• Journal of Intelligence and Information Systems
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• v.27 no.1
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• pp.65-82
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• 2021

Stock market investors are generally split into foreign investors, institutional investors, and individual investors. Compared to individual investor groups, professional investor groups such as foreign investors have an advantage in information and financial power and, as a result, foreign investors are known to show good investment performance among market participants. The purpose of this study is to propose an investment strategy that combines investor-specific transaction information and machine learning, and to analyze the portfolio investment performance of the proposed model using actual stock price and investor-specific transaction data. The Korea Exchange offers daily information on the volume of purchase and sale of each investor to securities firms. We developed a data collection program in C# programming language using an API provided by Daishin Securities Cybosplus, and collected 151 out of 200 KOSPI stocks with daily opening price, closing price and investor-specific net purchase data from January 2, 2007 to July 31, 2017. The self-organizing map model is an artificial neural network that performs clustering by unsupervised learning and has been introduced by Teuvo Kohonen since 1984. We implement competition among intra-surface artificial neurons, and all connections are non-recursive artificial neural networks that go from bottom to top. It can also be expanded to multiple layers, although many fault layers are commonly used. Linear functions are used by active functions of artificial nerve cells, and learning rules use Instar rules as well as general competitive learning. The core of the backpropagation model is the model that performs classification by supervised learning as an artificial neural network. We grouped and transformed investor-specific transaction volume data to learn backpropagation models through the self-organizing map model of artificial neural networks. As a result of the estimation of verification data through training, the portfolios were rebalanced monthly. For performance analysis, a passive portfolio was designated and the KOSPI 200 and KOSPI index returns for proxies on market returns were also obtained. Performance analysis was conducted using the equally-weighted portfolio return, compound interest rate, annual return, Maximum Draw Down, standard deviation, and Sharpe Ratio. Buy and hold returns of the top 10 market capitalization stocks are designated as a benchmark. Buy and hold strategy is the best strategy under the efficient market hypothesis. The prediction rate of learning data using backpropagation model was significantly high at 96.61%, while the prediction rate of verification data was also relatively high in the results of the 57.1% verification data. The performance evaluation of self-organizing map grouping can be determined as a result of a backpropagation model. This is because if the grouping results of the self-organizing map model had been poor, the learning results of the backpropagation model would have been poor. In this way, the performance assessment of machine learning is judged to be better learned than previous studies. Our portfolio doubled the return on the benchmark and performed better than the market returns on the KOSPI and KOSPI 200 indexes. In contrast to the benchmark, the MDD and standard deviation for portfolio risk indicators also showed better results. The Sharpe Ratio performed higher than benchmarks and stock market indexes. Through this, we presented the direction of portfolio composition program using machine learning and investor-specific transaction information and showed that it can be used to develop programs for real stock investment. The return is the result of monthly portfolio composition and asset rebalancing to the same proportion. Better outcomes are predicted when forming a monthly portfolio if the system is enforced by rebalancing the suggested stocks continuously without selling and re-buying it. Therefore, real transactions appear to be relevant.

• Journal of Intelligence and Information Systems
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• v.20 no.4
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• pp.89-105
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• 2014

