• 한국문헌정보학회지
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• 제58권2호
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• pp.269-288
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• 2024

KOS 레지스트리를 구조화하기 위해서는 수집된 KOS의 특성에 맞는 분류체계를 선정해야 한다. 이 연구에서는 다양한 분류체계를 적용하여 수집된 국내 KOS 를 분류하고, 그 결과를 바탕으로 KOS 레지스트리의 구조화를 위한 분류체계를 선정할 때 고려해야 할 시사점을 제공하고자 했다. 웹탐색을 통해 수집된 313개의 KOS 데이터를 대상으로 총 5종의 분류체계와 시소러스를 적용하여 분류하고 그 결과를 분석했다. 분석 결과, KOS 레지스트리의 국제적 연계를 위해서는 국외 분류체계를 적용하고, 국내 지식자원과 연계하거나 국내 연구자들에게 최적화하기 위해서는 국내 분류체계를 적용할 필요가 있었다. 그리고 KOS의 분야별 특성에 따라 연구 분야 KOS는 학문 분야를 기반으로 하는 분류체계를 적용하고, 공공 분야 KOS는 정부 업무기능을 기반으로 하는 분류체계를 적용하는 것을 검토할 필요가 있었다. 마지막으로 국내 KOS와 국제 KOS와의 연계를 강화할 필요가 있었고, 이를 위해서 복수의 분류체계를 적용할 필요가 있었다.

• 한국문헌정보학회지
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• 제45권1호
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• pp.29-49
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• 2011

한식은 세계화의 충분한 잠재력과 가능성을 갖고 있으며 한식의 다양성과 특수성이 국가경쟁력을 제공하는 국가 홍보 전략의 하나가 될 수 있다. 이를 위해서 가장 먼저 바탕이 되어야하는 것이 한식과 관련해서 쏟아져 나오는 정보의 조직화이다. 따라서 본 연구는 한식에 관한 자료의 분류 현황 및 사례 분석을 바탕으로 한식이 문헌분류표에 반영된 정도와 앞으로 개선되어야할 사항을 파악하고 DDC의 수정 전개안의 제안을 통해 DDC 22판 개정의 근거와 국내 도서관의 DDC 수정 전개 활용을 제공하고자 하였다.

• 한국음향학회지
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• 제28권5호
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• pp.471-476
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• 2009

본 논문에서는 변별적 가중치 학습 (discriminative weight training) 기반의 최적화된 가중치를 가지는 입력벡터를 구성하여 support vector machine (SVM)을 이용한 기존의 3GPP2 selectable mode vocoder (SMV)코덱의 음성/음악 분류 성능을 향상 시키는 방법을 제안한다. 구체적으로, 최소 분류 오차 minimum classification error (MCE) 방법을 도입하여, 최적화된 가중치를 각각의 특징벡터별로 부가한 SVM을 적용하여 기존의 가중치를 고려하지 않은 SVM 기반의 알고리즘과 비교하였으며, 우수한 음성/음악 분류 성능을 보였다.

• 한국수자원학회논문집
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• 제48권11호
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• pp.945-955
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• 2015

The technology of the SWG (Smart Water Grid) as one of most important national projects results in significant assignment that is closely associated with systematic management and effective operation. The individual component technics are required to establish directory and classification for the purpose of effectively managing their information related to research and development (R&D). The national science technology (S&T) standard classification tree which results in the representative example has been established with an intention to manage R&D information, human resource, and budget. It has been also revised every five years and then used in the various fields related to the evaluation, administration, and prediction of the national R&D projects. In addition, the standard classification system for R&D projects has been widely used in the UNESCO (United Nations Educational, Scientific and Cultural Organization) and EU (European Union) since the Frascati Manual was established in the Organization for Economic Cooperation and Development (OECD). Therefore, it is necessary for SWG techniques to develop the standard S&T classification tree for research management and evaluation. For this, it is essential to draw the core techniques for the SWG, which are incorporated with IT (Information Technology), NT (Nano Technology), and BT (Biology Technology).

• International Journal of Fuzzy Logic and Intelligent Systems
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• 제4권2호
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• pp.249-253
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• 2004

When we decide the software quality on the basis of the software measurement, the transitive property which is a requirement for an equivalence relation is not always satisfied. Therefore, we propose a scheme for classifying the software quality that employs a tolerance relation instead of an equivalence relation. Given the experimental data set, the proposed scheme generates the tolerant classes for elements in the experiment data set, and generates the tolerant ranges for classifying the software quality by clustering the means of the tolerance classes. Through the experiment, we showed that the proposed scheme could product very useful and valid results. That is, it has no problems that we use as the criteria for classifying the software quality the tolerant ranges generated by the proposed scheme.

• 전자공학회논문지B
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• 제32B권11호
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• pp.1536-1544
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• 1995

We propose a design scheme of a binary decision tree and apply it to the tire tread pattern recognition problem. In this scheme, a binary decision tree is constructed by using fuzzy C-means( FCM ) algorithm. All the available features are used while clustering. At each node, the best feature or feature subset among these available features is selected based on proposed similarity measure. The decision tree can be used for the classification of unknown patterns. The proposed design scheme is applied to the tire tread pattern recognition problem. The design procedure including feature extraction is described. Experimental results are given to show the usefulness of this scheme.

