• Journal of Intelligence and Information Systems
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• v.19 no.4
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• pp.81-96
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• 2013

Video information retrieval has become a very important issue because of the explosive increase in video data from Web content development. Meanwhile, content-based video analysis using visual features has been the main source for video information retrieval and browsing. Content in video can be represented with content-based analysis techniques, which can extract various features from audio-visual data such as frames, shots, colors, texture, or shape. Moreover, similarity between videos can be measured through content-based analysis. However, a movie that is one of typical types of video data is organized by story as well as audio-visual data. This causes a semantic gap between significant information recognized by people and information resulting from content-based analysis, when content-based video analysis using only audio-visual data of low level is applied to information retrieval of movie. The reason for this semantic gap is that the story line for a movie is high level information, with relationships in the content that changes as the movie progresses. Information retrieval related to the story line of a movie cannot be executed by only content-based analysis techniques. A formal model is needed, which can determine relationships among movie contents, or track meaning changes, in order to accurately retrieve the story information. Recently, story-based video analysis techniques have emerged using a social network concept for story information retrieval. These approaches represent a story by using the relationships between characters in a movie, but these approaches have problems. First, they do not express dynamic changes in relationships between characters according to story development. Second, they miss profound information, such as emotions indicating the identities and psychological states of the characters. Emotion is essential to understanding a character's motivation, conflict, and resolution. Third, they do not take account of events and background that contribute to the story. As a result, this paper reviews the importance and weaknesses of previous video analysis methods ranging from content-based approaches to story analysis based on social network. Also, we suggest necessary elements, such as character, background, and events, based on narrative structures introduced in the literature. We extract characters' emotional words from the script of the movie Pretty Woman by using the hierarchical attribute of WordNet, which is an extensive English thesaurus. WordNet offers relationships between words (e.g., synonyms, hypernyms, hyponyms, antonyms). We present a method to visualize the emotional pattern of a character over time. Second, a character's inner nature must be predetermined in order to model a character arc that can depict the character's growth and development. To this end, we analyze the amount of the character's dialogue in the script and track the character's inner nature using social network concepts, such as in-degree (incoming links) and out-degree (outgoing links). Additionally, we propose a method that can track a character's inner nature by tracing indices such as degree, in-degree, and out-degree of the character network in a movie through its progression. Finally, the spatial background where characters meet and where events take place is an important element in the story. We take advantage of the movie script to extracting significant spatial background and suggest a scene map describing spatial arrangements and distances in the movie. Important places where main characters first meet or where they stay during long periods of time can be extracted through this scene map. In view of the aforementioned three elements (character, event, background), we extract a variety of information related to the story and evaluate the performance of the proposed method. We can track story information extracted over time and detect a change in the character's emotion or inner nature, spatial movement, and conflicts and resolutions in the story.

• Journal of Intelligence and Information Systems
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• v.28 no.1
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• pp.263-286
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• 2022

This study acquires NBA statistical information for a total of 32 years from 1990 to 2022 using web crawling, observes variables of interest through exploratory data analysis, and generates related derived variables. Unused variables were removed through a purification process on the input data, and correlation analysis, t-test, and ANOVA were performed on the remaining variables. For the variable of interest, the difference in the mean between the groups that advanced to the playoffs and did not advance to the playoffs was tested, and then to compensate for this, the average difference between the three groups (higher/middle/lower) based on ranking was reconfirmed. Of the input data, only this year's season data was used as a test set, and 5-fold cross-validation was performed by dividing the training set and the validation set for model training. The overfitting problem was solved by comparing the cross-validation result and the final analysis result using the test set to confirm that there was no difference in the performance matrix. Because the quality level of the raw data is high and the statistical assumptions are satisfied, most of the models showed good results despite the small data set. This study not only predicts NBA game results or classifies whether or not to advance to the playoffs using machine learning, but also examines whether the variables of interest are included in the major variables with high importance by understanding the importance of input attribute. Through the visualization of SHAP value, it was possible to overcome the limitation that could not be interpreted only with the result of feature importance, and to compensate for the lack of consistency in the importance calculation in the process of entering/removing variables. It was found that a number of variables related to three points and errors classified as subjects of interest in this study were included in the major variables affecting advancing to the playoffs in the NBA. Although this study is similar in that it includes topics such as match results, playoffs, and championship predictions, which have been dealt with in the existing sports data analysis field, and comparatively analyzed several machine learning models for analysis, there is a difference in that the interest features are set in advance and statistically verified, so that it is compared with the machine learning analysis result. Also, it was differentiated from existing studies by presenting explanatory visualization results using SHAP, one of the XAI models.

