• The Journal of Korea Institute of Information, Electronics, and Communication Technology
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• v.12 no.3
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• pp.290-298
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• 2019

The proportion of farm households in the total population is decreasing every year. The aging of rural areas is expected to deepen. The aging of agriculture is continuing due to the aging of the aged population and the decline of the young population, and agricultural manpower shortage is emerging as a threat to agriculture and rural areas. The existing facility cultivation was concentrated on the production / yield per unit area. However, nowadays, not only production but also crop quality should be good so that the quality of crops must be improved because they can secure competitiveness in the market. Therefore, the government plans to increase the productivity by hi-techization of ICT infrastructure horticulture and to plan the dissemination of energy saving smart greenhouse. Therefore, it is necessary to develop a Smart Farm convergence service system based on a hybrid algorithm to enhance diversity and connectivity. Therefore, this study aims to develop smart farm convergence service system which collects data of growth environment of the rhizosphere environment of crops by wireless and monitor smartphone.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.20 no.1
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• pp.131-143
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• 2019

The purpose of this study is to analyze the research trend of the dementia prevention program in the elderly. Between 2000 and 2018, the Korean Research Information Service (Riss), Google Scholar Search, DBpia, Korea Academy of Science Information (Dissemination Prevention), Dementia Prevention Program, Dementia, The purpose of this study was to investigate the dementia prevention program for the elderly. Based on the analysis criteria and methods of the 404 papers, 36 papers were finally selected. The results of this study are as follows: First, as a result of analysis of the basic structure of the research data and program implementation structure, And, when applied quantitative research method, 25 cases showed a much higher tendency. As a result of analyzing trends of the implementation structure of dementia prevention program for the elderly, 11 were the most in the nursing home (elderly welfare hospital), and the proportion of elderly women was higher than that of male elderly. 65 years of age or older. Second, as a result of analyzing the type of intervention program for dementia prevention program, Third, the Korean version of the MMSE-K tool, which measures cognitive function, is the most frequently used dementia prevention program measurement tool and the result of analysis of effectiveness, Significant improvement in cognitive function. The results of this study suggest that the prevention of dementia for the elderly should be avoided from a fragmentary program and improve the cognitive function, mental behavior and lifestyle of the elderly, improve the healthy aging and quality of life, Suggesting that a program is required.

• Journal of the Korean Society of Marine Environment & Safety
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• v.28 no.1
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• pp.117-124
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• 2022

The International Maritime Organization (IMO) is tightening environmental regulations in the shipping sector to reduce air pollutants such as greenhouse gases emitted from ships. Meanwhile, the paradigm of the shipbuilding and shipping industries is shifting toward eco-friendly and high-efficiency ships worldwide. The Republic of Korea is also promoting a policy to expand the supply of eco-friendly ships from 2020 to disseminate them. In this article, a survey was conducted with 12 experts on the government's eco-friendly ship supply policy, and the priority of the policy was evaluated using the analytic hierarchy process (AHP). As a result of the comprehensive evaluation of the priorities for six priority tasks, "Securing the world's leading technology for future eco-friendly ships" for the development of carbon-free and low carbon ships was the highest. This study, which analyzed the importance of eco-friendly ship policies through AHP analysis, can be used as data to preemptively respond to international marine environmental regulations and to improve policy execution efficiency such as budget allocation and policy development regarding protecting national shipping and shipbuilding industries.

• The Journal of the Convergence on Culture Technology
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• v.8 no.5
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• pp.377-383
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• 2022

In today's modern society, along with the 4th industrial revolution, digitalization is rapidly progressing from everyday life to the entire industry. In the field of education, support is being provided at the government level, such as the dissemination of digital devices to create a digital educational environment, and the use of digital devices is remarkably increasing at the level of individual students as well. In the fashion design education field of universities, the number of students using tablet PCs and applications such as iPads is increasing and the scope of its application is expanding, from design concept setting and idea generation to design sketches in the early fashion design process, as well as diagramming and portfolio work. For fashion design sketching, it was found that students mainly use Procreate or Clip Studio applications with iPad. Therefore, by identifying the differentiated functions and characteristics of Procreate and Clip Studio, and examining design sketch examples using them, the possibility of effective use of iPad applications was presented and problems were also examined. Through this study, it is intended to provide basic data for fashion design education that can increase students' interest and achievement by using the iPad application.

