• Journal of information and communication convergence engineering
• /
• v.22 no.1
• /
• pp.56-63
• /
• 2024

Despite the rapid strides in content-based image retrieval, a notable disparity persists between the visual features of images and the semantic features discerned by humans. Hence, image retrieval based on the association of semantic similarities recognized by humans with visual similarities is a difficult task for most image-retrieval systems. Our study endeavors to bridge this gap by refining image semantics, aligning them more closely with human perception. Deep learning techniques are used to semantically classify images and retrieve those that are semantically similar to personalized images. Moreover, we introduce a keyword-based image retrieval, enabling automatic labeling of images in mobile environments. The proposed approach can improve the performance of a mobile device with limited resources and bandwidth by performing retrieval based on the visual features and keywords of the image on the mobile device.

• Spatial Information Research
• /
• v.23 no.5
• /
• pp.1-7
• /
• 2015

Twitter has the fast propagation and diffusion of information compare to other SNS. Therefore, many researches about detecting real-time event using twitter are progressing. Twitter real-time event detecting system assumes every twitter user as a sensor and analyzes their written tweet in order to detect the event. Researches that are related to this twitter have already obtained good results but confronted the limits because of some problems. Especially, many existing researches are using the method that can trace an event location by using GPS coordinate. However, it can be suggested a definite limitation through the present user's skeptical responses about making personal location information public. Therefore, this paper suggests the method that traces the location information in tweet contents text without using the provided location information from twitter. Associated words were grouped by using the keyword that extracted in tweet contents text. The place that the events have occurred and whether the events have surely occurred are detected by this experiment using this algorithm. Furthermore, this experiment demonstrated the necessity of the suggested methods by showing faster detection compare to the other existing media.

• Journal of Platform Technology
• /
• v.11 no.4
• /
• pp.19-34
• /
• 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 the Korea Society of Computer and Information
• /
• v.26 no.9
• /
• pp.189-200
• /
• 2021

Recently, Information and Communication Technology and SMART Phone Technology have been rapidly developed. According to the increase of data use, the era of big data has come. With the approach of non-contact society, QR Codes are becoming inseparable in our lives. In this paper, we are trying to figure out the implications of QR Code research based on Big Data in Korea. The purpose of this study is to first examine the previous studies on "QR Code" and conduct an analysis on keywords by field using Big Data. Second, for data visualization WordCloud analysis and network analysis are performed on "QR Code" frequent keyword. Third, we would like to present the research direction to future researchers regarding "QR Code". In the results, First of all, research trends showed that research is on the rise and that various fields are being utilized. Second, the results of the analysis of frequent keyword resulted in similar results overall, with some differences depending on the field and year. Third, we found that the visualization results according to the frequent keyword were also analyzed in the same way as the frequent keyword analysis results. The practical implications of the theoretical findings are as follows. First, 'QR Code' needs to be studied as a means of information delivery, not as a technical aspect. Second, it can be seen that "QR Code" is developing reflecting social trends or issues. With both theoretical and practical implications, we are trying to provide the strategic ways of QR-code in future.

• KSII Transactions on Internet and Information Systems (TIIS)
• /
• v.15 no.9
• /
• pp.3221-3242
• /
• 2021

The purpose of the study is to examine the current study trend of 'QR code' and suggest a direction for the future study of big data analysis: (1) Background: study trend of 'QR code' and analysis of the text by subject field and year; (2) Methodology: data scraping and collection, EXCEL summary, and preprocess and big data analysis by R x 64 4.0.2 program package; (3) the findings: first, the trend showed a continuous increase in 'QR code' studies in general and the findings were applied in various fields. Second, the analysis of frequent keywords showed somewhat different results by subject field and year, but the overall results were similar. Third, the visualization of the frequent keywords also showed similar results as that of frequent keyword analysis; and (4) the conclusions: in general, 'QR code' studies are used in various fields, and the trend is likely to increase in the future as well. And the findings of this study are a reflection that 'QR code' is an aspect of our social and cultural phenomena, so that it is necessary to think that 'QR code' is a tool and an application of information. An expansion of the scope of the analysis is expected to show us more meaningful indications on 'QR code' study trends and development potential.

