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As Director Bong Joon Ho’s film “Parasite” made history at the Academy Awards, becoming the first non-English language film to claim four oscars, his interpreter Sharon Choi also came into spotlight. This study aims to see how the public perceives interpreters on social media through the case of Sharon Choi by sampling the video-sharing website Youtube(www.youtube.com). A dataset of 4,192 comments was collected from the top 10 most viewed youtube videos on ‘Bong Joon Ho’s acceptance speech’ using Selenium, a Python library. Keyword analysis, association analysis and LDA topic modeling were conducted on the dataset to derive insights on users’ perception of the interpreter. The results show that the most frequently mentioned keyword in the comments was “tongyeok” meaning both “interpretation” and “interpreter” in Korean. The analysis of keywords associated with the word “interpretation” revealed that the interpreter’s delivery skills, expressive skills, understanding ability, and quick wits were the main reasons for the positive response towards the interpreter from the viewers. The four main topics derived from the comments were “Bong’s human qualities and acceptance speech”, “interpreter’s appearance and language ability”, “interpreter’s interpretation skills”, and “general comments about Bong’s award-winning and interpretation”.

This study applies text mining techniques to analyze aviation safety management and propose improvement strategies for aviation services. A comprehensive dataset of 6,297 aviation-related documents, including accident reports, customer feedback, and social media posts, was collected and analyzed to extract significant keywords and patterns related to both safety and service quality. Key safety-related terms such as "weather," "in-flight safety," "crash," and "maintenance" were identified, while service-related keywords included "customer service," "reservation," "on-time performance," and "customer satisfaction." Sentiment analysis was further applied to assess the emotional tone associated with these keywords, showing that safety-related terms often correlated with negative sentiments like "dangerous," "anxious," and "unsafe," whereas service-related terms were generally linked to positive sentiments such as "satisfactory," "reliable," and "convenient." The findings emphasize the importance of utilizing real-time customer feedback and safety reports to not only enhance service quality but also strengthen safety protocols. By integrating insights from both safety and service aspects, this research contributes to the development of proactive strategies that can lead to improved customer experience and enhanced aviation safety management. The study also provides a foundation for future research and policy-making within the aviation industry.

In this study, a total of 2,584 domestic research papers with the keywords "Aviation Safety" and "Aviation Accidents" were subjected to Text Mining analysis. Various text mining techniques, including keyword frequency analysis, word correlation analysis, network analysis, and topic modeling, were applied to examine the research trends in the field of aviation safety. The results revealed a significant increase in research using the keyword "Aviation Safety" since 2015, with over 300 papers published annually. Through keyword frequency analysis, it was observed that "Aircraft" was the most frequently mentioned term, followed by "Drones" and "Unmanned Aircraft." Phi coefficients were calculated for words closely related to "Aircraft," "Aviation," "Drones," and "Safety." Furthermore, topic modeling was employed to identify 12 distinct topics in the field of aviation safety and aviation accidents, allowing for an in-depth exploration of research trends.

Bitcoin’s core security requires honest participants to control at least 51% of the total hash power. However, it has been shown that several techniques can exploit the fair mining in the Bitcoin network. This study focuses on selfish mining, which is based on the idea, ”keeping blocks secret.” Herein, we analyze selfish mining regarding competition between mining pools. We emphasize that mining-related information is shared between mining pools and participants. Based on shared information about selfish mining, we have developed an effective and practical counter strategy.

A data stream is a massive unbounded sequence of data elements continuously generated at a rapid rate. The continuous characteristic of streaming data necessitates the use of algorithms that require only one scan over the stream for knowledge discovery. Data mining over data streams should support the flexible trade-off between processing time and mining accuracy. In many application areas, mining frequent itemsets has been suggested to find important frequent itemsets by considering the weight of itemsets. In this paper, we present an efficient algorithm WSFI (Weighted Support Frequent Itemsets)-Mine with normalized weight over data streams. Moreover, we propose a novel tree structure, called the Weighted Support FP-Tree (WSFP-Tree), that stores compressed crucial information about frequent itemsets. Empirical results show that our algorithm outperforms comparative algorithms under the windowed streaming model.

