• Asian Pacific Journal of Cancer Prevention
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• v.14 no.12
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• pp.7143-7147
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• 2013

Background: The survival outcomes for women presenting with early breast cancer are influenced by treatment decisions. In Malaysia, survival outcome is generally poor due to late presentation. Of those who present early, many refuse treatment for complementary therapy. Objective: This study aimed to explore the decision making experiences of women with early breast cancer. Materials and Methods: A qualitative study using individual in-depth interviews was conducted to capture the decision making process of women with early breast cancer in Malaysia. We used purposive sampling to recruit women yet to undergo surgical treatment. A total of eight participants consented and were interviewed using a semi-structured interview guide. These women were recruited from a period of one week after they were informed of their diagnoses. A topic guide, based on the Ottawa decision support framework (ODSF), was used to facilitate the interviews, which were audio recorded, transcribed and analysed using a thematic approach. Results: We identified four phases in the decision-making process of women with early breast cancer: discovery (pre-diagnosis); confirmatory ('receiving bad news'); deliberation; and decision (making a decision). These phases ranged from when women first discovered abnormalities in their breasts to them making final surgical treatment decisions. Information was vital in guiding these women. Support from family members, friends, healthcare professionals as well as survivors also has an influencing role. However, the final say on treatment decision was from themselves. Conclusions: The treatment decision for women with early breast cancer in Malaysia is a result of information they gather on their decision making journey. This journey starts with diagnosis. The women's spouses, friends, family members and healthcare professionals play different roles as information providers and supporters at different stages of treatment decisions. However, the final treatment decision is influenced mainly by women's own experiences, knowledge and understanding.

• Korean journal of communication and information
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• v.76
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• pp.151-182
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• 2016

The right to be forgotten (RTBF) has been a population notion to address privacy issues associated with the digitalization of information and the dissemination of such information over the global digital network. In May 2014, the European Court of Justice (ECJ) laid down a landmark RTBF decision to grant individuals the right to be de-listed from search results. ECJ's RTBF decision sparked an increased interest in RTBF in South Korea. Academic and non-academic commentators have provided a mistaken or outstretched interpretation of RTBF in claiming that removal of news articles should be read into RTBF in Korean law. Moreover, the Press Arbitration Commission of Korea (PAC) has proposed revising the Press Arbitration Act (PAA) to allow the alleged victims of news reporting to request the deletion of news stories. This article examines the notion of RTBF from its origin to the latest development abroad and also critically explores Korean laws regulation freedom of expression to evaluate if Korea needs the proposed PAA revision.

• Journal of Internet Computing and Services
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• v.14 no.6
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• pp.85-94
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• 2013

With the emergence of world wide web, in particular web 2.0 the rapidly growing amount of news articles has created a problem for users in selection of news articles according to their requirements. To overcome this problem different clustering mechanism has been proposed to broadly categorize news articles. However these techniques are totally machine oriented techniques and lack users' participation in the process of decision making for membership of clustering. In order to overcome the issue of zero-participation in the process of clustering news articles in this paper we have proposed a framework for clustering news articles by combining users' judgments that they post on twitter with the news articles to cluster the objects. We have employed twitter hash-tags for this purpose. Furthermore we have computed the credibility of users' based on frequency of retweets for their tweets in order to enhance the accuracy of the clustering membership function. In order to test performance of proposed methodology, we performed experiments on tweets messages tweeted during general election 2013 in Pakistan. Our results proved over claim that using users' output better outcome can be achieved then ordinary clustering algorithms.

• The Journal of Information Systems
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• v.30 no.3
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• pp.21-45
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• 2021

Purpose This paper analyzed the impacts of domestic stock market by a global pandemic such as COVID-19. We investigated how the overall pattern of the stock market changed due to the impact of the COVID-19 pandemic. In particular, we analyzed in depth the pattern of stock price, as well, tried to find what factors affect on stock market index(KOSPI) in the healthcare industry due to the COVID-19 pandemic. Design/methodology/approach We built a data warehouse from the databases in various industrial and economic fields to analyze the changes in the KOSPI due to COVID-19, particularly, the changes in the healthcare industry centered on bio-medicine. We collected daily stock price data of the KOSPI centered on the KOSPI-200 about two years before and one year after the outbreak of COVID-19. In addition, we also collected various news related to COVID-19 from the stock market by applying text mining techniques. We designed four experimental data sets to develop decision tree-based prediction models. Findings All prediction models from the four data sets showed the significant predictive power with explainable decision tree models. In addition, we derived significant 10 to 14 decision rules for each prediction model. The experimental results showed that the decision rules were enough to explain the domestic healthcare stock market patterns for before and after COVID-19.

