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Currently, there are many approaches for image transformations that are developed to cover the new technology in the huge amount of data treatment. In this paper, a study on Curvelet transformation coefficients was performed based on the aerial image to apply segmentation. This paper applies a lot of modifications on cut off frequencies on the filters, which is used to decompose the image on curvelet transformation. Two approaches are proposed and tested to look for the best segmentation result; the first one is based on designing filters manually, while the second evaluates the filter coefficients depending on the selected shape of the filters. The first technique gives acceptable segmentation and the second reaches the optimal result. One of the most important results is that the cut of frequency has a high effect on the segmentation; in addition, choosing filter parameters depended on the coefficients dimension of the curvelet transformation. Finally, the results show that the first approach underperformed the second one.

The process of improvement software quality began from early stages of software engineering development. It uses multiple quality metrics which are very important in software development. To calculate the standards quality of in software testing has been adopted. The software testing is focusing on the Software defect. In this paper is proposed new methods which combine the particle swarm optimization (PSO) to handle the best features Extraction with back-propagation networks to testing and evaluation of the data set . The paper depended database for NASA standards data. The result and experiment method improved quality performance for all classification methods used in the research"Combining Particle Swarm Optimization based Feature Selection and Bagging for Software Defect Prediction ".

In this paper many algorithmswereapplied to" zoom –out" the images, like the traditional algorithm (Linear Averaging) algorithm, then this algorithm was developed to a proposed algorithm (Block Averaging) to be the new one in this paper. another algorithm is obtained from another application which is involved as an enhancement smoothing algorithms and will be used as a (zooming-out) scheme, this algorithm is the (Median) algorithm. The obtained results of these algorithms were compared together to sign the better one. The MATLAB language is used to apply the algorithms. .

Recently the Wavelet Transform has gained a lot of popularity in the field of signal and image processing, this is due to its capability of providing both time and frequency information simultaneously.In this paper, focus on used Discrete Wavelet Transform 2-dimenssion )2D- DWT) based on conventional approach, convolution, to image processing and implementation by using FPGA(Field Programmable Gate Array) ,due to many researches were implemented on this Hardware in recent years ,using VHDL. In this paper, has been proposed two VHDL architectures to implementation the conventional of the Daubechies 5/3-tap biorthogonal filter bank, a simple – straightforward one and an optimized one, substituting the multipliers used for scaling with shift – add operations. The architecture of optimized approaches were designed and implemented on FPGA, type of Xilinx XC3S500E Spartan-3E.

In this paper, a new proposed method is developed, this method contains two algorithms, one for human iris recognition, the other is for extracting the features of the recognized iris. Many studies tried to extract the iris from images, most of those studies succeeded but after using very complex processing and filtering operations in addition to transforming the image to other domains (such as frequency domain) to achieve the required operations. The proposed method deduct those complexities operations to a minimum and requires only wavelet transform in a small part of the second algorithm, all other operations will be applied directly to the spatial domain. Experimental results shows the efficiency of the method as its being applied to the images of the Chinese Academy of Sciences – Institute of Automation “CASIA” iris database which contains a lot of deformations. Finally, Matlab R2010a (Ver. 7.10.0) was used to implement the algorithms presented in this paper because it facilitates handling images, arrays, and filters.

The didactic bags which are programs designed to replace the teacher in a certain scientific field, are the modernist didactic means which proved great activity in the didactic process and due to the importance of the images processing field of computers, so It has been constructed in this paper on designing of didactic bag in the bases of the images processing field which is considered as one of the classification of computer imaging. The research deals with the images processing in that the idioms. It deals the most famous idioms of images processing. It also treats of some of the images folders shapes. It deals with parts of images processing in detail as well as giving instance to illustrate each of these parts. It deals with the associating process with the parts of the images processing in that digital filtration and mathematical rolling, besides it deals with the special commands in the images processing in some programming languages as Matlab language, c++ language and visual basic language with giving some instances. The paper also contains a show for some internet sites in order to give the opportunity to the user to increase his knowledge and expertise if he wishes to sight the internet sites.

