• Journal of Preventive Medicine and Public Health
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• v.52 no.2
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• pp.115-122
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• 2019

Objectives: The aim of this study was to assess knowledge, attitudes, and practices regarding tuberculosis (TB) in the general population in Timor-Leste. Methods: In the nationally representative cross-sectional 2016 Timor-Leste Demographic and Health Survey, 4622 men (aged 15-59 years) and 12 607 women (aged 15-49 years) were randomly selected using stratified multistage sampling and interviewed. Results: Overall, 66.9% of men and 62.8% of women were aware of TB, 4.4% of men and 12.6% of women had TB courtesy stigma, and 83.3% of men and 88.6% of women reported intention to receive TB treatment. The $mean{\pm}standard$ deviation overall TB knowledge score was $3.9{\pm}2.0$ (out of 8) among men and $3.0{\pm}1.8$ among women. In a multivariable linear regression analysis, among both men and women, older age, higher education, rural residence, and sources of TB information (family/friends, school/workplace, health care provider, Internet, television, and newspaper) were associated with higher TB knowledge scores. In addition, among women, higher wealth status and having heard about TB from the radio were associated with higher TB knowledge scores. Negative associations with TB courtesy stigma were found for urban residence and having heard about TB from family or friends among men, and for older age, higher TB knowledge, and TB information sources (family/friends and school/workplace) among women. Among both men and women, higher TB knowledge scores and having heard of TB from a health care provider were associated with intention to receive TB treatment. Conclusions: This study identified socio-demographic risk factors for deficiences in population-based TB knowledge in Timor-Leste; these findings should be considered when designing TB communication, prevention, and control strategies.

• Proceedings of the Korea Database Society Conference
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• 1999.06a
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• pp.145-154
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• 1999

An intelligent system embedded with multiple sources of knowledge may provide more robust intelligence with highly ill structured problems than the system with a single source of knowledge. This paper proposes the hybrid knowledge integration mechanism that yields the cooperated knowledge by integrating expert, user, and machine knowledge within the fuzzy logic-driven framework, and then refines it with a genetic algorithm (GA) to enhance the reasoning performance. The proposed knowledge integration mechanism is applied for the prediction of Korea stock price index (KOSPI). Empirical results show that the proposed mechanism can make an intelligent system with the more adaptable and robust intelligence.

• Proceedings of the Korea Inteligent Information System Society Conference
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• 1999.03a
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• pp.145-154
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• 1999

An intelligent system embedded with multiple sources of knowledge may provide more robust intelligence with highly ill structured problems than the system with a single source of knowledge. This paper proposes th hybrid knowledge integration mechanism that yields the cooperated knowledge by integrating expert, user, and machine knowledge within the fuzzy logic-driven framework, and then refines it with a genetic algorithm (GA) to enhance the reasoning performance. The proposed knowledge integration mechanism is applied for the prediction of Korea stock price index (KOSPI). Empirical results show that the proposed mechanism can make an intelligent system with the more adaptable and robust intelligence.

• International Journal of Human Ecology
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• v.11 no.2
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• pp.51-61
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• 2010

Using data from the 2007 SCF, this study examined the use of information source for investment decisions of elderly consumers. The results indicated that many elderly consumers (about 88%) involved savings /investment decisions. The elderly used 'Experts' (39.48%) as a major information source for their investment decisions, followed by 'Friends' (24.18%). The results of the multinomial logit analysis suggested that the perceived value, the cost for search, knowledge, risk and some of the demographic factors were significantly related to the choice of the information sources for investments by elderly consumers.

• Journal of Information Science Theory and Practice
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• v.9 no.3
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• pp.42-55
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• 2021

Knowledge and technology resources are the most crucial sources for the achievement of sustainable development in competitive advantage. Meanwhile, few empirical studies have clarified the types of knowledge and technology resources that nonprofit organizations (NPOs) use and develop. This study aims to categorize knowledge and technology resources in NPOs that both researchers and practitioners can use to develop the nonprofit sector further. A qualitative research method was used for the study. Data were collected from 31 interviews with senior and founding members of NPOs in Thailand. Analysis of qualitative data identified five critical categories of knowledge resources: human resources, organizational practices, partnership or stakeholder involvement, operational practices, and other resources. This study also illustrates both internal and external technology resources, which are used in sample organizations. The study's findings contribute to developing a body of knowledge management literature related to the knowledge and technology resources of NPOs.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.17 no.5
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• pp.1413-1432
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• 2023

