• Journal of the Korea Institute of Information and Communication Engineering
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• v.19 no.4
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• pp.991-1000
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• 2015

This paper is studying for New Online Learning Model on Smart digital era. It can expect the result of learning degree on Online Learning Class. Using 7-factors of Online Class's operating policy, make the expectation model by 'decision tree' method. And through applying another class, we can getting a generality. Finally, Over the traditional Online Class model, we can take the real-time status of Online class learning degree. It is useful both students and teacher. It is the one of 'Smart learning Model'.

• Journal of Digital Contents Society
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• v.18 no.5
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• pp.933-942
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• 2017

In this paper, we propose a web crawler service method for collecting information efficiently about college students and job-seeker's external activities, competition, and scholarship. The proposed web crawler service uses Jsoup tree analysis and Json format data transmission method to avoid problems of duplicated crawling while crawling at high speed. After collecting relevant information for 24 hours, we were able to confirm that the web crawler service is running with an accuracy of 100%. It is expected that the web crawler service can be applied to various web sites in the future to improve the web crawler service.

• The Journal of the Korea Contents Association
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• v.22 no.1
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• pp.197-213
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• 2022

In this study, text mining analysis was performed on online big data for recognition and assessment of urban brand I Seoul U. To this end, TEXTOM, a processing program for data acquisition and analysis was used, and the 'I SEOUL U' keyword was selected as an analysis keyword. Keyword analysis shows the keywords associated with I Seoul U to be as follows: First, as a business and marketing term, keywords include pop-up store, gallery, co-branding, (festival, etc.), commodities, private companies and online. Second, as an event-related term, keywords include Han River, tree-planting day, tree planting, Hongdae, Christmas, Mapo, Jung-gu, Sejong University, and festival. Third, as a promotional term, keywords include robotics engineer Dr. Dennis Hong, Government, Art and Korea. In the N Gram analysis, as the city brand of Seoul, I Seoul U, in the public interest, was found to contribute to the commercial activities of private companies. In connection-oriented analysis, business and marketing, events, and promotions have been derived as categories. In matrix analysis, it was found that the products of the pop-up store are mainly developed, and products in the form of co-branding were being developed. In the topic modeling, a total of 10 topics were extracted and needs for commercial utilization and information for event festivals were mostly found.

• Journal of Korean Society of Forest Science
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• v.99 no.5
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• pp.706-710
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• 2010

Understanding of chemical composition and energy contents in tree is important to develope strategies of renewable energy policy to cope with climate change. Residual biomass as renewable energy source was evaluated and focused on the bark-containing branches. Chemical analysis studies were conducted for different part of yellow poplar (Liriodendron tulipifera), which were partitioned to inner bark, outer bark, small branches, medium branches, big branches and trunk. The variations in hydrophobic extractives, hydrophilic extractives, lignin, carbohydrate compositions, energy contents (higher heating value) and the ash content were determined. The inner and outer bark had higher ash content, hydrophobic and hydrophilic extractives content, and higher energy content than those of tree trunk. Polysaccharides content in inner and outer bark was quite lower than those of stem or branches. Based on the energy content of residual biomass, replacement of fossil fuel and greenhouse gas emission abatement were calculated.

• Journal of the Korean Society of Environmental Restoration Technology
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• v.5 no.3
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• pp.50-57
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• 2002

The restoration techniques of large disturbed land containing native forest resources and soil animals were investigated on the new campus area of Kyushu University in Japan. Important techniques to restore native forest and biodiversity in that area are transplantation of existing large trees, transplantation of the forest soil, transplantation of native tree stools, and the reuse of wood and bamboo chips. The benefits can be obtained by using these methods. Firstly, the native genetic resources that would be discarded as part of the land development can be reused. Secondary, the time taken to become a high growth forest as opposed to the practice of planting saplings or grass seeds can be reduced. At last, the native forest ecosystem containing various under-story vegetations and soil animals can be conserved and regenerated. In addition, big and small ponds were constructed in the biodiversity preservation zone to preserve rare plants, rare animals, and native aquatic animals. And these plants and animals were transplanted and moved to ponds.

• Journal of the Korea Society of Computer and Information
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• v.3 no.4
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• pp.49-54
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• 1998

AS it takes a considerable time for database operations for processing the condition part of active rule, the operations have an important effect on the efficiency of active database system. The processing time of operations should be minimized in order to improve the efficiency of system. The previous works are limited to basic database operations and the partial aggregate functions. In this paper, the processing technique using the structuralization and the state table of relations is suggested. The processing time for basic database operations can be reduced with the structuralization of relations to classification tree and the introduction of deletion information table. With the introduction of binary search tree and relation state table, the aggregate function which has a big of processing cost can be processed effectively and the function of the active database system can be maximized.