In this paper an image is hidden in another image using one of the hiding algorithms (Least Significant Bit) to produce the stego-cover image which used as an input with the cover to Radial basis function Network to produce the weights. Cover is delivered once to the recipient who can use it for unlimited number of messages. The weights are delivered to the recipient for each hidden message as a key. The recipient uses the cover with the weights to unhide the message. So that this method include two levels of security. The first one is hiding the message in the cover to produce stego-cover image. The second one is ciphering the embedded image using RBF Neural Network. This Network is considered as a target and the input to the Neural Network is the cover image. Then the weights, which represent the encrypted information are reconstructed. The recipient can use RBF Network to unhide the message by having the stego-cover image then the message. Matlab R2008a was used in this paper.

In this paper many algorithmswere applied to" zoom –in" the images, like traditional algorithms which is used (Linear Averaging) algorithm, (Convolution) algorithm and the proposed algorithms to be the new algorithms in this paper. These algorithms are obtained from another application which involves in Enhancement Smoothing Algorithms and will be used as a (zooming-in) color images algorithms which are include (Median) Algorithm and (Max & Min) Algorithm and sometimes called (Conservative Smoothing Algorithm). The obtained results of these algorithms were compared together. The MATLAB language is used to apply the algorithms.

Two new hybrid algorithms have been suggested in this paper, the first one utilizes four formula of self-scaling update matrix was used. The matrix is selected according to Buckley method in each step. The new algorithm has been compared with BFGS standard algorithm by means of (10) multi-dimensional standard functions. As for the second new hybrid algorithm, a new method is used to test the conjugate coefficient (β) which consists of Hestenes Stiefel (HS) and Dai and yuan (DY). Then it is compared with BFGS and PCG algorithms, which uses BFGS update, by means of (10) multi-dimesional standard functions. Numerical results in general indicates the efficiency of the algorithms proposed in this paper by using this number of non-linear functions in this domain.

We solve in this paper some machine scheduling problems with certain conditions; the solution had been made by using dynamic programming procedures with a fixed time for all the algorithms. Some of the algorithms described in the paper represent a modification to some mathematical programming algorithms and some of them are new or a generalization to others. The optimal solution of all these algorithms will be done with a fixed complexity time, which may lead us to solve them easier and faster.

In this paper, we have used one of the preconditioned conjugate gradient algorithm with the Quasi – Newton approximation; namely the BFGS preconditioned algorithm which was suggested by (AL-Bayati and Aref, 2001). In this paper we have suggested a new algorithm for constrained optimization with robust numerical results, for solving constrained optimization problems.

Nowadays, the field of Internet of Things (IoT) has become a new trend and one of the most attractive areas of research. It has a wide range of applications; starting from smart devices to developing smart cities. The main issue in this kind of applications is the limitation in network resources (e.g., energy, memory, connectivity, etc.). Most of the works in the literature deal with this issue in a traditional way. For instance, developing routing protocols that find the optimal path for data forwarding. This paper looks to this issue from a different angle. In this work, we aim at testing different mobility patterns and then investigate their impact of the consumption of network resources under particular distributions and data routing protocols. The aspect we aim to investigate and measure is the amount of data exchanged, which in turn affects the power and the memory consumption of a network. We also measure two more aspects; performance stability, and data coverage area. The results show that mobility models play a significant role in the overall network performance.

Data stream clustering refers to the process of grouping continuously arriving new data chunks into continuously changing groups to enable dynamic analysis of segmentation patterns. However, the main attention of research on clustering methods till now has been concerned with alteration of the methods updated for static datasets and changes of the available modified methods. Such methods presented only one type of final output clusters, i.e. convex or non-convex shape clusters. This paper presents a novel two-phase parallel hybrid clustering (HPPD) algorithm that identify convex and non-convex groups in online stage and mixed groups in offline stage from data stream. In this work, we first receive the data stream and apply pre-processing step to identify convex and non-convex clusters. Secondly, apply modified EINCKM to present online output convex clusters and modified EDDS to present online output non-convex clusters in parallel scheme. Thirdly, apply adaptive merging strategy in offline stage to give last composed output groups. The method is assessed on a synthetic dataset. The output results of the experiments have authenticate the activeness and effectiveness of the method.

In this paper, we investigated of a new self-scaling by use quasi-Newton method and conjugate gradient method. The new algorithm satisfies a quasi-newton condition and mutually conjugate, and practically proved its efficiency when compared with the well-known algorithms in this domain, by depending on efficiency measure, number of function, number of iteration, and number of constrained, NOF, NOI and NOC.