• Journal of Environmental Impact Assessment
• /
• v.26 no.5
• /
• pp.303-314
• /
• 2017

The purpose of this study was to determine domestic and foreign ecological asset research trends. We aimed to understand ecological assets research directions and trends by comprehensively analyzing 12 keywords, including those similar to keywords for comparable assets, to identify related fields and regions. Extensive analysis of domestic and foreign studies was conducted through keyword network analysis of textural information. This approach is helpful for understanding the flow of information and identifying research directions. Foreign studies based on sustainability were connected with 'Economic assessment', 'Management' and 'Policy' areas. It was difficult to determine domestic research trends because there are fewer domestic studies than foreign. There were studies that sought to identify economic value of developing regions. This research can be used to guide the research direction for future ecosystem asset analysis in Korea.

• The Journal of the Korea Contents Association
• /
• v.17 no.11
• /
• pp.389-397
• /
• 2017

Recently semantic web document is produced and added in repository in a cloud computing environment and requires an intelligent semantic agent for analytical classification of documents and information retrieval. The traditional methods of information retrieval uses keyword for query and delivers a document list returned by the search. Users carry a heavy workload for examination of contents because a former method of the information retrieval don't provide a lot of semantic similarity information. To solve these problems, we suggest a key word frequency and concept matching based semantic clustering model using hadoop and NoSQL to improve classification accuracy of the similarity. Implementation of our suggested technique in a cloud computing environment offers the ability to classify and discover similar document with improved accuracy of the classification. This suggested model is expected to be use in the semantic web retrieval system construction that can make it more flexible in retrieving proper document.

• Journal of the Korea Institute of Information and Communication Engineering
• /
• v.4 no.2
• /
• pp.443-451
• /
• 2000

Until now, there have been various kinds of science information databsae which databased the science technology information, but they do not satisfy the aspiration of the users. Therefore, in the position of the users, it suggests the technology information space as a now paradigm, which supplement the function of science information DB. ICPIS which inputs described papers with keywords, offers the itemized summary of these contents, the visual indication and comparison of similar thesis, and it also supplises the abundant summary information, survey information, more than ten volumes of info communication thesis with starting the casual relation extraction for the users, playing a significant role in ICPIS is called KP, and it is package of domain knowledge that unifies the extraction and structure narration of the technology information. ICPIS extracts the technology information among the thesis that are deserved by the natural language treatment in the itemized KP keywords described, and form the prescribed summary structure in KP.

• Korean Journal of Community Nutrition
• /
• v.23 no.2
• /
• pp.128-136
• /
• 2018

Objectives: The purpose of this study was to compare and analyse dieting practices and tendencies in 2010 and 2015 using big data. Methods: Keywords related to diet were collected from the portal site Naver from January 1, 2010 until December 31, 2010 for 2010 data and from January 1, 2015 until December 31, 2015 for 2015 data. Collected data were analyzed by simple frequency analysis, N-gram analysis, keyword network analysis, and seasonality analysis. Results: The results show that exercise had the highest frequency in simple frequency analysis in both years. However, weight reduction in 2010 and diet menu in 2015 appeared most frequently in N-gram analysis. In addition, keyword network analysis was categorized into three groups in 2010 (diet group, exercise group, and commercial weight control group) and four groups in 2015 (diet group, exercise group, commercial program for weight control group, and commercial food for weight control group). Analysis of seasonality showed that subjects' interests in diets increased steadily from February to July, although subjects were most interested in diets in July in both years. Conclusions: In this study, the number of data in 2015 steadily increased compared with 2010, and diet grouping could be further subdivided. In addition, it can be confirmed that a similar pattern appeared over a one-year cycle in 2010 and 2015. Therefore, dietary method is reflected in society, and it changes according to trends.

• Journal of the Korean Society of Clothing and Textiles
• /
• v.41 no.6
• /
• pp.1022-1038
• /
• 2017

Wearable smart products contributed to expand the concept of modern clothing in terms of closer communication between wearers and the surrounding environment. This study investigated the concept of wearable smart actuators and characteristics of actuating feedback. The representative product development trends over the past 10 years are examined based on the method of actuating feedback. A first keyword search from Naver and Google reused to select the final 58 products based on the second key keyword actuating feedback. The wearable smart actuator system works in a similar way to a human body system such as those of the information processing process. Actuating feedback is delivered directly to the user as the last signal of the system, which has visual, auditory and tactile feedback. It works in complex at the device to be delivered to the user. Actuator feedback is divided into three types of active and passive, depending on the user situation, the overall purpose of the product, the collected information, and the device dependency. Active actuating feedback determines and actuates the situation on behalf of the user when the user is in an emergency or a disability situation. Passive actuating feedback plays a role in environmental monitoring to help in a healthy daily life.