• Journal of Korean Society for Quality Management
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• v.51 no.3
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• pp.403-418
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• 2023

Purpose: The acceleration of technological innovation, specifically Industry 4.0, has triggered the emergence of a quality management paradigm known as Quality 4.0. This study aims to provide a systematic overview of dispersed studies on Quality 4.0 across various disciplines and to stimulate further academic discussions and industrial transformations. Methods: Text mining and machine learning approaches are applied to learn and identify key research topics, and the suggested key references are manually reviewed to develop a state-of-the-art overview of Quality 4.0. Results: 1) A total of 27 key research topics were identified based on the analysis of 1234 research papers related to Quality 4.0. 2) A relationship among the 27 key research topics was identified. 3) A multilevel framework consisting of technological enablers, business methods and strategies, goals, application industries of Quality 4.0 was developed. 4) The trends of key research topics was analyzed. Conclusion: The identification of 27 key research topics and the development of the Quality 4.0 framework contribute to a better understanding of Quality 4.0. This research lays the groundwork for future academic and industrial advancements in the field and encourages further discussions and transformations within the industry.

• Journal of Fashion Business
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• v.28 no.2
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• pp.139-151
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• 2024

In recent years, consumer clothing behaviors have undergone significant changes due to global phenomena such as climate change, pandemics, and advances in IT technology. Laundry behaviors closely connected to how consumers handle clothes and their clothing lifecycle have also experienced considerable transformations. However, research on laundry behavior has been limited despite its importance in understanding consumer clothing habits. This study employed text mining analysis of social data spanning the past decade to explore overall trends in consumer laundry behavior, aiming to understand key topics of interest and changes over time. Through LDA topic modeling analysis, nine topics were identified. They were grouped into subjects, targets, methods, and reasons related to laundry. Analyzing relative frequencies of keywords for each topic group revealed evolving consumer laundry behavior in response to societal changes. Over time, laundry behavior showed a dispersal of agents and locations, increased diversification of laundry targets, and a growing interest in various methods and reasons for doing laundry. This research sheds light on the broader context of laundry behavior, offering a more comprehensive understanding of consumer attitudes and perceptions than previous studies. It underscores the significance of laundry as a daily, socio-cultural aspect of our lives. Additionally, this study identifies changing customer values and suggests improvements and strategic branding for laundry services, providing practical implications.

• Journal of Korean Academy of Nursing
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• v.54 no.3
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• pp.358-371
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• 2024

Purpose: This study aimed to analyze the experiences of new nurses during their first year of hospital employment to gather data for the development of an evidence-based new nurse residency program focused on adaptability. Methods: This study was conducted at a tertiary hospital in Korea between March and August 2021 with 80 new nurses who wrote in critical reflective journals during their first year of work. NetMiner 4.5.0 was used to conduct a text network analysis of the critical reflective journals to uncover core keywords and topics across three periods. Results: In the journals, over time, degree centrality emerged as "study" and "patient understanding" for 1 to 3 months, "insufficient" and "stress" for 4 to 6 months, and "handover" and "preparation" for 7 to 12 months. Major sub-themes at 1 to 3 months were: "rounds," "intravenous-cannulation," "medical device," and "patient understanding"; at 4 to 6 months they were "admission," "discharge," "oxygen therapy," and "disease"; and at 7 to 12 months they were "burden," "independence," and "solution." Conclusion: These results provide valuable insights into the challenges and experiences encountered by new nurses during different stages of their field adaptation process. This information may highlight the best nurse leadership methods for improving institutional education and supporting new nurses' transitions to the hospital work environment.

• Journal of Web Engineering
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• v.21 no.3
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• pp.677-692
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• 2022

Recognizing the offensive, abusive, and profanity of multimedia content on the web has been a challenge to keep the web environment for user's freedom of speech. As profanity filtering function has been developed and applied in text, audio, and video context in platforms such as social media, entertainment, and education, the number of methods to trick the web-based application also has been increased and became a new issue to be solved. Compared to commonly developed toxic content detection systems that use lexicon and keyword-based detection, this work tries to embrace a different approach by the meaning of the sentence. Meaning representation is a way to grasp the meaning of linguistic input. This work proposed a data-driven approach utilizing Abstract meaning Representation to extract the meaning of the online text content into a convolutional neural network to detect level profanity. This work implements the proposed model in two kinds of datasets from the Offensive Language Identification Dataset and other datasets from the Offensive Hate dataset merged with the Twitter Sentiment Analysis dataset. The results indicate that the proposed model performs effectively, and can achieve a satisfactory accuracy in recognizing the level of online text content toxicity.

