Data Mining - Concepts and Techniques.

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」

IEEE Transactions on Pattern Analysis and Machine Intelligence

State-of-the-art, challenges, and open issues in the integration of Internet of things and cloud computing

Investigating the impact of data normalization on classification performance

This study is emphasized on different types of normalization. Each of which was tested against the ID3 methodology using the HSV data set. Number of leaf nodes, accuracy and tree growing time are three factors that were taken into account. Comparisons between different learning methods were accomplished as they were applied to each normalization method. A new matrix was designed to check for the best normalization method based on the factors and their priorities. Recommendations were concluded.

/pdf/data-mining-a-preprocessing-engine-4rm5x5353y.pdf

Data Mining: A Preprocessing Engine

This study is emphasized on different types of normalization. Each of which was tested against the ID3 methodology using the HSV data set. Number of leaf nodes, accuracy, and tree growing time are three factors that were taken into account. Comparisons between different learning methods were accomplished as they were applied to each normalization method. A simple matrix was designed to check for the best normalization method based on the factors and their priorities. Recommendations were concluded.

Normalization as a Preprocessing Engine for Data Mining and the Approach of Preference Matrix

In many applications, different kinds of moments have been utilized to classify images and object shapes. Moments are important features used in recognition of different types of images. In this paper, three kinds of moments: Geometrical, Zernike and Legendre Moments have been evaluated for classifying 3D object images using Nearest Neighbor classifier. Experiments are conducted using ETH-80 database, which contains 80 objects.

http://www.jatit.org/volumes/research-papers/Vol7No1/4Vol7No1.pdf

Object classification via geometrical, zernike and legendre moments

In this paper, a new face recognition system based on Haar wavelet transform (HWT) and Principal Component Analysis (PCA) using Levenberg-Marquardt backpropagation (LMBP) neural network is presented. The image face is preprocessed and detected. The Haar wavelet is used to form the coefficient matrix for the detected face. The image feature vector is obtained by computing PCA for the coefficient matrix of DWT. A comparison between the proposed recognition system using DWT, PCA and Discrete Cosine Transform (DCT) is also made. The experimental results indicated that the image faces can be recognized by the proposed face recognition system effectively.

Face Recognition Based on Haar Wavelet Transform and Principal Component Analysis via Levenberg-Marquardt Backpropagation Neural Network

Invisible watermarking methods have been applied in frequency domains, trying to embed a small image inside a large original image. The original bitmap image will be converted into frequency domain to obtain the discrete cosine transform (DCT) matrices from its blocks. The bits of the logo image are embedded in random color components of the original image, as well as in random positions in each selected block. These positions are alternating current (AC) coefficients of the DCT matrix. The randomness is obtained from RC4 pseudorandom bit generator that determines in which color component this logo image bits will be embedded. The embedded bits have been hidden in random blocks in the image, which are chosen according to a (semi-random) function proposed in this work.

Zyad Shaaban

Papers

Data Mining: A Preprocessing Engine

Normalization as a Preprocessing Engine for Data Mining and the Approach of Preference Matrix

Object classification via geometrical, zernike and legendre moments

Face Recognition Based on Haar Wavelet Transform and Principal Component Analysis via Levenberg-Marquardt Backpropagation Neural Network

Watermarking of digital images in frequency domain