After emergence of Internet, social media with highly interactive Web 2.0 applications has provided very user friendly means for consumers and companies to communicate with each other. Users have routinely published contents involving their opinions and interests in social media such as blogs, forums, chatting rooms, and discussion boards, and the contents are released real-time in the Internet. For that reason, many researchers and marketers regard social media contents as the source of information for business analytics to develop business insights, and many studies have reported results on mining business intelligence from Social media content. In particular, opinion mining and sentiment analysis, as a technique to extract, classify, understand, and assess the opinions implicit in text contents, are frequently applied into social media content analysis because it emphasizes determining sentiment polarity and extracting authors' opinions. A number of frameworks, methods, techniques and tools have been presented by these researchers. However, we have found some weaknesses from their methods which are often technically complicated and are not sufficiently user-friendly for helping business decisions and planning. In this study, we attempted to formulate a more comprehensive and practical approach to conduct opinion mining with visual deliverables. First, we described the entire cycle of practical opinion mining using Social media content from the initial data gathering stage to the final presentation session. Our proposed approach to opinion mining consists of four phases: collecting, qualifying, analyzing, and visualizing. In the first phase, analysts have to choose target social media. Each target media requires different ways for analysts to gain access. There are open-API, searching tools, DB2DB interface, purchasing contents, and so son. Second phase is pre-processing to generate useful materials for meaningful analysis. If we do not remove garbage data, results of social media analysis will not provide meaningful and useful business insights. To clean social media data, natural language processing techniques should be applied. The next step is the opinion mining phase where the cleansed social media content set is to be analyzed. The qualified data set includes not only user-generated contents but also content identification information such as creation date, author name, user id, content id, hit counts, review or reply, favorite, etc. Depending on the purpose of the analysis, researchers or data analysts can select a suitable mining tool. Topic extraction and buzz analysis are usually related to market trends analysis, while sentiment analysis is utilized to conduct reputation analysis. There are also various applications, such as stock prediction, product recommendation, sales forecasting, and so on. The last phase is visualization and presentation of analysis results. The major focus and purpose of this phase are to explain results of analysis and help users to comprehend its meaning. Therefore, to the extent possible, deliverables from this phase should be made simple, clear and easy to understand, rather than complex and flashy. To illustrate our approach, we conducted a case study on a leading Korean instant noodle company. We targeted the leading company, NS Food, with 66.5% of market share; the firm has kept No. 1 position in the Korean "Ramen" business for several decades. We collected a total of 11,869 pieces of contents including blogs, forum contents and news articles. After collecting social media content data, we generated instant noodle business specific language resources for data manipulation and analysis using natural language processing. In addition, we tried to classify contents in more detail categories such as marketing features, environment, reputation, etc. In those phase, we used free ware software programs such as TM, KoNLP, ggplot2 and plyr packages in R project. As the result, we presented several useful visualization outputs like domain specific lexicons, volume and sentiment graphs, topic word cloud, heat maps, valence tree map, and other visualized images to provide vivid, full-colored examples using open library software packages of the R project. Business actors can quickly detect areas by a swift glance that are weak, strong, positive, negative, quiet or loud. Heat map is able to explain movement of sentiment or volume in categories and time matrix which shows density of color on time periods. Valence tree map, one of the most comprehensive and holistic visualization models, should be very helpful for analysts and decision makers to quickly understand the "big picture" business situation with a hierarchical structure since tree-map can present buzz volume and sentiment with a visualized result in a certain period. This case study offers real-world business insights from market sensing which would demonstrate to practical-minded business users how they can use these types of results for timely decision making in response to on-going changes in the market. We believe our approach can provide practical and reliable guide to opinion mining with visualized results that are immediately useful, not just in food industry but in other industries as well.

• Journal of the Korean Institute of Traditional Landscape Architecture
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• v.32 no.3
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• pp.154-167
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• 2014

The results of this study conducted to identify the substance, regional characteristics or landscaping of Namwon Yonghogugok, which is the only valley of Jiri Mountain, based on Kim Samun's 'Yonghokugok-Gyeongseungannae(龍湖九曲景勝案內)', 'Yongseongji(龍城誌)' and position, meaning of letters carved and projection technique by ArcGIS10.0 on the rocks are as below. The feature landscapes of the canyon of Yonghogugok, which is an incised meander and one of the Eight beautiful scenery of Namwon, ponds, cliffs and rocks generated with metamorphic rocks and granites weathered by rapids torrents. As a result of measuring the GPS coordinates of the letters carved on the rocks, excluding the 3 Gok Hakseoam and the distances based on the origin and destination of the letters carved on the rocks using the API(Application Programming Interface) function of Daum map, the total distance of Yonghogugok was 3.5km and the average distance between the each Gok was 436.5m. It is assumed that Yonghogugok was designated by Sarim(士林) of the Kiho School(畿湖學派) related to Wondong Hyangyak(元洞鄕約) which is the main agent of Yonghojeongsa(龍湖精舍), the forerunner of Yonghoseowon(龍湖書院), between the late Joseon Dynasty and the early Japanese colonial era, in 1927. Its grounds are the existence of Yonghoyeongdang mentioned on 'Yonghojeongsilgi'(龍湖亭實記), records of 'Haeunyugo(荷隱遺稿)', 'Yonghopumje(龍湖品題)' of Bulshindang(佛神堂), 'Yonghojeongsadonggu Gapjachun(龍湖精舍洞口 甲子春)' letters carved on the rocks and 'Yonghogugok-Shipyeong(龍湖九曲十詠)' posted on Mokgandang of Yonghoseowon. Comprehensively considering the numerous poetry society lists carved on the stone wall of Punghodae(風乎臺), the Sixth Gok Yuseondae, its stone mortar, 'Bangjangjeildongcheon(方丈第一洞天)' of Bulshindang and Gyoryongdam(交龍潭), the Yonghoseokmun(龍湖石門) letters carved on the rocks, Yeogungseok adjacent to the First Gok and Fengshui facilities, centered on Yonghoseowon and Yonghojeong, Yonghogugok can be understood as a unique valley culture formed with the thoughts of Confucianism, Buddhism, Taoism and Fengshui. 'Yonghogugok-Gyeongseungannae' provides very useful information to understand the place name, called by locals and landscaping aspects of Yonghogugok in the late Joseon Dynasty. In addition, the meaning of "Nine dragons" and even though 12 chu(湫: pond) of Yonghogugok Yongchudong including Bulyeongchu, Guryongchu, Isuchu, Goieumchu and Daeyachu are mentioned on Yongseongji, a part of them cannot be confirmed now. Various place names and facilities relevant to Guryong adjacent to Yonghogugok are the core of the place identity. In addition, the accurate location identification and the delivery of the landscaping significance of the 12 ponds is expected to provide landscaping attractiveness of Yonghogugok and become very useful contents for landscaping storytelling and a keyword of storyboard.

