Machine learning-based techniques are used widely for the classification of images into various categories. The advancement of Convolutional Neural Network (CNN) affects the field of computer vision on a large scale. It has been applied to classify and localize objects in images. Among the fields of applications of CNN, it has been applied to understand huge unstructured astronomical data being collected every second. Galaxies have diverse and complex shapes and their morphology carries fundamental information about the whole universe. Studying these galaxies has been a tremendous task for the researchers around the world. Researchers have already applied some basic CNN models to predict the morphological classes

Digital image is widely used in computer applications. This paper introduces a proposed method of image zooming based upon inverse slantlet transform and image scaling. Slantlet transform (SLT) is based on the principle of designing different filters for different scales.

      First we apply SLT on color image, the idea of transform color image into slant, where large coefficients are mainly the   signal and smaller one represent the noise. By suitably modifying these coefficients , using scaling up image by  box and Bartlett filters so that the image scales up to 2X2 and then inverse slantlet transform from modifying coefficients using to the reconstructed image .

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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

Steganography is a mean of hiding information within a more obvious form of
communication. It exploits the use of host data to hide a piece of information in such a way
that it is imperceptible to human observer. The major goals of effective Steganography are
High Embedding Capacity, Imperceptibility and Robustness. This paper introduces a scheme
for hiding secret images that could be as much as 25% of the host image data. The proposed
algorithm uses orthogonal discrete cosine transform for host image. A scaling factor (a) in
frequency domain controls the quality of the stego images. Experimented results of secret
image recovery after applying JPEG coding to the stego-images are included.

Regulation of contracts for the representation of professional players has great theoretical and practical importance; The FIFA has issued a new regulation to deal with football intermediaries instead of the previously exist provision of the former players' agents. The FIFA also committed the national associations to use these new regulations through issuing new national provisions. Regarding the international and national regulations, we address the meaning of the football intermediaries, the characteristics of the contracts concluded by such intermediaries and its legal provisions, the rights of the football intermediaries, and the obligations incumbent on them.

As usage of internet grows in different applications around the world, many techniques were developed to guard an important data against from illegal access and modification from unauthorized users by embedding this data into visual media called host media. Audio hiding in an image is a challenge because of the large size of the audio signal. Some previous methods have been presented to reduce the data of the audio signal before embedding it in the cover image, however, these methods was at the cost of reducing the quality of the audio signal. In this paper, a Slantlet transform (SLT) based method is applied to obtain better performance in terms of audio quality. In addition, the data hiding scheme in the cover color image has been imple

A soliton is a solitary wave whose amplitude, shape, and velocity are conserved after a collision with another soliton. Solitons, in general, manifest themselves in a large variety of wave/particle systems in nature: practically in any system that possesses both dispersion (in time or space) and nonlinearity. Solitons have been identified in optics, plasmas, fluids, condensed matter, particle physics, and astrophysics. Yet over the past decade, the forefront of soliton research has shifted to neuroscience. The Soliton model in optical fiber is a recently developed model that attempts to explain how signals are propagated within optical fiber without dispersion. In this research, it proposes that the signals travel along the Single Mode O

The fetal heart rate (FHR) signal processing based on Artificial Neural Networks (ANN),Fuzzy Logic (FL) and frequency domain Discrete Wavelet Transform(DWT) were analysis in order to perform automatic analysis using personal computers. Cardiotocography (CTG) is a primary biophysical method of fetal monitoring. The assessment of the printed CTG traces was based on the visual analysis of patterns that describing the variability of fetal heart rate signal. Fetal heart rate data of pregnant women with pregnancy between 38 and 40 weeks of gestation were studied. The first stage in the system was to convert the cardiotocograghy (CTG) tracing in to digital series so that the system can be analyzed ,while the second stage ,the FHR time series was t

      In this research, the nonparametric technique has been presented to estimate the time-varying coefficients functions for the longitudinal balanced data that characterized by observations obtained through (n) from the independent subjects, each one of them is measured repeatedly by group of  specific time points (m). Although the measurements are independent among the different subjects; they are mostly connected within each subject and the applied techniques is the Local Linear kernel LLPK technique. To avoid the problems of dimensionality, and thick computation, the two-steps method has been used to estimate the coefficients functions by using the two former technique. Since, the two-