The most popular medium that being used by people on the internet nowadays is video streaming.  Nevertheless, streaming a video consumes much of the internet traffics. The massive quantity of internet usage goes for video streaming that disburses nearly 70% of the internet. Some constraints of interactive media might be detached; such as augmented bandwidth usage and lateness. The need for real-time transmission of video streaming while live leads to employing of Fog computing technologies which is an intermediary layer between the cloud and end user. The latter technology has been introduced to alleviate those problems by providing high real-time response and computational resources near to the

There has been a great deal of research into the considerable challenge of managing of traffic at road junctions; its application to vehicular ad hoc network (VANET) has proved to be of great interest in the developed world. Dynamic topology is one of the vital challenges facing VANET; as a result, routing of packets to their destination successfully and efficiently is a non-simplistic undertaking. This paper presents a MDORA, an efficient and uncomplicated algorithm enabling intelligent wireless vehicular communications. MDORA is a robust routing algorithm that facilitates reliable routing through communication between vehicles. As a position-based routing technique, the MDORA algorithm, vehicles' precise locations are used to establish th

A three-stage learning algorithm for deep multilayer perceptron (DMLP) with effective weight initialisation based on sparse auto-encoder is proposed in this paper, which aims to overcome difficulties in training deep neural networks with limited training data in high-dimensional feature space. At the first stage, unsupervised learning is adopted using sparse auto-encoder to obtain the initial weights of the feature extraction layers of the DMLP. At the second stage, error back-propagation is used to train the DMLP by fixing the weights obtained at the first stage for its feature extraction layers. At the third stage, all the weights of the DMLP obtained at the second stage are refined by error back-propagation. Network structures an

The Assignment model is a mathematical model that aims to express a real problem facing factories and companies which is characterized by the guarantee of its activity in order to make the appropriate decision to get the best allocation of machines or jobs or workers on machines in order to increase efficiency or profits to the highest possible level or reduce costs or time To the extent possible, and in this research has been using the method of labeling to solve the problem of the fuzzy assignment of real data has been approved by the tire factory Diwaniya, where the data included two factors are the factors of efficiency and cost, and was solved manually by a number of iterations until reaching the optimization solution,

One of the recent significant but challenging research studies in computational biology and bioinformatics is to unveil protein complexes from protein-protein interaction networks (PPINs). However, the development of a reliable algorithm to detect more complexes with high quality is still ongoing in many studies. The main contribution of this paper is to improve the effectiveness of the well-known modularity density ( ) model when used as a single objective optimization function in the framework of the canonical evolutionary algorithm (EA). To this end, the design of the EA is modified with a gene ontology-based mutation operator, where the aim is to make a positive collaboration between the modularity density model and the proposed

<p>In combinatorial testing development, the fabrication of covering arrays is the key challenge by the multiple aspects that influence it. A wide range of combinatorial problems can be solved using metaheuristic and greedy techniques. Combining the greedy technique utilizing a metaheuristic search technique like hill climbing (HC), can produce feasible results for combinatorial tests. Methods based on metaheuristics are used to deal with tuples that may be left after redundancy using greedy strategies; then the result utilization is assured to be near-optimal using a metaheuristic algorithm. As a result, the use of both greedy and HC algorithms in a single test generation system is a good candidate if constructed correctly. T

The study was conducted at the fields of the Department of Horticulture and Landscape Gardening,College of Agriculture, University of Baghdad during the growing seasons of 2013- 2014 .forPerformance of Evaluation Vegetative growth and yield traits and estimate some important geneticparameter on seven selected breed of tomato which (S1-S7 ) Pure line. the results found significantdifferences between breeds in all study trails except clusters flowering number .S1 significantly plantlength which reached 227.3 .Also S1,S2 and S4 were significantly increased the number fruit for plant,Fruit weight Increased in S3 ,S6 and plant yield. Increased in S1, S4 ,S5. Genetic variation valueswere low in Floral clusters , TSS and fruit firmest and medium i

       In recent years, encryption technology has been developed rapidly and many image encryption methods have been put forward. The chaos-based image encryption technique is a modern encryption system for images. To encrypt images, it uses random sequence chaos, which is an efficient way to solve the intractable problem of simple and highly protected image encryption. There are, however, some shortcomings in the technique of chaos-based image encryption, such limited accuracy issue. The approach focused on the chaotic system in this paper is to construct a dynamic IP permutation and S-Box substitution by following steps. First of all, use of a new IP table for more diffusion of al