• Journal of Intelligence and Information Systems
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• v.21 no.1
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• pp.65-82
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• 2015

Recently, there has been a surge of interest in finding core issues and analyzing emerging trends for the future. This represents efforts to devise national strategies and policies based on the selection of promising areas that can create economic and social added value. The existing studies, including those dedicated to the discovery of future promising fields, have mostly been dependent on qualitative research methods such as literature review and expert judgement. Deriving results from large amounts of information under this approach is both costly and time consuming. Efforts have been made to make up for the weaknesses of the conventional qualitative analysis approach designed to select key promising areas through discovery of future core issues and emerging trend analysis in various areas of academic research. There needs to be a paradigm shift in toward implementing qualitative research methods along with quantitative research methods like text mining in a mutually complementary manner. The change is to ensure objective and practical emerging trend analysis results based on large amounts of data. However, even such studies have had shortcoming related to their dependence on simple keywords for analysis, which makes it difficult to derive meaning from data. Besides, no study has been carried out so far to develop core issues and analyze emerging trends in special domains like the aviation industry. The change used to implement recent studies is being witnessed in various areas such as the steel industry, the information and communications technology industry, the construction industry in architectural engineering and so on. This study focused on retrieving aviation-related core issues and emerging trends from overall research papers pertaining to aviation through text mining, which is one of the big data analysis techniques. In this manner, the promising future areas for the air transport industry are selected based on objective data from aviation-related research papers. In order to compensate for the difficulties in grasping the meaning of single words in emerging trend analysis at keyword levels, this study will adopt topic analysis, which is a technique used to find out general themes latent in text document sets. The analysis will lead to the extraction of topics, which represent keyword sets, thereby discovering core issues and conducting emerging trend analysis. Based on the issues, it identified aviation-related research trends and selected the promising areas for the future. Research on core issue retrieval and emerging trend analysis for the aviation industry based on big data analysis is still in its incipient stages. So, the analysis targets for this study are restricted to data from aviation-related research papers. However, it has significance in that it prepared a quantitative analysis model for continuously monitoring the derived core issues and presenting directions regarding the areas with good prospects for the future. In the future, the scope is slated to expand to cover relevant domestic or international news articles and bidding information as well, thus increasing the reliability of analysis results. On the basis of the topic analysis results, core issues for the aviation industry will be determined. Then, emerging trend analysis for the issues will be implemented by year in order to identify the changes they undergo in time series. Through these procedures, this study aims to prepare a system for developing key promising areas for the future aviation industry as well as for ensuring rapid response. Additionally, the promising areas selected based on the aforementioned results and the analysis of pertinent policy research reports will be compared with the areas in which the actual government investments are made. The results from this comparative analysis are expected to make useful reference materials for future policy development and budget establishment.

• Journal of Internet Computing and Services
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• v.23 no.5
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• pp.145-154
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• 2022

Automatic Target Recognition (ATR) technology is emerging as a core technology of Future Combat Systems (FCS). Conventional ATR is performed based on IMINT (image information) collected from the SAR sensor, and various image-based deep learning models are used. However, with the development of IT and sensing technology, even though data/information related to ATR is expanding to HUMINT (human information) and SIGINT (signal information), ATR still contains image oriented IMINT data only is being used. In complex and diversified battlefield situations, it is difficult to guarantee high-level ATR accuracy and generalization performance with image data alone. Therefore, we propose a knowledge graph-based ATR method that can utilize image and text data simultaneously in this paper. The main idea of the knowledge graph and deep model-based ATR method is to convert the ATR image and text into graphs according to the characteristics of each data, align it to the knowledge graph, and connect the heterogeneous ATR data through the knowledge graph. In order to convert the ATR image into a graph, an object-tag graph consisting of object tags as nodes is generated from the image by using the pre-trained image object recognition model and the vocabulary of the knowledge graph. On the other hand, the ATR text uses the pre-trained language model, TF-IDF, co-occurrence word graph, and the vocabulary of knowledge graph to generate a word graph composed of nodes with key vocabulary for the ATR. The generated two types of graphs are connected to the knowledge graph using the entity alignment model for improvement of the ATR performance from images and texts. To prove the superiority of the proposed method, 227 documents from web documents and 61,714 RDF triples from dbpedia were collected, and comparison experiments were performed on precision, recall, and f1-score in a perspective of the entity alignment..

