다중혈관 관상동맥 환자에서 y-문합을 이용하여 양쪽 내흉동맥만을 사용한 우회술의 조기 성적

Machine Learning is the study of methods for programming computers to learn. Computers are applied to a wide range of tasks, and for most of these it is relatively easy for programmers to design and implement the necessary software. However, there are many tasks for which this is difficult or impossible. These can be divided into four general categories. First, there are problems for which there exist no human experts. For example, in modern automated manufacturing facilities, there is a need to predict machine failures before they occur by analyzing sensor readings. Because the machines are new, there are no human experts who can be interviewed by a programmer to provide the knowledge necessary to build a computer system. A machine learning system can study recorded data and subsequent machine failures and learn prediction rules. Second, there are problems where human experts exist, but where they are unable to explain their expertise. This is the case in many perceptual tasks, such as speech recognition, hand-writing recognition, and natural language understanding. Virtually all humans exhibit expert-level abilities on these tasks, but none of them can describe the detailed steps that they follow as they perform them. Fortunately, humans can provide machines with examples of the inputs and correct outputs for these tasks, so machine learning algorithms can learn to map the inputs to the outputs. Third, there are problems where phenomena are changing rapidly. In finance, for example, people would like to predict the future behavior of the stock market, of consumer purchases, or of exchange rates. These behaviors change frequently, so that even if a programmer could construct a good predictive computer program, it would need to be rewritten frequently. A learning program can relieve the programmer of this burden by constantly modifying and tuning a set of learned prediction rules. Fourth, there are applications that need to be customized for each computer user separately. Consider, for example, a program to filter unwanted electronic mail messages. Different users will need different filters. It is unreasonable to expect each user to program his or her own rules, and it is infeasible to provide every user with a software engineer to keep the rules up-to-date. A machine learning system can learn which mail messages the user rejects and maintain the filtering rules automatically. Machine learning addresses many of the same research questions as the fields of statistics, data mining, and psychology, but with differences of emphasis. Statistics focuses on understanding the phenomena that have generated the data, often with the goal of testing different hypotheses about those phenomena. Data mining seeks to find patterns in the data that are understandable by people. Psychological studies of human learning aspire to understand the mechanisms underlying the various learning behaviors exhibited by people (concept learning, skill acquisition, strategy change, etc.).

Machine learning

Wireless distributed microsensor systems will enable the reliable monitoring of a variety of environments for both civil and military applications. In this paper, we look at communication protocols, which can have signicant impact on the overall energy dissipation of these networks. Based on our ndings that the conventional protocols of direct transmission, minimum-transmission-energy, multihop routing, and static clustering may not be optimal for sensor networks, we propose LEACH (Low-Energy Adaptive Clustering Hierarchy), a clustering-based protocol that utilizes randomized rotation of local cluster base stations (cluster-heads) to evenly distribute the energy load among the sensors in the network. LEACH uses localized coordination to enable scalability and robustness for dynamic networks, and incorporates data fusion into the routing protocol to reduce the amount of information that must be transmitted to the base station. Simulations show that LEACH can achieve as much as a factor of 8 reduction in energy dissipation compared with conventional routing protocols. In addition, LEACH is able to distribute energy dissipation evenly throughout the sensors, doubling the useful system lifetime for the networks we simulated.

Energy-efficient communication protocols for wireless microsensor networks

Two paradigms characterize much of the research in the Information Systems discipline: behavioral science and design science The behavioral-science paradigm seeks to develop and verify theories that explain or predict human or organizational behavior The design-science paradigm seeks to extend the boundaries of human and organizational capabilities by creating new and innovative artifacts Both paradigms are foundational to the IS discipline, positioned as it is at the confluence of people, organizations, and technology Our objective is to describe the performance of design-science research in Information Systems via a concise conceptual framework and clear guidelines for understanding, executing, and evaluating the research In the design-science paradigm, knowledge and understanding of a problem domain and its solution are achieved in the building and application of the designed artifact Three recent exemplars in the research literature are used to demonstrate the application of these guidelines We conclude with an analysis of the challenges of performing high-quality design-science research in the context of the broader IS community

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Design science in information systems research

Knowledge is a broad and abstract notion that has defined epistemological debate in western philosophy since the classical Greek era. In the past few years, however, there has been a growing interest in treating knowledge as a significant organizational resource. Consistent with the interest in organizational knowledge and knowledge management (KM), IS researchers have begun promoting a class of information systems, referred to as knowledge management systems (KMS). The objective of KMS is to support creation, transfer, and application of knowledge in organizations. Knowledge and knowledge management are complex and multi-faceted concepts. Thus, effective development and implementation of KMS requires a foundation in several rich literatures.

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Review: Knowledge management and knowledge management systems: conceptual foundations and research issues

Electronic meeting systems

The authors critically review systems development in information systems (IS) research. Several classification schemes of research are described and systems development is identified as a developmental, engineering, and formulative type of research. A framework of research is proposed to explain the dual nature of systems development as a research methodology and a research domain in IS research. Progress in several disciplinary areas is reviewed to provide a basis to argue that systems development is a valid research methodology. A systems development research process is presented from a methodological perspective. Software engineering, the basic method is applying the systems development research methodology, is then discussed. A framework to classify IS research domain and various research methodologies in studying systems development is presented. It is suggested that systems development and empirical research methodologies are complementary to each other. It is further proposed that an integrated multidimensional and multimethodological approach will generate fruitful research results in IS research. >

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Systems development in information systems research

http://clintlalonde.net/wp-content/uploads/2012/09/instructionalvideo.pdf

Instructional video in e-learning: Assessing the impact of interactive video on learning effectiveness

In an e-learning environment that emphasizes learner-centered activity and system interactivity, remote learners can outperform traditional classroom students.

Can e-learning replace classroom learning?

As managers spend more of their time in meetings, the study of information technology to support meetings becomes increasingly important. Several unique systems to support meetings electronically have been developed in industry and universities. The PLEXSYS systems at the University of Arizona have been operational since 1985 and are now being implemented in industrial sites. This article proposes and defines a new term for information technology systems that support group meetings: electronic meeting systems (EMS). EMSs are more than group decision support systems (GDSS): they support more tasks than just decision making; they focus on communication. They move beyond the GDSS decision room where groups must meet at the same time in the same place, to meetings that can be conducted across time ands pace. The article then presents a model fo th EMS concept, which has three components: group process and outcomes; methods; and environment. Each of these component is explained in turn, and the implications derived form their study to date are discussed. Finally, the implementation of information technology for meeting support and its use in corporate settings will be addressed, as it has implications for productivity, meeting size, group member participat9ion, and the role of the IS department.

Jay F. Nunamaker

Papers

Electronic meeting systems

Systems development in information systems research

Instructional video in e-learning: Assessing the impact of interactive video on learning effectiveness

Can e-learning replace classroom learning?

Information technology to support electronic meetings