• Journal of Industrial Convergence
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• v.22 no.1
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• pp.149-162
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• 2024

This study is a longitudinal study of female older adults living alone, one of the most vulnerable groups in our society, to determine how their depression changes over time and what factors affect their depression. At the same time, considering that there is a large difference in age among the same older adults, this study divided the female older adults into the young-old and the old-old to see how the predictors of depression in each group differ from each other. The main findings are as follows First, depression among female older adults living alone appears to have a declining pattern over time. In the conditional model, factors affecting the initial level of the depression trajectory among women living alone were found to be associated with lower initial depression values among those living in metropolitan areas rather than non-metropolitan areas, better subjective health, and those who did not exercise. Next, we examined the factors affecting rate of change (slope) in depression among female living alone older adults and found that the higher the age, the larger the metropolitan area, the better the subjective health, the less socializing, and the more socializing, the greater the decrease in depression level. Finally, there were some differences in the pathways affecting the initial value and slope of depression among female older adults living alone between the early and late older adults. Specifically, the higher the initial level of participation in social activities, the greater the change in depression among the late older adults, while there was no significant relationship among the early older adults. In the early older adults, better initial subjective health was associated with a larger change in depression than in the late older adults. Only in the late older adults did those who regularly exercised in the early years have higher initial depression values than those who did not. Based on the results of the above analyses, suggestions were made to reduce depression among female older adults living alone.

• Journal of Korea Water Resources Association
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• v.57 no.6
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• pp.421-435
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• 2024

In April 2023, a wildfire broke out in Gangneung located in the east coast region due to the influence of the Yanggang-local wind. In this study, GIS-based RUSLE(Revised Universal Soil Loss Equation) and SEMMA (Soil Erosion Model for Mountain Areas) were used to evaluate the erosion rate due to vegetation recovery in a small watershed of the Gangneung WUI(Wildland-Urban Interface) fire. The small watershed of WUI fire has a low altitude range of 10-30 m and the average slope of 10.0±7.4° which corresponds to a gentle slope. The soil texture was loamy sand with a high organic content and the deep soil depth. As herbaceous layer regenerated profusely in the gully after the wildfire, the NDVI (Normalized Difference Vegetation Index) reached a maximum of 0.55. Simulation results of erosion rates showed that RUSLE ranged from 0.07-94.9 t/ha/storm and SEMMA ranged from 0.24-83.6 t/ha/storm. RUSLE overestimated the average erosion rate by 1.19-1.48 times compared to SEMMA. The erosion rates were estimated to be high in the middle slope where burned pine trees were widely distributed and the slope was steep and to be relatively low in the hollow below the gully where herbaceous layer recovers rapidly. SEMMA showed a rapid increase in erosion sensitivity under at certain vegetation covers with NDVI below 0.25 (Ic = 0.35) on post-fire hillslopes. Gentle slopes with high organic content and rapid recovery of natural vegetation had relatively low erosion rate compared to steep slopes. As subsequent infrastructure and human damages due to sediment disaster by heavy rain is anticipated in WUI fire areas, the research results may be used as basic data for targeted management and decision making on the implementation of emergency treatment after the wildfire.

• Atmosphere
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• v.24 no.3
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• pp.303-315
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• 2014

Terrestrial ecosystem plays the important role as carbon sink in the global carbon cycle. Understanding of interactions of terrestrial carbon cycle with climate is important for better prediction of future climate change. In this paper, terrestrial carbon cycle is investigated by Hadley Centre Global Environmental Model, version 2, Carbon Cycle (HadGEM2-CC) that considers vegetation dynamics and an interactive carbon cycle with climate. The simulation for future projection is based on the three (8.5/4.5/2.6) representative concentration pathways (RCPs) from 2006 to 2100 and compared with historical land carbon uptake from 1979 to 2005. Projected changes in ecological features such as production, respiration, net ecosystem exchange and climate condition show similar pattern in three RCPs, while the response amplitude in each RCPs are different. For all RCP scenarios, temperature and precipitation increase with rising of the atmospheric $CO_2$. Such climate conditions are favorable for vegetation growth and extension, causing future increase of terrestrial carbon uptakes in all RCPs. At the end of 21st century, the global average of gross and net primary productions and respiration increase in all RCPs and terrestrial ecosystem remains as carbon sink. This enhancement of land $CO_2$ uptake is attributed by the vegetated area expansion, increasing LAI, and early onset of growing season. After mid-21st century, temperature rising leads to excessive increase of soil respiration than net primary production and thus the terrestrial carbon uptake begins to fall since that time. Regionally the NEE average value of East-Asia ($90^{\circ}E-140^{\circ}E$, $20^{\circ}N{\sim}60^{\circ}N$) area is bigger than that of the same latitude band. In the end-$21^{st}$ the NEE mean values in East-Asia area are $-2.09PgC\;yr^{-1}$, $-1.12PgC\;yr^{-1}$, $-0.47PgC\;yr^{-1}$ and zonal mean NEEs of the same latitude region are $-1.12PgC\;yr^{-1}$, $-0.55PgC\;yr^{-1}$, $-0.17PgC\;yr^{-1}$ for RCP 8.5, 4.5, 2.6.

