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

Data Mining - Concepts and Techniques.

It is shown that, particularly for terrestrial cellular telephony, the interference-suppression feature of CDMA (code division multiple access) can result in a many-fold increase in capacity over analog and even over competing digital techniques. A single-cell system, such as a hubbed satellite network, is addressed, and the basic expression for capacity is developed. The corresponding expressions for a multiple-cell system are derived. and the distribution on the number of users supportable per cell is determined. It is concluded that properly augmented and power-controlled multiple-cell CDMA promises a quantum increase in current cellular capacity. >

On the capacity of a cellular CDMA system

Part of the groundbreaking Connecting with e-Learning series, A Guide to Authentic e-Learning provides effective, working examples to engage learners with authentic tasks in online settings. As technology continues to open up possibilities for innovative and effective teaching and learning opportunities, students and teachers are no longer content to accept familiar classroom or lecture-based pedagogies that rely on information delivery and little else. Situated and constructivist theories advocate that learning is best achieved in circumstances resembling the real-life application of knowledge. While there are multiple learning design models that share similar foundations, authentic e-learning tasks go beyond process to become complex, sustained activities that draw on realistic situations to produce realistic outcomes.

A Guide to Authentic e-Learning

Performance comparison of intrusion detection systems and application of machine learning to Snort system

The usage of mobile devices for mobile learning is becoming increasingly popular. There is a new brand of students in the universities now-a-days who are easily connected to technology and innovative mobile devices. We attempt to do an analysis on a survey done with university students on mobile device usage for mobile learning purposes. This is to find the learning trends within the student community so that some of these popular practices could be encouraged to enhance learning among the student community. Both the quantitative and qualitative approaches are adopted in the analysis. The results are discussed and conclusions drawn in the end.

The mobile devices and its mobile learning usage analysis

The technology of internet and wireless networks conceived the revolutionary concept of mobile learning or m-learning. Mobile learning through wireless enabled laptops within a university campus can make use of the learning management system that is already available through internet or intranet. Without restrictions within the four walls of computer labs or library, students can now access the learning resources anywhere in the campus where wireless access points or hotspots are located. We briefly investigate on the mobile learning concepts and also expand briefly on different wireless technologies, eventually emphasizing on a secure 802.11 network. Later an analysis of the student perceptions on mobile learning is presented through a survey and the future trends were discussed in conclusion.

Mobile technologies and its impact – an analysis in higher education context

Unsolicited or spam emails are on the rise, where one's email storage inbox is bombarded with emails that make no sense at all. This creates excess usage of traffic bandwidth and results in unnecessary wastage of network resources. We wanted to test the Bayesian spam detection scheme with context matching that we had developed by implementing the keyword stripping using the Porter Stemmer algorithm. This could make the keyword search more efficient, as the root or stem word is only considered. Experimental results on two public spam corpuses are also discussed at the end.

Biju Issac

Papers

Performance comparison of intrusion detection systems and application of machine learning to Snort system

The mobile devices and its mobile learning usage analysis

The mobile devices and its mobile learning usage analysis

Mobile technologies and its impact – an analysis in higher education context

Implementing spam detection using Bayesian and Porter Stemmer keyword stripping approaches