In this paper, has been proposed Algorithm for standard JPEG2000 part one for image compression. The proposed Algorithm was executed by using MATLAB7.11 environment, applied these algorithm on the gray and color images for type of the images natural, medical, Graphics images and remote sensing. Dependence on the Peak Signal-to-Noise Ratio (PSNR) for comparing the result of the proposed Algorithm by using the Daubechies filters 5/3 tap filter and 9/7 tap filter Biothogonal , Another comparison is held concerning the obtained results of the algorithm of ModJPEG and Color-SPECK. Proved the processing results Efficiency performance of proposed Algorithm.

Bank loans play a crucial role in the development of banks investment business. Nowadays, there are many risk-related issues associated with bank loans. With the advent of computerization systems, banks have become able to register borrowers' data according their criteria. In fact, there is a tremendous amount of borrowers’ data, which makes the process of load management a challenging task. Many studies have utilized data mining algorithms for the purpose of loans classification in terms of repayment or when the loans are not based on customers’ financial history. This kind of algorithms can help banks in making grant decisions for their customers. In this paper, the performance of machine learning algorithms has been compared for the purpose of classifying bank loan risks using the standard criteria and then choosing (Multilayer Perceptron) as it has given best accuracy compared to RandomForest, BayesNet, NaiveBayes and DTJ48 algorithms.

Nowadays, data from different real-time data streams are coming in. The classical relational database systems cannot manage such big data. Big data should be managed in a way that keeps the semantic relations between different concepts. Ontology is a powerful tool that comes from the concept of the semantic web, can formulate data schema as semantically connected objects. Ontology can be shared and reused across different domains and sites. In this paper, an ontology that captures the main concepts and their relationships in Mosul university is proposed. The main steps for OMU (ontology for Mosul University) development are showen using protégé ontology editor. Also, several queries are implemented to show how we can use inference engine to infer new and implicit knowledge from ontologies. visualization tools are used to visualize OMU ontology.

In this paper, we are using enhancing feedback control on a new continuous 4D autonomous hyper chaotic system proposed by Sadiq A. Mehdi and A. Hayder, Qasim [Analysis of a New Hyperchaotic System with six cross-product nonlinearities terms, 2017], this system has three critical points employs ten terms include six quadratic cross-product nonlinearity terms, We notice that when we apply any linear control method that relies on a single unit control added to the system, the system behavior in this case cannot control it, so we applied enhancing linear feedback control at origin and we noticed that a necessary condition for suppression is getting positive feedback coefficient. Theoretical analysis and numerical simulation check the validity of the results obtained.

In this paper, we derived a new parameter by equating the modified QN direction which is suggested by [7] and the standard CG method which satisfied the sufficient descent condition and the global convergence under some assumptions. The numerical experiment of the new algorithm perform better than previous standard algorithm depending on the number of calling the function (NOF) and number of iterations (NOI).

The amount of the available data increases the ability to analyze and understand. The internet revolution has added billions of customer’s review data in its depots. This has given an interest in sentiment analysis and opinion mining in the recent years. People have to depend on machines to classify and process the data as there are terabytes of review data in stock of a single product. So that prediction customer sentiments is very important to analyze the reviews as it not only helps in increasing profits but also goes a long way in improving and bringing out better products. In this paper , we present a survey regarding the presently available techniques and applications that appear in the field of opinion mining , such as , economy , security , marketing , spam detection , decision making , and elections expectation.

The process of classification and recognition of the digits is very important and accurate issue, it should depend on a strong, adequate rule, because there are differences in the representation of the digits for various type of font, rather than to get homogeneous classification for group of digits to show the form or the constant shape for the digit from groups of font , where to be one shape represents each group of the shapes and sizes. This paper proposed a new method for recognition images of Arabic digits with various types and size of font which captured by various kind of camera and various resolution depending on the characteristics of each digit which extracted from study of ten types of font with various size for each type. The characteristic of all shapes and sizes extracted for each digit. Then made a constant model for each digit to be compare with the digit image in any size or font type to recognize the digit in the image. A database of ten templates (one for each digit) built, and these templates are basic and general to recognize the Arabic digits with various font and size. The new method applied for recognition of Arabic digits in the images of license plate of the passenger cars. the recognition percentage was 98%.

In this paper a new algorithm is suggested to encrypt data, since it was to take advantage of properties of the chaotic, so it was entered as a key in encryption and hiding by entering values to the artificial neural network for training as well as hide in the picture, beside use the database for storage and retrieval information and increase the secret system. Through the overlap between the results of stages encryption and hide and artificial neural network algorithm was obtained exciting new strength from where you can not detect secret text only after obtaining random values of the chaotic algorithm and information about neural network algorithm as well as algorithm of work.