• Journal of Intelligence and Information Systems
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• v.24 no.2
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• pp.85-109
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• 2018

Recommender system recommends the items expected to be purchased by a customer in the future according to his or her previous purchase behaviors. It has been served as a tool for realizing one-to-one personalization for an e-commerce service company. Traditional recommender systems, especially the recommender systems based on collaborative filtering (CF), which is the most popular recommendation algorithm in both academy and industry, are designed to generate the items list for recommendation by using 'overall rating' - a single criterion. However, it has critical limitations in understanding the customers' preferences in detail. Recently, to mitigate these limitations, some leading e-commerce companies have begun to get feedback from their customers in a form of 'multicritera ratings'. Multicriteria ratings enable the companies to understand their customers' preferences from the multidimensional viewpoints. Moreover, it is easy to handle and analyze the multidimensional ratings because they are quantitative. But, the recommendation using multicritera ratings also has limitation that it may omit detail information on a user's preference because it only considers three-to-five predetermined criteria in most cases. Under this background, this study proposes a novel hybrid recommendation system, which selectively uses the results from 'traditional CF' and 'CF using multicriteria ratings'. Our proposed system is based on the premise that some people have holistic preference scheme, whereas others have composite preference scheme. Thus, our system is designed to use traditional CF using overall rating for the users with holistic preference, and to use CF using multicriteria ratings for the users with composite preference. To validate the usefulness of the proposed system, we applied it to a real-world dataset regarding the recommendation for POI (point-of-interests). Providing personalized POI recommendation is getting more attentions as the popularity of the location-based services such as Yelp and Foursquare increases. The dataset was collected from university students via a Web-based online survey system. Using the survey system, we collected the overall ratings as well as the ratings for each criterion for 48 POIs that are located near K university in Seoul, South Korea. The criteria include 'food or taste', 'price' and 'service or mood'. As a result, we obtain 2,878 valid ratings from 112 users. Among 48 items, 38 items (80%) are used as training dataset, and the remaining 10 items (20%) are used as validation dataset. To examine the effectiveness of the proposed system (i.e. hybrid selective model), we compared its performance to the performances of two comparison models - the traditional CF and the CF with multicriteria ratings. The performances of recommender systems were evaluated by using two metrics - average MAE(mean absolute error) and precision-in-top-N. Precision-in-top-N represents the percentage of truly high overall ratings among those that the model predicted would be the N most relevant items for each user. The experimental system was developed using Microsoft Visual Basic for Applications (VBA). The experimental results showed that our proposed system (avg. MAE = 0.584) outperformed traditional CF (avg. MAE = 0.591) as well as multicriteria CF (avg. AVE = 0.608). We also found that multicriteria CF showed worse performance compared to traditional CF in our data set, which is contradictory to the results in the most previous studies. This result supports the premise of our study that people have two different types of preference schemes - holistic and composite. Besides MAE, the proposed system outperformed all the comparison models in precision-in-top-3, precision-in-top-5, and precision-in-top-7. The results from the paired samples t-test presented that our proposed system outperformed traditional CF with 10% statistical significance level, and multicriteria CF with 1% statistical significance level from the perspective of average MAE. The proposed system sheds light on how to understand and utilize user's preference schemes in recommender systems domain.

