• Journal of Korean Society of Rural Planning
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• v.25 no.2
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• pp.63-74
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• 2019

As the awareness of food security has increased, the Korean government has established national projects, such as Saemangeum proclaimed land, to secure sources of grain. Saemangeum is a large-scale agricultural area that was constructed to maintain preparedness for unstable food markets. This study aims to develop a $6^{th}$ Industrialization Model (SIM) for Saemangeum Grain Complex by applying feasible strategies to wheat and two-rowed barley which have low self-sufficiency rates. In addition, this study estimates the potential economic value of each development strategy associated with a sixth industrialization model to create higher added values from production, processing and tourism experiences. The strategic plan for primary, secondary, and tertiary industries is to combine cultivating and processing wheat and two-rowed barley for sales and linking them to tourism experience. This study shows value added from the combination of the primary, secondary and tertiary industry of wheat and two-rowed barley are 7.5 and 23.0 times more than those of the primary and tertiary industry combination, respectively. Through branding Saemangeum Grain Complex's products, such as Saemanguem bread and craft beer, would further enhance the economic benefits derived from the complex.

• The Journal of The Korea Institute of Intelligent Transport Systems
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• v.19 no.6
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• pp.152-162
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• 2020

In the urban and rural complex, taxis move from downtown to rural areas for business purposes, and operate a combined surcharge system that preserves losses when they back to downtown. However, complaints related to the abolition of the compound surcharge system are increasing due to deformed operation that does not fit the purpose of the system. When the combinedsurcharge system is abolished, the taxi industry can be hit hard by the decrease in profits, and local governments are inevitable to support it. However, it is difficult to set the size of the subsidy considering the decrease of actual income. This study is to estimate the income reduction in the abolition of the combined surcharge system by scientific and objective method by analyzing the DTG data and the sales data collected from the digital driving recorder installed in the corporate taxi of the urban and rural complex area (e.g., Tongyeong city). This study is meaningful in that it used DTG data to solve the current issues in the real region and suggested the use of new DTG data.

• Journal of the Korea Society of Computer and Information
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• v.27 no.3
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• pp.173-182
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• 2022

In this paper, we propose a machine learning model that can prevent fraudulent transactions in advance and interpret them using the XAI approach. For the experiment, we collected a real data set of 12,258 mobile phone sales posts from Joonggonara, a major domestic online C2C resale trading platform. Characteristics of the text corresponding to the post body were extracted using Doc2vec, dimensionality was reduced through PCA, and various derived variables were created based on previous research. To mitigate the data imbalance problem in the preprocessing stage, a complex sampling method that combines oversampling and undersampling was applied. Then, various machine learning models were built to detect fraudulent postings. As a result of the analysis, LightGBM showed the best performance compared to other machine learning models. And as a result of SHAP, if the price is unreasonably low compared to the market price and if there is no indication of the transaction area, there was a high probability that it was a fraudulent post. Also, high price, no safe transaction, the more the courier transaction, and the higher the ratio of 0 in the price also led to fraud.

• The Journal of the Convergence on Culture Technology
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• v.7 no.4
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• pp.815-821
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• 2021

The advantage of big data is to collect a large amount of data on the Internet and refine and use valuable data. That is, the unstructured data is processed so that the user can analyze and utilize it from a necessary point of view. This paper is a relatively small project and is based on unstructured data that can be closely applied to real life and used for marketing. The subjects of the experiment were modeled on lodging companies in the Seoul metropolitan area an hour away from Seoul, and analyzed for the increase in lodging rates before and after marketing using big data. As an experiment that shows the effects of increasing sales, reducing costs, and increasing returns by users, we propose a system to determine and filter whether data input in the process of analyzing big data such as social networks can be used as accommodation-related information.

• Journal of Industrial Convergence
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• v.20 no.11
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• pp.197-203
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• 2022

The purpose of this study is to establish an S.I. (Space Identity) considering the spatial location and cultural specificity of Jecheon Jungang Market, the oldest and largest market in Jecheon with historical characteristics. To this end, we identify the flow of the cultural tourism market, investigate and analyze the current state of Jecheon Central Market, and present a direction based on storytelling for each space. The concept of space design was divided into space, time, people, and culture as coexistence, and merchants, products, and shopping malls share temporality and coexist in one space. Therefore, the facilities for each floor consisted of a gate, information board, lighting, rest area, design bench facility, information center, business compliance line, floor sign, and gate floor sign for each floor. Through this, it is necessary to establish a mid- to long-term development strategy by establishing a step-by-step promotion strategy to predict the economic effect of creating new demand and increasing sales in Jecheon Central Market.

