• Journal of Electrical Engineering and Technology
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• v.10 no.4
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• pp.1877-1885
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• 2015

This paper presents a new Nearest-Neighbors based weighted representation for images and weighted K-Nearest-Neighbors (WKNN) classifier to improve the precision of image classification using the Bag of Visual Words (BOVW) based models. Scale-invariant feature transform (SIFT) features are firstly extracted from images. Then, the K-means++ algorithm is adopted in place of the conventional K-means algorithm to generate a more effective visual dictionary. Furthermore, the histogram of visual words becomes more expressive by utilizing the proposed weighted vector quantization (WVQ). Finally, WKNN classifier is applied to enhance the properties of the classification task between images in which similar levels of background noise are present. Average precision and absolute change degree are calculated to assess the classification performance and the stability of K-means++ algorithm, respectively. Experimental results on three diverse datasets: Caltech-101, Caltech-256 and PASCAL VOC 2011 show that the proposed WVQ method and WKNN method further improve the performance of classification.

• International Journal of Advanced Culture Technology
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• v.8 no.3
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• pp.133-139
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• 2020

The purpose of this study is to contribute to the provision of basic data for psychological quarantine policy and counseling by examining the public perception of the "corona blue" phenomenon through analysis of social media big data. To do this, key words related to the word 'Corona Blue' were derived and analyzed using the big data analysis program 'Textom'. As a result of the analysis, words such as 'Corona 19', 'depression', 'problem' and 'overcome' were derived as key words. For the analysis results,"pride and awarenes as the public perception of Corona 19", "depression and anxiety as a group trauma as the corona blue phenomenon", "spreading a psychological quarantine culture and demanding social healing as the perception of overcoming corona Blue," and "hope for return to daily life and changes in daily life as the perception of post corona" were discussed. In conclusion, we have identified the need for active psychological support from the community By revealing that Corona Blue is a depression as a group trauma. At this time, it is confirmed that it is necessary to prioritize social healing and psychological quarantine for the main risk groups such as youth or the vulnerable, who are the socially weak.

• The KIPS Transactions:PartB
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• v.9B no.5
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• pp.689-700
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• 2002

In this paper, we developed Korean Morphological Analyzer for an automatic indexing that is essential for Information Retrieval. Since it is important to index large-scaled document set efficiently, we concentrated on maximizing the speed of word analysis, modularization and structuralization of the system without new concepts or ideas. In this respect, our system is characterized in terms of software engineering aspect to be used in real world rather than theoretical issues. First, a dictionary of words was structured. Then modules that analyze substantive words and inflected words were introduced. Furthermore numeral analyzer was developed. And we introduced an unknown word analyzer using the patterns of morpheme. This whole system was integrated into K-2000, an information retrieval system.

• Cross-Cultural Studies
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• v.31
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• pp.241-262
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• 2013

In this study, we select and analyze the slang that is represented in Elistratov's "Dictionary of Russian slang". Through the above analysis, some conclusions were drawn as follows: First, as a social and psychological phenomenon appears universal in all languages, the study of slang generates strict criteria for the analysis. Unlike literary language, listed in the dictionary slang expressions can become obsolete for their short period of usage by native speakers. Therefore, in the following research of the actual data, we have to validate words targeted for analysis. Second, as the result of the analysis it is metaphor for the most part studied rather than metonymy. The semantic derivations as a result of metonymy are used very frequently in real life. But in this study we mainly analyze words, therefore the number of words was less in metonymy than was expected. Third, the basic types of metaphor are appeared as similarity by form, function, and location, and there are varieties of intervening of subjectivity in similarity of emotional impression. Fourth, the metonymy is divided into three cases: the part meaning the whole, the whole meaning the part, and some thing meaning the reality of where it exists. Fifth, not only literary language, but also slang as the 'transitional process' is the most active way of development of new meanings, and there are two methods to transfer main meaning to second meaning.