• Journal of Intelligence and Information Systems
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• v.20 no.1
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• pp.49-65
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• 2014

Word of Mouth (WOM) is a behavior used by consumers to transfer or communicate their product or service experience to other consumers. Due to the popularity of social media such as Facebook, Twitter, blogs, and online communities, electronic WOM (e-WOM) has become important to the success of products or services. As a result, most enterprises pay close attention to e-WOM for their products or services. This is especially important for movies, as these are experiential products. This paper aims to identify the network factors of an online movie community that impact box office revenue using social network analysis. In addition to traditional WOM factors (volume and valence of WOM), network centrality measures of the online community are included as influential factors in box office revenue. Based on previous research results, we develop five hypotheses on the relationships between potential influential factors (WOM volume, WOM valence, degree centrality, betweenness centrality, closeness centrality) and box office revenue. The first hypothesis is that the accumulated volume of WOM in online product communities is positively related to the total revenue of movies. The second hypothesis is that the accumulated valence of WOM in online product communities is positively related to the total revenue of movies. The third hypothesis is that the average of degree centralities of reviewers in online product communities is positively related to the total revenue of movies. The fourth hypothesis is that the average of betweenness centralities of reviewers in online product communities is positively related to the total revenue of movies. The fifth hypothesis is that the average of betweenness centralities of reviewers in online product communities is positively related to the total revenue of movies. To verify our research model, we collect movie review data from the Internet Movie Database (IMDb), which is a representative online movie community, and movie revenue data from the Box-Office-Mojo website. The movies in this analysis include weekly top-10 movies from September 1, 2012, to September 1, 2013, with in total. We collect movie metadata such as screening periods and user ratings; and community data in IMDb including reviewer identification, review content, review times, responder identification, reply content, reply times, and reply relationships. For the same period, the revenue data from Box-Office-Mojo is collected on a weekly basis. Movie community networks are constructed based on reply relationships between reviewers. Using a social network analysis tool, NodeXL, we calculate the averages of three centralities including degree, betweenness, and closeness centrality for each movie. Correlation analysis of focal variables and the dependent variable (final revenue) shows that three centrality measures are highly correlated, prompting us to perform multiple regressions separately with each centrality measure. Consistent with previous research results, our regression analysis results show that the volume and valence of WOM are positively related to the final box office revenue of movies. Moreover, the averages of betweenness centralities from initial community networks impact the final movie revenues. However, both of the averages of degree centralities and closeness centralities do not influence final movie performance. Based on the regression results, three hypotheses, 1, 2, and 4, are accepted, and two hypotheses, 3 and 5, are rejected. This study tries to link the network structure of e-WOM on online product communities with the product's performance. Based on the analysis of a real online movie community, the results show that online community network structures can work as a predictor of movie performance. The results show that the betweenness centralities of the reviewer community are critical for the prediction of movie performance. However, degree centralities and closeness centralities do not influence movie performance. As future research topics, similar analyses are required for other product categories such as electronic goods and online content to generalize the study results.

• Journal of Intelligence and Information Systems
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• v.25 no.4
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• pp.141-154
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• 2019

