• Journal of Engineering Education Research
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• v.17 no.4
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• pp.43-47
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• 2014

The rapid development of science & technology and the globalization of society have accelerated the fractionation and specialization of academic disciplines. Accordingly, Korean colleges and universities are continually dropping antiquated courses to make room for new courses that better meet societal demands. With emphasis placed on providing students with a broader range of choices in terms of course selection, compulsory courses have given way to elective courses. On average, 4 year institutions of higher learning in Korea currently offer somewhere in the neighborhood of 1,000 different courses yearly. The classification of an ever growing list of courses offered and the practical use of such data would not be possible without the aid of computers. For example, if we were able to show the pre/post requisite relationship among various courses as well as the commonalities in substance among courses, such data generated regarding the interrelationship of different courses would undoubtedly greatly benefit the students, as well as the professors, during course registration. Furthermore, the GT system's relatively simple approach to course classification and coding will obviate the need for the development of a more complicated keyword based search engine, and hopefully contribute to the standardization of the course coding scheme in the future..Therefore, as a sample case project, this study will use GT to classify and code all courses offered at the College of Engineering of K University, thereby developing a system that will facilitate the scanning of relevant courses.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.13 no.4
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• pp.2162-2179
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• 2019

The performance of text classification is highly related to the feature selection methods. Usually, two tasks are performed when a feature selection method is applied to construct a feature set; 1) assign score to each feature and 2) select the top-N features. The selection of top-N features in the existing filter-based feature selection methods is biased by their discriminative power and the empirical process which is followed to determine the value of N. In order to improve the text classification performance by presenting a more illustrative feature set, we present an approach via a potent representation learning technique, namely DBN (Deep Belief Network). This algorithm learns via the semantic illustration of documents and uses feature vectors for their formulation. The nodes, iteration, and a number of hidden layers are the main parameters of DBN, which can tune to improve the classifier's performance. The results of experiments indicate the effectiveness of the proposed method to increase the classification performance and aid developers to make effective decisions in certain domains.

• Journal of Information Processing Systems
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• v.15 no.1
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• pp.100-115
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• 2019

Malicious code distribution on the Internet is one of the most critical Internet-based threats and distribution technology has evolved to bypass detection systems. As a new defense against the detection bypass technology of malicious attackers, this study proposes the automated tracing of malicious websites in a malware distribution network (MDN). The proposed technology extracts automated links and classifies websites into malicious and normal websites based on link structure. Even if attackers use a new distribution technology, website classification is possible as long as the connections are established through automated links. The use of a real web-browser and proxy server enables an adequate response to attackers' perception of analysis environments and evasion technology and prevents analysis environments from being infected by malicious code. The validity and accuracy of the proposed method for classification are verified using 20,000 links, 10,000 each from normal and malicious websites.

• International Journal of Oral Biology
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• v.43 no.2
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• pp.101-111
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• 2018

Understanding the classification of malocclusion is a crucial issue in Orthodontics. It can also help us to diagnose, treat, and understand malocclusion to establish a standard for definite class of patients. Principal component analysis (PCA) and k-means algorithms have been emerging as data analytic methods for cephalometric measurements, due to their intuitive concepts and application potentials. This study analyzed the macro- and meso-scale classification structure and feature basis vectors of 1020 (415 male, 605 female; mean age, 25 years) orthodontic patients using statistical preprocessing, PCA, random matrix theory (RMT) and k-means algorithms. RMT results show that 7 principal components (PCs) are significant standard in the extraction of features. Using k-means algorithms, 3 and 6 clusters were identified and the axes of PC1~3 were determined to be significant for patient classification. Macro-scale classification denotes skeletal Class I, II, III and PC1 means anteroposterior discrepancy of the maxilla and mandible and mandibular position. PC2 and PC3 means vertical pattern and maxillary position respectively; they played significant roles in the meso-scale classification. In conclusion, the typical patient profile (TPP) of each class showed that the data-based classification corresponds with the clinical classification of orthodontic patients. This data-based study can provide insight into the development of new diagnostic classifications.