• Nuclear Engineering and Technology
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• v.53 no.11
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• pp.3465-3473
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• 2021

For a long time, research into integrated deterministic-probabilistic safety assessment has been continuously conducted to point out and overcome the limitations of classical ET (event tree)/FT (fault tree) based PSA (probabilistic safety assessment). The current paper also attempts to assert the reason why a technical transformation from classical PSA is necessary with a re-interpretation of the categories of risk. In this study, residual risk was classified into interpolating- and extrapolating-censored categories, which represent risks that are difficult to identify through an interpolation or extrapolation of representative scenarios due to potential nonlinearity between hardware and human behaviors intertwined in time and space. The authors hypothesize that such risk can be dealt with only if the classical ETs/FTs are freely relocated, entailing large-scale computation associated with physical models. The functional elements that are favorable to find residual risk were inferred from previous studies. The authors then introduce their under-development enabling techniques, namely DICE (Dynamic Integrated Consequence Evaluation) and DeBATE (Deep learning-Based Accident Trend Estimation). This work can be considered as a preliminary initiative to find the bridging points between deterministic and probabilistic assessments on the pillars of big data technology.

• Journal of Korea Artificial Intelligence Association
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• v.1 no.1
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• pp.11-16
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• 2023

Accurate hospital case modeling and prediction are crucial for efficient healthcare. In this study, we demonstrate the implementation of regression analysis methods in machine learning systems utilizing mathematical statics and machine learning techniques. The developed machine learning model includes Bayesian linear, artificial neural network, decision tree, decision forest, and linear regression analysis models. Through the application of these algorithms, corresponding regression models were constructed and analyzed. The results suggest the potential of leveraging machine learning systems for medical research. The experiment aimed to create an Azure Machine Learning Studio tool for the speedy evaluation of multiple regression models. The tool faciliates the comparision of 5 types of regression models in a unified experiment and presents assessment results with performance metrics. Evaluation of regression machine learning models highlighted the advantages of boosted decision tree regression, and decision forest regression in hospital case prediction. These findings could lay the groundwork for the deliberate development of new directions in medical data processing and decision making. Furthermore, potential avenues for future research may include exploring methods such as clustering, classification, and anomaly detection in healthcare systems.

• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.13 no.4
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• pp.107-116
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• 2013

This algorithm suggests a method in which a minimum spanning tree can be obtained fast by reducing the number of an algorithm execution. The suggested algorithm performs a select-and-delete process. In the select process, firstly, it performs Borůvka's first stage for all the vertices of a graph. Then it re-performs Borůvka's first stage for specific vertices and reduces the population of the edges. In the delete process, it deletes the maximum weight edge if any cycle occurs between the 3 edges of the edges with the reduced population. After, among the remaining edges, applying the valency concept, it gets rid of maximum weight edges. Finally, it eliminates the maximum weight edges if a cycle happens among the vertices with a big valency. The select-and-delete algorithm was applied to 9 various graphs and was evaluated its applicability. The suggested select process is believed to be the vest way to choose the edges, since it showed that it chose less number of big edges from 6 graphs, and only from 3 graphs, comparing to the number of edges that is to be performed when using MST algorithm. When applied the delete process to Kruskal algorithm, the number of performances by Kruskal was less in 6 graphs, but 1 more in each 3 graph. Also, when using the suggested delete process, 1 graph performed only the 1st stage, 5 graphs till 2nd stage, and the remaining till 3rd stage. Finally, the select-and-delete algorithm showed its least number of performances among the MST algorithms.

• Journal of Korea Trade
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• v.24 no.7
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• pp.1-28
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• 2020

Purpose - This paper empirically investigates the predictors and main determinants of consumers' ratings of mobile applications in the Google Play Store. Using a linear and nonlinear model comparison to identify the function of users' review, in determining application rating across countries, this study estimates the direct effects of users' reviews on the application rating. In addition, extending our modelling into a sentimental analysis, this paper also aims to explore the effects of review polarity and subjectivity on the application rating, followed by an examination of the moderating effect of user reviews on the polarity-rating and subjectivity-rating relationships. Design/methodology - Our empirical model considers nonlinear association as well as linear causality between features and targets. This study employs competing theoretical frameworks - multiple regression, decision-tree and neural network models - to identify the predictors and main determinants of app ratings, using data from the Google Play Store. Using a cross-validation method, our analysis investigates the direct and moderating effects of predictors and main determinants of application ratings in a global app market. Findings - The main findings of this study can be summarized as follows: the number of user's review is positively associated with the ratings of a given app and it positively moderates the polarity-rating relationship. Applying the review polarity measured by a sentimental analysis to the modelling, it was found that the polarity is not significantly associated with the rating. This result best applies to the function of both positive and negative reviews in playing a word-of-mouth role, as well as serving as a channel for communication, leading to product innovation. Originality/value - Applying a proxy measured by binomial figures, previous studies have predominantly focused on positive and negative sentiment in examining the determinants of app ratings, assuming that they are significantly associated. Given the constraints to measurement of sentiment in current research, this paper employs sentimental analysis to measure the real integer for users' polarity and subjectivity. This paper also seeks to compare the suitability of three distinct models - linear regression, decision-tree and neural network models. Although a comparison between methodologies has long been considered important to the empirical approach, it has hitherto been underexplored in studies on the app market.