• International Journal of Computer Science & Network Security
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• v.24 no.2
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• pp.79-88
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• 2024

The use of the social media has become part of our daily life activities. The social web channels provide the content generation facility to its users who can share their views, opinions and experiences towards certain topics. The researchers are using the social media content for various research areas. Sentiment analysis, one of the most active research areas in last decade, is the process to extract reviews, opinions and sentiments of people. Sentiment analysis is applied in diverse sub-areas such as subjectivity analysis, polarity detection, and emotion detection. Stance classification has emerged as a new and interesting research area as it aims to determine whether the content writer is in favor, against or neutral towards the target topic or issue. Stance classification is significant as it has many research applications like rumor stance classifications, stance classification towards public forums, claim stance classification, neural attention stance classification, online debate stance classification, dialogic properties stance classification etc. This research study explores different feature sets such as lexical, sentiment-specific, dialog-based which have been extracted using the standard datasets in the relevant area. Supervised learning approaches of generative algorithms such as Naïve Bayes and discriminative machine learning algorithms such as Support Vector Machine, Naïve Bayes, Decision Tree and k-Nearest Neighbor have been applied and then ensemble-based algorithms like Random Forest and AdaBoost have been applied. The empirical based results have been evaluated using the standard performance measures of Accuracy, Precision, Recall, and F-measures.

• Journal of Preventive Medicine and Public Health
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• v.29 no.4 s.55
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• pp.877-897
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• 1996

In recent years, the Korean Medical Association has undertaken the feat of establishing the Korean Standard Terminology of Medical Procedures with the dedicated help of 32 medical academic societies. However, because the project is being conducted by several different circles, it has yet to see a clear system of classification. This thesis, therefore, proposes the three principles of scientific properties, usefulness and ideology as the basis for classification system and has developed the Classification System of Medical Procedures in Korea upon their foundation. The methodology and organization of this thesis as follows. First, by adopting scientific classification system of Feinstein(1988), an analysis of the classification systems of the medical procedures in the United States, Japan, Taiwan, WHO was carried out to reveal the framework and the basic principles in each system. Second, the direction of classification system has been constructed by applying the normative principle of medical field in order to show the future direction of the medical field and realize its ideology. Third, a finalized framework for the classification system will be presented as based on the direction of classification system. Of the three basis principles mentioned above, the analysis on the principles of usefulness was left out of this thesis due to the difficulty of establishing specific standards of analysis. The results of the study are as follows. The overall structure of the thesis is aimed at showing the 'Prevention-Therapy-Rehabilitation' quality of comprehensive health care and consists of six chapters; I. Prevention and Health Promotion II. Evaluation and Management III. Diagnostic Procedures IV. Endoscopy V. Therapeutic Procedures VI. Rehabilitation Chapter three Diagnostic Procedures is divided into four parts : Functional Diagnosis, Visual Diagnosis, Pathological Diagnosis, Biopsy and Sampling. Chapter five Therapeutic Procedures is divided into Psychiatry, Non-Invasive Therapy, Invasive Therapy, Anaesthesia and Radiation Oncology. Of these sub-divisions, Functional Diagnosis, Biopsy and Sampling, Endoscopy and Invasive Therapy employs the anatomical system of classification. On the other hand, Visual Diagnosis, Pathological Diagnosis, Anesthesia and Diagnostic Radiology, namely those divisions in which there is little or no overlapping in services with other divisions, used the classification system of its own division. The classification system introduced in this thesis can be further supplemented through the use of the cluster analysis by incorporating the advice and assistance of other specialists.

• KSTLE International Journal
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• v.1 no.1
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• pp.34-42
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• 2000

A method and results of classification of four different metallic wear debris were presented by using their color features. The color image of wear debris was used far the initial data, and the color properties of the debris were specified by HSI color model. Particles were characterized by a set of statistical features derived from the distribution of HSI color model components. The initial feature set was optimized by a principal component analysis, and multidimensional scaling procedure was used fer the definition of a classification plane. It was found that five features, which include mean values of H and S, median S, skewness of distribution of S and I, allow to distinguish copper based alloys, red and dark iron oxides and steel particles. In this work, a method of probabilistic decision-making of class label assignment was proposed, which was based on the analysis of debris-coordinates distribution in the classification plane. The obtained results demonstrated a good availability for the automated wear particle analysis.

• Proceedings of the KSRS Conference
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• 2002.10a
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• pp.23-28
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• 2002

Among effective feature extraction methods from the small-patched image set, independent component analysis (ICA) is recently well known stochastic manner to find informative basis images. The ICA simultaneously learns both basis images and independent components using high order statistic manners, because that information underlying between pixels are sensitive to high-order statistic models. The topographic ICA model is adapted in our experiment. This paper deals with an unsupervised classification strategies using learned ICA basis images. The experimental result by proposed classification technique shows superior performance than classic texture analysis techniques for the panchromatic KOMPSAT imagery.

• International Journal of Contents
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• v.14 no.4
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• pp.24-29
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• 2018

EEG-based brain-computer interfaces has focused on explicitly expressed intentions to assist physically impaired patients. For EEG-based-computer interfaces to function effectively, it should be able to understand users' implicit information. Since it is hard to gather EEG signals of human brains, we do not have enough training data which are essential for proper classification performance of implicit intention. In this paper, we improve the subject independent classification of implicit intention through the generation of additional training data. In the first stage, we perform the PCA (principal component analysis) of training data in a bid to remove redundant components in the components within the input data. After the dimension reduction by PCA, we train ICA (independent component analysis) network whose outputs are statistically independent. We can get additional training data by adding Gaussian noises to ICA outputs and projecting them to input data domain. Through simulations with EEG data provided by CNSL, KAIST, we improve the classification performance from 65.05% to 66.69% with Gamma components. The proposed sample generation method can be applied to any machine learning problem with fewer samples.

