A digital color images are the most important types of data currently being traded; they are used in many vital and important applications. Hence, the need for a small data representation of the image is an important issue. This paper will focus on analyzing different methods used to extract texture features for a color image. These features can be used as a primary key to identify and recognize the image. The proposed discrete wave equation DWE method of generating color image key will be presented, implemented and tested. This method showed that the percentage of reduction in the key size is 85% compared with other methods.

<p>In this work, we present a novel approach for extracting features of a digital wave file. This approach will be presented, implemented and tested. A signature or a key to any wave file will be created.  This signature will be reduced to minimize the efforts of digital signal processing applications. Hence, the features array can be used as key to recover a wave file from a database consisting of several wave files using reduced Local binary patterns (RLBP). Experimental results are presented and show that The proposed RLBP method is at least 3 times faster than CSLBP method, which mean that the proposed method is more efficient.</p>

Face feature extraction and classification is an attracting research area for its various applications. This paper proposes a hybrid technique based on modified local binary pattern (MLBP) and Layered-Recurrent neural network (L-RNN) to recognize the human faces. The proposed MLBP algorithm reduces the dimensions of extracted face images features. The classification process is conducted using L-RNN. The quasi-Newton back propagation algorithm is used to train the L-RNN. The proposed hybrid technique is examined on MUCT database and the performance analysis of different ANN techniques shows that the Hybrid technique is robust and has superior performance over conventional methods. It achieves the best classification rate of 98%.

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<p>This paper aims to present the details regarding telecommunication network security through cryptology protocols. The data was based on scientific data collection and the quantitative method was adopted. The questionnaire was developed and the primary respondents were approached who were working in 4 telecommunication networking companies namely Huawei, Ericsson, SK Telecom and Telefonica. The sample size of the research was 60 participants and the statistical analysis was used to analyze research. The finding shows that cryptology protocol such as SSH, SSL, Kerberos PGP and SET are implemented within the companies in order to secure network.</p>