• Proceedings of the KIEE Conference
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• 2003.11c
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• pp.830-835
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• 2003

This paper describes a sonic polygonal multiple reflection range sensor (SPMRS), which uses multiple reflection properties usually ignored in ultrasonic sensors as disturbances or noises. Targets such as a plane, corner, edge, or cylinder in indoor environments can easily be detected by the multiple reflection patterns obtained with a SPMRS system. Target classification and feature data extraction, such as distance and azimuth to the target, are computed simultaneously by considering the geometrical relationships between the detected targets, and finally the environment model is generated by refining the detected targets. In addition, the narrow field of view of a sonar range sensor is increased and the scanning time is reduced by active motion of the SPMRS stepping servomechanism.

• Journal of the Korea Safety Management & Science
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• v.15 no.4
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• pp.407-416
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• 2013

This paper proposes an approach to analyzing the relationship between technology and services, and to identifying promising service areas for technology-based firms with the analysis of business model (BM) patents. First, BM patents and technology patents are collected and classified into their relevant categories, respectively. Second, patent citation analysis is conducted to analyze the linkage and impacts between each technology and service field at macro level. Third, as a micro level analysis, patent co-classification analysis is employed to identify the interrelationships among specific technology and service areas. Finally, the promising service areas for technology-based firms seeking service areas for diversification is investigated with portfolio analysis. The working of the proposed approach is provided with the help of a case study of IT and mobile services. The proposed approach could guide and help managers of technology-based firms to discover the opportunity of the diversification to new areas in emerging service fields.

• Journal of Institute of Control, Robotics and Systems
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• v.6 no.8
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• pp.710-718
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• 2000

In this paper we described an approach to automation of visual inspection of solder joint defects of SMC(Surface Mounted Components) on PCBs(Printed Circuit Board) by using neural network and fuzzy rule-based classification method. Inherently the surface of the solder joints is curved tiny and specular reflective it induces difficulty of taking good image of the solder joints. And the shape of the solder joints tends to greatly vary with the soldering condition and the shapes are not identical to each other even though the solder joints belong to a set of the same soldering quality. This problem makes it difficult to classify the solder joints according to their qualities. Neural network and fuzzy rule-based classification method is proposed to effi-ciently make human-like classification criteria of the solder joint shapes. The performance of the proposed approach is tested on numerous samples of commercial computer PCB boards and compared with the results of the human inspector performance and the conventional Kohonen network.

• Journal of the Korean Applied Science and Technology
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• v.41 no.3
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• pp.673-684
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• 2024

This study aims to enhance the accuracy of severity classification by examining the usage patterns and characteristics of emergency department visits. It focuses on adult and elderly patients who visited a public hospital in Seoul. This descriptive study retrospectively reviewed the electronic medical records of patients who visited the emergency department of a public hospital between November and December 2023. The total number of participants was 1,033, with 46.4% (n=479) being elderly and 53.6% (n=554) being adults. The chief complaints of the participants were as follows: for the elderly, nervous system symptoms at 8.2% (n=85) and digestive symptoms at 7.5% (n=77) were the most common, while for adults, gastrointestinal symptoms at 11.0% (n=114) and trauma at 8.6% (n=89) were more prevalent. In the case of the elderly, patients classified as urgent accounted for the highest percentage at 23.9% (n=247), while for adults, non-emergency were more prevalent at 32.2% (n=333). The initial severity classification error rate for elderly patients in the urgent was 3.8%, indicating that the suitability of KTAS for elderly patients with high severity was low. To minimize severity classification errors and enhance KTAS accuracy, it's essential to address its current limitation of only classifying adults and children separately by developing a KTAS classification system that reflects the diverse characteristics of elderly patients.

• Journal of Information Management
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• v.27 no.2
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• pp.33-64
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• 1996

The systematic classification of database technologies is being much debated issue currently in the telecommunication and database industry. Such a rapid requirement toward standard classification model will enable many experts to characterize database technologies. The purpose of this study is to obtain a general overview and suggest a draft for the development of standard model associated with classification. This presented model is concentrating on information and database system. This presentation is catalogued by 5 subjects such as : general overview, information distribution, information retrieval systems, database systems, peripheral aspects related to database.

• 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.