• The KIPS Transactions:PartB
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• v.16B no.3
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• pp.225-232
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• 2009

Author name disambiguation is essential for improving performance of document indexing, retrieval, and web search. Author name disambiguation resolves the conflict when multiple authors share the same name label. This paper introduces a novel approach which exploits ontologies and WordNet-based category utility for author name disambiguation. Our method utilizes author knowledge in the form of populated ontology that uses various types of properties: titles, abstracts and co-authors of papers and authors' affiliation. Author ontology has been constructed in the artificial intelligence and semantic web areas semi-automatically using OWL API and heuristics. Author name disambiguation determines the correct author from various candidate authors in the populated author ontology. Candidate authors are evaluated using proposed WordNet-based category utility to resolve disambiguation. Category utility is a tradeoff between intra-class similarity and inter-class dissimilarity of author instances, where author instances are described in terms of attribute-value pairs. WordNet-based category utility has been proposed to exploit concept information in WordNet for semantic analysis for disambiguation. Experiments using the WordNet-based category utility increase the number of disambiguation by about 10% compared with that of category utility, and increase the overall amount of accuracy by around 98%.

• Journal of the Korean Home Economics Association
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• v.44 no.4 s.218
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• pp.133-144
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• 2006

The contemporary period is called "the age of sensibility" in which each individual consumer seeks to have her or his own products. Businesses are in need of design developments with an emphasis on customer sensitivity, and at the same time consumers must understand their own sensitivity to acquire information on designs that suit them. This research established a sensitivity and configuration database on designing men's jackets using the sensitivity engineering approach to clothing design information. The user interface was created on the Internet. Sixty-seven sensitivity terms of vocabulary appropriate for the assessment of men's jacket design were selected, and the different designs were classified into six items and 24 categories. Thirty men's jackets with different designs were produced for sensory testing and the results were analyzed in accordance with general linear I statistics. A sensitivity database was established for each category. My-sql, PHP, Java Script, and Html were used for the configuration database work. The configuration of items/categories, with the most appropriate sensitivity database information assigned to the selected sensitivity vocabulary, was programmed for display on the computer screen. The sensitivity vocabulary of a customer's choice for each factor was selected for the program to run, while the category and product configuration of the men's jacket most suitable for the search was displayed based on the user interface.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.41 no.7
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• pp.816-822
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• 2016

Content-Based image retrieval is a method to search by image features such as local color, texture, and other image content information, which is different from conventional tag or labeled text-based searching. In real life data, the number of images having tags or labels is relatively small, so it is hard to search the relevant images with text-based approach. Existing image search method only based on image feature similarity has limited performance and does not ensure that the results are what the user expected. In this study, we propose and validate a machine learning based approach to improve the performance of the image search engine. We note that when users search relevant images with a query image, they would expect the retrieved images belong to the same category as that of the query. Image classification method is combined with the traditional image feature similarity method. The proposed method is extensively validated on a public PASCAL VOC dataset consisting of 11,530 images from 20 categories.

• International Journal of Computer Science & Network Security
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• v.23 no.7
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• pp.131-140
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• 2023

Network security situational awareness systems helps in better managing the security concerns of a network, by monitoring for any anomalies in the network connections and recommending remedial actions upon detecting an attack. An Intrusion Detection System helps in identifying the security concerns of a network, by monitoring for any anomalies in the network connections. We have proposed a CRF based IDS system using genetic search feature selection algorithm for network security situational awareness to detect any anomalies in the network. The conditional random fields being discriminative models are capable of directly modeling the conditional probabilities rather than joint probabilities there by achieving better classification accuracy. The genetic search feature selection algorithm is capable of identifying the optimal subset among the features based on the best population of features associated with the target class. The proposed system, when trained and tested on the bench mark NSL-KDD dataset exhibited higher accuracy in identifying an attack and also classifying the attack category.

• Journal of the Korea Society of Computer and Information
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• v.29 no.6
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• pp.113-120
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• 2024

With the advancement of information and communication technology, we can easily generate various forms of data in our daily lives. To efficiently manage such a large amount of data, systematic classification into categories is essential. For effective search and navigation, data is organized into a tree-like hierarchical structure known as a category tree, which is commonly seen in news websites and Wikipedia. As a result, various techniques have been proposed to classify large volumes of documents into the terminal nodes of category trees. However, document classification methods using category trees face a problem: as the height of the tree increases, the number of terminal nodes multiplies exponentially, which increases the probability of misclassification and ultimately leads to a reduction in classification accuracy. Therefore, in this paper, we propose a new node expansion-based classification algorithm that satisfies the classification accuracy required by the application, while enabling detailed categorization. The proposed method uses a greedy approach to prioritize the expansion of nodes with high classification accuracy, thereby maximizing the overall classification accuracy of the category tree. Experimental results on real data show that the proposed technique provides improved performance over naive methods.

