• Culinary science and hospitality research
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• v.14 no.3
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• pp.45-57
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• 2008

The purpose of this study is to initiate a systematic approach to maximize profits through continuous development of menu and build a strong image of Western restaurants located inside hotels by identifying their guests' knowledge and concern and menu selection behavior in well being trend. Findings from the analysis are as follows. First, among the Western menu selection behavior, organic grain and seafood, seasonal event menu, less spicy and more natural cooking methods are favored as the most important consideration. Second, customers' knowledge and concern in well being trend and menu selection behavior were found to be statistically significant. Third, customers' awareness in health and obesity were found to be statistically significant to the concern in well being trend. Fourth, demographical characteristics of customers such as gender, marital status, age, income level and education were tested for their relationships with knowledge and concern in well being trend.

• Journal of Intelligence and Information Systems
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• v.21 no.1
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• pp.65-82
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• 2015

Recently, there has been a surge of interest in finding core issues and analyzing emerging trends for the future. This represents efforts to devise national strategies and policies based on the selection of promising areas that can create economic and social added value. The existing studies, including those dedicated to the discovery of future promising fields, have mostly been dependent on qualitative research methods such as literature review and expert judgement. Deriving results from large amounts of information under this approach is both costly and time consuming. Efforts have been made to make up for the weaknesses of the conventional qualitative analysis approach designed to select key promising areas through discovery of future core issues and emerging trend analysis in various areas of academic research. There needs to be a paradigm shift in toward implementing qualitative research methods along with quantitative research methods like text mining in a mutually complementary manner. The change is to ensure objective and practical emerging trend analysis results based on large amounts of data. However, even such studies have had shortcoming related to their dependence on simple keywords for analysis, which makes it difficult to derive meaning from data. Besides, no study has been carried out so far to develop core issues and analyze emerging trends in special domains like the aviation industry. The change used to implement recent studies is being witnessed in various areas such as the steel industry, the information and communications technology industry, the construction industry in architectural engineering and so on. This study focused on retrieving aviation-related core issues and emerging trends from overall research papers pertaining to aviation through text mining, which is one of the big data analysis techniques. In this manner, the promising future areas for the air transport industry are selected based on objective data from aviation-related research papers. In order to compensate for the difficulties in grasping the meaning of single words in emerging trend analysis at keyword levels, this study will adopt topic analysis, which is a technique used to find out general themes latent in text document sets. The analysis will lead to the extraction of topics, which represent keyword sets, thereby discovering core issues and conducting emerging trend analysis. Based on the issues, it identified aviation-related research trends and selected the promising areas for the future. Research on core issue retrieval and emerging trend analysis for the aviation industry based on big data analysis is still in its incipient stages. So, the analysis targets for this study are restricted to data from aviation-related research papers. However, it has significance in that it prepared a quantitative analysis model for continuously monitoring the derived core issues and presenting directions regarding the areas with good prospects for the future. In the future, the scope is slated to expand to cover relevant domestic or international news articles and bidding information as well, thus increasing the reliability of analysis results. On the basis of the topic analysis results, core issues for the aviation industry will be determined. Then, emerging trend analysis for the issues will be implemented by year in order to identify the changes they undergo in time series. Through these procedures, this study aims to prepare a system for developing key promising areas for the future aviation industry as well as for ensuring rapid response. Additionally, the promising areas selected based on the aforementioned results and the analysis of pertinent policy research reports will be compared with the areas in which the actual government investments are made. The results from this comparative analysis are expected to make useful reference materials for future policy development and budget establishment.

• Journal of the Korea Society of Computer and Information
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• v.28 no.10
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• pp.55-65
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• 2023

In this paper, we apply KeyBERT(Keyword extraction with Bidirectional Encoder Representations of Transformers) algorithm-driven topic extraction and topic frequency analysis to deep learning and reinforcement learning research to discover the rapidly changing trends in them. First, we crawled abstracts of research papers on deep learning and reinforcement learning, and temporally divided them into two groups. After pre-processing the crawled data, we extracted topics using KeyBERT algorithm, and then analyzed the extracted topics in terms of topic occurrence frequency. This analysis reveals that there are distinct trends in research work of all analyzed algorithms and applications, and we can clearly tell which topics are gaining more interest. The analysis also proves the effectiveness of the utilized topic extraction and topic frequency analysis in research trend analysis, and this trend analysis scheme is expected to be used for research trend analysis in other research fields. In addition, the analysis can provide insight into how deep learning will evolve in the near future, and provide guidance for select research topics and methodologies by informing researchers of research topics and methodologies which are recently attracting attention.

• Proceedings of the Korea Society of Design Studies Conference
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• 2001.05a
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• pp.152-153
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• 2001

• 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.