Spatio-temporal patterns extracted from historical trajectories of moving objects reveal important knowledge about movement behavior for high quality LBS services. Existing approaches transform trajectories into sequences of location symbols and derive frequent subsequences by applying conventional sequential pattern mining algorithms. However, spatio-temporal correlations may be lost due to the inappropriate approximations of spatial and temporal properties. In this paper, we address the problem of mining spatio-temporal patterns from trajectory data. The inefficient description of temporal information decreases the mining efficiency and the interpretability of the patterns. We provide a formal statement of efficient representation of spatio-temporal movements and propose a new approach to discover spatio-temporal patterns in trajectory data. The proposed method first finds meaningful spatio-temporal regions and extracts frequent spatio-temporal patterns based on a prefix-projection approach from the sequences of these regions. We experimentally analyze that the proposed method improves mining performance and derives more intuitive patterns.

Biological sequences such as DNA and amino acid sequences typically contain a large number of items. They have contiguous sequences that ordinarily consist of more than hundreds of frequent items. In biological sequences analysis(BSA), a frequent contiguous sequence search is one of the most important operations. Many studies have been done for mining sequential patterns efficiently. Most of the existing methods for mining sequential patterns are based on the Apriori algorithm. In particular, the prefixSpan algorithm is one of the most efficient sequential pattern mining schemes based on the Apriori algorithm. However, since the algorithm expands the sequential patterns from frequent patterns with length-1, it is not suitable for biological datasets with long frequent contiguous sequences. In recent years, the MacosVSpan algorithm was proposed based on the idea of the prefixSpan algorithm to significantly reduce its recursive process. However, the algorithm is still inefficient for mining frequent contiguous sequences from long biological data sequences. In this paper, we propose an efficient method to mine maximal frequent contiguous sequences in large biological data sequences by constructing the spanning tree with a fixed length. To verify the superiority of the proposed method, we perform experiments in various environments. The experiments show that the proposed method is much more efficient than MacosVSpan in terms of retrieval performance.

Along with the rapid development of the database technology, as well as the widespread application of the database management systems are more and more large. Now the data mining technology has already been applied in scientific research, financial investment, market marketing, insurance and medical health and so on, and obtains widespread application. We discuss data mining technology and analyze the questions of it. Therefore, the research in a new data mining method has important significance. Some literatures did not consider the differences between attributes, leading to redundancy when constructing concept lattices. The paper proposes a new method of uncertain data mining based on the concept lattice of connotation difference degree (c_diff). The method defines the two rules. The construction of a concept lattice can be accelerated by excluding attributes with poor discriminative power from the process. There is also a new technique of calculating c_diff, which does not scan the full database on each layer, therefore reducing the number of database scans. The experimental outcomes present that the proposed method can save considerable time and improve the accuracy of the data mining compared with U-Apriori algorithm.

Data mining is a technique for extracting valuable information from vast amounts of data by analyzing statistical and mathematical operations, rules, and relationships. In this study, we employed data mining technology to analyze the data concerning the painting poetry of Maehwado (plum blossom paintings) from the early Joseon Dynasty. The data was extracted from the Hanguk Munjip Chonggan (Korean Literary Collections in Classical Chinese) in the Hanguk Gojeon Jonghap database (Korea Classics DB). Using computer information processing techniques, we carried out web scraping and classification of the painting poetry from the Hanguk Munjip Chonggan. Subsequently, we narrowed down our focus to the painting poetry specifically related to Maehwado in the early Joseon Dynasty. Based on this, refined dataset, we conducted an in-depth analysis and interpretation of the text data at the syllable corpus level. As a result, we found a direct correlation between the corpus statistics for each syllable in Maehwado painting poetry and the symbolic meaning of plum blossoms.

PURPOSE: Text-mining has been shown to be useful for understanding the clinical characteristics and patients' concerns regarding a specific disease. Low back pain (LBP) is the most common disease in modern society and has a wide variety of causes and symptoms. On the other hand, it is difficult to understand the clinical characteristics and the needs as well as demands of patients with LBP because of the various clinical characteristics. This study examined online texts on LBP to determine of text-mining can help better understand general characteristics of LBP and its specific elements. METHODS: Online data from www.spine-health.com were used for text-mining. Keyword frequency analysis was performed first on the complete text of postings (full-text analysis). Only the sentences containing the highest frequency word, pain, were selected. Next, texts including the sentences were used to re-analyze the keyword frequency (pain-text analysis). RESULTS: Keyword frequency analysis showed that pain is of utmost concern. Full-text analysis was dominated by structural, pathological, and therapeutic words, whereas pain-text analysis was related mainly to the location and quality of the pain. CONCLUSION: The present study indicated that text-mining for a specific element (keyword) of a particular disease could enhance the understanding of the specific aspect of the disease. This suggests that a consideration of the text source is required when interpreting the results. Clinically, the present results suggest that clinicians pay more attention to the pain a patient is experiencing, and provide information based on medical knowledge.

