• Journal of Platform Technology
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• v.8 no.1
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• pp.33-40
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• 2020

The purpose of this study is to identify factors in selecting the elective ICT utilization lecture and to find positive and negative elements of the lecture through conducting topic modeling analysis of text mining of the narrative lecture evaluation. In order to do so, from pre-processing of data, keyword frequency analysis to wordcloud visualization and topic modeling analysis have been conducted from 'reasons of selecting the lecture,' 'improvements to be made on the lecture,' and 'what I liked about the lecture' categories regarding the ICT utilization lecture which was opened in the second semester of 2019 at M University. The analysis results show that students mostly registered for the ICT utilization lecture at M University to obtain a certificate and the fact being certified and taking the lecture can be done simultaneously is a positive element of taking the lecture. On the other hand, negative element included inconvenience of the classroom setting environment.

• Journal of Korean Biological Nursing Science
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• v.23 no.3
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• pp.170-179
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• 2021

Purpose: The aim of this study was to identify core keywords and topic groups in the 'Diabetes mellitus and mobile applications' field of research for better understanding research trends in the past 20 years. Methods: This study was a text-mining and topic modeling study including four steps such as 'collecting abstracts', 'extracting and cleaning semantic morphemes', 'building a co-occurrence matrix', and 'analyzing network features and clustering topic groups'. Results: A total of 789 papers published between 2002 and 2021 were found in databases (Springer). Among them, 435 words were extracted from 118 articles selected according to the conditions: 'analyzed by text network analysis and topic modeling'. The core keywords were 'self-management', 'intervention', 'health', 'support', 'technique' and 'system'. Through the topic modeling analysis, four themes were derived: 'intervention', 'blood glucose level control', 'self-management' and 'mobile health'. The main topic of this study was 'self-management'. Conclusion: While more recent work has investigated mobile applications, the highest feature was related to self-management in the diabetes care and prevention. Nursing interventions utilizing mobile application are expected to not only effective and powerful glycemic control and self-management tools, but can be also used for patient-driven lifestyle modification.

• International Journal of Computer Science & Network Security
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• v.22 no.1
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• pp.61-68
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• 2022

The present study was based on developing an AI based model to facilitate the academic registration needs of blind students. The model was developed to enable blind students to submit academic service requests and tasks with ease. The findings from previous studies formed the basis of the study where functionality gaps from the literary research identified by blind students were utilized when the system was devised. Primary simulation data were composed based on several thousand cases. As such, the current study develops a model based on archival insight. Given that the model is theoretical, it was partially applied to help determine how efficient the associated AI tools are and determine how effective they are in real-world settings by incorporating them into the portal that institutions currently use. In this paper, we argue that voice-activated personal assistant (VAPA), text mining, bag of words, and case-based reasoning (CBR) perform better together, compared with other classifiers for analyzing and classifying the text in academic request submission through the VAPA.

• The Journal of the Convergence on Culture Technology
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• v.8 no.5
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• pp.729-734
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• 2022

Various studies using accident cases are being conducted to identify the causes of accidents in the construction industry, but studies on the differences between public and private construction are insignificant. In this study, web scraping and text mining technologies were applied to analyze the causes of accidents by order type. Through statistical analysis and word cloud analysis of more than 10,000 structured and unstructured data collected, it was confirmed that there was a difference in the types and causes of accidents in public and private construction. In addition, it can contribute to the establishment of safety management measures in the future by identifying the correlation between major accident causes.

• Journal of Korea Trade
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• v.26 no.8
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• pp.37-57
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• 2022

Purpose -International diplomacy is key for the cohesive economic growth of countries around the world. This study aims to identify the major topics discussed and make sense of word pairs used in sentences by Chinese senior leaders during their diplomatic visits. It also compares the differences between key topics addressed during diplomatic visits to developed and developing countries. Design/methodology - We employed three methods: word frequency, co-word, and semantic network analysis. Text data are crawling state and official visit news released by the Ministry of Foreign Affairs of the People's Republic of China regarding diplomatic visits undertaken from 2015-2019. Findings - The results show economic and diplomatic relations most prominently during state and official visits. The discussion topics were classified according to nine centrality keywords most central to the structure and had the maximum influence in China. Moreover, the results showed that China's diplomatic issues and strategies differ between developed and developing countries. The topics mentioned in developing countries were more diverse. Originality/value - Our study proposes an effective approach to identify key topics in Chinese diplomatic talks with other countries. Moreover, it shows that discussion topics differ for developed and developing countries. The findings of this research can help researchers conduct empirical studies on diplomacy relationships and extend our method to other countries. Additionally, it can significantly help key policymakers gain insights into negotiations and establish a good diplomatic relationship with China.

