• Journal of the Korea Academia-Industrial cooperation Society
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• v.21 no.5
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• pp.543-550
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• 2020

Interest in food recipe retrieval systems has been increasing recently. Most computer-based recipe retrieval systems are searched by cooking name or ingredient name. Since each recipe provides information in different weighing units, recalculations to the desired amount are necessary and inconvenient. This paper introduces a computer system that addresses these inconveniences. The system is a chatbot system, based on web-based recipe recommendations, for users familiar with the use of messenger conversation systems. After selecting the most popular recipes by their names, and pre-processing to extract only information required for the recipes, the system recommends recipes based on the 100,000 data. Recipes are then searched by the names of food ingredients (included and excluded). Recalculations are performed based on the number of servings entered by the user. A satisfaction rate for the systems' recommendations was 90.5%.

• Convergence Security Journal
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• v.21 no.1
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• pp.115-120
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• 2021

An O2O-based real estate brokerage web sites or apps are increasing explosively. As a result, the environment has been changed from the existing offline-based real estate brokerage environment to the online-based environment, and consumers are getting very good feelings in terms of time, cost, and convenience. However, behind the convenience of online-based real estate brokerage services, users often suffer time and money damage due to false information or malicious false information. Therefore, in this study, in order to reduce the damage to consumers that may occur in the O2O-based real estate brokerage service, we designed a false property information filtering system that can determine the authenticity of registered property information using artificial intelligence technology. Through the proposed research method, it was shown that not only the authenticity of the property information registered in the online real estate service can be determined, but also the temporal and financial damage of consumers can be reduced.

• Journal of Wellbeing Management and Applied Psychology
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• v.2 no.2
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• pp.39-45
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• 2019

The purpose of this study is to solve the problem of revision or abbreviation of questionnaires based on the previous studies suggested by many existing empirical studies. In addition, this study aims to provide the theoretical basis of the research method which has been variously approached since it presents the methodology that can directly measure the research object. For this purpose, this study proposed a more elaborate analysis method using the differences in perception of individuals who are interested in cognitive research. Specifically, the perception gap(D) can be used as an independent variable, a dependent variable, and a moderating variable. And this study suggested an effective research approach using the measurement of perception difference. The difference of perception suggested that it can be used as a measure to overcome the limitations of existing researches used it as independent variables or mediating variables that measure only one factor of expectation and performance or importance and satisfaction. In addition, it is highly likely that various analyzes on the perception differences, which are the result of measuring target factors for the same person, will be quite effective in the situation where follow-up of respondents is difficult. This study is expected to overcome various limitations reported by empirical studies such as scale utilization problem and follow-up survey difficulty. In future research, it was expected that the limitation of the factor derivation process in the research approach could be complemented by web crawling and text mining of big data analysis.

• Journal of Korea Multimedia Society
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• v.22 no.11
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• pp.1313-1323
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• 2019

With the development of the Internet and the increase of smart phones, various services considering user convenience are increasing, so that users can check news in real time anytime and anywhere. However, online news is categorized by media and category, and it provides only a few related search terms, making it difficult to find related news related to keywords. In order to solve this problem, we propose a method to recommend related documents more accurately by applying Doc2Vec similarity to the specific keywords of news articles and weighting the title and contents of news articles. We collect news articles from Naver politics category by web crawling in Java environment, preprocess them, extract topics using LDA modeling, and find similarities using Doc2Vec. To supplement Doc2Vec, we apply TF-IDF to obtain TC(Title Contents) weights for the title and contents of news articles. Then we combine Doc2Vec similarity and TC weight to generate TC weight-similarity and evaluate the similarity between words using PMI technique to confirm the keyword association.

• Journal of the Korea Society of Computer and Information
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• v.20 no.6
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• pp.37-42
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• 2015

In this paper, we propose malware database in mobile memory for realtime malware URL detection and we support realtime malware URL detection engine, that is control the web service for more secure mobile service. Recently, mobile malware is on the rise and to be new threat on mobile environment. In particular the mobile characteristics, the damage of malware is more important, because it leads to monetary damages for the user. There are many researches in cybercriminals prevention and malware detection, but it is still insufficient. Additionally we propose the method for prevention Smishing within SMS, MMS. In the near future, mobile venders must build the secure mobile environment with fundamental measures based on our research.

• Journal of Korea Society of Industrial Information Systems
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• v.21 no.4
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• pp.1-11
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• 2016

Recently numerous studies are conducted to estimate the human personality from the online social activities. This paper develops a comprehensive model for political attitude estimation leveraging the Facebook Like information of the users. We designed a Facebook Crawler that efficiently collects data overcoming the difficulties in crawling Ajax enabled Facebook pages. We show that the category level selection can reduce the data analysis complexity utilizing the sparsity of the huge like-attitude matrix. In the Korean Facebook users' context, only 28 criteria (3% of the total) can estimate the political polarity of the user with high accuracy (AUC of 0.82).