• Journal of Convergence Korean Medicine
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• v.3 no.1
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• pp.5-13
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• 2022

Objectives: Smoking had a long negative impact on public health. The ingredients of a cigarette are major risk factors for several diseases. Owing to the problems about economic and quality of life, we need to ensure smoking cessation (SC). There are several approaches for SC including pharmacological therapy, nicotine replacement therapy, education, and behavioral intervention. However, due to some limitations, other alternative approaches are gaining popularity. Acupuncture has been reported to have few side effects and be more effective than some conventional treatments in several articles. However, there are no systematic reviews on the comparison of acupuncture combination treatment with other conventional monotherapies. Methods: Randomized controlled trials that used acupuncture as an adjunct treatment for SC will be searched and data will be summarized according to the predefined criteria. The primary outcome will be the abstinence rate, and secondary outcomes will be adverse events and biochemical indicators. We will use Review Manager to perform a meta-analysis, Cochrane Collaboration Risk of Bias tool for the risk of bias assessment, and the Grades of Recommendation, Assessment, Development and Evaluation approach to determine the quality of evidence. We will investigate the efficacy and safety of acupuncture combination treatment for SC with this study. Ethics and dissemination: This study will provide reliable clinical evidence on additional effect of acupuncture on smoking cessation. We will publish our results in a peer-review journal.

• Smart Media Journal
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• v.12 no.8
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• pp.9-17
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• 2023

The development of information and communication technology and the improvement of the development and dissemination of smart devices have caused changes in the form of tourism, and the concept of smart tourism has since emerged. In this regard, researches related to smart tourism has been conducted in various fields such as policy implementation and surveys, but there is a lack of research on application reviews. This study collects Trip Advisor application review data in the Google Play Store to identify usage of the application and user satisfaction through Latent Dirichlet Allocation (LDA) topic modeling. The analysis results in four topics, two of which are positive and the other two are negative. We found that users were satisfied with the application's recommendation system, but were dissatisfied when the filters they set during search were not applied or that reviews were not published after updates of the application. We suggest more categories can be added to the application to provide users with different experiences. In addition, it is expected that user satisfaction can be improved by identifying problems within the application, including the filter function, and checking the application environment and resolving the error occurring during the application usage.

• Journal of Intelligence and Information Systems
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• v.19 no.3
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• pp.93-111
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• 2013

As Internet and information technology (IT) continues to develop and evolve, the issue of big data has emerged at the foreground of scholarly and industrial attention. Big data is generally defined as data that exceed the range that can be collected, stored, managed and analyzed by existing conventional information systems and it also refers to the new technologies designed to effectively extract values from such data. With the widespread dissemination of IT systems, continual efforts have been made in various fields of industry such as R&D, manufacturing, and finance to collect and analyze immense quantities of data in order to extract meaningful information and to use this information to solve various problems. Since IT has converged with various industries in many aspects, digital data are now being generated at a remarkably accelerating rate while developments in state-of-the-art technology have led to continual enhancements in system performance. The types of big data that are currently receiving the most attention include information available within companies, such as information on consumer characteristics, information on purchase records, logistics information and log information indicating the usage of products and services by consumers, as well as information accumulated outside companies, such as information on the web search traffic of online users, social network information, and patent information. Among these various types of big data, web searches performed by online users constitute one of the most effective and important sources of information for marketing purposes because consumers search for information on the internet in order to make efficient and rational choices. Recently, Google has provided public access to its information on the web search traffic of online users through a service named Google Trends. Research that uses this web search traffic information to analyze the information search behavior of online users is now receiving much attention in academia and in fields of industry. Studies using web search traffic information can be broadly classified into two fields. The first field consists of empirical demonstrations that show how web search information can be used to forecast social phenomena, the purchasing power of consumers, the outcomes of political elections, etc. The other field focuses on using web search traffic information to observe consumer behavior, identifying the attributes of a product that consumers regard as important or tracking changes on consumers' expectations, for example, but relatively less research has been completed in this field. In particular, to the extent of our knowledge, hardly any studies related to brands have yet attempted to use web search traffic information to analyze the factors that influence consumers' purchasing activities. This study aims to demonstrate that consumers' web search traffic information can be used to derive the relations among brands and the relations between an individual brand and product attributes. When consumers input their search words on the web, they may use a single keyword for the search, but they also often input multiple keywords to seek related information (this is referred to as simultaneous searching). A consumer performs a simultaneous search either to simultaneously compare two product brands to obtain information on their similarities and differences, or to acquire more in-depth information about a specific attribute in a specific brand. Web search traffic information shows that the quantity of simultaneous searches using certain keywords increases when the relation is closer in the consumer's mind and it will be possible to derive the relations between each of the keywords by collecting this relational data and subjecting it to network analysis. Accordingly, this study proposes a method of analyzing how brands are positioned by consumers and what relationships exist between product attributes and an individual brand, using simultaneous search traffic information. It also presents case studies demonstrating the actual application of this method, with a focus on tablets, belonging to innovative product groups.