• The Journal of the Korea Contents Association
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• v.18 no.1
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• pp.336-344
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• 2018

This research proposes a suitable classification and analysis standard for digital exhibition to analyze digital exhibition. Through the previous studies on digital exhibition classification, the necessity of the new standard is suggested and the analysis standard which can be easily applied to the change of concept and form of the newly emerging digital exhibition is established. Digital exhibition should take into account the elements of audience participation that naturally arise from exhibition planning and interactive storytelling format. Classification and analysis of existing digital exhibition spaces are conceptual classification based on keywords. This is because traditional exhibition methodology has been applied in the process of classifying exhibitions and works. However, in digital exhibitions, the interactive aspect between exhibition space, works, and visitors become so important that it is necessary to perform a performative classification between the works and the audience in the digital exhibition. Accordingly, the way of participating directly or indirectly in the exhibition classification should be considered based on what the audience feel. In this research, the interpretation of the classification and composition of the exhibition is based on Benjamin's argument which the classification of the sensory experience of the audience and 'Aktualisierung' closely related to the interaction with the audience. We also present analysis standard for digital exhibition according to the structure of the art exhibition narrative based on the narrative structure of Chatman. This classification methodology will provide the exhibition information in a way that can be easily understood by the visitors and it will be a precedent research that secures the expansion and accessibility of the digital exhibition.

• Journal of the Korean Society for Library and Information Science
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• v.47 no.1
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• pp.57-79
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• 2013

The aim of this study is to reveal the categorizational and classificatory features of the subject-based classification (SBC) as a subject organization system. For this purpose, 12 SBC schemes of public libraries were selected and a comparative analysis was made between the traditional classification system, such as DDC and SBC in terms of the categorizational aspects, and canons for the classification. As a result, there were significant and considerable differences between the two types of classifications. This study concluded that SBC cannot be clearly explained and understood without a consideration of its essential and distinctive characteristics as a classification scheme.

• Journal of Korean Library and Information Science Society
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• v.29
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• pp.241-272
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• 1998

The purpose of this paper is to examine the analytic-assembling(faceted analysis) methods applied in enumerative-hierarchical classification schemes. (mainly in KDC) The methods are summarized as follows : 1. For the enumerative-hierarchical classification schemes, in principle the subjects are divided into subdivisions by only one facet at the same level, and step by step. However some subjects, for example 'library and information science' 'education' and others in KDC, are divided into subdivisions by multiple facets at same level like Colon Classification. 2. Most of enumerative-hierarchical classification schemes have various kinds of auxiliary tables, such as standard subdivisions, areas, periods, and languages. Each of them is considered as foci by a facet applied to subdivide all kinds of subjects or some special subjects into lower level. 3. To classify the compound subjects with phase relation, KDC provides ready-made classification numbers or notes that says 'divide by 001-999'(whole subjects) of 'divide by xxx-xxx'(limited scope of subjects). The ready-made compound subjects, or subdividing by whole or limited scope of subjects are similar to representation of phase relation in Colon Classification. Yet these analytic-assembling methods in KDC are needed to be supplemented and amended. Subdividing methods for faceted analysis have to be unified through the whole schedule. The auxiliary tables should be enlarged and subdivided more specifically. And for representation of phase relation, the linking signs can be useful in KDC as well as UDC and other analytic-assembling classification schemes like Colon Classification.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.35 no.9B
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• pp.1322-1329
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• 2010

As body sensor network (BSN) research becomes mature, the need for managing power consumption of sensor nodes has become evident since most of the applications are designed for continuous monitoring. Real time Electrocardiograph (ECG) analysis on sensor nodes is proposed as an optimal choice for saving power consumption by reducing data transmission overhead. Smart sensor nodes with the ability to categorize lately detected ECG cycles communicate with base station only when ECG cycles are classified as abnormal. In this paper, ECG classification algorithms are described, which categorize detected ECG cycles as normal or abnormal, or even more specific cardiac diseases. Our Euclidean distance (ED) based classification method is validated to be most power efficient and very accurate in determining normal or abnormal ECG cycles. A close comparison of power efficiency and classification accuracy between our ED classification algorithm and generalized linear model (GLM) based classification algorithm is provided. Through experiments we show that, CPU cycle power consumption of ED based classification algorithm can be reduced by 31.21% and overall power consumption can be reduced by 13.63% at most when compared with GLM based method. The accuracy of detecting NSR, APC, PVC, SVT, VT, and VF using GLM based method range from 55% to 99% meanwhile, we show that the accuracy of detecting normal and abnormal ECG cycles using our ED based method is higher than 86%.

• Journal of Korean Society of Archives and Records Management
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• v.5 no.2
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• pp.73-101
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• 2005

This study aims to identify the developing process of classification shemes for NGO records. And it chooses the KFEM(Korea Federation for Environmental Movenment) for case study, which is a representative NGO of Korea. This study proposes the classification principles in the form that the function classification and subject classification are combined. The development model of function classification schemes on the KFEM records is based on the Australian Standard Work Process Analysis for Recordkeeping(AS 5090) and the DIRKS (Designing and Implementing Recordkeeping Systems) methodology. Literature review, interviews, work process analysis, and questionnaire surveys have been employed as research methodology.