Reverse engineering is one of the important operations performed on programming codes to get their design presented graphically. Reverse engineering is performed on various types of databases to get their particular designs represented by different models of relationships. In this paper, we found that a tool for performing reverse engineering on Oracle10g database located inside Oracle10g server , It has been designed in order to get its entity relationship model. Oracle10g data dictionary has been considered in performing reverse engineering. This dictionary includes all information that describes databases which is utilized for getting the important information relevant to the tables. It was necessary for presenting the entity relationship model. XML tags have been used in presenting the information of the data dictionary. These tags were utilized in creating the graphical entity relationship model.

This paper has developed a method for hiding in images, as it was first encrypt the secret message chaoticlly using the chaotic encryption algorithm and secondly execute the steganography in two phases, the first divide the cover image (.BMP, .PNG) to a group of sections (Blocks) with the diagonal sequence and make hiding using the cell of the least Significant Bit (LSB) within (Bytes) of certain randomly, and then using the Genetic Algorithm (GA) and working at the expense of Peak Signal to Noise Ratio(PSNR) for each section after the steganography and then get the best PSNR value of the optimal section (ie, a better distribution of the random sites). The second include a final for all sections (Blocks) depending on the results of the first stage and the best for a random distribution of sites (Bytes) according to the results of genetic algorithm. Measuressuch as PSNR, BER, MSE and NC are used to prove the accuracy of the results and efficiency. The application implemented using Matlab 9.

Requirements engineering is the first phase of the software engineering process. This phase is the basis of other phases, so it is a very critical phase. Incomplete, ambiguous and misunderstanding requirements lead to fail in the product. For this reason, attention must be increased in requirements engineering to obtain accurate, complete and unambiguous requirements to satisfy users' needs in their products. In this paper, an attempt to analyze the requirement phase and produce analysis model is presented by using EA (Enterprise Architect) tool to offer a smart support for requirements engineering processes and introducing them as basis in requirements development in software engineering automation.

Among the various forms of malware, Botnets are emerging as the most serious threat, Botnets, remotely controlled by the attackers, and whose members are located in homes, schools, businesses, and governments around the world. This paper is a survey about Botnet and how Botnet is detected. The survey clarifies Botnet history, Botnet lifecycle, Botnet detection techniques, and proposed software has ability to detect (koobface) Botnet which attacks facebook website.

Mobile technology is rapidly developed, this developing leads the production of the multimedia smartphone that supports wireless LAN (WLAN) that are widely deployed because of its ease of use, flexibility, sharing application and support for multimedia transmission. In this paper, a system was designed and implemented to stream audio from the computer and displayed it on the smartphone model using the (Client Server) model. This work deals with real-time audio, using real-time streaming protocol (RTSP), the proposed work has achieved success of sending voice over the wireless network environments via heterogeneous operating system, Linux and Android.

In this paper addresses the multi-period single-item inventory problem with stochastic demands. In which the demand in each period is known but it varies from time to time for N replenishment periods. The main idea is to calculate expected the total minimum cost and the optimal quantity and the optimal replenishment periods using probabilistic dynamic programming. The results showed the optimal replenishment are (1, 10, 28), optimal quantity is (34974) and the expected total minimum cost is (883.487).

Data mining is the process of extracting hidden patterns from data. One of the most important activities in data mining is the association rule mining and the new head for data mining research area is privacy of mining. Privacy preserving data mining is a new research trend in privacy data for data mining and statistical database. Data mining can be applied on centered or distributed databases. Most efficient approaches for mining distributed databases suppose that all of the data at each site can be shared. Privacy concerns may prevent the sites from directly sharing the data, and some types of information about the data. Privacy Preserving Data Mining (PPDM) has become increasingly popular because it allows sharing of privacy sensitive data for analysis purposes. In this paper, the problem of privacy preserving association rule mining in horizontally distributed database is addressed by proposing a system to compute a global frequent itemsets or association rules from different sites without disclosing individual transactions. Indeed, a new algorithm is proposed to hide sensitive frequent itemsets or sensitive association rules from the global frequent itemsets by hiding them from each site individually. This can be done by modifying the original database for each site in order to decrease the support for each sensitive itemset or association rule. Experimental results show that the proposed algorithm hides rules in a distributed system with the good execution time, and with limited side effects. Also, the proposed system has the capability to calculate the global frequent itemsets from different sites and preserves the privacy for each site.

