• Journal of the Korea Society of Computer and Information
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• v.7 no.1
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• pp.88-93
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• 2002

In this thesis, we propose an Intelligent Agent based on CORBA technology which can exchange and share data under distributed computing environment using Agent Communication Language(ACL), Recently, due to the rapid development of network technology, the need for the database management appeared. So representative standard, CORBA have been applied and used under distributed environment. Agent applications are being developed for various fields such as electronic commerce, user interface, information retrieval. When a user searches goods, he navigates several servers on different platform until he finds desired goods. The Intelligent Agent presented in this thesis, does these tedious navigations on behalf of the user through communication between different platform.

• Journal of Digital Convergence
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• v.17 no.9
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• pp.135-145
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• 2019

Prolonged recession has caused the youth unemployment rate in Korea to remain at a high level of approximately 10% for years. Recently, the number of unemployed Koreans in their 30s and 40s has shown an upward trend. To expand the government's employment promotion and unemployment benefits from youth-centered policies to diverse age groups, including people in their 30s and 40s, prediction models for different age groups are required. Thus, we aimed to develop unemployment prediction models for specific age groups (30s and 40s) using available unemployment rates provided by Statistics Korea and Google search queries related to them. We first estimated multiple linear regressions (Model 1) using seasonal autoregressive integrated moving average approach with relevant unemployment rates. Then, we introduced Google search queries to obtain improved models (Model 2). For both groups, consequently, Model 2 additionally using web queries outperformed Model 1 during training and predictive periods. This result indicates that a web search query is still significant to improve the unemployment predictive models for Koreans. For practical application, this study needs to be furthered but will contribute to obtaining age-wise unemployment predictions.

• Journal of Intelligence and Information Systems
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• v.29 no.2
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• pp.129-148
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• 2023

Bitcoin is a blockchain technology-based digital currency that has been recognized as a representative cryptocurrency and a financial investment asset. Due to its highly volatile nature, Bitcoin has gained a lot of attention from investors and the public. Based on this popularity, numerous studies have been conducted on price and trend prediction using machine learning and deep learning. This study employed LSTM (Long Short Term Memory) and CNN (Convolutional Neural Networks), which have shown potential for predictive performance in the finance domain, to enhance the classification accuracy in Bitcoin price trend prediction. XAI(eXplainable Artificial Intelligence) techniques were applied to the predictive model to enhance its explainability and interpretability by providing a comprehensive explanation of the model. In the empirical experiment, CNN was applied to technical indicators and Google trend data to build a Bitcoin price trend prediction model, and the CNN model using both technical indicators and Google trend data clearly outperformed the other models using neural networks, SVM, and LSTM. Then SHAP(Shapley Additive exPlanations) was applied to the predictive model to obtain explanations about the output values. Important prediction drivers in input variables were extracted through global interpretation, and the interpretation of the predictive model's decision process for each instance was suggested through local interpretation. The results show that our proposed research framework demonstrates both improved classification accuracy and explainability by using CNN, Google trend data, and SHAP.

• Science of Emotion and Sensibility
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• v.12 no.4
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• pp.489-500
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• 2009

This research takes a user-centered approach to define relevance, the core concept in information retrieval. The literature on relevance has identified numerous factors affecting such a judgment. We examined the model of user relevance judgment that describes the relationship between user relevance criteria and different types of relevance with information search task. We consider 7 criteria of user relevance-topicality, novelty, reliability, understandability, specificity, richness, and interest-and 3 type of user relevance-cognitive relevance, situational relevance, and affective relevance. Data were collected from a semi-controlled survey and analyzed by a structural equation modeling. As a result, topicality and reliability were found to be the essential relevance criteria in all information retrieval tasks. In the fact search task, topicality, reliability, novelty, richness, and interest were found to be significant. In the problem solving search task, topicality, reliability, understandability, and specificity were found to be significant. In the decision making search task, topicality, reliability, novelty, understandability, richness, specificity, and interest were found to be significant. In addition, the relationships between types of user relevance were determined. This research made theoretical and practical contributions to the field of information retrieval by identifying a definite model of user relevance judgment.