• Journal of Yeungnam Medical Science
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• v.36 no.2
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• pp.78-84
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• 2019

Medical education research subjects are incredibly diverse and have changed over time. This work in particular aims to compare and analyze research trends in medical education through the words used in the titles of these research papers. Academic Medicine (the journal of the Association of American Medical Colleges), Medical Teacher (the journal of the Association of Medical Education in Europe), the Korean Journal of Medical Education (KJME), and Korean Medical Education Review (KMER) were selected and analyzed for the purposes of this research. From 2009 to 2018, Academic Medicine and Medical Teacher published approximately 10 to 20 times more papers than the KJME and KMER. Frequently used words in these titles include "medical," "student," "education," and "learning." The words "clinical" and "learning" were used relatively often (7.80% to 13.66%) in Korean journals and Medical Teacher, but Academic Medicine used these phrases relatively less often (6.47% and 4.41%, respectively). Concern with such various topics as problem-based learning, team-based learning, program evaluations, burnout, e-learning, and digital indicates that Medical Teacher seems to primarily deal with teaching and learning methodologies, and Academic Medicine handles all aspects of medical education. The KJME and KMER did not cover all subjects, as they publish smaller papers. However, it is anticipated that research on new subjects, such as artificial intelligence in medical education, will occur in the near future.

• Journal of IKEEE
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• v.23 no.4
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• pp.1393-1399
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• 2019

With the rapid growth of the Internet, millions of indiscriminate advertising SMS are sent every day because of the convenience of sending and receiving data. Although we still use methods to block spam words manually, we have been actively researching how to filter spam in a various ways as machine learning emerged. However, spam words and patterns are constantly changing to avoid being filtered, so existing machine learning mechanisms cannot detect or adapt to new words and patterns. Recently, the concept of Lifelong Learning emerged to overcome these limitations, using existing knowledge to keep learning new knowledge continuously. In this paper, we propose a method of spam filtering system using ensemble techniques of naive bayesian which is most commonly used in document classification and LLML(Lifelong Machine Learning). We validate the performance of lifelong learning by applying the model ELLA and the Naive Bayes most commonly used in existing spam filters.

• The Journal of Information Systems
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• v.30 no.4
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• pp.277-301
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• 2021

Purpose The purpose of this study is to analyze the sentiment responses of the general public to non-face-to-face work using text mining methodology. As the number of non-face-to-face complaints is increasing over time, it is difficult to review and analyze in traditional methods such as surveys, and there is a limit to reflect real-time issues. Approach This study has proposed a method of the research model, first by collecting and cleansing the data related to non-face-to-face work among tweets posted on Twitter. Second, topics and keywords are extracted from tweets using LDA(Latent Dirichlet Allocation), a topic modeling technique, and changes for each section are analyzed through DTM(Dynamic Topic Modeling). Third, the complaints of non-face-to-face work are analyzed through the classification of positive and negative polarity in the COVID-19 section. Findings As a result of analyzing 1.54 million tweets related to non-face-to-face work, the number of IDs using non-face-to-face work-related words increased 7.2 times and the number of tweets increased 4.8 times after COVID-19. The top frequently used words related to non-face-to-face work appeared in the order of remote jobs, cybersecurity, technical jobs, productivity, and software. The words that have increased after the COVID-19 were concerned about lockdown and dismissal, and business transformation and also mentioned as to secure business continuity and virtual workplace. New Normal was newly mentioned as a new standard. Negative opinions found to be increased in the early stages of COVID-19 from 34% to 43%, and then stabilized again to 36% through non-face-to-face work sentiment analysis. The complaints were, policies such as strengthening cybersecurity, activating communication to improve work productivity, and diversifying work spaces.

