• Journal of Korean Library and Information Science Society
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• v.41 no.4
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• pp.151-170
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• 2010

There is a growing interest in user-centered classification or reader-interest classification, as questions have arisen from the meanings and the effects of traditional library classification. American public libraries have used fiction genre classification called bookstore model as an alternative to the traditional classification schemes. As a result, accessibility to the collection was promoted and library service for their users was improved. This study intends to make a comprehensive inquiry about the philosophical background and functional features of genre classification. To the end, literature survey and interviews or e-mails with librarians in American public libraries were conducted.

• Journal of the Korea Society of Computer and Information
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• v.26 no.12
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• pp.133-142
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• 2021

Travel pattern recognition is widely used in many aspects such as user trajectory query, user behavior prediction, interest recommendation based on user location, user privacy protection and municipal transportation planning. Because the current recognition accuracy cannot meet the application requirements, the study of travel pattern recognition is the focus of trajectory data research. With the popularization of GPS navigation technology and intelligent mobile devices, a large amount of user mobile data information can be obtained from it, and many meaningful researches can be carried out based on this information. In the current travel pattern research method, the feature extraction of trajectory is limited to the basic attributes of trajectory (speed, angle, acceleration, etc.). In this paper, permutation entropy was used as an eigenvalue of trajectory to participate in the research of trajectory classification, and also used as an attribute to measure the complexity of time series. Velocity permutation entropy and angle permutation entropy were used as characteristics of trajectory to participate in the classification of travel patterns, and the accuracy of attribute classification based on permutation entropy used in this paper reached 81.47%.

• Journal of the Korean Society for Library and Information Science
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• v.52 no.4
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• pp.51-69
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• 2018

The purpose of this study is to analyze the potential use of the theme collection as a new classification method that reflects the interest of users in terms of classification and categorization. For this purpose, the background of the theme collection was identified based on the discussion of the library resource organization and the introduction of the curation service of bookstore. In addition, based on case analysis, which is building the theme collection, concrete concepts and characteristics of theme collection are derived. Based on the above discussion, the classification and categorization characteristics of public library themes collections were analyzed, and the characteristics and functions as a classification were compared with other categories relatively. Finally, the utility and applicability of the theme collection is presented and it is based on the discussions about the user-centered classification system design of the library in the future.

• Journal of Information Science Theory and Practice
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• v.5 no.3
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• pp.15-30
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• 2017

In some situations, you need information in order to solve a problem that has occurred. In information science, user needs are often described through very specific examples rather than through a classification of situation types in which information needs occur. Furthermore, information science often describes general human needs, typically with a reference to Maslow's classification of needs (1954), instead of actual information needs. Lexicography has also focused on information needs, but has developed a more abstract classification of types of information needs, though (until more recent research into lexicographical functions) with a particular interest in linguistic uncertainties and the lack of knowledge and skills in relation to one or several languages. In this article, we suggest a classification of information needs in which a tripartition has been made according to the different types of situations: communicative needs, cognitive needs, and operative needs. This is a classification that is relevant and useful in general in our modern information society and therefore also relevant for information science, including lexicography.

• Journal of the Korean BIBLIA Society for library and Information Science
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• v.23 no.4
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• pp.5-24
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• 2012

The aim of this study is 1) to analyse the historical development and current condition of the subject based classification(SBC), 2) to clarify the function and to categorize the specific kind of SBC, for its conceptualization and the development plan. For this purpose, almost 30 cases, for the period 1937 through now, were analyzed concerning their terms used in the names and the specific kinds as SBC. In addition, the analysis was made regarding how the SBC fulfill the selected main functions as a classificatory scheme and how SBC is inter-related with the other knowledge organization systems(KOS) such as classification and subject heading. Based on the above analysis, the conclusion addressed that SBC could be defined in consideration of the detailed function, type, information environment, and interconnection among the KOS, and suggested the future development plan of SBC as a classification scheme.

• Journal of KIISE:Computing Practices and Letters
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• v.16 no.1
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• pp.85-89
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• 2010

Most images are composed as union of the various objects which can describe meaning respectively. Unlike human perception, The general computer systems used for image processing analyze images based on low level features like color, texture and shape. The semantic gap between low level image features and the richness of user semantic knowledges can bring about unsatisfactory classification results from user expectation. In order to deal with this problem, we propose a semantic cue based image classification method using salient points from object of interest. Salient points are used to extract low level features from images and to link high level semantic concepts, and they represent distinct semantic information. The proposed algorithm can reduce semantic gap using salient points modeling which are used for image classification like human perception. and also it can improve classification accuracy of natural images according to their semantic concept relative to certain object information by using salient points. The experimental result shows both a high efficiency of the proposed methods and a good performance.

