• Journal of Digital Contents Society
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• v.19 no.3
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• pp.513-519
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• 2018

The objective of this study was to examine the trends on social network services. The abstracts of 308 articles were extracted from web of science database published between 1994 and 2016. Time series analysis and topic modeling of text mining were implemented. The topic modeling results showed that the research topics were mainly 20 topics: trust, support, satisfaction model, organization governance, mobile system, internet marketing, college student effect, opinion diffusion, customer, information privacy, health care, web collaboration, method, learning effectiveness, knowledge, individual theory, child support, algorithm, media participation, and context system. The time series regression results indicated that trust, support satisfaction model, and remains of the topics were hot topics. This study also provided suggestions for future research.

• Nutrition Research and Practice
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• v.14 no.1
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• pp.62-69
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• 2020

BACKGROUND/OBJECTIVES: A number of studies examined secular trends in blood lipid profiles using time series data of national surveys whereas few studies investigated individual-level factors contributing to such trends. The present study aimed to examine secular trends in dietary and modifiable factors and hyper-LDL-cholesterolemia (HC) prevalence and evaluate their associations using time series data of nationwide surveys. SUBJECTS/METHODS: The study included 41,073 Korean adults aged ≥ 30 years from the 2005, 2007-2009, 2010-2012, 2013-2015, and 2016 Korea National Health and Nutrition Examination Surveys. Stepwise logistic regression analysis was performed to select significant factors associated with HC, which was defined as serum LDL cholesterol levels ≥130 mg/dL. RESULTS: The following factors showed a positive association with HC (P < 0.05): for men having higher body mass index (BMI), being married, having an office job, and consuming higher dairy and vegetable oil products; for women having higher age or BMI, having no job or a non-office job, not in a low-income household, and consuming higher dairy products. In the given model, the 2016 survey data showed that a 2 kg/㎡ reduction in BMI of obese persons resulted in a decreased HC prevalence from 30.8% to 29.3% among men and from 33.6% to 32.5% among women. CONCLUSIONS: Based on these findings, it is suggested that primary prevention programs should advocate having proper BMI for Korean adults with a high-risk of HC. However, whether discouraging consumption of dairy and vegetable oil products can reduce HC prevalence warrants further studies with a prospective longitudinal design.

• Asian Pacific Journal of Cancer Prevention
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• v.14 no.2
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• pp.791-793
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• 2013

Colorectal cancer is one of the most common cancers. The aim of this study is determination its trends in Kerman province and individual cities separately until year 2016. This analytical and modeling study was based of cancer registry data of Kerman University of Medical Sciences, collected during 2001-2010. Among 20,351 cancer case, 792 were colorectal cancer cases in age group 18-93 years with a mean of 59.4 and standard deviation of 15.1. By applying time series and data trends, incidences were predicted until 2016 for the province and each city, with adjustment for population size. In colorectal cases, 413 (52%) were male, and 379 (48%) were female. The annual increasing rate in Kerman province overall was and can be expected to be 6%, and in the cities of the province Rafsanjan, Bardsir, Bam, Kerman, Baft, Sirjan, Jiroft, Kahnuj and Manujan had an increasing range from 5 to 14% by the year 2016. But in Ravar, Zarand and Shahrbabak reduction in rates of at least 2% could be predicted. The time series showed that the trend of colorectal cancer in female will increase 15% and in male 7% by year 2016. Given the trend of this cancer is increasing so that resources will be consumed in the treatment of the patients, efforts shoudlbe focused on prevention and early diagnosis of the disease. Screening could have an important role leading to improved survival.

• Electronics and Telecommunications Trends
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• v.36 no.1
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• pp.89-98
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• 2021

The Ministry of National Defense aims to create an environment in which transparent and reasonable defense policies can be implemented in real time by establishing the vision of smart defense innovation based on the Fourth Industrial Revolution and promoting innovation in technology-based defense operation systems. Artificial intelligence (AI) based defense technology is at the level of basic research worldwide, includes no domestic tasks, and involves classified military operation data and command control/decision information. Further, it is needed to secure independent technologies specialized for our military. In the army, military power continues to decline due to aging and declining population. In addition, it is expected that there will be more than 500,000 units should be managed simultaneously, to recognize the battle situation in real time on the future battlefields. Such a complex battlefield, command decisions will be limited by the experience and expertise of individual commanders. Accordingly, the study of AI core technologies supporting real-time combat command is actively pursued at home and abroad. It is necessary to strengthen future defense capabilities by identifying potential threats that commanders are likely to miss, improving the viability of the combat system, ensuring smart commanders always win conflicts and providing reasonable AI digital staff based on data science. This paper describes the recent research trends in AI military staff technology supporting commander decision-making, broken down into five key areas.

