Iraqi Journal for Computers and Informatics

This paper describe Web Content Filtering that
aimed to block out offensive material by using Distributed
Agents. The proposed system using FCM algorithm and other
page's features (Title, Metadata , Warning Message) to classify the websites (using as candidate) into two types:- white that considered acceptable, and black that contain harmful material taking the English Pornographic websites as a case study.

In this paper, overall system model, shown in Figure (1), of video compression-encryptiontransmitter/decompression-dencryption-receiver was designed and implemented. The modified video codec system has used and in addition to compression/decompression, the encryption/decryption video signal by using chaotic neural network (CNN) algorithm was done. Both of quantized vector data and motion vector data have been encrypted by CNN. The
compressed and encrypted video data stream has been sent to receiver by using orthogonal frequency division multiplexing (OFDM) modulation technique. The system model was designed according to video signal sample size of 176 × 144 (QCIF standard format) with rate of 30 frames per second. Overall system model integrates and operates successfully with acceptable performance results.

The important methods of data mining is large and from these methods is mining of association rule. The mining
of association rule gives huge number of the rules. These huge rules make analyst consuming more time when searching through the large rules for finding the interesting rules. One of the solutions for this problem is combing between one of the Association rules visualization method and generalization method. Association rules visualization method is graph-based method. Generalization method is Attribute Oriented Induction algorithm (AOI). AOI after combing calls Modified AOI because it removes and changes in the steps of the traditional AOI. The graph technique after combing also callsgrouped grap h method because it displays the aggregated that results rules from AOI. The results of this paper are ratio of compression that gives clarity of visualization. These results provide the ability for test and drill down in the rules or understand and roll up.

In this paper a new method to extract the closed friends to a certain twitter user is proposed. This algorithm can be used in many applications such as security and marketing. In the proposed algorithm four relation indicators are used. The shared twitter users, the common friends, the common followers and number of “retweeted from” are used to determine the closed friends of a certain twitter user. The great data downloading and mining tool KNIME is used with some R coding. The proposed model could ranks the shared twitter users according to their closeness to a certain twitter user using the four used relation indicator.

In this work an efficient method for hiding a speech in audio is proposed. The features of secret speech is extracted with LPC (Linear Predictive Coding), and these parameters embedded in audio in chaotic order. Discrete Wavelet Transform (DWT) is applied on audio frames to split the signal in high and low frequencies. The embedding parameters are embedded in high frequency. The stego audio is perceptually indistinguishable from the equivalent cover audio. The proposed method allows hiding a same duration of speech (secret) and audio (cover). The stego audio is subjected to objective tests such signal to noise ratio (SNR), signal to noise ratio segmental (SNRseg), Segmental Spectral SNR, Log Likelihood Ratio (LLR) and Correlation (Rxy) to determine the similarity with original audio.

DBSCAN (Density-Based Clustering of Applications with Noise )is one of the attractive algorithms among density-based clustering algorithms. It characterized by its ability to detect clusters of various sizes and shapes with the presence of noise, but its performance degrades when data have different densities .In this paper, we proposed a new technique to separate data based on its density with a new sampling technique , the purpose of these new techniques is for getting data with homogenous density .The experimental results on synthetic data and real world data show that the new technique enhanced the clustering of DBSCAN to large extent.

Levenshtein is a Minimum Edit Distance method; it is usually used in spell checking applications for generating candidates. The method computes the number of the required edit operations to transform one string to another and it can recognize three types of edit operations: deletion, insertion, and substitution of one letter. Damerau modified the Levenshtein method to consider another type of edit operations, the transposition of two adjacent letters, in addition to the considered three types. However, the modification suffers from the time complexity which was added to the original quadratic time complexity of the original method. In this paper, we proposed a modification for the original Levenshtein to consider the same four types using very small number of matching operations which resulted in a shorter execution time and a similarity measure is also achieved to exploit the resulted distance from any Edit Distance method for finding the amount of similarity between two given strings.

This paper deals with constructing a new generated Marshal – Olkin Uniform family distribution which include finding the probability density function ( ), cumulative distribution function ( ), this new compound is necessary to represent the time to failure data of complex system, so we insist to extend Marshal – Olkin distribution to another family called Marshal – Olkin Weibull, we deriving the ( ), ( ), and reliability function, then applying simulation procedure taking different sample size (and different set of initial values of ( ), each experiment repeated ( ), all results explained in tables.

the internet and its websites have huge using these days. These webs may have sensitive and secret information like military information, financial information and other important information that transfer through the networks. Only some people have the authorization to see and access this information. So information has to transfer in secret environment. SQL injection represents one of the most important things that thread these webs. In which unauthorized people can access to the data and information. This paper introduces a method that can be used to prevent SQL injection by converting the user input to static string, use this string as user input and compared with the database attributes that need to compare with, during the runtime. The goal behind converting the input to a string is to make user input as a single unit (one token) that cannot use as a SQL query statement. The system will call the database attribute in such away in which user cannot access to the sql statement to do the injection. And the sql query will be empty from any input tools that can use by user to injects the SQL.

Manipulating a huge amount of data sets makes the mechanism of searching very difficult to find the best features combinations in phylogenetic tree construction. This article has proposed a binary particle swarm optimization algorithm that randomly selects chloroplast genes to construct phylogenetic trees. Also, a simple mutation operator the article has suggested to speed up the search and facilitate the search process to find the best chloroplast genes combinations. These combinations can be evaluated according to the bootstrap values of all the nodes of the constructed tree.

Cloud computing-based technology has become in many modern organizations concerned with education institutions. It will likely have a significant impact on the educational environment in the future. Universities take advantage of available cloud applications offered by service providers and enable their own users/students to perform business and academic tasks.