• Journal of Korean society of Dental Hygiene
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• v.21 no.5
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• pp.639-644
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• 2021

Objectives: The purpose of this study was to analyze the factors that affected the number of citations for articles published in the Journal of Korean Society of Dental Hygiene based on previous studies. Methods: Information on papers including the number of citations was collected using a web crawling technique. The effect of the number of author keywords, the number of Medical Subject Headings (MeSH) keywords, MeSH match rate, abstract word count and keyword-abstract ratio on the number of citations was analyzed by multiple regression analysis. Results: The use of the MeSH keyword did not have a significant effect on the number of citations. Among the other factors, only the keyword-abstract ratio was statistically significant. Conclusions: Select a topic of constant interest in the field, write the title in detail using colons or asterisks if necessary, and do not repeat the words used in the title in keywords. Select specific keywords deeply related to the topic. In particular, choice words or phrases that are frequently used in the abstract. If the MeSH keyword selection contradicts the previous strategies, boldly give up the MeSH keyword.

• Journal of Korea Multimedia Society
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• v.22 no.1
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• pp.1-9
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• 2019

Single image rain removal is a typical inverse problem which decomposes the image into a background scene and a rain streak. Recent works have witnessed a substantial progress on the task due to the development of convolutional neural network (CNN). However, existing CNN-based approaches train the network with synthetically generated training examples. These data tend to make the network bias to the synthetic scenes. In this paper, we present an unsupervised framework for removing rain streaks from real-world rainy images. We focus on the natural phenomena that static rainy scenes capture a common background but different rain streak. From this observation, we train siamese network with the real rain image pairs, which outputs identical backgrounds from the pairs. To train our network, a real rainy dataset is constructed via web-crawling. We show that our unsupervised framework outperforms the recent CNN-based approaches, which are trained by supervised manner. Experimental results demonstrate that the effectiveness of our framework on both synthetic and real-world datasets, showing improved performance over previous approaches.

• Journal of the Korean Society of Food Culture
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• v.36 no.2
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• pp.168-183
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• 2021

Using the big data analysis of the Choseonwangjosilrok, this research aimed to figure out the fruits' types, prevalence, seasonal appearances as well as the royalty's perspective on fruits during Choseon period. Choseonwangjosilrok included nineteen kinds of fruits and five kinds of nuts, totaling 1,601 cases at 72.8% and 533 cases at 24.2% respectively. The text recorded fruits being used as: tributes for kings, gifts from kings to palace officials, tomb offerings, county specialties, trade goods or gifts to the foreign ambassadors, and medicine ingredients in oriental pharmacy. Seasonally the fruits appeared demonstrating an even distribution. Periodic characteristics were observed in decreasing quantity chronologically. From fifteenth century to nineteenth century, the fruits with timely features were seen: 804 times at 36.6%, 578 times at 26.3%, 490 times at 22.3%, 248 times at 11.3%, and 78 times at 3.5% respectively. In fifteenth century: citrons, quinces, pomegranates, cherries, permissions, watermelons, Korean melons, omija, walnuts, chestnuts, and pine nuts appeared most frequently. In sixteenth century: pears, grapes, apricots, peaches, and hazelnuts appeared most frequently. In seventeenth century: tangerines and dates appeared most frequently. In eighteenth century, trifoliate orange was the most frequently mentioned fruit.

• The Journal of Information Systems
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• v.31 no.2
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• pp.1-18
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• 2022

Purpose Customer review is a major factor in choosing certain restaurants. This study investigates the key factors affecting customer's evaluation about restaurants. With the recent intensification of competition among restaurants in the service industry, the analysis results are expected to provide in-depth insights for enhancing customer experiences. Design/methodology/approach We collected information and reviews provided at the restaurants in the Kakao Map platform. The information collected is based on the information of 3,785 restaurants in Daegu registered on Kakao Map. Based on the information collected, seven independent variables, including number of rating registered, number of reviews, presence or absence of safe restaurants, presence or absence of a posting about holding facilities, presence or absence of a posting about business hours, presence or absence of a posting about hashtags, and presence or absence of break times, were used. Dependent variable is restaurant rating. Multiple regression between independent variables and restaurant rating was carried out. Findings The results of the study confirmed that number of rating registered, presence or absence of a posting about business hours, and presence or absence of a posting about hash tags have an positive effects on the restaurant rating. The number of reviews had a negative effect on the restaurant rating. In addition, in order to confirm the role of customer's reviews, we carried out LDA topic modeling. We divided the topics into the positive review and the negative reviews.