• Smart Media Journal
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• v.6 no.3
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• pp.41-48
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• 2017

Event detection using social media has been widespread since social network services have been an active communication channel for connecting with others, diffusing news message. Especially, the real-time characteristic of social media has created the opportunity for supporting for real-time applications/systems. Social network such as Twitter is the potential data source to explore useful information by mining messages posted by the user community. This paper proposed a novel system for temporal event detection by analyzing social data. As a result, this information can be used by first responders, decision makers, or news agents to gain insight of the situation. The proposed approach takes advantages of deep learning methods that play core techniques on the main tasks including informative data identifying from a noisy environment and temporal event detection. The former is the responsibility of Convolutional Neural Network model trained from labeled Twitter data. The latter is for event detection supported by Recurrent Neural Network module. We demonstrated our approach and experimental results on the case study of earthquake situations. Our system is more adaptive than other systems used traditional methods since deep learning enables to extract the features of data without spending lots of time constructing feature by hand. This benefit makes our approach adaptive to extend to a new context of practice. Moreover, the proposed system promised to respond to acceptable delay within several minutes that will helpful mean for supporting news channel agents or belief plan in case of disaster events.

• The Journal of Asian Finance, Economics and Business
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• v.7 no.3
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• pp.87-99
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• 2020

The study aims to investigate factors that determine dividend payout policy using 336 non-financial firm year observations covering the period 2005 to 2016 in Malaysia. We found a significant positive relationship between corporate board size, board members average age, board tenure and dividend payout policy. We also found a strong negative effect and statistically insignificant relationship of board diversity, board independence, CEO duality and dividend payout policy. Additional, financial leverage has a negative effect on dividend payout policy. It is also noticed that firms with diverse boards are more likely to pay dividends and tend to pay larger dividends than those with non-diverse boards. Our results suggest that board diversity has a significant impact on dividend payout policy. Impact of board diversity on dividend payout policy is particularly conspicuous for firms with potentially greater agency problems. Our findings are consistent with the argument that corporate board traits enhancement positively affect the dividend payout policy which is beneficial for shareholders. This study offers useful insights into the current global debate on board traits and its implications for firms. The dividend payout policy signals good news to investors. Corporate board traits and firm's financial decision are the factors that disrupt the dividend decision.

• Journal of Information Technology Services
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• v.11 no.3
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• pp.39-48
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• 2012

It is essential that firm makes a rational and scientific decision making and creates a news value for the future direction. To do so, many firms attempt to collect meaningful data and find the filtered and refined implication for the better customer relationship and the active market drive through the various analytic tools. Among the possible IT solutions, utilization of 'Big Data' is becoming more attractive and necessary in such a way that it would help firms obtain the systemized and demanding information and facilitate their decision making process to keep up with the market needs. In this paper, it introduces the concepts and development of 'Big Data' recognized as a IT resource and solution under the rapidly changing firm environment. This study also presents the several firm cases using Big Data' and the Oracle's total data management and analytic solutions in order to support the application of 'Big Data'. Finally this paper provides a holistic viewpoint and realistic approach on use of 'Big Data' to create a new value.

• Journal of Multimedia Information System
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• v.6 no.4
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• pp.217-224
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• 2019

As the world's population grows, how to maintain the food supply is becoming a bigger problem. Now and in the future, big data will play a major role in decision making in the agriculture industry. The challenge is how to obtain valuable information to help us make future decisions. Big data helps us to see history clearer, to obtain hidden values, and make the right decisions for the government and farmers. To contribute to solving this challenge, we developed the Agriculture Big Data Analysis System. The system consists of agricultural big data collection, big data analysis, and big data visualization. First, we collected structured data like price, climate, yield, etc., and unstructured data, such as news, blogs, TV programs, etc. Using the data that we collected, we implement prediction algorithms like ARIMA, Decision Tree, LDA, and LSTM to show the results in data visualizations.

• Journal of the Korean Professional Engineers Association
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• v.44 no.6
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• pp.40-44
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• 2011

Nuclear energy in Korea began in 1958, when the Korea's atomic energy act was formulated and the relevant organizations were founded. Since then, notwithstanding the two catastrophe like TMI and Chernobyl accident, Korea made a wise decision to expand the peaceful uses of the nuclear energy as well as to localize the essential nuclear design technology of fuel and nuclear steam supply system. This decision resulted in the success of export of nuclear power plants as well as research reactor in 2010s. The Korea's nuclear policy, which well utilized 'international crisis in nuclear business' as 'opportunity of Korea to get. nuclear technology', is believed nice policy as a role model of nuclear new-comer countries. Based upon the success story of localization of nuclear technology, Korea had an eye for a niche market, which was a basis of development of SMART, Korea-made integral PWR. The operation of a SMART plant can sufficiently provide not only electricity but also fresh water for 100,000 residents. Last two years, Korea's nuclear industry team led by the Korea Atomic Energy Research Institute completed the standard design of SMART and applied to the Korea's regulatory body for standard design approval. Now the Korea's licensing authority is reviewing the design with the relevant documents, and the design team is doing its best to realize its hope to get the approval by the end of this year. From next year, the SMART business including construction and export will be explored by the KEPCO consortium.

• Journal of Industrial Convergence
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• v.17 no.3
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• pp.17-22
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• 2019

As the value of big data is recognized as important, it is possible to advance decision making by effectively introducing and improving the development and utilization of JAVA and R programs that can analyze vast amounts of existing and unstructured data to governments, public institutions and private businesses. In this study, news data was collated and analyzed through text mining techniques in order to establish marketing strategies based on consumers' airline preferences. This research is meaningful in establishing marketing strategies based on analysis results by analyzing consumers' airline preferences using high-level big data utilization program techniques for data that were difficult to obtain in the past.