• Title/Summary/Keyword: network-based business

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Research Trends and Knowledge Structure of Digital Transformation in Fashion (패션 영역에서 디지털 전환 관련 연구동향 및 지식구조)

  • Choi, Yeong-Hyeon;Jeong, Jinha;Lee, Kyu-Hye
    • Journal of Digital Convergence
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    • v.19 no.3
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    • pp.319-329
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    • 2021
  • This study aims to investigate Korean fashion-related research trends and knowledge structures on digital transformation through information-based approaches. Accordingly, we first identified the current status of the relevant research in Korean academic literature by year and journal; subsequently, we derived key research topics through network analysis, and then analyzed major research trends and knowledge structures by time. From 2010 to 2020, we collected 159 studies published on Korean academic platforms, cleansed data through Python 3.7, and measured centrality and network implementation through NodeXL 1.0.1. The results are as follows: first, related research has been actively conducted since 2016, mainly concentrated in clothing and art areas. Second, the online platform, AR/VR, appeared as the most frequently mentioned topic, and consumer psychological analysis, marketing strategy suggestion, and case analysis were used as the main research methods. Through clustering, major research contents for each sub-major of clothing were derived. Third, major subject by period was considered, which has, over time, changed from consumer-centered research to strategy suggestion, and design development research of platforms or services. This study contributes to enhancing insight into the fashion field on digital transformation, and can be used as a basic research to design research on related topics.

Social Network Analysis of Long-term Standby Demand for Special Transportation (특별교통수단 장기대기수요에 대한 사회 연결망 분석)

  • Park, So-Yeon;Jin, Min-Ha;Kang, Won-Sik;Park, Dae-Yeong;Kim, Keun-Wook
    • Journal of Digital Convergence
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    • v.19 no.5
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    • pp.93-103
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    • 2021
  • The special means of transportation introduced to improve the mobility of the transportation vulnerable met the number of legal standards in 2016, but lack of development in terms of quality, such as the existence of long waiting times. In order to streamline the operation of special means of transportation, long-term standby traffic, which is the top 25% of the wait time, was extracted from the Daegu Metropolitan Government's special transportation history data, and spatial autocorrelation analysis and social network analysis were conducted. As a result of the analysis, the correlation between the average waiting time of special transportation users and the space was high. As a result of the analysis of internal degree centrality, the peak time zone is mainly visited by general hospitals, while the off-peak time zone shows high long-term waiting demand for visits by lawmakers. The analysis of external degree centrality showed that residential-based traffic demand was high in both peak and off-peak hours. The results of this study are considered to contribute to the improvement of the quality of the operation of special transportation means, and the academic implications and limitations of the study are also presented.

Emotion Recognition System Using Neural Networks in Textile Images (신경망을 이용한 텍스타일 영상에서의 감성인식 시스템)

  • Kim, Na-Yeon;Shin, Yun-Hee;Kim, Soo-Jeong;Kim, Jee-In;Jeong, Karp-Joo;Koo, Hyun-Jin;Kim, Eun-Yi
    • Journal of KIISE:Software and Applications
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    • v.34 no.9
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    • pp.869-879
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    • 2007
  • This paper proposes a neural network based approach for automatic human emotion recognition in textile images. To investigate the correlation between the emotion and the pattern, the survey is conducted on 20 peoples, which shows that a emotion is deeply affected by a pattern. Accordingly, a neural network based classifier is used for recognizing the pattern included in textiles. In our system, two schemes are used for describing the pattern; raw-pixel data extraction scheme using auto-regressive method (RDES) and wavelet transformed data extraction scheme (WTDES). To assess the validity of the proposed method, it was applied to recognize the human emotions in 100 textiles, and the results shows that using WTDES guarantees better performance than using RDES. The former produced the accuracy of 71%, while the latter produced the accuracy of 90%. Although there are some differences according to the data extraction scheme, the proposed method shows the accuracy of 80% on average. This result confirmed that our system has the potential to be applied for various application such as textile industry and e-business.