An improvement to a Steganography method for hiding data using Gray Level Images is proposed in this paper. The method is an improvement over earlier method named Least Significant Bit (LSB). This method uses the 5th, and 6th bits of pixel value for insertion and retrieval of message by using the same bits of pixel value. The idea here, deals with images in different sizes and extensions (BMP, JPG, PNG) for obtaining better results and more efficient than its original one (LSB). The selection of pixel locations is done by using the pseudo random number generator, Which is uses the same key for insertion as well as retrieval of process. The experiments show the Superiority of the new idea through using the performance measures (PSNR, BER, NC).

Software Quality still a vague and multifaceted concept, which means different things to different people, metrics for object oriented design focuses on measurements that are applied to the class and design characteristics. These measurements allow designers to access the software early in process, making changes that will reduce complexity and improve the continuing capability of the design. This paper focused on a set of object oriented metrics that can be used to measure the quality of an object oriented design. We study carefully Metrics for object oriented design and focus on MOOD model.

Semantic Web is an extension to the current web. It will convert the way we use World Wide Web (WWW) by giving the machine the capability to process and infer data in web. It starts as a vision and becomes a future trend in web. Due to the huge data that is scattered as web pages in web today, adding semantic meaning to data stored in these pages became necessary for the next age of information technology. The Semantic Web will bring structure to the meaningful content of Web pages, creating an environment where software agents roaming from page to page can readily carry out sophisticated tasks for users. Several tools and new technologies have been emerged to help bring this vision to reality. In this paper, Semantic Web is defined and described with its layering architecture and supporting technologies and tools. An example is given to show how to use these tools to semantically representing data model. At last, challenges and difficulties faced building this web and made it an extension to the current web has been discussed.

In this paper, the Discrete Wavelet Transform is studied in purifying the sound signal from noise because of the good capabilities in this scope, especially when it is merged with both types of the (Thresholding), the solid and the flexible. The aim of this research is making comparisons between the types of deferent Discrete Wavelet Transform for both the filters which are used (Harr, Daubechies) in deferent levels (2, 3, 4, 5) with the additive of two types of noise to these filters (Gaussian White Noise) and (Random Noise). The decibel value that added to these filters was in the (5dB,10dB,15dB) values. Good results of the purification process are achived after computing the (Signal to Noise Ratio (SNR)) and (Mean Sequre Error (MSE)).

This paper proposes a new four-dimensional continuous autonomous hyperchaotic system based on the 3D Pan system by introducing a nonlinear state feedback controller. Dynamical behaviors of the new system are analyzed, both theoretically and numerically, including equilibrium points, Lyapunov exponents spectrum stability and bifurcation , finally, an illustrative example is given.

In this paper, we study the global convergence properties of the new class of preconditioned conjugate gradient descent algorithm, when applied to convex objective non-linear unconstrained optimization functions. We assume that a new inexact line search rule which is similar to the Armijo line-search rule is used. It's an estimation formula to choose a large step-size at each iteration and use the same formula to find the direction search. A new preconditioned conjugate gradient direction search is used to replace the conjugate gradient descent direction of ZIR-algorithm. Numerical results on twenty five well-know test functions with various dimensions show that the new inexact line-search and the new preconditioned conjugate gradient search directions are efficient for solving unconstrained nonlinear optimization problem in many situations.

Due to the fact that most of the application on EZT algorithm were applied on wavelet transformation. In the last ten years, the contourlet transformation shows that its efficiency is higher than the wavelet transformation due to its ability to deal with multidirections instead of the vertical and horizontal directions which are covered by the wavelet transformation. In this paper, the contourlet coefficient is adopted as inputs to the EZT (which normally are a wavelet coefficient ). Arranging the contourlet coefficient to be studied as input to EZT, the result of adopting modified contourlet coefficient was studied on two parameters (the file size and threshold value ) and tested by evaluating two factors (correlation and MSE). Comparing the result which we get it with the wavelet technique shows that the contourlet gives a result closer, to the original one depending on the correlation factor plus the PSNR factor. So, the proposed technique can be achieved.

