Authorship attribution is one of the important problem, with many applications of practical use in the real-world. Authorship identification determines the likelihood of a piece of writing produced by a particular author by examining the other writings of that author. Most of the research in this field is carried out by using instance based model. One of the disadvantages of this model is that it treats the different documents of each author differently. It produces a matrix per each document of the author, thus creating a huge number of matrices per author, i.e. the dimensionality is very high. This paper presents authorship identification using Author based Rank Vector Coordinates (ARVC) model. The advantage of the proposed ARVC model is that it integrates all the author's profile documents into a single integrated profile document (IPD) and thus overcomes the above disadvantage. To overcome the ambiguity created by common words of authors ARVC model removes the common words based on a threshold. Singular value decomposition (SVD) is used on IPD after removing the common words. To reduce the overall dimension of the matrix, without affecting its semantic meaning a rankbased vector coordinates are derived. The eigenvector features are derived on ARVC model. The present paper used cosine similarity measure for author attribution and carries out authorship attribution on English poems and editorial documents

Automatic fabric inspection is important for maintain the fabric quality. For a long time the fabric defects inspection process is still carried out with human visual inspection, and thus, insufficient and costly. Hence the automatic fabric defect inspection is required to reduce the cost and time waste caused by defects. The development of fully automated web inspection system requires robust and efficient fabric defect detection algorithms. The detection of local fabric defects is one of the most intriguing problems in computer vision. Texture analysis plays an important role in the automated visual inspection of texture images to detect their defects. The main aim of this study is to find independent components of the Regular Bands method of the patterned fabric images for the purpose of defect detection in this paper, Independent Component Analysis (ICA) is the proposed method that solves the problem of defect detection in patterned fabrics prior to Regular Bands (RB) method. Patterned fabric is built on the repetitive unit of its design. RB is an existing method that is based on periodicity. The proposed method ICA along with RB method tries to improve the efficiency and quality of the fabric with in less time.

In this paper, we proposed a model for text encryption using elliptic curve cryptography (ECC) for secure transmission of text and by incorporating the Arithmetic/Huffman data compression technique for effective utilization of channel bandwidth and enhancing the security.In this model, every character of text message is transformed into the elliptic curve points (X m ,Y m ), these elliptic curve points are converted into cipher text .The resulting size of cipher text becomes four times of the original text. For minimizing the channel bandwidth requirements, the encrypted text is compressed using the Arithmetic and Huffman compression technique in the following two ways by considering i)x-y coordinates of encrypted text and ii) x-co-ordinates of the encrypted text. The results of the above two cases are compared in terms of overall bandwidth required and saved for Arithmetic and Huffman compression.

With the fast progression of digital data exchange in electronic way, information security is becoming much more important in data storage and transmission. Cryptography has come up as a solution which plays a vital role in information security system against malicious attacks. The cryptography is most important aspect of communications security and becoming an important building block for computer security. This security mechanism uses some algorithms to scramble data into unreadable text which can be only being decoded or decrypted by party those possesses the associated key. These algorithms consume a significant amount of computing resources such as CPU time, memory and computation time. This paper analyses the performance of DES & 3DES which are widely used symmetric encryption algorithms i.e. Data Encryption Standard (DES) and triple Data Encryption Standard (3DES) in terms of time computation of encryption and decryption as well as avalanche effect of the both algorithms

Authorship attribution is one of the important problem, with many applications of practical use in the real-world. Authorship identification determines the likelihood of a piece of writing produced by a particular author by examining the other writings of that author. Every author has a unique style of writing pattern. This paper identifies the unique style of an author(s) using lexical stylometric features including function words using balanced training corpus. The present paper calculates the frequencies of the lexical based stylometric features by balancing training and test corpus on English editorial documents. The present paper compares various machine learning algorithms for the authorship attribution and achieved highest average accuracy 95.58 using Random Forest classifier and 92.59 using Multilayer Perceptron algorithms.