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.

Data Mining Practical Machine Learning Tools and Techniques

International Journal of Computer and Applications

Detecting and investigating crime by means of data mining: a general crime matching framework

Link prediction is an important task in data mining, which has widespread applications in social network research. Given a social network, the objective of this task is to predict future links which have not yet observed in the current state of the network. Owing to its importance, the link prediction task has received substantial attention from researchers in diverse disciplines; thus, a large number of methodologies for solving this problem have been proposed in recent decades. However, existing literatures lack a current and comprehensive analysis of existing link prediction methodologies. Couple of survey articles on link prediction are available, but they are out-dated as numerous link prediction methods have been proposed after these articles have been published. In this paper, we provide a systematic analysis of existing link prediction methodologies. Our analysis is comprehensive, it covers the earliest scoring-based methodologies and extends up to the most recent methodologies which are based on deep learning methods. We also categorize the link prediction methods based on their technical approach, and discuss the strength and weakness of various methods.

A systemic analysis of link prediction in social network

Nowadays, because of the undeniable impact of the Internet on all aspects of human life, security preserving has received more attention. To reach an acceptable level of security, Completely Automatic Public Turing test to tell Computer and Human Apart or simply CAPTCHA as a security preserving tool has been tailored for situations that need to prevent bots from doing a specific action; for example, signing up and downloading. Simultaneously, it should be also designed in such a way to allow humans to perform the same action. Despite its advantageous applications, there are several important issues such as security, usability, and accessibility that make its use controversial. In this paper, attempts were made to do a comprehensive review on various aspects and state-of-the-art of CAPTCHA in general and its alternatives in particular to help researchers easily focus on specific issues for the sake of proposing new solutions and ideas. Regarding the advancement in CAPTCHA development, new classifications were proposed to categorize different variations of CAPTCHAs and their problems and then compare them. Moreover, different types of CAPTCHAs' alternatives were classified and evaluated by introducing several proposed measures. This evaluation could come in handy for future studies that aim to develop new techniques for overcoming current deficiencies. Copyright © 2014 John Wiley & Sons, Ltd.

https://onlinelibrary.wiley.com/doi/pdf/10.1002/sec.1157

CAPTCHA and its Alternatives: A Review

Robust dynamic block-based image watermarking in DWT domain

A growing number of applications that generate massive streams of data need intelligent data processing and online analysis. Real-time surveillance systems, telecommunication systems, sensor networks and other dynamic environments are such examples. The imminent need for turning such data into useful information and knowledge augments the development of systems, algorithms and frameworks that address streaming challenges. The storage, querying and mining of such data sets are highly computationally challenging tasks. Mining data streams is concerned with extracting knowledge structures represented in models and patterns in non stopping streams of information. Generally, two main challenges are designing fast mining methods for data streams and need to promptly detect changing concepts and data distribution because of highly dynamic nature of data streams. The goal of this article is to analyze and classify the application of diverse data mining techniques in different challenges of data stream mining. In this paper, we present the theoretical foundations of data stream analysis and propose an analytical framework for data stream mining techniques.

Mohammad Reza Keyvanpour

Papers

Detecting and investigating crime by means of data mining: a general crime matching framework

A systemic analysis of link prediction in social network

CAPTCHA and its Alternatives: A Review

Robust dynamic block-based image watermarking in DWT domain

An analytical framework for data stream mining techniques based on challenges and requirements