Data hiding is the process of encoding extra information in an image by making small modification to its pixels. To be practical, the hidden data must be perceptually invisible yet robust to common signal processing operations. This paper introduces a scheme for hiding a signature image that could be as much as 25% of the host image data and hence could be used both in digital watermarking as well as image/data hiding. The proposed algorithm uses orthogonal discrete wavelet transforms with two zero moments and with improved time localization called discrete slantlet transform for both host and signature image. A scaling factor ? in frequency domain control the quality of the watermarked images. Experimental results of signature image

Even though image retrieval is considered as one of the most important research areas in the last two decades, there is still room for improvement since it is still not satisfying for many users. Two of the major problems which need to be improved are the accuracy and the speed of the image retrieval system, in order to achieve user satisfaction and also to make the image retrieval system suitable for all platforms. In this work, the proposed retrieval system uses features with spatial information to analyze the visual content of the image. Then, the feature extraction process is followed by applying the fuzzy c-means (FCM) clustering algorithm to reduce the search space and speed up the retrieval process. The experimental results show t

A loS.sless (reversible) data hiding (embedding) method  inside  an image  (translating medium)  - presented   in  the  present  work  using  L_SB (least  significant  bit). technique  which  enables  us to translate   data  using an  image  (host  image),  using  a  secret  key, to  be  undetectable  without losing  any  data  or  without   changing   the  size  and  the  external   scene (visible  properties) of the image, the hid-ing data is then can  be extracted (without  losing)   by reversing &n

In this paper, integrated quantum neural network (QNN), which is a class of feedforward

neural networks (FFNN’s), is performed through emerging quantum computing (QC) with artificial neural network(ANN) classifier. It is used in data classification technique, and here iris flower data is used as a classification signals. For this purpose independent component analysis (ICA) is used as a feature extraction technique after normalization of these signals, the architecture of (QNN’s) has inherently built in fuzzy, hidden units of these networks (QNN’s) to develop quantized representations of sample information provided by the training data set in various graded levels of certainty. Experimental results presented here show that

The research aims to identify the importance of using analytical procedures in the detection of creative accounting practices. To achieve this goal, (100) questionnaires were prepared and distributed to the auditors in the Federal Financial Supervision Bureau and the authorized auditors' offices and practitioners of the auditing profession in Iraq. For the purpose of testing the research hypothesis and analyzing data, some appropriate statistical methods have been used and the use of the statistical program (SPSS) to analyze the data. The results of the research showed that the analytical procedures and tests applied by the auditor have a role in revealing and limiting creative accounting practices and methods and that auditors u

Maximum likelihood estimation method, uniformly minimum variance unbiased estimation method and minimum mean square error estimation, as classical estimation procedures, are frequently used for parameter estimation in statistics, which assuming the parameter is constant , while Bayes method assuming the parameter is random variable and hence the Bayes estimator is an estimator which minimize the Bayes risk for each value the random observable and for square error lose function the Bayes estimator is the posterior mean. It is well known that the Bayesian estimation is hardly used as a parameter estimation technique due to some difficulties to finding a prior distribution.

The interest of this paper is that

     The printed Arabic character recognition are faced numerous challenges due to its character body which are changed depending on its position in any sentence (at beginning or in the middle or in the end of the word). This paper portrays recognition strategies. These strategies depend on new pre-processing processes, extraction the structural and numerical features to build databases for printed alphabetical Arabic characters. The database information that obtained from features extracted was applied in recognition stage. Minimum Distance Classifier technique (MDC) was used to classify and train the classes of characters. The procedure of one character against all characters (OAA) was used in determination the rate

   A comparison of double informative and non- informative priors assumed for the parameter of Rayleigh distribution is considered. Three different sets of double priors are included, for a single unknown parameter of Rayleigh distribution. We have assumed three double priors: the square root inverted gamma (SRIG) - the natural conjugate family of priors distribution, the square root inverted gamma – the non-informative distribution, and the natural conjugate family of priors - the non-informative distribution as double priors .The data is generating form three cases from Rayleigh distribution for different samples sizes (small, medium, and large). And Bayes estimators for the parameter is derived under a squared erro