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.

     Cloth simulation and animation has been the topic of research since the mid-80's in the field of computer graphics. Enforcing incompressible is very important in real time simulation. Although, there are great achievements in this regard, it still suffers from unnecessary time consumption in certain steps that is common in real time applications.   This research develops a real-time cloth simulator for a virtual human character (VHC) with wearable clothing. This research achieves success in cloth simulation on the VHC through enhancing the position-based dynamics (PBD) framework by computing a series of positional constraints which implement constant densities. Also, the self-collision and collision wit

       The current research aims to diagnose the nature of the relation between dynamic capabilities as an independent variable and competitive advantage as a respondent variable, and identify the role of each of them in achieving the required performance of the organizations and adapt to the rapid environmental changes. The research was applied to the Iraqi Cement State Company one of the formations of the Iraqi Ministry of Industry and Minerals, and based on the importance of the subject matter of the research and the importance of the research sample and company of inquiry, The researcher adopted the descriptive analytical method in completing his research. The sample of the research was Intention

An experimental study was conducted on pressure drop of water flow through vertical cylindrical packed beds in turbulent region and the influence of the operating parameters on its behavior. The bed packing was made of spherical and non-spherical particles (spheres, Rasching rings and intalox saddle) with aspect ratio range 3.46  D/dp  8.486 obtaining bed porosities 0.396 0.84 and Reynolds number 1217  21758. The system is consisted of 5 cm inside diameter Perspex column, 50 cm long; distilled water was pumped through the bed with flow rate 875, 1000, 1125, 1250,1375 and 1500 l/h and inlet water temperature 20, 30, 40 and 50 ˚C. The packed bed system was monitored by using LabVIEW program, were the result

Rate of penetration plays a vital role in field development process because the drilling operation is expensive and include the cost of equipment and materials used during the penetration of rock and efforts of the crew in order to complete the well without major problems. It’s important to finish the well as soon as possible to reduce the expenditures. So, knowing the rate of penetration in the area that is going to be drilled will help in speculation of the cost and that will lead to optimize drilling outgoings. In this research, an intelligent model was built using artificial intelligence to achieve this goal.  The model was built using adaptive neuro fuzzy inference system to predict the rate of penetration in

This paper presents a hybrid approach for solving null values problem; it hybridizes rough set theory with intelligent swarm algorithm. The proposed approach is a supervised learning model. A large set of complete data called learning data is used to find the decision rule sets that then have been used in solving the incomplete data problem. The intelligent swarm algorithm is used for feature selection which represents bees algorithm as heuristic search algorithm combined with rough set theory as evaluation function. Also another feature selection algorithm called ID3 is presented, it works as statistical algorithm instead of intelligent algorithm. A comparison between those two approaches is made in their performance for null values estima

Tourism plays an important role in Malaysia’s economic development as it can boost business opportunity in its surrounding economic. By apply data mining on tourism data for predicting the area of business opportunity is a good choice. Data mining is the process that takes data as input and produces outputs knowledge. Due to the population of travelling in Asia country has increased in these few years. Many entrepreneurs start their owns business but there are some problems such as wrongly invest in the business fields and bad services quality which affected their business income. The objective of this paper is to use data mining technology to meet the business needs and customer needs of tourism enterprises and find the most effective