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 paper presents a new approach to tackle the problem of recognizing machine-printed Arabic texts. Because of the difficulty of recognizing cursive Arabic words, the text has to be normalized and segmented to be ready for the recognition stage. The new scheme for recognizing Arabic characters depends on multiple parallel neural networks classifier. The classifier has two phases. The first phase categories the input character into one of eight groups. The second phase classifies the character into one of the Arabic character classes in the group. The system achieved high recognition rate. Keywords—Neural Networks, character recognition, feature extraction, multiple networks, Arabic text.

A New Recognition Scheme for Machine- Printed Arabic Texts based on Neural Networks

Summary Automatic license plate recognition plays an essential role in many applications and a number of methods have been proposed. These applications range from complex security systems to common areas and from parking admission to urban traffic control. License plate recognition has hard properties due to varied effects as fog, rain, shadows, irregular illumination conditions, variable distances, cars’ velocity, scene's angle on frame, plate rotation and conservation, number of vehicles in the scene and other. These effects make plate recognition much more complex and difficult than the traditional pattern recognition systems. The proposed system is composed of the following three stages: 1) detection and extraction of a license plate area by video camera 2) segmentation of the plate characters and digits; and 3) character and digit recognition. The plate image area extracted using the proposed system is segmented into disjoint characters for the character recognition phase, where the neural network classifier is used to recognize the characters based on edge moment invariants and principal component analysis features of wavelet coefficient matrix. The main goal of this research is to develop a new plate recognition system with intelligent issues surpass the systems introduced in literature and to reduce many of the restrictions in the working environment. here the part of summary.

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An Intelligent License Plate Recognition System

In the development of cluster-based energy-efficiency protocols for Wireless Sensor Networks (WSNs), a particularly challenging problem is how to dynamically organize the sensors into a wireless communication network and route sensed information from the field sensors to a remote base station in a matter that prolongs the lifetime of WSNs. This paper presents a new energy-efficient dynamic clustering algorithm for WSNs that automatically organizes the sensors into an appropriate number of clusters in the network. This algorithm, which is based on Harmony Search Algorithm, music based meta-heuristic optimization algorithm, eliminates the need to set the number of clusters a priori. In addition, a multi-objective approach is utilized in the cluster head selection algorithm in order to select the best set of cluster heads. Simulation results demonstrate that the proposed algorithm can achieve an optimal number of clusters, as well as prolong the network lifetime and increase the data delivery at the base station when compared to other well-known clustering-based routing protocols.

Musicians'-inspired clustering protocol for efficient energy Wireless Sensor Networks

Summary Copyright protection of digital images received increasing attention in the last decade due to massive digital artwork distribution via internet, so digital watermarking was a potential solution to such problem. Invisible watermarking scheme has been applied in frequency domain, to embed a logo image inside a large original image. 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 AC coefficients of the DCT matrix. The randomness are obtained from a Non-Linear Feedback Shift Register (NLFSR) pseudorandom bit generator that determines in which color component this logo image bits will embed, as well, the block number for hiding each bit has been chosen according to a (semirandom) function proposed in this work.

/pdf/watermarking-scheme-for-copyright-of-digital-images-4ibfbdcxgf.pdf

Watermarking scheme for copyright of digital images

Human face detection plays a major role in face recognition systems and has gained much attention in recent years. Various methods were proposed to detect faces in different orientations. The aim of this paper is to introduce a comparative study of four detection methods regarding the detection rate. These methods are: SMQT Features and SNOW Classifier (SFSC) method, Efficient and Rank Deficient Face Detection (ERDFD) method, Gabor-Feature Extraction and Neural Network (GFENN) method and An efficient face candidates selector Features (EFCSF) method.The experimental results of the methods have been performed on the wild data set (FDDB) using MatLab 7.9. SFSC method achieved higher detection rate.

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Zyad Shaaban

Papers

A New Recognition Scheme for Machine- Printed Arabic Texts based on Neural Networks

An Intelligent License Plate Recognition System

Musicians'-inspired clustering protocol for efficient energy Wireless Sensor Networks

Watermarking scheme for copyright of digital images

Face detection methods