• Journal of Intelligence and Information Systems
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• v.27 no.1
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• pp.103-128
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• 2021

Recently, investors' interest and the influence of stock-related information dissemination are being considered as significant factors that explain stock returns and volume. Besides, companies that develop, distribute, or utilize innovative new technologies such as artificial intelligence have a problem that it is difficult to accurately predict a company's future stock returns and volatility due to macro-environment and market uncertainty. Market uncertainty is recognized as an obstacle to the activation and spread of artificial intelligence technology, so research is needed to mitigate this. Hence, the purpose of this study is to propose a machine learning model that predicts the volatility of a company's stock price by using the internet search volume of artificial intelligence-related technology keywords as a measure of the interest of investors. To this end, for predicting the stock market, we using the VAR(Vector Auto Regression) and deep neural network LSTM (Long Short-Term Memory). And the stock price prediction performance using keyword search volume is compared according to the technology's social acceptance stage. In addition, we also conduct the analysis of sub-technology of artificial intelligence technology to examine the change in the search volume of detailed technology keywords according to the technology acceptance stage and the effect of interest in specific technology on the stock market forecast. To this end, in this study, the words artificial intelligence, deep learning, machine learning were selected as keywords. Next, we investigated how many keywords each week appeared in online documents for five years from January 1, 2015, to December 31, 2019. The stock price and transaction volume data of KOSDAQ listed companies were also collected and used for analysis. As a result, we found that the keyword search volume for artificial intelligence technology increased as the social acceptance of artificial intelligence technology increased. In particular, starting from AlphaGo Shock, the keyword search volume for artificial intelligence itself and detailed technologies such as machine learning and deep learning appeared to increase. Also, the keyword search volume for artificial intelligence technology increases as the social acceptance stage progresses. It showed high accuracy, and it was confirmed that the acceptance stages showing the best prediction performance were different for each keyword. As a result of stock price prediction based on keyword search volume for each social acceptance stage of artificial intelligence technologies classified in this study, the awareness stage's prediction accuracy was found to be the highest. The prediction accuracy was different according to the keywords used in the stock price prediction model for each social acceptance stage. Therefore, when constructing a stock price prediction model using technology keywords, it is necessary to consider social acceptance of the technology and sub-technology classification. The results of this study provide the following implications. First, to predict the return on investment for companies based on innovative technology, it is most important to capture the recognition stage in which public interest rapidly increases in social acceptance of the technology. Second, the change in keyword search volume and the accuracy of the prediction model varies according to the social acceptance of technology should be considered in developing a Decision Support System for investment such as the big data-based Robo-advisor recently introduced by the financial sector.

• Journal of Service Research and Studies
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• v.14 no.1
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• pp.63-76
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• 2024

Multi-family housing plays a crucial role as a living and experiencing space, and its environment has a direct impact on the well-being and stability of its residents. Therefore, Crime Prevention Design (CPTED) for multi-family housing is of utmost importance. However, crime-related data in China is not disclosed to the public because of its specificity, making it difficult for researchers to conduct further in-depth studies based on accurate crime data. As a result, the establishment and application of CPTED theory in terms of crime prevention is limited and delayed. This study aims to explore three aspects of CPTED in multi-family housing as perceived by home-buying consumers. It investigated consumer perception of the CPTED, the importance of each element and ways to increase awareness of CPTED in multifamily housing in order to effectively improve multifamily crime prevention design principles and further enhance public safety. This study examined the current state and future trends of CPTED in China by analyzing relevant research reports and literature, aiming to gain insights into the crime prevention awareness of Chinese homeowners. In addition, a survey was conducted on Chinese consumers to unravel the importance of CPTED and increase awareness of its various elements in multifamily-family. This study used a Likert scale and SPSS reliability analysis to determine the cognitive status of multi-family CPTED, the importance of each element, and proposed an improvement plan based on the analysis results. As this study was limited by the difficulty of implementation and the lack of validation of its practical effectiveness, it is recommended that future research needs to validate the effectiveness of crime prevention designs and produce more practical results. Furthermore, it is crucial to utilize this study to inform the implementation of security solutions that are tailored to the unique characteristics of each district. Additionally, it is important to offer guidance on how to enhance community safety by increasing residents' awareness of security through education and information dissemination. The author hopes that the representative multi-family CPTED awareness, the importance of each element, and plans for improvement shall be summarized from this study, and provide foundational data for the future development of CPTED based on the Chinese region.