• International Journal of Computer Science & Network Security
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• v.23 no.4
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• pp.48-54
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• 2023

Nowadays, a blind man finds it very difficult to cross the roads. They should be very vigilant with every step they take. To resolve this problem, Convolutional Neural Networks(CNN) is a best method to analyse the data and automate the model without intervention of human being. In this work, a traffic signal recognition system is designed using CNN for the visually impaired. To provide a safe walking environment, a voice message is given according to light state and timer state at that instance. The developed model consists of two phases, in the first phase the CNN model is trained to classify different images captured from traffic signals. Common Objects in Context (COCO) labelled dataset is used, which includes images of different classes like traffic lights, bicycles, cars etc. The traffic light object will be detected using this labelled dataset with help of object detection model. The CNN model detects the color of the traffic light and timer displayed on the traffic image. In the second phase, from the detected color of the light and timer value a text message is generated and sent to the text-to-speech conversion model to make voice guidance for the blind person. The developed traffic light recognition model recognizes traffic light color and countdown timer displayed on the signal for safe signal crossing. The countdown timer displayed on the signal was not considered in existing models which is very useful. The proposed model has given accurate results in different scenarios when compared to other models.

• The Journal of the Korea Contents Association
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• v.22 no.2
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• pp.88-106
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• 2022

This study examined the social discourse on consumer boycott and explored consumer experience using text mining of mass media and social media data and the in-depth interview. The result showed that the topics of online news related to the boycott included the causes of the boycott, the responses of each actor in the process of the boycott, and the effects of the boycott. In the result of the in-depth interviews, it was found that the boycott has been decentralized and the participants had the experience of exploring and verifying information on their own. In the boycott process, there were mixed experiences due to the absence of substitutes and the marketing influence, and positive experiences of expressing one's thoughts and strengthening beliefs through the boycott.

• Smart Media Journal
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• v.12 no.1
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• pp.32-46
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• 2023

Natural Language Processing (NLP) has grown tremendously in recent years. Typically, bilingual, and multilingual translation models have been deployed widely in machine translation and gained vast attention from the research community. On the contrary, few studies have focused on translating between spoken and sign languages, especially non-English languages. Prior works on Sign Language Translation (SLT) have shown that a mid-level sign gloss representation enhances translation performance. Therefore, this study presents a new large-scale Korean sign language dataset, the Museum-Commentary Korean Sign Gloss (MCKSG) dataset, including 3828 pairs of Korean sentences and their corresponding sign glosses used in Museum-Commentary contexts. In addition, we propose a translation framework based on self-supervised learning, where the pretext task is a text-to-text from a Korean sentence to its back-translation versions, then the pre-trained network will be fine-tuned on the MCKSG dataset. Using self-supervised learning help to overcome the drawback of a shortage of sign language data. Through experimental results, our proposed model outperforms a baseline BERT model by 6.22%.

• Proceedings of the Korean Society of Computer Information Conference
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• 2023.01a
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• pp.437-438
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• 2023

This paper takes the Pingjiang historical and cultural district of Suzhou city as an example, collects 1439 visitor review data from Ctrip.com with the help of Python technology, and uses web text analysis to conduct research on high-frequency words, semantic networks and emotional tendencies to comprehensively assess the tourist perception of the Grand Canal heritage. The study found that: natural and humanistic landscape, historical and cultural accumulation, and the style of Jiangnan Canal are fully reflected in the tourists' perception of Pingjiang historical and cultural district; tourists hold strong positive emotion towards Pingjiang Road, however, there is still more room for renovation and improvement of the historical and cultural district. Finally, countermeasure suggestions for improving the tourist perception of the Grand Canal heritage are given in terms of protection first, cultural integration and innovative utilization.

• Educational Technology International
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• v.16 no.2
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• pp.183-200
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• 2015

This study investigated the effect of learning achievements and cognitive load according to different types of presenting learning materials and epistemological beliefs (EB). Learning achievements in this study were composed by retention and transfer of ill-structured problem. A total of 80 college students participated in the study. Prior to the learning, students were guided to fill out a questionnaire regarding epistemological beliefs and a prior knowledge test. The students of each group studied with a different type of reading material: full text (FT), full text including key questions (KeyFT) and full text including a concept map (CmFT). After a session of study was finished, they were asked to complete the posttest: retention and transfer. The results showed that there was a significant difference in transfer achievements. CmFT outperformed higher scores than the other types. There was no significant difference in retention among the groups. It is strongly believed that the types of presenting learning materials may have affected the understanding of ill-structured problem solving skills. Students with sophisticated EB showed higher achievements on retention and transfer than naive-EB and mixed-EB. Even though the data showed decrease of the cognitive load on the type of materials and EB, there were no significant differences on the cognitive load. We should consider a positive effect of types of presenting learning materials and EB enhancing capabilities of solving ill-structured problems in real life.