Air  stripping  for  removal  of  Trichloroethylene  (TCE),  Chloroform  (CF)  and  Dichloromethane (DCM) from water were studied in a bubble column (0.073 m inside dia. and 1.08 m height with several sampling ports). The contaminated water was prepared from deionized water and VOCs. The presence of VOCs in feed solution was single, binary or ternary components. They were diluted to the concentrations ranged between 50 mg/l to 250 mg/l. The experiments were carried out in batch experiments which regard the bubble column as stirred tank and only gas was bubbled through stationary liquid. In this case transient measurements of VOC concentration in the liquid phase and the measured concentra

    The problem statement discussed in this paper is a new technique for the presentation of painterly rendering that uses a K-mean segmentation to divide the input image into a set of regions (depending on the grayscale of the regions). Segmenting the input image helps users use different brush strokes and easily change the strokes' shape, size, or orientation for different regions. Every region is painted using different brush kinds. The properties of the brush strokes are chosen depending on the region's details. The brush stroke properties, such as size, color, shape, location, and orientation, are extracted from the source image using statistical tools. The number of regions is set up manually and depends on the input image. This

In this paper, membrane-based computing image segmentation, both region-based and edge-based, is proposed for medical images that involve two types of neighborhood relations between pixels. These neighborhood relations—namely, 4-adjacency and 8-adjacency of a membrane computing approach—construct a family of tissue-like P systems for segmenting actual 2D medical images in a constant number of steps; the two types of adjacency were compared using different hardware platforms. The process involves the generation of membrane-based segmentation rules for 2D medical images. The rules are written in the P-Lingua format and appended to the input image for visualization. The findings show that the neighborhood relations between pixels o

Deepfake is a type of artificial intelligence used to create convincing images, audio, and video hoaxes and it concerns celebrities and everyone because they are easy to manufacture. Deepfake are hard to recognize by people and current approaches, especially high-quality ones. As a defense against Deepfake techniques, various methods to detect Deepfake in images have been suggested. Most of them had limitations, like only working with one face in an image. The face has to be facing forward, with both eyes and the mouth open, depending on what part of the face they worked on. Other than that, a few focus on the impact of pre-processing steps on the detection accuracy of the models. This paper introduces a framework design focused on this asp

Document analysis of images snapped by camera is a growing challenge. These photos are often poor-quality compound images, composed of various objects and text; this makes automatic analysis complicated. OCR is one of the image processing techniques which is used to perform automatic identification of texts. Existing image processing techniques need to manage many parameters in order to clearly recognize the text in such pictures. Segmentation is regarded one of these essential parameters. This paper discusses the accuracy of segmentation process and its effect over the recognition process. According to the proposed method, the images were firstly filtered using the wiener filter then the active contour algorithm could b

Shot boundary detection is the process of segmenting a video into basic units known as shots by discovering transition frames between shots. Researches have been conducted to accurately detect the shot boundaries. However, the acceleration of the shot detection process with higher accuracy needs improvement. A new method was introduced in this paper to find out the boundaries of abrupt shots in the video with high accuracy and lower computational cost. The proposed method consists of two stages. First, projection features were used to distinguish non boundary transitions and candidate transitions that may contain abrupt boundary. Only candidate transitions were conserved for next stage. Thus, the speed of shot detection was improved by r

The Purpose of this research is a comparison between two types of multivariate GARCH models BEKK and DVECH to forecast using financial time series which are the series of daily Iraqi dinar exchange rate with dollar, the global daily of Oil price with dollar and the global daily of gold price with dollar for the period from 01/01/2014 till 01/01/2016.The estimation, testing and forecasting process has been computed through the program RATS. Three time series have been transferred to the three asset returns to get the Stationarity, some tests were conducted including Ljung- Box, Multivariate Q and Multivariate ARCH to Returns Series and Residuals Series for both models with comparison between the estimation and for

One of the significant stages in computer vision is image segmentation which is fundamental for different applications, for example, robot control and military target recognition, as well as image analysis of remote sensing applications. Studies have dealt with the process of improving the classification of all types of data, whether text or audio or images, one of the latest studies in which researchers have worked to build a simple, effective, and high-accuracy model capable of classifying emotions from speech data, while several studies dealt with improving textual grouping. In this study, we seek to improve the classification of image division using a novel approach depending on two methods used to segment the images. The first

     Medical Ultrasound (US) has many features that make it widely used in the world. These features are safety, availability and low cost. However, despite these features, the ultrasound suffers from problems. These problems are speckle noise and artifacts. In this paper, a new method is proposed to improve US images by removing speckle noise and reducing artifacts to enhance the contrast of the image. The proposed method involves algorithms for image preprocessing and segmentation. A median filter is used to smooth the image in the pre-processing. Additionally, to obtain best results, applying median filter with different kernel values. We take the better output of the median filter and feed it into the Gaussian filter, which then