http://chineseinput.net/에서 pinyin(병음)방식으로 중국어를 변환할 수 있습니다.
변환된 중국어를 복사하여 사용하시면 됩니다.
- 中文 을 입력하시려면 zhongwen을 입력하시고 space를누르시면됩니다.
- 北京 을 입력하시려면 beijing을 입력하시고 space를 누르시면 됩니다.
RISS 인기검색어
오늘 본 자료
-
New Paradigms for Colonoscopic Management of Diminutive Colorectal Polyps: Predict^ Resect^ and Discard or Do Not Resect?
-
청소년의 진로성숙에 영향을 미치는 요인에 관한 연구 : 관악·동작구 교육복지특별지원 고등학교를 중심으로 = A Study on Factors Affecting Adolescents' Career Maturity: Focusing on High Schools in Gwanak-gu and Dongjak-gu in Seoul^ which Receive Special Support from the Education Welfare Program
- 검색키워드
- 저자 : 민무홍 (검색결과 5 건)
-
-
-
Discovering communities in social network service using the machine learning mixture
민무홍 성균관대학교 일반대학원 2012 국내석사
Nowadays, social network services such as Twitter and Facebook have been widely exploited. Social network services have attracted millions of users, many of whom have combined these services into their daily life. Many people can no longer imagine a life without the social network service. Since it is easy to extend their relations to others in the social network service, users usually have a huge user list. In the user list, various users, not only friends but also unfamiliar users such as celebrities, news media, or/and even unknown users, can be included. A user may add 'friends' with little or no actual connection such as corporations. If people use social network services for a long time, acquaintance lists will be expanded. Then, updated information of intimate friends is covered. In other words, it is difficult to see the information of the familiar friends. Another problem is that users were bored about creating groups for intimate friends from the huge user list. If the friend list is classified into communities, similar users? contents belong to each community. Accordingly, the social network service needs to be divided into the meaningful communities from the friends list of users? for better services. This research aims at the analysis of the huge user list in the social network service and the data mining of the meaningful communities. A mixed method combining Support Vector Machine method with some clustering methods was utilized in order to divide a huge users? list to the meaningful communities. This method needs to be optimized to the social network services. The connection information was collected from 168 seed users. And also from the 17047 following users. The collected data were applied to Support Vector Machine first of all. For Support Vector Machine, the characteristics of the social network service were analyzed to extract ten features. In addition, three machine learning tools were used such as SVMlight, WEKA, and libSVM for feature selection. The experiment of the clustering technique was performed, and the previous Support Vector Machine results were put into graph clustering algorithms in the experiment. The experimental results for detecting communities show that the test result using Support Vector Machine is improved by 10% on average than that without using Support Vector Machine. The first contribution of the research is to detect the meaningful communities from a huge user list in the social network service. The second contribution is to analyze the characteristics of the social network service and to extract many kinds of features for Support Vector Machine.
-
민무홍 Graduate School of Cybersecurity, Korea University 2021 국내박사
Advances in information technology and the widespread use of smartphones have led to enormous growth in the gambling industry over the past decade, primarily across a network of mobile apps, websites, and platforms. In order to combat growing issues with addiction, government agencies have responded with strict regulations regarding monetary limits and gambling locations. As a result, some foreigner bettors have found new ways to evade emerging regulations in lawful gambling environments. The absence of technical systems to identify anomalous activity has led to cases like the Walkerhill Incident (2016-2017) that occurred between June 2016 and September 2017 at Grand Walkerhill Seoul Hotel, when foreign bettors automated ticket purchases by modifying a Korean betting application called “MyCard”. This study proposes a method to detect and prevent anomalous activity. The analysis utilizes periodic transaction data in a time series provided by real-world horse racing records rather than artificial data. This study then used time series machine learning algorithms to identify anomalous transactions and conduct a comparative analysis of the results of existing statistical techniques and machine learning techniques. This study also demonstrates a process to detect anomalous transactions, as well as specific methodologies and systems to respond to new types of anomalies. The detection method and its systems are designed based on time series research, which takes a similar form to the data produced in the horse racing industry. The resulting analysis and discussion could prove useful in a wide variety of real-world applications, including the gambling industry that originally inspired the research.
-
연관 검색어 추천
이 검색어로 많이 본 자료
활용도 높은 자료