• KSII Transactions on Internet and Information Systems (TIIS)
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• 제14권6호
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• pp.2333-2353
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• 2020

This paper proposes a novel data prioritization and transmission mechanism to minimize the number of packets transmitted and reduce network overload using the constrained application protocol (CoAP) in resource-constrained networks. The proposed scheme adopts four classification parameters to classify and prioritize data from a sensor. With the packet prioritization scheme, the sensed data having the lowest priority is only delivered using the proposed keep-alive message notification to decrease the number of packets transmitted. The performance evaluation demonstrates that the proposed scheme shows the improvement of resource utilization in energy consumption, and bandwidth usage compared with the existing CoAP methods. Furthermore, the proposed scheme supports quality-of-service (QoS) per packet by differentiating transmission delays regarding priorities.

• International Journal of Computer Science & Network Security
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• 제24권7호
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• pp.108-117
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• 2024

The selection and recommendation of a suitable job applicant from the pool of thousands of applications are often daunting jobs for an employer. The recommendation and selection process significantly increases the workload of the concerned department of an employer. Thus, Resume Classification System using the Natural Language Processing (NLP) and Machine Learning (ML) techniques could automate this tedious process and ease the job of an employer. Moreover, the automation of this process can significantly expedite and transparent the applicants' selection process with mere human involvement. Nevertheless, various Machine Learning approaches have been proposed to develop Resume Classification Systems. However, this study presents an automated NLP and ML-based system that classifies the Resumes according to job categories with performance guarantees. This study employs various ML algorithms and NLP techniques to measure the accuracy of Resume Classification Systems and proposes a solution with better accuracy and reliability in different settings. To demonstrate the significance of NLP & ML techniques for processing & classification of Resumes, the extracted features were tested on nine machine learning models Support Vector Machine - SVM (Linear, SGD, SVC & NuSVC), Naïve Bayes (Bernoulli, Multinomial & Gaussian), K-Nearest Neighbor (KNN) and Logistic Regression (LR). The Term-Frequency Inverse Document (TF-IDF) feature representation scheme proven suitable for Resume Classification Task. The developed models were evaluated using F-Score_M, Recall_M, Precission_M, and overall Accuracy. The experimental results indicate that using the One-Vs-Rest-Classification strategy for this multi-class Resume Classification task, the SVM class of Machine Learning algorithms performed better on the study dataset with over 96% overall accuracy. The promising results suggest that NLP & ML techniques employed in this study could be used for the Resume Classification task.

• KSII Transactions on Internet and Information Systems (TIIS)
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• 제18권8호
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• pp.2214-2229
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• 2024

Wireless sensor network (WSN) consists of large number of sensor nodes that are deployed in geographical locations to collect sensed information, process data and communicate it to the control station for further processing. Due the unfriendly environment where the sensors are deployed, there exist many possibilities of malicious nodes which performs malicious activities in the network. Therefore, the security threats affect performance and life time of sensor networks, whereas various security aspects are there to address security issues in WSN namely Cryptography, Trust Management, Intrusion Detection System (IDS) and Intrusion Prevention Systems (IPS). However, IDS detect the malicious activities and produce an alarm. These malicious activities exploit vulnerabilities in the network layer and affect all layers in the network. Existing feature selection methods such as filter-based methods are not considering the redundancy of the selected features and wrapper method has high risk of overfitting the classification of intrusion. Due to overfitting, the classification algorithm fails to detect the intrusion in better manner. The main objective of this paper is to provide the efficient feature selection algorithm which was suitable for any type classification algorithm to detect the intrusion in an effective manner. This paper, the security of the network is addressed by proposing Feature Selection Algorithm using Chi Squared with Ensemble Method (FSChE). The proposed scheme employs the combination of decision tree along with the random forest classification algorithm to form ensemble classifier. The experimental results justify the feasibility of the proposed scheme in terms of attack detection, packet delivery ratio and time analysis by employing NSL KDD cup data Set. The obtained results shows that the proposed ensemble method increases the overall performance by 10% to 25% with respect to mentioned parameters.

• 한국비블리아학회지
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• 제27권4호
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• pp.229-249
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• 2016

본 연구는 개화기 조선 체류 서양인 기록물의 디지털 아카이브를 구축하여 지역문화콘텐츠로 활용하기 위하여 수행되었다. 서양인 기록물 22권을 대상으로 선정하여 10개의 대주제, 40개의 중주제, 239개의 소주제로 된 분류체계를 구성하고 38개 메타데이터 항목을 추출하였다. 텍스트 내용의 분석과 입력자료 유형을 분류하여 엑셀로 된 데이터베이스를 구축하고, 다양한 접근점에 의한 검색과 정보 제공을 위하여 웹기반의 디지털 아카이브 시스템을 개발하였다. 추후연구를 위하여 서양인 기록물 자료의 지속적인 발굴을 통한 아카이브 내용의 양적 확대방안, 개별 아카이브 시스템을 연계한 디지털 한국학 아카이브의 통합정보시스템 구축, 문화유산분야 분류체계 표준화와 패싯구조를 고려한 다차원적인 분류체계 개발, 메타데이터 포맷의 표준화를 통한 콘텐츠의 일관성 유지, 의미검색 기능과 데이터마이닝 기능을 활용한 온톨로지 구축을 제안하였다.