Rapid growth of internet technology and social media is progressing. Data mining technology has evolved to enable unstructured document representations in a variety of applications. Sentiment analysis is an important technology that can distinguish poor or high-quality content through text data of products, and it has proliferated during text mining. Sentiment analysis mainly analyzes people's opinions in text data by assigning predefined data categories as positive and negative. This has been studied in various directions in terms of accuracy from simple rule-based to dictionary-based approaches using predefined labels. In fact, sentiment analysis is one of the most active researches in natural language processing and is widely studied in text mining. When real online reviews aren't available for others, it's not only easy to openly collect information, but it also affects your business. In marketing, real-world information from customers is gathered on websites, not surveys. Depending on whether the website's posts are positive or negative, the customer response is reflected in the sales and tries to identify the information. However, many reviews on a website are not always good, and difficult to identify. The earlier studies in this research area used the reviews data of the Amazon.com shopping mal, but the research data used in the recent studies uses the data for stock market trends, blogs, news articles, weather forecasts, IMDB, and facebook etc. However, the lack of accuracy is recognized because sentiment calculations are changed according to the subject, paragraph, sentiment lexicon direction, and sentence strength. This study aims to classify the polarity analysis of sentiment analysis into positive and negative categories and increase the prediction accuracy of the polarity analysis using the pretrained IMDB review data set. First, the text classification algorithm related to sentiment analysis adopts the popular machine learning algorithms such as NB (naive bayes), SVM (support vector machines), XGboost, RF (random forests), and Gradient Boost as comparative models. Second, deep learning has demonstrated discriminative features that can extract complex features of data. Representative algorithms are CNN (convolution neural networks), RNN (recurrent neural networks), LSTM (long-short term memory). CNN can be used similarly to BoW when processing a sentence in vector format, but does not consider sequential data attributes. RNN can handle well in order because it takes into account the time information of the data, but there is a long-term dependency on memory. To solve the problem of long-term dependence, LSTM is used. For the comparison, CNN and LSTM were chosen as simple deep learning models. In addition to classical machine learning algorithms, CNN, LSTM, and the integrated models were analyzed. Although there are many parameters for the algorithms, we examined the relationship between numerical value and precision to find the optimal combination. And, we tried to figure out how the models work well for sentiment analysis and how these models work. This study proposes integrated CNN and LSTM algorithms to extract the positive and negative features of text analysis. The reasons for mixing these two algorithms are as follows. CNN can extract features for the classification automatically by applying convolution layer and massively parallel processing. LSTM is not capable of highly parallel processing. Like faucets, the LSTM has input, output, and forget gates that can be moved and controlled at a desired time. These gates have the advantage of placing memory blocks on hidden nodes. The memory block of the LSTM may not store all the data, but it can solve the CNN's long-term dependency problem. Furthermore, when LSTM is used in CNN's pooling layer, it has an end-to-end structure, so that spatial and temporal features can be designed simultaneously. In combination with CNN-LSTM, 90.33% accuracy was measured. This is slower than CNN, but faster than LSTM. The presented model was more accurate than other models. In addition, each word embedding layer can be improved when training the kernel step by step. CNN-LSTM can improve the weakness of each model, and there is an advantage of improving the learning by layer using the end-to-end structure of LSTM. Based on these reasons, this study tries to enhance the classification accuracy of movie reviews using the integrated CNN-LSTM model.

• Korean Journal of Soil Science and Fertilizer
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• v.40 no.5
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• pp.383-391
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• 2007

This study was conducted for an assessment through the estimation of soil loss by each catchment classified by soil catena. Ten catchments, which are Geumgang21, Namgang03, Dongjincheon, Gapyongcheon01, Gyongancheon02, Geumgang16, Byongsungcheon01, Daesincheon, Bukcheon02, Youngsangang08, were selected from the hydrologic unit map and the detailed soil digital map (1:25,000) for this study. The catchments like Geumgang21, Namgang03, Dongjincheon, Gapyongcheon01 and Gyongancheon02 were mainly composed with soils originated from gneiss. The catchments like Geumgang16, Byongsungcheon01, Daesincheon, Bukcheon02 and Youngsangang08 were mainly composed with soils originated from granites. The grades, which are divided into seven grades with A(very tolerable), B(tolerable), C(moderate), D(low), E(high), F(severe), G(very severe), of soil erosion estimated by USLE in catchments were distributed in most A and B because of paddy land and forestry. In detailed, the soil erosion grade of catchments mainly distributing soils originated from gneiss showed more the distribution of B and C than it of catchments mainly distributing soils originated from granites. The reason of results would be derived from topographic characteristics of soils originated from gneiss located at mountainous. The soil loss according to soil catena linked with Songsan and Jigok series, which are soils originated from gneiss was calculated with $7.66ton\;ha^{-1}\;yr^{-1}$. The soil loss of Geumgang16, Byongsungcheon01, Daesincheon, Bukcheon02 which have the soil catena linked with Samgak and Sangju soil series originated from granite, was calculated with $5.55ton\;ha^{-1}\;yr^{-1}$. The soil loss of Youngsangang08 which have the soil catena linked with Songjung and Baeksan soil series originated from granite was calculated with $9.6ton\;ha^{-1}\;yr^{-1}$, but the conclusion on soil loss in this kind of soil catena would be drawn from the analysis of more catchments. In conclusion, the results of this study inform that the classification of soil catena by catchments and estimation of soil loss according to soil catena would be effective for analysis on the grade of non-point pollution by soil erosion in a catchment.