The background subtraction is a leading technique adopted for detecting the moving objects in video surveillance systems. Various background subtraction models have been applied to tackle different challenges in many surveillance environments. In this paper, we propose a model of pixel-based color-histogram and Fuzzy C-means (FCM) to obtain the background model using cosine similarity (CS) to measure the closeness between the current pixel and the background model and eventually determine the background and foreground pixel according to a tuned threshold. The performance of this model is benchmarked on CDnet2014 dynamic scenes dataset using statistical metrics. The results show a better performance against the state-of the art

The aim of study To purify GPCR from a local strain of S. cerevisiae using Ion exchange and gel filtration chromatography techniques , by packing materials for columns which will be chosen of low cost comparing to the already used in published researches, which depend on the costly affinity chromatography and other expensive methods of purification. Local strain of S. cerevisiae chosen for extraction and purification of G-protein coupled receptor (GPCR) .The strains were obtained from biology department in Al- Mosul University, Iraq. The isolated colony was activated on Yeast Extract Pepton Dextrose Broth (YEPDB) and incubated at 30 C˚ for 24 h .Loop fully of the yeast culture was transferred to (10ml) of yeast extract peptone glucose

The use of worn-out agricultural nozzles in pesticide application has a negative effect on the efficiency and cost of the application process. It also has an effect on environmental pollution due to an excessive amount of pesticide being applied when spraying with worn-out nozzles. In this paper, the resistance to wear of three different internal design hydraulic nozzles was ascertained. Changes in the flow rate and spray distribution as a result of this wear were also investigated. The wear test was done inside a closed system, and it was accelerated using an abrasive material to generate 100 h of wear. The tested nozzles were the Turbo TeeJet (TT)-twin chambered, Turbo Twinjet (TTj60)-dual outlet, and Drift Guard (DG)-pre-orifice.

  In this research study, I tried to trace the epic effect to learn how it was understood and how it was used. Following the descriptive and analytical approach in the research, the first chapter dealt with a presentation of the methodological framework of the problem, the goal, the limits of the research, the importance and the need for it and the definition of terms, as well as the theoretical framework which consisted of two topics, including the impact of the epic theater on the world theater and the second the effect of the epic theater on the Arab theater, This came by tracing the epic impact on the world stage of the Greeks, the Middle Ages, the Renaissance, and the Arab theater of the twentieth century.
As for the second

Cryptography is the process of transforming message to avoid an unauthorized access of data. One of the main problems and an important part in cryptography with secret key algorithms is key. For higher level of secure communication key plays an important role. For increasing the level of security in any communication, both parties must have a copy of the secret key which, unfortunately, is not that easy to achieve. Triple Data Encryption Standard algorithm is weak due to its weak key generation, so that key must be reconfigured to make this algorithm more secure, effective, and strong. Encryption key enhances the Triple Data Encryption Standard algorithm securities. This paper proposed a combination of two efficient encryption algorithms to

     Community detection is an important and interesting topic for better understanding and analyzing complex network structures. Detecting hidden partitions in complex networks is proven to be an NP-hard problem that may not be accurately resolved using traditional methods. So it is solved using evolutionary computation methods and modeled in the literature as an optimization problem.  In recent years, many researchers have directed their research efforts toward addressing the problem of community structure detection by developing different algorithms and making use of single-objective optimization methods. In this study, we have continued that research line by improving the Particle Swarm Optimization (PSO) algorithm using a

Variable selection is an essential and necessary task in the statistical modeling field. Several studies have triedto develop and standardize the process of variable selection, but it isdifficultto do so. The first question a researcher needs to ask himself/herself what are the most significant variables that should be used to describe a given dataset’s response. In thispaper, a new method for variable selection using Gibbs sampler techniqueshas beendeveloped.First, the model is defined, and the posterior distributions for all the parameters are derived.The new variable selection methodis tested usingfour simulation datasets. The new approachiscompared with some existingtechniques: Ordinary Least Squared (OLS), Least Absolute Shrinkage

Wireless sensor networks (WSNs) represent one of the key technologies in internet of things (IoTs) networks. Since WSNs have finite energy sources, there is ongoing research work to develop new strategies for minimizing power consumption or enhancing traditional techniques. In this paper, a novel Gaussian mixture models (GMMs) algorithm is proposed for mobile wireless sensor networks (MWSNs) for energy saving. Performance evaluation of the clustering process with the GMM algorithm shows a remarkable energy saving in the network of up to 92%. In addition, a comparison with another clustering strategy that uses the K-means algorithm has been made, and the developed method has outperformed K-means with superior performance, saving ener

This paper proposes a new structure of the hybrid neural controller based on the identification model for nonlinear systems. The goal of this work is to employ the structure of the Modified Elman Neural Network (MENN) model into the NARMA-L2 structure instead of Multi-Layer Perceptron (MLP) model in order to construct a new hybrid neural structure that can be used as an identifier model and a nonlinear controller for the SISO linear or nonlinear systems. Weight parameters of the hybrid neural structure with its serial-parallel configuration are adapted by using the Back propagation learning algorithm. The ability of the proposed hybrid neural structure for nonlinear system has achieved a fast learning with minimum number

The transition structure is considered as the most important hydraulic structure controlling the w/s transtion, morever it decrease the scouring of outlet structure.

seven experiment samples for transition structure was used in this research at different angles ( 10° - 90° ).

       It was shown that froud number has a clear effect on the depth of the scouring, morever the high discharge rates cause an increase of the ratio between the length of the scour and its depth.

       In order to select the best flaring angle it was shown that the angle of 40° has the most discharge rate, least structure length and least angle scour depth, with the firmly of t