Recommender systems aim to recommend items to users by taking into account their probable interests. This study focuses on creating a model that utilizes multiple sources of information about users and items by employing a multimodality approach. The study addresses the task of how to gather information from different sources (modalities) and transform them into a uniform format, resulting in a multi-modal feature description for users and items. This work also aims to transform and represent the features extracted from different modalities so that the information is in a compatible format for integration and contains important, useful information for the prediction model. To achieve this goal, we propose a novel multi-modal recommendation model, which involves extracting latent features of users and items from a utility matrix using matrix factorization techniques. Various transformation techniques are utilized to extract features from other sources of information such as user reviews, item descriptions, and item categories. We also proposed the use of Principal Component Analysis (PCA) and Feature Selection techniques to reduce the data dimension and extract important features as well as remove noisy features to increase the accuracy of the model. We conducted several different experimental models based on different subsets of modalities on the MovieLens and Amazon sub-category datasets. According to the experimental results, the proposed model significantly enhances the accuracy of recommendations when compared to SVD, which is acknowledged as one of the most effective models for recommender systems. Specifically, the proposed model reduces the RMSE by a range of 4.8% to 21.43% and increases the Precision by a range of 2.07% to 26.49% for the Amazon datasets. Similarly, for the MovieLens dataset, the proposed model reduces the RMSE by 45.61% and increases the Precision by 14.06%. Additionally, the experimental results on both datasets demonstrate that combining information from multiple modalities in the proposed model leads to superior outcomes compared to relying on a single type of information.

• Journal of the Korea Safety Management & Science
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• v.2 no.2
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• pp.1-10
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• 2000

This study deals with the application of knowledge engineering and a methodology for the assessment and measurement of reliability, availability, maintainability, and safety of industrial systems using fault-tree representation. A fuzzy methodology for fault-tree evaluation seems to be an alternative solution to overcome the drawbacks of the conventional approach (insufficient information concerning the relative frequence of hazard events). To improve the quality of results, the membership functions must be approximated based on heuristic considerations. The purpose of this study is to describe the knowledge engineering approach, directed to integrate the various sources of knowledge involved in a FTA.

• Knowledge Management Research
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• v.12 no.2
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• pp.35-55
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• 2011

By providing a number of unprecedented knowledge sources, virtual communities have stimulated interests in knowledge sourcing Specifically, virtual communities have triggered fundamental changes in the ways that people interact with one another. Thus, there is a critical need to examine how people try to behave for sourcing knowledge in virtual communities, particularly in terms of the demand-side perspective of knowledge. To address the knowledge gap regarding knowledge sourcing behavior from the knowledge recipients' side, this study draws upon GPA(goal-plan-action) theory with both the social cognitive theory and the goal-setting theory as overarching theories. Furthermore, we apply past experience in virtual communities and trust in knowledge providers as moderating variables to deeply understand the knowledge recipients' behavior. We expect to present theoretical and practical implications through our findings, which are relevant to cognitive goal-setting and knowledge sourcing behavior. In the theoretical perspective, this study illustrates how knowledge recipients determine knowledge sourcing behavior. Based on our findings, this study has practical implications for virtual communities to effectively balance knowledge repositories and knowledge management systems with knowledge providers and recipients.

• Proceedings of the IEEK Conference
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• 2002.07b
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• pp.1165-1168
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• 2002

Knowledge acquisition is the process that extracts the required knowledge from available sources, such as experts, textbooks and databases, for incorporation into a knowledge-based system. Knowledge acquisition is described as the first step in building expert systems and a major bottleneck in the efficient development and application of effective knowledge based expert systems. One cause of the problem is that the process of human reasoning we need to understand for knowledge-based system development is not available for direct observation. Moreover, the expertise of interest is typically not reportable due to the compilation of knowledge which results from extensive practice in a domain of problem solving activity. This is also a problem of modeling knowledge, which has been described as not a problem of accessing and translating what is known, but the familiar scientific and engineering problem of formalizing models for the first time. And this formalization process is especially difficult for knowledge engineers who are often faced with the difficult task of creating a knowledge model of a domain unfamiliar to them. In this paper, we propose an automated knowledge acquisition tool which is based on an implementation of the Inferential Modeling Technique. The Inferential Modeling Technique is derived from the Inferential Model which is a domain-independent categorization of knowledge types and inferences ［Chan 1992］. The model can serve as a template of the types of knowledge in a knowledge model of any domain.

• Journal of the Korean earth science society
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• v.25 no.8
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• pp.656-662
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• 2004

The purposes of this study was to analyze the knowledge sources and of high school students' responses on tasks in scientific context and tasks in everyday context, and was to investigate the relationship between the consistency of responses and cognitive style. One hundred fifteen students participated in study. The students were asked to solve ten paris of problems about several concepts of circulation of atmosphere and water. Each pair of the problems consisted of two questions which were about the same concept but were about different context. That is to say, one questions related to scientific context and the other related to everyday context. Every questions included one subquestion in other to investigate the students' sources of knowledge. The students' responses on two questions in each problem were analyzed in order to compare the sources of knowledge and examine the influence of task context. The results of the study can be summarized as follows: First, students' knowledge sources varied by task contexts. That is to say, the students selected 'Science activities in school' as main source in questions related scientific context and chose 'Experience in daily life' as main source in questions related to everyday context. Second, inconsistency score on all paris of problem was significantly higher than consistency score (p<.01). This result indicated students' responses were much dependent on context. third, field-dependent students were more dependent on context, considering higher inconsistency score than field-dependent ones.