• International Journal of Computer Science & Network Security
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• v.23 no.9
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• pp.1-7
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• 2023

Demand prediction is an essential component of any business or supply chain. Large retailers need to keep track of tens of millions of items flows each day to ensure smooth operations and strong margins. The demand prediction is in the epicenter of this planning tornado. For business processes in retail companies that deal with a variety of products with short shelf life and foodstuffs, forecast accuracy is of the utmost importance due to the shifting demand pattern, which is impacted by an environment of dynamic and fast response. All sectors strive to produce the ideal quantity of goods at the ideal time, but for retailers, this issue is especially crucial as they also need to effectively manage perishable inventories. In light of this, this research aims to show how Machine Learning approaches can help with demand forecasting in retail and future sales predictions. This will be done in two steps. One by using historic data and another by using open data of weather conditions, fuel, Consumer Price Index (CPI), holidays, any specific events in that area etc. Several machine learning algorithms were applied and compared using the r-squared and mean absolute percentage error (MAPE) assessment metrics. The suggested method improves the effectiveness and quality of feature selection while using a small number of well-chosen features to increase demand prediction accuracy. The model is tested with a one-year weekly dataset after being trained with a two-year weekly dataset. The results show that the suggested expanded feature selection approach provides a very good MAPE range, a very respectable and encouraging value for anticipating retail demand in retail systems.

• International Journal of Computer Science & Network Security
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• v.23 no.8
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• pp.210-216
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• 2023

Demand prediction is an essential component of any business or supply chain. Large retailers need to keep track of tens of millions of items flows each day to ensure smooth operations and strong margins. The demand prediction is in the epicenter of this planning tornado. For business processes in retail companies that deal with a variety of products with short shelf life and foodstuffs, forecast accuracy is of the utmost importance due to the shifting demand pattern, which is impacted by an environment of dynamic and fast response. All sectors strive to produce the ideal quantity of goods at the ideal time, but for retailers, this issue is especially crucial as they also need to effectively manage perishable inventories. In light of this, this research aims to show how Machine Learning approaches can help with demand forecasting in retail and future sales predictions. This will be done in two steps. One by using historic data and another by using open data of weather conditions, fuel, Consumer Price Index (CPI), holidays, any specific events in that area etc. Several machine learning algorithms were applied and compared using the r-squared and mean absolute percentage error (MAPE) assessment metrics. The suggested method improves the effectiveness and quality of feature selection while using a small number of well-chosen features to increase demand prediction accuracy. The model is tested with a one-year weekly dataset after being trained with a two-year weekly dataset. The results show that the suggested expanded feature selection approach provides a very good MAPE range, a very respectable and encouraging value for anticipating retail demand in retail systems.

• Health Policy and Management
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• v.33 no.1
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• pp.75-84
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• 2023

Background: The purpose of this study was to identify the factors affecting social discrimination experience on depression in the early and late elderly by region. Methods: This study used data from the National Survey of Older Koreans 2020. The subject of the study was the elderly aged 65 or older, and it was analyzed as those who responded. In order to analyze the effect of social discrimination experiences on depression, it was analyzed through binary logistic regression analysis. Results: The results of this study showed that the elderly who experienced social discrimination had a significant effect on depression. In addition, when four groups experienced social discrimination when using restaurants or coffee shops, depression was commonly affected. In addition, when both city and rural areas experience social discrimination when using sales facilities in social discrimination in the elderly, city areas are 2.21 times more likely to experience depression and 3.52 times more likely to experience depression in rural areas. The late elderly are more likely to experience 3.04 times more likely to experience social discrimination when using restaurants or coffee shops in city areas, and 3.03 times more likely to experience depression when experiencing social discrimination to make major decisions in the family in rural areas. Conclusion: In conclusion, it is necessary to prepare alternatives to prevent depression and improve mental health suitable for the characteristics of age and residential area. In addition, it suggests that personal and social efforts are needed to solve the problem of social discrimination in order to reduce depression in the elderly.

• International Area Studies Review
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• v.13 no.1
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• pp.219-240
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• 2009

Through the research, we found that the organizational systems of social enterprises in Europe have both properties of limited companies and stock companies in traditional organizational structures such as cooperatives, mutual companies and associations. However, social enterprises pursue interests of entire community, allowing the interested parties to join them in comparison with cooperatives. And for finance, most of the countries mainly use public fund such as national grants, but they are using more sales revenue. However, in our country the organizational systems of social enterprises have been introduced as the government and academic circles discussed expansion of social employment within a short period. So Korean enterprises tend to depend on national support rather than profitable activities for finance. Therefore, we need to develop a Korean convention or social agreement for the organizational systems of social enterprises. Furthermore, it is important for social enterprises to secure safe finance through development of social services and social cultures such as donation.

• International Area Studies Review
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• v.13 no.1
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• pp.265-286
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• 2009

The purpose of this research can be said as follows. The close examination of competitive advantages of Korean enterprises who have been participating and dominating the management activities directly in India. And the Analysing of the correlation between the competitive advantages and the management performances of Korean firms there. That is, the factors which exercise their influence over the local management positively can be activated and developed reasonably and systematically while the others which exercise their influence over it negatively have to be eliminated, at least. The factors of competitive advantages on this paper are from ones which could generally be recognized on the basis of the preceding studies, and the management performances are divided by three sub-variables like sales, profits and management satisfaction. As empirically statistical method, Regression coefficient analysis as inferential statistics as well as Pearson's correlation as descriptive is implemented for this paper of testing some hypotheses.