• Journal of the Korean Mathematical Society
• /
• v.38 no.2
• /
• pp.283-296
• /
• 2001

The Heisenberg inequality associated with the uncertainty principle is extended to an infinite dimensional abstract Wiener space (H, B) with an abstract Wiener measure p$_1$. For $\phi$ $\in$ L$^2$(p$_1$) and T$\in$L(B, H), it is shown that (※Equations, See Full-text), where F(sub)$\phi$ is the Fourier-Wiener transform of $\phi$. The conditions when the equality holds also discussed.

• Journal of Intelligence and Information Systems
• /
• v.20 no.1
• /
• pp.133-148
• /
• 2014

Recently, with the advent of various information channels, the number of has continued to grow. The main cause of this phenomenon can be found in the significant increase of unstructured data, as the use of smart devices enables users to create data in the form of text, audio, images, and video. In various types of unstructured data, the user's opinion and a variety of information is clearly expressed in text data such as news, reports, papers, and various articles. Thus, active attempts have been made to create new value by analyzing these texts. The representative techniques used in text analysis are text mining and opinion mining. These share certain important characteristics; for example, they not only use text documents as input data, but also use many natural language processing techniques such as filtering and parsing. Therefore, opinion mining is usually recognized as a sub-concept of text mining, or, in many cases, the two terms are used interchangeably in the literature. Suppose that the purpose of a certain classification analysis is to predict a positive or negative opinion contained in some documents. If we focus on the classification process, the analysis can be regarded as a traditional text mining case. However, if we observe that the target of the analysis is a positive or negative opinion, the analysis can be regarded as a typical example of opinion mining. In other words, two methods (i.e., text mining and opinion mining) are available for opinion classification. Thus, in order to distinguish between the two, a precise definition of each method is needed. In this paper, we found that it is very difficult to distinguish between the two methods clearly with respect to the purpose of analysis and the type of results. We conclude that the most definitive criterion to distinguish text mining from opinion mining is whether an analysis utilizes any kind of sentiment lexicon. We first established two prediction models, one based on opinion mining and the other on text mining. Next, we compared the main processes used by the two prediction models. Finally, we compared their prediction accuracy. We then analyzed 2,000 movie reviews. The results revealed that the prediction model based on opinion mining showed higher average prediction accuracy compared to the text mining model. Moreover, in the lift chart generated by the opinion mining based model, the prediction accuracy for the documents with strong certainty was higher than that for the documents with weak certainty. Most of all, opinion mining has a meaningful advantage in that it can reduce learning time dramatically, because a sentiment lexicon generated once can be reused in a similar application domain. Additionally, the classification results can be clearly explained by using a sentiment lexicon. This study has two limitations. First, the results of the experiments cannot be generalized, mainly because the experiment is limited to a small number of movie reviews. Additionally, various parameters in the parsing and filtering steps of the text mining may have affected the accuracy of the prediction models. However, this research contributes a performance and comparison of text mining analysis and opinion mining analysis for opinion classification. In future research, a more precise evaluation of the two methods should be made through intensive experiments.

• Journal of the Korean Mathematical Society
• /
• v.37 no.6
• /
• pp.1071-1084
• /
• 2000

We show that a weak solution of the Cauchy problem for he nonlinear Schrodinger equation, {i∂(sub)t u + ∂$^2$(sub)x u = f(u,u), t∈(-T,T), x∈R, u(0,x) = ø(x).} in the negative solbolev space H(sup)s has a smoothing effect up to real analyticity if the initial data only have a single point singularity such as the Dirac delta measure. It is shown that for H(sup)s (R)(s>-3/4) data satisfying the condition (※Equations, See Full-text) the solution is analytic in both space and time variable. The argument is based on the recent progress on the well-posedness result by Bourgain [2] and Kenig-Ponce-Vega [18] and previous work by Kato-Ogawa [12]. We give an improved new argument in the regularity argument.

