• Proceedings of the Korean Information Science Society Conference
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• 2001.10b
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• pp.190-192
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• 2001

In Information Retrieval or Digital Library, one of the most important factors is to find out the exact information which users need. Exact keywords which represent the content of a document can be much help to find the exact information. In this paper, we evaluate an efficient keyword extraction system by recall and precision. The results presented here are based on the human evaluations of the quality and the appropriateness of keywords.

• Proceedings of the Korea Multimedia Society Conference
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• 2000.11a
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• pp.57-61
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• 2000

본 논문에서는 영상의 다양한 특징 정보 중에서 모양 특징을 이용한 영상 검색 시스템 을 제안한다. 모양 특징을 추출하기 위한 과정은 Chain Code를 이용 경계면의 좌표와 깊이를 구하는 과정, 경계면에 대한 무게 중심 추출 과정 그리고 영역의 넓이를 구하는 과정으로 구성되고, 무게 중심으로부터 경계면 가지 거리의 합, 표준 편차, 장축/단축 비율 등을 특징 정보로 이용한다. 각 질의 영상들의 특징 정보와 데이터베이스에 저장된 영상들의 특징 정보들을 비교하여 유사도 순위에 따라 후보영상들이 검색된다. 실험 대상으로는 170개의 폐곡선을 이루는 이진 도형 영상에 대한 검색 실험을 실시하였으며, 실험 결과 평균 Recall/Precision이 0.65/0.81을 보임으로써 제안된 방법이 유용함을 보였다.

• Proceedings of the Korean Information Science Society Conference
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• 2003.10b
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• pp.751-753
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• 2003

약물의 화학구조와 약리작용간의 관계는 'Medicinal Chemistry'에서 활발히 연구된다. 이에 도움이 되는 분야로 수많은 약물들에서 사용자가 지정한 구조를 부분구조로 가지는 약물들을 자동으로 빠르게 찾아내는 부분구조검색(Substructure Searching)이 있다. 1950년대부터 연구된 앞의 문제는 NP-Complete이나 미리 인덱스를 두어 성능을 높인 RS3 시스템(http://www.acelrys.com/rs3)이 미국 특허를 받았다. 이 시스템은 화학구조에 대한 설명을 대용량으로 기술하여 이를 RDBMS에 저장하고 검색하는 시스템이다. 하지만 이 시스템은 재현율(Recall)과 정도(Precision)가 매우 낮으므로, 본 논문에서는 새로운 인덱스를 개발하여 재현율과 정도를 향상시킨 기법을 제시한다.

• IE interfaces
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• v.19 no.1
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• pp.70-77
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• 2006

One of the problems in developing a collaborative recommendation system is the scalability. To alleviate the scalability problem efficiently, enhancing the performance of the recommendation system, we propose a new recommendation system using feature selection. In our experiments, the proposed system using about a third of all features shows the comparable performances when compared with using all features in light of precision, recall and number of computations, as the number of users and products increases.

• Journal of Korea Multimedia Society
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• v.19 no.2
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• pp.344-351
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• 2016

Recently, mobile application for english vocabulary learning is being developed actively. However, most mobile English vocabulary learning applications did not effectively connected with the technical advantages of mobile learning. Also,the study of mobile english vocabulary learning app are still insufficient. Therefore, this paper development a english vocabulary context-learning Agent that can practice context learning more reasonably using a location-based service, a character recognition technology and augmented reality technology based on smart phones. In order to evaluate the performance of the proposed agent, we have measured the precision and usability. As results of experiments, the precision of learning vocabulary is 89% and 'Match between system and the real world', 'User control and freedom', 'Recognition rather than recall', 'Aesthetic and minimalist design' appeared to be respectively 3.91, 3.80, 3.85, 4.01 in evaluation of usability. It were obtained significant results.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.12 no.10
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• pp.5159-5178
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• 2018

Network Intrusion detection is a rapidly growing field of information security due to its importance for modern IT infrastructure. Many supervised and unsupervised learning techniques have been devised by researchers from discipline of machine learning and data mining to achieve reliable detection of anomalies. In this paper, a deep convolutional neural network (DCNN) based intrusion detection system (IDS) is proposed, implemented and analyzed. Deep CNN core of proposed IDS is fine-tuned using Randomized search over configuration space. Proposed system is trained and tested on NSLKDD training and testing datasets using GPU. Performance comparisons of proposed DCNN model are provided with other classifiers using well-known metrics including Receiver operating characteristics (RoC) curve, Area under RoC curve (AuC), accuracy, precision-recall curve and mean average precision (mAP). The experimental results of proposed DCNN based IDS shows promising results for real world application in anomaly detection systems.

