• KSII Transactions on Internet and Information Systems (TIIS)
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• v.13 no.3
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• pp.1403-1417
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• 2019

With the rapid growth of multimedia data, research on cross-media feature learning has significance in many applications, such as multimedia search and recommendation. Existing methods are sensitive to noise and edge information in multimedia data. In this paper, we propose a semi-supervised method for cross-media feature learning by means of $L_{2,q}$ norm to improve the performance of cross-media retrieval, which is more robust and efficient than the previous ones. In our method, noise and edge information have less effect on the results of cross-media retrieval and the dynamic patch information of multimedia data is employed to increase the accuracy of cross-media retrieval. Our method can reduce the interference of noise and edge information and achieve fast convergence. Extensive experiments on the XMedia dataset illustrate that our method has better performance than the state-of-the-art methods.

• Steel and Composite Structures
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• v.32 no.1
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• pp.37-57
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• 2019

This paper presents experimental and analytical studies to evaluate the cyclic behaviour of Circular Reinforced Concrete column Steel beam (CRCS) connections. Two 3/4-scale CRCS specimens are tested under quasi-static reversed cyclic loading. Specimens were strengthened with a tube plate (TP) and a steel doubler plate (SDP). Furthermore; nine interior beam-through type RCS connections are simulated using nonlinear three-dimensional finite element method using ABAQUS software and are verified with experimental results. The results revealed that using the TP improves the performance of the panel zone by providing better confinement to the concrete. Utilizing the TP at the panel zone may absorb and distribute stress in this region. Results demonstrate that TP can be used instead of SDP. Test records indicate that specimens with TP, with and without SDP maintained their maximum strength up to 4% drift angle, satisfying the recommendation given by AISC341-2016 for composite special moment-resisting frames.

• Journal of information and communication convergence engineering
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• v.17 no.1
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• pp.41-48
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• 2019

In recent years, sentiment analysis research has become popular. The research results of sentiment analysis have achieved remarkable results in practical applications, such as in Amazon's book recommendation system and the North American movie box office evaluation system. Analyzing big data based on user preferences and evaluations and recommending hot-selling books and hot-rated movies to users in a targeted manner greatly improve book sales and attendance rate in movies [1, 2]. However, traditional machine learning-based sentiment analysis methods such as the Classification and Regression Tree (CART), Support Vector Machine (SVM), and k-nearest neighbor classification (kNN) had performed poorly in accuracy. In this paper, an improved kNN classification method is proposed. Through the improved method and normalizing of data, the purpose of improving accuracy is achieved. Subsequently, the three classification algorithms and the improved algorithm were compared based on experimental data. Experiments show that the improved method performs best in the kNN classification method, with an accuracy rate of 11.5% and a precision rate of 20.3%.

• Journal of information and communication convergence engineering
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• v.17 no.1
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• pp.8-13
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• 2019

Smart Farming has been regarded as an important application in information and communications technology (ICT) fields. Selecting crops for cultivation at the pre-production stage is critical for agricultural producers' final profits because over-production and under-production may result in uncountable losses, and it is necessary to predict crop production to prevent these losses. The ITU-T Recommendation for Smart Farming (Y.4450/Y.2238) defines plan/production consultation service at the pre-production stage; this type of service must trace crop production in a predictive way. Several research papers present that machine learning technology can be applied to predict crop production after related data are learned, but these technologies have little to do with standardized ICT services. This paper clarifies the relationship between agricultural consultation services and predicting crop production. A prediction scheme is proposed, and the results confirm the usability and superiority of machine learning for predicting crop production.

• Journal of information and communication convergence engineering
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• v.16 no.4
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• pp.228-234
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• 2018

In software development, the quality of a product often depends on whether its developers can rapidly find and contribute to the proper tasks. Currently, the word data of projects to which newcomers have previously contributed are mainly utilized to find appropriate source files in an ongoing project. However, because of the vocabulary gap between software projects, the accuracy of source file identification based on information retrieval is not guaranteed. In this paper, we propose a novel source file identification method to reduce the vocabulary gap between software projects. The proposed method employs DBPedia Spotlight to identify proper source files based on semantic similarity between source files of software projects. In an experiment based on the Spring Framework project, we evaluate the accuracy of the proposed method in the identification of contributable source files. The experimental results show that the proposed approach can achieve better accuracy than the existing method based on comparison of word vocabularies.

• International Journal of Advanced Culture Technology
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• v.8 no.4
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• pp.255-262
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• 2020

Recently, medical tourism is recognized as a high value-added industry because of its longer period of stay and higher expenditure than general tourism. In particular, although the number of medical tourists visiting Korea is increasing, the perception of Korean medical services is low. The purpose of this paper is to develop the app which, based on medical tourism type, recommends tourism destinations. Additionally, this proposed app can expand general tourism as well. It can provide tourists with medical information easily by sorting types tourists. Besides, as medical tourists normally stay long, we can take the advantage of post-treatment time. This app collects medical information data and tourist destination data, and categorizes the types of medical tourists into four categories: disease medical tourism, traditional medical tourism, cosmetic medical tourism, and recreational medical tourism. It provides medical information according to each type and recommends customized tourist destinations. User-based collaborative filtering is applied for tourist destination recommendations.

• Pediatric Gastroenterology, Hepatology & Nutrition
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• v.24 no.2
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• pp.119-126
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• 2021

The aim of our paper was to present current knowledge, review literature and available practice guidelines of international hepatological associations regarding the effect of severe acute respiratory syndrome coronavirus 2 coronavirus on the liver, patients with underline liver disease, awaiting on liver transplantation (LTx) or being after LTx in the pandemic coronavirus disease 2019 area.

• Proceedings of the Korea Information Processing Society Conference
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• 2013.11a
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• pp.1156-1157
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• 2013

The mobile application market has been growing extensively in recent years. Currently, Apple's App Store has more than 400,000 applications and Google's Android Market has above 150,000 applications. Such growth in volumes of mobile applications has created a need to develop a recommender system that assists the users to take the right choice, when searching for a mobile application. In this paper, we study the recommendation system building tools based on collaborative filtering. Specifically, we present a study on comparison analysis of collaborative filtering in Java and R statistical software. We implement the collaborative filtering using Java's Apache Mahout and R's recommenderlab package. We evaluate both methods and describe the advantages and disadvantages of using them in order to implement collaborative filtering.

• International Journal of Computer Science & Network Security
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• v.21 no.3
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• pp.172-176
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• 2021

This study is conducted with an educational institution to minimize the potential security hazards to an acceptable level where the vulnerabilities and threats are revealed in this institution. The paper provides a general structure for the risk management process in educational institutions, and some recommendation that increases security levels is presented.

• Journal of Information Technology Services
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• v.20 no.1
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• pp.1-9
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• 2021

After being introduced "4th Industrial Revolution" in the Davos Forum 2016, it is continuously talked about "4th Industrial Revolution" as an industrial and social innovation momentum for solving structural problems of economy and society. In this situation, our government established "the Committee of 4th Industrial Revolution" as a control tower and announced a policy "4th Industrial Revolution recommendation for government" in 2019. The Ministry of National Defense is pursuing "4th Industrial Revolution" policies and organizing a team "4th Industrial Revolution Smart Defense Innovation Team" in accordance with government-wide policies from 2019. In this paper, I will look at the background and status of the Smart Defense Innovation policy. And I am proposing development plans to create practical results as like "Strengthening governance for the Smart Defense Innovation" and "Implementation of the budget for the Smart Defense Innovation" and so on.