• Journal of Internet Computing and Services
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• v.19 no.6
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• pp.91-99
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• 2018

Sketch-based 3D object retrieval is a convenient way to search for various 3D data using human-drawn sketches as query. In this paper, we propose a new method of using Sketch CNN, Wasserstein CNN and Wasserstein center loss for sketch-based 3D object search. Specifically, Wasserstein center loss is a method of learning the center of each object category and reducing the Wasserstein distance between center and features of the same category. To do this, the proposed 3D object retrieval is performed as follows. Firstly, Wasserstein CNN extracts 2D images taken from various directions of 3D object using CNN, and extracts features of 3D data by computing the Wasserstein barycenters of features of each image. Secondly, the features of the sketch are extracted using a separate Sketch CNN. Finally, we learn the features of the extracted 3D object and the features of the sketch using the proposed Wasserstein center loss. In order to demonstrate the superiority of the proposed method, we evaluated two sets of benchmark data sets, SHREC 13 and SHREC 14, and the proposed method shows better performance in all conventional metrics compared to the state of the art methods.

• Journal of Convergence for Information Technology
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• v.9 no.9
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• pp.20-26
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• 2019

With the development of ICT technology and the promotion of smartphone penetration, purchasing services that purchase various products through online market are being activated. In particular, due to advances in storage and delivery technology, sales of short food materials can be purchased online. Therefore, in this paper, we propose an integrated solution that enables advertisement effect, ordering and delivery through a purchase service even if there is no sales knowledge and sales network in a small shopping mall where only offline sales can be performed. The proposed system is able to efficiently view the product information by category through image search for the product that the user desires, so that the seller of the registered product can efficiently sell without any additional advertisement.

• The KIPS Transactions:PartB
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• v.16B no.6
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• pp.479-488
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• 2009

This paper shows a reinforced construction scheme of training data for improvement of text classification by automatic search of boundary category. The documents laid on boundary area are usually misclassified as they are including multiple topics and features. which is the main factor that we focus on. In this paper, we propose an automated exploring methodology of optimal boundary category based on previous research. We consider the boundary area among target categories to new category to be required training, which are then added to the target category sementically. In experiments, we applied our method to complex documents by intentionally making errors in training process. The experimental results show that our system has high accuracy and reliability in noisy environment.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.25 no.3
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• pp.361-367
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• 2021

Information search in video streaming services such as YouTube is replacing traditional information search services. To find desired detailed information in such a video, users should repeatedly navigate several points in the video, resulting in a waste of time and network traffic. In this paper, we propose a method to assist users in searching for information in a video by using DBSCAN clustering and LSTM. Our LSTM model is trained with a dataset that consists of user search sequences and their final target points categorized by DBSCAN clustering algorithm. Then, our proposed method utilizes the trained model to suggest an expected category for the user's desired target point based on a partial search sequence that can be collected at the beginning of the search. Our experiment results show that the proposed method successfully finds user destination points with 98% accuracy and 7s of the time difference by average.

• Journal of Distribution Science
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• v.15 no.3
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• pp.61-72
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• 2017

Purpose - The ICT development is affecting the consumer behaviors in selecting channel or distribution system. This study aims to advance the theory on the influence and interaction with omni-channel behaviors. Specifically, analyzing moderating variable is category knowledge that effect between propensity of brand loyalty and its precedence factor which is perceived difference, perceived value, authenticity and consumer-brand relationship. Research design, data, and methodology - The subject of this research is consumers who purchase goods in omni-channel situation. The hypothesis of this research is derived from the literature of the preceding research analysis on brand loyalty, omni-channel and consumer behaviors. This study have constructs that were defined operationally with reference to previous studies, and the research model was designed to figure out the structural relationship among perceived difference, perceived value, authenticity, consumer-brand relationship and brand loyalty. From 2016 Sept. 1 to Dec. 31, a questionnaire survey was performed targeting customers using omni-channel. 327 questionnaire survey had conducted. 316 survey data were used for empirical analysis except data that had missing and wrong value. AMOS(structural equation) was used to confirm the hypothesis which developed by researcher. Results - The results of this study are as follows. First, an authenticity has significant effect on brand loyalty. Second, in the omni-channel situation, but perceived differentiation, perceived value, consumer-brand relationship does not affect brand loyalty. According to this result, it is judged that it is easy to search for information in the situation of omni-channel and integrated decision making is done without distinction between channels. Third, category knowledge has moderating effect between brand loyalty and precedence factors. When the category knowledge level is low, preceding factors have a significant effect on brand loyalty. when the category knowledge level is high, the preceding factors did not have a significant effect on brand loyalty except the authenticity. Conclusions - This study finds out omni-channel's phenomenon is different from other distribution channel phenomenon. In the situation of omni-channel, it is suggested that brand loyalty may be relatively low for a certain brand because it raises the knowledge level of the category. Then this study provides a managerial implications based on the role of the moderate effect on category knowledge, brand loyalty and omni-channel.