The performance of sewage pumps stations affected by many factors through its work time which produce undesired transportation efficiency. This paper is focus on the use of artificial neural network and multiple linear regression (MLR) models for prediction the major sewage pump station in Baghdad city. The data used in this work were obtained from Al-Habibia sewage pump station during specified records- three years in Al-Karkh district, Baghdad. Pumping capability of the stations was recognized by considering the influent input importance of discharge, total suspended solids (TSS) and biological oxygen demand (BOD).  In addition, the chemical oxygen demands (COD), pH and chloride (Cl). The proposed model performanc

A new features extraction approach is presented based on mathematical form the modify soil ratio (MSR) and skewness for numerous environmental studies. This approach is involved the investigate on the separation of features using frequency band combination by ratio to estimate the quantity of these features, and it is exhibited a particular aspect to determine the shape of features according to the position of brightness values in a digital scenes, especially when the utilizing the skewness. In this research, the marginal probability density function G(MSR) derivation for the MSR index is corrected, that mentioned in several sources including the source (Aim et al.). This index can be used on original input features space for three diffe

The purpose of this research is to investigate the impact of corrosive environment (corrosive ferric chloride of 1, 2, 5, 6% wt. at room temperature), immersion period of (48, 72, 96, 120, 144 hours), and surface roughness on pitting corrosion characteristics and use the data to build an artificial neural network and test its ability to predict the depth and intensity of pitting corrosion in a variety of conditions. Pit density and depth were calculated using a pitting corrosion test on carbon steel (C-4130). Pitting corrosion experimental tests were used to develop artificial neural network (ANN) models for predicting pitting corrosion characteristics. It was found that artificial neural network models were shown to be

      Applying 4K, (Ultra HD) Real-time video streaming via the internet network, with low bitrate and low latency, is the challenge this paper addresses. Compression technology and transfer links are the important elements that influence video quality. So, to deliver video over the internet or another fixed capacity medium, it is essential to compress the video to more controllable bitrates (customarily in the 1-20 Mbps range). In this study, the video quality is examined using the H.265/HEVC compression standard, and the relationship between quality of video and bitrate flow is investigated using various constant rate factors, GOP patterns, quantization parameters, RC-lookahead, and other types of video motion sequences. The ultra

Alzheimer's disease (AD) increasingly affects the elderly and is a major killer of those 65 and over. Different deep-learning methods are used for automatic diagnosis, yet they have some limitations. Deep Learning is one of the modern methods that were used to detect and classify a medical image because of the ability of deep Learning to extract the features of images automatically. However, there are still limitations to using deep learning to accurately classify medical images because extracting the fine edges of medical images is sometimes considered difficult, and some distortion in the images. Therefore, this research aims to develop A Computer-Aided Brain Diagnosis (CABD) system that can tell if a brain scan exhibits indications of

      Detecting and subtracting the Motion objects from backgrounds is one of the most important areas. The development of cameras and their widespread use in most areas of security, surveillance, and others made face this problem. The difficulty of this area is unstable in the classification of the pixels (foreground or background). This paper proposed a suggested background subtraction algorithm based on the histogram. The classification threshold is adaptively calculated according to many tests. The performance of the proposed algorithms was compared with state-of-the-art methods in complex dynamic scenes.