• The KIPS Transactions:PartD
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• v.15D no.4
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• pp.485-494
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• 2008

Huge amounts of various web items such as keywords, images, and web pages are being made widely available on the Web. The popularities of such web items continuously change over time, and mining temporal patterns in popularities of web items is an important problem that is useful for several web applications. For example, the temporal patterns in popularities of search keywords help web search enterprises predict future popular keywords, enabling them to make price decisions when marketing search keywords to advertisers. However, presence of millions of web items makes it difficult to scale up previous techniques for this problem. This paper proposes an efficient method for mining temporal patterns in popularities of web items. We treat the popularities of web items as time-series, and propose gapmeasure to quantify the similarity between the popularities of two web items. To reduce the computation overhead for this measure, an efficient method using the Fast Fourier Transform (FFT) is presented. We assume that the popularities of web items are not necessarily following any probabilistic distribution or periodic. For finding clusters of web items with similar popularity trends, we propose to use a density-based clustering algorithm based on the gap measure. Our experiments using the popularity trends of search keywords obtained from the Google Trends web site illustrate the scalability and usefulness of the proposed approach in real-world applications.

• Journal of Platform Technology
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• v.11 no.4
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• pp.19-34
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• 2023

In order to identify the trends and insights of 'startups' in the global era, we conducted an in-depth trend analysis of the global startup ecosystem using Google Trends, a big data analysis platform. For the validity of the analysis, we verified the correlation between the keywords 'startup' and 'global' through BIGKinds. We also conducted a network analysis based on the data extracted using Google Trends to determine the frequency of searches for the keyword or term 'startup'. The results showed a strong positive linear relationship between the keywords, indicating a statistically significant correlation (correlation coefficient: +0.8906). When exploring global startup trends using Google Trends, we found a terribly similar linear pattern of increasing and decreasing interest in each country over time, as shown in Figure 4. In particular, startup interest was low in the range of 35 to 76 from mid-2020 due to the COVID-19 pandemic, but there was a noticeable upward trend in startup interest after March 2022. In addition, we found that the interest in startups in each country except South Korea is very similar, and the related topics are startup company, technology, investment, funding, and keyword search terms such as best startup, tech, business, invest, health, and fintech are highly correlated.

• Journal of Intelligence and Information Systems
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• v.21 no.1
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• pp.143-159
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• 2015

Predicting IT trends has been a long and important subject for information systems research. IT trend prediction makes it possible to acknowledge emerging eras of innovation and allocate budgets to prepare against rapidly changing technological trends. Towards the end of each year, various domestic and global organizations predict and announce IT trends for the following year. For example, Gartner Predicts 10 top IT trend during the next year, and these predictions affect IT and industry leaders and organization's basic assumptions about technology and the future of IT, but the accuracy of these reports are difficult to verify. Social media data can be useful tool to verify the accuracy. As social media services have gained in popularity, it is used in a variety of ways, from posting about personal daily life to keeping up to date with news and trends. In the recent years, rates of social media activity in Korea have reached unprecedented levels. Hundreds of millions of users now participate in online social networks and communicate with colleague and friends their opinions and thoughts. In particular, Twitter is currently the major micro blog service, it has an important function named 'tweets' which is to report their current thoughts and actions, comments on news and engage in discussions. For an analysis on IT trends, we chose Tweet data because not only it produces massive unstructured textual data in real time but also it serves as an influential channel for opinion leading on technology. Previous studies found that the tweet data provides useful information and detects the trend of society effectively, these studies also identifies that Twitter can track the issue faster than the other media, newspapers. Therefore, this study investigates how frequently the predicted IT trends for the following year announced by public organizations are mentioned on social network services like Twitter. IT trend predictions for 2013, announced near the end of 2012 from two domestic organizations, the National IT Industry Promotion Agency (NIPA) and the National Information Society Agency (NIA), were used as a basis for this research. The present study analyzes the Twitter data generated from Seoul (Korea) compared with the predictions of the two organizations to analyze the differences. Thus, Twitter data analysis requires various natural language processing techniques, including the removal of stop words, and noun extraction for processing various unrefined forms of unstructured data. To overcome these challenges, we used SAS IRS (Information Retrieval Studio) developed by SAS to capture the trend in real-time processing big stream datasets of Twitter. The system offers a framework for crawling, normalizing, analyzing, indexing and searching tweet data. As a result, we have crawled the entire Twitter sphere in Seoul area and obtained 21,589 tweets in 2013 to review how frequently the IT trend topics announced by the two organizations were mentioned by the people in Seoul. The results shows that most IT trend predicted by NIPA and NIA were all frequently mentioned in Twitter except some topics such as 'new types of security threat', 'green IT', 'next generation semiconductor' since these topics non generalized compound words so they can be mentioned in Twitter with other words. To answer whether the IT trend tweets from Korea is related to the following year's IT trends in real world, we compared Twitter's trending topics with those in Nara Market, Korea's online e-Procurement system which is a nationwide web-based procurement system, dealing with whole procurement process of all public organizations in Korea. The correlation analysis show that Tweet frequencies on IT trending topics predicted by NIPA and NIA are significantly correlated with frequencies on IT topics mentioned in project announcements by Nara market in 2012 and 2013. The main contribution of our research can be found in the following aspects: i) the IT topic predictions announced by NIPA and NIA can provide an effective guideline to IT professionals and researchers in Korea who are looking for verified IT topic trends in the following topic, ii) researchers can use Twitter to get some useful ideas to detect and predict dynamic trends of technological and social issues.