• Proceedings of the Korean Society for Information Management Conference
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• 1995.08a
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• pp.17-20
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• 1995

본 연구는 질의어 확장 및 단기 이용자 모형 구축에 응용할 수 있는 하나의 기법으로서 이용자 질문구조의 전형을 통한 정보요구의 모형화를 실험을 통해 제시한다. 실험방법은 이용자의 질문을 시소러스를 통해 분석, 구조화 하고 그 질문구조에서 전형을 추출한 후 전형에 따라 요구하는 정보가 질문구조내에 일정하게 위치하는지를 알아보았다. 이러한 실험을 통해 6가지 질문구조 전형을 추출할 수 있었으며 질문구조의 전형을 이용한 정보요구 모형의 구축이 타당성이 있음을 입증하였고 지능형 정보검색 시스템에의 적용가능성을 논의하였다.

• Journal of the Korean Society for information Management
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• v.22 no.2 s.56
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• pp.125-145
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• 2005

This paper presents a semantic vector space retrieval model incorporating a word sense disambiguation algorithm in an attempt to improve retrieval effectiveness. Nine Korean homonyms are selected for the sense disambiguation and retrieval experiments. The total of approximately 120,000 news articles comprise the raw test collection and 18 queries including homonyms as query words are used for the retrieval experiments. A Naive Bayes classifier and EM algorithm representing supervised and unsupervised learning algorithms respectively are used for the disambiguation process. The Naive Bayes classifier achieved $92\%$ disambiguation accuracy. while the clustering performance of the EM algorithm is $67\%$ on the average. The retrieval effectiveness of the semantic vector space model incorporating the Naive Bayes classifier showed $39.6\%$ precision achieving about $7.4\%$ improvement. However, the retrieval effectiveness of the EM algorithm-based semantic retrieval is $3\%$ lower than the baseline retrieval without disambiguation. It is worth noting that the performances of disambiguation and retrieval depend on the distribution patterns of homonyms to be disambiguated as well as the characteristics of queries.

• Journal of the Korean Society for information Management
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• v.19 no.1
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• pp.47-69
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• 2002

The concept based retrieval model has shown a higher performance than those of the simple matching function method or the P-norm retrieval method introduced to compensate the demerits of the Boolean retrieval model. However. it takes too long to create a semantic-net knowledge base, which is essential in concept exploration. In order to solve such demerits. a method was sought out by creating a distributed knowledge base by subjects to reduce construction time without hindering the performance of retrieval.

• Journal of Korean Society of Transportation
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• v.33 no.1
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• pp.14-28
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• 2015

Numerous research has been conducted using internet search data since the mid-2000s. For example, Google Inc. developed a service predicting influenza patterns using the internet search data. The main objective of this study is to prove the hypothesis that highway traffic indices are similar to the internet search patterns. In order to achieve this objective, a model to predict the number of vehicles entering the expressway and space-mean speed was developed and the goodness-of-fit of the model was assessed. The results revealed several findings. First, it was shown that the Google search traffic was a good predictor for the TCS entering traffic volume model at sites with frequent commute trips, and it had a negative correlation with the TCS entering traffic volume. Second, the Naver search traffic was utilized for the TCS entering traffic volume model at sites with numerous recreational trips, and it was positively correlated with the TCS entering traffic volume. Third, it was uncovered that the VDS speed had a negative relationship with the search traffic on the time series diagram. Lastly, it was concluded that the transfer function noise time series model showed the better goodness-of-fit compared to the other time series model. It is expected that "Big Data" from the internet search data can be extensively applied in the transportation field if the sources of search traffic, time difference and aggregation units are explored in the follow-up studies.