• Journal of the Korean Institute of Landscape Architecture
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• v.49 no.4
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• pp.64-73
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• 2021

The purpose of this study is to present a method to assess the landscape aesthetic value of Bukhansan National Park using geotagged images that have been shared on social media sites. The method presented in this study consisted mainly of collecting geotagged image data, identifying landscape images, and analyzing the cumulative visibility by applying a target probability index. Ramblr is an application that supports outdoor activities with many users in Korea, from which a total of 110,954 geotagged images for Bukhansan National Park were collected and used to assess the landscape aesthetics. The collected geotagged images were interpreted using the Google Vision API, and were subsequently were divided into 11 landscape image types and 9 non-landscape image types through cluster analysis. As a result of analyzing the landscape types of Bukhansan National Park based on the extracted landscape images, landscape types related to topographical characteristics, such as peaks and mountain ranges, accounted for the largest portion, and forest landscapes, foliage landscapes, and waterscapes were also commonly found as major landscape types. In the derived landscape aesthetic value map, the higher the elevation and slope, the higher the overall landscape aesthetic value, according to the proportion and characteristics of these major landscape types. However, high landscape aesthetic values were also confirmed in some areas of lowlands with gentle slopes. In addition, the Bukhansan area was evaluated to have higher landscape aesthetics than the Dobongsan area. Despite the high elevation and slope, the Dobongsan area had a relatively low landscape aesthetic value. This shows that the aesthetic value of the landscape is strongly related not only to the physical environment but also to the recreational activities of visitors who are viewing the scenery. In this way, the landscape aesthetics assessment using the cumulative visibility of geotagged images is expected to be useful for planning and managing the landscape of Bukhansan National Park in the future, through allowing the geographical understanding of the landscape values based on people's perceptions and the identification of the regional deviations.

• Journal of Korean Society of Rural Planning
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• v.24 no.4
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• pp.147-155
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• 2018

The purpose of this study was to identify vulnerable area of emergency medical care. In the existing method, the emergency medical vulnerable area is set as an area that can not reach the emergency room within 30 minutes. In this study, we set up an area that can not reach within 30 minutes including the accessibility of 119 emergency center. To accomplish this, we obtained information on emergency room and 119 emergency center through Open API and constructed road network using digital map to perform accessibility analysis. As a result, 509 emergency room are located nationwide, 78.0% of them are concentrated in the region, 1,820 emergency center are located, and 61.0% of them are located in rural areas. The average access time from the center of the village to the emergency room was analyzed as 15.3 minutes, and the average access time considering the 119 emergency center was 21.8 minutes, 6.5 minutes more. As a result of considering the accessibility of 119 emergency center, vulnerable areas increased by 2.5 times, vulnerable population increased by 2.0 times, and calculating emergency medical care vulnerable areas, which account for more than 30% of the urban unit population, it was analyzed that it increased from 17 to 34 cities As a further study, it will be necessary to continuously monitor and research the real-time traffic information, medical personnel, medical field, and ambulance information to reflect the reality and to diagnose emergency medical care in the future.

• Journal of the Korea Society of Computer and Information
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• v.29 no.3
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• pp.99-106
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• 2024

In this paper, we design and implement the HealthSchedule app, which records exercise data based on the GPS sensor embedded in smartphones. This app utilizes the smartphone's GPS sensor to collect real-time location information of the user and displays the movement path to the designated destination. It records the user's actual path using latitude and longitude coordinates. Users register exercise activities and destination points when scheduling, and initiate the exercise. When measuring the current location, a lime green departure marker is generated, and the movement path is displayed in blue, with the destination marker and a surrounding 25-meter radius circle shown in sky blue. Using the coordinates of the starting point or the previous location and the current GPS sensor-transmitted location coordinates, it measures the distance traveled, time taken, and calculates the speed. Furthermore, it accumulates measurement data to provide information on the total distance traveled, movement path, and overall average speed. Even when reaching the destination during exercise, the movement path continues to accumulate until the completion button is clicked. The completion button is activated when the user moves into the sky blue circular area with a radius of 25 meters, centered around the initially set destination. This means that the user must reach the designated destination, and if they wish to continue exercising without clicking the completion button, they can do so. Depending on the selected exercise type, the app displays the calories burned, aiming to increase user engagement and a sense of accomplishment.