• Journal of People, Plants, and Environment
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• v.24 no.6
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• pp.563-572
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• 2021

Background and objective: The ongoing COVID-19 pandemic restricted daily life, forcing people to spend time indoors. With the growing interest in mental health issues and residential environments, 'pet plants' have been receiving attention during the unprecedented social distancing measures. This study aims to analyze the change in trends of pet plants before and during the COVID-19 pandemic and provide basic data for studies related to pet plants and directions of future development. Methods: A total of 2,016 news articles using the keyword 'pet plants' were collected on Naver News from January 1, 2018 to August 15, 2019 (609 articles) and January 1, 2020 to August 15, 2021 (1,407 articles). The texts were tokenized into words using KoNLPy package, ultimately coming up with 63,597 words. The analyses included frequency of keywords and topic modeling based on Latent Dirichlet Allocation (LDA) to identify the inherent meanings of related words and each topic. Results: Topic modeling generated three topics in each period (before and during the COVID-19), and the results showed that pet plants in daily life have become the object of 'emotional support' and 'healing' during social distancing. In particular, pet plants, which had been distributed as a solution to prevent solitary deaths and depression among seniors living alone, are now expanded to help resolve the social isolation of the general public suffering from COVID-19. The new term 'plant butler' became a new trend, and there was a change in the trend in which people shared their hobbies and information about pet plants and communicated with others in online. Conclusion: Based on these findings, the trend data of pet plants before and after the outbreak of COVID-19 can provide the basis for activating research on pet plants and setting the direction for development of related industries considering the continuous popularity and trend of indoor gardening and green hobby.

• Communications of Mathematical Education
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• v.32 no.3
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• pp.407-433
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• 2018

In order to analyze textbooks from a discursive approach, the purpose of this study is to structuralize an analytic framework based on previous literature review and apply it to analyzing the meanings and their syntheses developed by words and visual mediators appeared in the definition of graph in first-year middle school textbooks. The discursive approach consists of the communicational approach developed by Sfard(2008) and the systemic functional linguistics developed by Halliday(1985/2004). In this study, ideational meta-functions for ideational meanings and interpersonal meta-functions for interpersonal meanings were employed to analyze the meanings produced by words and visual mediators in textbooks, whereas textual meta-functions for textual meanings were used for analyzing the synthesized relationships between words and visual mediators. Results show that first, density in mathematical discourse was very high and subjects in mathematical activities were ambiguous in the ideational meanings of words, and behavior aspect was more emphasized than thinking aspect in the interpersonal meanings of words which request student participations. In the case of ideational meanings of visual mediators, there was a lack of narrative diagrams, whereas there were qualitative differences in the case of offer. Second, there was a need for promoting a wide range of diverse synthetic relationships between words and visual mediators for developing enriched mathematical meanings through the varying uses like specification, explanation, similarity, and complement. These results are so important that they provide a new analytic framework from a discursive approach to textbook analysis because not only words, but also visual mediators are analyzed as tools for producing meanings in mathematics textbooks and their synthetic relationships are also examined.

• Journal of the Korean Society of Marine Environment & Safety
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• v.25 no.4
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• pp.405-412
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• 2019

SOLAS requires that ECDIS be installed on ships of more than 500 gross tonnage engaged in international navigation until the first inspection arriving after July 1, 2018. Several accidents related to the use of ECDIS have occurred with its installation as a new major navigation instrument. The 12 incident reports issued by MAIB, BSU, BEAmer, DMAIB, and DSB were analyzed, and the cause of accident was determined to be related to the operation of the navigator and the ECDIS system. The text was analyzed using the R-program to quantitatively analyze words related to the cause of the accident. We used text mining techniques such as Wordcloud, Wordnetwork and Wordweight to represent the importance of words according to their frequency of derivation. Wordcloud uses the N-gram model as a way of expressing the frequency of used words in cloud form. As a result of the uni-gram analysis of the N-gram model, ECDIS words were obtained the most, and the bi-gram analysis results showed that the word "Safety Contour" was used most frequently. Based on the bi-gram analysis, the causative words are classified into the officer and the ECDIS system, and the related words are represented by Wordnetwork. Finally, the related words with the of icer and the ECDIS system were composed of word corpus, and Wordweight was applied to analyze the change in corpus frequency by year. As a result of analyzing the tendency of corpus variation with the trend line graph, more recently, the corpus of the officer has decreased, and conversely, the corpus of the ECDIS system is gradually increasing.