The principle of hybrid information hiding represented by process of information hiding message (multimedia files) embedded inside another message which is depended on hiding a certain media in another media using different domains (special domain, frequency domain) to provide protection and security for transformed data, this paper suggest an algorithm by using two different files first one compressed binary image, the compression method is run-length and hided this file inside the hostage media by use frequency domain which is represented by Discrete cosine transform (DCT), the second is text ciphered by Caesar method which is depended one secret key by using spatial domain. The data has been retrieved with no error for both text and binary image after deciphering and decompress process completed; the value of normalization correlation factor for both of them is equal to one.

The essential problem in the security field is how to detect information hiding. This paper proposes a new steganalysis scheme based on artificial neural network as a classifier to detect information hiding in colored and grayscale images. The statistical features extracted from Karhunen-Loève (KL) transform coefficients obtained from co-occurrence matrix of image. Then radial basis neural network (RBNN) trained using these features to discriminate whether the image contains hidden information or not. This system can be used to prevent the suspicious secret communication.

This paper develops a special conjugate gradient algorithm for solving unconstrained minimized problems. This development can be regarded as some kind of convex combination of the MPR and MLS methods. Experimental results indicate that the new algorithm is more efficient than the Polak and Ribiere - algorithm.

In this work, a new method is implemented for removing noise from gray scale image that depends on two-dimensional discrete wavelet transform and Threshold stage (hard threshold). This paper represents the algorithm to remove the speckle noise by using logarithm operation. This operation changes the multiplicative noise to additive noise. So that, the removing operation becomes easier. The Matlab program is used to build the Algorithm and measure the PSNR and other measurement criteria as (NMV, NV, NSD, ENL and PSNR) to study the effect of removing noise from corrupted image. The PSNR reaches to 24dB which is very satisfactory result in the reconstructed image, while the maximum value of ENL is 2.23 * 106, and the minimum value of NMV, NV, NSD which is equal to 6.79, 2.67*10-5, 46* 10-4 respectively gives a smoother and cleaner image. The universal Threshold is applied in high frequency coefficient (i.e. the LH, HL, and HH-sub band of image) to remove the speckle noise and the low frequency coefficient (LL-sub band of image) is still without any change.

Virtual LANs (VLANs) are introduced as some alternative switches to solve for the traffic problems of those LAN networks with Hubs. The idea is to create some collections in the whole LAN network, such collections are the so called VLANs. Appropriate switches (one switch or more of up to 24 ports) are designed to interconnect those VLANs. The design of such VLAN switches looks like the design of Look-Up tables to facilitate routing (similar to those used in CISCO networks). In this paper, an analytical model for such VLANs is proposed and then compared with single VLAN switch from the point of view of validity and speed characteristics.

This paper compares between two models: Common Genetic algorithm and the new Clonal selection theory in the field of Intrusion Detection. Genetic algorithms (GA) which is a model of genetic evolution, while Clonal selection theory (CST) is from models of the natural immune system NIS, the two models are from two different fields of Artificial Intelligence AI but they have portion of shared operations and objectives. The comparison to be done by applying the two models on some records of Knowledge Discovery and Data mining tools which is known by the name KDD data sets (its records the data of the interring packets to the computer system from the internet), to produce population ( in case of GA) or antibodies (in case of CST) can recognize these abnormal records.

Detecting circles is very important in the application of image processing especially in determining the object locations. In this paper, a new algorithm is proposed for circle detection, called Point Search Circle Detection (PSCD), which detects points and assumes them as inspection points on the circle circumference by using them to create a virtual circle to match it with the original image. Using matching operation leads to reduce computational operations and reduce the complexity and the running time of the algorithm. The proposed algorithm is highly accurate, has high speed and low storage requirements in comparing with other related algorithms. The proposed algorithm can precisely detect circles with various scales, crossed and nested circles in the binary images. The proposed algorithm was compared with Hough Transform (HT) method for circle detection by using many images with different numbers and radius of circles and different image dimensions. The proposed algorithm was more efficient, where the average ratio of the running time for the proposed algorithm to HT method was 1:646, and the accuracy of the proposed algorithm was 100% for circles detection. Both the proposed and HT algorithms are applied by using Matlab 7.2 language, PC equipment with 1.8MHz Pentium IV processor and 512MB RAM.

Steganographic techniques are generally used to maintain the confidentiality of valuable information and to protect it from any possible theft or unauthorized use especially over the internet.In this paper, Least Significant Bit LSB-based Steganographic techniques is used to embed large of data in different color space models, such as (RGB, HSV, YCbCr, YIQ, YUV). The idea can be summarized by transforming the RGB value of the secret image pixels into three separate components into the pixels of the cover image. The measures (MSE, SNR, PSNR) were used to compare between the color space models, the comparisons proved that steganography with color systems (RGB and HIS) shown a best results.