• Journal of Intelligence and Information Systems
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• v.18 no.4
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• pp.1-17
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• 2012

Personalized smart devices such as smartphones and smart pads are widely used. Unlike traditional feature phones, theses smart devices allow users to choose a variety of functions, which support not only daily experiences but also business operations. Actually, there exist a huge number of applications accessible by smart device users in online and mobile application markets. Users can choose apps that fit their own tastes and needs, which is impossible for conventional phone users. With the increase in app demand, the tastes and needs of app users are becoming more diverse. To meet these requirements, numerous apps with diverse functions are being released on the market, which leads to fierce competition. Unlike offline markets, online markets have a limitation in that purchasing decisions should be made without experiencing the items. Therefore, online customers rely more on item-related information that can be seen on the item page in which online markets commonly provide details about each item. Customers can feel confident about the quality of an item through the online information and decide whether to purchase it. The same is true of online app markets. To win the sales competition against other apps that perform similar functions, app developers need to focus on writing app descriptions to attract the attention of customers. If we can measure the effect of app descriptions on sales without regard to the app's price and quality, app descriptions that facilitate the sale of apps can be identified. This study intends to provide such a quantitative result for app developers who want to promote the sales of their apps. For this purpose, we collected app details including the descriptions written in Korean from one of the largest app markets in Korea, and then extracted keywords from the descriptions. Next, the impact of the keywords on sales performance was measured through our econometric model. Through this analysis, we were able to analyze the impact of each keyword itself, apart from that of the design or quality. The keywords, comprised of the attribute and evaluation of each app, are extracted by a morpheme analyzer. Our model with the keywords as its input variables was established to analyze their impact on sales performance. A regression analysis was conducted for each category in which apps are included. This analysis was required because we found the keywords, which are emphasized in app descriptions, different category-by-category. The analysis conducted not only for free apps but also for paid apps showed which keywords have more impact on sales performance for each type of app. In the analysis of paid apps in the education category, keywords such as 'search+easy' and 'words+abundant' showed higher effectiveness. In the same category, free apps whose keywords emphasize the quality of apps showed higher sales performance. One interesting fact is that keywords describing not only the app but also the need for the app have asignificant impact. Language learning apps, regardless of whether they are sold free or paid, showed higher sales performance by including the keywords 'foreign language study+important'. This result shows that motivation for the purchase affected sales. While item reviews are widely researched in online markets, item descriptions are not very actively studied. In the case of the mobile app markets, newly introduced apps may not have many item reviews because of the low quantity sold. In such cases, item descriptions can be regarded more important when customers make a decision about purchasing items. This study is the first trial to quantitatively analyze the relationship between an item description and its impact on sales performance. The results show that our research framework successfully provides a list of the most effective sales key terms with the estimates of their effectiveness. Although this study is performed for a specified type of item (i.e., mobile apps), our model can be applied to almost all of the items traded in online markets.

