Photometric techniques are one of the fundamentals and of great importance in the study of astronomical phenomena, including galaxies, and has witnessed a wide development during the last 100 years in equipment, sensitivity and accuracy in data analysis, especially after the direction toward space telescopes and the widespread use of a CCD camera. Therefore, in this research, an analytical study will be made to compare two types of galaxies, which are spiral and lenticular galaxies, using photometric techniques and compare the photometric parameters of each type with tables and illustrations. An analysis of the morphological of the two galaxies was done by using the Least Square Fitting Method, and it was fully explained in

In this study, two active galaxies (NGC4725, NGC4639) have been chosen to study their morphological and photometric properties, by using the IRAF ISOPHOTE ELLIPS task with griz-filters. Observations are obtained from the Sloan Digital Sky Survey (SDSS) which reaches now to the DATA Release (DR14). The data reduction of all images (bias and flat field) has been done by SDSS Pipeline. The surface photometric investigation was performed like the magnitude. Together with isophotal contour maps, surface brightness profiles and a bulge/disk decomposition of the images of the galaxies, although the disk position angle, ellipticity, and inclination of the galaxies have been done. Also, the color of galaxies was studied, where chromatic distribution

     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

<p class="0abstract">Image denoising is a technique for removing unwanted signals called the noise, which coupling with the original signal when transmitting them; to remove the noise from the original signal, many denoising methods are used. In this paper, the Multiwavelet Transform (MWT) is used to denoise the corrupted image by Choosing the HH coefficient for processing based on two different filters Tri-State Median filter and Switching Median filter. With each filter, various rules are used, such as Normal Shrink, Sure Shrink, Visu Shrink, and Bivariate Shrink. The proposed algorithm is applied Salt&amp; pepper noise with different levels for grayscale test images. The quality of the denoised image is evaluated by usi

In this paper, ceramic water filters were produced by using ten mixtures of different ratios of red clay and sawdust under different production conditions. The physical properties of these filters were tested. The production conditions include five press pressures ranged from 10 to 50MPa and a firing schedule having three different final temperatures of 1000, 1070, and 1100˚C. The tests results of the physical properties were used to obtain best compatibility between the hydraulic and the mechanical properties of these filters. Results showed that as the press pressure and the firing temperature are increased, the bulk density and the compressive and bending strengths of the produced filters are increased, while, the porosity and absorp

        The wavelet transform has become a useful computational tool for a variety of signal and image processing applications.

     The aim of this paper is to present the comparative study of various wavelet filters. Eleven different wavelet filters (Haar, Mallat, Symlets, Integer, Conflict, Daubechi 1, Daubechi 2, Daubechi 4, Daubechi 7, Daubechi 12 and Daubechi 20) are used to compress seven true color images of 256x256 as a samples. Image quality, parameters such as peak signal-to-noise ratio (PSNR), normalized mean square error have been used to evaluate the performance of wavelet filters.

   In our work PSNR is used as a measure of accuracy performanc

Astronomy image is regarded main source of information to discover outer space, therefore to know the basic contain for galaxy (Milky way), it was classified using Variable Precision Rough Sets technique to determine the different region within galaxy according different color in the image. From classified image we can determined the percentage for each class and then what is the percentage mean. In this technique a good classified image result and faster time required to done the classification process.

The proliferation of many editing programs based on artificial intelligence techniques has contributed to the emergence of deepfake technology. Deepfakes are committed to fabricating and falsifying facts by making a person do actions or say words that he never did or said. So that developing an algorithm for deepfakes detection is very important to discriminate real from fake media. Convolutional neural networks (CNNs) are among the most complex classifiers, but choosing the nature of the data fed to these networks is extremely important. For this reason, we capture fine texture details of input data frames using 16 Gabor filters indifferent directions and then feed them to a binary CNN classifier instead of using the red-green-blue