In last years, Steganography techniques involving audio file formats appear to be increasing in popularity. This may be attributed to the fact that current steganalysis tools available to the general public fall short when applied to audio files. Another factor that may be contributing to the increasing popularity is the widespread popularity of the MP3 file format and its ubiquitous presence on the Internet. In this paper, an introductory look at steganography and the important methods for hiding data in audio is shown, as well as the strengths and weaknesses of each method. An overview about sound and their file formats especially MP3 file format and its attribute and features. Here, some steganography techniques on sound (Low Bit Encoding, Phase Coding) has been used to apply them on MP3 file format which had been chosen as a cover for data transmission.

The best band selection from remote sensing image plays an important roles in multispectral and hyperspectral remote sensing image processing due to the intercorrelation that inherent in the multispectral images taken by remote sensing sensors. In this paper we use principle component analysis algorithm applied on remote sensing data and find covariance matrix for bands that should be processed then find eigen vector using Jacobi methods .The algorithm was applied on multispectral images of Thematic Mapper sensor , it concluded that the six band was the best band , the value of it’s eigen value was the biggest one and the value of signal to noise ratio equals to 74.7217. This algorithm is constructed using Visual C# 2008 that is characterized by efficient and high speed implementation.

Image fusion is an effective tool to integrate multi-source image, where the purpose of the process access to information integration concept, and make the resulting image more suited to human vision, as well as digital processing. This integration leads to a technology that joins the resources in order to increase and clarify the information that serve the visual and automatic interpretation. The current research aims to use wavelet transformation with two dimensions (2D-DWT) in the integration of multispectral images (images taken by visible band) and others taken by IR (Infrared band) to obtain a single and clear image, and shows in accurate details of the original images. This algorithm programmed by MATLAB tools (MATLAB R2008a) is applied to the two images, the dimensions of each image 512×512 pixels. Where the fusion process depends on two types of mathematical models, the Maximum, and Average. High pass Combination and Low pass Combination filters were used. Using filters db4, coif4 and sym4 and then the Entropies of images were calculated to measure the amount of information in the resulting image. This paper shows that the 2D-DWT is used to integrate images using the image fusion ,and it gives good results in increasing the amount of generated information, increase the efficiency of the image , increase reliability , and reduce the uncertainty-surrounding some of the features in the images which are not clear.

In this paper a new algorithm is suggested to encrypt data, as it was to benefit from human iris as one of Biometric properties in human body which distinguish the individual from the other to produce the encryption through extracting important features using Wavelet Transformation and then passing through a series of operations moderation as the first phase, at the second phase a chaotic function properties is used by leading it in encryption operation as a basic factor. Through the overlap between the results of above phases a new encryption algorithm is produced which show strength, and could not discover the encryption key until getting the biometric property and finding complete information about the chaotic used function in addition to the working algorithm .

In this paper the genetic algorithm has been used to estimate the parameter θ which exist in Boltzmann Distribution which controls the structure of the Ribo Nucleic Acid (RNA). Two algorithms have been suggested. The first found the value of the estimator which maximizes the likelihood function of Boltzmann Distribution. The second minimized the generation constraint of Boltzmann Distribution by using the genetic algorithm. Matlab (7.0) has been used in writing the programs of algorithms and achieved the following results: The maximum value for the likelihood estimator for Boltzmann Distribution appear at the value -4.1614 where the value of θ is 0.1457, and the minimum value for the Constraint Generation for Boltzmann Distribution appear at the value 0.951039101*17 where the value of θ is -4.4066.

In this paper audio files are compressed using counter propagation neural network (CPNN) which is one of the fastest neural networks in multi media. The utilized counter propagation neural network was trained on uncompressed sound file to obtain the final weights of this CPNN (Kohonen layer, Grossberg layer ). In compression operation: the sound signal segmented to number of frames equal in size. Then these frames are applied step by step, to the first layer of the neural network(kohonen layer) to obtain some compression results. The decompression operation done by retrieve stored information in resulted file. This information is applied to second layer of this CPNN (Grosberg layer) which will perform decompression operation and retrieve the original sound file. The proposed algorithm is applied on (.wav) audio files , The results show high performance in addition to short time in compression and decompression operation.