• Journal of the Korean Society of Food Science and Nutrition
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• v.45 no.6
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• pp.894-902
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• 2016

The purpose of this study was to analyze the attributes considered as important by parents in the selection of laver for infants through conjoint analysis techniques. A total of 917 questionnaires were distributed in January 2016, of which 211 were completed (23.0%). Statistical data analyses were performed using SPSS/Win 21.0 for descriptive statistics and conjoint analysis. The conjoint design was applied to evaluate the hypothetical laver for infants. According to the analysis of attributes and levels of laver for infants, the relative importance of each attribute was follows: seasoning (26.55%), flavor (19.33%), texture (18.75%), oil (15.15%), size (10.61%), and certification (9.61%). The results of the conjoint analysis indicate that parents raising infants preferred laver with the characteristics of non-seasoning, general flavor, softness, half-size, organic certification, and perilla oil. The most preferred laver for infants gained a 53.7% potential market share from choice simulation compared with laver being sold. Using utility and relative importance, the laver market for infants was classified into two segments. As a result of market segmentation, parents of cluster 1 preferred the laver model being sold (soy seasoning) while parents of cluster 2 preferred the optimized laver model (non-seasoning).

• KOREAN JOURNAL OF CROP SCIENCE
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• v.63 no.2
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• pp.98-105
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• 2018

The sale of brown rice batches composed of rice produced in different years is prohibited in Korea. Thus, new methods for the identification of the year of production are critical for maintaining the distribution of high quality brown rice. Here, we describe the exploitation of an enzyme that can be used to discriminate between freshly harvested and one-year-old brown rice. The degree of enzyme activity was visualized through freshness test with Guaiacol, Oxydol, and p-phenylenediamine reagents. With electronic eye equipment, we selected 29 color codes for identifying new brown rice and old brown rice. The discrimination power of selected color codes showed a minimum of 0.263 to a maximum of 0.922 and an average value of 0.62. The accuracy with which new brown rice and old brown rice could be identified was 100% in principal component analysis (PCA) and discriminant function analysis (DFA). The DFA analysis had greater discriminatory power than did the PCA analysis. A verification test using new brown rice, old brown rice, or a mixture of the two was then performed to validate our method. The accuracy of identification of new and old brown rice was 100% in both cases, whereas mixed brown rice samples were correctly classified at a rate of 96.9%. Additionally, in order to test whether the discriminant constructed in winter can be applied to samples collected in summer, new and old brown rice stored for 8 months were collected and tested. Both new and old brown rice collected in summer were classified as old brown rice and showed 50% identification accuracy. We were able to attribute these observations to changes in enzyme content over time, and therefore we conclude, it will be necessary to develop discriminants that are specific to distinct storage periods in the near future.

• Korean Chemical Engineering Research
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• v.52 no.5
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• pp.667-671
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• 2014

In this study, we suggest a facile method to control conditions of single component independently when preparing consisting two-component metal oxides nanofiber by simply dispersing nanoparticles in precursor solution. The well dispersed $SiO_2$ nanoparticles in $TiO_2$ nanofibers were successfully synthesized through a simple electrospinning process. The as-synthesized nanodfibers were investigated via FE-SEM, XRD and EDS for structural studies, furthermore, the analysis of UV-VIS and photocatalytic activity were carried out for demonstrate the effect of $SiO_2$ nanoparticles dispersed in $TiO_2$ nanofibers. As a result, $TiO_2$ nanofibres dispersed with $SiO_2$ nanoparticles have enhanced photocatalytic activity than that of $TiO_2$ nanofibres only. In this strategy, the introduction of $SiO_2$ nanoparticles in $TiO_2$ nanofibers were attribute to enlarge absorption in the visible region (380~440 nm). Additionally, $Br{\o}nsted$ acid sites generated in each metal oxide of Ti and Si increase OH radicals efficiently as well as it limit recombination loss by holding photogenerated electrons for high efficient photocatalytic activity.

• Journal of Korean Society for Geospatial Information Science
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• v.22 no.4
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• pp.175-181
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• 2014

Korean government proposed a new initiative 'government 3.0' with which the administration will open its dataset to the public before requests. City of Seoul is the front runner in disclosure of government data. If we know what kind of attributes are governing factors for any given segmentation, these outcomes can be applied to real world problems of marketing and business strategy, and administrative decision makings. However, with respect to city of Seoul, selection of optimal variables from the open dataset up to several thousands of attributes would require a humongous amount of computation time because it might require a combinatorial optimization while maximizing dissimilarity measures between clusters. In this study, we acquired 718 attribute dataset from Statistics Korea and conducted an analysis to select the most suitable variables, which differentiate Gangnam from other districts, using the Genetic algorithm and Dunn's index. Also, we utilized the Microsoft Azure cloud computing system to speed up the process time. As the result, the optimal 28 variables were finally selected, and the validation result showed that those 28 variables effectively group the Gangnam from other districts using the Ward's minimum variance and K-means algorithm.