• Journal of Intelligence and Information Systems
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• v.23 no.3
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• pp.155-175
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• 2017

As social data become into the spotlight, mainstream web search engines provide data indicate how many people searched specific keyword: Web Search Traffic data. Web search traffic information is collection of each crowd that search for specific keyword. In a various area, web search traffic can be used as one of useful variables that represent the attention of common users on specific interests. A lot of studies uses web search traffic data to nowcast or forecast social phenomenon such as epidemic prediction, consumer pattern analysis, product life cycle, financial invest modeling and so on. Also web search traffic data have begun to be applied to predict tourist inbound. Proper demand prediction is needed because tourism is high value-added industry as increasing employment and foreign exchange. Among those tourists, especially Chinese tourists: Youke is continuously growing nowadays, Youke has been largest tourist inbound of Korea tourism for many years and tourism profits per one Youke as well. It is important that research into proper demand prediction approaches of Youke in both public and private sector. Accurate tourism demands prediction is important to efficient decision making in a limited resource. This study suggests improved model that reflects latest issue of society by presented the attention from group of individual. Trip abroad is generally high-involvement activity so that potential tourists likely deep into searching for information about their own trip. Web search traffic data presents tourists' attention in the process of preparation their journey instantaneous and dynamic way. So that this study attempted select key words that potential Chinese tourists likely searched out internet. Baidu-Chinese biggest web search engine that share over 80%- provides users with accessing to web search traffic data. Qualitative interview with potential tourists helps us to understand the information search behavior before a trip and identify the keywords for this study. Selected key words of web search traffic are categorized by how much directly related to "Korean Tourism" in a three levels. Classifying categories helps to find out which keyword can explain Youke inbound demands from close one to far one as distance of category. Web search traffic data of each key words gathered by web crawler developed to crawling web search data onto Baidu Index. Using automatically gathered variable data, linear model is designed by multiple regression analysis for suitable for operational application of decision and policy making because of easiness to explanation about variables' effective relationship. After regression linear models have composed, comparing with model composed traditional variables and model additional input web search traffic data variables to traditional model has conducted by significance and R squared. after comparing performance of models, final model is composed. Final regression model has improved explanation and advantage of real-time immediacy and convenience than traditional model. Furthermore, this study demonstrates system intuitively visualized to general use -Youke Mining solution has several functions of tourist decision making including embed final regression model. Youke Mining solution has algorithm based on data science and well-designed simple interface. In the end this research suggests three significant meanings on theoretical, practical and political aspects. Theoretically, Youke Mining system and the model in this research are the first step on the Youke inbound prediction using interactive and instant variable: web search traffic information represents tourists' attention while prepare their trip. Baidu web search traffic data has more than 80% of web search engine market. Practically, Baidu data could represent attention of the potential tourists who prepare their own tour as real-time. Finally, in political way, designed Chinese tourist demands prediction model based on web search traffic can be used to tourism decision making for efficient managing of resource and optimizing opportunity for successful policy.

• Proceedings of the Korean Information Science Society Conference
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• 2003.10b
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• pp.577-579
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• 2003

본 논문에서는 생체인식 분야 중 얼굴인식의 검색 정확성 향상 및 검색 시간을 단축하기 위한 단계로 인종별 얼굴영상 데이터베이스에 대한 군집화 기법을 연구하였다. 우선, 일반적으로 얼굴 및 이미지 검색에 사용되는 다양한 특징을 추출하고, 추출한 다차원의 특징 데이터들로부터 다 인종 얼굴 데이터를 유사한 인종별로 정확하게 군집화 하기 위해 최적의 특징벡터를 자동으로 선택 할 수 있는 방법을 제안하였다. 군집결과 분석을 위해 자기 조직화 지도 모형을 이용하였는데, 이는 2차원 분석 및 가시화에 유용하며, 학습 후 코드북벡터를 사용하여 유사한 의미간의 거리부터 검색할 수 있는 특징을 가지고 있다. 특징추출에 관한 실험결과 인종별 구분을 위한 특징벡터로는 웨이블릿 주파수 성분(lowpass 성분)과 CbCr 특징벡터가 인종별 군집화에 가장 유용한 특징으로 선택되었으며. 추출된 특징을 바탕으로 semantic map을 구성하여 제안방법의 효율성을 제시하였다.