Data Mining - Concepts and Techniques.

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

We will review some of the major results in random graphs and some of the more challenging open problems. We will cover algorithmic and structural questions. We will touch on newer models, including those related to the WWW.

Random graphs

/pdf/a-break-in-the-clouds-towards-a-cloud-definition-svovsw1r5a.pdf

A Break in the Clouds: Towards a Cloud Definition

The objective of this paper is to give a comprehensive introduction to applied cryptography with an engineer or computer scientist in mind. The emphasis is on the knowledge needed to create practical systems which supports integrity, confidentiality, or authenticity. Topics covered includes an introduction to the concepts in cryptography, attacks against cryptographic systems, key use and handling, random bit generation, encryption modes, and message authentication codes. Recommendations on algorithms and further reading is given in the end of the paper. This paper should make the reader able to build, understand and evaluate system descriptions and designs based on the cryptographic components described in the paper.

[서평]「Applied Cryptography」

A high capacity text steganography scheme based on LZW compression and color coding

this paper, an attempt has been made to develop a statistical model for the sensor data stream, estimating density for distribution of data and flagging a particular value as an outlier in the best possible manner without compromising with the performance. A statistical modeling technique transforms the raw sensor readings into meaningful information which will yield effective output, hence offering a more reliable way to gain insight into the physical phenomena under observation. We have proposed a model that is based on the approximation of the sensor data distribution. Our approach takes into consideration various characteristics and features of streaming sensor data. We processed and evaluated our proposed scheme with a set of experiments with datasets which is taken from Intel Berkeley research lab. The experimental evaluation shows that our algorithm can achieve very high precision and recall rates for identifying outliers and demonstrate the effectiveness of the proposed approach.

/pdf/outlier-detection-of-data-in-wireless-sensor-networks-using-1r6nb4is1l.pdf

Outlier Detection of Data in Wireless Sensor Networks Using Kernel Density Estimation

In this paper, we propose a novel interpolation and a new reversible data hiding scheme for upscaling the original image and hiding secret data into the upscaled/interpolated image. This data hiding scheme considers the characteristics of the human visual system while embedding the secret data so that the existence of the secret data is not detected even after embedding a large amount of secret data. The proposed hiding scheme first divides pixel intensity ranges into groups and then adaptively embeds the secret data bits into the pixels based on the pixel intensity values. Therefore, the proposed scheme is able to maintain the visual quality of the stego-image. Experimental results show that the achieved PSNR by the proposed interpolation method is more than 30 dB for all the test images. Further, the results prove that the proposed data hiding scheme has superior performance than all the existing interpolation-based data hiding schemes.

A Reversible Data Hiding Scheme for Interpolated Images Based on Pixel Intensity Range

Multimedia data is more used on internet so it is desired to secure the data before transmitting. Various algorithms have been researched and proposed in this regards. This paper presents the hybrid approach for image security that combines both encryption and steganography. First the image is encrypted using proposed new version of AES algorithm, which is then hided into cover image using the steganography concept. Experimental results and analysis is shown. This hybrid approach provides greater security against attacks.

A hybrid approach for image security by combining encryption and steganography

Simulation modeling of cloud computing for smart grid using CloudSim

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