• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2017.10a
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• pp.146-147
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• 2017

Big data analysis is a technique for effectively analyzing unstructured data such as the Internet, social network services, web documents generated in the mobile environment, e-mail, and social data, as well as well formed structured data in a database. The most big data analysis techniques are data mining, machine learning, natural language processing, and pattern recognition, which were used in existing statistics and computer science. Global research institutes have identified analysis of big data as the most noteworthy new technology since 2011. Therefore, companies in most industries are making efforts to create new value through the application of big data. In this study, we analyzed using the Social Matrics which a big data analysis tool of Daum communications. We analyzed public perceptions of "Internet of things" keyword, one month as of october 8, 2017. The results of the big data analysis are as follows. First, the 1st related search keyword of the keyword of the "Internet of things" has been found to be technology (995). This study suggests theoretical implications based on the results.

• The KIPS Transactions:PartD
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• v.9D no.3
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• pp.511-522
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• 2002

WBI (Web-Based Instruction), a web-based tool for teaching the students at a long distance, makes possible to Interact between learners and instructors, provides a wide variety of learning materials, has an advantage of overcoming spatial constraints. In this paper, as a model of using the web for education, a web-based education-evaluation system has been designed and implemented. Web-based education-evaluation system has to be equipped with both of the online setting question mode and the upload setting question mode, the former makes questions on web and the latter uploads the setted questions on offline with settling a defeat of the existing systems on setting questions. And the system has to be equipped with the function of analyzing the questions that gives teacher several kinds of analysis information and makes possible to feedback to questions by adjusting the difficulty and revising the questions. In this paper, a system that reflects the above requirements has been designed and implemented with PHP script language and MySQL database system.

• Archives of Craniofacial Surgery
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• v.25 no.3
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• pp.116-122
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• 2024

Background: Due to the importance of evidence-based research in plastic surgery, the authors of this study aimed to assess the accuracy of ChatGPT in generating novel systematic review ideas within the field of craniofacial surgery. Methods: ChatGPT was prompted to generate 20 novel systematic review ideas for 10 different subcategories within the field of craniofacial surgery. For each topic, the chatbot was told to give 10 "general" and 10 "specific" ideas that were related to the concept. In order to determine the accuracy of ChatGPT, a literature review was conducted using PubMed, CINAHL, Embase, and Cochrane. Results: In total, 200 total systematic review research ideas were generated by ChatGPT. We found that the algorithm had an overall 57.5% accuracy at identifying novel systematic review ideas. ChatGPT was found to be 39% accurate for general topics and 76% accurate for specific topics. Conclusion: Craniofacial surgeons should use ChatGPT as a tool. We found that ChatGPT provided more precise answers with specific research questions than with general questions and helped narrow down the search scope, leading to a more relevant and accurate response. Beyond research purposes, ChatGPT can augment patient consultations, improve healthcare equity, and assist in clinical decision-making. With rapid advancements in artificial intelligence (AI), it is important for plastic surgeons to consider using AI in their clinical practice to improve patient-centered outcomes.

• Journal of the Korea Society of Computer and Information
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• v.28 no.10
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• pp.123-132
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• 2023

Git's commit messages manage the history of source changes during project progress or operation. By utilizing this historical data, project risks and project status can be identified, thereby reducing costs and improving time efficiency. A lot of research related to this is in progress, and among these research areas, there is research that classifies commit messages as a type of software maintenance. Among published studies, the maximum classification accuracy is reported to be 95%. In this paper, we began research with the purpose of utilizing solutions using the commit classification model, and conducted research to remove the limitation that the model with the highest accuracy among existing studies can only be applied to programs written in the JAVA language. To this end, we designed and implemented an additional step to standardize source change data into natural language using GPT. This text explains the process of extracting commit messages and source change data from Git, standardizing the source change data with GPT, and the learning process using the DistilBERT model. As a result of verification, an accuracy of 91% was measured. The proposed model was implemented and verified to ensure accuracy and to be able to classify without being dependent on a specific program. In the future, we plan to study a classification model using Bard and a management tool model helpful to the project using the proposed classification model.