• Journal of KIISE:Software and Applications
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• v.35 no.10
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• pp.608-615
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• 2008

In Knowledge Q&A services where information is created by unspecified users, document quality is an important factor of user satisfaction with search results. Previous work on quality prediction of Knowledge Q&A documents evaluate the quality of documents by using non-textual information, such as click counts and recommendation counts, and focus on enhancing retrieval performance by incorporating the quality measure into retrieval model. Although the non-textual information used in previous work was proven to be useful by experiments, data sparseness problem may occur when predicting the quality of newly created documents with such information. To solve data sparseness problem of non-textual features, this paper proposes new features for document quality prediction, namely text-confidence features, which indicate how trustworthy the content of a document is. The proposed features, extracted directly from the document content, are stable against data sparseness problem, compared to non-textual features that indirectly require participation of service users in order to be collected. Experiments conducted on real world Knowledge Q&A documents suggests that text-confidence features show performance comparable to the non-textual features. We believe the proposed features can be utilized as effective features for document quality prediction and improve the performance of Knowledge Q&A services in the future.

• Journal of Korea Society of Industrial Information Systems
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• v.28 no.4
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• pp.59-76
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• 2023

This study examines critical issues and topics related to services using YouTube videos and research publications. We analyzed 2,853 YouTube videos and 19,973 research papers related to services, released during the 2013-June, 2023 period, using text mining and network analysis. In addition, the collected data was divided into pre- and post-COVID-19 pandemic periods to explore how key issues and topics regarding services have changed. These papers were sequentially analyzed through text mining and network construction and procedures. The results indicate that the central themes of YouTube videos were IT, data, and solution, while academic research focused on service quality, quality, and customer satisfaction. Regarding ego network analysis, the key issues in YouTube video contents revolved primarily around words related to the service industry. Although it was found that they generally lacked specific industry fields, academic papers explored diverse issues in various service fields. The results of this study can be utilized to understand changes in customer concerns in the service industry from practical and academic perspectives.

• International Journal of Computer Science & Network Security
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• v.22 no.11
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• pp.381-389
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• 2022

With the collection of huge volumes of text, image, audio, video or combinations of these, in a word multimedia data, the need to explore them in order to discover possible new, unexpected and possibly valuable information for decision making was born. Starting from the already existing data mining, but not as its extension, multimedia mining appeared as a distinct field with increased complexity and many characteristic aspects. Later, the concept of big data was extended to multimedia, resulting in multimedia big data, which in turn attracted the multimedia big data mining process. This paper aims to survey multimedia data mining, starting from the general concept and following the transition from multimedia data mining to multimedia big data mining, through an up-to-date synthesis of works in the field, which is a novelty, from our best of knowledge.

• Journal of the Korean Society of Safety
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• v.32 no.3
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• pp.151-159
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• 2017

This study deals with characteristics and risk of a PM based on user survey result, road driving test and data analysis of PM accident. Text mining method is applied to extract PM accident data from Big Data, which are claim data of private insurance company. Road driving test and survey on safety, convenience, noise, overtake ability, steering ability, and climbing ability of PM are performed to evaluate user's safety and convenience considering domestic road condition. As the result of claim data analysis, annual average increase rate of PM accident is 47.4% and average compensation of personal mobility is higher than that of bicycle by maximum 1.5 times. 79.8% of PM accident is self-caused accident due to unskilled driving and age-specific diagnosis rate of driver over 60 is higher than that of under 60. Diagnosis rate of over 60 at lower limb, foot, rib and spine is especially higher than that of under 60. As the result of road driving test and user survey, satisfaction level on safety and convenience of PM is evaluated as close to that of bicycle and satisfaction level of PM is increased after boarding. Overtake ability, steering ability, and climbing ability of PM are evaluated as same or better than that of bicycle but warning equipment to pedestrian or bike such as horn is required because noise level of PM during driving is too low. Finally, user survey result shows that bicycle road is suitable for PM and safety standard, advance-education and insurance are required for PM. It is suggested that drivers' license for PM can be replaced by advance-education. Results of this study can be used to prepare safety measures and legal basis for PM operation.