• International Journal of Advanced Culture Technology
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• 제11권3호
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• pp.205-210
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• 2023

Stone cave paintings are continuous interactions as independent mediums in places such as text, images and stone cave architecture. Unlike Buddha statues, the narrative of the text always fascinates and guides the viewer to the timeliness of the image, that is, the narrative. In particular, in Buddhist art, Buddha statues are never simple images, and murals are never simple paintings. Before the Tang Dynasty, most unknown artists were artisans, and many artists still worked on murals in temples and palaces, and independent paintings such as scrolls and sides became an important form of painting after the Tang Dynasty, changing the mechanism of painting creation. In this paper, the graphic creation process prioritizes dedication and service, but we can still feel the creativity of the painters strongly. The historical resources of how to paint these paintings, the clues to the copies, and the precursor to the foreground, encourage the painters to constantly try to resemble each other and discover problems...Therefore, in this paper, it was confirmed that reinvention and creativity are very important, and that Dunhuang Buddhist art is the basis for artists' creation and the source of vitality.

• Asian Journal of Innovation and Policy
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• 제12권3호
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• pp.304-322
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• 2023

Text data mining ('big data methods') is one of the most widely used approaches during the COVID-19 pandemic. In particular, text data mining on Scopus databases or Web of Science (WoS). Text data mining is widely used to collect literature for later bibliometric analysis, and in the end, it becomes a literature review article. Therefore, in this article, we reveal the trend of publication of literature reviews in Scopus journals from Indonesia, Japan, South Korea, Vietnam, Singapore, and Malaysia. This article describes two essential parts, namely 1) a comparison of international publication trends and subject area of literature review publications, and 2) a comparison of Top 5 for Authors, Affiliation, Source Title, and Collaboration Country.

• Journal of Information Processing Systems
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• 제20권1호
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• pp.1-12
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• 2024

Nowadays, Islamic content is widely used in research, including Hadith and the Al-Quran. Both are mostly used in the field of natural language processing, especially in text classification research. One of the difficulties in learning the Al-Quran is ambiguity, while the Al-Quran is used as the main source of Islamic law and the life guidance of a Muslim in the world. This research was proposed to relieve people in learning the Al-Quran. We proposed a word embedding feature-based on Tensor Space Model as feature extraction, which is used to reduce the ambiguity. Based on the experiment results and the analysis, we prove that the proposed method yields the best performance with the Hamming loss 0.10317.

• 인터넷정보학회논문지
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• 제18권3호
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• pp.49-61
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• 2017

Wikipedia infoboxes have emerged as an important structured information source on the web. To compose infobox for an article, considerable amount of manual effort is required from an author. Due to this manual involvement, infobox suffers from inconsistency, data heterogeneity, incompleteness, schema drift etc. Prior works attempted to solve those problems by generating infobox automatically based on the corresponding article text. However, there are many articles in Wikipedia that do not have enough text content to generate infobox. In this paper, we present an automated approach to generate infobox for movie domain of Wikipedia by extracting information from several sources of the web instead of relying on article text only. The proposed methodology has been developed using semantic relations of article content and available semi-structured information of the web. It processes the article text through some classification processes to identify the template from the large pool of template list. Finally, it extracts the information for the corresponding template attributes from web and thus generates infobox. Through a comprehensive experimental evaluation the proposed scheme was demonstrated as an effective and efficient approach to generate Wikipedia infobox.

• International Journal of Computer Science & Network Security
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• 제22권10호
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• pp.223-229
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• 2022

LinkedIn is one of the most job hunting and career-growing applications in the world. There are a lot of opportunities and jobs available on LinkedIn. According to statistics, LinkedIn has 738M+ members. 14M+ open jobs on LinkedIn and 55M+ Companies listed on this mega-connected application. A lot of vacancies are available daily. LinkedIn data has been used for the research work carried out in this paper. This in turn can significantly tackle the challenges faced by LinkedIn and other job posting applications to improve the levels of jobs available in the industry. This research introduces Text Processing in natural language processing on datasets of LinkedIn which aims to find out the jobs that appear most in a month or/and year. Therefore, the large data became renewed into the required or needful source. This study thus uses Multinomial Naïve Bayes and Linear Support Vector Machine learning algorithms for text classification and developed a trained multilingual dataset. The results indicate the most needed job vacancies in any field. This will help students, job seekers, and entrepreneurs with their career decisions