Nowadays, people share and discuss scientific papers on social media such as the Web 2.0, big data, online forums, blogs, Twitter, Facebook and scholar community, etc. In addition to a variety of metrics such as numbers of citation, download, recommendation, etc., paper review text is also one of the effective resources for the study of scientific impact. The social media tools improve the research process: recording a series online scholarly behaviors. This paper aims to research the huge amount of paper reviews which have generated in the social media platforms to explore the implicit information about research papers. We implemented and shown the result of text mining on review texts using R language. And we found that Zika virus was the research hotspot and association research methods were widely used in 2016. We also mined the news review about one paper and derived the public opinion.

일반적으로 Object Detection을 수행하는 경우에서 객체가 있는 전경(Foreground)보다, 객체가 없 는 Background 영역이 더 많다. 이 문제는 객체의 클래스 불균형 문제를 일으키기 때문에 불균형한 학습으로 이루어질 수 있다. Classification의 경우에도 마찬가지이다. 한가지 클래스에 치우친 데이터 세트나, 불균형한 분포를 이루는 데이터 세트를 Imbalanced Dataset 이라고 하는데 불균형한 데이터 세트는 성능과 학습 과정에 치명적인 영향을 미치기 때문에 균형 잡힌 데이터 세트를 만드는 것은 상 당히 중요한 문제이다. 불균형한 문제를 해결하기 위해 Sample Selection 기법의 하나인, Hard Negative Mining 방법을 적용하고, 데이터를 비율에 맞게 추출하기 위해 K-Means Clustering 기법을 적용한다. 본 논문에서 소개되는 COVID-Net은 COVID-19에 대한 양성, 음성을 결정하는 Classification Network이지만, COVID-19의 데이터 세트는 전체 데이터의 약 25.2%만 차지하는 불균 형한 데이터 세트로 이루어져 있다. COVID-Net의 불균형한 데이터 세트로 인해 상대적으로 높은 COVID-19의 False Positive를 보여준다. 데이터 세트 이미지 클래스의 적절한 분포는 COVID-Net의 향상된 성능을 도출하기 때문에 Hard Negative Mining을 사용하여 균형 데이터 세트를 생성하여 Fine tuning을 실시하여 COVID-19 클래스의 더 높은 정확도를 보장하여 범유행 상황에서 임상의에 게 더욱 빠른 대처를 할 수 있게 해준다.

This paper presents a dynamic development of an ontology-based statistical information system supporting the collection, storage, processing, analysis, and the presentation of statistical knowledge at the national scale. To accomplish this, we propose a data mining technique to dynamically collect data relating to citizens from publicly available data sources; the collected data will then be structured, classified, categorized, and integrated into an ontology. Moreover, an intelligent platform is proposed in order to generate quantitative and qualitative statistical information based on the knowledge stored in the ontology. The main aims of our proposed system are to digitize administrative tasks and to provide reliable statistical information to governmental, economic, and social actors. The authorities will use the ontology-based statistical information system for strategic decision-making as it easily collects, produces, analyzes, and provides both quantitative and qualitative knowledge that will help to improve the administration and management of national political, social, and economic life.

Odoo is an enterprise resource planning information system providing modules to support the basic business function in companies. This research will look into the development of an additional module at Odoo. This module is a data mining module using Market Basket Analysis (MBA) using FP-Growth algorithm in managing OLTP of sales transaction to be useful information for users to improve the analysis of company business strategy. The FP-Growth algorithm used in the application was able to produce multidimensional association rules. The company will know more about their sales and customers' buying habits. Performing sales trend analysis will give a valuable insight into the inner-workings of the business. The testing of the module is using the data from X Supermarket. The final result of this module is generated from a data mining process in the form of association rule. The rule is presented in narrative and graphical form to be understood easier.