• Journal of Korea Technology Innovation Society
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• v.16 no.3
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• pp.650-671
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• 2013

Products elicit the consumer's purchasing behavior by satisfying their needs and are cognized as the combination of various needs attributes. Also R&D is referred as a series of technical development activities to meet the consumer's needs attributes. In particular, in the market-oriented R&D era, it could obtain the legitimacy by developing the R&D based on the needs attributes. In this study, we aimed to investigate the priority setting in R&D field, considering consumer's needs attributes. To be concrete, we tried to present the evolutional direction of desirable phased R&D according to 'the importance degree for consumers on the attributes (functions) of the certain products' and 'the urgency degree of technical quality to fulfill its needs'. To achieve this, we targeted SMART TV, the convergence product, which contains the uncertainty in terms of marketability and technological aspect, and analyzed the priority of the R&D in SMART TV field. Based on the result of the analysis, 4-steps product concept (ultra high definition TV, interactive TV, 3D/immersive TV, personalized TV) is derived by analyzing the evolutional direction of R&D in SMART TV field. This finding implies that the success possibilities of product could be enhanced during the process of the evolution of products that have multiple needs attributes, by pursuing the R&D which fulfills the needs attribute first required in the market. In addition, it provides a useful framework to design the R&D roadmap in an aspect of R&D strategy.

• Journal of the Korean Association of Geographic Information Studies
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• v.9 no.4
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• pp.1-11
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• 2006

This paper aims to develop a GIS(Geographic Information System) application model as a decision-making support system in order to evaluate the potential of forests according to their functions, or to classify forest functions. The forest functions analyzed in this study are as follows: production of timber, stable supply of water resources, forest hazards prevention, recreation in forests, conservation of living conditions and natural environment. Using a model possible to evaluate the potential of each forest function and to assort forest functions by making priority-based decisions according to the functions, as well as allowing for various possible analysis environments, its application has been reviewed. Factors for assessing the forest functions could be built by using the following three categories: four maps-topographical map, vegetation map, forest site map and basic forest land use map-whose quantitative drawings had already been made; other self-established maps, such as one indicating the location of sawmills, location map of expressway interchanges, and spatial data of national population distribution map; and attribute data of population and precipitation. The GIS application developed here contributes to the evaluation of forest functions in all the subject areas by map units and national forest management districts based upon the assessment system.

• Culinary science and hospitality research
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• v.19 no.1
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• pp.189-203
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• 2013

The purpose of this study is to examine medicinal food consumption patterns focusing on the consumers living in Gyeongju area, and it attempts to provide database about developing medicinal food products as tourist attractions grounded on the results. For this study, the data was analyzed using SPSS WIN 20.0 for an empirical analysis. Moreover, the survey questions were sent out to 300 people, and total 256 copies of questionnaire were returned for the sample data. For the result that "It will have a significant impact on the selecting attributes of medicinal food according to gender," there was a meaningful difference between gender on the average cost. For the result that "It will have a significant impact on the selecting attributes of medicinal food according to marital status," there was a significant difference between married and single for comparing tastes. For the result that "It will have a significant impact on the selecting attributes of medicinal food according to age," there was a meaningful difference on the degree of awareness, comparing tastes, and comparing health. For the result that "It will have a significant impact on the selecting attributes of medicinal food according to occupation and education," there was a meaningful difference on the degree of awareness. Based on the results of this study, medicinal restaurants should offer various menu items and services to prepare methods to remind consumers of their professionalism in order to enhance competitive power of medicinal food.