}C$ and two initial moisture content levels of 20.7, 26.2%(w.b.). The measured moisture ratios were fitted to Lewis and Page drying models by stepwise multiple regression analysis. Half response time of drying was affected by both drying temperature and initial moisture content at drying temperature of below 40$^{\circ}C$, but at above 40$^{\circ}C$ was mainly affected by drying temperature. Experimental constant(k) in Lewis model was a function of drying temperature, but K and N in Page model were function of drying temperature and initial moisture content. Moisture ratios predicted by two drying models agreed well with experimental values. But in the actual range of drying temperature above 30$^{\circ}C$ Page model was more suitable for predicting of drying rates.

• The Journal of Information Systems
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• v.31 no.4
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• pp.189-207
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• 2022

Purpose Previous research on customer engagement in SNS marketing has mainly addressed the conceptualization of type of word-of-mouth messages. However, there is a lack of researches about the effect of the brand-page characteristics on the type of word-of-mouth messages. Therefore, this study examined the effect of brand-page characteristics in terms of the type of word-of-mouth messages as the main objective and the effect of the type of word-of-mouth messages in terms of the brand loyalty as the secondary objective in the context of Facebook. Design/methodology/approach The empirical research was based on a poll done through 400 research candidates in the Facebook and the final 342 responses were collected and used in statistical data analysis. The adaptability, trust, and validity to measurement model were verified and the structural relationship in the research model was analyzed through these 342 responses. The collected data verified hypotheses established using the SPSS statistical package and structural equation model using AMOS. Findings The results showed that the BP-information provision had a non-significant effect on the factual word-of-mouth message and a significant effect on the evaluative word-of-mouth message. The BP-reliability had a significant effect on the factual word-of-mouth message and the evaluative word-of-mouth message. The BP-entertainment had a significant effect on the factual word-of-mouth message and the evaluative word-of-mouth effect. The BP-interaction had a non-significant effect on the factual word-of-mouth message and the evaluative word-of-mouth message. Finally, the factual word-of-mouth message and the evaluative word-of-mouth message had a significant effect on the brand loyalty.

• The Mathematical Education
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• v.62 no.3
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• pp.435-455
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• 2023

This study primarily focused on the development of an Explainable Artificial Intelligence (XAI) model to discern and analyze papers with significant impact in the field of mathematics education. To achieve this, meta-information from 29 domestic and international mathematics education journals was utilized to construct a comprehensive academic research network in mathematics education. This academic network was built by integrating five sub-networks: 'paper and its citation network', 'paper and author network', 'paper and journal network', 'co-authorship network', and 'author and affiliation network'. The Random Forest machine learning model was employed to evaluate the impact of individual papers within the mathematics education research network. The SHAP, an XAI model, was used to analyze the reasons behind the AI's assessment of impactful papers. Key features identified for determining impactful papers in the field of mathematics education through the XAI included 'paper network PageRank', 'changes in citations per paper', 'total citations', 'changes in the author's h-index', and 'citations per paper of the journal'. It became evident that papers, authors, and journals play significant roles when evaluating individual papers. When analyzing and comparing domestic and international mathematics education research, variations in these discernment patterns were observed. Notably, the significance of 'co-authorship network PageRank' was emphasized in domestic mathematics education research. The XAI model proposed in this study serves as a tool for determining the impact of papers using AI, providing researchers with strategic direction when writing papers. For instance, expanding the paper network, presenting at academic conferences, and activating the author network through co-authorship were identified as major elements enhancing the impact of a paper. Based on these findings, researchers can have a clear understanding of how their work is perceived and evaluated in academia and identify the key factors influencing these evaluations. This study offers a novel approach to evaluating the impact of mathematics education papers using an explainable AI model, traditionally a process that consumed significant time and resources. This approach not only presents a new paradigm that can be applied to evaluations in various academic fields beyond mathematics education but also is expected to substantially enhance the efficiency and effectiveness of research activities.

• Journal of Information Technology Applications and Management
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• v.13 no.2
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• pp.115-126
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• 2006

Recently, flash memories are one of best media to support portable computer's storages in mobile computing environment. We propose a new transaction recovery scheme for a flash memory database environment which is based on a flash media file system. We improved traditional shadow paging schemes by reusing old data pages which are supposed to be invalidated in the course of writing a new data page in the flash file system environment. In order to reuse these data pages, we exploit deferred cleaning list structure in our flash memory shadow paging (FMSP) scheme. FMSP scheme removes the additional storage overhead for keeping shadow pages and minimizes the I/O performance degradation caused by data page distribution phenomena of traditional shadow paging schemes. We also propose a simulation model to show the performance of FMSP. Based on the results of the performance evaluation, we conclude that FMSP outperforms the traditional scheme.