• Korean Journal of Poultry Science
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• v.17 no.4
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• pp.255-268
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• 1990

This study was carried out to investigate the time-trends of genetic parameters of the dosed flock population which selected for improving egg production. Data for two layer pure lines, Line-W (Single Comb White Leghorn) and Line-B (brown layer) which have been maintained at the Mani Breeding Farm were collected from 1980 to 1985 during 5 generations. The effective number of parents per generation ranged from 148 to 366 in Line-W and 85 to 355 in Line-B, and the cumulative expected inbreeding coefficients during 5 generations of selection were 15％ and 1.6％. So inbreeding could not be considered a critical factor on estimating the genetic parameters, heritabilities and genetic correlations Heritabilities of EN 300 and EN 400, primary two selected traits were significantly decreased during 5 generations but the estimates of the other 03its not showed the consistent decreasing pattern significantly. No time trends of probable consequence were evident in the genetic correlation coefficients of the traits studied. The reason for that situation was attributed to the fact that selection was conducted for multiple objectives and the relative importance of selection for the studied traits were not consistent by generations.

• The Journal of Industrial Distribution & Business
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• v.9 no.10
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• pp.83-89
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• 2018

Purpose - The purpose of this study examines when the optimism impact on financial asset price forecasting and the boundary condition of optimism in the financial asset price forecasting. People generally tend to optimistically forecast their future. Optimism is a nature of human beings and optimistic forecasting observed in daily life. But is it always observed in financial asset price forecasting? In this study, two factors were focused on considering whether the optimism that people have applied to predicting future performance of financial investment products (e.g., mutual fund). First, this study examined whether the degree of optimism varied depending on the direction of the prior price trend. Second, this study examined whether the degree of optimism varied according to the forecast period by dividing the future forecasted by people into three time horizon based on forecast period. Research design, data, and methodology - 2 (prior price trend: rising-up trend vs falling-down trend) × 3 (forecast time horizon: short term vs medium term vs long term) experimental design was used. Prior price trend was used between subject and forecast time horizon was used within subject design. 169 undergraduate students participated in the experiment. χ² analysis was used. In this study, prior price trend divided into two types: rising-up trend versus falling-down trend. Forecast time horizon divided into three types: short term (after one month), medium term (after one year), and long term (after five years). Results - Optimistic price forecasting and boundary condition was found. Participants who were exposed to falling-down trend did not make optimistic predictions in the short term, but over time they tended to be more optimistic about the future in the medium term and long term. However, participants who were exposed to rising-up trend were over-optimistic in the short term, but over time, less optimistic in the medium and long term. Optimistic price forecasting was found when participants forecasted in the long term. Exposure to prior price trends (rising-up trend vs falling-down trend) was a boundary condition of optimistic price forecasting. Conclusions - The results indicated that individuals were more likely to be impacted by prior price tends in the short term time horizon, while being optimistic in the long term time horizon.

• Journal of the Korea Safety Management & Science
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• v.5 no.3
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• pp.57-67
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• 2003

The trends in building construction these days are moving towards having better work space and greater suitability for the use of information technology, Therefore people can work in a more relaxed, delightful and pleasant environment. So accidents like fire could cause the mass destruction of human beings. In this paper, we estimated the escape time from a building and simulated the study results on computer to see how safe it would be in a real situation.