• Proceedings of the Korean Information Science Society Conference
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• 2001.04b
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• pp.418-420
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• 2001

최근 디지털 비디오 데이터의 사용이 급격히 증가하면서 보다 정확하게 샷을 검출하는 기법이 요구되고 있다. 비디오 정보를 이용하여 샷을 검출하는 역는 크게 이산코사인 변환의 결과값을 이용하는 방법과 움직임 보상의 결과값을 이용하는 방법으로 그룹화할 수 있는데 전자의 방법은 점진적인 변화를 검출할 수 있는 반면에 전체적인 검출율이 떨어진다는 단점이 있고, 후자의 방법은 전체적인 검출율은 높지만 점진적인 변화를 검출할 수 없다는 단점이 있다. 본 논문에서는 실험을 통하여 이러한 두 가지 방법의 특징을 살펴본 후 이 방법들을 이용한 새로운 샷 경계 검출 방법을 제안한다. 전체적으로 검출율을 높이는 데 목적을 두었기 때문에 매크로블록 타입을 이용하는 방법을 기본으로 하면서 히스토그램을 이용하는 방법을 추가하여 precision을 높일 수 있도록 하였다. 히스토그램을 이용하는 방법에서는 단순히 프레임과의 비교를 하던 기존의 방법에다 프레임들간의 차이의 차이를 이용하여 성능을 높일 수 있도록 하였다. 본 논문에서 제안한 알고리즘을 이용하여 실험을 한 결과 평균 0.96의 recall과 0.96의 precision을 보이고 있음을 알 수 있었다.

• Journal of the Korean Society for information Management
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• v.17 no.4
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• pp.67-81
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• 2000

This study discussed why Precision(& Recall) is not a good effectiveness measurement of IR system providing ranked results, reviewed other effectiveness measurements appropriate for ranked results, and proposed new measurements based on the average rank of relevant documents retrieved. The 18 case-sets of ranked results were used for evaluating 10 effectiveness measurements including proposed measurements. Simple measurements were significantly similar with the 11-Point Precision requiring complicated calculation.

• International Journal of Contents
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• v.17 no.4
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• pp.79-90
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• 2021

With the deepening of population aging, pension has become an urgent problem in most countries. Community smart pension can effectively resolve the problem of traditional pension, as well as meet the personalized and multi-level needs of the elderly. To predict the pension intention of the elderly in the community more accurately, this paper uses the decision tree classification method to classify the pension data. After missing value processing, normalization, discretization and data specification, the discretized sample data set is obtained. Then, by comparing the information gain and information gain rate of sample data features, the feature ranking is determined, and the C4.5 decision tree model is established. The model performs well in accuracy, precision, recall, AUC and other indicators under the condition of 10-fold cross-validation, and the precision was 89.5%, which can provide the certain basis for government decision-making.

• Smart Media Journal
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• v.12 no.11
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• pp.125-133
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• 2023

Traditional attire has assumed a pivotal role within the contemporary fashion industry. The objective of this study is to construct a computer vision model tailored to the recognition of traditional costumes originating from five distinct countries, namely India, Korea, Japan, Tanzania, and Vietnam. Leveraging a dataset comprising 1,608 images, we proceeded to train the cutting-edge computer vision model YOLOv8. The model yielded an impressive overall mean average precision (MAP) of 96%. Notably, the Indian sari exhibited a remarkable MAP of 99%, the Tanzanian kitenge 98%, the Japanese kimono 92%, the Korean hanbok 89%, and the Vietnamese ao dai 83%. Furthermore, the model demonstrated a commendable overall box precision score of 94.7% and a recall rate of 84.3%. Within the realm of the fashion industry, this model possesses considerable utility for trend projection and the facilitation of personalized recommendation systems.