Korean Customer Attitudes Towards SNS Shopping

  • Cho, Young-Sang;Heo, Jeong-Yoon;Youn, Myoung-Kil
    • Journal of Distribution Science
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    • v.10 no.8
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    • pp.7-14
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    • 2012
  • As a new format of retailing, social shopping on SNS has rapidly grown in recent. Although there is much literature associated with customer behaviours in the academic world, little attention has been paid to identifying the shopping patterns of SNS shoppers. This paper will, thus, identify how perceived value has an impact on the buying intention of SNS shoppers, after illustrating what kind of factor influences the formation process of perceived value in the Korean marketplace. Given that SNS shoppers are for the most part 20s as well as 30s, the authors handed out questionnaires to them. Furthermore, based on literature review results, the conceptualised research model was developed. Despite lack of literature, the authors developed five constructs like price reduction, quantity- and time-limited message, product ranges, information-sharing, and required number of shoppers. The researchers made a considerable effort to identify the relationship between research concepts and each variable, based on a few research analysis methods such as frequency analysis, the Varimax rotation technique used orthogonal rotation, Cronbach's Alpha, PCA (Principle Component Analysis), and the like. Amongst the 5 variables used to measure the degree of influences on the perceived value as a social shopping characteristic, it has been evident that price cut, required minimum shoppers, product variety, and information-sharing have a positive impact on the perceived value formation processes of SNS customers. Also, this research implies that SNS retailers can differentiate themselves from other retailers by differently using the above factors. From a practitioner's point of view, these factors should be strategically used to increase the social shopping opportunities of SNS users. It is, furthermore, evident that the perceived value formed by the above 4 factors have played an important role in the buying decision process of SNS customers. In a sense, whether customers are aware of higher price cut rates, information-sharing, required minimum shoppers, and product variety has a positive impact on making buying decisions. From a retailer's point of view, online shopping mall operators are able to use blog as well as twitter to improve the buying intention as a marketing tool of social network, because the business activities provided by social shopping retailers, like the rapid, accurate responses to customer requirements, the provision of a variety of information, and the communications between customers are closely related to buying intentions. There are a few research limitations to conduct this empirical research. It was not easy to review prior papers, due to its lack. In spite of the increasing number of SNS shoppers in Korea, little research attention has been paid to this kind of research topic by academicians, because buying products or services through SNS is in its infancy. With regard to research populations, it would be difficult to generalise the research findings in Korea, owing to unbalanced respondent distribution. Considering the above research limitations as well as the growth of social shopping, many authors should pay considerable attention to SNS-related issues in the future, and develop the more sophisticated criteria to measure the characteristics of SNS shoppers.

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Nonlinear Time Series Prediction Modeling by Weighted Average Defuzzification Based on NEWFM (NEWFM 기반 가중평균 역퍼지화에 의한 비선형 시계열 예측 모델링)

  • Chai, Soo-Han;Lim, Joon-Shik
    • Journal of the Korean Institute of Intelligent Systems
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    • v.17 no.4
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    • pp.563-568
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    • 2007
  • This paper presents a methodology for predicting nonlinear time series based on the neural network with weighted fuzzy membership functions (NEWFM). The degree of classification intensity is obtained by bounded sum of weighted fuzzy membership functions extracted by NEWFM, then weighted average defuzzification is used for predicting nonlinear time series. The experimental results demonstrate that NEWFM has the classification capability of 92.22% against the target class of GDP. The time series created by NEWFM model has a relatively close approximation to the GDP which is a typical business cycle indicator, and has been proved to be a useful indicator which has the turning point forecasting capability of average 12 months in the peak point and average 6 months in the trough point during 5th to 8th cyclical period. In addition, NEWFM measures the efficiency of the economic indexes by the feature selection and enables the users to forecast with reduced numbers of 7 among 10 leading indexes while improving the classification rate from 90% to 92.22%.

Strategies for the Development of Watermelon Industry Using Unstructured Big Data Analysis