• Journal of the Korea Institute of Information Security & Cryptology
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• v.34 no.3
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• pp.439-449
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• 2024

Recent rapid advancements in voice generation technology have enabled the natural synthesis of voices using text alone. However, this progress has led to an increase in malicious activities, such as voice phishing (voishing), where generated voices are exploited for criminal purposes. Numerous models have been developed to detect the presence of synthesized voices, typically by extracting features from the voice and using these features to determine the likelihood of voice generation.This paper proposes a new model for extracting voice features to address misuse cases arising from generated voices. It utilizes a deep learning-based audio codec model and the pre-trained natural language processing model BERT to extract novel voice features. To assess the suitability of the proposed voice feature extraction model for voice detection, four generated voice detection models were created using the extracted features, and performance evaluations were conducted. For performance comparison, three voice detection models based on Deepfeature proposed in previous studies were evaluated against other models in terms of accuracy and EER. The model proposed in this paper achieved an accuracy of 88.08%and a low EER of 11.79%, outperforming the existing models. These results confirm that the voice feature extraction method introduced in this paper can be an effective tool for distinguishing between generated and real voices.

• Journal of The Korean Association For Science Education
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• v.41 no.2
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• pp.71-82
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• 2021

Learning efficiency can be maximized by careful matching of text features to expected reader features (i.e., linguistic and cognitive abilities, and background knowledge). The present study aims to explore whether this systematic principle is reflected in the development of science textbooks. The current study examined science textbook texts on 20 measures provided by Auto-Kohesion, a Korean language analysis tool. In addition to surface-level features (basic counts, word-related measures, syntactic complexity measures) which have been commonly used in previous text analysis studies, the present study included cohesion-related features as well (noun overlap ratios, connectives, pronouns). The main findings demonstrate that the surface measures (e.g., word and sentence length, word frequency) overall increased in complexity with grade levels, whereas the majority of the other measures, particularly cohesion-related measures, did not systematically vary across grade levels. The current results suggest that students of lower grades are expected to experience learning difficulties and lowered motivation due to the challenging texts. Textbooks are also not likely to be suitable for students of higher grades to develop the ability to process difficulty level texts required for higher education. The current study suggests that various text-related features including cohesion-related measures need to be carefully considered in the process of textbook development.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.16 no.3
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• pp.830-860
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• 2022

[Introduction] Nowadays, many companies are shifting their businesses online due to the growing trend among customers to buy and shop online, as people prefer online purchasing products. [Problem] Users share a vast amount of information about products, making it difficult and challenging for the end-users to make certain decisions. [Motivation] Therefore, we need a mechanism to automatically analyze end-user opinions, thoughts, or feelings in the social media platform about the products that might be useful for the customers to make or change their decisions about buying or purchasing specific products. [Proposed Solution] For this purpose, we proposed an automated SentiDecpective approach, which classifies end-user reviews into negative, positive, and neutral sentiments and identifies deceptive crowd-users rating information in the social media platform to help the user in decision-making. [Methodology] For this purpose, we first collected 11781 end-users comments from the Amazon store and Flipkart web application covering distant products, such as watches, mobile, shoes, clothes, and perfumes. Next, we develop a coding guideline used as a base for the comments annotation process. We then applied the content analysis approach and existing VADER library to annotate the end-user comments in the data set with the identified codes, which results in a labelled data set used as an input to the machine learning classifiers. Finally, we applied the sentiment analysis approach to identify the end-users opinions and overcome the deceptive rating information in the social media platforms by first preprocessing the input data to remove the irrelevant (stop words, special characters, etc.) data from the dataset, employing two standard resampling approaches to balance the data set, i-e, oversampling, and under-sampling, extract different features (TF-IDF and BOW) from the textual data in the data set and then train & test the machine learning algorithms by applying a standard cross-validation approach (KFold and Shuffle Split). [Results/Outcomes] Furthermore, to support our research study, we developed an automated tool that automatically analyzes each customer feedback and displays the collective sentiments of customers about a specific product with the help of a graph, which helps customers to make certain decisions. In a nutshell, our proposed sentiments approach produces good results when identifying the customer sentiments from the online user feedbacks, i-e, obtained an average 94.01% precision, 93.69% recall, and 93.81% F-measure value for classifying positive sentiments.