• 한국동물학회:학술대회논문집
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• 한국동물학회 1998년도 한국생물과학협회 학술발표대회
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• pp.174.2-174
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• 1998

No Abstract, See Full Text

• 대한자원환경지질학회:학술대회논문집
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• 대한자원환경지질학회 2007년도 춘계 지질과학기술 공동학술대회
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• pp.523-523
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• 2007

See Full Text

• 한국동물학회:학술대회논문집
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• 한국동물학회 2000년도 한국생물과학협회 학술발표대회
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• pp.150.3-151
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• 2000

No Abstract, See Full Text

• 지식경영연구
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• 제23권3호
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• pp.193-212
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• 2022

많은 연구에서 온라인 리뷰 유용성에 영향을 미치는 다양한 요인을 발견하였다. 기존 연구에서는 주로 온라인 리뷰와 관련되는 정량적(예: 평점) 및 정서적(예: 감성점수) 요인이 리뷰 유용성에 미치는 영향을 조사했다. 온라인 리뷰는 제목과 내용을 동시에 포함하고 있지만, 기존 연구는 주로 리뷰 내용에 중점을 두고 있다. 그러나 리뷰 제목을 고려하지 않고 단순히 리뷰 내용만을 고려하면 리뷰 유용성에 영향을 미치는 요인을 조사할 때 한계가 존재한다. 이에 따라 리뷰 제목과 내용을 모두 고려하는 연구가 주목받고 있지만, 대부분의 연구는 리뷰 유용성에 대한 리뷰 내용과 제목의 영향을 독립적으로 조사하였다. 이는 리뷰 제목과 내용 간의 일치성이 리뷰 유용성에 미치는 잠재적인 영향을 간과할 수 있다. 따라서 본 연구에서는 단순 노출 효과 이론을 통해 리뷰 제목과 내용 간의 텍스트 일치성이 리뷰 유용성에 미치는 영향을 확인하고, 정보 선명성, 리뷰 길이 및 정보원 신뢰성의 역할도 고려하였다. 분석 결과, 리뷰 제목과 내용 간의 텍스트 일치성은 리뷰 유용성에 부정적인 영향을 미치는 것을 확인하였다. 또한, 정보 선명성과 정보원 신뢰성은 리뷰 유용성에 대한 텍스트 일치성의 부정적인 영향을 완화한다는 것을 발견했다.

• International Journal of Computer Science & Network Security
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• 제21권8호
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• pp.238-246
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• 2021

The rapid rise of the Internet and social media has resulted in a large number of text-based reviews being placed on sites such as social media. In the age of social media, utilizing machine learning technologies to analyze the emotional context of comments aids in the understanding of QoS for any product or service. The classification and analysis of user reviews aids in the improvement of QoS. (Quality of Services). Machine Learning algorithms have evolved into a powerful tool for analyzing user sentiment. Unlike traditional categorization models, which are based on a set of rules. In sentiment categorization, Bidirectional Long Short-Term Memory (BiLSTM) has shown significant results, and Convolution Neural Network (CNN) has shown promising results. Using convolutions and pooling layers, CNN can successfully extract local information. BiLSTM uses dual LSTM orientations to increase the amount of background knowledge available to deep learning models. The suggested hybrid model combines the benefits of these two deep learning-based algorithms. The data source for analysis and classification was user reviews of Indian Railway Services on Twitter. The suggested hybrid model uses the Keras Embedding technique as an input source. The suggested model takes in data and generates lower-dimensional characteristics that result in a categorization result. The suggested hybrid model's performance was compared using Keras and Word2Vec, and the proposed model showed a significant improvement in response with an accuracy of 95.19 percent.