  • LEE, Seung-In;SON, Chansoo;SHIM, Joonyong;LEE, Hyerim;LEE, Hye-Jin;CHO, Yongbeen
    • The Journal of Industrial Distribution & Business
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    • v.12 no.1
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    • pp.47-62
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    • 2021
  • Purpose: Our purpose in this study was to examine the strategies for the development of watermelon industry using unstructured big data analysis. That is, this study was to look the change of issues and consumer's perception about watermelon using big data and social network analysis and to investigate ways to strengthen the competitiveness of watermelon industry based on that. Methodology: For this purpose, the data was collected from Naver (blog, news) and Daum (blog, news) by TEXTOM 4.5 and the analysis period was set from 2015 to 2016 and from 2017-2018 and from 2019-2020 in order to understand change of issues and consumer's perception about watermelon or watermelon industry. For the data analysis, TEXTOM 4.5 was used to conduct key word frequency analysis, word cloud analysis and extraction of metrics data. UCINET 6.0 and NetDraw function of UCINET 6.0 were utilized to find the connection structure of words and to visualize the network relations, and to make a cluster of words. Results: The keywords related to the watermelon extracted such as 'the stalk end of a watermelon', 'E-mart', 'Haman', 'Gochang', and 'Lotte Mart' (news: 015-2016), 'apple watermelon', 'Haman', 'E-mart', 'Gochang', and' Mudeungsan watermelon' (news: 2017-2018), 'E-mart', 'apple watermelon', 'household', 'chobok', and 'donation' (news: 2019-2020), 'watermelon salad', 'taste', 'the heat', 'baby', and 'effect' (blog: 2015-2016), 'taste', 'watermelon juice', 'method', 'watermelon salad', and 'baby' (blog: 2017-2018), 'taste', 'effect', 'watermelon juice', 'method', and 'apple watermelon' (blog: 2019-2020) and the results from frequency and TF-IDF analysis presented. And in CONCOR analysis, appeared as four types, respectively. Conclusions: Based on the results, the authors discussed the strategies and policies for boosting the watermelon industry and limitations of this study and future research directions. The results of this study will help prioritize strategies and policies for boosting the consumption of the watermelon and contribute to improving the competitiveness of watermelon industry in Korea. Also, it is expected that this study will be used as a very important basis for agricultural big data studies to be conducted in the future and this study will offer watermelon producers and policy-makers practical points helpful in crafting tailor-made marketing strategies.

Paradigm of the Transformation of Potential-Forming Space Under the Impact of Intellectual-Innovation Determinants

  • Khanin, Semen;Derhaliuk, Marta;Stavroyany, Serhii;Kudlasevych, Olga;Didkivska, Lesia
    • International Journal of Computer Science & Network Security
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    • v.22 no.1
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    • pp.340-346
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    • 2022
  • The article examines the formation of the scientific paradigm of transformation of the potential-forming space of the regional economy under the influence of intellectual and innovative determinants. Based on the study of different scientific views on the nature and properties of potential-forming space through the study of approaches to understanding the concept of "space" clarified the complexity and multifaceted nature of the phenomenon and found that its characteristics are relevant to the industrial development model. It is revealed that the leading modern trends related to the spread of globalization and regionalization, rapid development of information and communication technologies, diffusion of innovations accompany the transition from industrial to post-industrial development and its development, which leads to new development: changes production, nature and relations between business entities, etc. It is proved that under such conditions, the region as a key element of the economic system, acquires a leading role in achieving sustainable and balanced development. These processes significantly affect the potential-forming space of the regional economy under the influence of intellectual and innovative determinants, leading to the need for its transformation and change in accordance with modern realities, which is reflected in thorough research on the formation of scientific paradigm based on the formation of its theoretical foundations and methodological basis. This study reveals the essence, role, functions, structure, process of formation of the scientific paradigm of transformation of the potential-forming space of the regional economy under the influence of intellectual and innovative determinants. It is proved that the formation of the modern scientific paradigm of transformation of the potential-forming space of the regional economy under the influence of intellectual and innovative determinants occurs in the context of building a post-industrial model of development, accompanied by consideration of the region as a spatial object territories from the physical plane to the spatial environment in which the development of human capital, innovation and self-development of the region. Taking into account the above, the article outlines the prerequisites and factors of formation of the scientific paradigm of transformation of the potential-forming space of the regional economy under the influence of intellectual and innovative determinants.

