• Journal of information and communication convergence engineering
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• v.19 no.3
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• pp.142-147
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• 2021

In this paper, we propose an intelligent research fund management system that applies artificial intelligence technology to an integrated research fund management system. By defining research fund management rules as work rules, a detection model learned using deep learning is designed, through which the disapproval status is presented for each research fund usage history. The disapproval detection system of the RCMS implemented in this study predicts whether the newly registered usage details are recognized or disapproved using an artificial intelligence model designed based on the use of an 8.87 million research fund registered in the RCMS. In addition, the item-detail recommendation system described herein presents the usage details according to the usage history item newly registered by the artificial intelligence model through a correlation between the research cost usage details and the item itself. The accuracy of the recommendation was shown to be 97.21%.

• Journal of Korea Multimedia Society
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• v.22 no.2
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• pp.250-259
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• 2019

Big data processing technology and artificial intelligence (AI) are increasingly attracting attention. Natural language processing is an important research area of artificial intelligence. In this paper, we use Korean news articles to extract topic distributions in documents and word distribution vectors in topics through LDA-based Topic Modeling. Then, we use Word2vec to vector words, and generate a weight matrix to derive the relevance SCORE considering the semantic relationship between the words. We propose a way to recommend documents in order of high score.

• Journal of the Korea Society of Computer and Information
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• v.24 no.2
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• pp.9-16
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• 2019

Computer-Assisted Pronunciation Training system is considered to be a useful tool for pronunciation learning for students who received elementary level English pronunciation education, especially for students who have difficulty in correcting their pronunciation in front of others or who are not able to receive face-to-face training. The conventional Computer-Assisted Pronunciation Training system shows the word to the user, the user pronounces the word, and then the system provides phoneme or audio feedback according to the pronunciation of the user. In this paper, we propose a Computer-Assisted Pronunciation Training system that can practice on the varying pronunciation according to positions of adjacent phonemes. To achieve this, the proposed system is implemented by recommending a series of words by focusing on adjacent phonemes for simplicity and clarity. Experimental results showed that word recommendation considering adjacent phonemes leads to improvement of pronunciation accuracy.

• Journal of Korea Multimedia Society
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• v.21 no.11
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• pp.1353-1361
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• 2018

With the availability of real-time educational data collection and analysis techniques, the education paradigm is shifting from educator-centric to data-driven lectures. However, most offline and online education frameworks collect students' feedback from question-answering data that can summarize their understanding but requires instructor's attention when students need additional help during lectures. This paper proposes a content restructure recommendation framework based on collected student feedback. We list the types of student feedback and implement a web-based framework that collects both implicit and explicit feedback for content restructuring. With a case study of four-week lectures with 50 students, we analyze the pattern of student feedback and quantitatively validate the effect of the proposed content restructuring measured by the level of student engagement.

• Journal of Information Processing Systems
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• v.15 no.5
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• pp.1029-1035
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• 2019

In recent years, artificial intelligence (AI) services have become one of the most essential parts to extend human capabilities in various fields such as face recognition for security, weather prediction, and so on. Various learning algorithms for existing AI services are utilized, such as classification, regression, and deep learning, to increase accuracy and efficiency for humans. Nonetheless, these services face many challenges such as fake news spread on social media, stock selection, and volatility delay in stock prediction systems and inaccurate movie-based recommendation systems. In this paper, various algorithms are presented to mitigate these issues in different systems and services. Convolutional neural network algorithms are used for detecting fake news in Korean language with a Word-Embedded model. It is based on k-clique and data mining and increased accuracy in personalized recommendation-based services stock selection and volatility delay in stock prediction. Other algorithms like multi-level fusion processing address problems of lack of real-time database.

• International Journal of Computer Science & Network Security
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• v.22 no.7
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• pp.43-50
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• 2022

Continuous development is the key of development issue in developing nations. Smart city measurement is prevalently carried through in the cities in which the nations have been classified as industrialized countries. In addition, cities in Europe becomes the models of smart city system. Smart city concept used in the cities in Europe applies six predominant features i.e. smart economic, smart mobility, smart environment, smart people, smart living, and smart governance. This paper focuses on figuring out city' development strategy in developing nations particularly Indonesia in regard with European Framework by way of Multi Expert Multi Criterion Decision Making (ME-MCDM). Recommendation is resulted from the tests using the data collected from one of the metropolis cities in Indonesia, whereby issuing recommendation must firstly implement smart education, secondly communication, thirdly smart government, and fourthly smart health, as well as simultaneously implement smart energy and smart mobility.

• Proceedings of the Korea Information Processing Society Conference
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• 2022.05a
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• pp.366-369
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• 2022

The glut of information aggravated the process of data analysis and other procedures including data mining. Many algorithms were devised in Big Data and Data Mining to solve such an intricate problem. In this paper, we conducted research about the comparison of several similarity measures and community detection algorithms in collaborative filtering for movie recommendation systems. Movielense data set was used to do an empirical experiment. We applied three different similarity measures: Cosine, Euclidean, and Pearson. Moreover, betweenness and eigenvector centrality were used to detect communities from the network. As a result, we elucidated which algorithm is more suitable than its counterpart in terms of recommendation accuracy.

• Journal of Global Scholars of Marketing Science
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• v.16 no.3
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• pp.17-37
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• 2006

The present study examines the relative roles of external recommendations and internally felt satisfaction in influencing patient loyalty to a doctor. It establishes that recommendations do result in preliminary loyalty formation in the patient to the doctor, but only until the formation of own experiences; the significance of external sources of recommendation in the determination of patient loyalty becomes insignificant thenceforth. Implications for the doctors are that they should strive at fostering bonds of emotional attachment in their present patients so that they become strongly loyal and spread positive word-of-mouth which could result in the doctor getting new patients as well. Probable extensions of this research are also discussed later in the paper.

• Proceedings of the Korea Information Processing Society Conference
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• 2023.11a
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• pp.406-409
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• 2023

With the explosion of information, recommendation algorithms are becoming increasingly important in providing people with appropriate content, enhancing their online experience. In this paper, we propose a recommender system using advanced deep reinforcement learning(DRL) techniques. This method is more adaptive and integrative than traditional methods. We selected the MovieLens dataset and employed the precision metric to assess the effectiveness of our algorithm. The result of our implementation outperforms other baseline techniques, delivering better results for Top-N item recommendations.

• Knowledge Management Research
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• v.11 no.2
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• pp.75-93
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• 2010

Even though there have been many efforts on driver's route recommendation, driver still should get involved to choose the driving path in a manual manner. Uncertain traffic information provided to the driver delays his arrival time and hence may cause diminished economic values. One of the solutions of reducing the uncertainty is to provide various kinds of traffic information, rather than send real-time information. Therefore, as the wireless communication technology improves and at the same time volume of utilizable traffic contents increases in geometrical progression, selecting traffic information based on driver's context in a timely and individual manner will be needed. Hence, the purpose of this paper is to propose a methodology that efficiently sends the rich traffic contents to the personal in-vehicle navigation. To do so, driver preference is modeled and then the recommendation algorithm of traffic information contents was developed using the preference model. Secondly, ontology based traffic situation analyzation method is suggested to automatically inference the noticeable information from the traffic context on driver's route. To show the feasibility of the idea proposed in this paper, an open API service is implemented in consideration of ease of use.