• Journal of the Korean Mathematical Society
• /
• v.37 no.6
• /
• pp.1043-1057
• /
• 2000

This paper is devoted to the investigation of mixed Fourier-Bessel transformation (※Equations, See Full-text) We apply the method of [6] which provides the estimate for weighted L(sub)$\infty$-norm of the spherical mean of │f│$^2$ via its weighted L$_1$-norm (generally it is wrong without the requirement of the non-negativity of f). We prove that in the case of Fourier-Bessel transformatin the mentioned method provides (in dependence on the relation between the dimension of the space of non-special variables n and the length of multiindex ν) similar estimates for weighted spherical means of │f│$^2$, the allowed powers of weights are also defined by multiindex ν. Further those estimates are applied to partial differential equations with singular Bessel operators with respect to y$_1$, …, y(sub)m and we obtain the corresponding estimates for solutions of the mentioned equations.

• International Journal of Computer Science & Network Security
• /
• v.24 no.7
• /
• pp.108-117
• /
• 2024

The selection and recommendation of a suitable job applicant from the pool of thousands of applications are often daunting jobs for an employer. The recommendation and selection process significantly increases the workload of the concerned department of an employer. Thus, Resume Classification System using the Natural Language Processing (NLP) and Machine Learning (ML) techniques could automate this tedious process and ease the job of an employer. Moreover, the automation of this process can significantly expedite and transparent the applicants' selection process with mere human involvement. Nevertheless, various Machine Learning approaches have been proposed to develop Resume Classification Systems. However, this study presents an automated NLP and ML-based system that classifies the Resumes according to job categories with performance guarantees. This study employs various ML algorithms and NLP techniques to measure the accuracy of Resume Classification Systems and proposes a solution with better accuracy and reliability in different settings. To demonstrate the significance of NLP & ML techniques for processing & classification of Resumes, the extracted features were tested on nine machine learning models Support Vector Machine - SVM (Linear, SGD, SVC & NuSVC), Naïve Bayes (Bernoulli, Multinomial & Gaussian), K-Nearest Neighbor (KNN) and Logistic Regression (LR). The Term-Frequency Inverse Document (TF-IDF) feature representation scheme proven suitable for Resume Classification Task. The developed models were evaluated using F-Score_M, Recall_M, Precission_M, and overall Accuracy. The experimental results indicate that using the One-Vs-Rest-Classification strategy for this multi-class Resume Classification task, the SVM class of Machine Learning algorithms performed better on the study dataset with over 96% overall accuracy. The promising results suggest that NLP & ML techniques employed in this study could be used for the Resume Classification task.

• Journal of the Korean Society for information Management
• /
• v.28 no.2
• /
• pp.53-63
• /
• 2011

This paper aims to find out the kinds of sub-topics that were researched in relation to Information Literacy (IL). The text mining method was applied to the articles with information literacy' in the fields of the descriptor, title and in the LISA Database. Also, out of 214 journals that published these articles, those with the top ten highest frequencies were listed and analyzed. Research results show that 908 articles on information literacy were published in 214 journals and User training' and Students' were major descriptors in the sub-topic area of information literacy. Also, Reference Services Review and The Journal of Academic Librarianship are two key journals in IL research as they have the highest frequency of related articles and have shown increasing trends.

• Journal of Korean Academy of Nursing
• /
• v.51 no.6
• /
• pp.720-731
• /
• 2021

Purpose: The purpose of this study was to identify the knowledge structure of health information (HI) for chronic obstructive pulmonary disease (COPD). Methods: Keywords or meaningful morphemes from HI presented on five health-related websites (HRWs) of one national HI institute and four hospitals, as well as HI needs among patients presented in nine literature, were reviewed, refined, and analyzed using text network analysis and their co-occurrence matrix was generated. Two networks of 61 and 35 keywords, respectively, were analyzed for degree, closeness, and betweenness centrality, as well as betweenness community analysis. Results: The most common keywords pertaining to HI on HRWs were lung, inhaler, smoking, dyspnea, and infection, focusing COPD treatment. In contrast, HI needs among patients were lung, medication, support, symptom, and smoking cessation, expanding to disease management. Two common sub-topic groups in HI on HRWs were COPD overview and medication administration, whereas three common sub-topic groups in HI needs among patients in the literature were COPD overview, self-management, and emotional management. Conclusion: The knowledge structure of HI on HRWs is medically oriented, while patients need supportive information. Thus, the support system for self-management and emotional management on HRWs must be informed according to the structure of patients' needs for HI. Healthcare providers should consider presenting COPD patient-centered information on HRWs.