Recommender Systems using Structural Hole and Collaborative Filtering (구조적 공백과 협업필터링을 이용한 추천시스템)

  • Kim, Mingun;Kim, Kyoung-Jae
    • Journal of Intelligence and Information Systems
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    • v.20 no.4
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    • pp.107-120
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    • 2014
  • This study proposes a novel recommender system using the structural hole analysis to reflect qualitative and emotional information in recommendation process. Although collaborative filtering (CF) is known as the most popular recommendation algorithm, it has some limitations including scalability and sparsity problems. The scalability problem arises when the volume of users and items become quite large. It means that CF cannot scale up due to large computation time for finding neighbors from the user-item matrix as the number of users and items increases in real-world e-commerce sites. Sparsity is a common problem of most recommender systems due to the fact that users generally evaluate only a small portion of the whole items. In addition, the cold-start problem is the special case of the sparsity problem when users or items newly added to the system with no ratings at all. When the user's preference evaluation data is sparse, two users or items are unlikely to have common ratings, and finally, CF will predict ratings using a very limited number of similar users. Moreover, it may produces biased recommendations because similarity weights may be estimated using only a small portion of rating data. In this study, we suggest a novel limitation of the conventional CF. The limitation is that CF does not consider qualitative and emotional information about users in the recommendation process because it only utilizes user's preference scores of the user-item matrix. To address this novel limitation, this study proposes cluster-indexing CF model with the structural hole analysis for recommendations. In general, the structural hole means a location which connects two separate actors without any redundant connections in the network. The actor who occupies the structural hole can easily access to non-redundant, various and fresh information. Therefore, the actor who occupies the structural hole may be a important person in the focal network and he or she may be the representative person in the focal subgroup in the network. Thus, his or her characteristics may represent the general characteristics of the users in the focal subgroup. In this sense, we can distinguish friends and strangers of the focal user utilizing the structural hole analysis. This study uses the structural hole analysis to select structural holes in subgroups as an initial seeds for a cluster analysis. First, we gather data about users' preference ratings for items and their social network information. For gathering research data, we develop a data collection system. Then, we perform structural hole analysis and find structural holes of social network. Next, we use these structural holes as cluster centroids for the clustering algorithm. Finally, this study makes recommendations using CF within user's cluster, and compare the recommendation performances of comparative models. For implementing experiments of the proposed model, we composite the experimental results from two experiments. The first experiment is the structural hole analysis. For the first one, this study employs a software package for the analysis of social network data - UCINET version 6. The second one is for performing modified clustering, and CF using the result of the cluster analysis. We develop an experimental system using VBA (Visual Basic for Application) of Microsoft Excel 2007 for the second one. This study designs to analyzing clustering based on a novel similarity measure - Pearson correlation between user preference rating vectors for the modified clustering experiment. In addition, this study uses 'all-but-one' approach for the CF experiment. In order to validate the effectiveness of our proposed model, we apply three comparative types of CF models to the same dataset. The experimental results show that the proposed model outperforms the other comparative models. In especial, the proposed model significantly performs better than two comparative modes with the cluster analysis from the statistical significance test. However, the difference between the proposed model and the naive model does not have statistical significance.

A Study on Foreign Exchange Rate Prediction Based on KTB, IRS and CCS Rates: Empirical Evidence from the Use of Artificial Intelligence (국고채, 금리 스왑 그리고 통화 스왑 가격에 기반한 외환시장 환율예측 연구: 인공지능 활용의 실증적 증거)

  • Lim, Hyun Wook;Jeong, Seung Hwan;Lee, Hee Soo;Oh, Kyong Joo
    • Knowledge Management Research
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    • v.22 no.4
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    • pp.71-85
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    • 2021
  • The purpose of this study is to find out which artificial intelligence methodology is most suitable for creating a foreign exchange rate prediction model using the indicators of bond market and interest rate market. KTBs and MSBs, which are representative products of the Korea bond market, are sold on a large scale when a risk aversion occurs, and in such cases, the USD/KRW exchange rate often rises. When USD liquidity problems occur in the onshore Korean market, the KRW Cross-Currency Swap price in the interest rate market falls, then it plays as a signal to buy USD/KRW in the foreign exchange market. Considering that the price and movement of products traded in the bond market and interest rate market directly or indirectly affect the foreign exchange market, it may be regarded that there is a close and complementary relationship among the three markets. There have been studies that reveal the relationship and correlation between the bond market, interest rate market, and foreign exchange market, but many exchange rate prediction studies in the past have mainly focused on studies based on macroeconomic indicators such as GDP, current account surplus/deficit, and inflation while active research to predict the exchange rate of the foreign exchange market using artificial intelligence based on the bond market and interest rate market indicators has not been conducted yet. This study uses the bond market and interest rate market indicator, runs artificial neural network suitable for nonlinear data analysis, logistic regression suitable for linear data analysis, and decision tree suitable for nonlinear & linear data analysis, and proves that the artificial neural network is the most suitable methodology for predicting the foreign exchange rates which are nonlinear and times series data. Beyond revealing the simple correlation between the bond market, interest rate market, and foreign exchange market, capturing the trading signals between the three markets to reveal the active correlation and prove the mutual organic movement is not only to provide foreign exchange market traders with a new trading model but also to be expected to contribute to increasing the efficiency and the knowledge management of the entire financial market.