• Sijohaknonchong
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• v.44
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• pp.105-138
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• 2016

Recently with the research function, "XML Digital collection of Sijo Texts in the Period of Modern Enlightenment" DB data is being provided through the Korean Research Memory (http://www.krm.or.kr) and the foundation for the constructing the contents of Sijo Texts in the Period of Modern Enlightenment has been laid. In this paper, by reviewing the characteristics and problems of Digital collection of Sijo Texts in the Period of Modern Enlightenment and searching for the improvement, I tried to find a way to make it into the content. This database has the primary meaning in the integrating and glancing at the vast amounts of Sijo in the Period of Modern Enlightenment to reaching 12,500 pieces. In addition, it is the first Sijo data base which is provide the variety of search features according to literature, name of poet, title of work, original text, per period, and etc. However, this database has the limits to verifying the overall aspects of the Sijo in the Period of Modern Enlightenment. The title and original text, which is written in the archaic word or Chinese character, could not be searched, because the standard type text of modern language is not formatted. And also the works and the individual Sijo works released after 1945 were missing in the database. It is inconvenient to extract the datum according to the poet, because poets are marked in the various ways such as one's real name, nom de plume and etc. To solve this kind of problems and improve the utilization of the database, I proposed the providing the standard type text of modern language, giving the index terms about content, providing the information on the work format and etc. Furthermore, if the Sijo database in the Period of Modern Enlightenment which is prepared the character of the Sijo Culture Information System could be built, it could be connected with the academic, educational contents. For the specific plan, I suggested as follow, - learning support materials for the Modern history and the national territory recognition on the Modern Age - source materials for studying indigenous animals and plants characters creating the commercial characters - applicability as the Sijo learning tool such as Sijo Game.

• Journal of the Korea Society of Computer and Information
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• v.17 no.8
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• pp.189-201
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• 2012

The purpose of this study is to develop an effective teaching model for the "Problem solving methods and procedures" section in the revised academic high school informatics curriculum, verify its effectiveness, make the subject more effective and appealing to teachers as well as students. The model includes a middle school level informatics curriculum for the students who have yet to learn the section. This development follows the ADDIE model, and the Python programming language is adopted for the model. Using the model, classes were conducted with two groups: high school computer club students and undergraduate students majoring in computer education. Of the undergraduate students 75% responded positively to the model. This model was applied in the actual high school classroom teaching for 23 class-hours in the spring semester 2012. The Pearson correlation coefficient that verifies the correspondence between the PSI score and the informatics midterm exam grade is .247, which reflects a weak positive correlation. The result of the study showed that the developed teaching model is an effective tool in educating students about the "problem solving methods and procedures". The model is to be a cornerstone of teaching/learning plans for informatics at academic high school as well as training materials for pre-service teachers.

• Journal of The Korean Association For Science Education
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• v.24 no.5
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• pp.1008-1017
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• 2004

In physics education, language is an fundamental learning tool as in other subjects. In writing activity, students can get fair opportunities to express their own ideas during the class. Even though there are various styles of writing, students are usually supposed to make a report in their science classes. But there have been few studies in science education on the tasks and features of student's science writing. In this research, different styles of writing tasks were designed for science classes, and students' writing was analysed in terms of conceptual and emotional aspects. Also the usefulness of each task type was discussed relating to school physics education. Four types of writing, i.e. , , , and writing were developed, and 'The reflection of light' was selected as the theme and given to students. Four types of writing were analysed in this paper. In each type of writing, students showed different features in their conception. They also showed emotional expressions in imaginative writing types, that is, and types. Based on these results, it is recommended that in physics teaching various types of writing need to be designed, developed and applied according to the aim of a particular lesson.