• Journal of Korean Elementary Science Education
• /
• v.36 no.4
• /
• pp.428-438
• /
• 2017

The purpose of this study is to analyze the visual attention of elementary school students according to the editorial design of the 2007 2009 revised elementary science textbook 'Structure and function of our body'. For this purpose, eye movements were collected while elementary school students were watching real textbooks wearing mobile eye tracker. The BeGaze 3.7 program of SMI company was used analyzing eye movements. Twenty-six elementary school students participated voluntarily in mobile tracking research. Elementary students learned the contents of textbook related to 'digestive organ' and 'respiratory organ' by using double reading learning strategy. The results of this study are as follows. First, as a result of pre- and post-knowledge tests, there was no statistically significant difference in learning effect between 2007 revised and 2009 revised textbook editing design. Second, elementary school students tended to give more visual attention to text than textbook illustrations. Third, the selective attention and persistent attention of elementary students showed a very strong positive correlation (.940), but the selective attention and self-control showed a strong positive correlation (.499). Fourth, students with high level of attention and low level showed high visual occupancy in text than in illustrations. Fifth, elementary school students preferred the 2009 revised science textbook to the 2007 revised.

• The Research Journal of the Costume Culture
• /
• v.14 no.2
• /
• pp.192-205
• /
• 2006

This study first aims on preparing a systematic analysis basis for the expression aspects of hairstyle in forms of postmodernism. Secondly, it studies the meanings and aspects the hairstyle trend through a post modernism approach about its expression methods based on the suggestive collection hairstyles. Third, it confirms that hairstyle is also one of fashion that can be represented as modern society and culture by explaining that post modernism appears in hairstyles. This study analyzes the expression aspects of hairstyle in forms of post modernism that appears in modern collections by preparing theoretical theories for this study based on former studies related to post modernism theory and clothes from a qualitative analysis. The results of this study prepared a theoretical analysis frame to study the expression aspects of hairstyle in forms of postmodernism first. Secondly, as a result of analyzing based on the analysis, they show that disoriginality of hairstyle can be freely interpreted by the author through borrowing and restoration, disformation through discord and incompletion, discenterization through non-westernization and sub-culture, and inter-text through many symbols and meanings. Thirdly, as the postmodernism movement that represents modern society, culture, and art movement is reflected on hairstyle, it can be considered as a small culture and a product of the era.

• KSII Transactions on Internet and Information Systems (TIIS)
• /
• v.8 no.1
• /
• pp.213-231
• /
• 2014

Existing image search systems allow users to search images by keywords, or by example images through content-based image retrieval (CBIR). On the other hand, users might learn more relevant textual information about an image from its text captions or surrounding contexts within documents or Web pages. Without such contexts, it's difficult to extract semantic description directly from the image content. In this paper, we propose an annotation watermarking system for users to embed text descriptions, and retrieve more relevant textual information from similar images. First, tags associated with an image are converted by two-dimensional code and embedded into the image by discrete wavelet transform (DWT). Next, for images without annotations, similar images can be obtained by CBIR techniques and embedded annotations can be extracted. Specifically, we use global features such as color ratios and dominant sub-image colors for preliminary filtering. Then, local features such as Scale-Invariant Feature Transform (SIFT) descriptors are extracted for similarity matching. This design can achieve good effectiveness with reasonable processing time in practical systems. Our experimental results showed good accuracy in retrieving similar images and extracting relevant tags from similar images.