• The Journal of the Korea Contents Association
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• v.4 no.4
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• pp.33-43
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• 2004

In this paper, a traffic trend analysis based SNMP algorithm is proposed for improving the problem of existing traffic analysis using SNMP. The existing traffic analysis method has a vulnerability that is taken much time In analyzing by using a threshold and not detected a harmful traffic at the point of transition. The method that is proposed in this paper can solve the problems that the existing method had, simultaneously using traffic trend analysis of the day, traffic trend analysis happening in each protocol and MIB object analysis responding to attacks instead of using the threshold. The algorithm proposed in this paper will analyze harmful traffic more quickly and more precisely; hence it can reduce the damage made by traffic flooding attacks. When traffic happens, it can detect the abnormality through the three analysis methods previously mentioned. After that, if abnormal traffic overlaps in at least two of the three methods, we can consider it as harmful traffic. The proposed algorithm will analyze harmful traffic more quickly and more precisely; hence it can reduce the damage made by traffic flooding attacks.

• Journal of Korea Water Resources Association
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• v.47 no.7
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• pp.643-656
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• 2014

The homogeneity analysis of temporal (monthly, seasonal and annual) climate aridity index trend was accomplished for 43 climate measurement stations in South Korea. Furthermore, 43 stations were grouped into 9 different regions and the temporal and regional homogeneity of climate aridity index trends in each region and entire 9 regions were analyzed. For analysis, monthly, seasonal and annual climate aridity indexes of 43 study stations were estimated using precipitation and potential evapotranspiration calculated from FAO Penman-Monteith equation. The Mann-Kendall statistical test for significant trend was accomplished using the estimated climate aridity indexes and the results of trend test (Z scores) were used to analyze the temporal and regional homogeneity of climate aridity index trends. The study results showed the temporal and regional homogeneity of climate aridity index trends for individual and entire 9 regions. However, the homogeneity and the extent of aridity index trend showed different patterns temporally and regionally.

• Asian Pacific Journal of Cancer Prevention
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• v.17 no.3
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• pp.1393-1397
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• 2016

Background: Breast cancer is the most common cancer and the first cause of cancer death in women worldwide, with infiltrating duct carcinoma as the most common morphology. This study aimed to investigate trend of breast cancer incidence by age groups and histological changes in Iranian women between 2003 and 2008. Materials and Methods: This is analytic study, carried out based on re-analysis of the Cancer Registry Center report of health deputy for women's breast cancer in Iran during a 6-year period (2003-2008). Statistical analysis for incidence time trends and morphology change percentage carried out joinpoint regression analysis using the software Joinpoint Regression Program. Results: A total of 36,340 cases were reported for Iranian women in the six years. Analytical trend showed an increasing incidence trend with significant annual percentage change (APC) of 15.2 (CI: 11.6 to 18.8). The lowest and highest significant increased trend were related to age groups of 40 to 44 years and above 85 years, respectively; with APCs of 13.0 and 25.1, respectively. Of total cases, 78.7% of cases were infiltrating duct carcinoma, decreasing from 82.0% in 2003 to 76.6% in 2008, which was significant with an APC equal to -1.76 (CI:-2.7 to -0.8). Conclusions: The incidence trend of breast cancer is rising in Iranian women. The highest incidence was observed in the age groups 45-65 and 80-85. In conclusion, to reduce breast cancer incidence and its burden, preventive and screening programs for breast cancer, especially in young women, are recommended in Iran.

• Journal of Fashion Business
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• v.6 no.5
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• pp.59-71
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• 2002

The purpose of this study was 1) to analyze the consumers' preference on the recent trend in men's wear according to their lifestyle and 2) to suggest a suitable direction for men's wear planning based on the lifestyle analysis. A survey was applied to obtain the data set responded from 310 male subjects who were aged between thirties and fourties. The main results of this study are summarized as follows: 1) Five types of recent fashion trends were identified through a qualitative analysis on the recent men's wear trend, which were 'Modern classic casual', 'Retro traditional casual', 'Authentic/Ethnic casual', 'Urban dandy street casual' and the 'Refined sportive casual'. 2) The three types of the respondents' lifestyle were identified in this research and named as 'pursuing sense', 'pursuing tradition' and 'conservative indifference'. Examining the preference on fashion trends according to subjects' lifestyle and etc., the preference level of the 'pursuing sense' group on trend was, in general, higher than that of the two other lifestyle groups. The most preferred trend style of 'pursuing sense' group was the 'Modern Classic'. Finally, a suitable direction for men's wear planning was suggested on the result of analysis in this research.

• Journal of the Korean Society of Hazard Mitigation
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• v.9 no.6
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• pp.17-30
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• 2009

In this study, the existence of possible deterministic longterm trend of precipitation amount, monthly maximum precipitation, rain day, the number of rain day greater than 20mm, 30mm, and 80mm was analyzed using the Mann-Kendall rank test and the data from 62 stations between 1905 and 2004 in South Korea. Results indicate that the annual and monthly rainfall amount increases and the number of rain days which have more than 80mm rainfall a day, increases. However the number of rain days decreases. Also, monthly trend analysis of precipitation amount and monthly maximum precipitation increases in Jan., May, Jun., Jul., Aug., and Sep. and they decrease in Mar., Apr., Oct., Nov., and Dec. Monthly trend of the number of rain day greater than 20mm, 30mm, and 80mm increases in Jun., Jul., Aug., and Sep. However results of Mann-Kedall test demonstrated that the ratio of stations, which have meaningful longterm trend in the significance level of 90% and 95%, is very low. It means that the random variability of the analyzed precipitation related data is much greater than their linear increment.