• International Journal of Computer Science & Network Security
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• v.22 no.10
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• pp.246-256
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• 2022

Recently, parents and teachers consider physical education as a minor subject for students in elementary and secondary schools. Physical education performance has become increasingly significant as parents and schools pay more attention to physical schooling. The sports mining with distribution analysis model considers different factors, including the games, comments, conversations, and connection made on numerous sports interests. Using different machine learning/deep learning approach, children's athletic and academic interests can be tracked over the course of their academic lives. There have been a number of studies that have focused on predicting the success of students in higher education. Sports interest prediction research at the secondary level is uncommon, but the secondary level is often used as a benchmark to describe students' educational development at higher levels. An Automated Student Interest Prediction on Sports Mining using DL Based Bi-directional Long Short-Term Memory model (BiLSTM) is presented in this article. Pre-processing of data, interest classification, and parameter tweaking are all the essential operations of the proposed model. Initially, data augmentation is used to expand the dataset's size. Secondly, a BiLSTM model is used to predict and classify user interests. Adagrad optimizer is employed for hyperparameter optimization. In order to test the model's performance, a dataset is used and the results are analysed using precision, recall, accuracy and F-measure. The proposed model achieved 95% accuracy on 400^th instances, where the existing techniques achieved 93.20% accuracy for the same. The proposed model achieved 95% of accuracy and precision for 60%-40% data, where the existing models achieved 93% for accuracy and precision.

• Journal of the Ergonomics Society of Korea
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• v.31 no.4
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• pp.601-607
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• 2012

Objective: The aim of this study is to classify between intentional and natural blinks in vision based eye tracking system. Through implementing the classification method, we expect that the great eye tracking method will be designed which will perform well both navigation and selection interactions. Background: Currently, eye tracking is widely used in order to increase immersion and interest of user by supporting natural user interface. Even though conventional eye tracking system is well focused on navigation interaction by tracking pupil movement, there is no breakthrough selection interaction method. Method: To determine classification threshold between intentional and natural blinks, we performed experiment by capturing eye images including intentional and natural blinks from 12 subjects. By analyzing successive eye images, two features such as eye closed duration and pupil size variation after eye open were collected. Then, the classification threshold was determined by performing SVM(Support Vector Machine) training. Results: Experimental results showed that the average detection accuracy of intentional blinks was 97.4% in wearable eye tracking system environments. Also, the detecting accuracy in non-wearable camera environment was 92.9% on the basis of the above used SVM classifier. Conclusion: By combining two features using SVM, we could implement the accurate selection interaction method in vision based eye tracking system. Application: The results of this research might help to improve efficiency and usability of vision based eye tracking method by supporting reliable selection interaction scheme.

• Journal of the Korean Society for Library and Information Science
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• v.52 no.3
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• pp.5-33
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• 2018

In this paper, we analyze a user's usage behavior, identify and track search intention and interest field based on the National Science and Technology Standard Classification, and use it to personalize science and technology information. In other words, we sought to satisfy both efficiency and satisfaction in searching for information that users want by improving scientific information search performance. We developed the personalization service of science and technology information and evaluated the suitability and usefulness of personalized information by comparing the search performance between expert experimental group and control group. As a result, the personalization service proposed in this study showed better search performance than comparative service and proved to provide higher usability.

• Journal of the Korean Society for Precision Engineering
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• v.21 no.12
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• pp.100-108
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• 2004

In reverse engineering, data acquisition methodology can generally be categorized into contacting and non-contacting types. Recently, researches on hybrid or sensor fusion of the two types have been increasing. In addition, efficient construction of a geometric model from the measurement data is required, where considerable amount of user interaction to classify and localize regions of interest is inevitable. Our research focuses on the classification of each bounded region into a pre-defined feature shape fer a hybrid measuring scheme, where the overall procedures are described as fellows. Firstly, the physical model is digitized by a non-contacting laser scanner which rapidly provides cloud-of-points data. Secondly, the overall digitized data are approximated to a z-map model. Each bounding curve of a region of interest (featured area) can be 1.aced out based on our previous research. Then each confined area is systematically classified into one of the pre-defined feature types such as floor, wall, strip or volume, followed by a more accurate measuring step via a contacting probe. Assigned to each feature is a specific digitizing path topology which may reflect its own geometric character. The research can play an important role in minimizing user interaction at the stage of digitizing path planning.