A Study on Differences of Contents and Tones of Arguments among Newspapers Using Text Mining Analysis (텍스트 마이닝을 활용한 신문사에 따른 내용 및 논조 차이점 분석)

  • Kam, Miah;Song, Min
    • Journal of Intelligence and Information Systems
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    • v.18 no.3
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    • pp.53-77
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    • 2012
  • This study analyses the difference of contents and tones of arguments among three Korean major newspapers, the Kyunghyang Shinmoon, the HanKyoreh, and the Dong-A Ilbo. It is commonly accepted that newspapers in Korea explicitly deliver their own tone of arguments when they talk about some sensitive issues and topics. It could be controversial if readers of newspapers read the news without being aware of the type of tones of arguments because the contents and the tones of arguments can affect readers easily. Thus it is very desirable to have a new tool that can inform the readers of what tone of argument a newspaper has. This study presents the results of clustering and classification techniques as part of text mining analysis. We focus on six main subjects such as Culture, Politics, International, Editorial-opinion, Eco-business and National issues in newspapers, and attempt to identify differences and similarities among the newspapers. The basic unit of text mining analysis is a paragraph of news articles. This study uses a keyword-network analysis tool and visualizes relationships among keywords to make it easier to see the differences. Newspaper articles were gathered from KINDS, the Korean integrated news database system. KINDS preserves news articles of the Kyunghyang Shinmun, the HanKyoreh and the Dong-A Ilbo and these are open to the public. This study used these three Korean major newspapers from KINDS. About 3,030 articles from 2008 to 2012 were used. International, national issues and politics sections were gathered with some specific issues. The International section was collected with the keyword of 'Nuclear weapon of North Korea.' The National issues section was collected with the keyword of '4-major-river.' The Politics section was collected with the keyword of 'Tonghap-Jinbo Dang.' All of the articles from April 2012 to May 2012 of Eco-business, Culture and Editorial-opinion sections were also collected. All of the collected data were handled and edited into paragraphs. We got rid of stop-words using the Lucene Korean Module. We calculated keyword co-occurrence counts from the paired co-occurrence list of keywords in a paragraph. We made a co-occurrence matrix from the list. Once the co-occurrence matrix was built, we used the Cosine coefficient matrix as input for PFNet(Pathfinder Network). In order to analyze these three newspapers and find out the significant keywords in each paper, we analyzed the list of 10 highest frequency keywords and keyword-networks of 20 highest ranking frequency keywords to closely examine the relationships and show the detailed network map among keywords. We used NodeXL software to visualize the PFNet. After drawing all the networks, we compared the results with the classification results. Classification was firstly handled to identify how the tone of argument of a newspaper is different from others. Then, to analyze tones of arguments, all the paragraphs were divided into two types of tones, Positive tone and Negative tone. To identify and classify all of the tones of paragraphs and articles we had collected, supervised learning technique was used. The Na$\ddot{i}$ve Bayesian classifier algorithm provided in the MALLET package was used to classify all the paragraphs in articles. After classification, Precision, Recall and F-value were used to evaluate the results of classification. Based on the results of this study, three subjects such as Culture, Eco-business and Politics showed some differences in contents and tones of arguments among these three newspapers. In addition, for the National issues, tones of arguments on 4-major-rivers project were different from each other. It seems three newspapers have their own specific tone of argument in those sections. And keyword-networks showed different shapes with each other in the same period in the same section. It means that frequently appeared keywords in articles are different and their contents are comprised with different keywords. And the Positive-Negative classification showed the possibility of classifying newspapers' tones of arguments compared to others. These results indicate that the approach in this study is promising to be extended as a new tool to identify the different tones of arguments of newspapers.